From e8b6b78363a9c8cd0b178b99e6d18feda191cbf6 Mon Sep 17 00:00:00 2001 From: Macqueen Ma-hnu Date: Mon, 19 Oct 2026 09:55:51 +0800 Subject: [PATCH 1/7] perf(pt): add opt-in SO2 edge-cohort inference backward Use a compiled single-cohort branch for small edge sets and retain a streamed runtime path for larger frozen Neo input gradients. Keep native fallback and explicit stride defaults, add 60 CUDA checks and cold-first-peak evidence. The switch remains disabled by default; multi-model and AOT deployment are not validated. N64 cold allocated peak falls 3.62%; no speedup is claimed. --- .../kernels/triton/sezm/so2_edge_cohort.py | 380 ++ .../kernels/triton/sezm/so2_value_path.py | 34 +- .../ecs-edge-cohort-benchmark-results.json | 5202 +++++++++++++++++ doc/development/ecs-edge-cohort-benchmarks.md | 205 + .../_so2_edge_cohort_benchmark_common.py | 131 + .../_so2_edge_cohort_benchmark_impl.py | 207 + .../kernels/benchmark_so2_edge_cohort.py | 131 + .../pt_expt/kernels/test_so2_edge_cohort.py | 201 + 8 files changed, 6487 insertions(+), 4 deletions(-) create mode 100644 deepmd/pt_expt/kernels/triton/sezm/so2_edge_cohort.py create mode 100644 doc/development/ecs-edge-cohort-benchmark-results.json create mode 100644 doc/development/ecs-edge-cohort-benchmarks.md create mode 100644 source/tests/pt_expt/kernels/_so2_edge_cohort_benchmark_common.py create mode 100644 source/tests/pt_expt/kernels/_so2_edge_cohort_benchmark_impl.py create mode 100644 source/tests/pt_expt/kernels/benchmark_so2_edge_cohort.py create mode 100644 source/tests/pt_expt/kernels/test_so2_edge_cohort.py diff --git a/deepmd/pt_expt/kernels/triton/sezm/so2_edge_cohort.py b/deepmd/pt_expt/kernels/triton/sezm/so2_edge_cohort.py new file mode 100644 index 0000000000..1bee615190 --- /dev/null +++ b/deepmd/pt_expt/kernels/triton/sezm/so2_edge_cohort.py @@ -0,0 +1,380 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +"""Experimental edge-cohort backward for frozen, narrow SO(2) inference. + +Keep native GEMM column parallelism and arithmetic, but traverse all layers +of one cohort before advancing to the next cohort. Scratch scales with the +cohort, not the entire graph. The saved activations and returned input gradient +still have full edge extent. This Python runtime launcher is an online-compile +prototype; standalone AOTInductor/C++ deployment is not validated. +""" + +from __future__ import annotations + +import torch +import triton +from torch import Tensor +from torch.library import wrap_triton + +from . import so2_value_path as sv + +COHORT_EDGES = 8192 +SINGLE_COHORT_MAX_EDGES = 16384 + + +def supports_edge_cohort( + grad_out: Tensor, z_all: Tensor, lmax: int, focus_dim: int, apply_alpha: bool +) -> bool: + """Restrict opt-in dispatch to the experimentally checked Neo layout.""" + return ( + grad_out.is_cuda + and z_all.device == grad_out.device + and grad_out.dtype == torch.float32 + and z_all.dtype == torch.float32 + and grad_out.is_contiguous() + and z_all.is_contiguous() + and lmax == 3 + and focus_dim == 32 + and apply_alpha + and grad_out.ndim == 3 + and z_all.ndim == 4 + and grad_out.shape[1:] == (2, 320) + and z_all.shape[:2] == (2, 2) + and z_all.shape[2] == grad_out.shape[0] + and z_all.shape[3] == 320 + ) + + +def cohort_input_backward_impl( + grad_out: Tensor, + z_all: Tensor, + alpha: Tensor, + w0t: Tensor, + w1t: Tensor, + gw: Tensor, + gwt: Tensor, + cohort_edges: int, +) -> Tensor: + """Compute input gradients without retaining unrelated forward outputs. + + Kernels infer scratch focus strides from the cohort edge count. Independent + saved/output strides retain full-graph layout, including partial cohorts. + Only an internally owned residual is overwritten: matrix input ``gz`` and + GEMM output never alias, and caller-owned tensors remain read-only. + """ + if not supports_edge_cohort(grad_out, z_all, 3, 32, True): + raise ValueError("edge cohorts require contiguous FP32 Neo inference tensors") + if cohort_edges <= 0: + raise ValueError("cohort_edges must be positive") + if not all(t.is_contiguous() for t in (alpha, w0t, w1t, gw, gwt)): + raise ValueError("edge-cohort parameters must be contiguous") + n_gated, n_focus, n_edge, row = z_all.shape + result = grad_out.new_empty((n_focus, n_edge, row)) + if n_edge == 0: + return result + cap = min(n_edge, cohort_edges) + g = grad_out.new_empty((n_focus, cap, row)) + gz = torch.empty_like(g) + sig = grad_out.new_empty((n_focus, cap, 96)) + bm, bn, bk, warps, stages = sv.stack_fp32_configs(32, 3)[2] + n_tiles = triton.cdiv(128, bn) + triton.cdiv(192, bn) + inside, point_config, recompute_config = sv._point_backward_schedule( + 32, 3, train=False + ) + for start in range(0, n_edge, cap): + count = min(cap, n_edge - start) + go = grad_out[start:] + a = alpha[start:] + z = z_all.view(-1)[start * row :] + grid = (triton.cdiv(count, bm) * n_tiles, n_focus) + sv._stack_gemm_bwd_kernel[grid]( + go, + go, + w0t, + w1t, + a, + g, + count, + n_gated, + L=3, + CF=32, + G_EDGE_MAJOR=True, + FOLD_ALPHA=True, + RES_IS_GZ=True, + BLOCK_M=bm, + BLOCK_N=bn, + BLOCK_K=bk, + num_warps=warps, + num_stages=stages, + ) + for layer in range(n_gated - 1, -1, -1): + if not inside: + rbm, rw, rs = recompute_config + sv._stack_recompute_kernel[(triton.cdiv(count, rbm), n_focus)]( + z, + gw, + sig, + count, + layer, + L=3, + CF=32, + BLOCK_M=rbm, + saved_edges=n_edge, + num_warps=rw, + num_stages=rs, + ) + pbm, pw, ps = point_config + sv._stack_point_bwd_kernel[(triton.cdiv(count, pbm), n_focus)]( + g, + z, + sig, + gwt, + gz, + sig, + g, + g, + count, + layer, + L=3, + CF=32, + GLOGIT_OUT=False, + GLOGIT_STORE=False, + RECOMPUTE_SIG=inside, + RECOVER_INPUT=False, + BLOCK_M=pbm, + saved_edges=n_edge, + num_warps=pw, + num_stages=ps, + ) + # Residual elements are loaded before their matching output store. + # Distinct (edge, focus, column) tiles own disjoint residual elements. + target = result.view(-1)[start * row :] if layer == 0 else g + sv._stack_gemm_bwd_kernel[grid]( + gz, + g, + w0t, + w1t, + gz, + target, + count, + layer, + L=3, + CF=32, + G_EDGE_MAJOR=False, + FOLD_ALPHA=False, + RES_IS_GZ=False, + BLOCK_M=bm, + BLOCK_N=bn, + BLOCK_K=bk, + output_edges=n_edge if layer == 0 else count, + num_warps=warps, + num_stages=stages, + ) + return result + + +# Small edge sets must not pay the opaque multi-cohort launcher overhead. +# The residual and returned result share internally owned storage; gz never +# aliases GEMM output. See the inference-only dispatcher below. +@torch.library.triton_op("sezm_triton::so2_single_cohort_input_bwd", mutates_args=()) +def _single_cohort_input_backward( + grad_out: Tensor, + z_all: Tensor, + alpha: Tensor, + w0t: Tensor, + w1t: Tensor, + gw: Tensor, + gwt: Tensor, + cohort_edges: int, +) -> Tensor: + """Compute one small cohort with compiled launchers and owned residual reuse.""" + if not supports_edge_cohort(grad_out, z_all, 3, 32, True): + raise ValueError("edge cohorts require contiguous FP32 Neo inference tensors") + if cohort_edges <= 0: + raise ValueError("cohort_edges must be positive") + if not all(t.is_contiguous() for t in (alpha, w0t, w1t, gw, gwt)): + raise ValueError("edge-cohort parameters must be contiguous") + n_gated, n_focus, n_edge, row = z_all.shape + result = grad_out.new_empty((n_focus, n_edge, row)) + if n_edge == 0: + return result + cap = n_edge + g = result + gz = torch.empty_like(g) + sig = grad_out.new_empty((n_focus, cap, 96)) + bm, bn, bk, warps, stages = sv.stack_fp32_configs(32, 3)[2] + n_tiles = triton.cdiv(128, bn) + triton.cdiv(192, bn) + inside, point_config, recompute_config = sv._point_backward_schedule( + 32, 3, train=False + ) + start = 0 + count = min(cap, n_edge - start) + go = grad_out[start:] + a = alpha[start:] + z = z_all.view(-1)[start * row :] + grid = (triton.cdiv(count, bm) * n_tiles, n_focus) + wrap_triton(sv._stack_gemm_bwd_kernel)[grid]( + go, + go, + w0t, + w1t, + a, + g, + count, + n_gated, + L=3, + CF=32, + G_EDGE_MAJOR=True, + FOLD_ALPHA=True, + RES_IS_GZ=True, + output_edges=n_edge, + BLOCK_M=bm, + BLOCK_N=bn, + BLOCK_K=bk, + num_warps=warps, + num_stages=stages, + ) + for layer in range(n_gated - 1, -1, -1): + if not inside: + rbm, rw, rs = recompute_config + wrap_triton(sv._stack_recompute_kernel)[triton.cdiv(count, rbm), n_focus]( + z, + gw, + sig, + count, + layer, + L=3, + CF=32, + BLOCK_M=rbm, + saved_edges=n_edge, + num_warps=rw, + num_stages=rs, + ) + pbm, pw, ps = point_config + wrap_triton(sv._stack_point_bwd_kernel)[triton.cdiv(count, pbm), n_focus]( + g, + z, + sig, + gwt, + gz, + sig, + g, + g, + count, + layer, + L=3, + CF=32, + GLOGIT_OUT=False, + GLOGIT_STORE=False, + RECOMPUTE_SIG=inside, + RECOVER_INPUT=False, + BLOCK_M=pbm, + saved_edges=n_edge, + num_warps=pw, + num_stages=ps, + ) + target = result.view(-1)[start * row :] if layer == 0 else g + wrap_triton(sv._stack_gemm_bwd_kernel)[grid]( + gz, + g, + w0t, + w1t, + gz, + target, + count, + layer, + L=3, + CF=32, + G_EDGE_MAJOR=False, + FOLD_ALPHA=False, + RES_IS_GZ=False, + BLOCK_M=bm, + BLOCK_N=bn, + BLOCK_K=bk, + output_edges=n_edge if layer == 0 else count, + num_warps=warps, + num_stages=stages, + ) + return result + + +@torch.library.custom_op("sezm_triton::so2_cohort_input_bwd", mutates_args=()) +def _cohort_input_backward( + grad_out: Tensor, + z_all: Tensor, + alpha: Tensor, + w0t: Tensor, + w1t: Tensor, + gw: Tensor, + gwt: Tensor, + cohort_edges: int, +) -> Tensor: + """Keep the runtime edge loop opaque without retaining x_local/u_final.""" + return cohort_input_backward_impl( + grad_out, z_all, alpha, w0t, w1t, gw, gwt, cohort_edges + ) + + +@_cohort_input_backward.register_fake +def _cohort_input_backward_fake( + grad_out: Tensor, + z_all: Tensor, + alpha: Tensor, + w0t: Tensor, + w1t: Tensor, + gw: Tensor, + gwt: Tensor, + cohort_edges: int, +) -> Tensor: + """Preserve full input-gradient shape under FakeTensor tracing.""" + return grad_out.new_empty(z_all.shape[1:]) + + +@torch.library.triton_op("sezm_triton::so2_cohort_alpha_bwd", mutates_args=()) +def _cohort_alpha_backward(grad_out: Tensor, x_local: Tensor, alpha: Tensor) -> Tensor: + """Expose alpha's short-lived output dependency to the graph scheduler.""" + n_edge, n_focus = alpha.shape + result = torch.empty_like(alpha) + if sv._has_no_edges(n_edge): + return result + bm, warps, stages = sv.gate_config(32, 3) + wrap_triton(sv._stack_grad_alpha_kernel)[(triton.cdiv(n_edge, bm), n_focus)]( + grad_out, + x_local, + alpha, + result, + n_edge, + L=3, + CF=32, + BLOCK_M=bm, + num_warps=warps, + num_stages=stages, + ) + return result + + +def cohort_backward( + grad_out: Tensor, + x_local: Tensor, + z_all: Tensor, + u_final: Tensor, + alpha: Tensor, + w0t: Tensor, + w1t: Tensor, + gw: Tensor, + gwt: Tensor, + lmax: int, + focus_dim: int, + apply_alpha: bool, +) -> tuple[Tensor, Tensor]: + """First-order dispatcher; training/higher derivatives never enter here.""" + del u_final + if not supports_edge_cohort(grad_out, z_all, lmax, focus_dim, apply_alpha): + raise ValueError("unsupported edge-cohort layout") + grad_alpha = _cohort_alpha_backward(grad_out, x_local, alpha) + input_backward = ( + _single_cohort_input_backward + if grad_out.shape[0] <= SINGLE_COHORT_MAX_EDGES + else _cohort_input_backward + ) + grad_u0 = input_backward(grad_out, z_all, alpha, w0t, w1t, gw, gwt, COHORT_EDGES) + return grad_u0, grad_alpha diff --git a/deepmd/pt_expt/kernels/triton/sezm/so2_value_path.py b/deepmd/pt_expt/kernels/triton/sezm/so2_value_path.py index 7a8ac3fd43..38db605a4a 100644 --- a/deepmd/pt_expt/kernels/triton/sezm/so2_value_path.py +++ b/deepmd/pt_expt/kernels/triton/sezm/so2_value_path.py @@ -91,6 +91,7 @@ annotations, ) +import os from typing import ( TYPE_CHECKING, NamedTuple, @@ -161,6 +162,10 @@ _ROTATE_MIX_BWD_CONFIG = (1, 2) # per-edge backward (warps, stages) +# Experimental, frozen first-order inference only. Read before compilation; +# changing the switch requires a new process/graph. Disabled by default. +_EDGE_COHORT_INFER = os.environ.get("DP_SO2_EDGE_COHORT_INFER", "0") == "1" + # ====================================================================== # Eager reference / fallback implementations @@ -1369,6 +1374,7 @@ def _stack_recompute_kernel( L: tl.constexpr, CF: tl.constexpr, BLOCK_M: tl.constexpr, + saved_edges=0, ): """Recompute the gate sigmoids from the saved pre-activation (backward).""" ROW: tl.constexpr = (3 * L + 1) * CF @@ -1386,7 +1392,8 @@ def _stack_recompute_kernel( cm = mm & (nc < CF)[None, :] wm = ((nc < CF)[:, None]) & ((nc < CF)[None, :]) - z_row = z_ptr + (layer * n_focus + fid) * n_edge * ROW + offs_m * ROW + z_stride = tl.where(saved_edges > 0, saved_edges, n_edge).to(tl.int64) + z_row = z_ptr + (layer * n_focus + fid) * z_stride * ROW + offs_m * ROW z_s = tl.load(z_row[:, None] + nc[None, :], mask=cm, other=0.0).to(tl.float32) sig_row = sig_ptr + (fid * n_edge + offs_m) * LG for g in tl.static_range(L): @@ -1420,6 +1427,7 @@ def _stack_point_bwd_kernel( RECOMPUTE_SIG: tl.constexpr, RECOVER_INPUT: tl.constexpr, BLOCK_M: tl.constexpr, + saved_edges=0, ): """Pointwise part of the gated-layer backward. @@ -1456,7 +1464,8 @@ def _stack_point_bwd_kernel( wm = ((nc < CF)[:, None]) & ((nc < CF)[None, :]) g_row = g_ptr + fid * n_edge * ROW + offs_m * ROW - z_row = z_ptr + (layer * n_focus + fid) * n_edge * ROW + offs_m * ROW + z_stride = tl.where(saved_edges > 0, saved_edges, n_edge).to(tl.int64) + z_row = z_ptr + (layer * n_focus + fid) * z_stride * ROW + offs_m * ROW gz_row = gz_ptr + fid * n_edge * ROW + offs_m * ROW sig_row = sig_ptr + (fid * n_edge + offs_m) * LG gl_row = gl_ptr + (fid * n_edge + offs_m) * LG @@ -2014,6 +2023,7 @@ def _stack_gemm_bwd_kernel( BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr, + output_edges=0, ): """Backward block GEMM ``g_u = residual + gz @ W^T`` over both blocks. @@ -2044,7 +2054,8 @@ def _stack_gemm_bwd_kernel( gz_row = gz_ptr + fid * ROW + offs_m * (n_focus * ROW) else: gz_row = gz_ptr + fid * n_edge * ROW + offs_m * ROW - gu_row = gu_ptr + fid * n_edge * ROW + offs_m * ROW + output_stride = tl.where(output_edges > 0, output_edges, n_edge).to(tl.int64) + gu_row = gu_ptr + fid * output_stride * ROW + offs_m * ROW if FOLD_ALPHA: alpha = tl.load(alpha_ptr + offs_m * n_focus + fid, mask=m_mask, other=0.0) @@ -2435,6 +2446,7 @@ def _launch_stack_point_backward( L=lmax, CF=focus_dim, BLOCK_M=block_m, + saved_edges=0, num_warps=warps, num_stages=stages, ) @@ -2458,6 +2470,7 @@ def _launch_stack_point_backward( RECOMPUTE_SIG=recompute_sigmoid, RECOVER_INPUT=recover, BLOCK_M=block_m, + saved_edges=0, num_warps=warps, num_stages=stages, ) @@ -3218,6 +3231,7 @@ def _stack_backward_traversal( BLOCK_M=block_m, BLOCK_N=block_n, BLOCK_K=block_k, + output_edges=0, num_warps=warps, num_stages=stages, ) @@ -3402,6 +3416,7 @@ def _stack_backward_traversal( BLOCK_M=block_m, BLOCK_N=block_n, BLOCK_K=block_k, + output_edges=0, num_warps=warps, num_stages=stages, ) @@ -4956,7 +4971,18 @@ def _mixing_stack_backward(ctx, grad_out, grad_z, grad_u): None, None, ) - grad_u0, grad_alpha = _mixing_stack_bwd_op( + backward_op = _mixing_stack_bwd_op + if _EDGE_COHORT_INFER: + from .so2_edge_cohort import ( + cohort_backward, + supports_edge_cohort, + ) + + if supports_edge_cohort( + grad_out, z_all, ctx.lmax, ctx.focus_dim, ctx.apply_alpha + ): + backward_op = cohort_backward + grad_u0, grad_alpha = backward_op( grad_out, x_local, z_all, diff --git a/doc/development/ecs-edge-cohort-benchmark-results.json b/doc/development/ecs-edge-cohort-benchmark-results.json new file mode 100644 index 0000000000..7a1225794f --- /dev/null +++ b/doc/development/ecs-edge-cohort-benchmark-results.json @@ -0,0 +1,5202 @@ +{ + "schema_version": 1, + "baseline_commit": "668b60cc2a9559f79abb9c33acd959833376344f", + "model": { + "name": "DPA4-Neo-OMat24-v20260627.pt", + "sha256": "8d8b2b02b9575cb9e0af420cc1e8b1d9ffcbc04752c2203d29f48ccd0cd5f07d" + }, + "environment": { + "gpu": "NVIDIA GeForce RTX 5090", + "driver": "595.91.07", + "torch": "2.11.0+cu128", + "triton": "3.6.0", + "cuda": "12.8", + "precision": "FP32 SO(2), TF32 and AMP disabled" + }, + "protocol": { + "gpu": 0, + "atoms": 64, + "edges": 10114, + "seed": 0, + "coordinate_std_angstrom": 0.02, + "warmup": 8, + "samples": 30, + "independent_processes_per_arm": 3, + "order": [ + "BASE", + "FIXED", + "FIXED", + "BASE", + "BASE", + "FIXED" + ], + "cache": "new process and empty Inductor/Triton caches per run; driver/system caches not purged", + "timing": "full ASE energy/force/virial/stress API including neighbor building and host gaps", + "primary_memory": "complete high-water mark from model loading through first JIT, warmup and 30 measured calls", + "steady_memory": "separate 30-call replay after resetting peak counters; reserved cache not emptied", + "tolerance": { + "atol": 1e-05, + "rtol": 0.0001, + "relative_denominator_floor": 1e-08 + } + }, + "summary": { + "groups": { + "BASE": { + "process_p50_ms": [ + 12.157610966823995, + 11.771477526053786, + 12.372727505862713 + ], + "p50_ms": 12.157610966823995, + "pooled_wall_ms": { + "median": 12.157610966823995, + "p90": 12.414685660041869, + "min": 11.702988063916564, + "max": 12.85580382682383 + }, + "pooled_event_ms": { + "median": 12.128463745117188, + "p90": 12.389702796936035, + "min": 11.678048133850098, + "max": 12.829216003417969 + }, + 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"local_input_alpha_gradients": "24 edge-count/distribution cases, bitwise equal, finite, caller tensors unchanged" + }, + "limitations": [ + "Latest cold-process matrix covers Neo N64 only; no multi-model/PT2 acceptance claim.", + "Small-path residual reuse overlaps the earlier internal inplace optimization; percentages are not additive.", + "Earlier opaque two-cohort N64 path regressed 6.85-7.81%; it is not the reported small path.", + "No stable latency speedup is claimed below 3%; full model outputs are tolerance-equivalent, not bitwise guaranteed." + ] +} \ No newline at end of file diff --git a/doc/development/ecs-edge-cohort-benchmarks.md b/doc/development/ecs-edge-cohort-benchmarks.md new file mode 100644 index 0000000000..f36a81e842 --- /dev/null +++ b/doc/development/ecs-edge-cohort-benchmarks.md @@ -0,0 +1,205 @@ +# Experimental SO(2) edge-cohort inference backward + +This opt-in experiment targets first-order input and alpha gradients of a frozen +DPA4-Neo model. It is not a training optimization and does not change the default +inference path. Enable `DP_SO2_EDGE_COHORT_INFER=1` **before importing the model**. +Use `DP_TRITON_INFER=2`, with TF32 and AMP disabled. Other model components retain +their original precision; the checkpoint is not globally cast to FP32. + +## Scope and implementation + +The checked layout is `F=2`, `Cf=32`, `lmax=3`, `ROW=320`, three mixing layers +and two gated layers, contiguous FP32 activations, and `apply_alpha=True`. +Unsupported layouts retain the existing dispatcher. Weight gradients, higher +derivatives and extra saved-output gradients retain the existing training path. +The switch defaults to zero. + +The existing backward traverses a full edge set for each layer. The large-edge +experiment instead traverses the whole reverse layer chain of one cohort before +advancing to the next cohort. Scratch buffers scale with cohort size (8192), +while saved forward activations and the returned gradient still scale with the +full edge count. This is not a claim that all full-edge storage disappears. + +For `E <= 16384`, a single-cohort `torch.library.triton_op` exposes `wrap_triton` +launches to Inductor and uses the internally owned returned gradient as the +residual buffer. This avoids the opaque Python multi-cohort launch overhead on +small systems. It does **not** fuse the reverse chain into one mega-kernel. +16384 is a tested dispatch boundary, not a proven optimum for every workload. + +Only internally owned residual elements are overwritten. Caller tensors remain +read-only, and GEMM matrix input `gz` never aliases GEMM output: independent +column tiles may still be reading `gz`. Saved and output strides account for +full-graph extent even when a cohort has a partial tail. Alpha backward remains +separate to preserve the forward-output lifetime contract. The single-cohort +residual reuse overlaps the internal inplace optimization discussed in #6058; +their percentages must not be added or presented as independent improvements. + +The five GEMM schedule parameters, gate/point/recompute schedules, weights and +arithmetic are unchanged. Four native `wrap_triton` launch sites additionally +pass `saved_edges=0` / `output_edges=0` explicitly so optional Python defaults do +not become `None` in compiled native calls. Static and dynamic native tests +cover the disabled-switch path. + +## Measurement protocol + +The baseline is unmodified commit +`668b60cc2a9559f79abb9c33acd959833376344f`. The checkpoint is +`DPA4-Neo-OMat24-v20260627.pt`, SHA-256 +`8d8b2b02b9575cb9e0af420cc1e8b1d9ffcbc04752c2203d29f48ccd0cd5f07d`. +It must be provided separately; it is not bundled in this PR. + +Environment: RTX 5090, driver 595.91.07, PyTorch 2.11.0+cu128, CUDA runtime 12.8, +Triton 3.6.0. The main table uses GPU 0 only. It runs the normal ASE API for +energy, forces, virial and stress of a 64-atom diamond cell (`nrep=2`, approximately +10114 edges), seed 0, position perturbations with standard deviation 0.02 Angstrom. +Timing includes neighbor construction and host work. CUDA Events bracket the +whole API call on the default stream, including host launch gaps: they are not +the sum of kernel durations. + +Three independent processes per arm run in the order BASE/ECS/ECS/BASE/BASE/ECS. +Each process uses new Inductor and Triton caches, eight warmup frames and thirty +measured frames. System/driver caches are not purged. First-call compilation +time is recorded separately and excluded from steady timings. P50 is the median +of the three process medians; P90 pools ninety samples per arm. A GPU 0 NVML +monitor checked for foreign compute processes every 250 ms; none was detected. +Clocks were recorded, not locked, and no global driver settings were changed. +The monitor covers compilation and idle time as well as execution; its clock +range is not a claim of constant clocks during timing. + +**Primary memory includes the first call and compilation.** Counters are not +reset between loading the model, first execution, warmup and the thirty timed +calls. Report the worst high-water mark across the three processes for both +allocated and reserved memory. A second replay resets only peak counters to +measure steady allocated memory; allocator caches are not emptied. Reserved +memory can therefore retain the earlier cold peak. These are full-model CUDA +allocator metrics, not SO(2)-exclusive storage or total driver/context memory. +A compilation scratch allocation still consumes real capacity and is not +subtracted from the primary metric. Sampled device memory is an auxiliary +measurement and can miss narrow peaks. + +Numeric results and all per-call timings are in +[the JSON evidence](ecs-edge-cohort-benchmark-results.json). + +## Results: cold-process Neo N64 + +MB below is decimal (`1 MB = 10^6 bytes`). First and complete-window peaks +were identical in all six processes. Each arm's three allocated peak readings +were also identical. Memory columns report the worst process, not its average. + +| Full-model metric | Unmodified baseline | Opt-in candidate | Change | +| --- | ---: | ---: | ---: | +| **First/complete allocated peak, MB** | **498.199552** | **480.156672** | **-3.6216%** | +| First/complete reserved peak, MB | 528.482304 | 507.510784 | -3.9683% | +| Steady allocated peak, MB, secondary | 397.536256 | 379.493376 | -4.5387% | +| Steady reserved peak, MB, cache retained | 528.482304 | 507.510784 | -3.9683% | +| Wall P50, ms, median of process medians | 12.157611 | 12.313776 | +1.2845% latency | +| Wall P90, ms, pooled 90 samples | 12.414686 | 12.432651 | +0.1447% latency | +| Wall min / max, ms | 11.702988 / 12.855804 | 12.185729 / 12.588670 | distribution, not a speedup | +| CUDA Event median, ms, pooled | 12.128464 | 12.314896 | whole API, not kernel sum | +| First call including JIT, seconds, range | 124.931-125.718 | 124.219-126.629 | excluded from steady P50 | + +Baseline process P50s: 12.157611 / 11.771478 / 12.372728 ms. +Candidate process P50s: 12.398105 / 12.254008 / 12.313776 ms. +The latency difference is within the chosen 3% noise band; there is **no claimed +stable speedup**. The result is a reproducible memory reduction with no repeat +of the original approximately 7.8% small-system regression, not proof of zero +time cost. First-call compilation latency itself is approximately two minutes +in this fresh-cache protocol and is not hidden. + +| Accuracy comparison, three frames x energy/forces/virial/stress | Pairs | Worst error/tolerance ratio | Result | +| --- | ---: | ---: | --- | +| Baseline self, independent processes | 3 | 0.282621 | pass | +| Candidate self, independent processes | 3 | 0.184105 | pass | +| All baseline-to-candidate process pairs | 9 | 0.256570 | pass | +| Six processes versus fresh pure-PyTorch output | 6 | 0.352841 | pass | + +Within-process worst ratios are 0.256139 (baseline), 0.354324 (candidate), and +0.211513 (pure PyTorch). These characterize observed noise, not permission to +relax the absolute/relative acceptance thresholds. All compared arrays are +finite. During the full GPU 0 process windows, the highest sampled temperature +was 46 C; clock samples ranged from idle 172 MHz to 2902 MHz. No throttling +cause is inferred from those whole-window statistics. + +## Correctness and limitations + +Use `abs(actual-reference) <= 1e-5 + 1e-4*abs(reference)` elementwise, without +relaxing thresholds on failure. Relative error uses +`max(abs(reference), 1e-8)` in the denominator. The evidence includes max/mean +absolute error, max relative error, finite checks and the maximum error/tolerance +ratio for each frame and physical output. + +The cold matrix is checked against a fresh pure-PyTorch reference with Triton +disabled, and also checks baseline-to-baseline, candidate-to-candidate and all +nine baseline-to-candidate process pairs. The pure-PyTorch reference runs on +GPU 1 in one process with three self-repeats; this is not a three-process Torch +noise matrix, and its timings are not mixed with GPU 0 performance. +Full-model floating-point reductions are not promised to be bitwise identical. + +The focused CUDA test file has 60 passing tests, including zero/tail edges, +legal near-zero alpha, caller-input immutability, native static/dynamic compile, +and dispatch across 16384/16385 edges. Local experimental checks of input/alpha +gradients cover 24 edge-count/distribution cases with bitwise equality. Training +and higher-order tests check dispatch guards, not numerical correctness of a +complete training run. + +Earlier opaque two-cohort N64 runs regressed by 6.85-7.81% despite reducing +memory. The compiled single-cohort branch addresses that small-system penalty; +it must not inherit the earlier 9.46% cold-peak reduction claim. Differences +below 3% are treated as noise, not a demonstrated speedup. Earlier diagnostic +workers produced different baseline timings; their numbers are not mixed into +this same-entry-point comparison, and the cross-day difference is not uniquely +attributed to a hardware cause. + +The latest cold-process matrix is Neo N64 only. Earlier larger-system or other +model runs with precision failures are not reclassified as passing. This draft +does not claim multi-model acceptance, standalone `.pt2`/AOTInductor/C++ deployment, +LAMMPS performance, new NCU measurements, or full training validation. The +large-edge runtime launcher remains experimental. + +## Reproduce + +Install a CUDA-enabled DeePMD build and its runtime dependencies, plus `ase`, +`pytest` and Triton. Use the same environment and checkpoint for both checkouts. +Create a clean baseline worktree at the commit above, and a worktree containing +this PR. Set the following paths to absolute local paths: + +```bash +PY=/path/to/environment/bin/python +PR_ROOT=/path/to/pr-checkout +BASE_ROOT=/path/to/baseline-checkout +MODEL=/path/to/DPA4-Neo-OMat24-v20260627.pt +RESULTS=/path/to/new-results +BENCH=$PR_ROOT/source/tests/pt_expt/kernels/benchmark_so2_edge_cohort.py + +# Each --out must be new. Default caches are inside that output directory. +$PY "$BENCH" --gpu 0 --mode torch --source-root "$BASE_ROOT" \ + --model "$MODEL" --out "$RESULTS/torch" +$PY "$BENCH" --gpu 0 --mode base --source-root "$BASE_ROOT" \ + --model "$MODEL" --out "$RESULTS/base-0" \ + --reference "$RESULTS/torch/outputs.npz" +$PY "$BENCH" --gpu 0 --mode ecs --source-root "$PR_ROOT" \ + --model "$MODEL" --out "$RESULTS/ecs-0" \ + --reference "$RESULTS/torch/outputs.npz" +``` + +Run three processes per arm in the order specified above, with unique output +directories; do not choose the fastest process. GPU 0 must be idle at launch. +The benchmark refuses a nonempty explicit `--cache-dir` unless +`--allow-warm-cache` is given; warm-cache results are a different experiment. +`--nrep 4` tests 512 atoms and `--nrep 8` tests 4096, but the current N64 result +does not establish their acceptance. A failed precision check exits nonzero. + +The CLI writes `result.json`, `manifest.json`, `outputs.npz`, first-call memory +and compiler caches. It records global memory including validation, in addition +to the primary normal-inference window and separate steady replay, and records +source hashes. The public CLI has also been run end-to-end on Torch/base/ECS +with fresh caches; its GPU 1 timings are kept separate from the main matrix. + +For generated-input CUDA tests, use a correctly installed PR checkout: + +```bash +DP_TRITON_INFER=2 DP_TF32_INFER=0 DP_AMP_INFER=0 \ + $PY -m pytest source/tests/pt_expt/kernels/test_so2_edge_cohort.py -q +``` + +No weights or generated compiler caches are tracked in the repository. diff --git a/source/tests/pt_expt/kernels/_so2_edge_cohort_benchmark_common.py b/source/tests/pt_expt/kernels/_so2_edge_cohort_benchmark_common.py new file mode 100644 index 0000000000..d0303669b4 --- /dev/null +++ b/source/tests/pt_expt/kernels/_so2_edge_cohort_benchmark_common.py @@ -0,0 +1,131 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +"""Isolated-process setup and serialization for the SO(2) benchmark.""" + +import hashlib +import json +import os +from pathlib import Path +import site +import subprocess +import sys + +LAB = Path(__file__).resolve().parent +ROOT = Path(__file__).resolve().parents[4] + + +def setup(gpu, out, level="2"): + out.mkdir(parents=True, exist_ok=False) + busy = subprocess.check_output( + [ + "nvidia-smi", + "-i", + str(gpu), + "--query-compute-apps=pid", + "--format=csv,noheader", + ], + text=True, + ).strip() + if busy: + raise RuntimeError(f"GPU {gpu} has compute processes: {busy}") + for key in list(os.environ): + if key.startswith("DP_"): + del os.environ[key] + os.environ.update( + CUDA_VISIBLE_DEVICES=str(gpu), + DP_COMPILE_INFER="1", + DP_COMPILE="0", + DP_TRITON_INFER=level, + DP_CUDA_INFER="0", + DP_TF32_INFER="0", + DP_AMP_INFER="0", + OMP_NUM_THREADS="1", + DP_INTRA_OP_PARALLELISM_THREADS="1", + DP_INTER_OP_PARALLELISM_THREADS="1", + PYTORCH_ALLOC_CONF=os.getenv("LAB_ALLOC_CONF", "expandable_segments:True"), + TORCH_FORCE_NO_WEIGHTS_ONLY_LOAD="1", + TORCHINDUCTOR_COMPILE_THREADS="1", + TORCHINDUCTOR_CACHE_DIR=str(out.resolve() / "inductor"), + TRITON_CACHE_DIR=str(out.resolve() / "triton"), + ) + sys.path.insert(0, str(ROOT)) + sys.meta_path[:] = [ + f + for f in sys.meta_path + if not type(f).__module__.startswith("_editable_skbc_deepmd_kit") + ] + import deepmd + + for installed in site.getsitepackages(): + p = Path(installed) / "deepmd" + if (p / "lib").is_dir(): + deepmd.__path__.append(str(p)) + assert Path(deepmd.__file__).resolve().parent.parent == ROOT + import torch + + torch.set_num_threads(1) + torch.set_float32_matmul_precision("highest") + torch.backends.cuda.matmul.allow_tf32 = False + torch.backends.cudnn.allow_tf32 = False + + +def write(path, obj): + Path(path).write_text( + json.dumps(obj, indent=2, ensure_ascii=False, allow_nan=False) + ) + + +def sha(path): + return hashlib.sha256(Path(path).read_bytes()).hexdigest() + + +def environment(): + import torch + import triton + + return { + "python": sys.version, + "torch": torch.__version__, + "triton": triton.__version__, + "cuda": torch.version.cuda, + "gpu": subprocess.check_output( + [ + "nvidia-smi", + "--query-gpu=index,name,uuid,driver_version,clocks.sm,clocks.mem,temperature.gpu,power.draw", + "--format=csv", + ], + text=True, + ), + "revision": subprocess.check_output( + ["git", "rev-parse", "HEAD"], cwd=ROOT, text=True + ).strip(), + "flags": { + k: v + for k, v in os.environ.items() + if k.startswith( + ( + "DP_", + "PBS_", + "COHORT_", + "TORCHINDUCTOR", + "TRITON_CACHE", + "CUDA_VISIBLE", + "PYTORCH_ALLOC", + ) + ) + }, + "harness_hashes": { + p.name: sha(p) for p in LAB.glob("*so2_edge_cohort*benchmark*.py") + }, + } + + +def stats(values): + import numpy as np + + return { + "median": float(np.median(values)), + "p90": float(np.percentile(values, 90)), + "min": float(min(values)), + "max": float(max(values)), + "samples": values, + } diff --git a/source/tests/pt_expt/kernels/_so2_edge_cohort_benchmark_impl.py b/source/tests/pt_expt/kernels/_so2_edge_cohort_benchmark_impl.py new file mode 100644 index 0000000000..ad207e309a --- /dev/null +++ b/source/tests/pt_expt/kernels/_so2_edge_cohort_benchmark_impl.py @@ -0,0 +1,207 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +# ruff: noqa: T201 -- manual benchmark CLI output +"""Measurement body executed in an isolated module namespace by the CLI.""" + +import argparse +from pathlib import Path +import time +import traceback + +from _so2_edge_cohort_benchmark_common import environment, setup, sha, stats, write + +p = argparse.ArgumentParser() +p.add_argument("--out", type=Path, required=True) +p.add_argument("--gpu", default="0") +p.add_argument("--variant", choices=("BASE", "TORCH"), default="BASE") +p.add_argument("--nrep", type=int, default=5) +p.add_argument( + "--model", + type=Path, + required=True, +) +p.add_argument("--reference", type=Path) +p.add_argument("--warmup", type=int, default=8) +p.add_argument("--samples", type=int, default=30) +p.add_argument("--system", choices=("diamond", "water"), default="diamond") +a = p.parse_args() +setup(a.gpu, a.out, "0" if a.variant == "TORCH" else "2") + +import numpy as np +import torch +from ase.build import bulk +from deepmd.calculator import DP +from deepmd.pt_expt.kernels.triton.sezm import so2_value_path as sv + +assert a.warmup >= 3 and a.samples >= 10 +manifest = environment() +manifest.update(model_sha256=sha(a.model), args={k: str(v) for k, v in vars(a).items()}) +manifest["neo_stack_fp32_configs"] = sv.stack_fp32_configs(32, 3) +write(a.out / "manifest.json", manifest) + +if a.system == "diamond": + base = bulk("C", "diamond", a=3.567, cubic=True).repeat((a.nrep,) * 3) +else: + from ase.build import molecule + + base = molecule("H2O") + base.set_cell([4.0, 4.0, 4.0]) + base.center() + base.pbc = True + base = base.repeat((a.nrep,) * 3) +rng = np.random.default_rng(0) +frames = [ + base.positions + 0.02 * rng.standard_normal(base.positions.shape) + for _ in range(max(3, a.warmup + a.samples)) +] +calc = DP(model=str(a.model)) +inner = calc.dp.get_model() +assert not inner.training, "Inference must not enable model AMP through training mode" +write(a.out / "model_definition.json", calc.dp.get_model_def_script()) + + +def evaluate(pos): + calc.reset() + atoms = base.copy() + atoms.positions = pos + atoms.calc = calc + forces = atoms.get_forces().copy() + return { + "energy": np.asarray(atoms.get_potential_energy()), + "forces": forces, + "virial": np.asarray(calc.results["virial"]).copy(), + "stress": atoms.get_stress().copy(), + } + + +def compare(got, ref): + result = {} + for key, value in got.items(): + expected = ref[key] + assert value.shape == expected.shape + finite = bool(np.isfinite(value).all() and np.isfinite(expected).all()) + if finite: + d = np.abs(value - expected) + ratio = d / (1e-5 + 1e-4 * np.abs(expected)) + result[key] = { + "passed": bool((ratio <= 1).all()), + "finite": True, + "max_abs": float(d.max()), + "mean_abs": float(d.mean()), + "max_relative": float((d / np.maximum(np.abs(expected), 1e-8)).max()), + "max_ratio": float(ratio.max()), + "bitwise_equal": bool(np.array_equal(value, expected)), + } + else: + result[key] = {"passed": False, "finite": False} + return result + + +result = {"variant": a.variant, "n_atoms": len(base), "status": "running"} +try: + torch.cuda.synchronize() + before = { + "allocated": torch.cuda.memory_allocated(), + "reserved": torch.cuda.memory_reserved(), + } + start = time.perf_counter() + evaluate(frames[0]) + torch.cuda.synchronize() + first_ms = 1000 * (time.perf_counter() - start) + first = { + "allocated": torch.cuda.max_memory_allocated(), + "reserved": torch.cuda.max_memory_reserved(), + } + print("first", first_ms, first, flush=True) + write(a.out / "first.json", {"first_ms": first_ms, "memory": first}) + for pos in frames[1 : a.warmup]: + evaluate(pos) + wall, gpu = [], [] + for pos in frames[a.warmup : a.warmup + a.samples]: + torch.cuda.synchronize() + s, e = ( + torch.cuda.Event(enable_timing=True), + torch.cuda.Event(enable_timing=True), + ) + start = time.perf_counter() + s.record() + evaluate(pos) + e.record() + torch.cuda.synchronize() + wall.append(1000 * (time.perf_counter() - start)) + gpu.append(s.elapsed_time(e)) + complete = { + "allocated": torch.cuda.max_memory_allocated(), + "reserved": torch.cuda.max_memory_reserved(), + } + torch.cuda.reset_peak_memory_stats() + for pos in frames[a.warmup : a.warmup + a.samples]: + evaluate(pos) + torch.cuda.synchronize() + steady = { + "allocated": torch.cuda.max_memory_allocated(), + "reserved": torch.cuda.max_memory_reserved(), + } + arrays = { + f"{i}_{k}": v + for i, pos in enumerate(frames[:3]) + for k, v in evaluate(pos).items() + } + np.savez_compressed(a.out / "outputs.npz", **arrays) + accuracy = {} + if a.reference: + with np.load(a.reference) as ref: + accuracy = compare(arrays, ref) + repeats = [] + for _ in range(3): + repeated = { + f"{i}_{k}": v + for i, pos in enumerate(frames[:3]) + for k, v in evaluate(pos).items() + } + repeats.append(compare(repeated, arrays)) + from torch._inductor.codecache import PyCodeCache + + wrappers = [ + str(m.__file__) + for m in PyCodeCache.modules + if "def call(" in Path(m.__file__).read_text() + ] + assert wrappers, "No compiled wrapper; eager must not be reported as compiled" + result.update( + first_ms=first_ms, + wall_ms=stats(wall), + event_elapsed_ms=stats(gpu), + event_scope="entire API call on default stream; includes host gaps, not kernel-sum", + throughput_atoms_per_s=len(base) / (np.median(wall) / 1000), + memory={ + "before": before, + "first": first, + "complete": complete, + "steady": steady, + }, + accuracy=accuracy, + precision_pass=all(v["passed"] for v in accuracy.values()) + if accuracy + else None, + finite=all(bool(np.isfinite(v).all()) for v in arrays.values()), + repeat_noise=repeats, + wrappers=wrappers, + gpu_end=environment()["gpu"], + status="completed", + ) +except Exception: + result["status"] = "failed" + result["exception"] = traceback.format_exc() + print(result["exception"], flush=True) +finally: + write(a.out / "result.json", result) + print( + { + k: v + for k, v in result.items() + if k not in ("repeat_noise", "accuracy", "wrappers") + }, + flush=True, + ) +if result["status"] != "completed": + raise SystemExit(1) diff --git a/source/tests/pt_expt/kernels/benchmark_so2_edge_cohort.py b/source/tests/pt_expt/kernels/benchmark_so2_edge_cohort.py new file mode 100644 index 0000000000..90b579489d --- /dev/null +++ b/source/tests/pt_expt/kernels/benchmark_so2_edge_cohort.py @@ -0,0 +1,131 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +# ruff: noqa: T201 -- manual benchmark CLI output +"""Reproduce full-model first-call, global and steady SO(2) memory results. + +This manual CUDA benchmark is not collected by pytest. Checkpoint files and +generated compiler caches are intentionally not included in the repository. +""" + +import argparse +import os +from pathlib import Path +import runpy +import sys + +import _so2_edge_cohort_benchmark_common as common + +parser = argparse.ArgumentParser(description=__doc__) +parser.add_argument("--model", required=True, type=Path) +parser.add_argument("--out", required=True, type=Path) +parser.add_argument( + "--source-root", type=Path, default=Path(__file__).resolve().parents[4] +) +parser.add_argument("--mode", choices=("base", "ecs", "torch"), default="base") +parser.add_argument("--gpu", default="0") +parser.add_argument("--nrep", type=int, default=2) +parser.add_argument("--warmup", type=int, default=8) +parser.add_argument("--samples", type=int, default=30) +parser.add_argument("--reference", type=Path) +parser.add_argument("--cache-dir", type=Path) +parser.add_argument("--allow-warm-cache", action="store_true") +args = parser.parse_args() +common.ROOT = args.source_root.resolve() +original_setup = common.setup + + +def setup(gpu, out, level="2"): + """Select code and precision before importing the model implementation.""" + original_setup(gpu, out, level) + os.environ["DP_SO2_EDGE_COHORT_INFER"] = "1" if args.mode == "ecs" else "0" + if args.cache_dir is not None: + cache = args.cache_dir.resolve() + if cache.exists() and any(cache.iterdir()) and not args.allow_warm_cache: + raise ValueError( + "nonempty cache: pass --allow-warm-cache or use a new directory" + ) + os.environ["TORCHINDUCTOR_CACHE_DIR"] = str(cache / "inductor") + os.environ["TRITON_CACHE_DIR"] = str(cache / "triton") + + +common.setup = setup +implementation = Path(__file__).with_name("_so2_edge_cohort_benchmark_impl.py") +sys.argv = [ + str(implementation), + "--model", + str(args.model), + "--out", + str(args.out), + "--gpu", + args.gpu, + "--nrep", + str(args.nrep), + "--warmup", + str(args.warmup), + "--samples", + str(args.samples), + "--variant", + "TORCH" if args.mode == "torch" else "BASE", +] +if args.reference is not None: + sys.argv += ["--reference", str(args.reference)] +namespace = runpy.run_path(str(implementation), run_name="__main__") +import torch + +result = namespace["result"] +result["variant"] = args.mode +memory = result["memory"] +# The implementation resets counters only for its separate steady replay. +# Preserve the preceding high-water mark, including model load and first JIT. +memory["after_validation"] = { + "allocated": torch.cuda.max_memory_allocated(), + "reserved": torch.cuda.max_memory_reserved(), +} +memory["global"] = { + key: max( + memory[phase][key] + for phase in ("first", "complete", "steady", "after_validation") + ) + for key in ("allocated", "reserved") +} +memory["global_first_observed_phase"] = next( + phase + for phase in ("first", "complete", "steady", "after_validation") + if memory[phase]["allocated"] == memory["global"]["allocated"] +) +result["protocol"] = { + "seed": 0, + "coordinate_std_angstrom": 0.02, + "cache": "warm_allowed" + if args.allow_warm_cache + else "new_inductor_and_triton_cache", + "primary_memory": "global high-water mark including model load and first JIT; never substitute steady", + "tf32": False, + "amp": False, + "weight_gradients": False, + "cold_scope": "new process and application compiler caches; driver/system caches are not purged", +} +result["source_hashes"] = { + str(p.relative_to(common.ROOT)): common.sha(p) + for p in [ + common.ROOT / "deepmd/pt_expt/kernels/triton/sezm/so2_value_path.py", + common.ROOT / "deepmd/pt_expt/kernels/triton/sezm/so2_edge_cohort.py", + ] + if p.exists() +} +assert torch.get_float32_matmul_precision() == "highest" +assert not torch.backends.cuda.matmul.allow_tf32 +assert not torch.backends.cudnn.allow_tf32 +common.write(args.out / "result.json", result) +if args.reference is not None and not result["precision_pass"]: + raise SystemExit("accuracy check failed; thresholds were not relaxed") +if not all(v["passed"] for repeat in result["repeat_noise"] for v in repeat.values()): + raise SystemExit("within-process reproducibility check failed") +print( + { + "mode": args.mode, + "p50_ms": result["wall_ms"]["median"], + "first_peak": memory["first"], + "global_peak": memory["global"], + "steady_peak": memory["steady"], + } +) diff --git a/source/tests/pt_expt/kernels/test_so2_edge_cohort.py b/source/tests/pt_expt/kernels/test_so2_edge_cohort.py new file mode 100644 index 0000000000..087ab83624 --- /dev/null +++ b/source/tests/pt_expt/kernels/test_so2_edge_cohort.py @@ -0,0 +1,201 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +"""Generated-input checks for the opt-in frozen Neo edge-cohort prototype.""" + +from __future__ import annotations + +from types import SimpleNamespace + +import pytest +import torch + +pytest.importorskip("triton") + +from deepmd.pt_expt.kernels.triton.sezm import so2_edge_cohort as ecs +from deepmd.pt_expt.kernels.triton.sezm import so2_value_path as sv + +pytestmark = pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA required") + + +def operands(edges: int, mode: str = "random") -> tuple: + """Generate well-conditioned operands without a downloaded checkpoint.""" + torch.manual_seed(719) + u = torch.randn(2, edges, 320, device="cuda") * 0.1 + if mode == "zero": + u.zero_() + alpha = torch.rand(edges, 2, device="cuda") * 0.8 + 0.1 + if mode == "tiny": + alpha.fill_(1e-5) + w0 = torch.randn(3, 2, 128, 128, device="cuda") * 0.01 + w1 = torch.randn(3, 2, 192, 192, device="cuda") * 0.01 + gw = torch.randn(2, 2, 32, 96, device="cuda") * 0.01 + out, z, uf = sv._mixing_stack_impl(u, alpha, w0, w1, gw, 3, 32, True) + go = torch.randn_like(out) * 0.1 + return ( + go, + out, + z, + uf, + alpha, + w0.transpose(2, 3).contiguous(), + w1.transpose(2, 3).contiguous(), + gw, + gw.transpose(2, 3).contiguous(), + 3, + 32, + True, + ) + + +@pytest.mark.parametrize("edges", [0, 1, 37, 129, 1025, 8193]) +@pytest.mark.parametrize("cap", [64, 8192]) +@pytest.mark.parametrize("mode", ["random", "zero", "tiny"]) +def test_cohort_matches_native(edges: int, cap: int, mode: str) -> None: + """Cover tail cohorts, two focus strides, legal tiny alpha and no mutation.""" + args = operands(edges, mode) + copies = [x.clone() for x in args[:9]] + expected_u, expected_a = sv._mixing_stack_bwd_impl(*args) + go, out, z, _, alpha, w0t, w1t, gw, gwt, *_ = args + actual_u = ecs.cohort_input_backward_impl(go, z, alpha, w0t, w1t, gw, gwt, cap) + actual_a = ecs._cohort_alpha_backward(go, out, alpha) + for actual, expected in ((actual_u, expected_u), (actual_a, expected_a)): + assert torch.isfinite(actual).all() + torch.testing.assert_close(actual, expected, rtol=0, atol=0) + for before, after in zip(copies, args[:9], strict=True): + assert torch.equal(before, after) + + +def test_compiled_dynamic_cohort() -> None: + """The opaque runtime edge loop supports online dynamic graph execution.""" + compiled = torch.compile(ecs.cohort_backward, fullgraph=True, dynamic=True) + for edges in (37, 129): + args = operands(edges) + actual = compiled(*args) + expected = sv._mixing_stack_bwd_impl(*args) + for a, b in zip(actual, expected, strict=True): + torch.testing.assert_close(a, b, atol=0, rtol=0) + + +@pytest.mark.parametrize("dynamic", [False, True]) +def test_compiled_native_backward(dynamic: bool) -> None: + """Optional strides must preserve the default native compilation path.""" + compiled = torch.compile(sv._mixing_stack_bwd_op, fullgraph=True, dynamic=dynamic) + for edges in (37, 129): + args = operands(edges) + expected = sv._mixing_stack_bwd_impl(*args) + actual = compiled(*args) + for a, b in zip(actual, expected, strict=True): + torch.testing.assert_close(a, b, atol=0, rtol=0) + + +@pytest.mark.parametrize("edges", [0, 37, 8193, 16384]) +@pytest.mark.parametrize("mode", ["random", "zero", "tiny"]) +def test_single_cohort_owned_output(edges: int, mode: str) -> None: + """Small-cohort reuse changes neither gradients nor caller-owned storage.""" + args = operands(edges, mode) + copies = [t.clone() for t in args[:9]] + go, out, z, _, alpha, w0t, w1t, gw, gwt, *_ = args + expected_u, expected_a = sv._mixing_stack_bwd_impl(*args) + actual_u = ecs._single_cohort_input_backward( + go, z, alpha, w0t, w1t, gw, gwt, ecs.COHORT_EDGES + ) + actual_a = ecs._cohort_alpha_backward(go, out, alpha) + for a, b in ((actual_u, expected_u), (actual_a, expected_a)): + assert torch.isfinite(a).all() + torch.testing.assert_close(a, b, atol=0, rtol=0) + for before, after in zip(copies, args[:9], strict=True): + assert torch.equal(before, after) + + +def test_compiled_cohort_bucket_boundary() -> None: + """A dynamic compiled graph handles both sides of the small-E boundary.""" + compiled = torch.compile(ecs.cohort_backward, fullgraph=True, dynamic=True) + for edges in (16384, 16385): + args = operands(edges) + expected = sv._mixing_stack_bwd_impl(*args) + actual = compiled(*args) + for a, b in zip(actual, expected, strict=True): + torch.testing.assert_close(a, b, atol=0, rtol=0) + + +@pytest.mark.parametrize( + "lmax,cf,apply", [(2, 32, True), (3, 64, True), (3, 32, False)] +) +def test_unsupported_layout(lmax: int, cf: int, apply: bool) -> None: + """Untested layouts must not enter the experimental implementation.""" + args = operands(1) + assert not ecs.supports_edge_cohort(args[0], args[2], lmax, cf, apply) + assert not ecs.supports_edge_cohort(args[0].cpu(), args[2].cpu(), 3, 32, True) + + +@pytest.mark.parametrize("reason", ["weights", "saved_z", "saved_u", "create_graph"]) +def test_training_dispatch_preserved(monkeypatch, reason: str) -> None: + """Weight/state/higher-order requests bypass ECS even when opted in.""" + args = operands(1) + go, out, z, uf, alpha, w0t, w1t, gw, *_ = args + u = torch.zeros_like(uf) + w0 = w0t.transpose(2, 3).contiguous() + w1 = w1t.transpose(2, 3).contiguous() + ctx = SimpleNamespace( + saved_tensors=(u, alpha, out, z, uf, w0, w1, gw), + needs_input_grad=( + True, + True, + reason == "weights", + False, + False, + False, + False, + False, + ), + lmax=3, + focus_dim=32, + apply_alpha=True, + ) + calls = [] + + def training(*unused): + calls.append(True) + return torch.zeros_like(u), torch.zeros_like(alpha), w0, w1, gw + + def forbidden(*unused): + raise AssertionError("ECS entered a training/higher-order request") + + monkeypatch.setattr(sv, "_EDGE_COHORT_INFER", True) + monkeypatch.setattr(sv, "_mixing_stack_train_bwd_op", training) + monkeypatch.setattr(ecs, "cohort_backward", forbidden) + with torch.set_grad_enabled(reason == "create_graph"): + sv._mixing_stack_backward( + ctx, + go, + torch.zeros_like(z) if reason == "saved_z" else None, + torch.zeros_like(uf) if reason == "saved_u" else None, + ) + assert calls == [True] + + +def test_frozen_dispatch(monkeypatch) -> None: + """Enabling the switch actually changes the frozen inference dispatch.""" + args = operands(37) + go, out, z, uf, alpha, w0t, w1t, gw, *_ = args + ctx = SimpleNamespace( + saved_tensors=( + uf, + alpha, + out, + z, + uf, + w0t.transpose(2, 3).contiguous(), + w1t.transpose(2, 3).contiguous(), + gw, + ), + needs_input_grad=(True, True, False, False, False, False, False, False), + lmax=3, + focus_dim=32, + apply_alpha=True, + ) + monkeypatch.setattr(sv, "_EDGE_COHORT_INFER", True) + expected = sv._mixing_stack_bwd_impl(*args) + with torch.no_grad(): + actual = sv._mixing_stack_backward(ctx, go, None, None) + for a, b in zip(actual[:2], expected, strict=True): + torch.testing.assert_close(a, b, atol=0, rtol=0) From 9e2e4c5de1b90cf2d6bc8b2706bc103a06778dae Mon Sep 17 00:00:00 2001 From: Macqueen Ma-hnu Date: Mon, 19 Oct 2026 15:30:57 +0800 Subject: [PATCH 2/7] test(pt): add counterbalanced ECS latency validation --- .../ecs-edge-cohort-benchmark-results.json | 4 +- doc/development/ecs-edge-cohort-benchmarks.md | 12 +- .../ecs-edge-cohort-controlled-raw.json.gz | Bin 0 -> 1311322 bytes .../ecs-edge-cohort-controlled-results.json | 4593 +++++++++++++++++ .../ecs-edge-cohort-controlled-timing.md | 235 + .../_so2_edge_cohort_benchmark_impl.py | 2 + .../_so2_edge_cohort_controlled_worker.py | 245 + .../kernels/analyze_so2_edge_cohort_clocks.py | 76 + .../kernels/analyze_so2_edge_cohort_paired.py | 229 + .../analyze_so2_edge_cohort_same_context.py | 104 + .../kernels/analyze_so2_edge_cohort_serial.py | 112 + .../benchmark_so2_edge_cohort_paired.py | 228 + .../benchmark_so2_edge_cohort_same_context.py | 326 ++ .../benchmark_so2_edge_cohort_serial.py | 191 + 14 files changed, 6352 insertions(+), 5 deletions(-) create mode 100644 doc/development/ecs-edge-cohort-controlled-raw.json.gz create mode 100644 doc/development/ecs-edge-cohort-controlled-results.json create mode 100644 doc/development/ecs-edge-cohort-controlled-timing.md create mode 100644 source/tests/pt_expt/kernels/_so2_edge_cohort_controlled_worker.py create mode 100644 source/tests/pt_expt/kernels/analyze_so2_edge_cohort_clocks.py create mode 100644 source/tests/pt_expt/kernels/analyze_so2_edge_cohort_paired.py create mode 100644 source/tests/pt_expt/kernels/analyze_so2_edge_cohort_same_context.py create mode 100644 source/tests/pt_expt/kernels/analyze_so2_edge_cohort_serial.py create mode 100644 source/tests/pt_expt/kernels/benchmark_so2_edge_cohort_paired.py create mode 100644 source/tests/pt_expt/kernels/benchmark_so2_edge_cohort_same_context.py create mode 100644 source/tests/pt_expt/kernels/benchmark_so2_edge_cohort_serial.py diff --git a/doc/development/ecs-edge-cohort-benchmark-results.json b/doc/development/ecs-edge-cohort-benchmark-results.json index 7a1225794f..c56875fa35 100644 --- a/doc/development/ecs-edge-cohort-benchmark-results.json +++ b/doc/development/ecs-edge-cohort-benchmark-results.json @@ -5197,6 +5197,6 @@ "Latest cold-process matrix covers Neo N64 only; no multi-model/PT2 acceptance claim.", "Small-path residual reuse overlaps the earlier internal inplace optimization; percentages are not additive.", "Earlier opaque two-cohort N64 path regressed 6.85-7.81%; it is not the reported small path.", - "No stable latency speedup is claimed below 3%; full model outputs are tolerance-equivalent, not bitwise guaranteed." + "Cold-process percentage differences alone are not significance tests; the fixed-3%-noise interpretation is retired by the controlled timing follow-up. Full model outputs are tolerance-equivalent, not bitwise guaranteed." ] -} \ No newline at end of file +} diff --git a/doc/development/ecs-edge-cohort-benchmarks.md b/doc/development/ecs-edge-cohort-benchmarks.md index f36a81e842..52a47a0d1e 100644 --- a/doc/development/ecs-edge-cohort-benchmarks.md +++ b/doc/development/ecs-edge-cohort-benchmarks.md @@ -1,5 +1,11 @@ # Experimental SO(2) edge-cohort inference backward +**Update:** the cold-process numbers below remain historical measurements, but +their earlier fixed-3%-noise interpretation is retired. See the +[controlled timing follow-up](ecs-edge-cohort-controlled-timing.md) for aligned +autotune choices, counterbalanced same-context normal-API measurements, +process-level uncertainty and negative experiments. + This opt-in experiment targets first-order input and alpha gradients of a frozen DPA4-Neo model. It is not a training optimization and does not change the default inference path. Enable `DP_SO2_EDGE_COHORT_INFER=1` **before importing the model**. @@ -100,8 +106,8 @@ were also identical. Memory columns report the worst process, not its average. Baseline process P50s: 12.157611 / 11.771478 / 12.372728 ms. Candidate process P50s: 12.398105 / 12.254008 / 12.313776 ms. -The latency difference is within the chosen 3% noise band; there is **no claimed -stable speedup**. The result is a reproducible memory reduction with no repeat +The single-batch latency difference alone is not a significance test; there is +**no claimed stable speedup**. The result is a reproducible memory reduction with no repeat of the original approximately 7.8% small-system regression, not proof of zero time cost. First-call compilation latency itself is approximately two minutes in this fresh-cache protocol and is not hidden. @@ -145,7 +151,7 @@ complete training run. Earlier opaque two-cohort N64 runs regressed by 6.85-7.81% despite reducing memory. The compiled single-cohort branch addresses that small-system penalty; it must not inherit the earlier 9.46% cold-peak reduction claim. Differences -below 3% are treated as noise, not a demonstrated speedup. Earlier diagnostic +are now evaluated with process-level uncertainty, not a fixed 3% cutoff. 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2, + "unchanged": true, + "count": 835 + }, + { + "run": 3, + "unchanged": true, + "count": 835 + }, + { + "run": 4, + "unchanged": true, + "count": 835 + }, + { + "run": 5, + "unchanged": true, + "count": 835 + } + ], + "interpretation": "finite n=3 independent pairs; a confidence interval is not proof of zero cost", + "cold_peak": "separate existing cold-process measurement, not replaced" + }, + "compiled_graph_replay_diagnostic": { + "processes": 1, + "blocks": 24, + "paired_change_percent": 0.226145777601847, + "conditional_block_bootstrap95_percent": [ + 0.0706033734667633, + 0.38418086800490775 + ], + "scope": "compiled graph only on same captured real inputs and CUDA context; one process, not population/API acceptance", + "stats": { + "BASE": { + "p50_ms": 7.320728036575019, + "p90_ms": 7.417856063693762 + }, + "ECS": { + "p50_ms": 7.336758426390588, + "p90_ms": 7.425044267438352 + } + } + } + }, + "natural_stride_not_adopted": [ + { + "run": "natural_stride_r0", + "status": { + "status": "completed", + "correctness_cases": 21, + "dtype": "float32", + "tf32": false, + "scope": "single small-stack input backward only, no alpha or forward/model timing" + }, + "paired_change_percent": 0.06525239059100577, + "conditional_block95_percent": [ + -0.028697385654204866, + 0.14577425505556788 + ], + "p50_ms": { + "old": 0.3089229576289654, + "natural": 0.3091339021921158 + } + }, + { + "run": "natural_stride_r1", + "status": { + "status": "completed", + "correctness_cases": 21, + "dtype": "float32", + "tf32": false, + "scope": "single small-stack input backward only, no alpha or forward/model timing" + }, + "paired_change_percent": 0.03573459119047764, + "conditional_block95_percent": [ + -0.0006084065066323472, + 0.07264982019066607 + ], + "p50_ms": { + "old": 0.31001504976302385, + "natural": 0.3102158661931753 + } + } + ], + "kernel_counts": { + "full_model_per_frame": 657, + "so2_per_frame": 32, + "single_stack_forward": 8, + "single_stack_backward": 8, + "scope": "new representative profiler traces; time under profiler is not used for speed conclusions" + }, + "wrapper_equivalence": { + "real_pr_kernel_definitions": 191, + "in_process_kernel_definitions": 191, + "identical_kernel_body_multiset": true, + "same_call_after_symbol_renaming": true, + "real_call": "2eb42115a595ebef74c6ffd358fcb1a3cb9ef2f39461cc8b91cd9de89b71ebb7", + "in_process_call": "2eb42115a595ebef74c6ffd358fcb1a3cb9ef2f39461cc8b91cd9de89b71ebb7" + }, + "first_peak_capacity": { + "scope": "retained independent cold-process measurements; source unchanged; new timing protocols do not replace first peak", + "baseline": { + "first": { + "allocated": 498199552, + "reserved": 528482304 + }, + "complete": { + "allocated": 498199552, + "reserved": 528482304 + }, + "steady": { + "allocated": 397536256, + "reserved": 528482304 + } + }, + "candidate": { + "first": { + "allocated": 480156672, + "reserved": 507510784 + }, + "complete": { + "allocated": 480156672, + "reserved": 507510784 + }, + "steady": { + "allocated": 379493376, + "reserved": 507510784 + } + }, + "first_alloc_reduction_percent": 3.621617066407956, + "steady_alloc_reduction_percent": 4.538675335313314 + }, + "invalid_diagnostics": [ + "Two launcher-inspection failures before controlled timing; no comparative result used.", + "One own-worker initialization cancelled after inspection incompatibility was identified.", + "Two graph-replay setup failures (bound method / missing no_grad); not candidate numerical failures." + ], + "limitations": [ + "Neo N64 only; no new multi-model/PT2/large-edge acceptance.", + "Only partial Megakernels scheduling ideas are used, not its runtime or a cross-layer mega-kernel.", + "No stable speedup or exact zero-cost guarantee; initialization-order effect observed, low-level mechanism not uniquely attributed.", + "The natural-stride experiment was not adopted; it did not demonstrate a repeatable benefit.", + "Large-edge make_fx/AOT dispatch must be checked separately; Dynamo boundary unit tests are not AOT deployment acceptance." + ], + "raw_evidence": { + "file": "ecs-edge-cohort-controlled-raw.json.gz", + "sha256": "213178e9d326af552766ffb5b0f002b924e56a9ede703b4b7748bcde912e329d", + "compressed_bytes": 1311322 + } +} \ No newline at end of file diff --git a/doc/development/ecs-edge-cohort-controlled-timing.md b/doc/development/ecs-edge-cohort-controlled-timing.md new file mode 100644 index 0000000000..9f90018e0e --- /dev/null +++ b/doc/development/ecs-edge-cohort-controlled-timing.md @@ -0,0 +1,235 @@ +# ECS controlled timing follow-up + +This follow-up investigates the previously reported N64 latency increase of +1.2845%. The kernel implementation is unchanged. It replaces the earlier +"less than 3% means noise" interpretation with counterbalanced measurements +and process-level uncertainty. No stable speedup or exact zero-cost guarantee +is claimed. + +## Main finding + +Four independent processes each held both implementations in **one CUDA +context**, using normal ASE energy/force/virial/stress calls. Two Python hash +seeds (0, 1) were each tested with baseline-first and ECS-first compilation. +Each process ran 24 symmetric ABBA blocks, 50 calls per round, eight warmups +before each round: 9600 measured calls per arm, 96 blocks in total. + +| Metric | Baseline | ECS | +| --- | ---: | ---: | +| Pooled normal-API P50, ms | 11.683581 | 11.708142 | +| Pooled normal-API P90, ms | 11.911459 | 11.932993 | +| Direct ratio of pooled P50s | reference | +0.2102% latency | +| Mean process-level paired effect | reference | **+0.0795% latency** | +| 95% Student-t interval over four independent process effects | — | **[-0.3866%, +0.5479%]** | + +The paired estimate averages log ratios of matched block medians within each +process, then gives each process equal weight. It is not the ratio of pooled +medians; both are reported rather than choosing one favorable number. Frames +are correlated within processes and are not 19200 independent experiments. + +The measured interval does not support a persistent +1.2845% penalty under +these controlled conditions. It also does not establish exact zero overhead, +or equivalence on other models, sizes, compilers or hardware. Four processes +remain a finite sample. There is no fixed 3% significance rule. + +| Process | First compiled model | Hash seed | Paired ECS latency change | +| --- | --- | ---: | ---: | +| 0 | Baseline | 0 | +0.2999% | +| 1 | ECS | 0 | -0.1536% | +| 2 | Baseline | 1 | +0.3656% | +| 3 | ECS | 1 | -0.1924% | + +Compilation/initialization order is an observed confound, not a uniquely +identified low-level cause. No claim is made that a particular cache bank, +Python hash lookup or GPU clock policy explains the entire historical result. + +## Why the old result was not an isolated ECS comparison + +The earlier independent cold runs also independently autotuned other Inductor +kernels. Compared with the first baseline process, the first candidate had +**41 different launch choices among 109 matching cached programs**. Other +candidate runs had 44/49 changed choices; other baseline runs themselves had +48/47 among 110. These are actual XBLOCK/warp/stage changes, not differences +in recorded autotune duration. This does not quantify how much of the 1.28% +each change caused, but it prevents assigning the whole difference to ECS. + +The five SO(2) GEMM schedule values remained `(64,64,32,4,2)`. A source option +such as `max_autotune=False` does not mean every generated pointwise/reduction +kernel has no runtime choice. In the controlled cache, all 110 anchor choices +were preserved. Shared semantic programs used identical actual configs, and +835 autotune/PTX/cubin file hashes remained unchanged during each main timed +window. Nothing was retuned halfway through the comparison. + +## Controls, diagnostics and rejected explanations + +These protocols have different scopes; do not pool their absolute times or +call their confidence intervals interchangeable. + +| Protocol | Independent groups | Paired change | Process t 95% interval / limitation | +| --- | ---: | ---: | --- | +| Two resident workers, GPU0, 12 ABBA blocks/group | 6 | +0.0206% | [-1.4295%, +1.4920%]; initialization-order bias remained | +| One worker at a time, GPU0, matched seeds, 400 formal calls/process | 3 pairs | -0.5510% | [-4.1574%, +3.1910%]; too few groups for a tight conclusion | +| Identical baseline versus baseline clone, GPU1 | 2 | +0.3511% | pair effects +0.8666% / -0.1618%; calibration only | +| Candidate source with ECS disabled versus original baseline, GPU1 | 2 | +0.5092% | pair effects +1.6908% / -0.6587%; no stable compatibility-overhead attribution | +| Same-context compiled-graph replay, GPU1 | 1 | +0.2261% | conditional block interval [+0.0706%, +0.3842%], not population/API acceptance | + +The last diagnostic intentionally excludes ASE, neighbor preprocessing and the +AOT runtime shell. It provides a small-overhead observation, not a substitute +for complete API performance. This model's energy inference path is +`make_fx -> aot_module_simplified -> compile_fx_inner`, **bypassing Dynamo**; +it would be incorrect to attribute the observed cost to Dynamo inference guards. + +Both GPUs were checked for foreign work on the measured card. Only one worker +executed at a time; known resident workers in the resident diagnostic were +allowed to keep idle contexts. GPU0 workers used CPU8, monitor CPU16; GPU1 used +CPU40, monitor CPU48, on their respective local NUMA nodes. NVML sampled actual +timing intervals at 50 ms. SM clocks remained dynamic, not locked. Memory +clocks, temperature, power and available clock-reason data are retained. +Whole-process idle/compilation readings are not called timing-window clocks. +No driver settings, global GPU clocks, or other users' processes were changed. + +As an explicitly secondary, post-stratified check, 90/96 primary blocks meet +all of: mean sampled SM-clock difference <=0.5%, memory clock 13801 MHz in both +arms, mean temperature difference <=2 C. Their paired estimate is +0.0861%, +process t 95% interval [-0.3873%, +0.5617%], consistent with the full result. +This is a hardware-matching criterion, **not** a latency-noise cutoff. All 96 +blocks remain in the primary analysis. Selection changes composition and +does not prove constant per-kernel clocks. In the full primary sampling, +baseline SM mean was 2776.84 MHz and candidate 2815.98 MHz; ten baseline +samples reported memory idle-clock 810 MHz. These readings are not hidden. + +In the resident experiment, average sampled SM clocks for first/second +initialized workers were approximately 2776.89/2775.65 MHz. That near equality +does not support blaming the whole initialization-order bias on downclocking. +Dynamic per-round frequency variation still prevents claiming constant clocks. +Outliers were retained; some individual API calls exceeded 30 ms. + +## Same-context setup and validation + +The manual harness first compiles one normal calculator. It then copies the +exact three reviewed kernel definitions and registered backward into the +experiment process, clears the **module-level** SeZM compiled cache, and +compiles the other calculator. Each retains its separate model-local cache. +This is necessary: the shared cache key does not independently prove that the +second experimental mode produced a new graph. + +The harness asserts distinct compiled wrappers and the expected ECS marker, +keeps the first model's cache unchanged, and checks all three reference frames. +Runtime transplantation is **benchmark setup only**; the production proposal +uses ordinary checked-in functions and does not generate or rewrite kernel +source. No checkpoint or production file is modified by the harness. + +To audit this method against the real PR integration, the generated candidate +has the same multiset of 191 embedded function bodies and the same entire +`call` AST after symbolic-size renaming. Normalized call SHA-256: +`2eb42115a595ebef74c6ffd358fcb1a3cb9ef2f39461cc8b91cd9de89b71ebb7`. +This counts source definitions, not runtime launches. The baseline remains the +unmodified commit `668b60cc2a9559f79abb9c33acd959833376344f`. + +Every same-context process passes three-frame energy/force/virial/stress +comparisons against the pure-PyTorch reference and its paired baseline, using +`atol=1e-5, rtol=1e-4`; worst error/tolerance ratio across these runs is 0.376674. +All compared outputs are finite. The reference is one fresh pure-PyTorch +process with three self-repeats, not a new multi-process Torch matrix. +The 60 focused CUDA tests were rerun: 60 passed, 14 deprecation warnings. + +## Bounded specialization attempt: not adopted + +A full single cohort has `count == E`, `start == 0`. Four runtime +`saved_edges=E` / `output_edges=E` operands were experimentally replaced with +the natural-stride sentinel zero. No floating arithmetic, launch count or tile +was changed. Each of two processes checked 21 generated cases: +E=0/1/37/129/8193/10114/16384, random/zero/tiny legal alpha; input gradients +were bitwise equal, finite, and caller-owned inputs unchanged. + +| Small-stack input-backward only | Original P50 ms | Natural stride P50 ms | Paired change | +| --- | ---: | ---: | ---: | +| Process 0 | 0.308923 | 0.309134 | +0.0653% | +| Process 1 | 0.310015 | 0.310216 | +0.0357% | + +Conditional block intervals include zero. The selected binary sets differ, +so it is not established that the compiler erased every difference; nevertheless +there is no measured benefit. This attempt is **not adopted** and is not an +end-to-end speedup. Alpha backward itself was unchanged. + +## Memory and the extent of Megakernels use + +The unchanged implementation retains the earlier independent cold-process +capacity result: **first/complete allocated peak 498.199552 -> 480.156672 MB +(-3.6216%)**, reserved 528.482304 -> 507.510784 MB (-3.9683%). Steady allocated +397.536256 -> 379.493376 MB (-4.5387%) is secondary. JIT/first execution is not +removed from the primary peak. Dual-model resident capacity is not substituted +for this single-model measurement. + +Current ECS borrows bounded-workset/dependency scheduling ideas; it does not +integrate the HazyResearch runtime or retain the reverse chain wholly on chip. +The new representative traces show **657 GPU launches/frame in both arms, +including 32 SO(2) launches**: two stacks, each 8 forward and 8 backward kernels. +There is no mega-kernel launch-count reduction in this proposal. + +The reference [Megakernels implementation](https://github.com/HazyResearch/Megakernels/blob/main/include/megakernel.cuh) +uses device loader/consumer/storer/controller roles and synchronization. Those +mechanisms are not present here. Earlier single-CTA persistent PBS experiments +in this project regressed at E=158002 (baseline backward about 5.047 ms, best of +12 PBS configurations about 8.780 ms); that route was not promoted. Fusion can +lose column parallelism, increase registers/spill and synchronization costs. +Fewer buffers or launches alone do not establish faster execution. + +## Reproduce and inspect + +Use the same checkpoint/environment as the original protocol. Provide absolute +paths for `BASE_ROOT`, `PR_ROOT`, `MODEL`, `TORCH_REFERENCE` and fresh result dirs. +CPU numbers below are specific to the reported dual-socket server; select valid +GPU-local cores on another host. + +```bash +PY=/path/to/cuda-environment/bin/python +TOOLS=$PR_ROOT/source/tests/pt_expt/kernels + +# Initialize a shared cache using normal BASE and ECS benchmark calls first. +# A nonempty explicit cache requires --allow-warm-cache in the original CLI. +$PY "$TOOLS/benchmark_so2_edge_cohort.py" --mode base --source-root "$BASE_ROOT" \ + --gpu 0 --model "$MODEL" --reference "$TORCH_REFERENCE" \ + --cache-dir "$CACHE" --out "$PREP_BASE" +$PY "$TOOLS/benchmark_so2_edge_cohort.py" --mode ecs --source-root "$PR_ROOT" \ + --gpu 0 --model "$MODEL" --reference "$TORCH_REFERENCE" \ + --cache-dir "$CACHE" --allow-warm-cache --out "$PREP_ECS" + +# Main same-context normal API; repeat all four seed/order combinations. +PYTHONHASHSEED=0 $PY "$TOOLS/benchmark_so2_edge_cohort_same_context.py" \ + --baseline-root "$BASE_ROOT" --candidate-root "$PR_ROOT" \ + --model "$MODEL" --reference "$TORCH_REFERENCE" --cache "$CACHE" \ + --gpu 0 --cpu 8 --monitor-cpu 16 --first BASE --blocks 24 --out "$NEW_OUT" +# Other combinations: seed0/ECS-first, seed1/BASE-first, seed1/ECS-first. +$PY "$TOOLS/analyze_so2_edge_cohort_same_context.py" \ + "$OUT0" "$OUT1" "$OUT2" "$OUT3" --out "$SUMMARY" +$PY "$TOOLS/analyze_so2_edge_cohort_clocks.py" \ + "$OUT0" "$OUT1" "$OUT2" "$OUT3" --out "$CLOCK_SENSITIVITY" + +# Secondary serialized control (never two resident contexts on the card). +$PY "$TOOLS/benchmark_so2_edge_cohort_serial.py" \ + --baseline-root "$BASE_ROOT" --candidate-root "$PR_ROOT" \ + --model "$MODEL" --reference "$TORCH_REFERENCE" --cache "$CACHE" \ + --gpu 0 --cpu 8 --monitor-cpu 16 --seeds 0 1 2 --rounds 8 --samples 50 \ + --conditioning-samples 200 --out "$SERIAL_OUT" +$PY "$TOOLS/analyze_so2_edge_cohort_serial.py" "$SERIAL_OUT" +``` + +`benchmark_so2_edge_cohort_paired.py` and its analyzer reproduce the secondary +resident-worker ABBA experiment (`--pairs 6 --blocks 12 --profile`). It is not +the primary estimator because of the observed resident-process bias. +Original standalone CLI behavior remains unchanged when no control hook is +provided; the shared measurement body is AST-identical to the measured copy. + +[Summary JSON](ecs-edge-cohort-controlled-results.json) includes complete +configuration, uncertainty, cache-choice changes and limitations. +[Compressed raw JSON](ecs-edge-cohort-controlled-raw.json.gz) includes per-call +wall/Event samples, conditional experiments, correctness and representative +profile summaries. It excludes private manifests, model weights and compiler +caches. Its SHA-256 is recorded in the summary; decompress with `python -m gzip +-d` or read with Python's `gzip.open`. + +Unresolved gates remain: wider model/size matrices, large-edge make_fx/AOT +dispatch and deployment, PT2/C++ acceptance, and the exact low-level cause of +initialization bias. Passing a Dynamo boundary unit test is not an AOT runtime +bucket-dispatch acceptance test. Default-off draft status remains unchanged. diff --git a/source/tests/pt_expt/kernels/_so2_edge_cohort_benchmark_impl.py b/source/tests/pt_expt/kernels/_so2_edge_cohort_benchmark_impl.py index ad207e309a..2728d023b4 100644 --- a/source/tests/pt_expt/kernels/_so2_edge_cohort_benchmark_impl.py +++ b/source/tests/pt_expt/kernels/_so2_edge_cohort_benchmark_impl.py @@ -129,6 +129,8 @@ def compare(got, ref): torch.cuda.synchronize() wall.append(1000 * (time.perf_counter() - start)) gpu.append(s.elapsed_time(e)) + if globals().get("_controlled_rounds") is not None: + globals()["_controlled_rounds"](globals()) complete = { "allocated": torch.cuda.max_memory_allocated(), "reserved": torch.cuda.max_memory_reserved(), diff --git a/source/tests/pt_expt/kernels/_so2_edge_cohort_controlled_worker.py b/source/tests/pt_expt/kernels/_so2_edge_cohort_controlled_worker.py new file mode 100644 index 0000000000..3b21737f42 --- /dev/null +++ b/source/tests/pt_expt/kernels/_so2_edge_cohort_controlled_worker.py @@ -0,0 +1,245 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +# ruff: noqa: T201 -- manual benchmark CLI output +"""Resident worker preserving the public benchmark's pre-validation entry.""" + +import argparse +import ast +import hashlib +import json +import os +from pathlib import Path +import runpy +import subprocess +import sys +import time + +HERE = Path(__file__).resolve().parent +import _so2_edge_cohort_benchmark_common as common + +p = argparse.ArgumentParser() +p.add_argument("--baseline-root", type=Path, required=True) +p.add_argument("--candidate-root", type=Path, required=True) +p.add_argument("--model", type=Path, required=True) +p.add_argument("--reference", type=Path, required=True) +p.add_argument( + "--variant", choices=("BASE", "BASE2", "NATIVE", "ECS", "ECS2"), required=True +) +p.add_argument("--out", type=Path, required=True) +p.add_argument("--cache", type=Path, required=True) +p.add_argument("--gpu", default="0") +p.add_argument("--cpu", type=int, default=8) +a = p.parse_args() +os.sched_setaffinity(0, {a.cpu}) +init = json.loads(sys.stdin.readline()) +allowed = set(init["pids"]) +original_check = subprocess.check_output + + +def checked(cmd, *args, **kwargs): + value = original_check(cmd, *args, **kwargs) + if isinstance(cmd, list) and "--query-compute-apps=pid" in cmd: + foreign = {int(x) for x in value.split()} - allowed + if foreign: + raise RuntimeError(f"foreign compute processes: {foreign}") + return "" + return value + + +common.ROOT = ( + a.baseline_root.resolve() + if a.variant in ("BASE", "BASE2") + else a.candidate_root.resolve() +) +original_setup = common.setup + + +def setup(*args, **kwargs): + subprocess.check_output = checked + try: + original_setup(*args, **kwargs) + finally: + subprocess.check_output = original_check + os.environ.update( + DP_SO2_EDGE_COHORT_INFER="1" if a.variant in ("ECS", "ECS2") else "0", + TORCHINDUCTOR_CACHE_DIR=str(a.cache / "inductor"), + TRITON_CACHE_DIR=str(a.cache / "triton"), + ) + + +common.setup = setup + + +def emit(value): + print("@CONTROL@" + json.dumps(value), flush=True) + + +def callback(ns): + import torch + from torch._dynamo.utils import counters + from torch._inductor.codecache import PyCodeCache + + wrappers, kernels = [], [] + for module in PyCodeCache.modules: + path = Path(module.__file__) + if "def call(" in path.read_text(): + wrappers.append({"path": str(path), "sha256": common.sha(path)}) + for name, kernel in vars(module).items(): + if not hasattr(kernel, "launchers") or not hasattr( + kernel, "triton_meta" + ): + continue + source = ast.parse(kernel.fn.src) + for node in ast.walk(source): + if isinstance(node, ast.FunctionDef): + node.name = "kernel" + identity = { + "body": ast.dump(source, include_attributes=False), + "signature": { + k: str(v) + for k, v in kernel.triton_meta.get("signature", {}).items() + }, + "constants": kernel.triton_meta.get("constants", {}), + } + launches = [] + for launch in kernel.launchers: + compiled = next( + ( + r.kernel + for r in kernel.compile_results + if str(r.config) == str(launch.config) + ), + None, + ) + binary = ( + getattr(compiled, "asm", {}).get("cubin") + if compiled is not None + else None + ) + launches.append( + { + "config": str(launch.config), + "cubin_sha256": hashlib.sha256(binary).hexdigest() + if binary is not None + else None, + "n_regs": getattr(launch, "n_regs", None), + "n_spills": getattr(launch, "n_spills", None), + "shared": getattr(launch, "shared", None), + } + ) + kernels.append( + { + "name": name, + "semantic_sha256": hashlib.sha256( + json.dumps(identity, sort_keys=True, default=str).encode() + ).hexdigest(), + "launchers": launches, + } + ) + first = { + "variant": a.variant, + "pid": os.getpid(), + "entry_wall_ms": common.stats(ns["wall"]), + "entry_event_ms": common.stats(ns["gpu"]), + "first_ms": ns["first_ms"], + "first_peak": ns["first"], + "wrappers": wrappers, + "kernels": kernels, + "source_sha256": { + str(path.relative_to(common.ROOT)): common.sha(path) + for path in (common.ROOT / "deepmd/pt_expt/kernels/triton/sezm").glob( + "so2_*cohort.py" + ) + }, + "counters": {k: dict(v) for k, v in counters.items()}, + } + common.write(a.out / "ready.json", first) + emit(dict(status="ready", **first)) + rounds = [] + evaluate, frames = ns["evaluate"], ns["frames"] + for line in sys.stdin: + command = json.loads(line) + if command["action"] == "stop": + break + if command["action"] == "profile": + from torch.profiler import ProfilerActivity, profile, record_function + + for pos in frames[:8]: + evaluate(pos) + torch.cuda.synchronize() + with profile( + activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA] + ) as prof: + for i in range(5): + torch.cuda.synchronize() + with record_function(f"CONTROLLED_FRAME_{i}"): + evaluate(frames[8 + i]) + torch.cuda.synchronize() + trace = a.out / "trace.json" + prof.export_chrome_trace(str(trace)) + emit({"status": "profiled", "variant": a.variant, "trace": str(trace)}) + continue + if command.get("cpu") is not None: + os.sched_setaffinity(0, {command["cpu"]}) + for i in range(command.get("warmup", 8)): + evaluate(frames[i % len(frames)]) + torch.cuda.synchronize() + torch.cuda.reset_peak_memory_stats() + pre_counters = {k: dict(v) for k, v in counters.items()} + start_ns = time.monotonic_ns() + wall, events = [], [] + for i in range(command.get("samples", 50)): + torch.cuda.synchronize() + s, e = ( + torch.cuda.Event(enable_timing=True), + torch.cuda.Event(enable_timing=True), + ) + start = time.perf_counter() + s.record() + evaluate(frames[8 + i % 30]) + e.record() + torch.cuda.synchronize() + wall.append(1000 * (time.perf_counter() - start)) + events.append(s.elapsed_time(e)) + record = { + "variant": a.variant, + "pid": os.getpid(), + "command": command, + "start_monotonic_ns": start_ns, + "end_monotonic_ns": time.monotonic_ns(), + "cpu": sorted(os.sched_getaffinity(0)), + "wall_ms": common.stats(wall), + "event_ms": common.stats(events), + "peak": { + "allocated": torch.cuda.max_memory_allocated(), + "reserved": torch.cuda.max_memory_reserved(), + }, + "counters_before": pre_counters, + "counters_after": {k: dict(v) for k, v in counters.items()}, + } + rounds.append(record) + common.write(a.out / "rounds.json", rounds) + emit(record) + + +sys.argv = [ + str(HERE / "_so2_edge_cohort_benchmark_impl.py"), + "--gpu", + a.gpu, + "--out", + str(a.out), + "--model", + str(a.model), + "--nrep", + "2", + "--reference", + str(a.reference), +] +ns = runpy.run_path( + str(HERE / "_so2_edge_cohort_benchmark_impl.py"), + init_globals={"_controlled_rounds": callback}, + run_name="__main__", +) +result = ns["result"] +if not result["precision_pass"] or not result["finite"]: + raise SystemExit("accuracy failure") +emit({"status": "completed", "variant": a.variant, "precision_pass": True}) diff --git a/source/tests/pt_expt/kernels/analyze_so2_edge_cohort_clocks.py b/source/tests/pt_expt/kernels/analyze_so2_edge_cohort_clocks.py new file mode 100644 index 0000000000..710163c1cc --- /dev/null +++ b/source/tests/pt_expt/kernels/analyze_so2_edge_cohort_clocks.py @@ -0,0 +1,76 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +# ruff: noqa: T201 -- manual benchmark CLI output +"""Supplement only: match observed clock/temperature bins, never replace raw trials.""" + +import argparse +import json +from pathlib import Path + +import numpy as np +from scipy.stats import t + +p = argparse.ArgumentParser(description=__doc__) +p.add_argument("directories", type=Path, nargs="+") +p.add_argument("--out", type=Path, required=True) +p.add_argument("--memory-clock", type=int, default=13801) +a = p.parse_args() +result = [] +for root in a.directories: + rows = json.loads((root / "same_context_rounds.json").read_text()) + monitor = json.loads((root / "same_context_monitor.json").read_text()) + kept = [] + for block in sorted({r["command"]["block"] for r in rows}): + group = [r for r in rows if r["command"]["block"] == block] + clocks = {} + temperatures = {} + memory = {} + effects = {} + for arm in ("BASE", "ECS"): + rounds = [r for r in group if r["variant"] == arm] + samples = [ + m + for r in rounds + for m in monitor + if r["start_monotonic_ns"] <= m["t_ns"] <= r["end_monotonic_ns"] + ] + clocks[arm] = np.mean([m["sm_mhz"] for m in samples]) + temperatures[arm] = np.mean([m["temperature"] for m in samples]) + memory[arm] = {m["mem_mhz"] for m in samples} + effects[arm] = np.mean(np.log([r["wall_ms"]["median"] for r in rounds])) + if ( + abs(clocks["ECS"] / clocks["BASE"] - 1) <= 0.005 + and memory["BASE"] == memory["ECS"] == {a.memory_clock} + and abs(temperatures["ECS"] - temperatures["BASE"]) <= 2 + ): + kept.append( + { + "block": block, + "logratio": float(effects["ECS"] - effects["BASE"]), + "sm_mhz": {k: float(v) for k, v in clocks.items()}, + } + ) + result.append( + { + "run": root.name, + "retained_blocks": len(kept), + "blocks": kept, + "mean_logratio": float(np.mean([r["logratio"] for r in kept])) + if kept + else None, + } + ) +logs = np.array([r["mean_logratio"] for r in result if r["mean_logratio"] is not None]) +radius = t.ppf(0.975, len(logs) - 1) * logs.std(ddof=1) / np.sqrt(len(logs)) +summary = { + "criteria": f"block mean SM clock difference <=0.5%, both memory clocks {a.memory_clock}MHz, mean temperature difference <=2C", + "primary": False, + "caveat": "post-stratified sensitivity analysis; conditioning can change composition and does not prove constant clocks or replace full results", + "processes": result, + "mean_change_percent": float(100 * np.expm1(logs.mean())), + "process_t95_percent": ( + 100 * np.expm1(logs.mean() + np.array([-1, 1]) * radius) + ).tolist(), +} +a.out.write_text(json.dumps(summary, indent=2)) +print(json.dumps({k: v for k, v in summary.items() if k != "processes"}, indent=2)) +print([(r["run"], r["retained_blocks"]) for r in result]) diff --git a/source/tests/pt_expt/kernels/analyze_so2_edge_cohort_paired.py b/source/tests/pt_expt/kernels/analyze_so2_edge_cohort_paired.py new file mode 100644 index 0000000000..bd72c76f2e --- /dev/null +++ b/source/tests/pt_expt/kernels/analyze_so2_edge_cohort_paired.py @@ -0,0 +1,229 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +# ruff: noqa: T201 -- manual benchmark CLI output +"""Analyze ABBA blocks using process/temporal clusters, not iid frame counts.""" + +import argparse +import ast +from collections import Counter, defaultdict +import json +from pathlib import Path + +import numpy as np +from scipy.stats import t as student_t + +p = argparse.ArgumentParser() +p.add_argument("directory", type=Path) +a = p.parse_args() +root = a.directory +records = json.loads((root / "rounds.json").read_text()) +control = json.loads((root / "controller.json").read_text()) +assert control["status"] == "completed", ( + "incomplete/failed trials cannot be final evidence" +) +arms = ast.literal_eval(control["args"]["variants"]) +blocks = defaultdict(list) +for row in records: + blocks[(row["pair"], row["command"]["block"])].append(row) +effects, absolute, by_pair = [], [], defaultdict(list) +for (pair, block), rows in sorted(blocks.items()): + assert len(rows) == 4 + per_arm = { + arm: [r["wall_ms"]["median"] for r in rows if r["variant"] == arm] + for arm in arms + } + assert all(len(v) == 2 for v in per_arm.values()) + logratio = float( + np.mean(np.log(per_arm[arms[1]])) - np.mean(np.log(per_arm[arms[0]])) + ) + difference = float(np.mean(per_arm[arms[1]]) - np.mean(per_arm[arms[0]])) + effects.append( + { + "pair": pair, + "block": block, + "logratio": logratio, + "percent": 100 * np.expm1(logratio), + "difference_ms": difference, + } + ) + by_pair[pair].append(logratio) + absolute.append(difference) +rng = np.random.default_rng(20261019) +matrix = np.asarray(list(by_pair.values())) +draws = [] +for _ in range(20000): + pair_indices = rng.integers(0, len(matrix), size=len(matrix)) + resampled = [ + rng.choice(matrix[i], size=len(matrix[i]), replace=True).mean() + for i in pair_indices + ] + draws.append(np.mean(resampled)) +ci = 100 * np.expm1(np.percentile(draws, [2.5, 97.5])) +pair_means = matrix.mean(axis=1) +process_t_ci = None +upper95 = None +if len(pair_means) > 1: + se = pair_means.std(ddof=1) / np.sqrt(len(pair_means)) + radius = student_t.ppf(0.975, len(pair_means) - 1) * se + process_t_ci = ( + 100 * np.expm1([pair_means.mean() - radius, pair_means.mean() + radius]) + ).tolist() + upper95 = float( + 100 + * np.expm1(pair_means.mean() + student_t.ppf(0.95, len(pair_means) - 1) * se) + ) +stats = {} +for arm in arms: + rows = [r for r in records if r["variant"] == arm] + samples = [v for r in rows for v in r["wall_ms"]["samples"]] + stats[arm] = { + "rounds": len(rows), + "samples": len(samples), + "median_of_round_medians_ms": float( + np.median([r["wall_ms"]["median"] for r in rows]) + ), + "pooled_p50_ms": float(np.median(samples)), + "pooled_p90_ms": float(np.percentile(samples, 90)), + "min_ms": float(min(samples)), + "max_ms": float(max(samples)), + "event_p50_ms": float( + np.median([v for r in rows for v in r["event_ms"]["samples"]]) + ), + } +monitor = json.loads((root / "monitor.json").read_text()) +telemetry = [] +for row in records: + values = [ + m + for m in monitor + if row["start_monotonic_ns"] <= m["t_ns"] <= row["end_monotonic_ns"] + ] + telemetry.append( + { + "pair": row["pair"], + "block": row["command"]["block"], + "position": row["command"]["position"], + "arm": row["variant"], + "count": len(values), + "sm_mhz": { + str(k): v for k, v in Counter(m["sm_mhz"] for m in values).items() + }, + "mem_mhz": { + str(k): v for k, v in Counter(m["mem_mhz"] for m in values).items() + }, + "temperature_range": [ + min(m["temperature"] for m in values), + max(m["temperature"] for m in values), + ] + if values + else None, + "mean_power_w": float(np.mean([m["power_mw"] / 1000 for m in values])) + if values + else None, + } + ) +ready = json.loads((root / "ready.json").read_text()) +kernel_audits = [] +for pair in by_pair: + arm_kernels = [] + for arm in arms: + entry = next(r for r in ready if r["pair"] == pair and r["variant"] == arm) + grouped = defaultdict(list) + for kernel in entry.get("kernels", []): + grouped[kernel["semantic_sha256"]].extend(kernel["launchers"]) + arm_kernels.append(grouped) + shared = set(arm_kernels[0]) & set(arm_kernels[1]) + different = [] + identical_binaries = 0 + for identity in shared: + configs = [{v["config"] for v in group[identity]} for group in arm_kernels] + if configs[0] != configs[1]: + different.append( + {"semantic_sha256": identity, "configs": [sorted(v) for v in configs]} + ) + bins = [ + {v["cubin_sha256"] for v in group[identity] if v["cubin_sha256"]} + for group in arm_kernels + ] + if bins[0] and bins[0] == bins[1]: + identical_binaries += 1 + before = json.loads((root / f"cache_pair{pair}_before.json").read_text()) + after = json.loads((root / f"cache_pair{pair}_after.json").read_text()) + kernel_audits.append( + { + "pair": pair, + "shared_semantic_programs": len(shared), + "common_programs_identical_cubin": identical_binaries, + "different_configs": different, + "all_compiler_artifact_hashes_unchanged": before == after, + "fingerprinted_artifacts": len(before), + } + ) +profiles = [] +for path in sorted(root.glob("pair*_*/*.json")): + if path.name != "trace.json": + continue + events = json.loads(path.read_text())["traceEvents"] + frames = [ + v + for v in events + if v.get("name", "").startswith("CONTROLLED_FRAME_") and v.get("ph") == "X" + ] + for frame in frames: + kernels = [ + v + for v in events + if v.get("cat") == "kernel" + and v.get("ph") == "X" + and frame["ts"] <= v["ts"] + and v["ts"] + v["dur"] <= frame["ts"] + frame["dur"] + ] + mixed = [v for v in kernels if v["name"].startswith("_stack_")] + profiles.append( + { + "run": path.parent.name, + "frame": frame["name"], + "kernel_count": len(kernels), + "kernel_sum_ms": sum(v["dur"] for v in kernels) / 1000, + "mixing_kernel_count": len(mixed), + "mixing_kernel_sum_ms": sum(v["dur"] for v in mixed) / 1000, + "mixing_names": dict(Counter(v["name"] for v in mixed)), + } + ) +summary = { + "arms": arms, + "process_pairs": len(by_pair), + "blocks": len(effects), + "paired_logmean_change_percent": float(100 * np.expm1(matrix.mean())), + "mean_block_difference_ms": float(np.mean(absolute)), + "hierarchical_bootstrap_95_percent": ci.tolist(), + "process_pair_t_95_percent": process_t_ci, + "process_pair_t_one_sided_upper95_percent": upper95, + "t_interval_caveat": "t interval over independent process-pair means; n=2 is calibration only, not a precise population estimate", + "bootstrap_draws": 20000, + "bootstrap_seed": 20261019, + "interval_method": "resample independent process pairs, then ABBA blocks within each selected pair; frame samples are not iid replicates", + "per_pair_change_percent": [float(100 * np.expm1(row.mean())) for row in matrix], + "interpretation": "positive interval: repeatable cost in this controlled setup" + if ci[0] > 0 + else "negative interval: repeatable improvement in this controlled setup" + if ci[1] < 0 + else "interval crosses zero: unresolved sign, not proof of zero cost", + "stats": stats, + "block_effects": effects, + "interval_telemetry": telemetry, + "kernel_audits": kernel_audits, + "profiles": profiles, + "monitor_errors": control["errors"], + "memory_scope": "resident timing only; retain independent cold-first/full-window capacity measurements separately", +} +(root / "summary.json").write_text(json.dumps(summary, indent=2)) +print( + json.dumps( + { + k: v + for k, v in summary.items() + if k not in ("block_effects", "interval_telemetry", "profiles") + }, + indent=2, + ) +) diff --git a/source/tests/pt_expt/kernels/analyze_so2_edge_cohort_same_context.py b/source/tests/pt_expt/kernels/analyze_so2_edge_cohort_same_context.py new file mode 100644 index 0000000000..43fb585ffc --- /dev/null +++ b/source/tests/pt_expt/kernels/analyze_so2_edge_cohort_same_context.py @@ -0,0 +1,104 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +# ruff: noqa: T201 -- manual benchmark CLI output +"""Cluster normal same-context API contrasts by independent process.""" + +import argparse +import json +from pathlib import Path + +import numpy as np +from scipy.stats import t + +p = argparse.ArgumentParser() +p.add_argument("directories", type=Path, nargs="+") +p.add_argument("--out", type=Path, required=True) +a = p.parse_args() +rng = np.random.default_rng(20261019) +processes = [] +all_rows = [] +for root in a.directories: + status = json.loads((root / "same_context_status.json").read_text()) + assert status["status"] == "completed" + rows = json.loads((root / "same_context_rounds.json").read_text()) + all_rows.extend(rows) + effects = [] + for block in sorted({r["command"]["block"] for r in rows}): + group = [r for r in rows if r["command"]["block"] == block] + m = { + arm: np.mean( + np.log([r["wall_ms"]["median"] for r in group if r["variant"] == arm]) + ) + for arm in ("BASE", "ECS") + } + effects.append(float(m["ECS"] - m["BASE"])) + draws = np.mean(rng.choice(effects, (20000, len(effects))), axis=1) + meta = json.loads((root / "same_context_manifest.json").read_text()) + processes.append( + { + "run": root.name, + "first": meta["first"], + "blocks": len(effects), + "logmean": float(np.mean(effects)), + "change_percent": float(100 * np.expm1(np.mean(effects))), + "conditional_block_ci95_percent": ( + 100 * np.expm1(np.percentile(draws, [2.5, 97.5])) + ).tolist(), + "worst_error_ratio": max( + v["max_ratio"] + for case in meta["accuracy"].values() + for v in case.values() + ), + "wrappers": { + arm: entry["sha256"] for arm, entry in meta["wrappers"].items() + }, + } + ) +logs = np.array([p["logmean"] for p in processes]) +se = logs.std(ddof=1) / np.sqrt(len(logs)) if len(logs) > 1 else None +interval = ( + ( + 100 + * np.expm1(logs.mean() + np.array([-1, 1]) * t.ppf(0.975, len(logs) - 1) * se) + ).tolist() + if se is not None + else None +) +summary = { + "processes": processes, + "process_count": len(processes), + "mean_change_percent": float(100 * np.expm1(logs.mean())), + "process_t95_percent": interval, + "confidence_note": "processes, not frames, are independent replicates; conditional block intervals do not establish population equivalence", + "stats": { + arm: { + "samples": sum( + len(r["wall_ms"]["samples"]) for r in all_rows if r["variant"] == arm + ), + "p50_ms": float( + np.median( + [ + v + for r in all_rows + if r["variant"] == arm + for v in r["wall_ms"]["samples"] + ] + ) + ), + "p90_ms": float( + np.percentile( + [ + v + for r in all_rows + if r["variant"] == arm + for v in r["wall_ms"]["samples"] + ], + 90, + ) + ), + } + for arm in ("BASE", "ECS") + }, + "scope": "normal full ASE API, both implementations in one CUDA context; two calculators, actual master and exact PR definitions; no Dynamo inference frontend", +} +a.out.write_text(json.dumps(summary, indent=2)) +print(json.dumps(summary, indent=2)) diff --git a/source/tests/pt_expt/kernels/analyze_so2_edge_cohort_serial.py b/source/tests/pt_expt/kernels/analyze_so2_edge_cohort_serial.py new file mode 100644 index 0000000000..638ea9f6fc --- /dev/null +++ b/source/tests/pt_expt/kernels/analyze_so2_edge_cohort_serial.py @@ -0,0 +1,112 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +# ruff: noqa: T201 -- manual benchmark CLI output +"""Matched-seed independent-process analysis of serialized normal API runs.""" + +import argparse +import json +from pathlib import Path + +import numpy as np +from scipy.stats import t + +p = argparse.ArgumentParser() +p.add_argument("directory", type=Path) +a = p.parse_args() +root = a.directory +assert json.loads((root / "controller.json").read_text())["status"] == "completed" +rows = json.loads((root / "rounds.json").read_text()) +formal = [r for r in rows if r["command"]["phase"] == "formal"] +ready = json.loads((root / "ready.json").read_text()) +seeds = sorted({r["seed"] for r in formal}) +pairs = [] +for seed in seeds: + stats = {} + for arm in ("BASE", "ECS"): + group = [r for r in formal if r["seed"] == seed and r["variant"] == arm] + values = [v for r in group for v in r["wall_ms"]["samples"]] + stats[arm] = { + "p50_ms": float(np.median(values)), + "p90_ms": float(np.percentile(values, 90)), + "round_logmean_ms": float( + np.exp(np.mean(np.log([r["wall_ms"]["median"] for r in group]))) + ), + "round_medians_ms": [r["wall_ms"]["median"] for r in group], + } + change = float( + np.log(stats["ECS"]["round_logmean_ms"]) + - np.log(stats["BASE"]["round_logmean_ms"]) + ) + pairs.append( + { + "seed": seed, + "arms": stats, + "logratio": change, + "percent": float(100 * np.expm1(change)), + } + ) +logs = np.array([p["logratio"] for p in pairs]) +se = logs.std(ddof=1) / np.sqrt(len(logs)) if len(logs) > 1 else None +ci = ( + ( + 100 + * np.expm1(logs.mean() + np.array([-1, 1]) * t.ppf(0.975, len(logs) - 1) * se) + ).tolist() + if se is not None + else None +) +monitor = json.loads((root / "monitor.json").read_text()) +telemetry = [] +for r in formal: + vals = [ + m + for m in monitor + if r["start_monotonic_ns"] <= m["t_ns"] <= r["end_monotonic_ns"] + ] + telemetry.append( + { + "run": r["run"], + "seed": r["seed"], + "arm": r["variant"], + "block": r["command"]["block"], + "mean_sm_mhz": float(np.mean([m["sm_mhz"] for m in vals])), + "mem_mhz": sorted({m["mem_mhz"] for m in vals}), + "temperature_range": [ + min(m["temperature"] for m in vals), + max(m["temperature"] for m in vals), + ], + "clock_reason_masks": sorted({m["clock_reasons"] for m in vals}), + } + ) +artifacts = [] +for entry in ready: + before = json.loads((root / f"cache_{entry['run']}_before.json").read_text()) + after = json.loads((root / f"cache_{entry['run']}_after.json").read_text()) + assert before == after + artifacts.append({"run": entry["run"], "unchanged": True, "count": len(before)}) +stats = {} +for arm in ("BASE", "ECS"): + values = [v for r in formal if r["variant"] == arm for v in r["wall_ms"]["samples"]] + stats[arm] = { + "samples": len(values), + "p50_ms": float(np.median(values)), + "p90_ms": float(np.percentile(values, 90)), + "min_ms": float(min(values)), + "max_ms": float(max(values)), + } +summary = { + "scope": "normal API; one CUDA process at a time; matched Python hash seeds; see controller arguments", + "pairs": pairs, + "mean_change_percent": float(100 * np.expm1(logs.mean())), + "process_pair_t_95_percent": ci, + "stats": stats, + "interval_telemetry": telemetry, + "compiler_artifacts": artifacts, + "interpretation": "finite independent process pairs; a confidence interval is not proof of zero cost", + "cold_peak": "separate existing cold-process measurement, not replaced", +} +(root / "summary.json").write_text(json.dumps(summary, indent=2)) +print( + json.dumps( + {k: v for k, v in summary.items() if k != "interval_telemetry"}, indent=2 + ) +) diff --git a/source/tests/pt_expt/kernels/benchmark_so2_edge_cohort_paired.py b/source/tests/pt_expt/kernels/benchmark_so2_edge_cohort_paired.py new file mode 100644 index 0000000000..7b651909d6 --- /dev/null +++ b/source/tests/pt_expt/kernels/benchmark_so2_edge_cohort_paired.py @@ -0,0 +1,228 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +# ruff: noqa: T201 -- manual benchmark CLI output +"""Sequential resident-worker ABBA trials with fixed caches and interval NVML.""" + +import argparse +import hashlib +import json +import os +from pathlib import Path +import subprocess +import sys +import threading +import time + +import pynvml as nv + +HERE = Path(__file__).resolve().parent +p = argparse.ArgumentParser() +p.add_argument("--out", type=Path, required=True) +p.add_argument("--baseline-root", type=Path, required=True) +p.add_argument("--candidate-root", type=Path, required=True) +p.add_argument("--model", type=Path, required=True) +p.add_argument("--reference", type=Path, required=True) +p.add_argument("--cache", type=Path, required=True) +p.add_argument("--variants", nargs=2, default=["BASE", "ECS"]) +p.add_argument("--pairs", type=int, default=3) +p.add_argument("--blocks", type=int, default=12) +p.add_argument("--samples", type=int, default=50) +p.add_argument("--cpu", type=int, default=8) +p.add_argument("--gpu", default="0") +p.add_argument("--monitor-cpu", type=int, default=16) +p.add_argument("--profile", action="store_true") +a = p.parse_args() +out = a.out.resolve() +out.mkdir(parents=True, exist_ok=False) +a.cache = a.cache.resolve() +a.cache.mkdir(parents=True, exist_ok=True) +os.sched_setaffinity(0, {a.monitor_cpu}) +nv.nvmlInit() +h = nv.nvmlDeviceGetHandleByIndex(int(a.gpu)) +assert not nv.nvmlDeviceGetComputeRunningProcesses(h), f"GPU{a.gpu} not idle" +workers = {} +stop = threading.Event() +monitor, errors, records, ready = [], [], [], [] + + +def watch(): + while not stop.wait(0.05): + try: + pids = {v.pid for v in nv.nvmlDeviceGetComputeRunningProcesses(h)} + foreign = pids - {w.pid for w in workers.values()} + if foreign: + errors.append(f"foreign compute pids: {sorted(foreign)}") + for w in list(workers.values()): + if w.poll() is None: + w.terminate() + return + monitor.append( + { + "t_ns": time.monotonic_ns(), + "pids": sorted(pids), + "sm_mhz": nv.nvmlDeviceGetClockInfo(h, nv.NVML_CLOCK_SM), + "mem_mhz": nv.nvmlDeviceGetClockInfo(h, nv.NVML_CLOCK_MEM), + "temperature": nv.nvmlDeviceGetTemperature( + h, nv.NVML_TEMPERATURE_GPU + ), + "power_mw": nv.nvmlDeviceGetPowerUsage(h), + "utilization": nv.nvmlDeviceGetUtilizationRates(h).gpu, + "pstate": nv.nvmlDeviceGetPerformanceState(h), + } + ) + except Exception as exc: + errors.append(repr(exc)) + return + + +def write(name, value): + (out / name).write_text(json.dumps(value, indent=2)) + + +def fingerprint(include_binaries=False): + patterns = ["*.best_config"] + (["*.cubin", "*.ptx"] if include_binaries else []) + return { + str(path.relative_to(a.cache)): hashlib.sha256(path.read_bytes()).hexdigest() + for pattern in patterns + for path in sorted(a.cache.rglob(pattern)) + } + + +def message(worker, log): + while True: + line = worker.stdout.readline() + if not line: + raise RuntimeError(f"worker exited: {worker.poll()} {errors}") + log.write(line) + log.flush() + if line.startswith("@CONTROL@"): + return json.loads(line[len("@CONTROL@") :]) + + +thread = threading.Thread(target=watch, daemon=True) +thread.start() +logs = [] +try: + for pair in range(a.pairs): + pair_ready = [] + init_order = a.variants if pair % 2 == 0 else list(reversed(a.variants)) + for variant in init_order: + log = (out / f"pair{pair}_{variant}.log").open("x") + logs.append(log) + worker = subprocess.Popen( + [ + sys.executable, + str(HERE / "_so2_edge_cohort_controlled_worker.py"), + "--baseline-root", + str(a.baseline_root.resolve()), + "--candidate-root", + str(a.candidate_root.resolve()), + "--model", + str(a.model.resolve()), + "--reference", + str(a.reference.resolve()), + "--variant", + variant, + "--out", + str(out / f"pair{pair}_{variant}"), + "--cache", + str(a.cache), + "--cpu", + str(a.cpu), + "--gpu", + a.gpu, + ], + stdin=subprocess.PIPE, + stdout=subprocess.PIPE, + stderr=log, + text=True, + bufsize=1, + ) + workers[variant] = worker + worker.stdin.write( + json.dumps({"pids": [w.pid for w in workers.values()]}) + "\n" + ) + worker.stdin.flush() + r = message(worker, log) + assert r["status"] == "ready" + r["pair"] = pair + ready.append(r) + pair_ready.append(r) + write("ready.json", ready) + print("READY", pair, variant, r["entry_wall_ms"]["median"], flush=True) + frozen = fingerprint() + binary_before = fingerprint(True) + write(f"cache_pair{pair}_before.json", binary_before) + for block in range(a.blocks): + left, right = a.variants if block % 2 == 0 else list(reversed(a.variants)) + for position, variant in enumerate((left, right, right, left)): + worker = workers[variant] + command = { + "action": "measure", + "pair": pair, + "block": block, + "position": position, + "warmup": 8, + "samples": a.samples, + "cpu": a.cpu, + } + assert fingerprint() == frozen, "autotune cache changed before timing" + worker.stdin.write(json.dumps(command) + "\n") + worker.stdin.flush() + log = logs[-2 + init_order.index(variant)] + r = message(worker, log) + assert fingerprint() == frozen, "autotune cache changed during timing" + r["pair"] = pair + records.append(r) + write("rounds.json", records) + print( + "ROUND", + pair, + block, + position, + variant, + r["wall_ms"]["median"], + flush=True, + ) + if a.profile: + for variant in init_order: + worker = workers[variant] + worker.stdin.write('{"action":"profile"}\n') + worker.stdin.flush() + profiled = message(worker, logs[-2 + init_order.index(variant)]) + assert profiled["status"] == "profiled" + write(f"profile_pair{pair}_{variant}.json", profiled) + after = fingerprint(True) + write(f"cache_pair{pair}_after.json", after) + assert after == binary_before, ( + "compiled binaries changed during measured blocks" + ) + for variant in init_order: + worker = workers[variant] + worker.stdin.write('{"action":"stop"}\n') + worker.stdin.flush() + r = message(worker, logs[-2 + init_order.index(variant)]) + assert r["status"] == "completed" and r["precision_pass"] + worker.wait(timeout=30) + assert worker.returncode == 0 + workers.clear() +finally: + for worker in workers.values(): + if worker.poll() is None: + worker.terminate() + worker.wait(timeout=30) + stop.set() + thread.join(timeout=5) + write("monitor.json", monitor) + write( + "controller.json", + { + "args": {k: str(v) for k, v in vars(a).items()}, + "errors": errors, + "status": "completed" + if not errors and len(records) == a.pairs * a.blocks * 4 + else "failed", + }, + ) + for log in logs: + log.close() + nv.nvmlShutdown() diff --git a/source/tests/pt_expt/kernels/benchmark_so2_edge_cohort_same_context.py b/source/tests/pt_expt/kernels/benchmark_so2_edge_cohort_same_context.py new file mode 100644 index 0000000000..0e623a7b10 --- /dev/null +++ b/source/tests/pt_expt/kernels/benchmark_so2_edge_cohort_same_context.py @@ -0,0 +1,326 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +# ruff: noqa: T201 -- manual benchmark CLI output +"""Normal ASE API A/B in one CUDA context, preserving the original baseline. + +Experimental setup copies exactly the three reviewed kernel definitions and +registered backward into memory *after* compiling the baseline. No production +file, kernel arithmetic, or checkpoint is edited. Each model keeps its own +compiled cache. Inference bypasses Dynamo; stale module-level cache reuse is +explicitly prohibited and compiled graph provenance is checked. +""" + +import argparse +import ast +import hashlib +import importlib.util +import os +from pathlib import Path +import runpy +import sys +import threading +import time + +HERE = Path(__file__).resolve().parent +import _so2_edge_cohort_benchmark_common as common + +p = argparse.ArgumentParser() +p.add_argument("--baseline-root", type=Path, required=True) +p.add_argument("--candidate-root", type=Path, required=True) +p.add_argument("--model", type=Path, required=True) +p.add_argument("--reference", type=Path, required=True) +p.add_argument("--cache", type=Path, required=True) +p.add_argument("--out", type=Path, required=True) +p.add_argument("--first", choices=("BASE", "ECS"), default="BASE") +p.add_argument("--gpu", default="0") +p.add_argument("--cpu", type=int, default=8) +p.add_argument("--monitor-cpu", type=int, default=16) +p.add_argument("--blocks", type=int, default=24) +a = p.parse_args() +os.sched_setaffinity(0, {a.cpu}) +PUBLIC = a.candidate_root.resolve() +common.ROOT = a.baseline_root.resolve() +original_setup = common.setup +state = {} + + +def install_candidate(): + from deepmd.pt_expt.kernels.triton.sezm import so2_value_path as sv + + if not state: + state.update( + sv=sv, + backward=sv._mixing_stack_backward, + kernels={ + name: getattr(sv, name) + for name in ( + "_stack_gemm_bwd_kernel", + "_stack_point_bwd_kernel", + "_stack_recompute_kernel", + ) + }, + ) + source = PUBLIC / "deepmd/pt_expt/kernels/triton/sezm/so2_value_path.py" + tree = ast.parse(source.read_text()) + names = set(state["kernels"]) | {"_mixing_stack_backward"} + definitions = [ + n + for n in ast.walk(tree) + if isinstance(n, ast.FunctionDef) and n.name in names + ] + assert len(definitions) == 4 + scope = dict(vars(sv), _EDGE_COHORT_INFER=True) + exec( + compile(ast.Module(body=definitions, type_ignores=[]), str(source), "exec"), + scope, + ) + state["candidate_definitions"] = {name: scope[name] for name in names} + name = "deepmd.pt_expt.kernels.triton.sezm.so2_edge_cohort" + path = PUBLIC / "deepmd/pt_expt/kernels/triton/sezm/so2_edge_cohort.py" + spec = importlib.util.spec_from_file_location(name, path) + module = importlib.util.module_from_spec(spec) + sys.modules[name] = module + spec.loader.exec_module(module) + for name in state["kernels"]: + setattr(sv, name, state["candidate_definitions"][name]) + sv._mixing_stack_op.register_autograd( + state["candidate_definitions"]["_mixing_stack_backward"], + setup_context=sv._mixing_stack_setup_context, + ) + + +def restore_baseline(): + sv = state["sv"] + for name, kernel in state["kernels"].items(): + setattr(sv, name, kernel) + sv._mixing_stack_op.register_autograd( + state["backward"], setup_context=sv._mixing_stack_setup_context + ) + + +def setup(*args, **kwargs): + original_setup(*args, **kwargs) + os.environ.update( + TORCHINDUCTOR_CACHE_DIR=str(a.cache.resolve() / "inductor"), + TRITON_CACHE_DIR=str(a.cache.resolve() / "triton"), + ) + if a.first == "ECS": + install_candidate() + + +common.setup = setup + + +def callback(ns): + import numpy as np + import torch + import pynvml as nv + from deepmd.calculator import DP + from deepmd.pt.model.model import sezm_model + from torch._inductor.codecache import PyCodeCache + + first = ns["calc"] + model = ns["inner"] + assert model.compiled_core_compute_cache + first_cache = dict(model.compiled_core_compute_cache) + old_modules = { + str(m.__file__) + for m in PyCodeCache.modules + if "def call(" in Path(m.__file__).read_text() + } + assert len(old_modules) == 1 + if a.first == "BASE": + install_candidate() + else: + restore_baseline() + sezm_model._SEZM_COMPILE_CACHE.clear() + sezm_model._SEZM_TASK_BUF_ORDER.clear() + second = DP(model=str(a.model)) + assert not second.dp.get_model().compiled_core_compute_cache + calculators = {a.first: first, ("ECS" if a.first == "BASE" else "BASE"): second} + base, frames = ns["base"], ns["frames"] + + def evaluate(calc, pos): + calc.reset() + atoms = base.copy() + atoms.positions = pos + atoms.calc = calc + forces = atoms.get_forces().copy() + return { + "energy": np.asarray(atoms.get_potential_energy()), + "forces": forces, + "virial": np.asarray(calc.results["virial"]).copy(), + "stress": atoms.get_stress().copy(), + } + + started = time.perf_counter() + for pos in frames[:8]: + evaluate(second, pos) + torch.cuda.synchronize() + assert model.compiled_core_compute_cache == first_cache, "first graph cache changed" + modules = { + str(m.__file__) + for m in PyCodeCache.modules + if "def call(" in Path(m.__file__).read_text() + } + new_modules = modules - old_modules + assert len(new_modules) == 1, ( + "second mode silently reused a graph or compiled multiple entries" + ) + wrappers = { + a.first: next(iter(old_modules)), + ("ECS" if a.first == "BASE" else "BASE"): next(iter(new_modules)), + } + assert "so2_single_cohort" in Path(wrappers["ECS"]).read_text() + assert "so2_single_cohort" not in Path(wrappers["BASE"]).read_text() + with np.load(a.reference) as ref: + accuracy = {} + outputs = {} + for arm, calc in calculators.items(): + values = { + f"{i}_{k}": v + for i, pos in enumerate(frames[:3]) + for k, v in evaluate(calc, pos).items() + } + outputs[arm] = values + accuracy[arm] = ns["compare"](values, ref) + assert all(v["passed"] and v["finite"] for v in accuracy[arm].values()) + np.savez_compressed(a.out / f"{arm}_outputs.npz", **values) + accuracy["paired"] = ns["compare"](outputs["ECS"], outputs["BASE"]) + assert all(v["passed"] for v in accuracy["paired"].values()) + cache = a.cache.resolve() + + def artifacts(): + return { + str(p.relative_to(cache)): hashlib.sha256(p.read_bytes()).hexdigest() + for pattern in ("*.best_config", "*.cubin", "*.ptx") + for p in cache.rglob(pattern) + } + + frozen = artifacts() + common.write( + a.out / "same_context_manifest.json", + { + "first": a.first, + "second_compile_s": time.perf_counter() - started, + "wrappers": { + k: {"path": v, "sha256": common.sha(Path(v))} + for k, v in wrappers.items() + }, + "accuracy": accuracy, + "original_entry_wall_ms": ns["wall"], + "seed": os.environ.get("PYTHONHASHSEED"), + "method": "two normal ASE calculators; distinct AOT compiled caches; same process/context; exact candidate kernel/backward definitions transplanted only for tracing", + }, + ) + nv.nvmlInit() + h = nv.nvmlDeviceGetHandleByIndex(int(a.gpu)) + stop = threading.Event() + monitor = [] + monitor_errors = [] + + def watch(): + os.sched_setaffinity(0, {a.monitor_cpu}) + while not stop.wait(0.05): + pids = {p.pid for p in nv.nvmlDeviceGetComputeRunningProcesses(h)} + if pids != {os.getpid()}: + monitor_errors.append(f"foreign CUDA pid {pids}") + return + monitor.append( + { + "t_ns": time.monotonic_ns(), + "sm_mhz": nv.nvmlDeviceGetClockInfo(h, nv.NVML_CLOCK_SM), + "mem_mhz": nv.nvmlDeviceGetClockInfo(h, nv.NVML_CLOCK_MEM), + "temperature": nv.nvmlDeviceGetTemperature( + h, nv.NVML_TEMPERATURE_GPU + ), + "clock_reasons": nv.nvmlDeviceGetCurrentClocksThrottleReasons(h), + } + ) + + thread = threading.Thread(target=watch, daemon=True) + thread.start() + records = [] + try: + for block in range(a.blocks): + left, right = ("BASE", "ECS") if block % 2 == 0 else ("ECS", "BASE") + for position, arm in enumerate((left, right, right, left)): + calc = calculators[arm] + for pos in frames[:8]: + evaluate(calc, pos) + torch.cuda.synchronize() + wall = [] + events = [] + start_ns = time.monotonic_ns() + for i in range(50): + torch.cuda.synchronize() + s = torch.cuda.Event(enable_timing=True) + e = torch.cuda.Event(enable_timing=True) + start = time.perf_counter() + s.record() + evaluate(calc, frames[8 + i % 30]) + e.record() + torch.cuda.synchronize() + wall.append(1000 * (time.perf_counter() - start)) + events.append(s.elapsed_time(e)) + records.append( + { + "pair": 0, + "variant": arm, + "command": {"block": block, "position": position}, + "wall_ms": common.stats(wall), + "event_ms": common.stats(events), + "start_monotonic_ns": start_ns, + "end_monotonic_ns": time.monotonic_ns(), + } + ) + common.write(a.out / "same_context_rounds.json", records) + assert not monitor_errors, str(monitor_errors) + print( + "CONTEXT_API", + block, + position, + arm, + records[-1]["wall_ms"]["median"], + flush=True, + ) + finally: + stop.set() + thread.join(timeout=5) + nv.nvmlShutdown() + common.write(a.out / "same_context_monitor.json", monitor) + assert not monitor_errors, str(monitor_errors) + assert artifacts() == frozen + assert model.compiled_core_compute_cache == first_cache + common.write( + a.out / "same_context_status.json", + { + "status": "completed", + "accuracy_passed": True, + "cache_unchanged": True, + "first": a.first, + "blocks": a.blocks, + "samples_per_arm": a.blocks * 2 * 50, + }, + ) + restore_baseline() + + +sys.argv = [ + str(HERE / "_so2_edge_cohort_benchmark_impl.py"), + "--out", + str(a.out), + "--gpu", + a.gpu, + "--nrep", + "2", + "--model", + str(a.model), + "--reference", + str(a.reference), +] +ns = runpy.run_path( + str(HERE / "_so2_edge_cohort_benchmark_impl.py"), + init_globals={"_controlled_rounds": callback}, + run_name="__main__", +) +assert ns["result"]["precision_pass"] and ns["result"]["finite"] diff --git a/source/tests/pt_expt/kernels/benchmark_so2_edge_cohort_serial.py b/source/tests/pt_expt/kernels/benchmark_so2_edge_cohort_serial.py new file mode 100644 index 0000000000..23dc31fc47 --- /dev/null +++ b/source/tests/pt_expt/kernels/benchmark_so2_edge_cohort_serial.py @@ -0,0 +1,191 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +# ruff: noqa: T201 -- manual benchmark CLI output +"""One CUDA process at a time: remove resident-context initialization bias.""" + +import argparse +import hashlib +import json +import os +from pathlib import Path +import subprocess +import sys +import threading +import time + +import pynvml as nv + +HERE = Path(__file__).resolve().parent +p = argparse.ArgumentParser(description=__doc__) +p.add_argument("--out", type=Path, required=True) +p.add_argument("--cache", type=Path, required=True) +p.add_argument("--baseline-root", type=Path, required=True) +p.add_argument("--candidate-root", type=Path, required=True) +p.add_argument("--model", type=Path, required=True) +p.add_argument("--reference", type=Path, required=True) +p.add_argument("--gpu", default="0") +p.add_argument("--cpu", type=int, default=8) +p.add_argument("--monitor-cpu", type=int, default=16) +p.add_argument("--seeds", type=int, nargs="+", default=[0, 1, 2]) +p.add_argument("--rounds", type=int, default=8) +p.add_argument("--samples", type=int, default=50) +p.add_argument("--conditioning-samples", type=int, default=200) +a = p.parse_args() +assert a.samples >= 10 and a.rounds >= 1 +out = a.out.resolve() +out.mkdir(parents=True, exist_ok=False) +cache = a.cache.resolve() +os.sched_setaffinity(0, {a.monitor_cpu}) +nv.nvmlInit() +handle = nv.nvmlDeviceGetHandleByIndex(int(a.gpu)) +cases = [ + (arm, seed) + for i, seed in enumerate(a.seeds) + for arm in (("BASE", "ECS") if i % 2 == 0 else ("ECS", "BASE")) +] +worker = None +stop = threading.Event() +monitor, errors, ready, records = [], [], [], [] + + +def write(name, value): + (out / name).write_text(json.dumps(value, indent=2)) + + +def watch(): + while not stop.wait(0.05): + pids = {v.pid for v in nv.nvmlDeviceGetComputeRunningProcesses(handle)} + allowed = {worker.pid} if worker is not None else set() + if pids - allowed: + errors.append(f"foreign GPU0 pids: {pids - allowed}") + if worker is not None and worker.poll() is None: + worker.terminate() + return + monitor.append( + { + "t_ns": time.monotonic_ns(), + "pids": sorted(pids), + "sm_mhz": nv.nvmlDeviceGetClockInfo(handle, nv.NVML_CLOCK_SM), + "mem_mhz": nv.nvmlDeviceGetClockInfo(handle, nv.NVML_CLOCK_MEM), + "temperature": nv.nvmlDeviceGetTemperature( + handle, nv.NVML_TEMPERATURE_GPU + ), + "power_mw": nv.nvmlDeviceGetPowerUsage(handle), + "clock_reasons": nv.nvmlDeviceGetCurrentClocksThrottleReasons(handle), + } + ) + + +def fingerprint(): + return { + str(p.relative_to(cache)): hashlib.sha256(p.read_bytes()).hexdigest() + for pattern in ("*.best_config", "*.cubin", "*.ptx") + for p in cache.rglob(pattern) + } + + +def message(log): + while True: + line = worker.stdout.readline() + if not line: + raise RuntimeError(f"worker ended {worker.poll()}") + log.write(line) + log.flush() + if line.startswith("@CONTROL@"): + return json.loads(line[len("@CONTROL@") :]) + + +assert not nv.nvmlDeviceGetComputeRunningProcesses(handle) +thread = threading.Thread(target=watch, daemon=True) +thread.start() +try: + for run, (variant, seed) in enumerate(cases): + assert not nv.nvmlDeviceGetComputeRunningProcesses(handle) + env = dict(os.environ, PYTHONHASHSEED=str(seed)) + with (out / f"{run}_{variant}.log").open("x") as log: + worker = subprocess.Popen( + [ + sys.executable, + str(HERE / "_so2_edge_cohort_controlled_worker.py"), + "--baseline-root", + str(a.baseline_root.resolve()), + "--candidate-root", + str(a.candidate_root.resolve()), + "--model", + str(a.model.resolve()), + "--reference", + str(a.reference.resolve()), + "--variant", + variant, + "--out", + str(out / f"{run}_{variant}"), + "--cache", + str(cache), + "--gpu", + a.gpu, + "--cpu", + str(a.cpu), + ], + stdin=subprocess.PIPE, + stdout=subprocess.PIPE, + stderr=log, + text=True, + bufsize=1, + env=env, + ) + worker.stdin.write(json.dumps({"pids": [worker.pid]}) + "\n") + worker.stdin.flush() + r = message(log) + assert r["status"] == "ready" + r.update(run=run, seed=seed) + ready.append(r) + write("ready.json", ready) + print("READY", run, variant, seed, r["entry_wall_ms"]["median"], flush=True) + frozen = fingerprint() + write(f"cache_{run}_before.json", frozen) + for block in range(a.rounds + 1): + command = { + "action": "measure", + "pair": seed, + "block": block, + "position": run % 2, + "samples": a.conditioning_samples if block == 0 else a.samples, + "warmup": 8, + "cpu": a.cpu, + "phase": "conditioning" if block == 0 else "formal", + } + worker.stdin.write(json.dumps(command) + "\n") + worker.stdin.flush() + r = message(log) + r.update(run=run, seed=seed, pair=seed) + records.append(r) + write("rounds.json", records) + assert fingerprint() == frozen, "autotune/cache changed in timed runs" + print("ROUND", run, variant, block, r["wall_ms"]["median"], flush=True) + write(f"cache_{run}_after.json", fingerprint()) + worker.stdin.write('{"action":"stop"}\n') + worker.stdin.flush() + r = message(log) + assert r["status"] == "completed" and r["precision_pass"] + worker.wait(timeout=30) + assert worker.returncode == 0 + worker = None +finally: + if worker is not None: + if worker.poll() is None: + worker.terminate() + worker.wait(timeout=30) + stop.set() + thread.join(timeout=5) + write("monitor.json", monitor) + write( + "controller.json", + { + "errors": errors, + "status": "completed" + if len(records) == len(cases) * (a.rounds + 1) and not errors + else "failed", + "args": {k: str(v) for k, v in vars(a).items()}, + "protocol": "one CUDA process at a time; matched hash seeds; conditioning separated from formal rounds", + }, + ) + nv.nvmlShutdown() From 0079b9dbc379622f4a0c44f12601e9944a864f6b Mon Sep 17 00:00:00 2001 From: Macqueen Ma-hnu Date: Mon, 19 Oct 2026 18:13:17 +0800 Subject: [PATCH 3/7] docs(pt): disclose ECS multi-model accuracy and N4096 limits --- .../ecs-edge-cohort-multimodel-accuracy.md | 206 + .../ecs-edge-cohort-multimodel-results.json | 10004 ++++++++++++++++ .../ecs-edge-cohort-multimodel-summary.md | 52 + .../ecs-edge-cohort-multimodel-update.md | 62 + ...-edge-cohort-n4096-historical-results.json | 1 + 5 files changed, 10325 insertions(+) create mode 100644 doc/development/ecs-edge-cohort-multimodel-accuracy.md create mode 100644 doc/development/ecs-edge-cohort-multimodel-results.json create mode 100644 doc/development/ecs-edge-cohort-multimodel-summary.md create mode 100644 doc/development/ecs-edge-cohort-multimodel-update.md create mode 100644 doc/development/ecs-edge-cohort-n4096-historical-results.json diff --git a/doc/development/ecs-edge-cohort-multimodel-accuracy.md b/doc/development/ecs-edge-cohort-multimodel-accuracy.md new file mode 100644 index 0000000000..8d487d6bd6 --- /dev/null +++ b/doc/development/ecs-edge-cohort-multimodel-accuracy.md @@ -0,0 +1,206 @@ +# ECS 多模型多尺寸精度测试 + +> 精度优先；未放宽阈值。误差范围为每对输出max_abs的min～max，不是置信区间。基线SHA与模型SHA见JSON。 + +模式：TORCH=纯PyTorch编译；ECS=当前PR候选（非Neo为回退）；ECS_RUNTIME=强制真实8192分批。单位：能量/位力eV，力eV/Å，stress eV/ų。所有结果均不是DFT预测误差。 + +| 模型 | 原子数 | 对照 | 范围 | 完成进程数 | 能量max_abs范围 | 力max_abs范围 | 位力max_abs范围 | stress max_abs范围 | 最差误差/容差 | 状态 | +| --- | ---: | --- | --- | --- | --- | --- | --- | --- | ---: | --- | +| Mini | 512 | TORCH vs TORCH | within_process | 3/3 | 1.788e-07～1.907e-05 | 5.743e-06～7.458e-06 | 1.941e-05～1.008e-04 | 6.477e-09～3.453e-08 | 2.3256 | FAIL | +| Mini | 512 | TORCH vs TORCH | across_process | 3/3 | 0.000e+00～2.682e-05 | 5.574e-06～8.402e-06 | 2.161e-05～1.017e-04 | 7.439e-09～3.501e-08 | 2.0800 | FAIL | +| Mini | 512 | BASE vs BASE | within_process | 3/3 | 8.941e-07～2.909e-05 | 5.665e-06～7.634e-06 | 1.822e-05～8.902e-05 | 5.847e-09～3.065e-08 | 1.3209 | FAIL | +| Mini | 512 | BASE vs BASE | across_process | 3/3 | 2.384e-07～3.576e-05 | 5.394e-06～8.533e-06 | 1.780e-05～9.780e-05 | 6.128e-09～3.367e-08 | 1.5409 | FAIL | +| Mini | 512 | ECS vs ECS | within_process | 3/3 | 2.384e-07～2.784e-05 | 5.507e-06～8.241e-06 | 2.388e-05～1.102e-04 | 7.637e-09～3.793e-08 | 2.3481 | FAIL | +| Mini | 512 | ECS vs ECS | across_process | 3/3 | 2.980e-07～2.706e-05 | 5.538e-06～8.991e-06 | 8.570e-06～1.185e-04 | 2.600e-09～4.078e-08 | 2.7696 | FAIL | +| Mini | 512 | BASE vs TORCH | cross_implementation | 3/3 | 0.000e+00～3.856e-05 | 7.663e-06～1.194e-05 | 8.734e-05～2.392e-04 | 3.007e-08～8.237e-08 | 2.0564 | FAIL | +| Mini | 512 | ECS vs TORCH | cross_implementation | 3/3 | 1.192e-07～3.630e-05 | 7.538e-06～1.362e-05 | 8.123e-05～2.064e-04 | 2.797e-08～7.105e-08 | 2.6359 | FAIL | +| Mini | 512 | ECS vs BASE | cross_implementation | 3/3 | 1.192e-07～3.225e-05 | 5.317e-06～8.227e-06 | 7.091e-06～1.170e-04 | 2.441e-09～4.027e-08 | 2.1640 | FAIL | +| Mini | 1000 | TORCH vs TORCH | within_process | 3/3 | 7.749e-07～3.952e-05 | 5.942e-06～8.479e-06 | 3.820e-05～1.486e-04 | 6.734e-09～2.619e-08 | 0.6828 | PASS | +| Mini | 1000 | TORCH vs TORCH | across_process | 3/3 | 4.172e-07～5.251e-05 | 6.128e-06～8.859e-06 | 2.881e-05～1.862e-04 | 4.861e-09～3.283e-08 | 0.7997 | PASS | +| Mini | 1000 | BASE vs BASE | within_process | 3/3 | 5.364e-07～3.475e-05 | 5.791e-06～8.084e-06 | 2.814e-05～9.627e-05 | 4.864e-09～1.697e-08 | 0.7113 | PASS | +| Mini | 1000 | BASE vs BASE | across_process | 3/3 | 1.788e-07～5.239e-05 | 5.877e-06～9.181e-06 | 1.580e-05～1.545e-04 | 2.721e-09～2.723e-08 | 0.7256 | PASS | +| Mini | 1000 | ECS vs ECS | within_process | 3/3 | 2.384e-07～3.195e-05 | 5.835e-06～8.294e-06 | 3.425e-05～1.184e-04 | 6.019e-09～2.087e-08 | 1.1129 | FAIL | +| Mini | 1000 | ECS vs ECS | across_process | 3/3 | 2.384e-07～4.518e-05 | 5.955e-06～8.708e-06 | 1.679e-05～1.293e-04 | 2.959e-09～2.279e-08 | 1.3280 | FAIL | +| Mini | 1000 | BASE vs TORCH | cross_implementation | 3/3 | 1.192e-07～6.008e-05 | 8.291e-06～1.272e-05 | 1.898e-04～4.217e-04 | 3.346e-08～7.434e-08 | 0.9772 | PASS | +| Mini | 1000 | ECS vs TORCH | cross_implementation | 3/3 | 5.960e-08～4.542e-05 | 7.863e-06～1.166e-05 | 2.127e-04～3.895e-04 | 3.749e-08～6.865e-08 | 1.0280 | FAIL | +| Mini | 1000 | ECS vs BASE | cross_implementation | 3/3 | 2.384e-07～5.299e-05 | 5.502e-06～9.310e-06 | 1.849e-05～1.536e-04 | 3.259e-09～2.707e-08 | 1.2858 | FAIL | +| Mini | 4096 | TORCH vs TORCH | within_process | 3/3 | 5.960e-08～6.229e-05 | 7.276e-06～9.493e-06 | 5.062e-05～4.385e-04 | 2.066e-09～1.887e-08 | 12.0785 | FAIL | +| Mini | 4096 | TORCH vs TORCH | across_process | 3/3 | 5.960e-08～8.333e-05 | 6.791e-06～9.902e-06 | 5.727e-05～3.834e-04 | 2.084e-09～1.650e-08 | 18.0056 | FAIL | +| Mini | 4096 | BASE vs BASE | within_process | 3/3 | 3.099e-06～1.399e-04 | 7.088e-06～8.594e-06 | 4.909e-05～2.918e-04 | 2.043e-09～1.256e-08 | 7.5328 | FAIL | +| Mini | 4096 | BASE vs BASE | across_process | 3/3 | 3.576e-07～1.013e-04 | 6.853e-06～9.191e-06 | 4.477e-05～3.680e-04 | 1.894e-09～1.584e-08 | 8.7236 | FAIL | +| Mini | 4096 | ECS vs ECS | within_process | 3/3 | 8.941e-07～6.741e-05 | 6.751e-06～9.570e-06 | 8.410e-05～3.134e-04 | 3.504e-09～1.342e-08 | 7.6971 | FAIL | +| Mini | 4096 | ECS vs ECS | across_process | 3/3 | 2.980e-07～8.285e-05 | 6.717e-06～1.040e-05 | 3.166e-05～3.210e-04 | 1.362e-09～1.381e-08 | 8.5980 | FAIL | +| Mini | 4096 | BASE vs TORCH | cross_implementation | 3/3 | 1.073e-06～1.630e-04 | 9.423e-06～1.370e-05 | 8.078e-04～1.487e-03 | 3.476e-08～6.401e-08 | 16.3663 | FAIL | +| Mini | 4096 | ECS vs TORCH | cross_implementation | 3/3 | 1.609e-06～1.435e-04 | 9.178e-06～1.420e-05 | 7.984e-04～1.493e-03 | 3.436e-08～6.425e-08 | 14.4638 | FAIL | +| Mini | 4096 | ECS vs BASE | cross_implementation | 3/3 | 0.000e+00～1.230e-04 | 6.839e-06～9.656e-06 | 4.190e-05～3.580e-04 | 1.803e-09～1.541e-08 | 10.4884 | FAIL | +| Nano | 512 | TORCH vs TORCH | within_process | 3/3 | 1.788e-07～5.186e-05 | 8.767e-06～1.729e-05 | 4.055e-05～1.745e-04 | 1.365e-08～6.008e-08 | 5.1961 | FAIL | +| Nano | 512 | TORCH vs TORCH | across_process | 3/3 | 8.941e-07～5.031e-05 | 8.257e-06～1.488e-05 | 3.397e-05～1.774e-04 | 1.067e-08～6.108e-08 | 8.1770 | FAIL | +| Nano | 512 | BASE vs BASE | within_process | 3/3 | 6.557e-07～5.233e-05 | 8.955e-06～1.241e-05 | 4.078e-05～1.411e-04 | 1.404e-08～4.859e-08 | 4.3052 | FAIL | +| Nano | 512 | BASE vs BASE | across_process | 3/3 | 3.576e-07～4.852e-05 | 8.638e-06～1.356e-05 | 2.392e-05～1.993e-04 | 7.193e-09～6.862e-08 | 5.5042 | FAIL | +| Nano | 512 | ECS vs ECS | within_process | 3/3 | 2.384e-07～4.870e-05 | 8.850e-06～1.174e-05 | 2.709e-05～1.370e-04 | 9.077e-09～4.717e-08 | 3.9393 | FAIL | +| Nano | 512 | ECS vs ECS | across_process | 3/3 | 2.980e-07～6.038e-05 | 8.533e-06～1.260e-05 | 2.392e-05～2.191e-04 | 8.234e-09～7.544e-08 | 5.7037 | FAIL | +| Nano | 512 | BASE vs TORCH | cross_implementation | 3/3 | 2.980e-06～1.152e-04 | 8.867e-06～1.386e-05 | 1.153e-05～2.624e-04 | 3.971e-09～9.033e-08 | 9.4910 | FAIL | +| Nano | 512 | ECS vs TORCH | cross_implementation | 3/3 | 4.172e-07～1.128e-04 | 8.495e-06～1.357e-05 | 2.651e-05～2.647e-04 | 9.126e-09～9.113e-08 | 8.6337 | FAIL | +| Nano | 512 | ECS vs BASE | cross_implementation | 3/3 | 4.172e-07～5.412e-05 | 8.595e-06～1.269e-05 | 2.358e-05～2.068e-04 | 8.119e-09～7.121e-08 | 6.5564 | FAIL | +| Nano | 1000 | TORCH vs TORCH | within_process | 3/3 | 1.788e-07～6.706e-05 | 9.814e-06～1.328e-05 | 5.537e-05～2.621e-04 | 9.760e-09～4.621e-08 | 5.8245 | FAIL | +| Nano | 1000 | TORCH vs TORCH | across_process | 3/3 | 8.941e-07～7.910e-05 | 9.717e-06～1.356e-05 | 3.931e-05～2.863e-04 | 6.794e-09～5.047e-08 | 7.5700 | FAIL | +| Nano | 1000 | BASE vs BASE | within_process | 3/3 | 1.073e-06～5.555e-05 | 8.786e-06～1.246e-05 | 4.376e-05～2.458e-04 | 7.638e-09～4.333e-08 | 6.4322 | FAIL | +| Nano | 1000 | BASE vs BASE | across_process | 3/3 | 5.960e-08～7.153e-05 | 9.121e-06～1.302e-05 | 5.281e-05～2.499e-04 | 9.309e-09～4.405e-08 | 9.2287 | FAIL | +| Nano | 1000 | ECS vs ECS | within_process | 3/3 | 1.192e-06～5.585e-05 | 9.348e-06～1.242e-05 | 4.847e-05～2.198e-04 | 8.545e-09～3.874e-08 | 4.9302 | FAIL | +| Nano | 1000 | ECS vs ECS | across_process | 3/3 | 5.960e-08～6.944e-05 | 9.239e-06～1.430e-05 | 2.857e-05～2.319e-04 | 5.036e-09～4.087e-08 | 6.9685 | FAIL | +| Nano | 1000 | BASE vs TORCH | cross_implementation | 3/3 | 1.049e-05～1.621e-04 | 9.189e-06～1.489e-05 | 3.178e-05～2.637e-04 | 5.601e-09～4.649e-08 | 9.2328 | FAIL | +| Nano | 1000 | ECS vs TORCH | cross_implementation | 3/3 | 1.264e-05～1.663e-04 | 9.170e-06～1.386e-05 | 1.395e-05～3.074e-04 | 2.459e-09～5.418e-08 | 8.0526 | FAIL | +| Nano | 1000 | ECS vs BASE | cross_implementation | 3/3 | 0.000e+00～7.570e-05 | 9.041e-06～1.358e-05 | 3.918e-05～2.690e-04 | 6.906e-09～4.742e-08 | 8.1419 | FAIL | +| Nano | 4096 | TORCH vs TORCH | within_process | 3/3 | 3.755e-06～1.802e-04 | 1.138e-05～1.498e-05 | 9.571e-05～6.979e-04 | 4.119e-09～3.003e-08 | 2.3384 | FAIL | +| Nano | 4096 | TORCH vs TORCH | across_process | 3/3 | 5.960e-08～2.024e-04 | 1.065e-05～1.443e-05 | 9.505e-05～6.595e-04 | 3.965e-09～2.838e-08 | 2.8159 | FAIL | +| Nano | 4096 | BASE vs BASE | within_process | 3/3 | 2.384e-06～1.985e-04 | 1.098e-05～1.587e-05 | 1.133e-04～3.956e-04 | 4.860e-09～1.703e-08 | 1.3307 | FAIL | +| Nano | 4096 | BASE vs BASE | across_process | 3/3 | 1.907e-06～2.370e-04 | 1.060e-05～1.419e-05 | 5.914e-05～4.544e-04 | 2.379e-09～1.956e-08 | 2.5569 | FAIL | +| Nano | 4096 | ECS vs ECS | within_process | 3/3 | 3.278e-06～1.050e-04 | 1.101e-05～1.347e-05 | 6.502e-05～7.514e-04 | 2.450e-09～3.233e-08 | 1.2806 | FAIL | +| Nano | 4096 | ECS vs ECS | across_process | 3/3 | 1.788e-07～1.045e-04 | 1.095e-05～1.462e-05 | 8.139e-05～6.386e-04 | 3.503e-09～2.748e-08 | 2.1274 | FAIL | +| Nano | 4096 | BASE vs TORCH | cross_implementation | 3/3 | 9.382e-05～4.325e-04 | 1.070e-05～1.538e-05 | 8.534e-05～1.047e-03 | 3.673e-09～4.504e-08 | 3.3957 | FAIL | +| Nano | 4096 | ECS vs TORCH | cross_implementation | 3/3 | 1.401e-04～4.290e-04 | 1.060e-05～1.602e-05 | 9.420e-05～1.060e-03 | 3.617e-09～4.560e-08 | 3.0524 | FAIL | +| Nano | 4096 | ECS vs BASE | cross_implementation | 3/3 | 5.960e-08～1.978e-04 | 1.056e-05～1.622e-05 | 7.473e-05～6.500e-04 | 3.042e-09～2.797e-08 | 2.4384 | FAIL | +| Neo | 512 | TORCH vs TORCH | within_process | 3/3 | 5.960e-08～1.943e-05 | 3.261e-06～4.747e-06 | 1.320e-05～7.500e-05 | 4.517e-09～2.582e-08 | 0.6232 | PASS | +| Neo | 512 | TORCH vs TORCH | across_process | 3/3 | 4.768e-07～1.884e-05 | 3.108e-06～5.441e-06 | 9.732e-06～7.461e-05 | 2.689e-09～2.569e-08 | 0.6851 | PASS | +| Neo | 512 | BASE vs BASE | within_process | 3/3 | 5.960e-08～2.521e-05 | 3.148e-06～4.934e-06 | 1.098e-05～4.944e-05 | 3.581e-09～1.702e-08 | 0.6621 | PASS | +| Neo | 512 | BASE vs BASE | across_process | 3/3 | 2.384e-07～2.354e-05 | 3.135e-06～5.108e-06 | 9.663e-06～6.807e-05 | 3.327e-09～2.344e-08 | 0.7136 | PASS | +| Neo | 512 | ECS vs ECS | within_process | 3/3 | 1.192e-07～1.627e-05 | 3.263e-06～4.824e-06 | 5.221e-06～4.653e-05 | 1.513e-09～1.602e-08 | 0.7407 | PASS | +| Neo | 512 | ECS vs ECS | across_process | 3/3 | 0.000e+00～1.740e-05 | 3.247e-06～4.922e-06 | 1.290e-05～6.690e-05 | 3.928e-09～2.303e-08 | 0.8633 | PASS | +| Neo | 512 | BASE vs TORCH | cross_implementation | 3/3 | 0.000e+00～3.117e-05 | 4.321e-06～6.981e-06 | 7.577e-05～1.771e-04 | 2.609e-08～6.098e-08 | 1.1932 | FAIL | +| Neo | 512 | ECS vs TORCH | cross_implementation | 3/3 | 1.192e-07～2.927e-05 | 4.556e-06～7.410e-06 | 7.720e-05～1.594e-04 | 2.658e-08～5.487e-08 | 1.0295 | FAIL | +| Neo | 512 | ECS vs BASE | cross_implementation | 3/3 | 0.000e+00～2.331e-05 | 3.149e-06～5.301e-06 | 7.728e-06～7.278e-05 | 2.660e-09～2.506e-08 | 0.9591 | PASS | +| Neo | 512 | ECS_RUNTIME vs ECS_RUNTIME | within_process | 3/3 | 4.172e-07～1.824e-05 | 3.403e-06～4.479e-06 | 1.187e-05～4.710e-05 | 2.963e-09～1.622e-08 | 1.0518 | FAIL | +| Neo | 512 | ECS_RUNTIME vs ECS_RUNTIME | across_process | 3/3 | 5.960e-08～1.431e-05 | 3.251e-06～5.502e-06 | 6.900e-06～5.424e-05 | 2.375e-09～1.867e-08 | 0.9080 | PASS | +| Neo | 512 | ECS_RUNTIME vs BASE | cross_implementation | 3/3 | 5.960e-08～2.301e-05 | 3.042e-06～5.382e-06 | 9.281e-06～6.285e-05 | 2.715e-09～2.164e-08 | 1.0467 | FAIL | +| Neo | 512 | ECS_RUNTIME vs TORCH | cross_implementation | 3/3 | 5.960e-08～2.372e-05 | 4.383e-06～7.113e-06 | 7.607e-05～1.648e-04 | 2.619e-08～5.675e-08 | 1.0368 | FAIL | +| Neo | 512 | ECS_RUNTIME vs ECS | cross_implementation | 3/3 | 0.000e+00～2.110e-05 | 3.024e-06～5.139e-06 | 6.926e-06～6.315e-05 | 2.384e-09～2.174e-08 | 1.1043 | FAIL | +| Neo | 1000 | TORCH vs TORCH | within_process | 3/3 | 5.960e-07～2.939e-05 | 3.633e-06～5.042e-06 | 1.796e-05～8.294e-05 | 2.496e-09～1.462e-08 | 0.4762 | PASS | +| Neo | 1000 | TORCH vs TORCH | across_process | 3/3 | 1.192e-07～2.736e-05 | 3.471e-06～5.588e-06 | 1.483e-05～1.008e-04 | 2.181e-09～1.777e-08 | 0.4978 | PASS | +| Neo | 1000 | BASE vs BASE | within_process | 3/3 | 7.153e-07～2.074e-05 | 3.335e-06～4.864e-06 | 1.662e-05～7.371e-05 | 2.929e-09～1.299e-08 | 0.3498 | PASS | +| Neo | 1000 | BASE vs BASE | across_process | 3/3 | 5.960e-08～1.848e-05 | 3.363e-06～5.305e-06 | 1.574e-05～1.023e-04 | 2.277e-09～1.802e-08 | 0.5508 | PASS | +| Neo | 1000 | ECS vs ECS | within_process | 3/3 | 6.557e-07～3.302e-05 | 3.544e-06～4.635e-06 | 1.599e-05～7.633e-05 | 2.645e-09～1.346e-08 | 0.3679 | PASS | +| Neo | 1000 | ECS vs ECS | across_process | 3/3 | 2.980e-07～3.064e-05 | 3.362e-06～5.389e-06 | 1.043e-05～1.018e-04 | 1.839e-09～1.794e-08 | 0.5019 | PASS | +| Neo | 1000 | BASE vs TORCH | cross_implementation | 3/3 | 0.000e+00～2.736e-05 | 4.921e-06～7.257e-06 | 1.523e-04～2.917e-04 | 2.685e-08～5.141e-08 | 0.6108 | PASS | +| Neo | 1000 | ECS vs TORCH | cross_implementation | 3/3 | 1.192e-07～4.029e-05 | 5.026e-06～8.691e-06 | 1.513e-04～3.357e-04 | 2.667e-08～5.917e-08 | 0.5704 | PASS | +| Neo | 1000 | ECS vs BASE | cross_implementation | 3/3 | 0.000e+00～3.189e-05 | 3.352e-06～5.042e-06 | 1.021e-05～1.058e-04 | 1.644e-09～1.866e-08 | 0.5937 | PASS | +| Neo | 1000 | ECS_RUNTIME vs ECS_RUNTIME | within_process | 3/3 | 2.384e-07～2.861e-05 | 3.570e-06～4.866e-06 | 1.601e-05～1.015e-04 | 2.815e-09～1.790e-08 | 0.4164 | PASS | +| Neo | 1000 | ECS_RUNTIME vs ECS_RUNTIME | across_process | 3/3 | 2.384e-07～2.778e-05 | 3.505e-06～5.059e-06 | 1.429e-05～1.104e-04 | 2.186e-09～1.946e-08 | 0.3538 | PASS | +| Neo | 1000 | ECS_RUNTIME vs BASE | cross_implementation | 3/3 | 5.960e-08～2.718e-05 | 3.289e-06～4.839e-06 | 1.072e-05～1.038e-04 | 1.889e-09～1.829e-08 | 0.6028 | PASS | +| Neo | 1000 | ECS_RUNTIME vs TORCH | cross_implementation | 3/3 | 5.960e-08～3.272e-05 | 4.816e-06～7.409e-06 | 1.576e-04～3.508e-04 | 2.778e-08～6.184e-08 | 0.5441 | PASS | +| Neo | 1000 | ECS_RUNTIME vs ECS | cross_implementation | 3/3 | 5.960e-08～3.481e-05 | 3.344e-06～5.309e-06 | 1.161e-05～1.169e-04 | 1.612e-09～2.061e-08 | 0.5110 | PASS | +| Neo | 4096 | BASE vs BASE | within_process | 3/3 | 2.384e-07～4.643e-05 | 4.007e-06～5.565e-06 | 4.326e-05～1.764e-04 | 1.428e-09～7.592e-09 | 2.0354 | FAIL | +| Neo | 4096 | BASE vs BASE | across_process | 3/3 | 4.172e-07～5.627e-05 | 3.912e-06～5.939e-06 | 2.275e-05～2.513e-04 | 9.788e-10～1.081e-08 | 2.8536 | FAIL | +| Neo | 4096 | ECS vs ECS | within_process | 3/3 | 2.384e-07～5.406e-05 | 4.204e-06～5.183e-06 | 2.174e-05～1.084e-04 | 5.853e-10～4.666e-09 | 2.2326 | FAIL | +| Neo | 4096 | ECS vs ECS | across_process | 3/3 | 3.576e-07～8.285e-05 | 4.080e-06～5.679e-06 | 2.126e-05～1.705e-04 | 5.108e-10～7.337e-09 | 2.8543 | FAIL | +| Neo | 4096 | ECS vs BASE | cross_implementation | 3/3 | 5.960e-08～6.813e-05 | 4.013e-06～5.806e-06 | 2.113e-05～2.482e-04 | 6.362e-10～1.068e-08 | 3.2394 | FAIL | +| Neo | 4096 | ECS_RUNTIME vs ECS_RUNTIME | within_process | 3/3 | 3.576e-07～5.656e-05 | 4.011e-06～5.388e-06 | 2.321e-05～2.127e-04 | 9.989e-10～9.155e-09 | 3.0024 | FAIL | +| Neo | 4096 | ECS_RUNTIME vs ECS_RUNTIME | across_process | 3/3 | 2.384e-07～6.253e-05 | 4.014e-06～6.203e-06 | 2.701e-05～1.597e-04 | 9.890e-10～6.871e-09 | 2.9657 | FAIL | +| Neo | 4096 | ECS_RUNTIME vs BASE | cross_implementation | 3/3 | 5.960e-08～6.562e-05 | 4.056e-06～6.068e-06 | 1.560e-05～2.561e-04 | 6.714e-10～1.102e-08 | 3.5269 | FAIL | +| Neo | 4096 | ECS_RUNTIME vs ECS | cross_implementation | 3/3 | 5.960e-08～7.308e-05 | 3.972e-06～5.791e-06 | 2.029e-05～1.955e-04 | 8.732e-10～8.411e-09 | 3.1677 | FAIL | +| Pro | 64 | TORCH vs TORCH | within_process | 1/1 | 5.960e-08～1.520e-06 | 7.994e-07～1.084e-06 | 1.829e-06～4.709e-06 | 3.323e-09～1.297e-08 | 0.0749 | PASS（样本未齐） | +| Pro | 64 | BASE vs BASE | within_process | 1/1 | 2.086e-07～2.235e-06 | 7.070e-07～9.719e-07 | 1.706e-06～4.243e-06 | 4.698e-09～1.169e-08 | 0.0727 | PASS（样本未齐） | +| Pro | 64 | ECS vs ECS | within_process | 2/2 | 2.980e-08～3.010e-06 | 6.763e-07～9.147e-07 | 2.130e-06～7.250e-06 | 3.415e-09～1.997e-08 | 0.0893 | PASS（样本未齐） | +| Pro | 64 | ECS vs ECS | across_process | 2/2 | 2.980e-08～4.113e-06 | 6.775e-07～1.166e-06 | 1.785e-06～6.365e-06 | 2.901e-09～1.753e-08 | 0.1206 | PASS（样本未齐） | +| Pro | 64 | BASE vs TORCH | cross_implementation | 1/1 | 1.788e-07～3.934e-06 | 1.086e-06～1.643e-06 | 2.214e-06～9.813e-06 | 5.613e-09～2.703e-08 | 0.0992 | PASS（样本未齐） | +| Pro | 64 | ECS vs TORCH | cross_implementation | 2/1 | 2.086e-07～4.351e-06 | 9.742e-07～1.560e-06 | 2.394e-06～1.112e-05 | 5.669e-09～3.062e-08 | 0.1440 | PASS（样本未齐） | +| Pro | 64 | ECS vs BASE | cross_implementation | 2/1 | 5.960e-08～3.636e-06 | 6.614e-07～1.147e-06 | 1.603e-06～6.327e-06 | 2.958e-09～1.743e-08 | 0.0945 | PASS（样本未齐） | + +## 所有进程状态（含OOM/失败，不能略去） + +| 运行 | 状态 | 参数量 | opaque ECS调用 | single路径标记 | +| --- | --- | ---: | ---: | --- | +| Mini_n1000_BASE_p0 | completed | 654988 | 0 | False | +| Mini_n1000_BASE_p1 | completed | 654988 | 0 | False | +| Mini_n1000_BASE_p2 | completed | 654988 | 0 | False | +| Mini_n1000_ECS_p0 | completed | 654988 | 0 | False | +| Mini_n1000_ECS_p1 | completed | 654988 | 0 | False | +| Mini_n1000_ECS_p2 | completed | 654988 | 0 | False | +| Mini_n1000_TORCH_p0 | completed | 654988 | 0 | False | +| Mini_n1000_TORCH_p1 | completed | 654988 | 0 | False | +| Mini_n1000_TORCH_p2 | completed | 654988 | 0 | False | +| Mini_n4096_BASE_p0 | completed | 654988 | 0 | False | +| Mini_n4096_BASE_p1 | completed | 654988 | 0 | False | +| Mini_n4096_BASE_p2 | completed | 654988 | 0 | False | +| Mini_n4096_ECS_p0 | completed | 654988 | 0 | False | +| Mini_n4096_ECS_p1 | completed | 654988 | 0 | False | +| Mini_n4096_ECS_p2 | completed | 654988 | 0 | False | +| Mini_n4096_TORCH_p0 | completed | 654988 | 0 | False | +| Mini_n4096_TORCH_p1 | completed | 654988 | 0 | False | +| Mini_n4096_TORCH_p2 | completed | 654988 | 0 | False | +| Mini_n512_BASE_p0 | completed | 654988 | 0 | False | +| Mini_n512_BASE_p1 | completed | 654988 | 0 | False | +| Mini_n512_BASE_p2 | completed | 654988 | 0 | False | +| Mini_n512_ECS_p0 | completed | 654988 | 0 | False | +| Mini_n512_ECS_p1 | completed | 654988 | 0 | False | +| Mini_n512_ECS_p2 | completed | 654988 | 0 | False | +| Mini_n512_TORCH_p0 | completed | 654988 | 0 | False | +| Mini_n512_TORCH_p1 | completed | 654988 | 0 | False | +| Mini_n512_TORCH_p2 | completed | 654988 | 0 | False | +| Nano_n1000_BASE_p0 | completed | 480268 | 0 | False | +| Nano_n1000_BASE_p1 | completed | 480268 | 0 | False | +| Nano_n1000_BASE_p2 | completed | 480268 | 0 | False | +| Nano_n1000_ECS_p0 | completed | 480268 | 0 | False | +| Nano_n1000_ECS_p1 | completed | 480268 | 0 | False | +| Nano_n1000_ECS_p2 | completed | 480268 | 0 | False | +| Nano_n1000_TORCH_p0 | completed | 480268 | 0 | False | +| Nano_n1000_TORCH_p1 | completed | 480268 | 0 | False | +| Nano_n1000_TORCH_p2 | completed | 480268 | 0 | False | +| Nano_n4096_BASE_p0 | completed | 480268 | 0 | False | +| Nano_n4096_BASE_p1 | completed | 480268 | 0 | False | +| Nano_n4096_BASE_p2 | completed | 480268 | 0 | False | +| Nano_n4096_ECS_p0 | completed | 480268 | 0 | False | +| Nano_n4096_ECS_p1 | completed | 480268 | 0 | False | +| Nano_n4096_ECS_p2 | completed | 480268 | 0 | False | +| Nano_n4096_TORCH_p0 | completed | 480268 | 0 | False | +| Nano_n4096_TORCH_p1 | completed | 480268 | 0 | False | +| Nano_n4096_TORCH_p2 | completed | 480268 | 0 | False | +| Nano_n512_BASE_p0 | completed | 480268 | 0 | False | +| Nano_n512_BASE_p1 | completed | 480268 | 0 | False | +| Nano_n512_BASE_p2 | completed | 480268 | 0 | False | +| Nano_n512_ECS_p0 | completed | 480268 | 0 | False | +| Nano_n512_ECS_p1 | completed | 480268 | 0 | False | +| Nano_n512_ECS_p2 | completed | 480268 | 0 | False | +| Nano_n512_TORCH_p0 | completed | 480268 | 0 | False | +| Nano_n512_TORCH_p1 | completed | 480268 | 0 | False | +| Nano_n512_TORCH_p2 | completed | 480268 | 0 | False | +| Neo_n1000_BASE_p0 | completed | 1124910 | 0 | False | +| Neo_n1000_BASE_p1 | completed | 1124910 | 0 | False | +| Neo_n1000_BASE_p2 | completed | 1124910 | 0 | False | +| Neo_n1000_ECS_RUNTIME_p0 | completed | 1124910 | 2 | False | +| Neo_n1000_ECS_RUNTIME_p1 | completed | 1124910 | 2 | False | +| Neo_n1000_ECS_RUNTIME_p2 | completed | 1124910 | 2 | False | +| Neo_n1000_ECS_p0 | completed | 1124910 | 0 | True | +| Neo_n1000_ECS_p1 | completed | 1124910 | 0 | True | +| Neo_n1000_ECS_p2 | completed | 1124910 | 0 | True | +| Neo_n1000_TORCH_p0 | completed | 1124910 | 0 | False | +| Neo_n1000_TORCH_p1 | completed | 1124910 | 0 | False | +| Neo_n1000_TORCH_p2 | completed | 1124910 | 0 | False | +| Neo_n4096_BASE_p0 | completed | 1124910 | 0 | False | +| Neo_n4096_BASE_p1 | completed | 1124910 | 0 | False | +| Neo_n4096_BASE_p2 | completed | 1124910 | 0 | False | +| Neo_n4096_ECS_RUNTIME_p0 | completed | 1124910 | 2 | False | +| Neo_n4096_ECS_RUNTIME_p1 | completed | 1124910 | 2 | False | +| Neo_n4096_ECS_RUNTIME_p2 | completed | 1124910 | 2 | False | +| Neo_n4096_ECS_p0 | completed | 1124910 | 0 | True | +| Neo_n4096_ECS_p1 | completed | 1124910 | 0 | True | +| Neo_n4096_ECS_p2 | completed | 1124910 | 0 | True | +| Neo_n4096_TORCH_p0 | oom | 1124910 | 0 | False | +| Neo_n4096_TORCH_p1 | oom | 1124910 | 0 | False | +| Neo_n4096_TORCH_p2 | oom | 1124910 | 0 | False | +| Neo_n512_BASE_p0 | completed | 1124910 | 0 | False | +| Neo_n512_BASE_p1 | completed | 1124910 | 0 | False | +| Neo_n512_BASE_p2 | completed | 1124910 | 0 | False | +| Neo_n512_ECS_RUNTIME_p0 | completed | 1124910 | 2 | False | +| Neo_n512_ECS_RUNTIME_p1 | completed | 1124910 | 2 | False | +| Neo_n512_ECS_RUNTIME_p2 | completed | 1124910 | 2 | False | +| Neo_n512_ECS_p0 | completed | 1124910 | 0 | True | +| Neo_n512_ECS_p1 | completed | 1124910 | 0 | True | +| Neo_n512_ECS_p2 | completed | 1124910 | 0 | True | +| Neo_n512_TORCH_p0 | completed | 1124910 | 0 | False | +| Neo_n512_TORCH_p1 | completed | 1124910 | 0 | False | +| Neo_n512_TORCH_p2 | completed | 1124910 | 0 | False | +| Pro_n512_TORCH_p0 | oom | 22678990 | 0 | False | +| Pro_n64_BASE_p0 | completed | 22678990 | 0 | False | +| Pro_n64_ECS_p0 | completed | 22678990 | 0 | False | +| Pro_n64_ECS_p1 | completed | 22678990 | 0 | False | +| Pro_n64_TORCH_p0 | completed | 22678990 | 0 | False | diff --git a/doc/development/ecs-edge-cohort-multimodel-results.json b/doc/development/ecs-edge-cohort-multimodel-results.json new file mode 100644 index 0000000000..33c7790b62 --- /dev/null +++ b/doc/development/ecs-edge-cohort-multimodel-results.json @@ -0,0 +1,10004 @@ +{ + "threshold": { + "atol": 1e-05, + "rtol": 0.0001, + "relative_floor": 1e-08 + }, + "range_definition": "min/max of max_abs over same-frame comparisons; NOT a confidence interval", + "statuses": [ + { + "run": "Mini_n1000_BASE_p0", + "model": "Mini", + "n": 1000, + "mode": "BASE", + "gpu": "1", + "process": "p0", + "status": "completed", + "raw_status": "completed", + "model_sha256": "13edeea63448c0f8a0b38e08fd8ba196fb1e2fad5f12486b1cd23320829da268", + "routes": [ + { + "sha256": "8625a6f9f07d0c8b3385d292657152a70bcc721e0a68e8a84ec14b464b2720ee", + "opaque_cohort_calls": 0, + "single_cohort_named_calls": 0, + "stack_kernel_definitions": 15, + "single_cohort_marker": false + } + ], + "runtime_cohort_calls": [], + "finite": true, + "parameter_count": 654988, + "first_peak": { + "allocated": 1884744704, + "reserved": 2017460224 + }, + "complete_peak": { + "allocated": 1884744704, + "reserved": 2017460224 + } + }, + { + "run": "Mini_n1000_BASE_p1", + "model": "Mini", + "n": 1000, + "mode": "BASE", + "gpu": "1", + "process": "p1", + "status": "completed", + "raw_status": "completed", + "model_sha256": "13edeea63448c0f8a0b38e08fd8ba196fb1e2fad5f12486b1cd23320829da268", + "routes": [ + { + "sha256": "8625a6f9f07d0c8b3385d292657152a70bcc721e0a68e8a84ec14b464b2720ee", + "opaque_cohort_calls": 0, + "single_cohort_named_calls": 0, + "stack_kernel_definitions": 15, + "single_cohort_marker": false + } + ], + "runtime_cohort_calls": [], + "finite": true, + "parameter_count": 654988, + "first_peak": { + "allocated": 1884744704, + "reserved": 2017460224 + }, + "complete_peak": { + "allocated": 1884744704, + "reserved": 2017460224 + } + }, + { + "run": "Mini_n1000_BASE_p2", + "model": "Mini", + "n": 1000, + "mode": "BASE", + "gpu": "1", + "process": "p2", + "status": "completed", + "raw_status": "completed", + "model_sha256": "13edeea63448c0f8a0b38e08fd8ba196fb1e2fad5f12486b1cd23320829da268", + "routes": [ + { + "sha256": "8625a6f9f07d0c8b3385d292657152a70bcc721e0a68e8a84ec14b464b2720ee", + "opaque_cohort_calls": 0, + "single_cohort_named_calls": 0, + "stack_kernel_definitions": 15, + "single_cohort_marker": false + } + ], + "runtime_cohort_calls": [], + "finite": true, + "parameter_count": 654988, + "first_peak": { + "allocated": 1884744704, + "reserved": 2017460224 + }, + "complete_peak": { + "allocated": 1884744704, 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"right_completed_processes": 1, + "all_passed": true, + "metrics": { + "energy": { + "comparisons": 54, + "failures": 0, + "all_finite": true, + "all_bitwise": false, + "max_abs_range": [ + 5.960464477539063e-08, + 3.635883331298828e-06 + ], + "worst_mean_abs": 3.635883331298828e-06, + "worst_max_relative": 6.268637063973441e-09, + "worst_tolerance_ratio": 6.267556472732592e-05 + }, + "forces": { + "comparisons": 54, + "failures": 0, + "all_finite": true, + "all_bitwise": false, + "max_abs_range": [ + 6.613807812527739e-07, + 1.1465652276498872e-06 + ], + "worst_mean_abs": 2.557871741359907e-07, + "worst_max_relative": 0.0001767185463139161, + "worst_tolerance_ratio": 0.08654445754001032 + }, + "virial": { + "comparisons": 54, + "failures": 0, + "all_finite": true, + "all_bitwise": false, + "max_abs_range": [ + 1.6028361553566128e-06, + 6.326886797580755e-06 + ], + "worst_mean_abs": 2.3277401853816215e-06, + "worst_max_relative": 1.3916530971727104e-05, + "worst_tolerance_ratio": 0.09450762132471897 + }, + "stress": { + "comparisons": 54, + "failures": 0, + "all_finite": true, + "all_bitwise": false, + "max_abs_range": [ + 2.9580262948282476e-09, + 1.7425720703206915e-08 + ], + "worst_mean_abs": 7.0962514801856174e-09, + "worst_max_relative": 1.220163929743474e-05, + "worst_tolerance_ratio": 0.0014213551835974513 + } + } + } + ] +} \ No newline at end of file diff --git a/doc/development/ecs-edge-cohort-multimodel-summary.md b/doc/development/ecs-edge-cohort-multimodel-summary.md new file mode 100644 index 0000000000..1206c462f4 --- /dev/null +++ b/doc/development/ecs-edge-cohort-multimodel-summary.md @@ -0,0 +1,52 @@ +# ECS 精度验收汇总（2026-10-19） + +> 以精度为先；下表数值为最差误差÷容差，≤1才满足该项既定判据。未满3个进程注明未齐。OOM不是PASS。 + +已记录95个进程，其中91个完整执行；标准矩阵108进程，加真实Neo分批9进程，共计划117。此轮不是性能测试。 + +## 模型与尺寸总表 + +| 模型 | 原子数 | Torch自身 | BASE自身 | PR候选自身 | PR vs BASE | PR vs Torch | PR实际路线 | +| --- | ---: | ---: | ---: | ---: | ---: | ---: | --- | +| Neo | 512 | 0.685 | 0.714 | 0.863 | 0.959 | 1.030 | single（非8192运行时分批） | +| Neo | 1000 | 0.498 | 0.551 | 0.502 | 0.594 | 0.570 | single（非8192运行时分批） | +| Neo | 4096 | OOM/缺参照 | 2.854 | 2.854 | 3.239 | OOM/缺参照 | single（非8192运行时分批） | +| Nano | 512 | 8.177 | 5.504 | 5.704 | 6.556 | 8.634 | 回退原生，不能算ECS验证 | +| Nano | 1000 | 7.570 | 9.229 | 6.968 | 8.142 | 8.053 | 回退原生，不能算ECS验证 | +| Nano | 4096 | 2.816 | 2.557 | 2.127 | 2.438 | 3.052 | 回退原生，不能算ECS验证 | +| Mini | 512 | 2.326 | 1.541 | 2.770 | 2.164 | 2.636 | 回退原生，不能算ECS验证 | +| Mini | 1000 | 0.800 | 0.726 | 1.328 | 1.286 | 1.028 | 回退原生，不能算ECS验证 | +| Mini | 4096 | 18.006 | 8.724 | 8.598 | 10.488 | 14.464 | 回退原生，不能算ECS验证 | +| Pro | 64 | 0.075（1/3，未齐） | 0.073（1/3，未齐） | 0.121（2/3，未齐） | 0.095（1/3，未齐） | 0.144（1/3，未齐） | 回退原生，不能算ECS验证 | +| Pro | 512 | OOM/缺参照 | 待测 | 待测 | 待测 | OOM/缺参照 | 回退原生，不能算ECS验证 | +| Pro | 4096 | 待测 | 待测 | 待测 | 待测 | 待测 | 回退原生，不能算ECS验证 | + +## 真正8192边运行时分批：与PR单批路径分开 + +| 模型 | 原子数 | 自身 | 对BASE | 对Torch | 对PR单批 | 已观察真实cohort | +| --- | ---: | ---: | ---: | ---: | ---: | --- | +| Neo | 512 | 1.052 | 1.047 | 1.037 | 1.104 | [(80898, 8192, 10)] | +| Neo | 1000 | 0.416 | 0.603 | 0.544 | 0.511 | [(158002, 8192, 20)] | +| Neo | 4096 | 3.002 | 3.527 | OOM/缺参照 | 3.168 | [(647170, 8192, 80)] | + +## 4096原子位力最大绝对误差范围（eV） + +范围是各对输出max_abs的min~max，非置信区间；所有比较仅对同一帧进行。 + +| 模型 | Torch自身 | BASE自身 | PR自身 | PR vs BASE | PR vs Torch | +| --- | --- | --- | --- | --- | --- | +| Neo | OOM/缺参照 | 2.275e-05 ~ 2.513e-04 | 2.126e-05 ~ 1.705e-04 | 2.113e-05 ~ 2.482e-04 | OOM/缺参照 | +| Nano | 9.505e-05 ~ 6.979e-04 | 5.914e-05 ~ 4.544e-04 | 6.502e-05 ~ 7.514e-04 | 7.473e-05 ~ 6.500e-04 | 9.420e-05 ~ 1.060e-03 | +| Mini | 5.062e-05 ~ 4.385e-04 | 4.477e-05 ~ 3.680e-04 | 3.166e-05 ~ 3.210e-04 | 4.190e-05 ~ 3.580e-04 | 7.984e-04 ~ 1.493e-03 | +| Pro | 待测 | 待测 | 待测 | 待测 | 待测 | + +## 必须保留的限制 + +- 判据仍为逐元素atol=1e-5、rtol=1e-4，finite必需；relative分母max(abs(ref),1e-8)。不因BASE自身波动自动放宽。所有输出误差详情见ACCURACY_TABLE.md与pair_details.json。 +- Torch是关闭Triton的编译路径，不是无限精度真值。Neo4096该路径三次OOM，因此不能宣称该规模对Torch参照通过。 +- BASE固定PR原基线668b60cc；当前远端master4f78c2d0不属于本矩阵。原模型权重不改、TF32/AMP关闭；不混合FP16x3。 +- 非Neo模型的PR候选是安全回退，不是多模型ECS性能成果。ECS_RUNTIME实验只在独立进程把分支阈值置0，不改PR源文件；真实调用计数确认实际分批。 +- Pro是官方登记的Pro-MPtrj，不是OMat24-Pro；官方MD5/HF SHA256匹配。旧fitting type只在读取时兼容映射，严格权重检查零缺失；三路同样处理，见models/pro_mptrj/COMPATIBILITY.md。 +- 本轮只有碳晶胞/三帧扰动的实现等价性测试，不是DFT预测准确率、所有化学体系或完整MD轨迹验收。 +- 能量/位力eV、力eV/Å、stress eV/ų。相同数值atol跨单位不是相同物理精度；stress通过不能覆盖virial失败。任何阈值调整需事前约定，本轮未调整。 +- 九次调用包含首次编译，用于误差样本，不报稳态P50或加速比。详见SCIENCE_AND_PROTOCOL.md；原始NPZ逐次保留。 diff --git a/doc/development/ecs-edge-cohort-multimodel-update.md b/doc/development/ecs-edge-cohort-multimodel-update.md new file mode 100644 index 0000000000..46200e720f --- /dev/null +++ b/doc/development/ecs-edge-cohort-multimodel-update.md @@ -0,0 +1,62 @@ +# ECS multi-model and N4096 follow-up (2026-10-19) + +This is a measurement update for the default-off Draft PR, not a claim of multi-model production acceptance. The production implementation is unchanged from PR head `9e2e4c5de1b90cf2d6bc8b2706bc103a06778dae`; BASE is pinned to `668b60cc2a9559f79abb9c33acd959833376344f`, not subsequent upstream master. + +## Read the results by execution path + +- **PR candidate:** unmodified current PR code. In the measured Neo make_fx/AOT deployment graphs, the small-edge single-cohort branch is specialized even for N4096. Those runs are **not** evidence of an 8192-edge workspace bound at large E. +- **ECS_RUNTIME diagnostic:** separate processes set `SINGLE_COHORT_MAX_EDGES=0` before compilation, without editing production files. Runtime instrumentation confirms `(E, capacity, batches)` of `(80898,8192,10)`, `(158002,8192,20)` and `(647170,8192,80)`. At N4096 the final batch contains two edges. This override is not a fix submitted in the PR. +- **Nano, Mini, Pro:** the Neo-specific eligibility guard falls back to the native implementation. These are compatibility/repeatability observations, **not evidence of ECS acceleration or of generalized ECS support**. + +The associated [snapshot](ecs-edge-cohort-multimodel-summary.md), [full accuracy table](ecs-edge-cohort-multimodel-accuracy.md), and [machine-readable summary](ecs-edge-cohort-multimodel-results.json) retain incomplete runs and OOM explicitly. Pro measurements are still in progress at this snapshot; no pending cell is a pass. + +## Protocol and scientific gate + +RTX 5090; PyTorch 2.11.0+cu128, Triton 3.6.0; strict level-2 FP32 mixing, TF32/AMP disabled. Normal ASE energy/forces/virial/stress evaluation, diamond carbon at 3.567 Angstrom, seed0 and three 0.02-Angstrom-perturbed frames. Neo/Nano/Mini use N512/1000/4096; Pro uses N64/512/4096. For each model/size/mode: three independent processes, three frames, three repeats per frame. Completed groups therefore retain 27 evaluations per mode. Every comparison uses the same frame; inputs and model identity are checked. All modes for a model/size use the same physical GPU. + +TORCH means the normal compiled path with `DP_TRITON_INFER=0`, not eager execution or an exact reference. BASE uses level2; PR candidate additionally enables `DP_SO2_EDGE_COHORT_INFER=1`. Each model/mode has its own cache reused by subsequent repeats; this is not a latency study with matched autotune selections. The short user-requested pause does not become a timing sample. + +Every output must be finite and satisfy `abs(candidate-reference) <= 1e-5 + 1e-4*abs(reference)`. Max-relative uses `max(abs(reference),1e-8)`. Summary ratios are worst elementwise error/tolerance; <=1 is within the criterion. Full tables include energy, force, virial and stress separately and distinguish within-process repeats from across-process repeats. Ranges are min/max of pairwise max-absolute error, not confidence intervals. BASE or TORCH self-failure does **not** turn a candidate failure into a pass. All-pairs comparisons are descriptive, not independent statistical replicates. + +The N4096 Neo compiled TORCH path OOMed in all three processes. BASE self-repeat worst ratio is 2.854, PR self-repeat 2.854, PR-vs-BASE 3.239; actual runtime batching-vs-BASE is 3.527. Thus **N4096 is not accepted under the fixed criterion**. Existing fluctuations complicate attribution, but do not establish that the candidate is harmless. No threshold was relaxed. + +Energy and virial are in eV, force in eV/Angstrom, stress in eV/Angstrom^3; identical numerical tolerances across units are not equivalent physical tolerances. This is implementation-equivalence testing on perturbed carbon crystals, not DFT predictive-accuracy or long-MD validation. + +## N4096 historical performance, not a current-PR speed claim + +The following is explicitly the **2026-10-18 runtime prototype**, each arm one GPU0 process, 8 warmups and 30 synchronized wall-clock samples through the normal full API, plus separate memory replay. First/JIT execution is excluded from steady timing and included in the full memory window. Decimal GB=1e9 bytes. It is not the controlled N64 latency study and not a new performance measurement of the current PR. + +| Strategy | Frame P50 ms | Time reduction | Steady allocated GB | Steady reduction | Full-window peak including first GB | Full-peak reduction | +| --- | ---: | ---: | ---: | ---: | ---: | ---: | +| Historical BASE | 156.495125 | — | 23.970400 | — | 24.061593 | — | +| Historical runtime ECS prototype | 152.996582 | 2.2356% | 22.256657 | 7.1494% | 22.256657 | 7.5013% | + +BASE/candidate P90: 156.614295/153.179559 ms. Candidate first-call-only allocated peak is 22.255608 GB; the full window is slightly higher at 22.256657 GB and is the primary capacity value above. The historical third-frame virial check failed (error/tolerance 1.394), and TORCH OOMed. These are **unaccepted historical observations**, not a stable speedup claim or a substitute for new precision/latency acceptance. The newer multi-process results further prohibit treating this historical row as accepted. + +[Historical per-call timing, memory and accuracy JSON](ecs-edge-cohort-n4096-historical-results.json) contains both original result objects and the aggregate row. It is deliberately separate from the current accuracy snapshot. + +## Model identity + +- Neo: `DPA4-Neo-OMat24-v20260627.pt`, SHA256 `8d8b2b02b9575cb9e0af420cc1e8b1d9ffcbc04752c2203d29f48ccd0cd5f07d`. +- Nano/Mini: official OMat24 v20260805 checkpoints; exact SHA256 and loaded parameter counts are retained in the machine-readable snapshot. +- Pro: **DPA-4.0.1-Pro-MPtrj v2026.06.05**, not OMat24-Pro. [Official registry](https://github.com/janosh/matbench-discovery/blob/main/models/deepmd/dpa-4.0.1-pro-mptrj.yml), [official record](https://doi.org/10.6084/m9.figshare.32647224). Downloaded mirror revision `452f673645030b1daeda91c75cf6e1b1c346ee5f` of `JinukMoon/oh-my-mlip-mirror-dpa4`; 273318000 bytes, official MD5 `2174bb3095b197489f978d31393c1e06`, SHA256 `446a6e893101fc52190cfccabb9d2328863d651aca36981fd8d3401c71a61221`, both verified. +- Pro's legacy checkpoint fitting type `ener` is unsupported by the current reader. All three modes apply the same **in-memory metadata-only** alias `ener -> dpa4_ener`, referring to the same historical SeZMEnergyFittingNet class; tensor contents and original file are unchanged. Strict CPU state loading has zero missing/unexpected keys. Therefore Pro BASE is the native computation with disclosed reader compatibility, not proof that the unmodified default reader loads this checkpoint directly. + +## Reproduction and limitations of published evidence + +The public benchmark already provides normal API runs for the three primary modes. For example, from the candidate checkout, with a prebuilt compatible CUDA installation, an idle selected GPU and separate output/cache directories: + +```bash +python source/tests/pt_expt/kernels/benchmark_so2_edge_cohort.py \ + --source-root /absolute/path/to/base-checkout --model /absolute/path/to/neo.pt \ + --mode base --gpu 0 --nrep 8 --warmup 8 --samples 30 \ + --out /absolute/path/to/new-base-output --cache-dir /absolute/path/to/new-base-cache +python source/tests/pt_expt/kernels/benchmark_so2_edge_cohort.py \ + --source-root /absolute/path/to/candidate-checkout --model /absolute/path/to/neo.pt \ + --mode ecs --gpu 0 --nrep 8 --warmup 8 --samples 30 \ + --out /absolute/path/to/new-ecs-output --cache-dir /absolute/path/to/new-ecs-cache +``` + +`nrep=2,4,5,8` corresponds to diamond N64,512,1000,4096. Use `--mode torch` for the compiled TORCH reference. Repeat independently with fresh outputs; do not compare across cards or source revisions. These commands exercise the current PR's deployment route and **do not force runtime batching**, and are not an exact replay of the new three-frame/repeat all-pairs matrix. Generated graph route inspection is mandatory. + +This supplement publishes complete aggregate metrics/route counts and the historical per-call result objects, not model weights, compiler caches, full matrix output-array archives or its independent orchestration harness. Therefore it does not claim that the original full matrix can be audited end-to-end from GitHub alone yet. Existing focused unit tests do not replace full-model numerical acceptance or PT2/C++ deployment validation. Keep this PR Draft and default-off; dynamic dispatch correction, unresolved accuracy failures and portable full-matrix reproduction remain gates. diff --git a/doc/development/ecs-edge-cohort-n4096-historical-results.json b/doc/development/ecs-edge-cohort-n4096-historical-results.json new file mode 100644 index 0000000000..fc93180ad3 --- /dev/null +++ b/doc/development/ecs-edge-cohort-n4096-historical-results.json @@ -0,0 +1 @@ +{"label": "2026-10-18 historical prototype; not current PR accepted performance", "metrics": [{"label": "N4096 GPU0", "bases": ["model_n4096_BASE_r0"], "candidates": ["model_n4096_SPLIT_r0"], "base": {"p50": 156.49512538220733, "p90": 156.6142953466624, "complete": 24061592576, "first": 24061592576, "steady": 23970400256}, "candidate": {"p50": 152.99658162984997, "p90": 153.1795586925, "complete": 22256656896, "first": 22255608320, "steady": 22256656896}, "time_reduction_pct": 2.235560848181617, "throughput_increase_pct": 2.2866809931881393, "peak_reduction_pct": 7.501314280420135}], "base": {"variant": "BASE", "n_atoms": 4096, "status": "completed", "first_ms": 127721.33345692419, "wall_ms": {"median": 156.49512538220733, "p90": 156.6142953466624, "min": 156.24970709905028, "max": 156.7487590946257, "samples": [156.6109189298004, 156.6446830984205, 156.6600319929421, 156.5163000486791, 156.5106299240142, 156.38837916776538, 156.33732313290238, 156.52387496083975, 156.60078101791441, 156.53209085576236, 156.48909914307296, 156.39495220966637, 156.56973188742995, 156.49431897327304, 156.4414391759783, 156.60207299515605, 156.4169730991125, 156.50827600620687, 156.49593179114163, 156.46439278498292, 156.32704296149313, 156.4743521157652, 156.4188168849796, 156.34497697465122, 156.7487590946257, 156.57230583019555, 156.5067518968135, 156.24970709905028, 156.46444307640195, 156.38035396113992]}, "event_elapsed_ms": {"median": 156.46929168701172, "p90": 156.5800567626953, "min": 156.22454833984375, "max": 156.7229766845703, "samples": [156.55673217773438, 156.6096649169922, 156.6345977783203, 156.49026489257812, 156.48431396484375, 156.36288452148438, 156.31173706054688, 156.49862670898438, 156.57498168945312, 156.5063934326172, 156.463623046875, 156.36764526367188, 156.54428100585938, 156.46847534179688, 156.41448974609375, 156.57676696777344, 156.3907470703125, 156.48294067382812, 156.47010803222656, 156.4393310546875, 156.30213928222656, 156.44851684570312, 156.39334106445312, 156.31983947753906, 156.7229766845703, 156.54534912109375, 156.4803466796875, 156.22454833984375, 156.43887329101562, 156.35494995117188]}, "event_scope": "entire API call on default stream; includes host gaps, not kernel-sum", "throughput_atoms_per_s": 26173.339201437473, "memory": {"before": {"allocated": 5105152, "reserved": 10485760}, "first": {"allocated": 24061592576, "reserved": 25612517376}, "complete": {"allocated": 24061592576, "reserved": 25612517376}, "steady": {"allocated": 23970400256, "reserved": 25612517376}}, "accuracy": {}, "precision_pass": null, "finite": true, "repeat_noise": [{"0_energy": {"passed": true, "finite": true, "max_abs": 2.9265880584716797e-05, "mean_abs": 2.9265880584716797e-05, "max_relative": 7.886113191931168e-10, "max_ratio": 7.886091941720186e-06, "bitwise_equal": false}, "0_forces": {"passed": true, "finite": true, "max_abs": 4.884153433692973e-06, "mean_abs": 9.149693440342201e-07, "max_relative": 0.00889732273496248, "max_ratio": 0.2910700292909392, "bitwise_equal": false}, "0_virial": {"passed": true, "finite": true, "max_abs": 5.8138769023940995e-05, "mean_abs": 2.451276976723883e-05, "max_relative": 8.539738394836927e-06, "max_ratio": 0.08416117881003406, "bitwise_equal": false}, "0_stress": {"passed": true, "finite": true, "max_abs": 2.17191859625248e-09, "mean_abs": 1.0305605540217744e-09, "max_relative": 7.413133353615878e-06, "max_ratio": 0.0002165573843127067, "bitwise_equal": false}, "1_energy": {"passed": true, "finite": true, "max_abs": 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.../ecs-validation-20261025/README.md | 74 + .../ecs-validation-20261025/REPORT.md | 171 + .../ecs-validation-20261025/analyze.py | 250 + .../port-to-2ba617bc.patch | 720 + .../pro_cpu_load_check.json | 318 + .../ecs-validation-20261025/provenance.json | 2749 ++ .../ecs-validation-20261025/report.py | 223 + .../ecs-validation-20261025/run_matrix.py | 119 + .../ecs-validation-20261025/runs.json | 23956 ++++++++++++++++ .../ecs-validation-20261025/summary.json | 11584 ++++++++ .../ecs-validation-20261025/validation.json | 165 + .../ecs-validation-20261025/worker.py | 370 + 16 files changed, 44663 insertions(+), 2685 deletions(-) create mode 100644 doc/development/ecs-validation-20261025/ACCURACY_TABLE.md create mode 100644 doc/development/ecs-validation-20261025/README.md create mode 100644 doc/development/ecs-validation-20261025/REPORT.md create mode 100644 doc/development/ecs-validation-20261025/analyze.py create mode 100644 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4.697629295974393e-09, - 1.1685465088617786e-08 + 7.889928172605254e-10, + 1.9628784205666383e-08 ], - "worst_mean_abs": 4.6480661980813414e-09, - "worst_max_relative": 6.783770186736631e-06, - "worst_tolerance_ratio": 0.0009496721878047293 + "worst_mean_abs": 1.0324958575396934e-08, + "worst_max_relative": 2.0425068195041572e-05, + "worst_tolerance_ratio": 0.0015786812437780847 } } }, { "model": "Pro", - "n": 64, - "left": "ECS", - "right": "ECS", + "n": 512, + "left": "BASE", + "right": "BASE", "scope": "within_process", - "left_completed_processes": 2, - "right_completed_processes": 2, + "left_completed_processes": 3, + "right_completed_processes": 3, "all_passed": true, "metrics": { "energy": { - "comparisons": 18, + "comparisons": 27, "failures": 0, "all_finite": true, "all_bitwise": false, "max_abs_range": [ - 2.9802322387695312e-08, - 3.0100345611572266e-06 + 5.960464477539063e-08, + 7.12275505065918e-06 ], - "worst_mean_abs": 3.0100345611572266e-06, - "worst_max_relative": 5.1896093782334826e-09, - "worst_tolerance_ratio": 5.1887147904010125e-05 + "worst_mean_abs": 7.12275505065918e-06, + "worst_max_relative": 1.535558534169057e-09, + "worst_tolerance_ratio": 1.5355254305572864e-05 }, "forces": { - "comparisons": 18, + "comparisons": 27, "failures": 0, "all_finite": true, "all_bitwise": false, "max_abs_range": [ - 6.762762065903871e-07, - 9.147038504409011e-07 + 8.576752588140835e-07, + 1.2281044208251046e-06 ], - "worst_mean_abs": 2.6035555022660417e-07, - "worst_max_relative": 0.0001570453217049622, - "worst_tolerance_ratio": 0.06976879806235865 + "worst_mean_abs": 2.367302973371732e-07, + "worst_max_relative": 0.0033962597714922776, + "worst_tolerance_ratio": 0.08957289998000123 }, "virial": { - "comparisons": 18, + "comparisons": 27, "failures": 0, "all_finite": true, "all_bitwise": false, "max_abs_range": [ - 2.130165203340084e-06, - 7.2501097605481846e-06 + 4.5147943201762075e-06, + 2.1660835486159158e-05 ], - "worst_mean_abs": 2.288475109536571e-06, - "worst_max_relative": 1.314665606591251e-05, - "worst_tolerance_ratio": 0.08927939747581534 + "worst_mean_abs": 8.955624374627922e-06, + "worst_max_relative": 3.769365576609372e-05, + "worst_tolerance_ratio": 0.2729805447207528 }, "stress": { - "comparisons": 18, + "comparisons": 27, "failures": 0, "all_finite": true, "all_bitwise": false, "max_abs_range": [ - 3.41465599199009e-09, - 1.9968491898197938e-08 + 1.114540369717698e-09, + 7.45737361793064e-09 ], - "worst_mean_abs": 8.343270145941273e-09, - "worst_max_relative": 1.1413206530463464e-05, - "worst_tolerance_ratio": 0.001628759918593722 + "worst_mean_abs": 3.626988874223889e-09, + "worst_max_relative": 2.70958655516476e-05, + "worst_tolerance_ratio": 0.0006061566337325134 } } }, { "model": "Pro", - "n": 64, - "left": "ECS", - "right": "ECS", + "n": 512, + "left": "BASE", + "right": "BASE", "scope": "across_process", - "left_completed_processes": 2, - "right_completed_processes": 2, + "left_completed_processes": 3, + "right_completed_processes": 3, "all_passed": true, "metrics": { "energy": { - "comparisons": 27, + "comparisons": 81, "failures": 0, "all_finite": true, "all_bitwise": false, "max_abs_range": [ 2.9802322387695312e-08, - 4.112720489501953e-06 + 8.165836334228516e-06 ], - "worst_mean_abs": 4.112720489501953e-06, - "worst_max_relative": 7.090753394402829e-09, - "worst_tolerance_ratio": 7.089531086279943e-05 + "worst_mean_abs": 8.165836334228516e-06, + "worst_max_relative": 1.7604311228549021e-09, + "worst_tolerance_ratio": 1.7603931714338762e-05 }, "forces": { - "comparisons": 27, + "comparisons": 81, "failures": 0, "all_finite": true, "all_bitwise": false, "max_abs_range": [ - 6.774947376442242e-07, - 1.166314316236594e-06 + 8.544495440077071e-07, + 1.3003742784789551e-06 ], - "worst_mean_abs": 2.5948061258459126e-07, - "worst_max_relative": 0.0002078401348765092, - "worst_tolerance_ratio": 0.07848616207961301 + "worst_mean_abs": 2.433386927180002e-07, + "worst_max_relative": 0.00370881190888481, + "worst_tolerance_ratio": 0.07696327320957047 }, "virial": { - "comparisons": 27, + "comparisons": 81, "failures": 0, "all_finite": true, "all_bitwise": false, "max_abs_range": [ - 1.7853426452774102e-06, - 6.365343558911718e-06 + 3.975478726214732e-06, + 1.7980439707798723e-05 ], - "worst_mean_abs": 2.5288423598255093e-06, - "worst_max_relative": 1.775710232290522e-05, - "worst_tolerance_ratio": 0.12058891956353995 + "worst_mean_abs": 6.964719292117887e-06, + "worst_max_relative": 3.88064961458411e-05, + "worst_tolerance_ratio": 0.28104283383259954 }, "stress": { - "comparisons": 27, + "comparisons": 81, "failures": 0, "all_finite": true, "all_bitwise": false, "max_abs_range": [ - 2.901221097671887e-09, - 1.7531639587980985e-08 + 5.997253580249454e-10, + 6.190290155472233e-09 ], - "worst_mean_abs": 8.637307409463767e-09, - "worst_max_relative": 1.5286664639320423e-05, - "worst_tolerance_ratio": 0.0014299946183736045 + "worst_mean_abs": 2.8201696906331123e-09, + "worst_max_relative": 3.0517400075554895e-05, + "worst_tolerance_ratio": 0.0005031644646046572 } } }, { "model": "Pro", - "n": 64, - "left": "BASE", - "right": "TORCH", - "scope": "cross_implementation", - "left_completed_processes": 1, - "right_completed_processes": 1, + "n": 512, + "left": "ECS", + "right": "ECS", + "scope": "within_process", + "left_completed_processes": 3, + "right_completed_processes": 3, "all_passed": true, "metrics": { "energy": { @@ -9824,12 +10833,12 @@ "all_finite": true, "all_bitwise": false, "max_abs_range": [ - 1.7881393432617188e-07, - 3.933906555175781e-06 + 2.384185791015625e-07, + 8.165836334228516e-06 ], - "worst_mean_abs": 3.933906555175781e-06, - "worst_max_relative": 6.785721039869355e-09, - "worst_tolerance_ratio": 6.784550751044958e-05 + "worst_mean_abs": 8.165836334228516e-06, + "worst_max_relative": 1.7604311200951038e-09, + "worst_tolerance_ratio": 1.760393168674197e-05 }, "forces": { "comparisons": 27, @@ -9837,12 +10846,12 @@ "all_finite": true, "all_bitwise": false, "max_abs_range": [ - 1.0858841097416416e-06, - 1.6431010747552932e-06 + 8.434803326906604e-07, + 1.2891272938020393e-06 ], - "worst_mean_abs": 4.018208583552359e-07, - "worst_max_relative": 0.00020495963725084666, - "worst_tolerance_ratio": 0.08233770655480728 + "worst_mean_abs": 2.3701967932559728e-07, + "worst_max_relative": 0.004445660054855727, + "worst_tolerance_ratio": 0.07794902742189604 }, "virial": { "comparisons": 27, @@ -9850,12 +10859,12 @@ "all_finite": true, "all_bitwise": false, "max_abs_range": [ - 2.213546985929682e-06, - 9.812836475830977e-06 + 3.4945092304283776e-06, + 1.5322660473771066e-05 ], - "worst_mean_abs": 2.5592508900502195e-06, - "worst_max_relative": 1.3238162780371968e-05, - "worst_tolerance_ratio": 0.09924368170280601 + "worst_mean_abs": 8.65937363957952e-06, + "worst_max_relative": 3.245924575414504e-05, + "worst_tolerance_ratio": 0.24862768555982143 }, "stress": { "comparisons": 27, @@ -9863,140 +10872,140 @@ "all_finite": true, "all_bitwise": false, "max_abs_range": [ - 5.612997816539789e-09, - 2.7026838510246787e-08 + 7.061008548692183e-10, + 5.275272229460226e-09 ], - "worst_mean_abs": 8.475510785332842e-09, - "worst_max_relative": 1.1534144768634636e-05, - "worst_tolerance_ratio": 0.0022080128243433354 + "worst_mean_abs": 3.3281194893243745e-09, + "worst_max_relative": 1.777806110928477e-05, + "worst_tolerance_ratio": 0.0004284302835379078 } } }, { "model": "Pro", - "n": 64, + "n": 512, "left": "ECS", - "right": "TORCH", - "scope": "cross_implementation", - "left_completed_processes": 2, - "right_completed_processes": 1, + "right": "ECS", + "scope": "across_process", + "left_completed_processes": 3, + "right_completed_processes": 3, "all_passed": true, "metrics": { "energy": { - "comparisons": 54, + "comparisons": 81, "failures": 0, "all_finite": true, "all_bitwise": false, "max_abs_range": [ - 2.086162567138672e-07, - 4.351139068603516e-06 + 5.960464477539063e-08, + 7.539987564086914e-06 ], - "worst_mean_abs": 4.351139068603516e-06, - "worst_max_relative": 7.50322622700948e-09, - "worst_tolerance_ratio": 7.501932572719295e-05 + "worst_mean_abs": 7.539987564086914e-06, + "worst_max_relative": 1.625507569956708e-09, + "worst_tolerance_ratio": 1.6254725272212222e-05 }, "forces": { - "comparisons": 54, + "comparisons": 81, "failures": 0, "all_finite": true, "all_bitwise": false, "max_abs_range": [ - 9.742261148915077e-07, - 1.5597756521934514e-06 + 8.076244775168107e-07, + 1.5195523195288274e-06 ], - "worst_mean_abs": 4.0320700836019946e-07, - "worst_max_relative": 0.00024979604792357515, - "worst_tolerance_ratio": 0.09760162552832756 + "worst_mean_abs": 2.3866009174275626e-07, + "worst_max_relative": 0.005405906946654774, + "worst_tolerance_ratio": 0.09230084116887002 }, "virial": { - "comparisons": 54, + "comparisons": 81, "failures": 0, "all_finite": true, "all_bitwise": false, "max_abs_range": [ - 2.3937603048640455e-06, - 1.1116557359969192e-05 + 3.3652813486995115e-06, + 1.891471632120556e-05 ], - "worst_mean_abs": 2.8792532694412977e-06, - "worst_max_relative": 2.1197254101867084e-05, - "worst_tolerance_ratio": 0.14395101210017142 + "worst_mean_abs": 8.586179844116693e-06, + "worst_max_relative": 2.8012208135760616e-05, + "worst_tolerance_ratio": 0.20651552488716088 }, "stress": { - "comparisons": 54, + "comparisons": 81, "failures": 0, "all_finite": true, "all_bitwise": false, "max_abs_range": [ - 5.668819750637502e-09, - 3.061758965458572e-08 + 9.447033147078066e-10, + 6.51194209666528e-09 ], - "worst_mean_abs": 9.543924728352507e-09, - "worst_max_relative": 1.9299542793057537e-05, - "worst_tolerance_ratio": 0.002501366579823094 + "worst_mean_abs": 3.2346261699035066e-09, + "worst_max_relative": 2.4068683410404667e-05, + "worst_tolerance_ratio": 0.000530263496296573 } } }, { "model": "Pro", - "n": 64, + "n": 512, "left": "ECS", "right": "BASE", "scope": "cross_implementation", - "left_completed_processes": 2, - "right_completed_processes": 1, + "left_completed_processes": 3, + "right_completed_processes": 3, "all_passed": true, "metrics": { "energy": { - "comparisons": 54, + "comparisons": 243, "failures": 0, "all_finite": true, "all_bitwise": false, "max_abs_range": [ - 5.960464477539063e-08, - 3.635883331298828e-06 + 2.9802322387695312e-08, + 9.059906005859375e-06 ], - "worst_mean_abs": 3.635883331298828e-06, - "worst_max_relative": 6.268637063973441e-09, - "worst_tolerance_ratio": 6.267556472732592e-05 + "worst_mean_abs": 9.059906005859375e-06, + "worst_max_relative": 1.9531790525757608e-09, + "worst_tolerance_ratio": 1.9531369458898076e-05 }, "forces": { - "comparisons": 54, + "comparisons": 243, "failures": 0, "all_finite": true, "all_bitwise": false, "max_abs_range": [ - 6.613807812527739e-07, - 1.1465652276498872e-06 + 8.428170579222183e-07, + 1.6497713382079837e-06 ], - "worst_mean_abs": 2.557871741359907e-07, - "worst_max_relative": 0.0001767185463139161, - "worst_tolerance_ratio": 0.08654445754001032 + "worst_mean_abs": 2.422898338375736e-07, + "worst_max_relative": 0.005169339914793636, + "worst_tolerance_ratio": 0.10245559618495009 }, "virial": { - "comparisons": 54, + "comparisons": 243, "failures": 0, "all_finite": true, "all_bitwise": false, "max_abs_range": [ - 1.6028361553566128e-06, - 6.326886797580755e-06 + 2.4501980202540352e-06, + 2.0536486857736236e-05 ], - "worst_mean_abs": 2.3277401853816215e-06, - "worst_max_relative": 1.3916530971727104e-05, - "worst_tolerance_ratio": 0.09450762132471897 + "worst_mean_abs": 8.182635733982529e-06, + "worst_max_relative": 3.856703373854477e-05, + "worst_tolerance_ratio": 0.279305619586037 }, "stress": { - "comparisons": 54, + "comparisons": 243, "failures": 0, "all_finite": true, "all_bitwise": false, "max_abs_range": [ - 2.9580262948282476e-09, - 1.7425720703206915e-08 + 5.848777862851526e-10, + 7.070283847970815e-09 ], - "worst_mean_abs": 7.0962514801856174e-09, - "worst_max_relative": 1.220163929743474e-05, - "worst_tolerance_ratio": 0.0014213551835974513 + "worst_mean_abs": 3.0359155135010474e-09, + "worst_max_relative": 2.8521296126945394e-05, + "worst_tolerance_ratio": 0.0005746928364611155 } } } diff --git a/doc/development/ecs-edge-cohort-multimodel-summary.md b/doc/development/ecs-edge-cohort-multimodel-summary.md index 1206c462f4..1653f68ec0 100644 --- a/doc/development/ecs-edge-cohort-multimodel-summary.md +++ b/doc/development/ecs-edge-cohort-multimodel-summary.md @@ -1,8 +1,10 @@ +> Historical baseline668b60cc, completed117-process archive (102 complete/15 OOM), finalized2026-10-26. This is not the newer master2ba617bc comparison; see ecs-validation-20261025/REPORT.md. + # ECS 精度验收汇总（2026-10-19） > 以精度为先；下表数值为最差误差÷容差，≤1才满足该项既定判据。未满3个进程注明未齐。OOM不是PASS。 -已记录95个进程，其中91个完整执行；标准矩阵108进程，加真实Neo分批9进程，共计划117。此轮不是性能测试。 +已记录117个进程，其中102个完整执行；标准矩阵108进程，加真实Neo分批9进程，共计划117。此轮不是性能测试。 ## 模型与尺寸总表 @@ -17,9 +19,9 @@ | Mini | 512 | 2.326 | 1.541 | 2.770 | 2.164 | 2.636 | 回退原生，不能算ECS验证 | | Mini | 1000 | 0.800 | 0.726 | 1.328 | 1.286 | 1.028 | 回退原生，不能算ECS验证 | | Mini | 4096 | 18.006 | 8.724 | 8.598 | 10.488 | 14.464 | 回退原生，不能算ECS验证 | -| Pro | 64 | 0.075（1/3，未齐） | 0.073（1/3，未齐） | 0.121（2/3，未齐） | 0.095（1/3，未齐） | 0.144（1/3，未齐） | 回退原生，不能算ECS验证 | -| Pro | 512 | OOM/缺参照 | 待测 | 待测 | 待测 | OOM/缺参照 | 回退原生，不能算ECS验证 | -| Pro | 4096 | 待测 | 待测 | 待测 | 待测 | 待测 | 回退原生，不能算ECS验证 | +| Pro | 64 | 0.130 | 0.122 | 0.121 | 0.152 | 0.144 | 回退原生，不能算ECS验证 | +| Pro | 512 | OOM/缺参照 | 0.281 | 0.249 | 0.279 | OOM/缺参照 | 回退原生，不能算ECS验证 | +| Pro | 4096 | OOM/缺参照 | OOM/缺参照 | OOM/缺参照 | OOM/缺参照 | OOM/缺参照 | 回退原生，不能算ECS验证 | ## 真正8192边运行时分批：与PR单批路径分开 @@ -38,7 +40,7 @@ | Neo | OOM/缺参照 | 2.275e-05 ~ 2.513e-04 | 2.126e-05 ~ 1.705e-04 | 2.113e-05 ~ 2.482e-04 | OOM/缺参照 | | Nano | 9.505e-05 ~ 6.979e-04 | 5.914e-05 ~ 4.544e-04 | 6.502e-05 ~ 7.514e-04 | 7.473e-05 ~ 6.500e-04 | 9.420e-05 ~ 1.060e-03 | | Mini | 5.062e-05 ~ 4.385e-04 | 4.477e-05 ~ 3.680e-04 | 3.166e-05 ~ 3.210e-04 | 4.190e-05 ~ 3.580e-04 | 7.984e-04 ~ 1.493e-03 | -| Pro | 待测 | 待测 | 待测 | 待测 | 待测 | +| Pro | OOM/缺参照 | OOM/缺参照 | OOM/缺参照 | OOM/缺参照 | OOM/缺参照 | ## 必须保留的限制 diff --git a/doc/development/ecs-edge-cohort-multimodel-update.md b/doc/development/ecs-edge-cohort-multimodel-update.md index 46200e720f..b011945994 100644 --- a/doc/development/ecs-edge-cohort-multimodel-update.md +++ b/doc/development/ecs-edge-cohort-multimodel-update.md @@ -1,3 +1,5 @@ +> **2026-10-26 status update:** the old-base matrix is now complete (117 processes,102 complete,15 OOM); earlier pending-Pro statements below describe the historical publication snapshot. The new135-process master2ba617bc comparison is in [ecs-validation-20261025](ecs-validation-20261025/README.md). Neither matrix establishes general production acceptance. + # ECS multi-model and N4096 follow-up (2026-10-19) This is a measurement update for the default-off Draft PR, not a claim of multi-model production acceptance. The production implementation is unchanged from PR head `9e2e4c5de1b90cf2d6bc8b2706bc103a06778dae`; BASE is pinned to `668b60cc2a9559f79abb9c33acd959833376344f`, not subsequent upstream master. diff --git a/doc/development/ecs-validation-20261025/ACCURACY_TABLE.md b/doc/development/ecs-validation-20261025/ACCURACY_TABLE.md new file mode 100644 index 0000000000..a68839dd9c --- /dev/null +++ b/doc/development/ecs-validation-20261025/ACCURACY_TABLE.md @@ -0,0 +1,266 @@ +# ECS 多模型多尺寸精度测试 + +> 精度优先；未放宽阈值。误差范围为每对输出max_abs的min～max，不是置信区间。基线SHA与模型SHA见JSON。 + +模式：TORCH=纯PyTorch编译；ECS=当前PR候选（非Neo为回退）；ECS_RUNTIME=强制真实8192分批。单位：能量/位力eV，力eV/Å，stress eV/ų。所有结果均不是DFT预测误差。 + +| 模型 | 原子数 | 对照 | 范围 | 完成进程数 | 能量max_abs范围 | 力max_abs范围 | 位力max_abs范围 | stress max_abs范围 | 最差误差/容差 | 状态 | +| --- | ---: | --- | --- | --- | --- | --- | --- | --- | ---: | --- | +| Mini | 96 | TORCH vs TORCH | within_process | 3/3 | 1.788e-07～1.234e-05 | 4.585e-06～8.618e-06 | 1.402e-05～6.538e-05 | 2.375e-08～1.200e-07 | 1.1463 | FAIL | +| Mini | 96 | TORCH vs TORCH | across_process | 3/3 | 0.000e+00～1.246e-05 | 4.587e-06～8.061e-06 | 7.968e-06～6.371e-05 | 1.463e-08～1.170e-07 | 1.5990 | FAIL | +| Mini | 96 | BASE vs BASE | within_process | 3/3 | 1.192e-07～9.894e-06 | 4.567e-06～6.735e-06 | 1.251e-05～4.619e-05 | 2.294e-08～8.481e-08 | 1.8636 | FAIL | +| Mini | 96 | BASE vs BASE | across_process | 3/3 | 5.960e-08～1.049e-05 | 4.451e-06～7.551e-06 | 1.017e-05～4.644e-05 | 1.809e-08～8.526e-08 | 1.7011 | FAIL | +| Mini | 96 | ECS vs ECS | within_process | 3/3 | 3.576e-07～1.740e-05 | 4.543e-06～7.322e-06 | 7.101e-06～7.174e-05 | 1.285e-08～1.317e-07 | 2.1289 | FAIL | +| Mini | 96 | ECS vs ECS | across_process | 3/3 | 5.960e-08～1.699e-05 | 4.675e-06～8.280e-06 | 8.842e-06～5.695e-05 | 1.572e-08～1.046e-07 | 2.1372 | FAIL | +| Mini | 96 | BASE vs TORCH | cross_implementation | 3/3 | 0.000e+00～1.836e-05 | 5.616e-06～1.080e-05 | 1.972e-05～8.237e-05 | 3.388e-08～1.512e-07 | 1.7538 | FAIL | +| Mini | 96 | ECS vs TORCH | cross_implementation | 3/3 | 0.000e+00～1.651e-05 | 5.894e-06～1.135e-05 | 1.419e-05～9.370e-05 | 2.501e-08～1.720e-07 | 1.9425 | FAIL | +| Mini | 96 | ECS vs BASE | cross_implementation | 3/3 | 0.000e+00～1.663e-05 | 4.214e-06～8.067e-06 | 8.232e-06～8.385e-05 | 1.474e-08～1.540e-07 | 1.9484 | FAIL | +| Mini | 512 | TORCH vs TORCH | within_process | 3/3 | 4.172e-07～3.606e-05 | 5.870e-06～7.873e-06 | 2.876e-05～1.708e-04 | 9.493e-09～5.882e-08 | 2.6615 | FAIL | +| Mini | 512 | TORCH vs TORCH | across_process | 3/3 | 1.788e-07～3.493e-05 | 5.543e-06～8.601e-06 | 2.627e-05～1.340e-04 | 9.046e-09～4.613e-08 | 2.1807 | FAIL | +| Mini | 512 | BASE vs BASE | within_process | 3/3 | 7.153e-07～3.594e-05 | 5.392e-06～8.266e-06 | 2.302e-05～9.672e-05 | 7.924e-09～3.330e-08 | 2.4707 | FAIL | +| Mini | 512 | BASE vs BASE | across_process | 3/3 | 1.192e-07～2.855e-05 | 5.616e-06～8.582e-06 | 1.416e-05～1.244e-04 | 4.757e-09～4.283e-08 | 2.6165 | FAIL | +| Mini | 512 | ECS vs ECS | within_process | 3/3 | 5.960e-07～2.813e-05 | 5.659e-06～9.532e-06 | 1.953e-05～1.400e-04 | 6.450e-09～4.821e-08 | 1.9644 | FAIL | +| Mini | 512 | ECS vs ECS | across_process | 3/3 | 0.000e+00～2.646e-05 | 5.619e-06～8.691e-06 | 1.610e-05～1.445e-04 | 5.544e-09～4.975e-08 | 3.1341 | FAIL | +| Mini | 512 | BASE vs TORCH | cross_implementation | 3/3 | 5.960e-08～3.952e-05 | 7.673e-06～1.210e-05 | 9.353e-05～2.595e-04 | 3.220e-08～8.933e-08 | 2.7111 | FAIL | +| Mini | 512 | ECS vs TORCH | cross_implementation | 3/3 | 5.960e-08～3.040e-05 | 7.604e-06～1.289e-05 | 9.723e-05～2.485e-04 | 3.347e-08～8.555e-08 | 3.0424 | FAIL | +| Mini | 512 | ECS vs BASE | cross_implementation | 3/3 | 5.960e-08～3.386e-05 | 5.100e-06～8.801e-06 | 1.883e-05～1.636e-04 | 6.483e-09～5.634e-08 | 3.0773 | FAIL | +| Mini | 4096 | TORCH vs TORCH | within_process | 3/3 | 5.364e-07～7.576e-05 | 6.887e-06～1.004e-05 | 9.010e-05～3.499e-04 | 3.878e-09～1.506e-08 | 10.2623 | FAIL | +| Mini | 4096 | TORCH vs TORCH | across_process | 3/3 | 1.788e-07～7.707e-05 | 6.973e-06～8.997e-06 | 6.730e-05～3.137e-04 | 2.584e-09～1.350e-08 | 8.9322 | FAIL | +| Mini | 4096 | BASE vs BASE | within_process | 3/3 | 2.086e-06～9.507e-05 | 6.874e-06～9.211e-06 | 8.355e-05～2.777e-04 | 3.595e-09～1.195e-08 | 10.3628 | FAIL | +| Mini | 4096 | BASE vs BASE | across_process | 3/3 | 7.749e-07～1.156e-04 | 7.001e-06～9.328e-06 | 5.222e-05～3.119e-04 | 2.040e-09～1.342e-08 | 12.0549 | FAIL | +| Mini | 4096 | ECS vs ECS | within_process | 3/3 | 5.960e-07～7.117e-05 | 7.187e-06～1.007e-05 | 5.141e-05～3.646e-04 | 2.061e-09～1.569e-08 | 6.1755 | FAIL | +| Mini | 4096 | ECS vs ECS | across_process | 3/3 | 5.364e-07～8.416e-05 | 7.002e-06～9.466e-06 | 4.396e-05～3.296e-04 | 1.892e-09～1.418e-08 | 7.3596 | FAIL | +| Mini | 4096 | BASE vs TORCH | cross_implementation | 3/3 | 3.576e-07～1.388e-04 | 9.301e-06～1.306e-05 | 7.796e-04～1.488e-03 | 3.355e-08～6.403e-08 | 12.5994 | FAIL | +| Mini | 4096 | ECS vs TORCH | cross_implementation | 3/3 | 1.311e-06～1.465e-04 | 9.302e-06～1.398e-05 | 8.275e-04～1.521e-03 | 3.561e-08～6.544e-08 | 9.8370 | FAIL | +| Mini | 4096 | ECS vs BASE | cross_implementation | 3/3 | 2.384e-07～1.084e-04 | 6.848e-06～1.005e-05 | 3.919e-05～3.468e-04 | 1.687e-09～1.492e-08 | 11.9793 | FAIL | +| Nano | 96 | TORCH vs TORCH | within_process | 3/3 | 4.768e-07～2.658e-05 | 7.035e-06～1.130e-05 | 1.325e-05～7.839e-05 | 2.318e-08～1.439e-07 | 5.5420 | FAIL | +| Nano | 96 | TORCH vs TORCH | across_process | 3/3 | 0.000e+00～2.420e-05 | 6.704e-06～1.136e-05 | 1.439e-05～7.676e-05 | 2.480e-08～1.409e-07 | 5.4077 | FAIL | +| Nano | 96 | BASE vs BASE | within_process | 3/3 | 1.192e-07～1.299e-05 | 6.957e-06～1.220e-05 | 1.406e-05～7.483e-05 | 2.405e-08～1.374e-07 | 5.7063 | FAIL | +| Nano | 96 | BASE vs BASE | across_process | 3/3 | 1.788e-07～1.878e-05 | 6.726e-06～1.142e-05 | 1.593e-05～8.811e-05 | 2.581e-08～1.618e-07 | 8.3241 | FAIL | +| Nano | 96 | ECS vs ECS | within_process | 3/3 | 0.000e+00～1.794e-05 | 7.266e-06～1.080e-05 | 1.871e-05～8.601e-05 | 3.432e-08～1.579e-07 | 7.3608 | FAIL | +| Nano | 96 | ECS vs ECS | across_process | 3/3 | 1.192e-07～2.122e-05 | 7.020e-06～1.150e-05 | 1.177e-05～8.074e-05 | 2.161e-08～1.482e-07 | 6.4631 | FAIL | +| Nano | 96 | BASE vs TORCH | cross_implementation | 3/3 | 1.788e-07～3.499e-05 | 6.454e-06～1.286e-05 | 1.219e-05～9.567e-05 | 2.155e-08～1.757e-07 | 9.4133 | FAIL | +| Nano | 96 | ECS vs TORCH | cross_implementation | 3/3 | 0.000e+00～3.982e-05 | 6.870e-06～1.269e-05 | 1.227e-05～9.163e-05 | 2.207e-08～1.682e-07 | 8.0976 | FAIL | +| Nano | 96 | ECS vs BASE | cross_implementation | 3/3 | 0.000e+00～2.354e-05 | 6.351e-06～1.155e-05 | 1.137e-05～1.014e-04 | 2.003e-08～1.862e-07 | 9.1350 | FAIL | +| Nano | 512 | TORCH vs TORCH | within_process | 3/3 | 8.941e-07～3.463e-05 | 8.984e-06～1.488e-05 | 4.949e-05～1.978e-04 | 1.683e-08～6.809e-08 | 7.7673 | FAIL | +| Nano | 512 | TORCH vs TORCH | across_process | 3/3 | 3.576e-07～6.020e-05 | 8.737e-06～1.247e-05 | 3.414e-05～2.010e-04 | 1.174e-08～6.921e-08 | 7.8147 | FAIL | +| Nano | 512 | BASE vs BASE | within_process | 3/3 | 1.371e-06～5.305e-05 | 8.899e-06～1.278e-05 | 4.218e-05～1.863e-04 | 1.452e-08～6.415e-08 | 4.2656 | FAIL | +| Nano | 512 | BASE vs BASE | across_process | 3/3 | 8.941e-07～6.390e-05 | 8.700e-06～1.347e-05 | 1.671e-05～1.607e-04 | 5.273e-09～5.534e-08 | 4.6439 | FAIL | +| Nano | 512 | ECS vs ECS | within_process | 3/3 | 1.907e-06～4.596e-05 | 8.712e-06～1.348e-05 | 3.527e-05～1.795e-04 | 1.214e-08～6.181e-08 | 7.0513 | FAIL | +| Nano | 512 | ECS vs ECS | across_process | 3/3 | 1.073e-06～5.358e-05 | 8.399e-06～1.288e-05 | 1.549e-05～2.203e-04 | 5.095e-09～7.585e-08 | 7.1989 | FAIL | +| Nano | 512 | BASE vs TORCH | cross_implementation | 3/3 | 2.980e-07～1.107e-04 | 9.036e-06～1.477e-05 | 2.200e-05～2.575e-04 | 6.814e-09～8.866e-08 | 7.1940 | FAIL | +| Nano | 512 | ECS vs TORCH | cross_implementation | 3/3 | 6.676e-06～1.102e-04 | 8.284e-06～1.325e-05 | 2.255e-05～2.560e-04 | 7.763e-09～8.813e-08 | 9.4950 | FAIL | +| Nano | 512 | ECS vs BASE | cross_implementation | 3/3 | 0.000e+00～6.342e-05 | 8.381e-06～1.570e-05 | 2.904e-05～1.981e-04 | 9.659e-09～6.821e-08 | 6.2237 | FAIL | +| Nano | 4096 | TORCH vs TORCH | within_process | 3/3 | 1.848e-06～1.260e-04 | 1.119e-05～1.436e-05 | 5.544e-05～3.779e-04 | 2.386e-09～1.626e-08 | 2.4958 | FAIL | +| Nano | 4096 | TORCH vs TORCH | across_process | 3/3 | 7.749e-07～1.612e-04 | 1.085e-05～1.484e-05 | 7.050e-05～4.504e-04 | 3.034e-09～1.938e-08 | 2.6872 | FAIL | +| Nano | 4096 | BASE vs BASE | within_process | 3/3 | 1.091e-05～1.924e-04 | 1.079e-05～1.563e-05 | 8.116e-05～4.467e-04 | 3.240e-09～1.922e-08 | 2.2079 | FAIL | +| Nano | 4096 | BASE vs BASE | across_process | 3/3 | 7.749e-07～1.666e-04 | 1.064e-05～1.480e-05 | 9.755e-05～5.898e-04 | 4.198e-09～2.538e-08 | 3.8540 | FAIL | +| Nano | 4096 | ECS vs ECS | within_process | 3/3 | 2.801e-06～1.946e-04 | 1.118e-05～1.424e-05 | 9.154e-05～4.406e-04 | 3.940e-09～1.896e-08 | 2.3048 | FAIL | +| Nano | 4096 | ECS vs ECS | across_process | 3/3 | 1.132e-06～2.133e-04 | 1.075e-05～1.605e-05 | 6.212e-05～5.332e-04 | 2.673e-09～2.294e-08 | 4.1555 | FAIL | +| Nano | 4096 | BASE vs TORCH | cross_implementation | 3/3 | 1.556e-04～4.887e-04 | 1.069e-05～1.613e-05 | 5.668e-05～7.157e-04 | 2.388e-09～3.080e-08 | 4.1304 | FAIL | +| Nano | 4096 | ECS vs TORCH | cross_implementation | 3/3 | 1.753e-04～5.112e-04 | 1.066e-05～1.554e-05 | 7.483e-05～7.943e-04 | 3.220e-09～3.418e-08 | 4.4355 | FAIL | +| Nano | 4096 | ECS vs BASE | cross_implementation | 3/3 | 2.384e-07～2.006e-04 | 1.020e-05～1.522e-05 | 3.754e-05～6.358e-04 | 1.501e-09～2.736e-08 | 4.1590 | FAIL | +| Neo | 96 | TORCH vs TORCH | within_process | 3/3 | 0.000e+00～1.222e-05 | 2.529e-06～4.930e-06 | 4.051e-06～2.011e-05 | 6.975e-09～3.692e-08 | 0.5965 | PASS | +| Neo | 96 | TORCH vs TORCH | across_process | 3/3 | 1.788e-07～1.091e-05 | 2.529e-06～4.594e-06 | 4.727e-06～2.222e-05 | 8.078e-09～4.080e-08 | 0.4535 | PASS | +| Neo | 96 | BASE vs BASE | within_process | 3/3 | 4.768e-07～7.212e-06 | 2.483e-06～3.981e-06 | 3.664e-06～2.443e-05 | 4.932e-09～4.486e-08 | 0.3532 | PASS | +| Neo | 96 | BASE vs BASE | across_process | 3/3 | 5.960e-08～1.073e-05 | 2.437e-06～4.474e-06 | 3.807e-06～1.991e-05 | 6.350e-09～3.656e-08 | 0.4794 | PASS | +| Neo | 96 | ECS vs ECS | within_process | 3/3 | 5.960e-08～6.557e-06 | 2.617e-06～4.197e-06 | 2.986e-06～2.181e-05 | 5.483e-09～4.005e-08 | 0.4760 | PASS | +| Neo | 96 | ECS vs ECS | across_process | 3/3 | 1.192e-07～6.855e-06 | 2.545e-06～4.369e-06 | 3.881e-06～2.690e-05 | 7.126e-09～4.938e-08 | 0.5175 | PASS | +| Neo | 96 | BASE vs TORCH | cross_implementation | 3/3 | 0.000e+00～1.115e-05 | 3.603e-06～7.196e-06 | 1.558e-05～4.824e-05 | 2.862e-08～8.858e-08 | 0.5819 | PASS | +| Neo | 96 | ECS vs TORCH | cross_implementation | 3/3 | 0.000e+00～1.127e-05 | 3.614e-06～7.185e-06 | 1.331e-05～5.232e-05 | 2.154e-08～9.607e-08 | 0.5914 | PASS | +| Neo | 96 | ECS vs BASE | cross_implementation | 3/3 | 0.000e+00～1.037e-05 | 2.304e-06～5.034e-06 | 2.399e-06～2.851e-05 | 2.107e-09～5.235e-08 | 0.5769 | PASS | +| Neo | 512 | TORCH vs TORCH | within_process | 3/3 | 1.192e-07～1.776e-05 | 3.427e-06～4.336e-06 | 1.278e-05～5.254e-05 | 4.298e-09～1.809e-08 | 0.4674 | PASS | +| Neo | 512 | TORCH vs TORCH | across_process | 3/3 | 4.172e-07～1.734e-05 | 3.283e-06～4.901e-06 | 1.086e-05～6.951e-05 | 3.545e-09～2.393e-08 | 0.7340 | PASS | +| Neo | 512 | BASE vs BASE | within_process | 3/3 | 1.192e-07～2.247e-05 | 3.262e-06～4.286e-06 | 7.840e-06～6.508e-05 | 2.699e-09～2.240e-08 | 0.6034 | PASS | +| Neo | 512 | BASE vs BASE | across_process | 3/3 | 5.960e-08～1.889e-05 | 2.996e-06～5.144e-06 | 8.897e-06～6.434e-05 | 2.589e-09～2.215e-08 | 0.5044 | PASS | +| Neo | 512 | ECS vs ECS | within_process | 3/3 | 9.537e-07～1.884e-05 | 3.293e-06～5.012e-06 | 9.156e-06～5.708e-05 | 3.152e-09～1.965e-08 | 0.8473 | PASS | +| Neo | 512 | ECS vs ECS | across_process | 3/3 | 1.192e-07～2.056e-05 | 3.137e-06～4.858e-06 | 6.668e-06～5.049e-05 | 2.046e-09～1.738e-08 | 0.8394 | PASS | +| Neo | 512 | BASE vs TORCH | cross_implementation | 3/3 | 0.000e+00～2.021e-05 | 4.529e-06～7.150e-06 | 7.708e-05～1.673e-04 | 2.654e-08～5.758e-08 | 0.8254 | PASS | +| Neo | 512 | ECS vs TORCH | cross_implementation | 3/3 | 2.980e-07～2.718e-05 | 4.555e-06～7.020e-06 | 7.569e-05～1.572e-04 | 2.606e-08～5.411e-08 | 1.0809 | FAIL | +| Neo | 512 | ECS vs BASE | cross_implementation | 3/3 | 1.192e-07～2.670e-05 | 3.075e-06～5.255e-06 | 4.760e-06～7.743e-05 | 1.186e-09～2.666e-08 | 0.8589 | PASS | +| Neo | 4096 | BASE vs BASE | within_process | 3/3 | 1.788e-07～5.084e-05 | 3.915e-06～5.114e-06 | 2.331e-05～1.037e-04 | 1.003e-09～4.284e-09 | 1.3472 | FAIL | +| Neo | 4096 | BASE vs BASE | across_process | 3/3 | 5.364e-07～5.960e-05 | 4.095e-06～5.564e-06 | 2.725e-05～1.374e-04 | 1.066e-09～5.913e-09 | 1.7886 | FAIL | +| Neo | 4096 | ECS vs ECS | within_process | 3/3 | 2.086e-06～5.770e-05 | 4.087e-06～5.089e-06 | 3.967e-05～2.099e-04 | 1.707e-09～9.031e-09 | 2.6929 | FAIL | +| Neo | 4096 | ECS vs ECS | across_process | 3/3 | 1.788e-07～5.788e-05 | 4.010e-06～5.939e-06 | 2.293e-05～2.537e-04 | 8.575e-10～1.092e-08 | 3.4631 | FAIL | +| Neo | 4096 | ECS vs BASE | cross_implementation | 3/3 | 1.788e-07～6.694e-05 | 4.074e-06～6.208e-06 | 1.792e-05～1.883e-04 | 6.963e-10～8.102e-09 | 2.7953 | FAIL | +| NeoCu | 96 | TORCH vs TORCH | within_process | 3/3 | 1.192e-07～3.338e-06 | 3.603e-07～5.453e-07 | 1.724e-06～1.361e-05 | 1.519e-09～1.200e-08 | 0.1450 | PASS | +| NeoCu | 96 | TORCH vs TORCH | across_process | 3/3 | 0.000e+00～3.636e-06 | 3.280e-07～6.306e-07 | 1.289e-06～1.229e-05 | 1.134e-09～1.083e-08 | 0.1805 | PASS | +| NeoCu | 96 | BASE vs BASE | within_process | 3/3 | 5.960e-08～4.292e-06 | 3.674e-07～6.496e-07 | 1.058e-06～1.315e-05 | 9.327e-10～1.159e-08 | 0.2355 | PASS | +| NeoCu | 96 | BASE vs BASE | across_process | 3/3 | 0.000e+00～4.590e-06 | 3.296e-07～5.808e-07 | 1.086e-06～1.663e-05 | 9.277e-10～1.466e-08 | 0.2466 | PASS | +| NeoCu | 96 | ECS vs ECS | within_process | 3/3 | 1.192e-07～4.292e-06 | 3.467e-07～5.431e-07 | 1.491e-06～1.204e-05 | 1.314e-09～1.061e-08 | 0.2134 | PASS | +| NeoCu | 96 | ECS vs ECS | across_process | 3/3 | 0.000e+00～4.411e-06 | 3.581e-07～6.476e-07 | 9.126e-07～1.739e-05 | 8.043e-10～1.533e-08 | 0.2576 | PASS | +| NeoCu | 96 | BASE vs TORCH | cross_implementation | 3/3 | 0.000e+00～6.855e-06 | 3.360e-07～6.391e-07 | 3.217e-06～2.707e-05 | 2.835e-09～2.386e-08 | 0.2037 | PASS | +| NeoCu | 96 | ECS vs TORCH | cross_implementation | 3/3 | 0.000e+00～5.782e-06 | 3.374e-07～6.297e-07 | 3.805e-06～2.837e-05 | 3.353e-09～2.500e-08 | 0.2452 | PASS | +| NeoCu | 96 | ECS vs BASE | cross_implementation | 3/3 | 0.000e+00～5.841e-06 | 3.036e-07～6.730e-07 | 5.077e-07～1.968e-05 | 4.474e-10～1.734e-08 | 0.2689 | PASS | +| NeoCu | 512 | TORCH vs TORCH | within_process | 3/3 | 0.000e+00～1.115e-05 | 4.107e-07～5.963e-07 | 3.552e-06～3.151e-05 | 5.869e-10～5.207e-09 | 0.2895 | PASS | +| NeoCu | 512 | TORCH vs TORCH | across_process | 3/3 | 3.576e-07～1.138e-05 | 4.138e-07～6.573e-07 | 2.813e-06～4.521e-05 | 4.174e-10～7.471e-09 | 0.2794 | PASS | +| NeoCu | 512 | BASE vs BASE | within_process | 3/3 | 1.192e-07～8.345e-06 | 4.176e-07～5.775e-07 | 4.065e-06～3.114e-05 | 6.577e-10～5.145e-09 | 0.3275 | PASS | +| NeoCu | 512 | BASE vs BASE | across_process | 3/3 | 0.000e+00～1.007e-05 | 4.184e-07～6.110e-07 | 1.907e-06～3.137e-05 | 3.124e-10～5.184e-09 | 0.3759 | PASS | +| NeoCu | 512 | ECS vs ECS | within_process | 3/3 | 6.557e-07～6.557e-06 | 4.320e-07～7.186e-07 | 2.182e-06～4.652e-05 | 3.606e-10～7.687e-09 | 0.2868 | PASS | +| NeoCu | 512 | ECS vs ECS | across_process | 3/3 | 5.960e-08～1.085e-05 | 4.208e-07～6.428e-07 | 2.016e-06～4.702e-05 | 3.332e-10～7.770e-09 | 0.4818 | PASS | +| NeoCu | 512 | BASE vs TORCH | cross_implementation | 3/3 | 0.000e+00～1.919e-05 | 4.175e-07～7.357e-07 | 4.216e-05～1.130e-04 | 6.966e-09～1.867e-08 | 0.3659 | PASS | +| NeoCu | 512 | ECS vs TORCH | cross_implementation | 3/3 | 5.960e-08～1.860e-05 | 4.282e-07～7.158e-07 | 3.795e-05～1.295e-04 | 6.271e-09～2.139e-08 | 0.3641 | PASS | +| NeoCu | 512 | ECS vs BASE | cross_implementation | 3/3 | 0.000e+00～1.073e-05 | 3.929e-07～6.560e-07 | 1.895e-06～4.981e-05 | 2.801e-10～8.231e-09 | 0.4547 | PASS | +| NeoCu | 4096 | TORCH vs TORCH | within_process | 3/3 | 8.345e-07～1.687e-05 | 5.199e-07～6.523e-07 | 7.247e-06～8.839e-05 | 1.497e-10～1.826e-09 | 0.3163 | PASS | +| NeoCu | 4096 | TORCH vs TORCH | across_process | 3/3 | 5.960e-08～2.599e-05 | 5.038e-07～7.152e-07 | 8.798e-06～1.036e-04 | 1.817e-10～2.140e-09 | 0.3102 | PASS | +| NeoCu | 4096 | BASE vs BASE | within_process | 3/3 | 7.153e-07～4.148e-05 | 5.221e-07～7.069e-07 | 5.660e-06～7.616e-05 | 9.913e-11～1.573e-09 | 0.2691 | PASS | +| NeoCu | 4096 | BASE vs BASE | across_process | 3/3 | 1.192e-07～3.791e-05 | 5.172e-07～6.890e-07 | 8.123e-06～1.054e-04 | 1.678e-10～2.178e-09 | 0.3652 | PASS | +| NeoCu | 4096 | ECS vs ECS | within_process | 3/3 | 1.788e-07～2.778e-05 | 5.439e-07～7.169e-07 | 7.803e-06～1.111e-04 | 1.612e-10～2.295e-09 | 0.2754 | PASS | +| NeoCu | 4096 | ECS vs ECS | across_process | 3/3 | 5.960e-08～3.821e-05 | 5.166e-07～7.105e-07 | 7.015e-06～9.401e-05 | 1.449e-10～1.942e-09 | 0.4257 | PASS | +| NeoCu | 4096 | BASE vs TORCH | cross_implementation | 3/3 | 3.046e-05～8.726e-05 | 5.351e-07～8.014e-07 | 4.939e-04～6.932e-04 | 1.020e-08～1.432e-08 | 0.4284 | PASS | +| NeoCu | 4096 | ECS vs TORCH | cross_implementation | 3/3 | 2.074e-05～8.494e-05 | 5.446e-07～7.932e-07 | 4.744e-04～6.950e-04 | 9.798e-09～1.435e-08 | 0.4357 | PASS | +| NeoCu | 4096 | ECS vs BASE | cross_implementation | 3/3 | 5.960e-08～3.868e-05 | 5.229e-07～7.972e-07 | 5.578e-06～1.249e-04 | 9.600e-11～2.581e-09 | 0.3727 | PASS | +| Pro | 96 | TORCH vs TORCH | within_process | 3/3 | 2.980e-08～3.546e-06 | 7.446e-07～1.197e-06 | 1.965e-06～9.613e-06 | 2.423e-09～1.765e-08 | 0.1584 | PASS | +| Pro | 96 | TORCH vs TORCH | across_process | 3/3 | 5.960e-08～4.083e-06 | 7.445e-07～1.667e-06 | 1.298e-06～8.369e-06 | 1.832e-09～1.537e-08 | 0.1908 | PASS | +| Pro | 96 | BASE vs BASE | within_process | 3/3 | 2.980e-08～2.086e-06 | 6.661e-07～1.003e-06 | 1.850e-06～6.390e-06 | 2.572e-09～1.173e-08 | 0.1034 | PASS | +| Pro | 96 | BASE vs BASE | across_process | 3/3 | 2.980e-08～3.368e-06 | 6.060e-07～1.153e-06 | 1.505e-06～8.763e-06 | 2.631e-09～1.609e-08 | 0.1715 | PASS | +| Pro | 96 | ECS vs ECS | within_process | 3/3 | 2.980e-08～2.652e-06 | 7.281e-07～1.056e-06 | 1.903e-06～7.248e-06 | 1.676e-09～1.331e-08 | 0.1771 | PASS | +| Pro | 96 | ECS vs ECS | across_process | 3/3 | 0.000e+00～3.338e-06 | 6.510e-07～1.144e-06 | 1.396e-06～7.585e-06 | 1.931e-09～1.393e-08 | 0.2516 | PASS | +| Pro | 96 | BASE vs TORCH | cross_implementation | 3/3 | 0.000e+00～5.037e-06 | 1.030e-06～1.919e-06 | 6.303e-06～1.613e-05 | 1.023e-08～2.962e-08 | 0.1988 | PASS | +| Pro | 96 | ECS vs TORCH | cross_implementation | 3/3 | 0.000e+00～5.871e-06 | 1.039e-06～1.691e-06 | 5.839e-06～1.643e-05 | 9.968e-09～3.017e-08 | 0.2784 | PASS | +| Pro | 96 | ECS vs BASE | cross_implementation | 3/3 | 0.000e+00～3.725e-06 | 6.266e-07～1.310e-06 | 1.210e-06～9.066e-06 | 1.710e-09～1.665e-08 | 0.2276 | PASS | +| Pro | 512 | BASE vs BASE | within_process | 3/3 | 1.192e-07～7.242e-06 | 8.882e-07～1.212e-06 | 3.637e-06～1.652e-05 | 6.287e-10～5.687e-09 | 0.2266 | PASS | +| Pro | 512 | BASE vs BASE | across_process | 3/3 | 2.980e-08～1.019e-05 | 8.338e-07～1.360e-06 | 3.104e-06～2.018e-05 | 8.452e-10～6.947e-09 | 0.2604 | PASS | +| Pro | 512 | ECS vs ECS | within_process | 3/3 | 2.980e-08～7.421e-06 | 8.405e-07～1.356e-06 | 3.452e-06～1.421e-05 | 1.188e-09～4.893e-09 | 0.2375 | PASS | +| Pro | 512 | ECS vs ECS | across_process | 3/3 | 2.980e-08～8.345e-06 | 8.582e-07～1.497e-06 | 2.475e-06～1.641e-05 | 6.944e-10～5.648e-09 | 0.2399 | PASS | +| Pro | 512 | ECS vs BASE | cross_implementation | 3/3 | 0.000e+00～1.121e-05 | 8.272e-07～1.477e-06 | 2.360e-06～1.753e-05 | 5.223e-10～6.034e-09 | 0.2884 | PASS | + +## 所有进程状态（含OOM/失败，不能略去） + +| 运行 | 状态 | 参数量 | opaque ECS调用 | single路径标记 | +| --- | --- | ---: | ---: | --- | +| Mini_n4096_BASE_p0 | completed | 654988 | 0 | False | +| Mini_n4096_BASE_p1 | completed | 654988 | 0 | False | +| Mini_n4096_BASE_p2 | completed | 654988 | 0 | False | +| Mini_n4096_ECS_p0 | completed | 654988 | 0 | False | +| Mini_n4096_ECS_p1 | completed | 654988 | 0 | False | +| Mini_n4096_ECS_p2 | completed | 654988 | 0 | False | +| Mini_n4096_TORCH_p0 | completed | 654988 | 0 | False | +| Mini_n4096_TORCH_p1 | completed | 654988 | 0 | False | +| Mini_n4096_TORCH_p2 | completed | 654988 | 0 | False | +| Mini_n512_BASE_p0 | completed | 654988 | 0 | False | +| Mini_n512_BASE_p1 | completed | 654988 | 0 | False | +| Mini_n512_BASE_p2 | completed | 654988 | 0 | False | +| Mini_n512_ECS_p0 | completed | 654988 | 0 | False | +| Mini_n512_ECS_p1 | completed | 654988 | 0 | False | +| Mini_n512_ECS_p2 | completed | 654988 | 0 | False | +| Mini_n512_TORCH_p0 | completed | 654988 | 0 | False | +| Mini_n512_TORCH_p1 | completed | 654988 | 0 | False | +| Mini_n512_TORCH_p2 | completed | 654988 | 0 | False | +| Mini_n96_BASE_p0 | completed | 654988 | 0 | False | +| Mini_n96_BASE_p1 | completed | 654988 | 0 | False | +| Mini_n96_BASE_p2 | completed | 654988 | 0 | False | +| Mini_n96_ECS_p0 | completed | 654988 | 0 | False | +| Mini_n96_ECS_p1 | completed | 654988 | 0 | False | +| Mini_n96_ECS_p2 | completed | 654988 | 0 | False | +| Mini_n96_TORCH_p0 | completed | 654988 | 0 | False | +| Mini_n96_TORCH_p1 | completed | 654988 | 0 | False | +| Mini_n96_TORCH_p2 | completed | 654988 | 0 | False | +| Nano_n4096_BASE_p0 | completed | 480268 | 0 | False | +| Nano_n4096_BASE_p1 | completed | 480268 | 0 | False | +| Nano_n4096_BASE_p2 | completed | 480268 | 0 | False | +| Nano_n4096_ECS_p0 | completed | 480268 | 0 | False | +| Nano_n4096_ECS_p1 | completed | 480268 | 0 | False | +| Nano_n4096_ECS_p2 | completed | 480268 | 0 | False | +| Nano_n4096_TORCH_p0 | completed | 480268 | 0 | False | +| Nano_n4096_TORCH_p1 | completed | 480268 | 0 | False | +| Nano_n4096_TORCH_p2 | completed | 480268 | 0 | False | +| Nano_n512_BASE_p0 | completed | 480268 | 0 | False | +| Nano_n512_BASE_p1 | completed | 480268 | 0 | False | +| Nano_n512_BASE_p2 | completed | 480268 | 0 | False | +| Nano_n512_ECS_p0 | completed | 480268 | 0 | False | +| Nano_n512_ECS_p1 | completed | 480268 | 0 | False | +| Nano_n512_ECS_p2 | completed | 480268 | 0 | False | +| Nano_n512_TORCH_p0 | completed | 480268 | 0 | False | +| Nano_n512_TORCH_p1 | completed | 480268 | 0 | False | +| Nano_n512_TORCH_p2 | completed | 480268 | 0 | False | +| Nano_n96_BASE_p0 | completed | 480268 | 0 | False | +| Nano_n96_BASE_p1 | completed | 480268 | 0 | False | +| Nano_n96_BASE_p2 | completed | 480268 | 0 | False | +| Nano_n96_ECS_p0 | completed | 480268 | 0 | False | +| Nano_n96_ECS_p1 | completed | 480268 | 0 | False | +| Nano_n96_ECS_p2 | completed | 480268 | 0 | False | +| Nano_n96_TORCH_p0 | completed | 480268 | 0 | False | +| Nano_n96_TORCH_p1 | completed | 480268 | 0 | False | +| Nano_n96_TORCH_p2 | completed | 480268 | 0 | False | +| NeoCu_n4096_BASE_p0 | completed | 1124910 | 0 | False | +| NeoCu_n4096_BASE_p1 | completed | 1124910 | 0 | False | +| NeoCu_n4096_BASE_p2 | completed | 1124910 | 0 | False | +| NeoCu_n4096_ECS_p0 | completed | 1124910 | 0 | True | +| NeoCu_n4096_ECS_p1 | completed | 1124910 | 0 | True | +| NeoCu_n4096_ECS_p2 | completed | 1124910 | 0 | True | +| NeoCu_n4096_TORCH_p0 | completed | 1124910 | 0 | False | +| NeoCu_n4096_TORCH_p1 | completed | 1124910 | 0 | False | +| NeoCu_n4096_TORCH_p2 | completed | 1124910 | 0 | False | +| NeoCu_n512_BASE_p0 | completed | 1124910 | 0 | False | +| NeoCu_n512_BASE_p1 | completed | 1124910 | 0 | False | +| NeoCu_n512_BASE_p2 | completed | 1124910 | 0 | False | +| NeoCu_n512_ECS_p0 | completed | 1124910 | 0 | True | +| NeoCu_n512_ECS_p1 | completed | 1124910 | 0 | True | +| NeoCu_n512_ECS_p2 | completed | 1124910 | 0 | True | +| NeoCu_n512_TORCH_p0 | completed | 1124910 | 0 | False | +| NeoCu_n512_TORCH_p1 | completed | 1124910 | 0 | False | +| NeoCu_n512_TORCH_p2 | completed | 1124910 | 0 | False | +| NeoCu_n96_BASE_p0 | completed | 1124910 | 0 | False | +| NeoCu_n96_BASE_p1 | completed | 1124910 | 0 | False | +| NeoCu_n96_BASE_p2 | completed | 1124910 | 0 | False | +| NeoCu_n96_ECS_p0 | completed | 1124910 | 0 | True | +| NeoCu_n96_ECS_p1 | completed | 1124910 | 0 | True | +| NeoCu_n96_ECS_p2 | completed | 1124910 | 0 | True | +| NeoCu_n96_TORCH_p0 | completed | 1124910 | 0 | False | +| NeoCu_n96_TORCH_p1 | completed | 1124910 | 0 | False | +| NeoCu_n96_TORCH_p2 | completed | 1124910 | 0 | False | +| Neo_n4096_BASE_p0 | completed | 1124910 | 0 | False | +| Neo_n4096_BASE_p1 | completed | 1124910 | 0 | False | +| Neo_n4096_BASE_p2 | completed | 1124910 | 0 | False | +| Neo_n4096_ECS_p0 | completed | 1124910 | 0 | True | +| Neo_n4096_ECS_p1 | completed | 1124910 | 0 | True | +| Neo_n4096_ECS_p2 | completed | 1124910 | 0 | True | +| Neo_n4096_TORCH_p0 | oom | 1124910 | 0 | False | +| Neo_n4096_TORCH_p1 | oom | 1124910 | 0 | False | +| Neo_n4096_TORCH_p2 | oom | 1124910 | 0 | False | +| Neo_n512_BASE_p0 | completed | 1124910 | 0 | False | +| Neo_n512_BASE_p1 | completed | 1124910 | 0 | False | +| Neo_n512_BASE_p2 | completed | 1124910 | 0 | False | +| Neo_n512_ECS_p0 | completed | 1124910 | 0 | True | +| Neo_n512_ECS_p1 | completed | 1124910 | 0 | True | +| Neo_n512_ECS_p2 | completed | 1124910 | 0 | True | +| Neo_n512_TORCH_p0 | completed | 1124910 | 0 | False | +| Neo_n512_TORCH_p1 | completed | 1124910 | 0 | False | +| Neo_n512_TORCH_p2 | completed | 1124910 | 0 | False | +| Neo_n96_BASE_p0 | completed | 1124910 | 0 | False | +| Neo_n96_BASE_p1 | completed | 1124910 | 0 | False | +| Neo_n96_BASE_p2 | completed | 1124910 | 0 | False | +| Neo_n96_ECS_p0 | completed | 1124910 | 0 | True | +| Neo_n96_ECS_p1 | completed | 1124910 | 0 | True | +| Neo_n96_ECS_p2 | completed | 1124910 | 0 | True | +| Neo_n96_TORCH_p0 | completed | 1124910 | 0 | False | +| Neo_n96_TORCH_p1 | completed | 1124910 | 0 | False | +| Neo_n96_TORCH_p2 | completed | 1124910 | 0 | False | +| Pro_n4096_BASE_p0 | oom | 22678990 | 0 | False | +| Pro_n4096_BASE_p1 | oom | 22678990 | 0 | False | +| Pro_n4096_BASE_p2 | oom | 22678990 | 0 | False | +| Pro_n4096_ECS_p0 | oom | 22678990 | 0 | False | +| Pro_n4096_ECS_p1 | oom | 22678990 | 0 | False | +| Pro_n4096_ECS_p2 | oom | 22678990 | 0 | False | +| Pro_n4096_TORCH_p0 | oom | 22678990 | 0 | False | +| Pro_n4096_TORCH_p1 | oom | 22678990 | 0 | False | +| Pro_n4096_TORCH_p2 | oom | 22678990 | 0 | False | +| Pro_n512_BASE_p0 | completed | 22678990 | 0 | False | +| Pro_n512_BASE_p1 | completed | 22678990 | 0 | False | +| Pro_n512_BASE_p2 | completed | 22678990 | 0 | False | +| Pro_n512_ECS_p0 | completed | 22678990 | 0 | False | +| Pro_n512_ECS_p1 | completed | 22678990 | 0 | False | +| Pro_n512_ECS_p2 | completed | 22678990 | 0 | False | +| Pro_n512_TORCH_p0 | oom | 22678990 | 0 | False | +| Pro_n512_TORCH_p1 | oom | 22678990 | 0 | False | +| Pro_n512_TORCH_p2 | oom | 22678990 | 0 | False | +| Pro_n96_BASE_p0 | completed | 22678990 | 0 | False | +| Pro_n96_BASE_p1 | completed | 22678990 | 0 | False | +| Pro_n96_BASE_p2 | completed | 22678990 | 0 | False | +| Pro_n96_ECS_p0 | completed | 22678990 | 0 | False | +| Pro_n96_ECS_p1 | completed | 22678990 | 0 | False | +| Pro_n96_ECS_p2 | completed | 22678990 | 0 | False | +| Pro_n96_TORCH_p0 | completed | 22678990 | 0 | False | +| Pro_n96_TORCH_p1 | completed | 22678990 | 0 | False | +| Pro_n96_TORCH_p2 | completed | 22678990 | 0 | False | diff --git a/doc/development/ecs-validation-20261025/README.md b/doc/development/ecs-validation-20261025/README.md new file mode 100644 index 0000000000..529ec8ab0a --- /dev/null +++ b/doc/development/ecs-validation-20261025/README.md @@ -0,0 +1,74 @@ +# Manual ECS validation artifact (2026-10-25 measurements; 2026-10-26 publication) + +## What is being compared + +This artifact records **135 process outcomes: 120 completed, 15 OOM**, not 135 numerical passes. Each complete arm has three fresh processes, three identical perturbed frames repeated three times per process. The three columns are compiled pure TORCH, native master, and the isolated candidate port. Sources are pinned; raw results are not mixed across trees. + +The master/candidate port uses `2ba617bc3db67adee2b8ba00c71e9fa1c2d2bb8c`, which includes merged #6058 residual-inplace. The existing personal PR still has the original668 base; **this evidence update does not silently rebase that PR or replace its production source**. `port-to-2ba617bc.patch` reproduces the tested three-file port on the pinned master. Generated source hashes are checked against the measurement manifests. + +## Files + +- `REPORT.md`: explicit three-column strict and empirical-band PASS/FAIL, per-output errors, P50/P90, CUDA Event time, first/full/steady allocated peaks, changes and route limits. +- `ACCURACY_TABLE.md`: separate within/across-process and implementation comparisons, strict failures and ranges. +- `summary.json`: complete aggregate metrics including mean_abs/max_relative, finite status, route counts, checkpoint identity and per-process peaks. +- `runs.json`: all135 original worker result/manifest records, including per-call timing samples and exceptions. OOM is retained. +- `provenance.json`: pinned sources, per-run NPZ hashes, source-consistency checks and publication-time environment snapshot. +- `validation.json`: prior60-test focused CUDA result and the published worker's separate complete Neo96/GPU1 smoke (3warmups/10samples,finite). The GPU0 attempt correctly stopped on a busy GPU. Smoke is not pooled into the135-run matrix and is not a new cross-reference accuracy acceptance. +- `worker.py`, `run_matrix.py`, `analyze.py`, `report.py`: standalone manual tools, not added to routine GPU CI. `pro_cpu_load_check.json` is the historical metadata-compatibility proof for the verified Pro checkpoint, not a new numerical reference. + +The original input/output NPZ archives total approximately47MiB and remain in the local experiment archive; they are **not committed here**. JSON timing/config/error summaries are inspectable, and the published harness permits fresh reproduction, but the published subset does not itself permit recomputing every original output comparison. NPZ hashes in provenance support later transfer/integrity checking; a hash is not a substitute for the arrays. + +## Checkpoints and scope + +Neo: DPA4-Neo-OMat24-v20260627.pt, SHA256 `8d8b2b02b9575cb9e0af420cc1e8b1d9ffcbc04752c2203d29f48ccd0cd5f07d`. Nano/Mini: official OMat24 v20260805; SHA256 and loaded counts in manifests. Pro: DPA-4.0.1-Pro-MPtrj v2026.06.05, **not OMat24-Pro**; SHA256 `446a6e893101fc52190cfccabb9d2328863d651aca36981fd8d3401c71a61221`, official MD5 `2174bb3095b197489f978d31393c1e06`,273318000 bytes. [Official Pro record](https://doi.org/10.6084/m9.figshare.32647224). + +Pro's historical fitting type alias is mapped in memory only (`ener -> dpa4_ener`), equally for all arms. Copy the included proof as `cpu_load_check.json` beside the verified Pro checkpoint before using the worker. This proof records strict CPU load on the historical reader; it is not evidence of passing inference on every current environment. The worker requires the exact verified Pro SHA; it does not patch arbitrary checkpoint files. No checkpoint tensors are changed or uploaded. + +The candidate guard supports the fixed Neo layout. Nano/Mini/Pro execute **native fallback**, not optimized ECS. Their timing variation cannot be credited to ECS. Neo's measured AOT graphs specialize the single-cohort path, including4096; this is not evidence of actual bounded8192-edge runtime batches. No deployment fix is included in this evidence publication. + +## Measurement protocol and limits + +- RTX5090 x2, PyTorch2.11.0+cu128, Triton3.6.0, CUDA runtime12.8. Driver595.91.07 observed on the same server at publication. The supplied environment snapshot is **post-run**, not a clock/temperature trace of the timed window. This matrix did not collect continuous GPU telemetry, unlike the earlier controlled N64 study. +- Each worker checks for existing GPU compute clients before initialization. Same physical GPU for all arms/repeats of a model/system/size: Neo/Pro GPU0, Nano/Mini GPU1. Two GPUs may run distinct cases simultaneously; CPU/package contention is not excluded. No global clock/driver changes, no killed foreign process. +- FP32 mixing, TF32/AMP off, no FP16x3. Preserve original non-mixing model precision. Normal ASE energy/forces/virial/stress API includes neighbor construction. +- Diamond C3.567Angstrom; bccCu2.87Angstrom. Seed0, sigma0.02Angstrom, three exact same frames checked across arms. Diamond N96 repeats(2,2,3), N512(4,4,4), N4096(8,8,8). Cu96(4,4,3),512(8,8,4),4096(16,16,8). +- Accuracy9calls includes first compilation but those calls are not latency samples. Then8 warmups and30 synchronized wall/Event samples; separate30call steady-memory replay. Full peak takes the maximum including first execution and subsequent phases; steady reset is never substituted for first capacity. Allocated/reserved bytes retained; decimalGB=1e9B. +- Explicit cache per model/system/mode is reused in later independent repeats. The first worker was cold; later repeats benefit from that cache. All generic autotune choices are **not** frozen across arms. Therefore these independent-process quantiles do not establish a stable isolated sub-percent ECS cost/benefit. Earlier controlled N64 evidence remains a different experiment. +- Median of three process P50/P90 values, worst process capacity. Timing/accuracy/profile are distinct; no profiled timings are included. No DFT/long-MD accuracy, H20,10k-atoms, PT2/C++ or new training acceptance claim. + +## Numerical criteria + +Strict elementwise criterion: finite and `abs(got-ref)<=1e-5+1e-4*abs(ref)`. Max-relative denominator `max(abs(ref),1e-8)`. Energy/virial in eV, force eV/Angstrom, stress eV/Angstrom^3; same numeric tolerance across units does not imply same physical accuracy. + +Meeting-band diagnostic: separately per output/model/system/size, worst same-frame cross-implementation max_abs divided by pure-TORCH's own worst repeat max_abs (within and across processes). <=3 is marked band PASS; it is an empirical sampled repeat band, not a formal error bound. Strict failures remain failures; zero self-band requires zero deviation. Missing/OOM reference is N/A, not PASS. A maximum over more repeat samples may increase; this protocol does not allow enlarging the sample set only to inflate tolerance. + +## Reproduce on explicit source trees + +Use a compatible CUDA/native DeePMD installation. Set source roots explicitly; the worker checks imported package provenance and avoids silently using an unrelated editable checkout. Obtain checkpoints separately under their licenses. Start with idle GPUs and new output/cache directories. + +```bash +git clone https://github.com/deepmodeling/deepmd-kit.git base +git -C base checkout --detach 2ba617bc3db67adee2b8ba00c71e9fa1c2d2bb8c +git clone --shared base candidate +git -C candidate apply /absolute/path/to/this-artifact/port-to-2ba617bc.patch +``` + +Create a local model-map JSON with Neo/Nano/Mini/Pro mapped to absolute checkpoint paths (not committed weights). For Pro, place its checked compatibility proof beside the checkpoint as described above. Then, using the appropriate Python environment: + +```bash +python run_matrix.py --base-root /absolute/base --candidate-root /absolute/candidate \ + --model-map /absolute/models.json --out-root /absolute/new-results \ + --gpu 0 --models Neo Pro +python run_matrix.py --base-root /absolute/base --candidate-root /absolute/candidate \ + --model-map /absolute/models.json --out-root /absolute/new-results \ + --gpu 1 --models Nano Mini +python report.py --root /absolute/new-results +``` + +The two commands can use different GPUs concurrently. Do not run two workers on one GPU. Default is all three sizes and three independent processes; `--sizes 96` permits an initial smoke, not full acceptance. Existing outcomes are not overwritten; explicit `--skip-existing` only resumes, not a new rerun. The public runner reproduces the calculation/protocol with explicit paths; orchestration/path handling is adapted from the local runner. Validate it on the target environment rather than claiming a new135-process run from `--help` success. + +## Conclusion and open gates + +Current-master Neo4096: P50 **156.512587->156.594862ms**, full allocated peak **22.313645->22.806378GB** (increase2.2082%). Neo96: **17.992521->18.035012ms**, full peak **0.646372->0.658145GB** (increase1.8213%). Small timing differences lack matched-autotune/process uncertainty; the observed memory increases are retained. No new positive ECS performance claim is made. + +Remaining: fix real dynamic deployment dispatch; resolve scientific acceptance/OOM reference limitations; replace the fine-grained development switch with an agreed existing-level/internal policy and tuning integration where appropriate; retain optional-Triton CPU safety; add proportionate actual engagement/guard/alias tests; obtain real upstream CI approval. Keep personal PR Draft/default-off. This update is evidence for human review, **not an upstream submission or ready-to-merge declaration**. diff --git a/doc/development/ecs-validation-20261025/REPORT.md b/doc/development/ecs-validation-20261025/REPORT.md new file mode 100644 index 0000000000..611ab06412 --- /dev/null +++ b/doc/development/ecs-validation-20261025/REPORT.md @@ -0,0 +1,171 @@ +# ECS current-master comparison — measured 2026-10-25, finalized 2026-10-26 + +BASE: 2ba617bc3db67adee2b8ba00c71e9fa1c2d2bb8c, including merged residual-inplace #6058. Old668-base numbers are not substituted. Candidate is the isolated port of PR#2; its AOT branch-specialization issue remains unresolved. + +Recorded 135/135 process results: {'completed': 120, 'oom': 15}. Missing cells are NOT passes. Three processes ×3 frames ×3 accuracy repeats per arm; 8 extra warmups/30 wall+Event samples, separate steady replay. Same physical GPU within each case. TF32/AMP off. + +## Three-column PASS/FAIL overview + +Strict columns cover all four fields: Torch self-repeat; master self-repeat and versus Torch; candidate self-repeat, versus Torch and versus master. A missing Torch reference is N/A, never PASS. The meeting band is a separately reported empirical diagnostic, not a rigorous error bound or a waiver of strict failure. + +| Model/system | Atoms | Compiled Torch | Current master | Candidate | +| --- | ---: | --- | --- | --- | +| Neo | 96 | strict self=PASS | strict self=PASS; vs Torch=PASS; band=PASS (2.171x) | strict self=PASS; vs Torch=PASS; vs master=PASS; band=PASS (2.355x) | +| Neo | 512 | strict self=PASS | strict self=PASS; vs Torch=PASS; band=PASS (2.406x) | strict self=PASS; vs Torch=FAIL; vs master=PASS; band=PASS (2.261x) | +| Neo | 4096 | strict self=N/A | strict self=FAIL; vs Torch=N/A; band=N/A | strict self=FAIL; vs Torch=N/A; vs master=FAIL; band=N/A | +| NeoCu | 96 | strict self=PASS | strict self=PASS; vs Torch=PASS; band=PASS (1.989x) | strict self=PASS; vs Torch=PASS; vs master=PASS; band=PASS (2.084x) | +| NeoCu | 512 | strict self=PASS | strict self=PASS; vs Torch=PASS; band=PASS (2.499x) | strict self=PASS; vs Torch=PASS; vs master=PASS; band=PASS (2.863x) | +| NeoCu | 4096 | strict self=PASS | strict self=PASS; vs Torch=PASS; band=FAIL (6.690x) | strict self=PASS; vs Torch=PASS; vs master=PASS; band=FAIL (6.707x) | +| Nano | 96 | strict self=FAIL | strict self=FAIL; vs Torch=FAIL; band=PASS (1.316x) | strict self=FAIL; vs Torch=FAIL; vs master=FAIL; band=PASS (1.498x) | +| Nano | 512 | strict self=FAIL | strict self=FAIL; vs Torch=FAIL; band=PASS (1.839x) | strict self=FAIL; vs Torch=FAIL; vs master=FAIL; band=PASS (1.831x) | +| Nano | 4096 | strict self=FAIL | strict self=FAIL; vs Torch=FAIL; band=FAIL (3.031x) | strict self=FAIL; vs Torch=FAIL; vs master=FAIL; band=FAIL (3.171x) | +| Mini | 96 | strict self=FAIL | strict self=FAIL; vs Torch=FAIL; band=PASS (1.474x) | strict self=FAIL; vs Torch=FAIL; vs master=FAIL; band=PASS (1.433x) | +| Mini | 512 | strict self=FAIL | strict self=FAIL; vs Torch=FAIL; band=PASS (1.519x) | strict self=FAIL; vs Torch=FAIL; vs master=FAIL; band=PASS (1.498x) | +| Mini | 4096 | strict self=FAIL | strict self=FAIL; vs Torch=FAIL; band=FAIL (4.252x) | strict self=FAIL; vs Torch=FAIL; vs master=FAIL; band=FAIL (4.346x) | +| Pro | 96 | strict self=PASS | strict self=PASS; vs Torch=PASS; band=PASS (1.678x) | strict self=PASS; vs Torch=PASS; vs master=PASS; band=PASS (1.709x) | +| Pro | 512 | strict self=N/A | strict self=PASS; vs Torch=N/A; band=N/A | strict self=PASS; vs Torch=N/A; vs master=PASS; band=N/A | +| Pro | 4096 | strict self=N/A | strict self=N/A; vs Torch=N/A; band=N/A | strict self=N/A; vs Torch=N/A; vs master=N/A; band=N/A | + +## Three accuracy columns + +Each field: worst pairwise max-absolute difference over same-frame outputs; self includes within/across-process repeats. Scientific band multiplier = worst deviation from compiled TORCH / TORCH own worst repeat max_abs, separately per field/model/size/system. <=3 is the meeting criterion, not a replacement for the fixed strict elementwise gate. Zero repeat band requires zero cross difference. Energy/virial eV, force eV/Angstrom, stress eV/Angstrom^3. + +| Model/system | Atoms | Output | Compiled Torch | Current master | Candidate | +| --- | ---: | --- | --- | --- | --- | +| Neo | 96 | energy | self max_abs=1.222e-05 | self max_abs=1.073e-05; vs Torch=1.115e-05; band×0.912 | self max_abs=6.855e-06; vs Torch=1.127e-05; band×0.922 | +| Neo | 96 | forces | self max_abs=4.93e-06 | self max_abs=4.474e-06; vs Torch=7.196e-06; band×1.46 | self max_abs=4.369e-06; vs Torch=7.185e-06; band×1.46 | +| Neo | 96 | virial | self max_abs=2.222e-05 | self max_abs=2.443e-05; vs Torch=4.824e-05; band×2.17 | self max_abs=2.69e-05; vs Torch=5.232e-05; band×2.35 | +| Neo | 96 | stress | self max_abs=4.08e-08 | self max_abs=4.486e-08; vs Torch=8.858e-08; band×2.17 | self max_abs=4.938e-08; vs Torch=9.607e-08; band×2.35 | +| Neo | 512 | energy | self max_abs=1.776e-05 | self max_abs=2.247e-05; vs Torch=2.021e-05; band×1.14 | self max_abs=2.056e-05; vs Torch=2.718e-05; band×1.53 | +| Neo | 512 | forces | self max_abs=4.901e-06 | self max_abs=5.144e-06; vs Torch=7.15e-06; band×1.46 | self max_abs=5.012e-06; vs Torch=7.02e-06; band×1.43 | +| Neo | 512 | virial | self max_abs=6.951e-05 | self max_abs=6.508e-05; vs Torch=0.0001673; band×2.41 | self max_abs=5.708e-05; vs Torch=0.0001572; band×2.26 | +| Neo | 512 | stress | self max_abs=2.393e-08 | self max_abs=2.24e-08; vs Torch=5.758e-08; band×2.41 | self max_abs=1.965e-08; vs Torch=5.411e-08; band×2.26 | +| Neo | 4096 | energy | OOM | self max_abs=5.96e-05; Torch ref unavailable | self max_abs=5.788e-05; Torch ref unavailable | +| Neo | 4096 | forces | OOM | self max_abs=5.564e-06; Torch ref unavailable | self max_abs=5.939e-06; Torch ref unavailable | +| Neo | 4096 | virial | OOM | self max_abs=0.0001374; Torch ref unavailable | self max_abs=0.0002537; Torch ref unavailable | +| Neo | 4096 | stress | OOM | self max_abs=5.913e-09; Torch ref unavailable | self max_abs=1.092e-08; Torch ref unavailable | +| NeoCu | 96 | energy | self max_abs=3.636e-06 | self max_abs=4.59e-06; vs Torch=6.855e-06; band×1.89 | self max_abs=4.411e-06; vs Torch=5.782e-06; band×1.59 | +| NeoCu | 96 | forces | self max_abs=6.306e-07 | self max_abs=6.496e-07; vs Torch=6.391e-07; band×1.01 | self max_abs=6.476e-07; vs Torch=6.297e-07; band×0.999 | +| NeoCu | 96 | virial | self max_abs=1.361e-05 | self max_abs=1.663e-05; vs Torch=2.707e-05; band×1.99 | self max_abs=1.739e-05; vs Torch=2.837e-05; band×2.08 | +| NeoCu | 96 | stress | self max_abs=1.2e-08 | self max_abs=1.466e-08; vs Torch=2.386e-08; band×1.99 | self max_abs=1.533e-08; vs Torch=2.5e-08; band×2.08 | +| NeoCu | 512 | energy | self max_abs=1.138e-05 | self max_abs=1.007e-05; vs Torch=1.919e-05; band×1.69 | self max_abs=1.085e-05; vs Torch=1.86e-05; band×1.63 | +| NeoCu | 512 | forces | self max_abs=6.573e-07 | self max_abs=6.11e-07; vs Torch=7.357e-07; band×1.12 | self max_abs=7.186e-07; vs Torch=7.158e-07; band×1.09 | +| NeoCu | 512 | virial | self max_abs=4.521e-05 | self max_abs=3.137e-05; vs Torch=0.000113; band×2.5 | self max_abs=4.702e-05; vs Torch=0.0001295; band×2.86 | +| NeoCu | 512 | stress | self max_abs=7.471e-09 | self max_abs=5.184e-09; vs Torch=1.867e-08; band×2.5 | self max_abs=7.77e-09; vs Torch=2.139e-08; band×2.86 | +| NeoCu | 4096 | energy | self max_abs=2.599e-05 | self max_abs=4.148e-05; vs Torch=8.726e-05; band×3.36 | self max_abs=3.821e-05; vs Torch=8.494e-05; band×3.27 | +| NeoCu | 4096 | forces | self max_abs=7.152e-07 | self max_abs=7.069e-07; vs Torch=8.014e-07; band×1.12 | self max_abs=7.169e-07; vs Torch=7.932e-07; band×1.11 | +| NeoCu | 4096 | virial | self max_abs=0.0001036 | self max_abs=0.0001054; vs Torch=0.0006932; band×6.69 | self max_abs=0.0001111; vs Torch=0.000695; band×6.71 | +| NeoCu | 4096 | stress | self max_abs=2.14e-09 | self max_abs=2.178e-09; vs Torch=1.432e-08; band×6.69 | self max_abs=2.295e-09; vs Torch=1.435e-08; band×6.71 | +| Nano | 96 | energy | self max_abs=2.658e-05 | self max_abs=1.878e-05; vs Torch=3.499e-05; band×1.32 | self max_abs=2.122e-05; vs Torch=3.982e-05; band×1.5 | +| Nano | 96 | forces | self max_abs=1.136e-05 | self max_abs=1.22e-05; vs Torch=1.286e-05; band×1.13 | self max_abs=1.15e-05; vs Torch=1.269e-05; band×1.12 | +| Nano | 96 | virial | self max_abs=7.839e-05 | self max_abs=8.811e-05; vs Torch=9.567e-05; band×1.22 | self max_abs=8.601e-05; vs Torch=9.163e-05; band×1.17 | +| Nano | 96 | stress | self max_abs=1.439e-07 | self max_abs=1.618e-07; vs Torch=1.757e-07; band×1.22 | self max_abs=1.579e-07; vs Torch=1.682e-07; band×1.17 | +| Nano | 512 | energy | self max_abs=6.02e-05 | self max_abs=6.39e-05; vs Torch=0.0001107; band×1.84 | self max_abs=5.358e-05; vs Torch=0.0001102; band×1.83 | +| Nano | 512 | forces | self max_abs=1.488e-05 | self max_abs=1.347e-05; vs Torch=1.477e-05; band×0.992 | self max_abs=1.348e-05; vs Torch=1.325e-05; band×0.89 | +| Nano | 512 | virial | self max_abs=0.000201 | self max_abs=0.0001863; vs Torch=0.0002575; band×1.28 | self max_abs=0.0002203; vs Torch=0.000256; band×1.27 | +| Nano | 512 | stress | self max_abs=6.921e-08 | self max_abs=6.415e-08; vs Torch=8.866e-08; band×1.28 | self max_abs=7.585e-08; vs Torch=8.813e-08; band×1.27 | +| Nano | 4096 | energy | self max_abs=0.0001612 | self max_abs=0.0001924; vs Torch=0.0004887; band×3.03 | self max_abs=0.0002133; vs Torch=0.0005112; band×3.17 | +| Nano | 4096 | forces | self max_abs=1.484e-05 | self max_abs=1.563e-05; vs Torch=1.613e-05; band×1.09 | self max_abs=1.605e-05; vs Torch=1.554e-05; band×1.05 | +| Nano | 4096 | virial | self max_abs=0.0004504 | self max_abs=0.0005898; vs Torch=0.0007157; band×1.59 | self max_abs=0.0005332; vs Torch=0.0007943; band×1.76 | +| Nano | 4096 | stress | self max_abs=1.938e-08 | self max_abs=2.538e-08; vs Torch=3.08e-08; band×1.59 | self max_abs=2.294e-08; vs Torch=3.418e-08; band×1.76 | +| Mini | 96 | energy | self max_abs=1.246e-05 | self max_abs=1.049e-05; vs Torch=1.836e-05; band×1.47 | self max_abs=1.74e-05; vs Torch=1.651e-05; band×1.33 | +| Mini | 96 | forces | self max_abs=8.618e-06 | self max_abs=7.551e-06; vs Torch=1.08e-05; band×1.25 | self max_abs=8.28e-06; vs Torch=1.135e-05; band×1.32 | +| Mini | 96 | virial | self max_abs=6.538e-05 | self max_abs=4.644e-05; vs Torch=8.237e-05; band×1.26 | self max_abs=7.174e-05; vs Torch=9.37e-05; band×1.43 | +| Mini | 96 | stress | self max_abs=1.2e-07 | self max_abs=8.526e-08; vs Torch=1.512e-07; band×1.26 | self max_abs=1.317e-07; vs Torch=1.72e-07; band×1.43 | +| Mini | 512 | energy | self max_abs=3.606e-05 | self max_abs=3.594e-05; vs Torch=3.952e-05; band×1.1 | self max_abs=2.813e-05; vs Torch=3.04e-05; band×0.843 | +| Mini | 512 | forces | self max_abs=8.601e-06 | self max_abs=8.582e-06; vs Torch=1.21e-05; band×1.41 | self max_abs=9.532e-06; vs Torch=1.289e-05; band×1.5 | +| Mini | 512 | virial | self max_abs=0.0001708 | self max_abs=0.0001244; vs Torch=0.0002595; band×1.52 | self max_abs=0.0001445; vs Torch=0.0002485; band×1.45 | +| Mini | 512 | stress | self max_abs=5.882e-08 | self max_abs=4.283e-08; vs Torch=8.933e-08; band×1.52 | self max_abs=4.975e-08; vs Torch=8.555e-08; band×1.45 | +| Mini | 4096 | energy | self max_abs=7.707e-05 | self max_abs=0.0001156; vs Torch=0.0001388; band×1.8 | self max_abs=8.416e-05; vs Torch=0.0001465; band×1.9 | +| Mini | 4096 | forces | self max_abs=1.004e-05 | self max_abs=9.328e-06; vs Torch=1.306e-05; band×1.3 | self max_abs=1.007e-05; vs Torch=1.398e-05; band×1.39 | +| Mini | 4096 | virial | self max_abs=0.0003499 | self max_abs=0.0003119; vs Torch=0.001488; band×4.25 | self max_abs=0.0003646; vs Torch=0.001521; band×4.35 | +| Mini | 4096 | stress | self max_abs=1.506e-08 | self max_abs=1.342e-08; vs Torch=6.403e-08; band×4.25 | self max_abs=1.569e-08; vs Torch=6.544e-08; band×4.35 | +| Pro | 96 | energy | self max_abs=4.083e-06 | self max_abs=3.368e-06; vs Torch=5.037e-06; band×1.23 | self max_abs=3.338e-06; vs Torch=5.871e-06; band×1.44 | +| Pro | 96 | forces | self max_abs=1.667e-06 | self max_abs=1.153e-06; vs Torch=1.919e-06; band×1.15 | self max_abs=1.144e-06; vs Torch=1.691e-06; band×1.01 | +| Pro | 96 | virial | self max_abs=9.613e-06 | self max_abs=8.763e-06; vs Torch=1.613e-05; band×1.68 | self max_abs=7.585e-06; vs Torch=1.643e-05; band×1.71 | +| Pro | 96 | stress | self max_abs=1.765e-08 | self max_abs=1.609e-08; vs Torch=2.962e-08; band×1.68 | self max_abs=1.393e-08; vs Torch=3.017e-08; band×1.71 | +| Pro | 512 | energy | OOM | self max_abs=1.019e-05; Torch ref unavailable | self max_abs=8.345e-06; Torch ref unavailable | +| Pro | 512 | forces | OOM | self max_abs=1.36e-06; Torch ref unavailable | self max_abs=1.497e-06; Torch ref unavailable | +| Pro | 512 | virial | OOM | self max_abs=2.018e-05; Torch ref unavailable | self max_abs=1.641e-05; Torch ref unavailable | +| Pro | 512 | stress | OOM | self max_abs=6.947e-09; Torch ref unavailable | self max_abs=5.648e-09; Torch ref unavailable | +| Pro | 4096 | energy | OOM | OOM | OOM | +| Pro | 4096 | forces | OOM | OOM | OOM | +| Pro | 4096 | virial | OOM | OOM | OOM | +| Pro | 4096 | stress | OOM | OOM | OOM | + +Strict per-element atol1e-5/rtol1e-4 and finite results, mean_abs/max_relative and within/across detail remain in ACCURACY_TABLE.md and summary.json. Pure-reference OOM prevents band acceptance. Band PASS does not silently erase strict FAIL. + +## Timing and memory — process medians and worst capacity + +Compilation excluded from timing, included in first/full memory. This independent-process protocol does not freeze all generated autotune choices: no stable isolated speedup is inferred solely from these percentages. GB=1e9B. Both allocated/reserved raw bytes are retained in result.json. Numbers are preliminary until 3 processes per arm complete. + +| Model/system | Atoms | Mode | Processes | Wall P50 ms | Wall P90 ms | Event P50 ms | First alloc GB | Full peak incl first GB | Steady alloc GB | +| --- | ---: | --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | +| Neo | 96 | TORCH | 3/3 | 13.075581 | 13.151851 | 13.030096 | 1.118502 | 1.118502 | 1.017839 | +| Neo | 96 | BASE | 3/3 | 17.992521 | 18.385345 | 17.950175 | 0.646372 | 0.646372 | 0.545709 | +| Neo | 96 | ECS | 3/3 | 18.035012 | 18.224658 | 17.991696 | 0.658145 | 0.658145 | 0.557481 | +| Neo | 512 | TORCH | 3/3 | 46.979462 | 47.018433 | 46.936768 | 5.331018 | 5.331018 | 5.331018 | +| Neo | 512 | BASE | 3/3 | 23.472956 | 23.531686 | 23.427423 | 2.809001 | 2.809001 | 2.809001 | +| Neo | 512 | ECS | 3/3 | 23.494863 | 23.648258 | 23.449760 | 2.871368 | 2.871368 | 2.871368 | +| Neo | 4096 | BASE | 3/3 | 156.512587 | 156.684338 | 156.465652 | 22.313645 | 22.313645 | 22.313645 | +| Neo | 4096 | ECS | 3/3 | 156.594862 | 156.777619 | 156.533646 | 22.806378 | 22.806378 | 22.806378 | +| NeoCu | 96 | TORCH | 3/3 | 12.232151 | 12.297081 | 12.188800 | 0.541385 | 0.541385 | 0.440721 | +| NeoCu | 96 | BASE | 3/3 | 18.154909 | 18.366497 | 18.111904 | 0.353757 | 0.353757 | 0.253004 | +| NeoCu | 96 | ECS | 3/3 | 18.259421 | 18.342752 | 18.215392 | 0.355033 | 0.355033 | 0.254369 | +| NeoCu | 512 | TORCH | 3/3 | 22.897386 | 22.935968 | 22.853473 | 2.253040 | 2.253040 | 2.253040 | +| NeoCu | 512 | BASE | 3/3 | 18.317986 | 18.392029 | 18.274000 | 1.249555 | 1.249555 | 1.249555 | +| NeoCu | 512 | ECS | 3/3 | 18.244627 | 18.391290 | 18.201713 | 1.254748 | 1.254748 | 1.254748 | +| NeoCu | 4096 | TORCH | 3/3 | 160.911316 | 161.083811 | 160.864113 | 17.866908 | 17.866908 | 17.866908 | +| NeoCu | 4096 | BASE | 3/3 | 80.131557 | 80.263306 | 80.085968 | 9.842755 | 9.842755 | 9.842755 | +| NeoCu | 4096 | ECS | 3/3 | 80.314757 | 80.400489 | 80.269539 | 9.873422 | 9.873422 | 9.873422 | +| Nano | 96 | TORCH | 3/3 | 10.842765 | 10.894773 | 10.799536 | 0.267505 | 0.267505 | 0.168914 | +| Nano | 96 | BASE | 3/3 | 10.827782 | 10.899461 | 10.785792 | 0.204831 | 0.204831 | 0.104168 | +| Nano | 96 | ECS | 3/3 | 10.780998 | 10.826602 | 10.738672 | 0.204831 | 0.204831 | 0.104168 | +| Nano | 512 | TORCH | 3/3 | 11.856695 | 11.910365 | 11.813136 | 0.845872 | 0.845872 | 0.845872 | +| Nano | 512 | BASE | 3/3 | 11.192140 | 11.325888 | 11.147888 | 0.500028 | 0.500028 | 0.500028 | +| Nano | 512 | ECS | 3/3 | 11.160740 | 11.249917 | 11.116624 | 0.500028 | 0.500028 | 0.500028 | +| Nano | 4096 | TORCH | 3/3 | 56.467913 | 56.592532 | 56.422001 | 6.678115 | 6.678115 | 6.678115 | +| Nano | 4096 | BASE | 3/3 | 33.593906 | 33.720488 | 33.547775 | 3.910505 | 3.910505 | 3.910505 | +| Nano | 4096 | ECS | 3/3 | 33.589447 | 33.682449 | 33.544529 | 3.910505 | 3.910505 | 3.910505 | +| Mini | 96 | TORCH | 3/3 | 11.962016 | 12.010488 | 11.917488 | 0.400473 | 0.400473 | 0.299810 | +| Mini | 96 | BASE | 3/3 | 11.099674 | 11.319123 | 11.057840 | 0.286303 | 0.286303 | 0.190500 | +| Mini | 96 | ECS | 3/3 | 11.180259 | 11.242529 | 11.135952 | 0.286303 | 0.286303 | 0.190500 | +| Mini | 512 | TORCH | 3/3 | 18.303323 | 18.349090 | 18.260481 | 1.528662 | 1.528662 | 1.528662 | +| Mini | 512 | BASE | 3/3 | 12.184330 | 12.276598 | 12.140736 | 0.944773 | 0.945822 | 0.945822 | +| Mini | 512 | ECS | 3/3 | 12.208581 | 12.249058 | 12.166288 | 0.944773 | 0.945822 | 0.945822 | +| Mini | 4096 | TORCH | 3/3 | 122.270842 | 122.379773 | 122.217281 | 12.115697 | 12.115697 | 12.115697 | +| Mini | 4096 | BASE | 3/3 | 61.097768 | 61.183442 | 61.052145 | 7.452142 | 7.453190 | 7.453190 | +| Mini | 4096 | ECS | 3/3 | 60.989576 | 61.053372 | 60.944431 | 7.452142 | 7.453190 | 7.453190 | +| Pro | 96 | TORCH | 3/3 | 125.952058 | 126.149409 | 125.908112 | 11.443602 | 11.443602 | 11.443602 | +| Pro | 96 | BASE | 3/3 | 66.992101 | 67.231234 | 66.948528 | 5.139665 | 5.139665 | 5.039002 | +| Pro | 96 | ECS | 3/3 | 67.021606 | 67.312684 | 66.977631 | 5.139665 | 5.139665 | 5.039002 | +| Pro | 512 | BASE | 3/3 | 330.464768 | 331.377085 | 330.418015 | 25.395441 | 25.395441 | 25.395441 | +| Pro | 512 | ECS | 3/3 | 330.612567 | 331.352630 | 330.561584 | 25.395441 | 25.395441 | 25.395441 | + +## Route and acceptance limits + +Nano/Mini/Pro candidate is native fallback, not demonstrated ECS acceleration. Neo dispatch must be checked against generated wrappers; current single-cohort specialization must not be described as 80 runtime batches. Pro-MPtrj is not OMat24-Pro; original checkpoint unmodified, reader-only legacy metadata alias applies equally to all arms. H20/PT2/C++/training and unmeasured larger models are not accepted by this matrix. + +Upstream review also requires focused guard/alias tests, no new public fine-grained development switch for a merge-ready path, freeze-time policy/selection where applicable, optional-Triton CPU safety and real CI approval. Current port keeps the old default-off experimental switch pending policy resolution. Do not submit as ready-to-merge. + +## Candidate vs current-master changes + +Positive percentage means reduction/improvement; negative means increase. Medians of independent process quantiles, not a controlled paired significance estimate. Fallback timing variation is not attributed to ECS. + +| Model/system | Atoms | Time reduction % | First-inclusive full alloc reduction % | Steady alloc reduction % | Candidate route | +| --- | ---: | ---: | ---: | ---: | --- | +| Neo | 96 | -0.2362 | -1.8213 | -2.1573 | single-cohort specialized, not bounded runtime batching | +| Neo | 512 | -0.0933 | -2.2202 | -2.2202 | single-cohort specialized, not bounded runtime batching | +| Neo | 4096 | -0.0526 | -2.2082 | -2.2082 | single-cohort specialized, not bounded runtime batching | +| NeoCu | 96 | -0.5757 | -0.3605 | -0.5395 | single-cohort specialized, not bounded runtime batching | +| NeoCu | 512 | 0.4005 | -0.4156 | -0.4156 | single-cohort specialized, not bounded runtime batching | +| NeoCu | 4096 | -0.2286 | -0.3116 | -0.3116 | single-cohort specialized, not bounded runtime batching | +| Nano | 96 | 0.4321 | 0.0000 | 0.0000 | native fallback | +| Nano | 512 | 0.2806 | 0.0000 | 0.0000 | native fallback | +| Nano | 4096 | 0.0133 | 0.0000 | 0.0000 | native fallback | +| Mini | 96 | -0.7260 | 0.0000 | 0.0000 | native fallback | +| Mini | 512 | -0.1990 | 0.0000 | 0.0000 | native fallback | +| Mini | 4096 | 0.1771 | 0.0000 | 0.0000 | native fallback | +| Pro | 96 | -0.0440 | 0.0000 | 0.0000 | native fallback | +| Pro | 512 | -0.0447 | 0.0000 | 0.0000 | native fallback | +| Pro | 4096 | N/A | N/A | N/A | incomplete/OOM | diff --git a/doc/development/ecs-validation-20261025/analyze.py b/doc/development/ecs-validation-20261025/analyze.py new file mode 100644 index 0000000000..c8db191f33 --- /dev/null +++ b/doc/development/ecs-validation-20261025/analyze.py @@ -0,0 +1,250 @@ +"""All-pairs scientific-accuracy ranges; never relax thresholds.""" +# ruff: noqa: T201, ANN001, ANN201, RUF001 -- standalone manual experiment, not imported package code + +import argparse +from itertools import combinations, product +import json +from pathlib import Path + +import numpy as np + +P = argparse.ArgumentParser() +P.add_argument("--root", type=Path, default=Path(__file__).resolve().parent) +a = P.parse_args() +root = a.root +all_results = [] +cases = {} +for d in sorted((root / "results").glob("*")): + if not (d / "result.json").exists(): + continue + result = json.loads((d / "result.json").read_text()) + manifest = ( + json.loads((d / "manifest.json").read_text()) + if (d / "manifest.json").exists() + else {} + ) + pieces = d.name.split("_") + model, n, mode, proc = pieces[0], pieces[1], "_".join(pieces[2:-1]), pieces[-1] + entry = { + "run": d.name, + "model": model, + "n": int(n[1:]), + "mode": mode, + "gpu": manifest.get("args", {}).get("gpu"), + "process": proc, + "status": "oom" + if "deepmd.utils.errors.OutOfMemoryError:" in result.get("exception", "") + else result["status"], + "raw_status": result["status"], + "model_sha256": manifest.get("model_sha256"), + "routes": result.get("routes"), + "runtime_cohort_calls": result.get("runtime_cohort_calls", []), + "finite": result.get("finite"), + "parameter_count": result.get("parameter_count"), + "first_peak": result.get("first_peak"), + "complete_peak": result.get("complete_peak"), + } + all_results.append(entry) + if result["status"] != "completed": + continue + with np.load(d / "outputs.npz") as z: + arrays = {k: z[k] for k in z.files} + with np.load(d / "inputs.npz") as z: + inputs = {k: z[k] for k in z.files} + key = (model, int(n[1:])) + group = cases.setdefault(key, {"inputs": inputs, "modes": {}}) + for k, v in inputs.items(): + if not np.array_equal(group["inputs"][k], v): + raise ValueError(f"inputs differ: {d}/{k}") + group["modes"].setdefault(mode, []).append((proc, arrays)) + + +for model in {r["model"] for r in all_results}: + hashes = { + r["model_sha256"] + for r in all_results + if r["model"] == model and r["model_sha256"] + } + assert len(hashes) == 1, f"checkpoint changed inside the matrix: {model}" +for model, n in {(r["model"], r["n"]) for r in all_results}: + devices = { + r["gpu"] + for r in all_results + if (r["model"], r["n"]) == (model, n) and r["gpu"] is not None + } + assert len(devices) == 1, f"cross-GPU case mixing: {model}/{n}" + + +def error(got, ref): + finite = bool(np.isfinite(got).all() and np.isfinite(ref).all()) + if not finite: + return {"finite": False, "passed": False} + diff = np.abs(got - ref) + ratio = diff / (1e-5 + 1e-4 * np.abs(ref)) + worst = int(np.argmax(ratio)) + return { + "finite": True, + "passed": bool((ratio <= 1).all()), + "max_abs": float(diff.max()), + "mean_abs": float(diff.mean()), + "max_relative": float((diff / np.maximum(np.abs(ref), 1e-8)).max()), + "max_ratio": float(ratio.max()), + "bitwise": bool(np.array_equal(got, ref)), + "worst_ratio_element": { + "index": [int(v) for v in np.unravel_index(worst, ratio.shape)], + "got": float(got.flat[worst]), + "reference": float(ref.flat[worst]), + "abs_error": float(diff.flat[worst]), + "tolerance": float(1e-5 + 1e-4 * abs(ref.flat[worst])), + }, + } + + +groups = [] +details = [] +for (model, n), case in sorted(cases.items()): + modes = case["modes"] + for left, right in [ + ("TORCH", "TORCH"), + ("BASE", "BASE"), + ("ECS", "ECS"), + ("BASE", "TORCH"), + ("ECS", "TORCH"), + ("ECS", "BASE"), + ("ECS_RUNTIME", "ECS_RUNTIME"), + ("ECS_RUNTIME", "BASE"), + ("ECS_RUNTIME", "TORCH"), + ("ECS_RUNTIME", "ECS"), + ]: + if left not in modes or right not in modes: + continue + for scope in ( + ["within_process", "across_process"] + if left == right + else ["cross_implementation"] + ): + raw = [] + for frame in range(3): + la = [(p, r, arr) for p, arr in modes[left] for r in range(3)] + ra = [(p, r, arr) for p, arr in modes[right] for r in range(3)] + pairs = combinations(la, 2) if left == right else product(la, ra) + for (lp, lr, lv), (rp, rr, rv) in pairs: + if scope == "within_process" and lp != rp: + continue + if scope == "across_process" and lp == rp: + continue + for out in ("energy", "forces", "virial", "stress"): + raw.append( + { + "frame": frame, + "left_process": lp, + "left_repeat": lr, + "right_process": rp, + "right_repeat": rr, + "output": out, + **error( + lv[f"{lr}_{frame}_{out}"], rv[f"{rr}_{frame}_{out}"] + ), + } + ) + if not raw: + continue + record = { + "model": model, + "n": n, + "left": left, + "right": right, + "scope": scope, + "left_completed_processes": len(modes[left]), + "right_completed_processes": len(modes[right]), + "all_passed": all(v["passed"] for v in raw), + "metrics": {}, + } + for out in ("energy", "forces", "virial", "stress"): + rows = [r for r in raw if r["output"] == out] + finite = [r for r in rows if r["finite"]] + record["metrics"][out] = { + "comparisons": len(rows), + "failures": sum(not r["passed"] for r in rows), + "all_finite": len(finite) == len(rows), + "all_bitwise": all(r.get("bitwise", False) for r in rows), + "max_abs_range": [ + min(r["max_abs"] for r in finite), + max(r["max_abs"] for r in finite), + ] + if finite + else None, + "worst_mean_abs": max( + (r["mean_abs"] for r in finite), default=None + ), + "worst_max_relative": max( + (r["max_relative"] for r in finite), default=None + ), + "worst_tolerance_ratio": max( + (r["max_ratio"] for r in finite), default=None + ), + } + groups.append(record) + details.append( + { + "model": model, + "n": n, + "left": left, + "right": right, + "scope": scope, + "pairs": raw, + } + ) +summary = { + "threshold": {"atol": 1e-5, "rtol": 1e-4, "relative_floor": 1e-8}, + "range_definition": "min/max of max_abs over same-frame comparisons; NOT a confidence interval", + "statuses": all_results, + "comparisons": groups, +} +(root / "summary.json").write_text(json.dumps(summary, indent=2)) +(root / "pair_details.json").write_text(json.dumps(details, indent=2)) +lines = [ + "# ECS 多模型多尺寸精度测试", + "", + "> 精度优先；未放宽阈值。误差范围为每对输出max_abs的min～max，不是置信区间。基线SHA与模型SHA见JSON。", + "", + "模式：TORCH=纯PyTorch编译；ECS=当前PR候选（非Neo为回退）；ECS_RUNTIME=强制真实8192分批。单位：能量/位力eV，力eV/Å，stress eV/ų。所有结果均不是DFT预测误差。", + "", + "| 模型 | 原子数 | 对照 | 范围 | 完成进程数 | 能量max_abs范围 | 力max_abs范围 | 位力max_abs范围 | stress max_abs范围 | 最差误差/容差 | 状态 |", + "| --- | ---: | --- | --- | --- | --- | --- | --- | --- | ---: | --- |", +] + + +def fmt(r): + return "～".join(f"{v:.3e}" for v in r) if r else "nonfinite" + + +for g in groups: + m = g["metrics"] + worst = max(v["worst_tolerance_ratio"] or 0 for v in m.values()) + lines.append( + f"| {g['model']} | {g['n']} | {g['left']} vs {g['right']} | {g['scope']} | {g['left_completed_processes']}/{g['right_completed_processes']} | {fmt(m['energy']['max_abs_range'])} | {fmt(m['forces']['max_abs_range'])} | {fmt(m['virial']['max_abs_range'])} | {fmt(m['stress']['max_abs_range'])} | {worst:.4f} | {'PASS' if g['all_passed'] else 'FAIL'}{'（样本未齐）' if min(g['left_completed_processes'], g['right_completed_processes']) < 3 else ''} |" + ) +lines += [ + "", + "## 所有进程状态（含OOM/失败，不能略去）", + "", + "| 运行 | 状态 | 参数量 | opaque ECS调用 | single路径标记 |", + "| --- | --- | ---: | ---: | --- |", +] +for r in all_results: + routes = r["routes"] or [] + lines.append( + f"| {r['run']} | {r['status']} | {r['parameter_count']} | {sum(x['opaque_cohort_calls'] for x in routes)} | {any(x['single_cohort_marker'] for x in routes)} |" + ) +(root / "ACCURACY_TABLE.md").write_text("\n".join(lines) + "\n") +print( + "runs", + len(all_results), + "completed", + sum(r["status"] == "completed" for r in all_results), + "comparisons", + len(groups), + "failed_groups", + sum(not g["all_passed"] for g in groups), +) diff --git a/doc/development/ecs-validation-20261025/port-to-2ba617bc.patch b/doc/development/ecs-validation-20261025/port-to-2ba617bc.patch new file mode 100644 index 0000000000..25d65b4d65 --- /dev/null +++ b/doc/development/ecs-validation-20261025/port-to-2ba617bc.patch @@ -0,0 +1,720 @@ +diff --git a/deepmd/pt_expt/kernels/triton/sezm/so2_value_path.py b/deepmd/pt_expt/kernels/triton/sezm/so2_value_path.py +index a02f4288..d874c8ed 100644 +--- a/deepmd/pt_expt/kernels/triton/sezm/so2_value_path.py ++++ b/deepmd/pt_expt/kernels/triton/sezm/so2_value_path.py +@@ -91,6 +91,7 @@ from __future__ import ( + annotations, + ) + ++import os + from typing import ( + TYPE_CHECKING, + NamedTuple, +@@ -161,6 +162,10 @@ _MAX_MIXER_RANK = 4 + + _ROTATE_MIX_BWD_CONFIG = (1, 2) # per-edge backward (warps, stages) + ++# Experimental, frozen first-order inference only. Read before compilation; ++# changing the switch requires a new process/graph. Disabled by default. ++_EDGE_COHORT_INFER = os.environ.get("DP_SO2_EDGE_COHORT_INFER", "0") == "1" ++ + + # ====================================================================== + # Eager reference / fallback implementations +@@ -1369,6 +1374,7 @@ if SO2_VALUE_PATH_TRITON_AVAILABLE: + L: tl.constexpr, + CF: tl.constexpr, + BLOCK_M: tl.constexpr, ++ saved_edges=0, + ): + """Recompute the gate sigmoids from the saved pre-activation (backward).""" + ROW: tl.constexpr = (3 * L + 1) * CF +@@ -1386,7 +1392,8 @@ if SO2_VALUE_PATH_TRITON_AVAILABLE: + cm = mm & (nc < CF)[None, :] + wm = ((nc < CF)[:, None]) & ((nc < CF)[None, :]) + +- z_row = z_ptr + (layer * n_focus + fid) * n_edge * ROW + offs_m * ROW ++ z_stride = tl.where(saved_edges > 0, saved_edges, n_edge).to(tl.int64) ++ z_row = z_ptr + (layer * n_focus + fid) * z_stride * ROW + offs_m * ROW + z_s = tl.load(z_row[:, None] + nc[None, :], mask=cm, other=0.0).to(tl.float32) + sig_row = sig_ptr + (fid * n_edge + offs_m) * LG + for g in tl.static_range(L): +@@ -1420,6 +1427,7 @@ if SO2_VALUE_PATH_TRITON_AVAILABLE: + RECOMPUTE_SIG: tl.constexpr, + RECOVER_INPUT: tl.constexpr, + BLOCK_M: tl.constexpr, ++ saved_edges=0, + ): + """Pointwise part of the gated-layer backward. + +@@ -1456,7 +1464,8 @@ if SO2_VALUE_PATH_TRITON_AVAILABLE: + wm = ((nc < CF)[:, None]) & ((nc < CF)[None, :]) + + g_row = g_ptr + fid * n_edge * ROW + offs_m * ROW +- z_row = z_ptr + (layer * n_focus + fid) * n_edge * ROW + offs_m * ROW ++ z_stride = tl.where(saved_edges > 0, saved_edges, n_edge).to(tl.int64) ++ z_row = z_ptr + (layer * n_focus + fid) * z_stride * ROW + offs_m * ROW + gz_row = gz_ptr + fid * n_edge * ROW + offs_m * ROW + sig_row = sig_ptr + (fid * n_edge + offs_m) * LG + gl_row = gl_ptr + (fid * n_edge + offs_m) * LG +@@ -2014,6 +2023,7 @@ if SO2_VALUE_PATH_TRITON_AVAILABLE: + BLOCK_M: tl.constexpr, + BLOCK_N: tl.constexpr, + BLOCK_K: tl.constexpr, ++ output_edges=0, + ): + """Backward block GEMM ``g_u = residual + gz @ W^T`` over both blocks. + +@@ -2044,7 +2054,8 @@ if SO2_VALUE_PATH_TRITON_AVAILABLE: + gz_row = gz_ptr + fid * ROW + offs_m * (n_focus * ROW) + else: + gz_row = gz_ptr + fid * n_edge * ROW + offs_m * ROW +- gu_row = gu_ptr + fid * n_edge * ROW + offs_m * ROW ++ output_stride = tl.where(output_edges > 0, output_edges, n_edge).to(tl.int64) ++ gu_row = gu_ptr + fid * output_stride * ROW + offs_m * ROW + if FOLD_ALPHA: + alpha = tl.load(alpha_ptr + offs_m * n_focus + fid, mask=m_mask, other=0.0) + +@@ -2435,6 +2446,7 @@ def _launch_stack_point_backward( + L=lmax, + CF=focus_dim, + BLOCK_M=block_m, ++ saved_edges=0, + num_warps=warps, + num_stages=stages, + ) +@@ -2458,6 +2470,7 @@ def _launch_stack_point_backward( + RECOMPUTE_SIG=recompute_sigmoid, + RECOVER_INPUT=recover, + BLOCK_M=block_m, ++ saved_edges=0, + num_warps=warps, + num_stages=stages, + ) +@@ -3222,6 +3235,7 @@ def _stack_backward_traversal( + BLOCK_M=block_m, + BLOCK_N=block_n, + BLOCK_K=block_k, ++ output_edges=0, + num_warps=warps, + num_stages=stages, + ) +@@ -3406,6 +3420,7 @@ def _stack_backward_traversal( + BLOCK_M=block_m, + BLOCK_N=block_n, + BLOCK_K=block_k, ++ output_edges=0, + num_warps=warps, + num_stages=stages, + ) +@@ -4960,7 +4975,18 @@ def _mixing_stack_backward(ctx, grad_out, grad_z, grad_u): + None, + None, + ) +- grad_u0, grad_alpha = _mixing_stack_bwd_op( ++ backward_op = _mixing_stack_bwd_op ++ if _EDGE_COHORT_INFER: ++ from .so2_edge_cohort import ( ++ cohort_backward, ++ supports_edge_cohort, ++ ) ++ ++ if supports_edge_cohort( ++ grad_out, z_all, ctx.lmax, ctx.focus_dim, ctx.apply_alpha ++ ): ++ backward_op = cohort_backward ++ grad_u0, grad_alpha = backward_op( + grad_out, + x_local, + z_all, +diff --git a/deepmd/pt_expt/kernels/triton/sezm/so2_edge_cohort.py b/deepmd/pt_expt/kernels/triton/sezm/so2_edge_cohort.py +new file mode 100644 +--- /dev/null ++++ b/deepmd/pt_expt/kernels/triton/sezm/so2_edge_cohort.py +@@ -0,0 +1,380 @@ ++# SPDX-License-Identifier: LGPL-3.0-or-later ++"""Experimental edge-cohort backward for frozen, narrow SO(2) inference. ++ ++Keep native GEMM column parallelism and arithmetic, but traverse all layers ++of one cohort before advancing to the next cohort. Scratch scales with the ++cohort, not the entire graph. The saved activations and returned input gradient ++still have full edge extent. This Python runtime launcher is an online-compile ++prototype; standalone AOTInductor/C++ deployment is not validated. ++""" ++ ++from __future__ import annotations ++ ++import torch ++import triton ++from torch import Tensor ++from torch.library import wrap_triton ++ ++from . import so2_value_path as sv ++ ++COHORT_EDGES = 8192 ++SINGLE_COHORT_MAX_EDGES = 16384 ++ ++ ++def supports_edge_cohort( ++ grad_out: Tensor, z_all: Tensor, lmax: int, focus_dim: int, apply_alpha: bool ++) -> bool: ++ """Restrict opt-in dispatch to the experimentally checked Neo layout.""" ++ return ( ++ grad_out.is_cuda ++ and z_all.device == grad_out.device ++ and grad_out.dtype == torch.float32 ++ and z_all.dtype == torch.float32 ++ and grad_out.is_contiguous() ++ and z_all.is_contiguous() ++ and lmax == 3 ++ and focus_dim == 32 ++ and apply_alpha ++ and grad_out.ndim == 3 ++ and z_all.ndim == 4 ++ and grad_out.shape[1:] == (2, 320) ++ and z_all.shape[:2] == (2, 2) ++ and z_all.shape[2] == grad_out.shape[0] ++ and z_all.shape[3] == 320 ++ ) ++ ++ ++def cohort_input_backward_impl( ++ grad_out: Tensor, ++ z_all: Tensor, ++ alpha: Tensor, ++ w0t: Tensor, ++ w1t: Tensor, ++ gw: Tensor, ++ gwt: Tensor, ++ cohort_edges: int, ++) -> Tensor: ++ """Compute input gradients without retaining unrelated forward outputs. ++ ++ Kernels infer scratch focus strides from the cohort edge count. Independent ++ saved/output strides retain full-graph layout, including partial cohorts. ++ Only an internally owned residual is overwritten: matrix input ``gz`` and ++ GEMM output never alias, and caller-owned tensors remain read-only. ++ """ ++ if not supports_edge_cohort(grad_out, z_all, 3, 32, True): ++ raise ValueError("edge cohorts require contiguous FP32 Neo inference tensors") ++ if cohort_edges <= 0: ++ raise ValueError("cohort_edges must be positive") ++ if not all(t.is_contiguous() for t in (alpha, w0t, w1t, gw, gwt)): ++ raise ValueError("edge-cohort parameters must be contiguous") ++ n_gated, n_focus, n_edge, row = z_all.shape ++ result = grad_out.new_empty((n_focus, n_edge, row)) ++ if n_edge == 0: ++ return result ++ cap = min(n_edge, cohort_edges) ++ g = grad_out.new_empty((n_focus, cap, row)) ++ gz = torch.empty_like(g) ++ sig = grad_out.new_empty((n_focus, cap, 96)) ++ bm, bn, bk, warps, stages = sv.stack_fp32_configs(32, 3)[2] ++ n_tiles = triton.cdiv(128, bn) + triton.cdiv(192, bn) ++ inside, point_config, recompute_config = sv._point_backward_schedule( ++ 32, 3, train=False ++ ) ++ for start in range(0, n_edge, cap): ++ count = min(cap, n_edge - start) ++ go = grad_out[start:] ++ a = alpha[start:] ++ z = z_all.view(-1)[start * row :] ++ grid = (triton.cdiv(count, bm) * n_tiles, n_focus) ++ sv._stack_gemm_bwd_kernel[grid]( ++ go, ++ go, ++ w0t, ++ w1t, ++ a, ++ g, ++ count, ++ n_gated, ++ L=3, ++ CF=32, ++ G_EDGE_MAJOR=True, ++ FOLD_ALPHA=True, ++ RES_IS_GZ=True, ++ BLOCK_M=bm, ++ BLOCK_N=bn, ++ BLOCK_K=bk, ++ num_warps=warps, ++ num_stages=stages, ++ ) ++ for layer in range(n_gated - 1, -1, -1): ++ if not inside: ++ rbm, rw, rs = recompute_config ++ sv._stack_recompute_kernel[(triton.cdiv(count, rbm), n_focus)]( ++ z, ++ gw, ++ sig, ++ count, ++ layer, ++ L=3, ++ CF=32, ++ BLOCK_M=rbm, ++ saved_edges=n_edge, ++ num_warps=rw, ++ num_stages=rs, ++ ) ++ pbm, pw, ps = point_config ++ sv._stack_point_bwd_kernel[(triton.cdiv(count, pbm), n_focus)]( ++ g, ++ z, ++ sig, ++ gwt, ++ gz, ++ sig, ++ g, ++ g, ++ count, ++ layer, ++ L=3, ++ CF=32, ++ GLOGIT_OUT=False, ++ GLOGIT_STORE=False, ++ RECOMPUTE_SIG=inside, ++ RECOVER_INPUT=False, ++ BLOCK_M=pbm, ++ saved_edges=n_edge, ++ num_warps=pw, ++ num_stages=ps, ++ ) ++ # Residual elements are loaded before their matching output store. ++ # Distinct (edge, focus, column) tiles own disjoint residual elements. ++ target = result.view(-1)[start * row :] if layer == 0 else g ++ sv._stack_gemm_bwd_kernel[grid]( ++ gz, ++ g, ++ w0t, ++ w1t, ++ gz, ++ target, ++ count, ++ layer, ++ L=3, ++ CF=32, ++ G_EDGE_MAJOR=False, ++ FOLD_ALPHA=False, ++ RES_IS_GZ=False, ++ BLOCK_M=bm, ++ BLOCK_N=bn, ++ BLOCK_K=bk, ++ output_edges=n_edge if layer == 0 else count, ++ num_warps=warps, ++ num_stages=stages, ++ ) ++ return result ++ ++ ++# Small edge sets must not pay the opaque multi-cohort launcher overhead. ++# The residual and returned result share internally owned storage; gz never ++# aliases GEMM output. See the inference-only dispatcher below. ++@torch.library.triton_op("sezm_triton::so2_single_cohort_input_bwd", mutates_args=()) ++def _single_cohort_input_backward( ++ grad_out: Tensor, ++ z_all: Tensor, ++ alpha: Tensor, ++ w0t: Tensor, ++ w1t: Tensor, ++ gw: Tensor, ++ gwt: Tensor, ++ cohort_edges: int, ++) -> Tensor: ++ """Compute one small cohort with compiled launchers and owned residual reuse.""" ++ if not supports_edge_cohort(grad_out, z_all, 3, 32, True): ++ raise ValueError("edge cohorts require contiguous FP32 Neo inference tensors") ++ if cohort_edges <= 0: ++ raise ValueError("cohort_edges must be positive") ++ if not all(t.is_contiguous() for t in (alpha, w0t, w1t, gw, gwt)): ++ raise ValueError("edge-cohort parameters must be contiguous") ++ n_gated, n_focus, n_edge, row = z_all.shape ++ result = grad_out.new_empty((n_focus, n_edge, row)) ++ if n_edge == 0: ++ return result ++ cap = n_edge ++ g = result ++ gz = torch.empty_like(g) ++ sig = grad_out.new_empty((n_focus, cap, 96)) ++ bm, bn, bk, warps, stages = sv.stack_fp32_configs(32, 3)[2] ++ n_tiles = triton.cdiv(128, bn) + triton.cdiv(192, bn) ++ inside, point_config, recompute_config = sv._point_backward_schedule( ++ 32, 3, train=False ++ ) ++ start = 0 ++ count = min(cap, n_edge - start) ++ go = grad_out[start:] ++ a = alpha[start:] ++ z = z_all.view(-1)[start * row :] ++ grid = (triton.cdiv(count, bm) * n_tiles, n_focus) ++ wrap_triton(sv._stack_gemm_bwd_kernel)[grid]( ++ go, ++ go, ++ w0t, ++ w1t, ++ a, ++ g, ++ count, ++ n_gated, ++ L=3, ++ CF=32, ++ G_EDGE_MAJOR=True, ++ FOLD_ALPHA=True, ++ RES_IS_GZ=True, ++ output_edges=n_edge, ++ BLOCK_M=bm, ++ BLOCK_N=bn, ++ BLOCK_K=bk, ++ num_warps=warps, ++ num_stages=stages, ++ ) ++ for layer in range(n_gated - 1, -1, -1): ++ if not inside: ++ rbm, rw, rs = recompute_config ++ wrap_triton(sv._stack_recompute_kernel)[triton.cdiv(count, rbm), n_focus]( ++ z, ++ gw, ++ sig, ++ count, ++ layer, ++ L=3, ++ CF=32, ++ BLOCK_M=rbm, ++ saved_edges=n_edge, ++ num_warps=rw, ++ num_stages=rs, ++ ) ++ pbm, pw, ps = point_config ++ wrap_triton(sv._stack_point_bwd_kernel)[triton.cdiv(count, pbm), n_focus]( ++ g, ++ z, ++ sig, ++ gwt, ++ gz, ++ sig, ++ g, ++ g, ++ count, ++ layer, ++ L=3, ++ CF=32, ++ GLOGIT_OUT=False, ++ GLOGIT_STORE=False, ++ RECOMPUTE_SIG=inside, ++ RECOVER_INPUT=False, ++ BLOCK_M=pbm, ++ saved_edges=n_edge, ++ num_warps=pw, ++ num_stages=ps, ++ ) ++ target = result.view(-1)[start * row :] if layer == 0 else g ++ wrap_triton(sv._stack_gemm_bwd_kernel)[grid]( ++ gz, ++ g, ++ w0t, ++ w1t, ++ gz, ++ target, ++ count, ++ layer, ++ L=3, ++ CF=32, ++ G_EDGE_MAJOR=False, ++ FOLD_ALPHA=False, ++ RES_IS_GZ=False, ++ BLOCK_M=bm, ++ BLOCK_N=bn, ++ BLOCK_K=bk, ++ output_edges=n_edge if layer == 0 else count, ++ num_warps=warps, ++ num_stages=stages, ++ ) ++ return result ++ ++ ++@torch.library.custom_op("sezm_triton::so2_cohort_input_bwd", mutates_args=()) ++def _cohort_input_backward( ++ grad_out: Tensor, ++ z_all: Tensor, ++ alpha: Tensor, ++ w0t: Tensor, ++ w1t: Tensor, ++ gw: Tensor, ++ gwt: Tensor, ++ cohort_edges: int, ++) -> Tensor: ++ """Keep the runtime edge loop opaque without retaining x_local/u_final.""" ++ return cohort_input_backward_impl( ++ grad_out, z_all, alpha, w0t, w1t, gw, gwt, cohort_edges ++ ) ++ ++ ++@_cohort_input_backward.register_fake ++def _cohort_input_backward_fake( ++ grad_out: Tensor, ++ z_all: Tensor, ++ alpha: Tensor, ++ w0t: Tensor, ++ w1t: Tensor, ++ gw: Tensor, ++ gwt: Tensor, ++ cohort_edges: int, ++) -> Tensor: ++ """Preserve full input-gradient shape under FakeTensor tracing.""" ++ return grad_out.new_empty(z_all.shape[1:]) ++ ++ ++@torch.library.triton_op("sezm_triton::so2_cohort_alpha_bwd", mutates_args=()) ++def _cohort_alpha_backward(grad_out: Tensor, x_local: Tensor, alpha: Tensor) -> Tensor: ++ """Expose alpha's short-lived output dependency to the graph scheduler.""" ++ n_edge, n_focus = alpha.shape ++ result = torch.empty_like(alpha) ++ if sv._has_no_edges(n_edge): ++ return result ++ bm, warps, stages = sv.gate_config(32, 3) ++ wrap_triton(sv._stack_grad_alpha_kernel)[(triton.cdiv(n_edge, bm), n_focus)]( ++ grad_out, ++ x_local, ++ alpha, ++ result, ++ n_edge, ++ L=3, ++ CF=32, ++ BLOCK_M=bm, ++ num_warps=warps, ++ num_stages=stages, ++ ) ++ return result ++ ++ ++def cohort_backward( ++ grad_out: Tensor, ++ x_local: Tensor, ++ z_all: Tensor, ++ u_final: Tensor, ++ alpha: Tensor, ++ w0t: Tensor, ++ w1t: Tensor, ++ gw: Tensor, ++ gwt: Tensor, ++ lmax: int, ++ focus_dim: int, ++ apply_alpha: bool, ++) -> tuple[Tensor, Tensor]: ++ """First-order dispatcher; training/higher derivatives never enter here.""" ++ del u_final ++ if not supports_edge_cohort(grad_out, z_all, lmax, focus_dim, apply_alpha): ++ raise ValueError("unsupported edge-cohort layout") ++ grad_alpha = _cohort_alpha_backward(grad_out, x_local, alpha) ++ input_backward = ( ++ _single_cohort_input_backward ++ if grad_out.shape[0] <= SINGLE_COHORT_MAX_EDGES ++ else _cohort_input_backward ++ ) ++ grad_u0 = input_backward(grad_out, z_all, alpha, w0t, w1t, gw, gwt, COHORT_EDGES) ++ return grad_u0, grad_alpha +diff --git a/source/tests/pt_expt/kernels/test_so2_edge_cohort.py b/source/tests/pt_expt/kernels/test_so2_edge_cohort.py +new file mode 100644 +--- /dev/null ++++ b/source/tests/pt_expt/kernels/test_so2_edge_cohort.py +@@ -0,0 +1,201 @@ ++# SPDX-License-Identifier: LGPL-3.0-or-later ++"""Generated-input checks for the opt-in frozen Neo edge-cohort prototype.""" ++ ++from __future__ import annotations ++ ++from types import SimpleNamespace ++ ++import pytest ++import torch ++ ++pytest.importorskip("triton") ++ ++from deepmd.pt_expt.kernels.triton.sezm import so2_edge_cohort as ecs ++from deepmd.pt_expt.kernels.triton.sezm import so2_value_path as sv ++ ++pytestmark = pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA required") ++ ++ ++def operands(edges: int, mode: str = "random") -> tuple: ++ """Generate well-conditioned operands without a downloaded checkpoint.""" ++ torch.manual_seed(719) ++ u = torch.randn(2, edges, 320, device="cuda") * 0.1 ++ if mode == "zero": ++ u.zero_() ++ alpha = torch.rand(edges, 2, device="cuda") * 0.8 + 0.1 ++ if mode == "tiny": ++ alpha.fill_(1e-5) ++ w0 = torch.randn(3, 2, 128, 128, device="cuda") * 0.01 ++ w1 = torch.randn(3, 2, 192, 192, device="cuda") * 0.01 ++ gw = torch.randn(2, 2, 32, 96, device="cuda") * 0.01 ++ out, z, uf = sv._mixing_stack_impl(u, alpha, w0, w1, gw, 3, 32, True) ++ go = torch.randn_like(out) * 0.1 ++ return ( ++ go, ++ out, ++ z, ++ uf, ++ alpha, ++ w0.transpose(2, 3).contiguous(), ++ w1.transpose(2, 3).contiguous(), ++ gw, ++ gw.transpose(2, 3).contiguous(), ++ 3, ++ 32, ++ True, ++ ) ++ ++ ++@pytest.mark.parametrize("edges", [0, 1, 37, 129, 1025, 8193]) ++@pytest.mark.parametrize("cap", [64, 8192]) ++@pytest.mark.parametrize("mode", ["random", "zero", "tiny"]) ++def test_cohort_matches_native(edges: int, cap: int, mode: str) -> None: ++ """Cover tail cohorts, two focus strides, legal tiny alpha and no mutation.""" ++ args = operands(edges, mode) ++ copies = [x.clone() for x in args[:9]] ++ expected_u, expected_a = sv._mixing_stack_bwd_impl(*args) ++ go, out, z, _, alpha, w0t, w1t, gw, gwt, *_ = args ++ actual_u = ecs.cohort_input_backward_impl(go, z, alpha, w0t, w1t, gw, gwt, cap) ++ actual_a = ecs._cohort_alpha_backward(go, out, alpha) ++ for actual, expected in ((actual_u, expected_u), (actual_a, expected_a)): ++ assert torch.isfinite(actual).all() ++ torch.testing.assert_close(actual, expected, rtol=0, atol=0) ++ for before, after in zip(copies, args[:9], strict=True): ++ assert torch.equal(before, after) ++ ++ ++def test_compiled_dynamic_cohort() -> None: ++ """The opaque runtime edge loop supports online dynamic graph execution.""" ++ compiled = torch.compile(ecs.cohort_backward, fullgraph=True, dynamic=True) ++ for edges in (37, 129): ++ args = operands(edges) ++ actual = compiled(*args) ++ expected = sv._mixing_stack_bwd_impl(*args) ++ for a, b in zip(actual, expected, strict=True): ++ torch.testing.assert_close(a, b, atol=0, rtol=0) ++ ++ ++@pytest.mark.parametrize("dynamic", [False, True]) ++def test_compiled_native_backward(dynamic: bool) -> None: ++ """Optional strides must preserve the default native compilation path.""" ++ compiled = torch.compile(sv._mixing_stack_bwd_op, fullgraph=True, dynamic=dynamic) ++ for edges in (37, 129): ++ args = operands(edges) ++ expected = sv._mixing_stack_bwd_impl(*args) ++ actual = compiled(*args) ++ for a, b in zip(actual, expected, strict=True): ++ torch.testing.assert_close(a, b, atol=0, rtol=0) ++ ++ ++@pytest.mark.parametrize("edges", [0, 37, 8193, 16384]) ++@pytest.mark.parametrize("mode", ["random", "zero", "tiny"]) ++def test_single_cohort_owned_output(edges: int, mode: str) -> None: ++ """Small-cohort reuse changes neither gradients nor caller-owned storage.""" ++ args = operands(edges, mode) ++ copies = [t.clone() for t in args[:9]] ++ go, out, z, _, alpha, w0t, w1t, gw, gwt, *_ = args ++ expected_u, expected_a = sv._mixing_stack_bwd_impl(*args) ++ actual_u = ecs._single_cohort_input_backward( ++ go, z, alpha, w0t, w1t, gw, gwt, ecs.COHORT_EDGES ++ ) ++ actual_a = ecs._cohort_alpha_backward(go, out, alpha) ++ for a, b in ((actual_u, expected_u), (actual_a, expected_a)): ++ assert torch.isfinite(a).all() ++ torch.testing.assert_close(a, b, atol=0, rtol=0) ++ for before, after in zip(copies, args[:9], strict=True): ++ assert torch.equal(before, after) ++ ++ ++def test_compiled_cohort_bucket_boundary() -> None: ++ """A dynamic compiled graph handles both sides of the small-E boundary.""" ++ compiled = torch.compile(ecs.cohort_backward, fullgraph=True, dynamic=True) ++ for edges in (16384, 16385): ++ args = operands(edges) ++ expected = sv._mixing_stack_bwd_impl(*args) ++ actual = compiled(*args) ++ for a, b in zip(actual, expected, strict=True): ++ torch.testing.assert_close(a, b, atol=0, rtol=0) ++ ++ ++@pytest.mark.parametrize( ++ "lmax,cf,apply", [(2, 32, True), (3, 64, True), (3, 32, False)] ++) ++def test_unsupported_layout(lmax: int, cf: int, apply: bool) -> None: ++ """Untested layouts must not enter the experimental implementation.""" ++ args = operands(1) ++ assert not ecs.supports_edge_cohort(args[0], args[2], lmax, cf, apply) ++ assert not ecs.supports_edge_cohort(args[0].cpu(), args[2].cpu(), 3, 32, True) ++ ++ ++@pytest.mark.parametrize("reason", ["weights", "saved_z", "saved_u", "create_graph"]) ++def test_training_dispatch_preserved(monkeypatch, reason: str) -> None: ++ """Weight/state/higher-order requests bypass ECS even when opted in.""" ++ args = operands(1) ++ go, out, z, uf, alpha, w0t, w1t, gw, *_ = args ++ u = torch.zeros_like(uf) ++ w0 = w0t.transpose(2, 3).contiguous() ++ w1 = w1t.transpose(2, 3).contiguous() ++ ctx = SimpleNamespace( ++ saved_tensors=(u, alpha, out, z, uf, w0, w1, gw), ++ needs_input_grad=( ++ True, ++ True, ++ reason == "weights", ++ False, ++ False, ++ False, ++ False, ++ False, ++ ), ++ lmax=3, ++ focus_dim=32, ++ apply_alpha=True, ++ ) ++ calls = [] ++ ++ def training(*unused): ++ calls.append(True) ++ return torch.zeros_like(u), torch.zeros_like(alpha), w0, w1, gw ++ ++ def forbidden(*unused): ++ raise AssertionError("ECS entered a training/higher-order request") ++ ++ monkeypatch.setattr(sv, "_EDGE_COHORT_INFER", True) ++ monkeypatch.setattr(sv, "_mixing_stack_train_bwd_op", training) ++ monkeypatch.setattr(ecs, "cohort_backward", forbidden) ++ with torch.set_grad_enabled(reason == "create_graph"): ++ sv._mixing_stack_backward( ++ ctx, ++ go, ++ torch.zeros_like(z) if reason == "saved_z" else None, ++ torch.zeros_like(uf) if reason == "saved_u" else None, ++ ) ++ assert calls == [True] ++ ++ ++def test_frozen_dispatch(monkeypatch) -> None: ++ """Enabling the switch actually changes the frozen inference dispatch.""" ++ args = operands(37) ++ go, out, z, uf, alpha, w0t, w1t, gw, *_ = args ++ ctx = SimpleNamespace( ++ saved_tensors=( ++ uf, ++ alpha, ++ out, ++ z, ++ uf, ++ 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type=Path, default=SCRIPT_DIR) +HERE = P.parse_args().root.resolve() +subprocess.run( + [sys.executable, str(SCRIPT_DIR / "analyze.py"), "--root", str(HERE)], check=True +) +data = json.loads((HERE / "summary.json").read_text()) +states = data["statuses"] +groups = data["comparisons"] + + +def select(model, n, left, right): + return [ + g + for g in groups + if (g["model"], g["n"], g["left"], g["right"]) == (model, n, left, right) + ] + + +def band(model, n, left, right, field): + gs = select(model, n, left, right) + if not gs: + return None + return max(g["metrics"][field]["max_abs_range"][1] for g in gs) + + +def strict_status(model, n, left, right): + gs = select(model, n, left, right) + if not gs: + return "N/A" + if ( + min( + min(g["left_completed_processes"], g["right_completed_processes"]) + for g in gs + ) + < 3 + ): + return "INCOMPLETE" + return "PASS" if all(g["all_passed"] for g in gs) else "FAIL" + + +def band_status(model, n, mode): + ratios = [] + for field in ("energy", "forces", "virial", "stress"): + cross = band(model, n, mode, "TORCH", field) + own = band(model, n, "TORCH", "TORCH", field) + if cross is None or own is None: + return "N/A" + ratios.append(cross / own if own else (0 if cross == 0 else float("inf"))) + return ("PASS" if max(ratios) <= 3 else "FAIL") + f" ({max(ratios):.3f}x)" + + +def summary_cell(model, n, mode, field): + ss = [s for s in states if (s["model"], s["n"], s["mode"]) == (model, n, mode)] + good = sum(s["status"] == "completed" for s in ss) + if not good: + return "OOM" if any(s["status"] == "oom" for s in ss) else "pending/failed" + own = band(model, n, mode, mode, field) + text = f"self max_abs={own:.4g}" if own is not None else "self pending" + if mode != "TORCH": + cross = band(model, n, mode, "TORCH", field) + pure = band(model, n, "TORCH", "TORCH", field) + text += ( + f"; vs Torch={cross:.4g}" + if cross is not None + else "; Torch ref unavailable" + ) + if cross is not None and pure is not None: + ratio = cross / pure if pure else (0 if cross == 0 else float("inf")) + text += f"; band×{ratio:.3g}" + return text + (f" ({good}/3 processes)" if good < 3 else "") + + +lines = [ + "# ECS current-master comparison — measured 2026-10-25, finalized 2026-10-26", + "", + "BASE: 2ba617bc3db67adee2b8ba00c71e9fa1c2d2bb8c, including merged residual-inplace #6058. Old668-base numbers are not substituted. Candidate is the isolated port of PR#2; its AOT branch-specialization issue remains unresolved.", + "", + f"Recorded {len(states)}/135 process results: {dict(Counter(s['status'] for s in states))}. Missing cells are NOT passes. Three processes ×3 frames ×3 accuracy repeats per arm; 8 extra warmups/30 wall+Event samples, separate steady replay. Same physical GPU within each case. TF32/AMP off.", + "", + "## Three accuracy columns", + "", + "Each field: worst pairwise max-absolute difference over same-frame outputs; self includes within/across-process repeats. Scientific band multiplier = worst deviation from compiled TORCH / TORCH own worst repeat max_abs, separately per field/model/size/system. <=3 is the meeting criterion, not a replacement for the fixed strict elementwise gate. Zero repeat band requires zero cross difference. Energy/virial eV, force eV/Angstrom, stress eV/Angstrom^3.", + "", + "| Model/system | Atoms | Output | Compiled Torch | Current master | Candidate |", + "| --- | ---: | --- | --- | --- | --- |", +] +cases = [ + (m, n) for m in ("Neo", "NeoCu", "Nano", "Mini", "Pro") for n in (96, 512, 4096) +] +gate_lines = [ + "## Three-column PASS/FAIL overview", + "", + "Strict columns cover all four fields: Torch self-repeat; master self-repeat and versus Torch; candidate self-repeat, versus Torch and versus master. A missing Torch reference is N/A, never PASS. The meeting band is a separately reported empirical diagnostic, not a rigorous error bound or a waiver of strict failure.", + "", + "| Model/system | Atoms | Compiled Torch | Current master | Candidate |", + "| --- | ---: | --- | --- | --- |", +] +for model, n in cases: + torch_gate = strict_status(model, n, "TORCH", "TORCH") + master_gate = f"strict self={strict_status(model, n, 'BASE', 'BASE')}; vs Torch={strict_status(model, n, 'BASE', 'TORCH')}; band={band_status(model, n, 'BASE')}" + candidate_gate = f"strict self={strict_status(model, n, 'ECS', 'ECS')}; vs Torch={strict_status(model, n, 'ECS', 'TORCH')}; vs master={strict_status(model, n, 'ECS', 'BASE')}; band={band_status(model, n, 'ECS')}" + gate_lines.append( + f"| {model} | {n} | strict self={torch_gate} | {master_gate} | {candidate_gate} |" + ) +first_table = lines.index("## Three accuracy columns") +lines[first_table:first_table] = [*gate_lines, ""] +for model, n in cases: + for field in ("energy", "forces", "virial", "stress"): + cells = [ + summary_cell(model, n, mode, field) for mode in ("TORCH", "BASE", "ECS") + ] + lines.append(f"| {model} | {n} | {field} | " + " | ".join(cells) + " |") +lines += [ + "", + "Strict per-element atol1e-5/rtol1e-4 and finite results, mean_abs/max_relative and within/across detail remain in ACCURACY_TABLE.md and summary.json. Pure-reference OOM prevents band acceptance. Band PASS does not silently erase strict FAIL.", + "", + "## Timing and memory — process medians and worst capacity", + "", + "Compilation excluded from timing, included in first/full memory. This independent-process protocol does not freeze all generated autotune choices: no stable isolated speedup is inferred solely from these percentages. GB=1e9B. Both allocated/reserved raw bytes are retained in result.json. Numbers are preliminary until 3 processes per arm complete.", + "", + "| Model/system | Atoms | Mode | Processes | Wall P50 ms | Wall P90 ms | Event P50 ms | First alloc GB | Full peak incl first GB | Steady alloc GB |", + "| --- | ---: | --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: |", +] +for model, n in cases: + for mode in ("TORCH", "BASE", "ECS"): + ss = [ + s + for s in states + if (s["model"], s["n"], s["mode"], s["status"]) + == (model, n, mode, "completed") + ] + if not ss: + continue + raw = [ + json.loads((HERE / "results" / s["run"] / "result.json").read_text()) + for s in ss + ] + vals = [ + np.median([r["steady_wall_ms"][k] for r in raw]) for k in ("median", "p90") + ] + vals += [np.median([r["steady_event_ms"]["median"] for r in raw])] + vals += [ + max(r[k]["allocated"] for r in raw) / 1e9 + for k in ("first_peak", "complete_peak", "steady_peak") + ] + lines.append( + f"| {model} | {n} | {mode} | {len(raw)}/3 | " + + " | ".join(f"{v:.6f}" for v in vals) + + " |" + ) +lines += [ + "", + "## Route and acceptance limits", + "", + "Nano/Mini/Pro candidate is native fallback, not demonstrated ECS acceleration. Neo dispatch must be checked against generated wrappers; current single-cohort specialization must not be described as 80 runtime batches. Pro-MPtrj is not OMat24-Pro; original checkpoint unmodified, reader-only legacy metadata alias applies equally to all arms. H20/PT2/C++/training and unmeasured larger models are not accepted by this matrix.", + "", + "Upstream review also requires focused guard/alias tests, no new public fine-grained development switch for a merge-ready path, freeze-time policy/selection where applicable, optional-Triton CPU safety and real CI approval. Current port keeps the old default-off experimental switch pending policy resolution. Do not submit as ready-to-merge.", +] +lines += [ + "", + "## Candidate vs current-master changes", + "", + "Positive percentage means reduction/improvement; negative means increase. Medians of independent process quantiles, not a controlled paired significance estimate. Fallback timing variation is not attributed to ECS.", + "", + "| Model/system | Atoms | Time reduction % | First-inclusive full alloc reduction % | Steady alloc reduction % | Candidate route |", + "| --- | ---: | ---: | ---: | ---: | --- |", +] +for model, n in cases: + by_mode = {} + for mode in ("BASE", "ECS"): + rows = [ + json.loads((HERE / "results" / s["run"] / "result.json").read_text()) + for s in states + if (s["model"], s["n"], s["mode"], s["status"]) + == (model, n, mode, "completed") + ] + if len(rows) == 3: + by_mode[mode] = ( + float(np.median([r["steady_wall_ms"]["median"] for r in rows])), + max(r["complete_peak"]["allocated"] for r in rows), + max(r["steady_peak"]["allocated"] for r in rows), + ) + if len(by_mode) != 2: + lines.append(f"| {model} | {n} | N/A | N/A | N/A | incomplete/OOM |") + continue + changes = [ + 100 * (b - c) / b for b, c in zip(by_mode["BASE"], by_mode["ECS"], strict=True) + ] + route = ( + "single-cohort specialized, not bounded runtime batching" + if model.startswith("Neo") + else "native fallback" + ) + lines.append( + f"| {model} | {n} | " + + " | ".join(f"{v:.4f}" for v in changes) + + f" | {route} |" + ) +(HERE / "REPORT.md").write_text("\n".join(lines) + "\n") +# Generated public evidence only; never copy advisor or private handoff notes. +public_dir = HERE / "candidate/doc/development" +if public_dir.is_dir(): + (public_dir / "ecs-current-master-validation.md").write_text( + "\n".join(lines) + "\n" + ) + (public_dir / "ecs-current-master-validation.json").write_text( + json.dumps(data, indent=2, ensure_ascii=False, allow_nan=False) + "\n" + ) +print("REPORT updated", len(states)) diff --git a/doc/development/ecs-validation-20261025/run_matrix.py b/doc/development/ecs-validation-20261025/run_matrix.py new file mode 100644 index 0000000000..fb4214b4f1 --- /dev/null +++ b/doc/development/ecs-validation-20261025/run_matrix.py @@ -0,0 +1,119 @@ +"""Run the disclosed three-arm manual CUDA matrix with explicit source/model paths.""" + +# ruff: noqa: T201 -- manual benchmark +import argparse +import json +import os +from pathlib import Path +import subprocess +import sys + +P = argparse.ArgumentParser(description=__doc__) +P.add_argument("--base-root", type=Path, required=True) +P.add_argument("--candidate-root", type=Path, required=True) +P.add_argument("--model-map", type=Path, required=True) +P.add_argument("--out-root", type=Path, required=True) +P.add_argument("--gpu", type=int, required=True) +P.add_argument( + "--models", nargs="+", choices=["Neo", "Nano", "Mini", "Pro"], required=True +) +P.add_argument( + "--sizes", type=int, nargs="+", choices=[96, 512, 4096], default=[96, 512, 4096] +) +P.add_argument("--repeats", type=int, default=3) +P.add_argument("--skip-existing", action="store_true") +a = P.parse_args() +models = json.loads(a.model_map.read_text()) +assert a.repeats >= 3 +for root in (a.base_root, a.candidate_root): + assert (root / "deepmd").is_dir(), root +records = [] +for model in a.models: + checkpoint = Path(models[model]).resolve() + assert checkpoint.is_file(), checkpoint + for system in ["diamond", "bcccu"] if model == "Neo" else ["diamond"]: + dims = ( + {96: (2, 2, 3), 512: (4, 4, 4), 4096: (8, 8, 8)} + if system == "diamond" + else {96: (4, 4, 3), 512: (8, 8, 4), 4096: (16, 16, 8)} + ) + label = model if system == "diamond" else model + "Cu" + for n in a.sizes: + for repeat in range(a.repeats): + for mode in ( + ["TORCH", "BASE", "ECS"] + if repeat % 2 == 0 + else ["ECS", "BASE", "TORCH"] + ): + out = ( + a.out_root.resolve() + / "results" + / f"{label}_n{n}_{mode}_p{repeat}" + ) + if (out / "result.json").exists(): + if a.skip_existing: + continue + raise FileExistsError( + f"Use a fresh output root; not overwriting {out}" + ) + busy = subprocess.check_output( + [ + "nvidia-smi", + "-i", + str(a.gpu), + "--query-compute-apps=pid", + "--format=csv,noheader", + ], + text=True, + ).strip() + if busy: + raise RuntimeError( + f"GPU busy; do not kill other processes: {busy}" + ) + out.parent.mkdir(parents=True, exist_ok=True) + command = [ + sys.executable, + str(Path(__file__).with_name("worker.py")), + "--root", + str( + ( + a.candidate_root if mode == "ECS" else a.base_root + ).resolve() + ), + "--model", + str(checkpoint), + "--out", + str(out), + "--cache", + str(a.out_root.resolve() / "cache" / f"{label}_{mode}"), + "--gpu", + str(a.gpu), + "--mode", + mode, + "--nrep", + "2", + "--repeat-cell", + *map(str, dims[n]), + "--system", + system, + "--warmup", + "8", + "--samples", + "30", + ] + print("START", out.name, flush=True) + with out.with_suffix(".log").open("w") as stream: + proc = subprocess.run( + command, + stdout=stream, + stderr=subprocess.STDOUT, + env=dict(os.environ, PYTHONHASHSEED=str(repeat % 2)), + check=False, + ) + records.append( + {"run": out.name, "exit": proc.returncode, "command": command} + ) + (a.out_root / f"commands_gpu{a.gpu}.json").write_text( + json.dumps(records, indent=2) + ) + print("DONE", out.name, proc.returncode, flush=True) diff --git a/doc/development/ecs-validation-20261025/runs.json b/doc/development/ecs-validation-20261025/runs.json new file mode 100644 index 0000000000..f1dcf559b2 --- /dev/null +++ b/doc/development/ecs-validation-20261025/runs.json @@ -0,0 +1,23956 @@ +{ + "protocol": "Three-mode current-master comparison; independent-process timing, not matched-autotune significance", + "runs": [ + { + "run": "Mini_n4096_BASE_p0", + "result": { + "status": "completed", + "mode": "BASE", + "n_atoms": 4096, + "started": 1792867222.9062676, + "threshold": { + "atol": 1e-05, + "rtol": 0.0001, + "relative_denominator_floor": 1e-08 + }, + "system": "diamond", + "input_sha256": "eda961dd45b711e42fad51b8dfde87d6ca736b34ac729d646c98f67481308f83", + "parameter_count": 654988, + "first_peak": { + "allocated": 7452141568, + "reserved": 8095006720 + }, + "steady_peak": { + "allocated": 7453190144, + "reserved": 8095006720 + }, + "full_window_peak": { + "allocated": 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"complete_peak": { + "allocated": 22806378496, + "reserved": 23953670144 + }, + "elapsed_s": 90.64900159835815 + }, + "manifest": { + "args": { + "root": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate", + "model": "/home/kmpl/mkl/project/DPA4-Neo-OMat24-v20260627.pt", + "out": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/results/Neo_n4096_ECS_p2", + "cache": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Neo_ECS", + "gpu": "0", + "mode": "ECS", + "nrep": "2", + "seed": "0", + "repeat_cell": "[8, 8, 8]", + "system": "diamond", + "samples": "30", + "warmup": "8" + }, + "model_sha256": "8d8b2b02b9575cb9e0af420cc1e8b1d9ffcbc04752c2203d29f48ccd0cd5f07d", + "torch": "2.11.0+cu128", + "triton": "3.6.0", + "cuda": "12.8", + "baseline_revision": "2ba617bc3db67adee2b8ba00c71e9fa1c2d2bb8c", + "source_hashes": { + "deepmd/pt_expt/kernels/triton/sezm/so2_value_path.py": "0fb6ae06e62af5dfafe8b7c9cfdce975714a9ffb577dc4f3581ed13706735462", + "deepmd/pt_expt/kernels/triton/sezm/so2_edge_cohort.py": "08c67a81883ff70f19ce9dad8809037585e0c5b0949ecceab747bb4179c44474" + }, + "flags": { + "PYTHONHASHSEED": "0", + "DP_COMPILE_INFER": "1", + "DP_COMPILE": "0", + "DP_TRITON_INFER": "2", + "DP_CUDA_INFER": "0", + "DP_TF32_INFER": "0", + "DP_AMP_INFER": "0", + "DP_SO2_EDGE_COHORT_INFER": "1", + "DP_INTRA_OP_PARALLELISM_THREADS": "1", + "DP_INTER_OP_PARALLELISM_THREADS": "1", + "PYTORCH_ALLOC_CONF": "expandable_segments:True", + "TORCHINDUCTOR_COMPILE_THREADS": "1", + "TORCHINDUCTOR_CACHE_DIR": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Neo_ECS/inductor", + "TRITON_CACHE_DIR": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Neo_ECS/triton" + } + } + }, + { + "run": "Neo_n4096_TORCH_p0", + "result": { + "status": "oom", + "mode": "TORCH", + "n_atoms": 4096, + "started": 1792865623.9983976, + "threshold": { + "atol": 1e-05, + "rtol": 0.0001, + "relative_denominator_floor": 1e-08 + }, + "system": "diamond", + "input_sha256": "eda961dd45b711e42fad51b8dfde87d6ca736b34ac729d646c98f67481308f83", + "parameter_count": 1124910, + "exception": "Traceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 156, in execute\n n_batch, result = callable(batch_nframes, start_index)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 243, in execute_with_batch_size\n return (end_index - start_index), callable(\n ^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 730, in _eval_model\n batch_output = model(\n ^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/train/wrapper.py\", line 201, in forward\n model_pred = self._forward_without_loss(task_key, input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/train/wrapper.py\", line 249, in _forward_without_loss\n model_pred = self.model[task_key](**input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 839, in forward\n model_ret = self.forward_common(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 1053, in forward_common\n return self.forward_common_lower(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 1217, in forward_common_lower\n model_predict = compiled_core_compute(\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 2399, in compiled\n return dict(zip(_keys, _fn(*args)))\n ^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/aot_autograd.py\", line 1200, in forward\n return compiled_fn(full_args)\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 580, in runtime_wrapper\n all_outs = call_func_at_runtime_with_args(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/utils.py\", line 138, in call_func_at_runtime_with_args\n out = normalize_as_list(f(args))\n ^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 783, in wrapper\n return compiled_fn(runtime_args)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 1011, in inner_fn\n outs = compiled_fn(args)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/output_code.py\", line 656, in __call__\n return self.current_callable(inputs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/utils.py\", line 3401, in run\n out = model(new_inputs)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Neo_TORCH/inductor/7p/c7pndtfxe3mrtfgixzsmsf3r355pce3qwesz5k6bbekdcu6zibmr.py\", line 21401, in call\n buf200 = empty_strided_cuda((2, s29, 10, 32), (320*s29, 320, 32, 1), torch.float32)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\ntorch.OutOfMemoryError: CUDA out of memory. Tried to allocate 1.54 GiB. GPU 0 has a total capacity of 31.36 GiB of which 441.06 MiB is free. Including non-PyTorch memory, this process has 30.89 GiB memory in use. Of the allocated memory 30.08 GiB is allocated by PyTorch, and 131.51 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://docs.pytorch.org/docs/stable/notes/cuda.html#optimizing-memory-usage-with-pytorch-cuda-alloc-conf)\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 243, in \n out = evaluate(pos)\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 224, in evaluate\n forces = sample.get_forces().copy()\n ^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/atoms.py\", line 2015, in get_forces\n forces = self._calc.get_forces(self)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/abc.py\", line 31, in get_forces\n return self.get_property('forces', atoms)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/calculator.py\", line 517, in get_property\n self.calculate(atoms, [name], system_changes)\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/calculator.py\", line 143, in calculate\n e, f, v = self.dp.eval(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/infer/deep_pot.py\", line 206, in eval\n results = self.deep_eval.eval(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 556, in eval\n out = self._eval_func(self._eval_model, numb_test, natoms)(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 637, in eval_func\n return self.auto_batch_size.execute_all(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 272, in execute_all\n n_batch, result = self.execute(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 169, in execute\n raise OutOfMemoryError(\ndeepmd.utils.errors.OutOfMemoryError: The callable still throws an out-of-memory (OOM) error even when batch size is 1!\n", + "partial_arrays": 0, + "complete_peak": { + "allocated": 32302947328, + "reserved": 32440844288 + }, + "elapsed_s": 73.36892628669739 + }, + "manifest": { + "args": { + "root": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base", + "model": "/home/kmpl/mkl/project/DPA4-Neo-OMat24-v20260627.pt", + "out": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/results/Neo_n4096_TORCH_p0", + "cache": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Neo_TORCH", + "gpu": "0", + "mode": "TORCH", + "nrep": "2", + "seed": "0", + "repeat_cell": "[8, 8, 8]", + "system": "diamond", + "samples": "30", + "warmup": "8" + }, + "model_sha256": "8d8b2b02b9575cb9e0af420cc1e8b1d9ffcbc04752c2203d29f48ccd0cd5f07d", + "torch": "2.11.0+cu128", + "triton": "3.6.0", + "cuda": "12.8", + "baseline_revision": "2ba617bc3db67adee2b8ba00c71e9fa1c2d2bb8c", + "source_hashes": { + "deepmd/pt_expt/kernels/triton/sezm/so2_value_path.py": "9c8b01082cf01fdcd41fe58e0884364e2166d7acbd4c41f75dda625829a64dbf" + }, + "flags": { + "PYTHONHASHSEED": "0", + "DP_COMPILE_INFER": "1", + "DP_COMPILE": "0", + "DP_TRITON_INFER": "0", + "DP_CUDA_INFER": "0", + "DP_TF32_INFER": "0", + "DP_AMP_INFER": "0", + "DP_SO2_EDGE_COHORT_INFER": "0", + "DP_INTRA_OP_PARALLELISM_THREADS": "1", + "DP_INTER_OP_PARALLELISM_THREADS": "1", + "PYTORCH_ALLOC_CONF": "expandable_segments:True", + "TORCHINDUCTOR_COMPILE_THREADS": "1", + "TORCHINDUCTOR_CACHE_DIR": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Neo_TORCH/inductor", + "TRITON_CACHE_DIR": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Neo_TORCH/triton" + } + } + }, + { + "run": "Neo_n4096_TORCH_p1", + "result": { + "status": "oom", + "mode": "TORCH", + "n_atoms": 4096, + "started": 1792866073.2576334, + "threshold": { + "atol": 1e-05, + "rtol": 0.0001, + "relative_denominator_floor": 1e-08 + }, + "system": "diamond", + "input_sha256": "eda961dd45b711e42fad51b8dfde87d6ca736b34ac729d646c98f67481308f83", + "parameter_count": 1124910, + "exception": "Traceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 156, in execute\n n_batch, result = callable(batch_nframes, start_index)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 243, in execute_with_batch_size\n return (end_index - start_index), callable(\n ^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 730, in _eval_model\n batch_output = model(\n ^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/train/wrapper.py\", line 201, in forward\n model_pred = self._forward_without_loss(task_key, input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/train/wrapper.py\", line 249, in _forward_without_loss\n model_pred = self.model[task_key](**input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 839, in forward\n model_ret = self.forward_common(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 1053, in forward_common\n return self.forward_common_lower(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 1217, in forward_common_lower\n model_predict = compiled_core_compute(\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 2399, in compiled\n return dict(zip(_keys, _fn(*args)))\n ^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/aot_autograd.py\", line 1200, in forward\n return compiled_fn(full_args)\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 580, in runtime_wrapper\n all_outs = call_func_at_runtime_with_args(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/utils.py\", line 138, in call_func_at_runtime_with_args\n out = normalize_as_list(f(args))\n ^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 783, in wrapper\n return compiled_fn(runtime_args)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 1011, in inner_fn\n outs = compiled_fn(args)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/output_code.py\", line 656, in __call__\n return self.current_callable(inputs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/utils.py\", line 3401, in run\n out = model(new_inputs)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Neo_TORCH/inductor/7p/c7pndtfxe3mrtfgixzsmsf3r355pce3qwesz5k6bbekdcu6zibmr.py\", line 21401, in call\n buf200 = empty_strided_cuda((2, s29, 10, 32), (320*s29, 320, 32, 1), torch.float32)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\ntorch.OutOfMemoryError: CUDA out of memory. Tried to allocate 1.54 GiB. GPU 0 has a total capacity of 31.36 GiB of which 441.06 MiB is free. Including non-PyTorch memory, this process has 30.89 GiB memory in use. Of the allocated memory 30.08 GiB is allocated by PyTorch, and 131.51 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://docs.pytorch.org/docs/stable/notes/cuda.html#optimizing-memory-usage-with-pytorch-cuda-alloc-conf)\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 243, in \n out = evaluate(pos)\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 224, in evaluate\n forces = sample.get_forces().copy()\n ^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/atoms.py\", line 2015, in get_forces\n forces = self._calc.get_forces(self)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/abc.py\", line 31, in get_forces\n return self.get_property('forces', atoms)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/calculator.py\", line 517, in get_property\n self.calculate(atoms, [name], system_changes)\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/calculator.py\", line 143, in calculate\n e, f, v = self.dp.eval(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/infer/deep_pot.py\", line 206, in eval\n results = self.deep_eval.eval(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 556, in eval\n out = self._eval_func(self._eval_model, numb_test, natoms)(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 637, in eval_func\n return self.auto_batch_size.execute_all(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 272, in execute_all\n n_batch, result = self.execute(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 169, in execute\n raise OutOfMemoryError(\ndeepmd.utils.errors.OutOfMemoryError: The callable still throws an out-of-memory (OOM) error even when batch size is 1!\n", + "partial_arrays": 0, + "complete_peak": { + "allocated": 32302947328, + "reserved": 32440844288 + }, + "elapsed_s": 73.83922123908997 + }, + "manifest": { + "args": { + "root": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base", + "model": "/home/kmpl/mkl/project/DPA4-Neo-OMat24-v20260627.pt", + "out": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/results/Neo_n4096_TORCH_p1", + "cache": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Neo_TORCH", + "gpu": "0", + "mode": "TORCH", + "nrep": "2", + "seed": "0", + "repeat_cell": "[8, 8, 8]", + "system": "diamond", + "samples": "30", + "warmup": "8" + }, + "model_sha256": "8d8b2b02b9575cb9e0af420cc1e8b1d9ffcbc04752c2203d29f48ccd0cd5f07d", + "torch": "2.11.0+cu128", + "triton": "3.6.0", + "cuda": "12.8", + "baseline_revision": "2ba617bc3db67adee2b8ba00c71e9fa1c2d2bb8c", + "source_hashes": { + "deepmd/pt_expt/kernels/triton/sezm/so2_value_path.py": "9c8b01082cf01fdcd41fe58e0884364e2166d7acbd4c41f75dda625829a64dbf" + }, + "flags": { + "PYTHONHASHSEED": "1", + "DP_COMPILE_INFER": "1", + "DP_COMPILE": "0", + "DP_TRITON_INFER": "0", + "DP_CUDA_INFER": "0", + "DP_TF32_INFER": "0", + "DP_AMP_INFER": "0", + "DP_SO2_EDGE_COHORT_INFER": "0", + "DP_INTRA_OP_PARALLELISM_THREADS": "1", + "DP_INTER_OP_PARALLELISM_THREADS": "1", + "PYTORCH_ALLOC_CONF": "expandable_segments:True", + "TORCHINDUCTOR_COMPILE_THREADS": "1", + "TORCHINDUCTOR_CACHE_DIR": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Neo_TORCH/inductor", + "TRITON_CACHE_DIR": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Neo_TORCH/triton" + } + } + }, + { + "run": "Neo_n4096_TORCH_p2", + "result": { + "status": "oom", + "mode": "TORCH", + "n_atoms": 4096, + "started": 1792866152.7280385, + "threshold": { + "atol": 1e-05, + "rtol": 0.0001, + "relative_denominator_floor": 1e-08 + }, + "system": "diamond", + "input_sha256": "eda961dd45b711e42fad51b8dfde87d6ca736b34ac729d646c98f67481308f83", + "parameter_count": 1124910, + "exception": "Traceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 156, in execute\n n_batch, result = callable(batch_nframes, start_index)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 243, in execute_with_batch_size\n return (end_index - start_index), callable(\n ^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 730, in _eval_model\n batch_output = model(\n ^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/train/wrapper.py\", line 201, in forward\n model_pred = self._forward_without_loss(task_key, input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/train/wrapper.py\", line 249, in _forward_without_loss\n model_pred = self.model[task_key](**input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 839, in forward\n model_ret = self.forward_common(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 1053, in forward_common\n return self.forward_common_lower(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 1217, in forward_common_lower\n model_predict = compiled_core_compute(\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 2399, in compiled\n return dict(zip(_keys, _fn(*args)))\n ^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/aot_autograd.py\", line 1200, in forward\n return compiled_fn(full_args)\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 580, in runtime_wrapper\n all_outs = call_func_at_runtime_with_args(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/utils.py\", line 138, in call_func_at_runtime_with_args\n out = normalize_as_list(f(args))\n ^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 783, in wrapper\n return compiled_fn(runtime_args)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 1011, in inner_fn\n outs = compiled_fn(args)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/output_code.py\", line 656, in __call__\n return self.current_callable(inputs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/utils.py\", line 3401, in run\n out = model(new_inputs)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Neo_TORCH/inductor/7p/c7pndtfxe3mrtfgixzsmsf3r355pce3qwesz5k6bbekdcu6zibmr.py\", line 21401, in call\n buf200 = empty_strided_cuda((2, s29, 10, 32), (320*s29, 320, 32, 1), torch.float32)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\ntorch.OutOfMemoryError: CUDA out of memory. Tried to allocate 1.54 GiB. GPU 0 has a total capacity of 31.36 GiB of which 441.06 MiB is free. Including non-PyTorch memory, this process has 30.89 GiB memory in use. Of the allocated memory 30.08 GiB is allocated by PyTorch, and 131.51 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://docs.pytorch.org/docs/stable/notes/cuda.html#optimizing-memory-usage-with-pytorch-cuda-alloc-conf)\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 243, in \n out = evaluate(pos)\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 224, in evaluate\n forces = sample.get_forces().copy()\n ^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/atoms.py\", line 2015, in get_forces\n forces = self._calc.get_forces(self)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/abc.py\", line 31, in get_forces\n return self.get_property('forces', atoms)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/calculator.py\", line 517, in get_property\n self.calculate(atoms, [name], system_changes)\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/calculator.py\", line 143, in calculate\n e, f, v = self.dp.eval(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/infer/deep_pot.py\", line 206, in eval\n results = self.deep_eval.eval(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 556, in eval\n out = self._eval_func(self._eval_model, numb_test, natoms)(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 637, in eval_func\n return self.auto_batch_size.execute_all(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 272, in execute_all\n n_batch, result = self.execute(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 169, in execute\n raise OutOfMemoryError(\ndeepmd.utils.errors.OutOfMemoryError: The callable still throws an out-of-memory (OOM) error even when batch size is 1!\n", + "partial_arrays": 0, + "complete_peak": { + "allocated": 32302947328, + "reserved": 32440844288 + }, + "elapsed_s": 71.68084454536438 + }, + "manifest": { + "args": { + "root": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base", + "model": "/home/kmpl/mkl/project/DPA4-Neo-OMat24-v20260627.pt", + "out": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/results/Neo_n4096_TORCH_p2", + "cache": 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156, in execute\n n_batch, result = callable(batch_nframes, start_index)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 243, in execute_with_batch_size\n return (end_index - start_index), callable(\n ^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 730, in _eval_model\n batch_output = model(\n ^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/train/wrapper.py\", line 201, in forward\n model_pred = self._forward_without_loss(task_key, input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/train/wrapper.py\", line 249, in _forward_without_loss\n model_pred = self.model[task_key](**input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 839, in forward\n model_ret = self.forward_common(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 1053, in forward_common\n return self.forward_common_lower(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 1217, in forward_common_lower\n model_predict = compiled_core_compute(\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 2399, in compiled\n return dict(zip(_keys, _fn(*args)))\n ^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/aot_autograd.py\", line 1200, in forward\n return compiled_fn(full_args)\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 580, in runtime_wrapper\n all_outs = call_func_at_runtime_with_args(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/utils.py\", line 138, in call_func_at_runtime_with_args\n out = normalize_as_list(f(args))\n ^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 783, in wrapper\n return compiled_fn(runtime_args)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 1011, in inner_fn\n outs = compiled_fn(args)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/output_code.py\", line 656, in __call__\n return self.current_callable(inputs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/utils.py\", line 3401, in run\n out = model(new_inputs)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_BASE/inductor/am/camspdo6kx5gnjlafl7iexnzw6e5snfddhd3trsr3e3n2cztlycx.py\", line 20919, in call\n buf64 = empty_strided_cuda((s46, 6, 64), (384, 64, 1), torch.float32)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\ntorch.OutOfMemoryError: CUDA out of memory. Tried to allocate 950.00 MiB. GPU 0 has a total capacity of 31.36 GiB of which 871.06 MiB is free. Including non-PyTorch memory, this process has 30.47 GiB memory in use. Of the allocated memory 29.74 GiB is allocated by PyTorch, and 57.85 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://docs.pytorch.org/docs/stable/notes/cuda.html#optimizing-memory-usage-with-pytorch-cuda-alloc-conf)\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 243, in \n out = evaluate(pos)\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 224, in evaluate\n forces = sample.get_forces().copy()\n ^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/atoms.py\", line 2015, in get_forces\n forces = self._calc.get_forces(self)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/abc.py\", line 31, in get_forces\n return self.get_property('forces', atoms)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/calculator.py\", line 517, in get_property\n self.calculate(atoms, [name], system_changes)\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/calculator.py\", line 143, in calculate\n e, f, v = self.dp.eval(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/infer/deep_pot.py\", line 206, in eval\n results = self.deep_eval.eval(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 556, in eval\n out = self._eval_func(self._eval_model, numb_test, natoms)(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 637, in eval_func\n return self.auto_batch_size.execute_all(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 272, in execute_all\n n_batch, result = self.execute(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 169, in execute\n raise OutOfMemoryError(\ndeepmd.utils.errors.OutOfMemoryError: The callable still throws an out-of-memory (OOM) error even when batch size is 1!\n", + "partial_arrays": 0, + "complete_peak": { + "allocated": 31929301504, + "reserved": 31989956608 + }, + "elapsed_s": 188.92400097846985 + }, + "manifest": { + "args": { + "root": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base", + "model": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_accuracy_20261019/models/pro_mptrj/pro_download.pt", + "out": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/results/Pro_n4096_BASE_p0", + "cache": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_BASE", + "gpu": "0", + "mode": "BASE", + "nrep": "2", + "seed": "0", + "repeat_cell": "[8, 8, 8]", + "system": "diamond", + "samples": "30", + "warmup": "8" + }, + "model_sha256": "446a6e893101fc52190cfccabb9d2328863d651aca36981fd8d3401c71a61221", + "torch": "2.11.0+cu128", + "triton": "3.6.0", + "cuda": "12.8", + "baseline_revision": "2ba617bc3db67adee2b8ba00c71e9fa1c2d2bb8c", + "source_hashes": { + "deepmd/pt_expt/kernels/triton/sezm/so2_value_path.py": "9c8b01082cf01fdcd41fe58e0884364e2166d7acbd4c41f75dda625829a64dbf" + }, + "flags": { + "PYTHONHASHSEED": "0", + "DP_COMPILE_INFER": "1", + "DP_COMPILE": "0", + "DP_TRITON_INFER": "2", + "DP_CUDA_INFER": "0", + "DP_TF32_INFER": "0", + "DP_AMP_INFER": "0", + "DP_SO2_EDGE_COHORT_INFER": "0", + "DP_INTRA_OP_PARALLELISM_THREADS": "1", + "DP_INTER_OP_PARALLELISM_THREADS": "1", + "PYTORCH_ALLOC_CONF": "expandable_segments:True", + "TORCHINDUCTOR_COMPILE_THREADS": "1", + "TORCHINDUCTOR_CACHE_DIR": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_BASE/inductor", + "TRITON_CACHE_DIR": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_BASE/triton" + } + } + }, + { + "run": "Pro_n4096_BASE_p1", + "result": { + "status": "oom", + "mode": "BASE", + "n_atoms": 4096, + "started": 1792873213.2061002, + "threshold": { + "atol": 1e-05, + "rtol": 0.0001, + "relative_denominator_floor": 1e-08 + }, + "system": "diamond", + "input_sha256": "eda961dd45b711e42fad51b8dfde87d6ca736b34ac729d646c98f67481308f83", + "legacy_compatibility": "Pro-only in-memory fitting alias ener -> dpa4_ener; strict CPU state check passed; original checkpoint unchanged", + "parameter_count": 22678990, + "exception": "Traceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 156, in execute\n n_batch, result = callable(batch_nframes, start_index)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 243, in execute_with_batch_size\n return (end_index - start_index), callable(\n ^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 730, in _eval_model\n batch_output = model(\n ^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/train/wrapper.py\", line 201, in forward\n model_pred = self._forward_without_loss(task_key, input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/train/wrapper.py\", line 249, in _forward_without_loss\n model_pred = self.model[task_key](**input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 839, in forward\n model_ret = self.forward_common(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 1053, in forward_common\n return self.forward_common_lower(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 1217, in forward_common_lower\n model_predict = compiled_core_compute(\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 2399, in compiled\n return dict(zip(_keys, _fn(*args)))\n ^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/aot_autograd.py\", line 1200, in forward\n return compiled_fn(full_args)\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 580, in runtime_wrapper\n all_outs = call_func_at_runtime_with_args(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/utils.py\", line 138, in call_func_at_runtime_with_args\n out = normalize_as_list(f(args))\n ^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 783, in wrapper\n return compiled_fn(runtime_args)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 1011, in inner_fn\n outs = compiled_fn(args)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/output_code.py\", line 656, in __call__\n return self.current_callable(inputs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/utils.py\", line 3401, in run\n out = model(new_inputs)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_BASE/inductor/am/camspdo6kx5gnjlafl7iexnzw6e5snfddhd3trsr3e3n2cztlycx.py\", line 20919, in call\n buf64 = empty_strided_cuda((s46, 6, 64), (384, 64, 1), torch.float32)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\ntorch.OutOfMemoryError: CUDA out of memory. Tried to allocate 950.00 MiB. GPU 0 has a total capacity of 31.36 GiB of which 871.06 MiB is free. Including non-PyTorch memory, this process has 30.47 GiB memory in use. Of the allocated memory 29.74 GiB is allocated by PyTorch, and 57.85 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://docs.pytorch.org/docs/stable/notes/cuda.html#optimizing-memory-usage-with-pytorch-cuda-alloc-conf)\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 243, in \n out = evaluate(pos)\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 224, in evaluate\n forces = sample.get_forces().copy()\n ^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/atoms.py\", line 2015, in get_forces\n forces = self._calc.get_forces(self)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/abc.py\", line 31, in get_forces\n return self.get_property('forces', atoms)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/calculator.py\", line 517, in get_property\n self.calculate(atoms, [name], system_changes)\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/calculator.py\", line 143, in calculate\n e, f, v = self.dp.eval(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/infer/deep_pot.py\", line 206, in eval\n results = self.deep_eval.eval(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 556, in eval\n out = self._eval_func(self._eval_model, numb_test, natoms)(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 637, in eval_func\n return self.auto_batch_size.execute_all(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 272, in execute_all\n n_batch, result = self.execute(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 169, in execute\n raise OutOfMemoryError(\ndeepmd.utils.errors.OutOfMemoryError: The callable still throws an out-of-memory (OOM) error even when batch size is 1!\n", + "partial_arrays": 0, + "complete_peak": { + "allocated": 31929301504, + "reserved": 31989956608 + }, + "elapsed_s": 186.5676712989807 + }, + "manifest": { + "args": { + "root": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base", + "model": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_accuracy_20261019/models/pro_mptrj/pro_download.pt", + "out": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/results/Pro_n4096_BASE_p1", + "cache": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_BASE", + "gpu": "0", + "mode": "BASE", + "nrep": "2", + "seed": "0", + "repeat_cell": "[8, 8, 8]", + "system": "diamond", + "samples": "30", + "warmup": "8" + }, + "model_sha256": "446a6e893101fc52190cfccabb9d2328863d651aca36981fd8d3401c71a61221", + "torch": "2.11.0+cu128", + "triton": "3.6.0", + "cuda": "12.8", + "baseline_revision": "2ba617bc3db67adee2b8ba00c71e9fa1c2d2bb8c", + "source_hashes": { + "deepmd/pt_expt/kernels/triton/sezm/so2_value_path.py": "9c8b01082cf01fdcd41fe58e0884364e2166d7acbd4c41f75dda625829a64dbf" + }, + "flags": { + "PYTHONHASHSEED": "1", + "DP_COMPILE_INFER": "1", + "DP_COMPILE": "0", + "DP_TRITON_INFER": "2", + "DP_CUDA_INFER": "0", + "DP_TF32_INFER": "0", + "DP_AMP_INFER": "0", + "DP_SO2_EDGE_COHORT_INFER": "0", + "DP_INTRA_OP_PARALLELISM_THREADS": "1", + "DP_INTER_OP_PARALLELISM_THREADS": "1", + "PYTORCH_ALLOC_CONF": "expandable_segments:True", + "TORCHINDUCTOR_COMPILE_THREADS": "1", + "TORCHINDUCTOR_CACHE_DIR": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_BASE/inductor", + "TRITON_CACHE_DIR": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_BASE/triton" + } + } + }, + { + "run": "Pro_n4096_BASE_p2", + "result": { + "status": "oom", + "mode": "BASE", + "n_atoms": 4096, + "started": 1792873698.0892181, + "threshold": { + "atol": 1e-05, + "rtol": 0.0001, + "relative_denominator_floor": 1e-08 + }, + "system": "diamond", + "input_sha256": "eda961dd45b711e42fad51b8dfde87d6ca736b34ac729d646c98f67481308f83", + "legacy_compatibility": "Pro-only in-memory fitting alias ener -> dpa4_ener; strict CPU state check passed; original checkpoint unchanged", + "parameter_count": 22678990, + "exception": "Traceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 156, in execute\n n_batch, result = callable(batch_nframes, start_index)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 243, in execute_with_batch_size\n return (end_index - start_index), callable(\n ^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 730, in _eval_model\n batch_output = model(\n ^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/train/wrapper.py\", line 201, in forward\n model_pred = self._forward_without_loss(task_key, input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/train/wrapper.py\", line 249, in _forward_without_loss\n model_pred = self.model[task_key](**input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 839, in forward\n model_ret = self.forward_common(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 1053, in forward_common\n return self.forward_common_lower(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 1217, in forward_common_lower\n model_predict = compiled_core_compute(\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 2399, in compiled\n return dict(zip(_keys, _fn(*args)))\n ^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/aot_autograd.py\", line 1200, in forward\n return compiled_fn(full_args)\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 580, in runtime_wrapper\n all_outs = call_func_at_runtime_with_args(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/utils.py\", line 138, in call_func_at_runtime_with_args\n out = normalize_as_list(f(args))\n ^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 783, in wrapper\n return compiled_fn(runtime_args)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 1011, in inner_fn\n outs = compiled_fn(args)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/output_code.py\", line 656, in __call__\n return self.current_callable(inputs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/utils.py\", line 3401, in run\n out = model(new_inputs)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_BASE/inductor/am/camspdo6kx5gnjlafl7iexnzw6e5snfddhd3trsr3e3n2cztlycx.py\", line 20919, in call\n buf64 = empty_strided_cuda((s46, 6, 64), (384, 64, 1), torch.float32)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\ntorch.OutOfMemoryError: CUDA out of memory. Tried to allocate 950.00 MiB. GPU 0 has a total capacity of 31.36 GiB of which 871.06 MiB is free. Including non-PyTorch memory, this process has 30.47 GiB memory in use. Of the allocated memory 29.74 GiB is allocated by PyTorch, and 57.85 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://docs.pytorch.org/docs/stable/notes/cuda.html#optimizing-memory-usage-with-pytorch-cuda-alloc-conf)\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 243, in \n out = evaluate(pos)\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 224, in evaluate\n forces = sample.get_forces().copy()\n ^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/atoms.py\", line 2015, in get_forces\n forces = self._calc.get_forces(self)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/abc.py\", line 31, in get_forces\n return self.get_property('forces', atoms)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/calculator.py\", line 517, in get_property\n self.calculate(atoms, [name], system_changes)\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/calculator.py\", line 143, in calculate\n e, f, v = self.dp.eval(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/infer/deep_pot.py\", line 206, in eval\n results = self.deep_eval.eval(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 556, in eval\n out = self._eval_func(self._eval_model, numb_test, natoms)(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 637, in eval_func\n return self.auto_batch_size.execute_all(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 272, in execute_all\n n_batch, result = self.execute(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 169, in execute\n raise OutOfMemoryError(\ndeepmd.utils.errors.OutOfMemoryError: The callable still throws an out-of-memory (OOM) error even when batch size is 1!\n", + "partial_arrays": 0, + "complete_peak": { + "allocated": 31929301504, + "reserved": 31989956608 + }, + "elapsed_s": 188.6032009124756 + }, + "manifest": { + "args": { + "root": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base", + "model": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_accuracy_20261019/models/pro_mptrj/pro_download.pt", + "out": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/results/Pro_n4096_BASE_p2", + "cache": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_BASE", + "gpu": "0", + "mode": "BASE", + "nrep": "2", + "seed": "0", + "repeat_cell": "[8, 8, 8]", + "system": "diamond", + "samples": "30", + "warmup": "8" + }, + "model_sha256": "446a6e893101fc52190cfccabb9d2328863d651aca36981fd8d3401c71a61221", + "torch": "2.11.0+cu128", + "triton": "3.6.0", + "cuda": "12.8", + "baseline_revision": "2ba617bc3db67adee2b8ba00c71e9fa1c2d2bb8c", + "source_hashes": { + "deepmd/pt_expt/kernels/triton/sezm/so2_value_path.py": "9c8b01082cf01fdcd41fe58e0884364e2166d7acbd4c41f75dda625829a64dbf" + }, + "flags": { + "PYTHONHASHSEED": "0", + "DP_COMPILE_INFER": "1", + "DP_COMPILE": "0", + "DP_TRITON_INFER": "2", + "DP_CUDA_INFER": "0", + "DP_TF32_INFER": "0", + "DP_AMP_INFER": "0", + "DP_SO2_EDGE_COHORT_INFER": "0", + "DP_INTRA_OP_PARALLELISM_THREADS": "1", + "DP_INTER_OP_PARALLELISM_THREADS": "1", + "PYTORCH_ALLOC_CONF": "expandable_segments:True", + "TORCHINDUCTOR_COMPILE_THREADS": "1", + "TORCHINDUCTOR_CACHE_DIR": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_BASE/inductor", + "TRITON_CACHE_DIR": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_BASE/triton" + } + } + }, + { + "run": "Pro_n4096_ECS_p0", + "result": { + "status": "oom", + "mode": "ECS", + "n_atoms": 4096, + "started": 1792872829.7394366, + "threshold": { + "atol": 1e-05, + "rtol": 0.0001, + "relative_denominator_floor": 1e-08 + }, + "system": "diamond", + "input_sha256": "eda961dd45b711e42fad51b8dfde87d6ca736b34ac729d646c98f67481308f83", + "legacy_compatibility": "Pro-only in-memory fitting alias ener -> dpa4_ener; strict CPU state check passed; original checkpoint unchanged", + "parameter_count": 22678990, + "exception": "Traceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/utils/batch_size.py\", line 156, in execute\n n_batch, result = callable(batch_nframes, start_index)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/utils/batch_size.py\", line 243, in execute_with_batch_size\n return (end_index - start_index), callable(\n ^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/infer/deep_eval.py\", line 730, in _eval_model\n batch_output = model(\n ^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/train/wrapper.py\", line 201, in forward\n model_pred = self._forward_without_loss(task_key, input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/train/wrapper.py\", line 249, in _forward_without_loss\n model_pred = self.model[task_key](**input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/model/model/sezm_model.py\", line 839, in forward\n model_ret = self.forward_common(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/model/model/sezm_model.py\", line 1053, in forward_common\n return self.forward_common_lower(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/model/model/sezm_model.py\", line 1217, in forward_common_lower\n model_predict = compiled_core_compute(\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/model/model/sezm_model.py\", line 2399, in compiled\n return dict(zip(_keys, _fn(*args)))\n ^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/aot_autograd.py\", line 1200, in forward\n return compiled_fn(full_args)\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 580, in runtime_wrapper\n all_outs = call_func_at_runtime_with_args(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/utils.py\", line 138, in call_func_at_runtime_with_args\n out = normalize_as_list(f(args))\n ^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 783, in wrapper\n return compiled_fn(runtime_args)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 1011, in inner_fn\n outs = compiled_fn(args)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/output_code.py\", line 656, in __call__\n return self.current_callable(inputs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/utils.py\", line 3401, in run\n out = model(new_inputs)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_ECS/inductor/wu/cwu5rkg3ws24k2ch22id4pkjawrgdklubhesfbxm3qaciqae7gpf.py\", line 20939, in call\n buf64 = empty_strided_cuda((s46, 6, 64), (384, 64, 1), torch.float32)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\ntorch.OutOfMemoryError: CUDA out of memory. Tried to allocate 950.00 MiB. GPU 0 has a total capacity of 31.36 GiB of which 871.06 MiB is free. Including non-PyTorch memory, this process has 30.47 GiB memory in use. Of the allocated memory 29.74 GiB is allocated by PyTorch, and 57.85 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://docs.pytorch.org/docs/stable/notes/cuda.html#optimizing-memory-usage-with-pytorch-cuda-alloc-conf)\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 243, in \n out = evaluate(pos)\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 224, in evaluate\n forces = sample.get_forces().copy()\n ^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/atoms.py\", line 2015, in get_forces\n forces = self._calc.get_forces(self)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/abc.py\", line 31, in get_forces\n return self.get_property('forces', atoms)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/calculator.py\", line 517, in get_property\n self.calculate(atoms, [name], system_changes)\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/calculator.py\", line 143, in calculate\n e, f, v = self.dp.eval(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/infer/deep_pot.py\", line 206, in eval\n results = self.deep_eval.eval(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/infer/deep_eval.py\", line 556, in eval\n out = self._eval_func(self._eval_model, numb_test, natoms)(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/infer/deep_eval.py\", line 637, in eval_func\n return self.auto_batch_size.execute_all(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/utils/batch_size.py\", line 272, in execute_all\n n_batch, result = self.execute(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/utils/batch_size.py\", line 169, in execute\n raise OutOfMemoryError(\ndeepmd.utils.errors.OutOfMemoryError: The callable still throws an out-of-memory (OOM) error even when batch size is 1!\n", + "partial_arrays": 0, + "complete_peak": { + "allocated": 31929301504, + "reserved": 31989956608 + }, + "elapsed_s": 186.17430138587952 + }, + "manifest": { + "args": { + "root": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate", + "model": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_accuracy_20261019/models/pro_mptrj/pro_download.pt", + "out": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/results/Pro_n4096_ECS_p0", + "cache": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_ECS", + "gpu": "0", + "mode": "ECS", + "nrep": "2", + "seed": "0", + "repeat_cell": "[8, 8, 8]", + "system": "diamond", + "samples": "30", + "warmup": "8" + }, + "model_sha256": "446a6e893101fc52190cfccabb9d2328863d651aca36981fd8d3401c71a61221", + "torch": "2.11.0+cu128", + "triton": "3.6.0", + "cuda": "12.8", + "baseline_revision": "2ba617bc3db67adee2b8ba00c71e9fa1c2d2bb8c", + "source_hashes": { + "deepmd/pt_expt/kernels/triton/sezm/so2_value_path.py": "0fb6ae06e62af5dfafe8b7c9cfdce975714a9ffb577dc4f3581ed13706735462", + "deepmd/pt_expt/kernels/triton/sezm/so2_edge_cohort.py": "08c67a81883ff70f19ce9dad8809037585e0c5b0949ecceab747bb4179c44474" + }, + "flags": { + "PYTHONHASHSEED": "0", + "DP_COMPILE_INFER": "1", + "DP_COMPILE": "0", + "DP_TRITON_INFER": "2", + "DP_CUDA_INFER": "0", + "DP_TF32_INFER": "0", + "DP_AMP_INFER": "0", + "DP_SO2_EDGE_COHORT_INFER": "1", + "DP_INTRA_OP_PARALLELISM_THREADS": "1", + "DP_INTER_OP_PARALLELISM_THREADS": "1", + "PYTORCH_ALLOC_CONF": "expandable_segments:True", + "TORCHINDUCTOR_COMPILE_THREADS": "1", + "TORCHINDUCTOR_CACHE_DIR": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_ECS/inductor", + "TRITON_CACHE_DIR": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_ECS/triton" + } + } + }, + { + "run": "Pro_n4096_ECS_p1", + "result": { + "status": "oom", + "mode": "ECS", + "n_atoms": 4096, + "started": 1792873021.7048163, + "threshold": { + "atol": 1e-05, + "rtol": 0.0001, + "relative_denominator_floor": 1e-08 + }, + "system": "diamond", + "input_sha256": "eda961dd45b711e42fad51b8dfde87d6ca736b34ac729d646c98f67481308f83", + "legacy_compatibility": "Pro-only in-memory fitting alias ener -> dpa4_ener; strict CPU state check passed; original checkpoint unchanged", + "parameter_count": 22678990, + "exception": "Traceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/utils/batch_size.py\", line 156, in execute\n n_batch, result = callable(batch_nframes, start_index)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/utils/batch_size.py\", line 243, in execute_with_batch_size\n return (end_index - start_index), callable(\n ^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/infer/deep_eval.py\", line 730, in _eval_model\n batch_output = model(\n ^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/train/wrapper.py\", line 201, in forward\n model_pred = self._forward_without_loss(task_key, input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/train/wrapper.py\", line 249, in _forward_without_loss\n model_pred = self.model[task_key](**input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/model/model/sezm_model.py\", line 839, in forward\n model_ret = self.forward_common(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/model/model/sezm_model.py\", line 1053, in forward_common\n return self.forward_common_lower(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/model/model/sezm_model.py\", line 1217, in forward_common_lower\n model_predict = compiled_core_compute(\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/model/model/sezm_model.py\", line 2399, in compiled\n return dict(zip(_keys, _fn(*args)))\n ^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/aot_autograd.py\", line 1200, in forward\n return compiled_fn(full_args)\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 580, in runtime_wrapper\n all_outs = call_func_at_runtime_with_args(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/utils.py\", line 138, in call_func_at_runtime_with_args\n out = normalize_as_list(f(args))\n ^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 783, in wrapper\n return compiled_fn(runtime_args)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 1011, in inner_fn\n outs = compiled_fn(args)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/output_code.py\", line 656, in __call__\n return self.current_callable(inputs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/utils.py\", line 3401, in run\n out = model(new_inputs)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_ECS/inductor/wu/cwu5rkg3ws24k2ch22id4pkjawrgdklubhesfbxm3qaciqae7gpf.py\", line 20939, in call\n buf64 = empty_strided_cuda((s46, 6, 64), (384, 64, 1), torch.float32)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\ntorch.OutOfMemoryError: CUDA out of memory. Tried to allocate 950.00 MiB. GPU 0 has a total capacity of 31.36 GiB of which 871.06 MiB is free. Including non-PyTorch memory, this process has 30.47 GiB memory in use. Of the allocated memory 29.74 GiB is allocated by PyTorch, and 57.85 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://docs.pytorch.org/docs/stable/notes/cuda.html#optimizing-memory-usage-with-pytorch-cuda-alloc-conf)\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 243, in \n out = evaluate(pos)\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 224, in evaluate\n forces = sample.get_forces().copy()\n ^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/atoms.py\", line 2015, in get_forces\n forces = self._calc.get_forces(self)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/abc.py\", line 31, in get_forces\n return self.get_property('forces', atoms)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/calculator.py\", line 517, in get_property\n self.calculate(atoms, [name], system_changes)\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/calculator.py\", line 143, in calculate\n e, f, v = self.dp.eval(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/infer/deep_pot.py\", line 206, in eval\n results = self.deep_eval.eval(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/infer/deep_eval.py\", line 556, in eval\n out = self._eval_func(self._eval_model, numb_test, natoms)(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/infer/deep_eval.py\", line 637, in eval_func\n return self.auto_batch_size.execute_all(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/utils/batch_size.py\", line 272, in execute_all\n n_batch, result = self.execute(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/utils/batch_size.py\", line 169, in execute\n raise OutOfMemoryError(\ndeepmd.utils.errors.OutOfMemoryError: The callable still throws an out-of-memory (OOM) error even when batch size is 1!\n", + "partial_arrays": 0, + "complete_peak": { + "allocated": 31929301504, + "reserved": 31989956608 + }, + "elapsed_s": 185.50847601890564 + }, + "manifest": { + "args": { + "root": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate", + "model": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_accuracy_20261019/models/pro_mptrj/pro_download.pt", + "out": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/results/Pro_n4096_ECS_p1", + "cache": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_ECS", + "gpu": "0", + "mode": "ECS", + "nrep": "2", + "seed": "0", + "repeat_cell": "[8, 8, 8]", + "system": "diamond", + "samples": "30", + "warmup": "8" + }, + "model_sha256": "446a6e893101fc52190cfccabb9d2328863d651aca36981fd8d3401c71a61221", + "torch": "2.11.0+cu128", + "triton": "3.6.0", + "cuda": "12.8", + "baseline_revision": "2ba617bc3db67adee2b8ba00c71e9fa1c2d2bb8c", + "source_hashes": { + "deepmd/pt_expt/kernels/triton/sezm/so2_value_path.py": "0fb6ae06e62af5dfafe8b7c9cfdce975714a9ffb577dc4f3581ed13706735462", + "deepmd/pt_expt/kernels/triton/sezm/so2_edge_cohort.py": "08c67a81883ff70f19ce9dad8809037585e0c5b0949ecceab747bb4179c44474" + }, + "flags": { + "PYTHONHASHSEED": "1", + "DP_COMPILE_INFER": "1", + "DP_COMPILE": "0", + "DP_TRITON_INFER": "2", + "DP_CUDA_INFER": "0", + "DP_TF32_INFER": "0", + "DP_AMP_INFER": "0", + "DP_SO2_EDGE_COHORT_INFER": "1", + "DP_INTRA_OP_PARALLELISM_THREADS": "1", + "DP_INTER_OP_PARALLELISM_THREADS": "1", + "PYTORCH_ALLOC_CONF": "expandable_segments:True", + "TORCHINDUCTOR_COMPILE_THREADS": "1", + "TORCHINDUCTOR_CACHE_DIR": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_ECS/inductor", + "TRITON_CACHE_DIR": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_ECS/triton" + } + } + }, + { + "run": "Pro_n4096_ECS_p2", + "result": { + "status": "oom", + "mode": "ECS", + "n_atoms": 4096, + "started": 1792873892.4796147, + "threshold": { + "atol": 1e-05, + "rtol": 0.0001, + "relative_denominator_floor": 1e-08 + }, + "system": "diamond", + "input_sha256": "eda961dd45b711e42fad51b8dfde87d6ca736b34ac729d646c98f67481308f83", + "legacy_compatibility": "Pro-only in-memory fitting alias ener -> dpa4_ener; strict CPU state check passed; original checkpoint unchanged", + "parameter_count": 22678990, + "exception": "Traceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/utils/batch_size.py\", line 156, in execute\n n_batch, result = callable(batch_nframes, start_index)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/utils/batch_size.py\", line 243, in execute_with_batch_size\n return (end_index - start_index), callable(\n ^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/infer/deep_eval.py\", line 730, in _eval_model\n batch_output = model(\n ^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/train/wrapper.py\", line 201, in forward\n model_pred = self._forward_without_loss(task_key, input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/train/wrapper.py\", line 249, in _forward_without_loss\n model_pred = self.model[task_key](**input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/model/model/sezm_model.py\", line 839, in forward\n model_ret = self.forward_common(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/model/model/sezm_model.py\", line 1053, in forward_common\n return self.forward_common_lower(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/model/model/sezm_model.py\", line 1217, in forward_common_lower\n model_predict = compiled_core_compute(\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/model/model/sezm_model.py\", line 2399, in compiled\n return dict(zip(_keys, _fn(*args)))\n ^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/aot_autograd.py\", line 1200, in forward\n return compiled_fn(full_args)\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 580, in runtime_wrapper\n all_outs = call_func_at_runtime_with_args(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/utils.py\", line 138, in call_func_at_runtime_with_args\n out = normalize_as_list(f(args))\n ^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 783, in wrapper\n return compiled_fn(runtime_args)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 1011, in inner_fn\n outs = compiled_fn(args)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/output_code.py\", line 656, in __call__\n return self.current_callable(inputs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/utils.py\", line 3401, in run\n out = model(new_inputs)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_ECS/inductor/wu/cwu5rkg3ws24k2ch22id4pkjawrgdklubhesfbxm3qaciqae7gpf.py\", line 20939, in call\n buf64 = empty_strided_cuda((s46, 6, 64), (384, 64, 1), torch.float32)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\ntorch.OutOfMemoryError: CUDA out of memory. Tried to allocate 950.00 MiB. GPU 0 has a total capacity of 31.36 GiB of which 871.06 MiB is free. Including non-PyTorch memory, this process has 30.47 GiB memory in use. Of the allocated memory 29.74 GiB is allocated by PyTorch, and 57.85 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://docs.pytorch.org/docs/stable/notes/cuda.html#optimizing-memory-usage-with-pytorch-cuda-alloc-conf)\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 243, in \n out = evaluate(pos)\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 224, in evaluate\n forces = sample.get_forces().copy()\n ^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/atoms.py\", line 2015, in get_forces\n forces = self._calc.get_forces(self)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/abc.py\", line 31, in get_forces\n return self.get_property('forces', atoms)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/calculator.py\", line 517, in get_property\n self.calculate(atoms, [name], system_changes)\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/calculator.py\", line 143, in calculate\n e, f, v = self.dp.eval(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/infer/deep_pot.py\", line 206, in eval\n results = self.deep_eval.eval(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/infer/deep_eval.py\", line 556, in eval\n out = self._eval_func(self._eval_model, numb_test, natoms)(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/infer/deep_eval.py\", line 637, in eval_func\n return self.auto_batch_size.execute_all(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/utils/batch_size.py\", line 272, in execute_all\n n_batch, result = self.execute(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/utils/batch_size.py\", line 169, in execute\n raise OutOfMemoryError(\ndeepmd.utils.errors.OutOfMemoryError: The callable still throws an out-of-memory (OOM) error even when batch size is 1!\n", + "partial_arrays": 0, + "complete_peak": { + "allocated": 31929301504, + "reserved": 31989956608 + }, + "elapsed_s": 187.12241053581238 + }, + "manifest": { + "args": { + "root": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate", + "model": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_accuracy_20261019/models/pro_mptrj/pro_download.pt", + "out": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/results/Pro_n4096_ECS_p2", + "cache": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_ECS", + "gpu": "0", + "mode": "ECS", + "nrep": "2", + "seed": "0", + "repeat_cell": "[8, 8, 8]", + "system": "diamond", + "samples": "30", + "warmup": "8" + }, + "model_sha256": "446a6e893101fc52190cfccabb9d2328863d651aca36981fd8d3401c71a61221", + "torch": "2.11.0+cu128", + "triton": "3.6.0", + "cuda": "12.8", + "baseline_revision": "2ba617bc3db67adee2b8ba00c71e9fa1c2d2bb8c", + "source_hashes": { + "deepmd/pt_expt/kernels/triton/sezm/so2_value_path.py": "0fb6ae06e62af5dfafe8b7c9cfdce975714a9ffb577dc4f3581ed13706735462", + "deepmd/pt_expt/kernels/triton/sezm/so2_edge_cohort.py": "08c67a81883ff70f19ce9dad8809037585e0c5b0949ecceab747bb4179c44474" + }, + "flags": { + "PYTHONHASHSEED": "0", + "DP_COMPILE_INFER": "1", + "DP_COMPILE": "0", + "DP_TRITON_INFER": "2", + "DP_CUDA_INFER": "0", + "DP_TF32_INFER": "0", + "DP_AMP_INFER": "0", + "DP_SO2_EDGE_COHORT_INFER": "1", + "DP_INTRA_OP_PARALLELISM_THREADS": "1", + "DP_INTER_OP_PARALLELISM_THREADS": "1", + "PYTORCH_ALLOC_CONF": "expandable_segments:True", + "TORCHINDUCTOR_COMPILE_THREADS": "1", + "TORCHINDUCTOR_CACHE_DIR": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_ECS/inductor", + "TRITON_CACHE_DIR": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_ECS/triton" + } + } + }, + { + "run": "Pro_n4096_TORCH_p0", + "result": { + "status": "oom", + "mode": "TORCH", + "n_atoms": 4096, + "started": 1792872488.735287, + "threshold": { + "atol": 1e-05, + "rtol": 0.0001, + "relative_denominator_floor": 1e-08 + }, + "system": "diamond", + "input_sha256": "eda961dd45b711e42fad51b8dfde87d6ca736b34ac729d646c98f67481308f83", + "legacy_compatibility": "Pro-only in-memory fitting alias ener -> dpa4_ener; strict CPU state check passed; original checkpoint unchanged", + "parameter_count": 22678990, + "exception": "Traceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 156, in execute\n n_batch, result = callable(batch_nframes, start_index)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 243, in execute_with_batch_size\n return (end_index - start_index), callable(\n ^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 730, in _eval_model\n batch_output = model(\n ^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/train/wrapper.py\", line 201, in forward\n model_pred = self._forward_without_loss(task_key, input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/train/wrapper.py\", line 249, in _forward_without_loss\n model_pred = self.model[task_key](**input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 839, in forward\n model_ret = self.forward_common(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 1053, in forward_common\n return self.forward_common_lower(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 1217, in forward_common_lower\n model_predict = compiled_core_compute(\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 2399, in compiled\n return dict(zip(_keys, _fn(*args)))\n ^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/aot_autograd.py\", line 1200, in forward\n return compiled_fn(full_args)\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 580, in runtime_wrapper\n all_outs = call_func_at_runtime_with_args(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/utils.py\", line 138, in call_func_at_runtime_with_args\n out = normalize_as_list(f(args))\n ^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 783, in wrapper\n return compiled_fn(runtime_args)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 1011, in inner_fn\n outs = compiled_fn(args)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/output_code.py\", line 656, in __call__\n return self.current_callable(inputs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/utils.py\", line 3401, in run\n out = model(new_inputs)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_TORCH/inductor/db/cdbxhl4ag2mms4oooq2iu5tx2nwa73j2ckthin7jon7hvaejblzz.py\", line 21624, in call\n buf83 = empty_strided_cuda((s29, 6, 64), (384, 64, 1), torch.float32)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\ntorch.OutOfMemoryError: CUDA out of memory. Tried to allocate 950.00 MiB. GPU 0 has a total capacity of 31.36 GiB of which 631.06 MiB is free. Including non-PyTorch memory, this process has 30.70 GiB memory in use. Of the allocated memory 29.98 GiB is allocated by PyTorch, and 49.85 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://docs.pytorch.org/docs/stable/notes/cuda.html#optimizing-memory-usage-with-pytorch-cuda-alloc-conf)\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 243, in \n out = evaluate(pos)\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 224, in evaluate\n forces = sample.get_forces().copy()\n ^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/atoms.py\", line 2015, in get_forces\n forces = self._calc.get_forces(self)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/abc.py\", line 31, in get_forces\n return self.get_property('forces', atoms)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/calculator.py\", line 517, in get_property\n self.calculate(atoms, [name], system_changes)\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/calculator.py\", line 143, in calculate\n e, f, v = self.dp.eval(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/infer/deep_pot.py\", line 206, in eval\n results = self.deep_eval.eval(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 556, in eval\n out = self._eval_func(self._eval_model, numb_test, natoms)(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 637, in eval_func\n return self.auto_batch_size.execute_all(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 272, in execute_all\n n_batch, result = self.execute(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 169, in execute\n raise OutOfMemoryError(\ndeepmd.utils.errors.OutOfMemoryError: The callable still throws an out-of-memory (OOM) error even when batch size is 1!\n", + "partial_arrays": 0, + "complete_peak": { + "allocated": 32189347328, + "reserved": 32598130688 + }, + "elapsed_s": 137.5577781200409 + }, + "manifest": { + "args": { + "root": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base", + "model": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_accuracy_20261019/models/pro_mptrj/pro_download.pt", + "out": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/results/Pro_n4096_TORCH_p0", + "cache": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_TORCH", + "gpu": "0", + "mode": "TORCH", + "nrep": "2", + "seed": "0", + "repeat_cell": "[8, 8, 8]", + "system": "diamond", + "samples": "30", + "warmup": "8" + }, + "model_sha256": "446a6e893101fc52190cfccabb9d2328863d651aca36981fd8d3401c71a61221", + "torch": "2.11.0+cu128", + "triton": "3.6.0", + "cuda": "12.8", + "baseline_revision": "2ba617bc3db67adee2b8ba00c71e9fa1c2d2bb8c", + "source_hashes": { + "deepmd/pt_expt/kernels/triton/sezm/so2_value_path.py": "9c8b01082cf01fdcd41fe58e0884364e2166d7acbd4c41f75dda625829a64dbf" + }, + "flags": { + "PYTHONHASHSEED": "0", + "DP_COMPILE_INFER": "1", + "DP_COMPILE": "0", + "DP_TRITON_INFER": "0", + "DP_CUDA_INFER": "0", + "DP_TF32_INFER": "0", + "DP_AMP_INFER": "0", + "DP_SO2_EDGE_COHORT_INFER": "0", + "DP_INTRA_OP_PARALLELISM_THREADS": "1", + "DP_INTER_OP_PARALLELISM_THREADS": "1", + "PYTORCH_ALLOC_CONF": "expandable_segments:True", + "TORCHINDUCTOR_COMPILE_THREADS": "1", + "TORCHINDUCTOR_CACHE_DIR": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_TORCH/inductor", + "TRITON_CACHE_DIR": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_TORCH/triton" + } + } + }, + { + "run": "Pro_n4096_TORCH_p1", + "result": { + "status": "oom", + "mode": "TORCH", + "n_atoms": 4096, + "started": 1792873405.5164213, + "threshold": { + "atol": 1e-05, + "rtol": 0.0001, + "relative_denominator_floor": 1e-08 + }, + "system": "diamond", + "input_sha256": "eda961dd45b711e42fad51b8dfde87d6ca736b34ac729d646c98f67481308f83", + "legacy_compatibility": "Pro-only in-memory fitting alias ener -> dpa4_ener; strict CPU state check passed; original checkpoint unchanged", + "parameter_count": 22678990, + "exception": "Traceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 156, in execute\n n_batch, result = callable(batch_nframes, start_index)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 243, in execute_with_batch_size\n return (end_index - start_index), callable(\n ^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 730, in _eval_model\n batch_output = model(\n ^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/train/wrapper.py\", line 201, in forward\n model_pred = self._forward_without_loss(task_key, input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/train/wrapper.py\", line 249, in _forward_without_loss\n model_pred = self.model[task_key](**input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 839, in forward\n model_ret = self.forward_common(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 1053, in forward_common\n return self.forward_common_lower(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 1217, in forward_common_lower\n model_predict = compiled_core_compute(\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 2399, in compiled\n return dict(zip(_keys, _fn(*args)))\n ^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/aot_autograd.py\", line 1200, in forward\n return compiled_fn(full_args)\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 580, in runtime_wrapper\n all_outs = call_func_at_runtime_with_args(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/utils.py\", line 138, in call_func_at_runtime_with_args\n out = normalize_as_list(f(args))\n ^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 783, in wrapper\n return compiled_fn(runtime_args)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 1011, in inner_fn\n outs = compiled_fn(args)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/output_code.py\", line 656, in __call__\n return self.current_callable(inputs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/utils.py\", line 3401, in run\n out = model(new_inputs)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_TORCH/inductor/db/cdbxhl4ag2mms4oooq2iu5tx2nwa73j2ckthin7jon7hvaejblzz.py\", line 21624, in call\n buf83 = empty_strided_cuda((s29, 6, 64), (384, 64, 1), torch.float32)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\ntorch.OutOfMemoryError: CUDA out of memory. Tried to allocate 950.00 MiB. GPU 0 has a total capacity of 31.36 GiB of which 631.06 MiB is free. Including non-PyTorch memory, this process has 30.70 GiB memory in use. Of the allocated memory 29.98 GiB is allocated by PyTorch, and 49.85 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://docs.pytorch.org/docs/stable/notes/cuda.html#optimizing-memory-usage-with-pytorch-cuda-alloc-conf)\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 243, in \n out = evaluate(pos)\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 224, in evaluate\n forces = sample.get_forces().copy()\n ^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/atoms.py\", line 2015, in get_forces\n forces = self._calc.get_forces(self)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/abc.py\", line 31, in get_forces\n return self.get_property('forces', atoms)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/calculator.py\", line 517, in get_property\n self.calculate(atoms, [name], system_changes)\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/calculator.py\", line 143, in calculate\n e, f, v = self.dp.eval(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/infer/deep_pot.py\", line 206, in eval\n results = self.deep_eval.eval(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 556, in eval\n out = self._eval_func(self._eval_model, numb_test, natoms)(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 637, in eval_func\n return self.auto_batch_size.execute_all(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 272, in execute_all\n n_batch, result = self.execute(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 169, in execute\n raise OutOfMemoryError(\ndeepmd.utils.errors.OutOfMemoryError: The callable still throws an out-of-memory (OOM) error even when batch size is 1!\n", + "partial_arrays": 0, + "complete_peak": { + "allocated": 32189347328, + "reserved": 32598130688 + }, + "elapsed_s": 138.13616728782654 + }, + "manifest": { + "args": { + "root": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base", + "model": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_accuracy_20261019/models/pro_mptrj/pro_download.pt", + "out": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/results/Pro_n4096_TORCH_p1", + "cache": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_TORCH", + "gpu": "0", + "mode": "TORCH", + "nrep": "2", + "seed": "0", + "repeat_cell": "[8, 8, 8]", + "system": "diamond", + "samples": "30", + "warmup": "8" + }, + "model_sha256": "446a6e893101fc52190cfccabb9d2328863d651aca36981fd8d3401c71a61221", + "torch": "2.11.0+cu128", + "triton": "3.6.0", + "cuda": "12.8", + "baseline_revision": "2ba617bc3db67adee2b8ba00c71e9fa1c2d2bb8c", + "source_hashes": { + "deepmd/pt_expt/kernels/triton/sezm/so2_value_path.py": "9c8b01082cf01fdcd41fe58e0884364e2166d7acbd4c41f75dda625829a64dbf" + }, + "flags": { + "PYTHONHASHSEED": "1", + "DP_COMPILE_INFER": "1", + "DP_COMPILE": "0", + "DP_TRITON_INFER": "0", + "DP_CUDA_INFER": "0", + "DP_TF32_INFER": "0", + "DP_AMP_INFER": "0", + "DP_SO2_EDGE_COHORT_INFER": "0", + "DP_INTRA_OP_PARALLELISM_THREADS": "1", + "DP_INTER_OP_PARALLELISM_THREADS": "1", + "PYTORCH_ALLOC_CONF": "expandable_segments:True", + "TORCHINDUCTOR_COMPILE_THREADS": "1", + "TORCHINDUCTOR_CACHE_DIR": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_TORCH/inductor", + "TRITON_CACHE_DIR": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_TORCH/triton" + } + } + }, + { + "run": "Pro_n4096_TORCH_p2", + "result": { + "status": "oom", + "mode": "TORCH", + "n_atoms": 4096, + "started": 1792873552.185923, + "threshold": { + "atol": 1e-05, + "rtol": 0.0001, + "relative_denominator_floor": 1e-08 + }, + "system": "diamond", + "input_sha256": "eda961dd45b711e42fad51b8dfde87d6ca736b34ac729d646c98f67481308f83", + "legacy_compatibility": "Pro-only in-memory fitting alias ener -> dpa4_ener; strict CPU state check passed; original checkpoint unchanged", + "parameter_count": 22678990, + "exception": "Traceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 156, in execute\n n_batch, result = callable(batch_nframes, start_index)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 243, in execute_with_batch_size\n return (end_index - start_index), callable(\n ^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 730, in _eval_model\n batch_output = model(\n ^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/train/wrapper.py\", line 201, in forward\n model_pred = self._forward_without_loss(task_key, input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/train/wrapper.py\", line 249, in _forward_without_loss\n model_pred = self.model[task_key](**input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 839, in forward\n model_ret = self.forward_common(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 1053, in forward_common\n return self.forward_common_lower(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 1217, in forward_common_lower\n model_predict = compiled_core_compute(\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 2399, in compiled\n return dict(zip(_keys, _fn(*args)))\n ^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/aot_autograd.py\", line 1200, in forward\n return compiled_fn(full_args)\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 580, in runtime_wrapper\n all_outs = call_func_at_runtime_with_args(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/utils.py\", line 138, in call_func_at_runtime_with_args\n out = normalize_as_list(f(args))\n ^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 783, in wrapper\n return compiled_fn(runtime_args)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 1011, in inner_fn\n outs = compiled_fn(args)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/output_code.py\", line 656, in __call__\n return self.current_callable(inputs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/utils.py\", line 3401, in run\n out = model(new_inputs)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_TORCH/inductor/db/cdbxhl4ag2mms4oooq2iu5tx2nwa73j2ckthin7jon7hvaejblzz.py\", line 21624, in call\n buf83 = empty_strided_cuda((s29, 6, 64), (384, 64, 1), torch.float32)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\ntorch.OutOfMemoryError: CUDA out of memory. Tried to allocate 950.00 MiB. GPU 0 has a total capacity of 31.36 GiB of which 631.06 MiB is free. Including non-PyTorch memory, this process has 30.70 GiB memory in use. Of the allocated memory 29.98 GiB is allocated by PyTorch, and 49.85 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://docs.pytorch.org/docs/stable/notes/cuda.html#optimizing-memory-usage-with-pytorch-cuda-alloc-conf)\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 243, in \n out = evaluate(pos)\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 224, in evaluate\n forces = sample.get_forces().copy()\n ^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/atoms.py\", line 2015, in get_forces\n forces = self._calc.get_forces(self)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/abc.py\", line 31, in get_forces\n return self.get_property('forces', atoms)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/calculator.py\", line 517, in get_property\n self.calculate(atoms, [name], system_changes)\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/calculator.py\", line 143, in calculate\n e, f, v = self.dp.eval(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/infer/deep_pot.py\", line 206, in eval\n results = self.deep_eval.eval(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 556, in eval\n out = self._eval_func(self._eval_model, numb_test, natoms)(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 637, in eval_func\n return self.auto_batch_size.execute_all(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 272, in execute_all\n n_batch, result = self.execute(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 169, in execute\n raise OutOfMemoryError(\ndeepmd.utils.errors.OutOfMemoryError: The callable still throws an out-of-memory (OOM) error even when batch size is 1!\n", + "partial_arrays": 0, + "complete_peak": { + "allocated": 32189347328, + "reserved": 32598130688 + }, + "elapsed_s": 137.1274483203888 + }, + "manifest": { + "args": { + "root": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base", + "model": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_accuracy_20261019/models/pro_mptrj/pro_download.pt", + "out": 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start_index)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 243, in execute_with_batch_size\n return (end_index - start_index), callable(\n ^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 730, in _eval_model\n batch_output = model(\n ^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/train/wrapper.py\", line 201, in forward\n model_pred = self._forward_without_loss(task_key, input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/train/wrapper.py\", line 249, in _forward_without_loss\n model_pred = self.model[task_key](**input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 839, in forward\n model_ret = self.forward_common(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 1053, in forward_common\n return self.forward_common_lower(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 1217, in forward_common_lower\n model_predict = compiled_core_compute(\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 2399, in compiled\n return dict(zip(_keys, _fn(*args)))\n ^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/aot_autograd.py\", line 1200, in forward\n return compiled_fn(full_args)\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 580, in runtime_wrapper\n all_outs = call_func_at_runtime_with_args(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/utils.py\", line 138, in call_func_at_runtime_with_args\n out = normalize_as_list(f(args))\n ^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 783, in wrapper\n return compiled_fn(runtime_args)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 1011, in inner_fn\n outs = compiled_fn(args)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/output_code.py\", line 656, in __call__\n return self.current_callable(inputs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/utils.py\", line 3401, in run\n out = model(new_inputs)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_TORCH/inductor/b6/cb6y74z5wjdb23bsj7sgwz7n4l6tpvrsmehqafsc5rp6ytf6afxd.py\", line 23148, in call\n buf476 = empty_strided_cuda((1, 16*s29, 128), (2048*s29, 128, 1), torch.float32)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\ntorch.OutOfMemoryError: CUDA out of memory. Tried to allocate 634.00 MiB. GPU 0 has a total capacity of 31.36 GiB of which 153.06 MiB is free. Including non-PyTorch memory, this process has 31.17 GiB memory in use. Of the allocated memory 30.43 GiB is allocated by PyTorch, and 62.92 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://docs.pytorch.org/docs/stable/notes/cuda.html#optimizing-memory-usage-with-pytorch-cuda-alloc-conf)\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 243, in \n out = evaluate(pos)\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 224, in evaluate\n forces = sample.get_forces().copy()\n ^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/atoms.py\", line 2015, in get_forces\n forces = self._calc.get_forces(self)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/abc.py\", line 31, in get_forces\n return self.get_property('forces', atoms)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/calculator.py\", line 517, in get_property\n self.calculate(atoms, [name], system_changes)\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/calculator.py\", line 143, in calculate\n e, f, v = self.dp.eval(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/infer/deep_pot.py\", line 206, in eval\n results = self.deep_eval.eval(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 556, in eval\n out = self._eval_func(self._eval_model, numb_test, natoms)(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 637, in eval_func\n return self.auto_batch_size.execute_all(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 272, in execute_all\n n_batch, result = self.execute(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 169, in execute\n raise OutOfMemoryError(\ndeepmd.utils.errors.OutOfMemoryError: The callable still throws an out-of-memory (OOM) error even when batch size is 1!\n", + "partial_arrays": 0, + "complete_peak": { + "allocated": 32676858368, + "reserved": 32847691776 + }, + "elapsed_s": 138.26069927215576 + }, + "manifest": { + "args": { + "root": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base", + "model": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_accuracy_20261019/models/pro_mptrj/pro_download.pt", + "out": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/results/Pro_n512_TORCH_p0", + "cache": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_TORCH", + "gpu": "0", + "mode": "TORCH", + "nrep": "2", + "seed": "0", + "repeat_cell": "[4, 4, 4]", + "system": "diamond", + "samples": "30", + "warmup": "8" + }, + "model_sha256": "446a6e893101fc52190cfccabb9d2328863d651aca36981fd8d3401c71a61221", + "torch": "2.11.0+cu128", + "triton": "3.6.0", + "cuda": "12.8", + "baseline_revision": "2ba617bc3db67adee2b8ba00c71e9fa1c2d2bb8c", + "source_hashes": { + "deepmd/pt_expt/kernels/triton/sezm/so2_value_path.py": "9c8b01082cf01fdcd41fe58e0884364e2166d7acbd4c41f75dda625829a64dbf" + }, + "flags": { + "PYTHONHASHSEED": "0", + "DP_COMPILE_INFER": "1", + "DP_COMPILE": "0", + "DP_TRITON_INFER": "0", + "DP_CUDA_INFER": "0", + "DP_TF32_INFER": "0", + "DP_AMP_INFER": "0", + "DP_SO2_EDGE_COHORT_INFER": "0", + "DP_INTRA_OP_PARALLELISM_THREADS": "1", + "DP_INTER_OP_PARALLELISM_THREADS": "1", + "PYTORCH_ALLOC_CONF": "expandable_segments:True", + "TORCHINDUCTOR_COMPILE_THREADS": "1", + "TORCHINDUCTOR_CACHE_DIR": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_TORCH/inductor", + "TRITON_CACHE_DIR": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_TORCH/triton" + } + } + }, + { + "run": "Pro_n512_TORCH_p1", + "result": { + "status": "oom", + "mode": "TORCH", + "n_atoms": 512, + "started": 1792871760.549256, + "threshold": { + "atol": 1e-05, + "rtol": 0.0001, + "relative_denominator_floor": 1e-08 + }, + "system": "diamond", + "input_sha256": "bf70d484ff4dc5117009b05a348a5f4a7a201d16571d4aa92768d0110eb584e1", + "legacy_compatibility": "Pro-only in-memory fitting alias ener -> dpa4_ener; strict CPU state check passed; original checkpoint unchanged", + "parameter_count": 22678990, + "exception": "Traceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 156, in execute\n n_batch, result = callable(batch_nframes, start_index)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 243, in execute_with_batch_size\n return (end_index - start_index), callable(\n ^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 730, in _eval_model\n batch_output = model(\n ^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/train/wrapper.py\", line 201, in forward\n model_pred = self._forward_without_loss(task_key, input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/train/wrapper.py\", line 249, in _forward_without_loss\n model_pred = self.model[task_key](**input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 839, in forward\n model_ret = self.forward_common(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 1053, in forward_common\n return self.forward_common_lower(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 1217, in forward_common_lower\n model_predict = compiled_core_compute(\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 2399, in compiled\n return dict(zip(_keys, _fn(*args)))\n ^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/aot_autograd.py\", line 1200, in forward\n return compiled_fn(full_args)\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 580, in runtime_wrapper\n all_outs = call_func_at_runtime_with_args(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/utils.py\", line 138, in call_func_at_runtime_with_args\n out = normalize_as_list(f(args))\n ^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 783, in wrapper\n return compiled_fn(runtime_args)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 1011, in inner_fn\n outs = compiled_fn(args)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/output_code.py\", line 656, in __call__\n return self.current_callable(inputs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/utils.py\", line 3401, in run\n out = model(new_inputs)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_TORCH/inductor/b6/cb6y74z5wjdb23bsj7sgwz7n4l6tpvrsmehqafsc5rp6ytf6afxd.py\", line 23148, in call\n buf476 = empty_strided_cuda((1, 16*s29, 128), (2048*s29, 128, 1), torch.float32)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\ntorch.OutOfMemoryError: CUDA out of memory. Tried to allocate 634.00 MiB. GPU 0 has a total capacity of 31.36 GiB of which 153.06 MiB is free. Including non-PyTorch memory, this process has 31.17 GiB memory in use. Of the allocated memory 30.43 GiB is allocated by PyTorch, and 62.92 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://docs.pytorch.org/docs/stable/notes/cuda.html#optimizing-memory-usage-with-pytorch-cuda-alloc-conf)\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 243, in \n out = evaluate(pos)\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 224, in evaluate\n forces = sample.get_forces().copy()\n ^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/atoms.py\", line 2015, in get_forces\n forces = self._calc.get_forces(self)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/abc.py\", line 31, in get_forces\n return self.get_property('forces', atoms)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/calculator.py\", line 517, in get_property\n self.calculate(atoms, [name], system_changes)\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/calculator.py\", line 143, in calculate\n e, f, v = self.dp.eval(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/infer/deep_pot.py\", line 206, in eval\n results = self.deep_eval.eval(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 556, in eval\n out = self._eval_func(self._eval_model, numb_test, natoms)(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 637, in eval_func\n return self.auto_batch_size.execute_all(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 272, in execute_all\n n_batch, result = self.execute(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 169, in execute\n raise OutOfMemoryError(\ndeepmd.utils.errors.OutOfMemoryError: The callable still throws an out-of-memory (OOM) error even when batch size is 1!\n", + "partial_arrays": 0, + "complete_peak": { + "allocated": 32676858368, + "reserved": 32847691776 + }, + "elapsed_s": 136.69677186012268 + }, + "manifest": { + "args": { + "root": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base", + "model": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_accuracy_20261019/models/pro_mptrj/pro_download.pt", + "out": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/results/Pro_n512_TORCH_p1", + "cache": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_TORCH", + "gpu": "0", + "mode": "TORCH", + "nrep": "2", + "seed": "0", + "repeat_cell": "[4, 4, 4]", + "system": "diamond", + "samples": "30", + "warmup": "8" + }, + "model_sha256": "446a6e893101fc52190cfccabb9d2328863d651aca36981fd8d3401c71a61221", + "torch": "2.11.0+cu128", + "triton": "3.6.0", + "cuda": "12.8", + "baseline_revision": "2ba617bc3db67adee2b8ba00c71e9fa1c2d2bb8c", + "source_hashes": { + "deepmd/pt_expt/kernels/triton/sezm/so2_value_path.py": "9c8b01082cf01fdcd41fe58e0884364e2166d7acbd4c41f75dda625829a64dbf" + }, + "flags": { + "PYTHONHASHSEED": "1", + "DP_COMPILE_INFER": "1", + "DP_COMPILE": "0", + "DP_TRITON_INFER": "0", + "DP_CUDA_INFER": "0", + "DP_TF32_INFER": "0", + "DP_AMP_INFER": "0", + "DP_SO2_EDGE_COHORT_INFER": "0", + "DP_INTRA_OP_PARALLELISM_THREADS": "1", + "DP_INTER_OP_PARALLELISM_THREADS": "1", + "PYTORCH_ALLOC_CONF": "expandable_segments:True", + "TORCHINDUCTOR_COMPILE_THREADS": "1", + "TORCHINDUCTOR_CACHE_DIR": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_TORCH/inductor", + "TRITON_CACHE_DIR": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_TORCH/triton" + } + } + }, + { + "run": "Pro_n512_TORCH_p2", + "result": { + "status": "oom", + "mode": "TORCH", + "n_atoms": 512, + "started": 1792871905.773682, + "threshold": { + "atol": 1e-05, + "rtol": 0.0001, + "relative_denominator_floor": 1e-08 + }, + "system": "diamond", + "input_sha256": "bf70d484ff4dc5117009b05a348a5f4a7a201d16571d4aa92768d0110eb584e1", + "legacy_compatibility": "Pro-only in-memory fitting alias ener -> dpa4_ener; strict CPU state check passed; original checkpoint unchanged", + "parameter_count": 22678990, + "exception": "Traceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 156, in execute\n n_batch, result = callable(batch_nframes, start_index)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 243, in execute_with_batch_size\n return (end_index - start_index), callable(\n ^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 730, in _eval_model\n batch_output = model(\n ^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/train/wrapper.py\", line 201, in forward\n model_pred = self._forward_without_loss(task_key, input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/train/wrapper.py\", line 249, in _forward_without_loss\n model_pred = self.model[task_key](**input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 839, in forward\n model_ret = self.forward_common(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 1053, in forward_common\n return self.forward_common_lower(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 1217, in forward_common_lower\n model_predict = compiled_core_compute(\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 2399, in compiled\n return dict(zip(_keys, _fn(*args)))\n ^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/aot_autograd.py\", line 1200, in forward\n return compiled_fn(full_args)\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 580, in runtime_wrapper\n all_outs = call_func_at_runtime_with_args(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/utils.py\", line 138, in call_func_at_runtime_with_args\n out = normalize_as_list(f(args))\n ^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 783, in wrapper\n return compiled_fn(runtime_args)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 1011, in inner_fn\n outs = compiled_fn(args)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/output_code.py\", line 656, in __call__\n return self.current_callable(inputs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/utils.py\", line 3401, in run\n out = model(new_inputs)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_TORCH/inductor/b6/cb6y74z5wjdb23bsj7sgwz7n4l6tpvrsmehqafsc5rp6ytf6afxd.py\", line 23148, in call\n buf476 = empty_strided_cuda((1, 16*s29, 128), (2048*s29, 128, 1), torch.float32)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\ntorch.OutOfMemoryError: CUDA out of memory. Tried to allocate 634.00 MiB. GPU 0 has a total capacity of 31.36 GiB of which 153.06 MiB is free. Including non-PyTorch memory, this process has 31.17 GiB memory in use. Of the allocated memory 30.43 GiB is allocated by PyTorch, and 62.92 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://docs.pytorch.org/docs/stable/notes/cuda.html#optimizing-memory-usage-with-pytorch-cuda-alloc-conf)\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 243, in \n out = evaluate(pos)\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 224, in evaluate\n forces = sample.get_forces().copy()\n ^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/atoms.py\", line 2015, in get_forces\n forces = self._calc.get_forces(self)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/abc.py\", line 31, in get_forces\n return self.get_property('forces', atoms)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/calculator.py\", line 517, in get_property\n self.calculate(atoms, [name], system_changes)\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/calculator.py\", line 143, in calculate\n e, f, v = self.dp.eval(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/infer/deep_pot.py\", line 206, in eval\n results = self.deep_eval.eval(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 556, in eval\n out = self._eval_func(self._eval_model, numb_test, natoms)(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 637, in eval_func\n return self.auto_batch_size.execute_all(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 272, in execute_all\n n_batch, result = self.execute(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 169, in execute\n raise OutOfMemoryError(\ndeepmd.utils.errors.OutOfMemoryError: The callable still throws an out-of-memory (OOM) error even when batch size is 1!\n", + "partial_arrays": 0, + "complete_peak": { + "allocated": 32676858368, + "reserved": 32847691776 + }, + "elapsed_s": 137.19549584388733 + }, + "manifest": { + "args": { + "root": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base", + "model": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_accuracy_20261019/models/pro_mptrj/pro_download.pt", + "out": 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"out": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/public_smoke_gpu1/Neo_n96_ECS_p0", + "cache": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Neo_ECS", + "gpu": "1", + "mode": "ECS", + "nrep": "2", + "seed": "0", + "repeat_cell": "[2, 2, 3]", + "system": "diamond", + "samples": "10", + "warmup": "3" + }, + "model_sha256": "8d8b2b02b9575cb9e0af420cc1e8b1d9ffcbc04752c2203d29f48ccd0cd5f07d", + "torch": "2.11.0+cu128", + "triton": "3.6.0", + "cuda": "12.8", + "baseline_revision": "2ba617bc3db67adee2b8ba00c71e9fa1c2d2bb8c", + "source_hashes": { + "deepmd/pt_expt/kernels/triton/sezm/so2_value_path.py": "0fb6ae06e62af5dfafe8b7c9cfdce975714a9ffb577dc4f3581ed13706735462", + "deepmd/pt_expt/kernels/triton/sezm/so2_edge_cohort.py": "08c67a81883ff70f19ce9dad8809037585e0c5b0949ecceab747bb4179c44474" + }, + "flags": { + "DP_COMPILE_INFER": "1", + "DP_COMPILE": "0", + "DP_TRITON_INFER": "2", + "DP_CUDA_INFER": "0", + "DP_TF32_INFER": "0", + "DP_AMP_INFER": "0", + "DP_SO2_EDGE_COHORT_INFER": "1", + "DP_INTRA_OP_PARALLELISM_THREADS": "1", + "DP_INTER_OP_PARALLELISM_THREADS": "1", + "PYTORCH_ALLOC_CONF": "expandable_segments:True", + "TORCHINDUCTOR_COMPILE_THREADS": "1", + "TORCHINDUCTOR_CACHE_DIR": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Neo_ECS/inductor", + "TRITON_CACHE_DIR": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Neo_ECS/triton" + } + }, + "scope": "GPU1, Neo96,3warmup/10samples, not pooled into135-run results; finite execution is not a new cross-reference precision pass" + }, + "gpu0_probe": { + "status": "blocked_busy", + "pids": "2030231" + }, + "lint": "candidate full-repository Ruff check/format/diff-check passed; published Python whitelist Ruff/format passed. Generated compiler-cache files are not source lint targets.", + "artifact_patch": "git apply --check passed against clean2ba617bc" +} diff --git a/doc/development/ecs-validation-20261025/worker.py b/doc/development/ecs-validation-20261025/worker.py new file mode 100644 index 0000000000..bf32b9fddf --- /dev/null +++ b/doc/development/ecs-validation-20261025/worker.py @@ -0,0 +1,370 @@ +"""Accuracy-first full API matrix. No production source mutation.""" +# ruff: noqa: T201, TID253, ANN001, ANN002, ANN003, ANN201 -- standalone manual experiment, not imported package code + +import argparse +import copy +import hashlib +import json +import os +from pathlib import Path +import subprocess +import sys +import time +import traceback + +P = argparse.ArgumentParser() +P.add_argument("--root", type=Path, required=True) +P.add_argument("--model", type=Path, required=True) +P.add_argument("--out", type=Path, required=True) +P.add_argument("--cache", type=Path, required=True) +P.add_argument("--gpu", type=int, required=True) +P.add_argument( + "--mode", + choices=["TORCH", "BASE", "ECS", "ECS_RUNTIME", "TORCH_EAGER"], + required=True, +) +P.add_argument("--nrep", type=int, required=True) +P.add_argument("--seed", type=int, default=0) +P.add_argument("--repeat-cell", type=int, nargs=3) +P.add_argument("--system", choices=["diamond", "bcccu"], default="diamond") +P.add_argument("--samples", type=int, default=30) +P.add_argument("--warmup", type=int, default=8) +a = P.parse_args() +a.out.mkdir(parents=True, exist_ok=False) + + +def write(name, obj): + (a.out / name).write_text( + json.dumps(obj, indent=2, ensure_ascii=False, allow_nan=False) + ) + + +def sha(p): + return hashlib.sha256(p.read_bytes()).hexdigest() + + +busy = subprocess.check_output( + [ + "nvidia-smi", + "-i", + str(a.gpu), + "--query-compute-apps=pid", + "--format=csv,noheader", + ], + text=True, +).strip() +if busy: + write("result.json", {"status": "blocked_busy", "pids": busy}) + raise SystemExit(3) +for k in list(os.environ): + if k.startswith("DP_"): + del os.environ[k] +os.environ.update( + CUDA_VISIBLE_DEVICES=str(a.gpu), + DP_COMPILE_INFER="0" if a.mode == "TORCH_EAGER" else "1", + DP_COMPILE="0", + DP_TRITON_INFER="0" if a.mode.startswith("TORCH") else "2", + DP_CUDA_INFER="0", + DP_TF32_INFER="0", + DP_AMP_INFER="0", + DP_SO2_EDGE_COHORT_INFER="1" if a.mode.startswith("ECS") else "0", + OMP_NUM_THREADS="1", + DP_INTRA_OP_PARALLELISM_THREADS="1", + DP_INTER_OP_PARALLELISM_THREADS="1", + PYTORCH_ALLOC_CONF="expandable_segments:True", + TORCH_FORCE_NO_WEIGHTS_ONLY_LOAD="1", + TORCHINDUCTOR_COMPILE_THREADS="1", + TORCHINDUCTOR_CACHE_DIR=str(a.cache / "inductor"), + TRITON_CACHE_DIR=str(a.cache / "triton"), +) +sys.path.insert(0, str(a.root)) +sys.meta_path[:] = [ + f + for f in sys.meta_path + if not type(f).__module__.startswith("_editable_skbc_deepmd_kit") +] +import site +import deepmd + +for installed in site.getsitepackages(): + p = Path(installed) / "deepmd" + if (p / "lib").is_dir(): + deepmd.__path__.append(str(p)) +assert Path(deepmd.__file__).resolve().parent.parent == a.root.resolve() +import numpy as np +import torch +import triton + +torch.set_num_threads(1) +torch.set_float32_matmul_precision("highest") +torch.backends.cuda.matmul.allow_tf32 = False +torch.backends.cudnn.allow_tf32 = False +torch.cuda.reset_peak_memory_stats() +manifest = { + "args": {k: str(v) for k, v in vars(a).items()}, + "model_sha256": sha(a.model), + "torch": torch.__version__, + "triton": triton.__version__, + "cuda": torch.version.cuda, + "baseline_revision": subprocess.check_output( + ["git", "rev-parse", "HEAD"], cwd=a.root, text=True + ).strip(), + "source_hashes": { + str(p.relative_to(a.root)): sha(p) + for p in [ + a.root / "deepmd/pt_expt/kernels/triton/sezm/so2_value_path.py", + a.root / "deepmd/pt_expt/kernels/triton/sezm/so2_edge_cohort.py", + ] + if p.exists() + }, + "flags": { + k: v + for k, v in os.environ.items() + if k.startswith( + ("DP_", "TORCHINDUCTOR", "TRITON_CACHE", "PYTORCH_ALLOC", "PYTHONHASH") + ) + }, +} +write("manifest.json", manifest) +result = { + "status": "running", + "mode": a.mode, + "n_atoms": (8 if a.system == "diamond" else 2) + * int(np.prod(a.repeat_cell if a.repeat_cell else [a.nrep] * 3)), + "started": time.time(), + "threshold": {"atol": 1e-5, "rtol": 1e-4, "relative_denominator_floor": 1e-8}, +} +arrays = {} +runtime_calls = [] +try: + from ase.build import bulk + from deepmd.calculator import DP + + repeat_cell = tuple(a.repeat_cell) if a.repeat_cell else (a.nrep,) * 3 + atoms = ( + bulk("C", "diamond", a=3.567, cubic=True) + if a.system == "diamond" + else bulk("Cu", "bcc", a=2.87, cubic=True) + ).repeat(repeat_cell) + result["n_atoms"] = len(atoms) + result["system"] = a.system + rng = np.random.default_rng(a.seed) + frames = np.stack( + [ + atoms.positions + 0.02 * rng.standard_normal(atoms.positions.shape) + for _ in range(3) + ] + ) + np.savez_compressed( + a.out / "inputs.npz", + positions=frames, + cell=np.asarray(atoms.cell), + numbers=atoms.numbers, + ) + result["input_sha256"] = sha(a.out / "inputs.npz") + if a.mode == "ECS_RUNTIME": + from deepmd.pt_expt.kernels.triton.sezm import so2_edge_cohort as ec + + ec.SINGLE_COHORT_MAX_EDGES = 0 + original_runtime = ec.cohort_input_backward_impl + + def traced_runtime(*args, **kwargs): + edges = args[0].shape[0] + cap = args[7] if len(args) > 7 else kwargs["cohort_edges"] + runtime_calls.append( + {"edges": edges, "cohort": cap, "chunks": (edges + cap - 1) // cap} + ) + return original_runtime(*args, **kwargs) + + ec.cohort_input_backward_impl = traced_runtime + original_load = torch.load + if ( + manifest["model_sha256"] + == "446a6e893101fc52190cfccabb9d2328863d651aca36981fd8d3401c71a61221" + ): + assert ( + manifest["model_sha256"] + == "446a6e893101fc52190cfccabb9d2328863d651aca36981fd8d3401c71a61221" + ) + proof = json.loads((a.model.parent / "cpu_load_check.json").read_text()) + assert proof["strict_missing"] == proof["strict_unexpected"] == [] + + def legacy_load(file, *args, **kwargs): + obj = original_load(file, *args, **kwargs) + if ( + isinstance(file, (str, Path)) + and Path(file).resolve() == a.model.resolve() + ): + state = obj["model"] if "model" in obj else obj + state["_extra_state"] = copy.deepcopy(state["_extra_state"]) + fit = state["_extra_state"]["model_params"]["fitting_net"] + assert fit["type"] == "ener" + fit["type"] = "dpa4_ener" + return obj + + torch.load = legacy_load + result["legacy_compatibility"] = ( + "Pro-only in-memory fitting alias ener -> dpa4_ener; strict CPU state check passed; original checkpoint unchanged" + ) + try: + calc = DP(model=str(a.model)) + finally: + torch.load = original_load + inner = calc.dp.get_model() + assert not inner.training + definition = calc.dp.get_model_def_script() + write("model_definition.json", definition) + result["parameter_count"] = sum(p.numel() for p in inner.parameters()) + + def evaluate(pos): + calc.reset() + sample = atoms.copy() + sample.positions = pos + sample.calc = calc + forces = sample.get_forces().copy() + return { + "energy": np.asarray(sample.get_potential_energy()), + "forces": forces, + "virial": np.asarray(calc.results["virial"]).copy(), + "stress": sample.get_stress().copy(), + } + + wall = [] + events = [] + for repeat in range(3): + for frame, pos in enumerate(frames): + torch.cuda.synchronize() + s, e = ( + torch.cuda.Event(enable_timing=True), + torch.cuda.Event(enable_timing=True), + ) + start = time.perf_counter() + s.record() + out = evaluate(pos) + e.record() + torch.cuda.synchronize() + elapsed = 1000 * (time.perf_counter() - start) + wall.append(elapsed) + events.append(s.elapsed_time(e)) + for k, v in out.items(): + arrays[f"{repeat}_{frame}_{k}"] = v + np.savez_compressed(a.out / "outputs.npz", **arrays) + if repeat == 0 and frame == 0: + result["first_peak"] = { + "allocated": torch.cuda.max_memory_allocated(), + "reserved": torch.cuda.max_memory_reserved(), + } + write( + "progress.json", + { + "repeat": repeat, + "frame": frame, + "elapsed_ms": elapsed, + "finite": all(bool(np.isfinite(v).all()) for v in out.values()), + }, + ) + print(a.mode, len(atoms), repeat, frame, elapsed, flush=True) + # Accuracy collection is separate from warmed timing and steady memory. + assert a.warmup >= 3 and a.samples >= 10 + for i in range(a.warmup): + evaluate(frames[i % len(frames)]) + timed_wall, timed_event = [], [] + for i in range(a.samples): + torch.cuda.synchronize() + begin = torch.cuda.Event(enable_timing=True) + end = torch.cuda.Event(enable_timing=True) + tick = time.perf_counter() + begin.record() + measured = evaluate(frames[i % len(frames)]) + end.record() + torch.cuda.synchronize() + assert all(bool(np.isfinite(v).all()) for v in measured.values()) + timed_wall.append(1000 * (time.perf_counter() - tick)) + timed_event.append(begin.elapsed_time(end)) + full_peak = { + "allocated": torch.cuda.max_memory_allocated(), + "reserved": torch.cuda.max_memory_reserved(), + } + torch.cuda.reset_peak_memory_stats() + for i in range(a.samples): + evaluate(frames[i % len(frames)]) + torch.cuda.synchronize() + result["steady_peak"] = { + "allocated": torch.cuda.max_memory_allocated(), + "reserved": torch.cuda.max_memory_reserved(), + } + result["full_window_peak"] = full_peak + result["steady_wall_ms"] = { + "median": float(np.median(timed_wall)), + "p90": float(np.percentile(timed_wall, 90)), + "min": min(timed_wall), + "max": max(timed_wall), + "samples": timed_wall, + } + result["steady_event_ms"] = { + "median": float(np.median(timed_event)), + "p90": float(np.percentile(timed_event, 90)), + "samples": timed_event, + } + from torch._inductor.codecache import PyCodeCache + + wrappers = [] + route = [] + for m in PyCodeCache.modules: + path = Path(m.__file__) + text = path.read_text() + if "def call(" not in text: + continue + wrappers.append(str(path)) + route.append( + { + "sha256": sha(path), + "opaque_cohort_calls": text.count("so2_cohort_input_bwd.default("), + "single_cohort_named_calls": text.count("so2_single_cohort_input_bwd"), + "stack_kernel_definitions": text.count("def _stack_"), + "single_cohort_marker": "so2_single_cohort_input_bwd" in text, + } + ) + if a.mode != "TORCH_EAGER": + assert wrappers, "compiled path missing" + assert torch.get_float32_matmul_precision() == "highest" + assert not torch.backends.cuda.matmul.allow_tf32 + assert not torch.backends.cudnn.allow_tf32 + assert not torch.is_autocast_enabled("cuda") + result.update( + status="completed", + finite=all(bool(np.isfinite(v).all()) for v in arrays.values()), + evaluations=9, + runtime_cohort_calls=runtime_calls, + precision_runtime={ + "matmul_precision": torch.get_float32_matmul_precision(), + "matmul_tf32": torch.backends.cuda.matmul.allow_tf32, + "cudnn_tf32": torch.backends.cudnn.allow_tf32, + "outer_autocast": torch.is_autocast_enabled("cuda"), + }, + wall_ms_including_first=wall, + event_ms_including_first=events, + wrappers=wrappers, + routes=route, + complete_peak={ + k: max(full_peak[k], result["steady_peak"][k]) for k in full_peak + }, + ) +except Exception as exc: + result.update( + status="oom" + if isinstance(exc, torch.OutOfMemoryError) + or type(exc).__name__ == "OutOfMemoryError" + else "failed", + exception=traceback.format_exc(), + partial_arrays=len(arrays), + complete_peak={ + "allocated": torch.cuda.max_memory_allocated(), + "reserved": torch.cuda.max_memory_reserved(), + }, + ) + print(result["exception"], flush=True) +finally: + result["elapsed_s"] = time.time() - result["started"] + write("result.json", result) +raise SystemExit(0 if result["status"] == "completed" else 1) From 9ae1715cd7893311cf82ccc6fbeb3677a60eac52 Mon Sep 17 00:00:00 2001 From: Macqueen Ma-hnu Date: Mon, 26 Oct 2026 14:49:56 +0800 Subject: [PATCH 5/7] chore(pt): trim edge-cohort PR to implementation and regression tests --- .../ecs-edge-cohort-benchmark-results.json | 5202 ---- doc/development/ecs-edge-cohort-benchmarks.md | 211 - .../ecs-edge-cohort-controlled-raw.json.gz | Bin 1311322 -> 0 bytes .../ecs-edge-cohort-controlled-results.json | 4593 --- .../ecs-edge-cohort-controlled-timing.md | 235 - .../ecs-edge-cohort-multimodel-accuracy.md | 206 - .../ecs-edge-cohort-multimodel-results.json | 11013 ------- .../ecs-edge-cohort-multimodel-summary.md | 54 - 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a/doc/development/ecs-edge-cohort-benchmark-results.json b/doc/development/ecs-edge-cohort-benchmark-results.json deleted file mode 100644 index c56875fa35..0000000000 --- a/doc/development/ecs-edge-cohort-benchmark-results.json +++ /dev/null @@ -1,5202 +0,0 @@ -{ - "schema_version": 1, - "baseline_commit": "668b60cc2a9559f79abb9c33acd959833376344f", - "model": { - "name": "DPA4-Neo-OMat24-v20260627.pt", - "sha256": "8d8b2b02b9575cb9e0af420cc1e8b1d9ffcbc04752c2203d29f48ccd0cd5f07d" - }, - "environment": { - "gpu": "NVIDIA GeForce RTX 5090", - "driver": "595.91.07", - "torch": "2.11.0+cu128", - "triton": "3.6.0", - "cuda": "12.8", - "precision": "FP32 SO(2), TF32 and AMP disabled" - }, - "protocol": { - "gpu": 0, - "atoms": 64, - "edges": 10114, - "seed": 0, - "coordinate_std_angstrom": 0.02, - "warmup": 8, - "samples": 30, - "independent_processes_per_arm": 3, - "order": [ - "BASE", - "FIXED", - "FIXED", - "BASE", - "BASE", - "FIXED" - ], - "cache": "new process and empty 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3.496542490211141e-06, - "mean_abs": 1.577094147923545e-06, - "max_relative": 1.244098685834877e-05, - "max_ratio": 0.07940815473083772, - "bitwise_equal": false - }, - "2_stress": { - "passed": true, - "finite": true, - "max_abs": 9.630292879120317e-09, - "mean_abs": 4.75344163040151e-09, - "max_relative": 5.728141736537804e-06, - "max_ratio": 0.0008201686919741789, - "bitwise_equal": false - } - } - ] - } - ], - "torch_reference": { - "gpu": 1, - "process_count": 1, - "self_repeats": 3, - "worst_self_ratio": 0.2115126973181738, - "caveat": "accuracy reference only; GPU1 timing not mixed into GPU0 performance table" - }, - "test_summary": { - "focused_cuda_passed": 60, - "training_and_higher_order": "dispatch guard tests, not full training validation", - "local_input_alpha_gradients": "24 edge-count/distribution cases, bitwise equal, finite, caller tensors unchanged" - }, - "limitations": [ - "Latest cold-process matrix covers Neo N64 only; no multi-model/PT2 acceptance claim.", - "Small-path residual reuse overlaps the earlier internal inplace optimization; percentages are not additive.", - "Earlier opaque two-cohort N64 path regressed 6.85-7.81%; it is not the reported small path.", - "Cold-process percentage differences alone are not significance tests; the fixed-3%-noise interpretation is retired by the controlled timing follow-up. Full model outputs are tolerance-equivalent, not bitwise guaranteed." - ] -} diff --git a/doc/development/ecs-edge-cohort-benchmarks.md b/doc/development/ecs-edge-cohort-benchmarks.md deleted file mode 100644 index 52a47a0d1e..0000000000 --- a/doc/development/ecs-edge-cohort-benchmarks.md +++ /dev/null @@ -1,211 +0,0 @@ -# Experimental SO(2) edge-cohort inference backward - -**Update:** the cold-process numbers below remain historical measurements, but -their earlier fixed-3%-noise interpretation is retired. See the -[controlled timing follow-up](ecs-edge-cohort-controlled-timing.md) for aligned -autotune choices, counterbalanced same-context normal-API measurements, -process-level uncertainty and negative experiments. - -This opt-in experiment targets first-order input and alpha gradients of a frozen -DPA4-Neo model. It is not a training optimization and does not change the default -inference path. Enable `DP_SO2_EDGE_COHORT_INFER=1` **before importing the model**. -Use `DP_TRITON_INFER=2`, with TF32 and AMP disabled. Other model components retain -their original precision; the checkpoint is not globally cast to FP32. - -## Scope and implementation - -The checked layout is `F=2`, `Cf=32`, `lmax=3`, `ROW=320`, three mixing layers -and two gated layers, contiguous FP32 activations, and `apply_alpha=True`. -Unsupported layouts retain the existing dispatcher. Weight gradients, higher -derivatives and extra saved-output gradients retain the existing training path. -The switch defaults to zero. - -The existing backward traverses a full edge set for each layer. The large-edge -experiment instead traverses the whole reverse layer chain of one cohort before -advancing to the next cohort. Scratch buffers scale with cohort size (8192), -while saved forward activations and the returned gradient still scale with the -full edge count. This is not a claim that all full-edge storage disappears. - -For `E <= 16384`, a single-cohort `torch.library.triton_op` exposes `wrap_triton` -launches to Inductor and uses the internally owned returned gradient as the -residual buffer. This avoids the opaque Python multi-cohort launch overhead on -small systems. It does **not** fuse the reverse chain into one mega-kernel. -16384 is a tested dispatch boundary, not a proven optimum for every workload. - -Only internally owned residual elements are overwritten. Caller tensors remain -read-only, and GEMM matrix input `gz` never aliases GEMM output: independent -column tiles may still be reading `gz`. Saved and output strides account for -full-graph extent even when a cohort has a partial tail. Alpha backward remains -separate to preserve the forward-output lifetime contract. The single-cohort -residual reuse overlaps the internal inplace optimization discussed in #6058; -their percentages must not be added or presented as independent improvements. - -The five GEMM schedule parameters, gate/point/recompute schedules, weights and -arithmetic are unchanged. Four native `wrap_triton` launch sites additionally -pass `saved_edges=0` / `output_edges=0` explicitly so optional Python defaults do -not become `None` in compiled native calls. Static and dynamic native tests -cover the disabled-switch path. - -## Measurement protocol - -The baseline is unmodified commit -`668b60cc2a9559f79abb9c33acd959833376344f`. The checkpoint is -`DPA4-Neo-OMat24-v20260627.pt`, SHA-256 -`8d8b2b02b9575cb9e0af420cc1e8b1d9ffcbc04752c2203d29f48ccd0cd5f07d`. -It must be provided separately; it is not bundled in this PR. - -Environment: RTX 5090, driver 595.91.07, PyTorch 2.11.0+cu128, CUDA runtime 12.8, -Triton 3.6.0. The main table uses GPU 0 only. It runs the normal ASE API for -energy, forces, virial and stress of a 64-atom diamond cell (`nrep=2`, approximately -10114 edges), seed 0, position perturbations with standard deviation 0.02 Angstrom. -Timing includes neighbor construction and host work. CUDA Events bracket the -whole API call on the default stream, including host launch gaps: they are not -the sum of kernel durations. - -Three independent processes per arm run in the order BASE/ECS/ECS/BASE/BASE/ECS. -Each process uses new Inductor and Triton caches, eight warmup frames and thirty -measured frames. System/driver caches are not purged. First-call compilation -time is recorded separately and excluded from steady timings. P50 is the median -of the three process medians; P90 pools ninety samples per arm. A GPU 0 NVML -monitor checked for foreign compute processes every 250 ms; none was detected. -Clocks were recorded, not locked, and no global driver settings were changed. -The monitor covers compilation and idle time as well as execution; its clock -range is not a claim of constant clocks during timing. - -**Primary memory includes the first call and compilation.** Counters are not -reset between loading the model, first execution, warmup and the thirty timed -calls. Report the worst high-water mark across the three processes for both -allocated and reserved memory. A second replay resets only peak counters to -measure steady allocated memory; allocator caches are not emptied. Reserved -memory can therefore retain the earlier cold peak. These are full-model CUDA -allocator metrics, not SO(2)-exclusive storage or total driver/context memory. -A compilation scratch allocation still consumes real capacity and is not -subtracted from the primary metric. Sampled device memory is an auxiliary -measurement and can miss narrow peaks. - -Numeric results and all per-call timings are in -[the JSON evidence](ecs-edge-cohort-benchmark-results.json). - -## Results: cold-process Neo N64 - -MB below is decimal (`1 MB = 10^6 bytes`). First and complete-window peaks -were identical in all six processes. Each arm's three allocated peak readings -were also identical. Memory columns report the worst process, not its average. - -| Full-model metric | Unmodified baseline | Opt-in candidate | Change | -| --- | ---: | ---: | ---: | -| **First/complete allocated peak, MB** | **498.199552** | **480.156672** | **-3.6216%** | -| First/complete reserved peak, MB | 528.482304 | 507.510784 | -3.9683% | -| Steady allocated peak, MB, secondary | 397.536256 | 379.493376 | -4.5387% | -| Steady reserved peak, MB, cache retained | 528.482304 | 507.510784 | -3.9683% | -| Wall P50, ms, median of process medians | 12.157611 | 12.313776 | +1.2845% latency | -| Wall P90, ms, pooled 90 samples | 12.414686 | 12.432651 | +0.1447% latency | -| Wall min / max, ms | 11.702988 / 12.855804 | 12.185729 / 12.588670 | distribution, not a speedup | -| CUDA Event median, ms, pooled | 12.128464 | 12.314896 | whole API, not kernel sum | -| First call including JIT, seconds, range | 124.931-125.718 | 124.219-126.629 | excluded from steady P50 | - -Baseline process P50s: 12.157611 / 11.771478 / 12.372728 ms. -Candidate process P50s: 12.398105 / 12.254008 / 12.313776 ms. -The single-batch latency difference alone is not a significance test; there is -**no claimed stable speedup**. The result is a reproducible memory reduction with no repeat -of the original approximately 7.8% small-system regression, not proof of zero -time cost. First-call compilation latency itself is approximately two minutes -in this fresh-cache protocol and is not hidden. - -| Accuracy comparison, three frames x energy/forces/virial/stress | Pairs | Worst error/tolerance ratio | Result | -| --- | ---: | ---: | --- | -| Baseline self, independent processes | 3 | 0.282621 | pass | -| Candidate self, independent processes | 3 | 0.184105 | pass | -| All baseline-to-candidate process pairs | 9 | 0.256570 | pass | -| Six processes versus fresh pure-PyTorch output | 6 | 0.352841 | pass | - -Within-process worst ratios are 0.256139 (baseline), 0.354324 (candidate), and -0.211513 (pure PyTorch). These characterize observed noise, not permission to -relax the absolute/relative acceptance thresholds. All compared arrays are -finite. During the full GPU 0 process windows, the highest sampled temperature -was 46 C; clock samples ranged from idle 172 MHz to 2902 MHz. No throttling -cause is inferred from those whole-window statistics. - -## Correctness and limitations - -Use `abs(actual-reference) <= 1e-5 + 1e-4*abs(reference)` elementwise, without -relaxing thresholds on failure. Relative error uses -`max(abs(reference), 1e-8)` in the denominator. The evidence includes max/mean -absolute error, max relative error, finite checks and the maximum error/tolerance -ratio for each frame and physical output. - -The cold matrix is checked against a fresh pure-PyTorch reference with Triton -disabled, and also checks baseline-to-baseline, candidate-to-candidate and all -nine baseline-to-candidate process pairs. The pure-PyTorch reference runs on -GPU 1 in one process with three self-repeats; this is not a three-process Torch -noise matrix, and its timings are not mixed with GPU 0 performance. -Full-model floating-point reductions are not promised to be bitwise identical. - -The focused CUDA test file has 60 passing tests, including zero/tail edges, -legal near-zero alpha, caller-input immutability, native static/dynamic compile, -and dispatch across 16384/16385 edges. Local experimental checks of input/alpha -gradients cover 24 edge-count/distribution cases with bitwise equality. Training -and higher-order tests check dispatch guards, not numerical correctness of a -complete training run. - -Earlier opaque two-cohort N64 runs regressed by 6.85-7.81% despite reducing -memory. The compiled single-cohort branch addresses that small-system penalty; -it must not inherit the earlier 9.46% cold-peak reduction claim. Differences -are now evaluated with process-level uncertainty, not a fixed 3% cutoff. Earlier diagnostic -workers produced different baseline timings; their numbers are not mixed into -this same-entry-point comparison, and the cross-day difference is not uniquely -attributed to a hardware cause. - -The latest cold-process matrix is Neo N64 only. Earlier larger-system or other -model runs with precision failures are not reclassified as passing. This draft -does not claim multi-model acceptance, standalone `.pt2`/AOTInductor/C++ deployment, -LAMMPS performance, new NCU measurements, or full training validation. The -large-edge runtime launcher remains experimental. - -## Reproduce - -Install a CUDA-enabled DeePMD build and its runtime dependencies, plus `ase`, -`pytest` and Triton. Use the same environment and checkpoint for both checkouts. -Create a clean baseline worktree at the commit above, and a worktree containing -this PR. Set the following paths to absolute local paths: - -```bash -PY=/path/to/environment/bin/python -PR_ROOT=/path/to/pr-checkout -BASE_ROOT=/path/to/baseline-checkout -MODEL=/path/to/DPA4-Neo-OMat24-v20260627.pt -RESULTS=/path/to/new-results -BENCH=$PR_ROOT/source/tests/pt_expt/kernels/benchmark_so2_edge_cohort.py - -# Each --out must be new. Default caches are inside that output directory. -$PY "$BENCH" --gpu 0 --mode torch --source-root "$BASE_ROOT" \ - --model "$MODEL" --out "$RESULTS/torch" -$PY "$BENCH" --gpu 0 --mode base --source-root "$BASE_ROOT" \ - --model "$MODEL" --out "$RESULTS/base-0" \ - --reference "$RESULTS/torch/outputs.npz" -$PY "$BENCH" --gpu 0 --mode ecs --source-root "$PR_ROOT" \ - --model "$MODEL" --out "$RESULTS/ecs-0" \ - --reference "$RESULTS/torch/outputs.npz" -``` - -Run three processes per arm in the order specified above, with unique output -directories; do not choose the fastest process. GPU 0 must be idle at launch. -The benchmark refuses a nonempty explicit `--cache-dir` unless -`--allow-warm-cache` is given; warm-cache results are a different experiment. -`--nrep 4` tests 512 atoms and `--nrep 8` tests 4096, but the current N64 result -does not establish their acceptance. A failed precision check exits nonzero. - -The CLI writes `result.json`, `manifest.json`, `outputs.npz`, first-call memory -and compiler caches. It records global memory including validation, in addition -to the primary normal-inference window and separate steady replay, and records -source hashes. The public CLI has also been run end-to-end on Torch/base/ECS -with fresh caches; its GPU 1 timings are kept separate from the main matrix. - -For generated-input CUDA tests, use a correctly installed PR checkout: - -```bash -DP_TRITON_INFER=2 DP_TF32_INFER=0 DP_AMP_INFER=0 \ - $PY -m pytest source/tests/pt_expt/kernels/test_so2_edge_cohort.py -q -``` - -No weights or generated compiler caches are tracked in the repository. diff --git a/doc/development/ecs-edge-cohort-controlled-raw.json.gz b/doc/development/ecs-edge-cohort-controlled-raw.json.gz deleted file mode 100644 index 5b5065bbaaa1ce4d71ff8d353974c23dd0419c31..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 1311322 zcmV)AK*YZviwFP!000021MIyGZzIXEE%;Xm0T0;4>*i!eevkJp;A{KsF812KxYrN3 zfMEz(RaxEKkwjgRnsM8h|Gp=xieF-uNERtITg{f;(<-tmGb1BToQRCf`sdAkTEr?Y z;?0l$yqTAu(|x@CInCyi>BG&B^1}^2N;f~=kRba}ijOkfe7Gs%Jl#zm(rr1tzn?7d zv9-RiqAV!}V?ymhEQ(Mn70W7^;FVI&Dy?hWJh^|oOZVwv5f^A<{fLAo6wWKFVkmuC 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"run": 3, - "unchanged": true, - "count": 835 - }, - { - "run": 4, - "unchanged": true, - "count": 835 - }, - { - "run": 5, - "unchanged": true, - "count": 835 - } - ], - "interpretation": "finite n=3 independent pairs; a confidence interval is not proof of zero cost", - "cold_peak": "separate existing cold-process measurement, not replaced" - }, - "compiled_graph_replay_diagnostic": { - "processes": 1, - "blocks": 24, - "paired_change_percent": 0.226145777601847, - "conditional_block_bootstrap95_percent": [ - 0.0706033734667633, - 0.38418086800490775 - ], - "scope": "compiled graph only on same captured real inputs and CUDA context; one process, not population/API acceptance", - "stats": { - "BASE": { - "p50_ms": 7.320728036575019, - "p90_ms": 7.417856063693762 - }, - "ECS": { - "p50_ms": 7.336758426390588, - "p90_ms": 7.425044267438352 - } - } - } - }, - "natural_stride_not_adopted": [ - { - "run": "natural_stride_r0", - "status": { - "status": "completed", - "correctness_cases": 21, - "dtype": "float32", - "tf32": false, - "scope": "single small-stack input backward only, no alpha or forward/model timing" - }, - "paired_change_percent": 0.06525239059100577, - "conditional_block95_percent": [ - -0.028697385654204866, - 0.14577425505556788 - ], - "p50_ms": { - "old": 0.3089229576289654, - "natural": 0.3091339021921158 - } - }, - { - "run": "natural_stride_r1", - "status": { - "status": "completed", - "correctness_cases": 21, - "dtype": "float32", - "tf32": false, - "scope": "single small-stack input backward only, no alpha or forward/model timing" - }, - "paired_change_percent": 0.03573459119047764, - "conditional_block95_percent": [ - -0.0006084065066323472, - 0.07264982019066607 - ], - "p50_ms": { - "old": 0.31001504976302385, - "natural": 0.3102158661931753 - } - } - ], - "kernel_counts": { - "full_model_per_frame": 657, - "so2_per_frame": 32, - "single_stack_forward": 8, - "single_stack_backward": 8, - "scope": "new representative profiler traces; time under profiler is not used for speed conclusions" - }, - "wrapper_equivalence": { - "real_pr_kernel_definitions": 191, - "in_process_kernel_definitions": 191, - "identical_kernel_body_multiset": true, - "same_call_after_symbol_renaming": true, - "real_call": "2eb42115a595ebef74c6ffd358fcb1a3cb9ef2f39461cc8b91cd9de89b71ebb7", - "in_process_call": "2eb42115a595ebef74c6ffd358fcb1a3cb9ef2f39461cc8b91cd9de89b71ebb7" - }, - "first_peak_capacity": { - "scope": "retained independent cold-process measurements; source unchanged; new timing protocols do not replace first peak", - "baseline": { - "first": { - "allocated": 498199552, - "reserved": 528482304 - }, - "complete": { - "allocated": 498199552, - "reserved": 528482304 - }, - "steady": { - "allocated": 397536256, - "reserved": 528482304 - } - }, - "candidate": { - "first": { - "allocated": 480156672, - "reserved": 507510784 - }, - "complete": { - "allocated": 480156672, - "reserved": 507510784 - }, - "steady": { - "allocated": 379493376, - "reserved": 507510784 - } - }, - "first_alloc_reduction_percent": 3.621617066407956, - "steady_alloc_reduction_percent": 4.538675335313314 - }, - "invalid_diagnostics": [ - "Two launcher-inspection failures before controlled timing; no comparative result used.", - "One own-worker initialization cancelled after inspection incompatibility was identified.", - "Two graph-replay setup failures (bound method / missing no_grad); not candidate numerical failures." - ], - "limitations": [ - "Neo N64 only; no new multi-model/PT2/large-edge acceptance.", - "Only partial Megakernels scheduling ideas are used, not its runtime or a cross-layer mega-kernel.", - "No stable speedup or exact zero-cost guarantee; initialization-order effect observed, low-level mechanism not uniquely attributed.", - "The natural-stride experiment was not adopted; it did not demonstrate a repeatable benefit.", - "Large-edge make_fx/AOT dispatch must be checked separately; Dynamo boundary unit tests are not AOT deployment acceptance." - ], - "raw_evidence": { - "file": "ecs-edge-cohort-controlled-raw.json.gz", - "sha256": "213178e9d326af552766ffb5b0f002b924e56a9ede703b4b7748bcde912e329d", - "compressed_bytes": 1311322 - } -} \ No newline at end of file diff --git a/doc/development/ecs-edge-cohort-controlled-timing.md b/doc/development/ecs-edge-cohort-controlled-timing.md deleted file mode 100644 index 9f90018e0e..0000000000 --- a/doc/development/ecs-edge-cohort-controlled-timing.md +++ /dev/null @@ -1,235 +0,0 @@ -# ECS controlled timing follow-up - -This follow-up investigates the previously reported N64 latency increase of -1.2845%. The kernel implementation is unchanged. It replaces the earlier -"less than 3% means noise" interpretation with counterbalanced measurements -and process-level uncertainty. No stable speedup or exact zero-cost guarantee -is claimed. - -## Main finding - -Four independent processes each held both implementations in **one CUDA -context**, using normal ASE energy/force/virial/stress calls. Two Python hash -seeds (0, 1) were each tested with baseline-first and ECS-first compilation. -Each process ran 24 symmetric ABBA blocks, 50 calls per round, eight warmups -before each round: 9600 measured calls per arm, 96 blocks in total. - -| Metric | Baseline | ECS | -| --- | ---: | ---: | -| Pooled normal-API P50, ms | 11.683581 | 11.708142 | -| Pooled normal-API P90, ms | 11.911459 | 11.932993 | -| Direct ratio of pooled P50s | reference | +0.2102% latency | -| Mean process-level paired effect | reference | **+0.0795% latency** | -| 95% Student-t interval over four independent process effects | — | **[-0.3866%, +0.5479%]** | - -The paired estimate averages log ratios of matched block medians within each -process, then gives each process equal weight. It is not the ratio of pooled -medians; both are reported rather than choosing one favorable number. Frames -are correlated within processes and are not 19200 independent experiments. - -The measured interval does not support a persistent +1.2845% penalty under -these controlled conditions. It also does not establish exact zero overhead, -or equivalence on other models, sizes, compilers or hardware. Four processes -remain a finite sample. There is no fixed 3% significance rule. - -| Process | First compiled model | Hash seed | Paired ECS latency change | -| --- | --- | ---: | ---: | -| 0 | Baseline | 0 | +0.2999% | -| 1 | ECS | 0 | -0.1536% | -| 2 | Baseline | 1 | +0.3656% | -| 3 | ECS | 1 | -0.1924% | - -Compilation/initialization order is an observed confound, not a uniquely -identified low-level cause. No claim is made that a particular cache bank, -Python hash lookup or GPU clock policy explains the entire historical result. - -## Why the old result was not an isolated ECS comparison - -The earlier independent cold runs also independently autotuned other Inductor -kernels. Compared with the first baseline process, the first candidate had -**41 different launch choices among 109 matching cached programs**. Other -candidate runs had 44/49 changed choices; other baseline runs themselves had -48/47 among 110. These are actual XBLOCK/warp/stage changes, not differences -in recorded autotune duration. This does not quantify how much of the 1.28% -each change caused, but it prevents assigning the whole difference to ECS. - -The five SO(2) GEMM schedule values remained `(64,64,32,4,2)`. A source option -such as `max_autotune=False` does not mean every generated pointwise/reduction -kernel has no runtime choice. In the controlled cache, all 110 anchor choices -were preserved. Shared semantic programs used identical actual configs, and -835 autotune/PTX/cubin file hashes remained unchanged during each main timed -window. Nothing was retuned halfway through the comparison. - -## Controls, diagnostics and rejected explanations - -These protocols have different scopes; do not pool their absolute times or -call their confidence intervals interchangeable. - -| Protocol | Independent groups | Paired change | Process t 95% interval / limitation | -| --- | ---: | ---: | --- | -| Two resident workers, GPU0, 12 ABBA blocks/group | 6 | +0.0206% | [-1.4295%, +1.4920%]; initialization-order bias remained | -| One worker at a time, GPU0, matched seeds, 400 formal calls/process | 3 pairs | -0.5510% | [-4.1574%, +3.1910%]; too few groups for a tight conclusion | -| Identical baseline versus baseline clone, GPU1 | 2 | +0.3511% | pair effects +0.8666% / -0.1618%; calibration only | -| Candidate source with ECS disabled versus original baseline, GPU1 | 2 | +0.5092% | pair effects +1.6908% / -0.6587%; no stable compatibility-overhead attribution | -| Same-context compiled-graph replay, GPU1 | 1 | +0.2261% | conditional block interval [+0.0706%, +0.3842%], not population/API acceptance | - -The last diagnostic intentionally excludes ASE, neighbor preprocessing and the -AOT runtime shell. It provides a small-overhead observation, not a substitute -for complete API performance. This model's energy inference path is -`make_fx -> aot_module_simplified -> compile_fx_inner`, **bypassing Dynamo**; -it would be incorrect to attribute the observed cost to Dynamo inference guards. - -Both GPUs were checked for foreign work on the measured card. Only one worker -executed at a time; known resident workers in the resident diagnostic were -allowed to keep idle contexts. GPU0 workers used CPU8, monitor CPU16; GPU1 used -CPU40, monitor CPU48, on their respective local NUMA nodes. NVML sampled actual -timing intervals at 50 ms. SM clocks remained dynamic, not locked. Memory -clocks, temperature, power and available clock-reason data are retained. -Whole-process idle/compilation readings are not called timing-window clocks. -No driver settings, global GPU clocks, or other users' processes were changed. - -As an explicitly secondary, post-stratified check, 90/96 primary blocks meet -all of: mean sampled SM-clock difference <=0.5%, memory clock 13801 MHz in both -arms, mean temperature difference <=2 C. Their paired estimate is +0.0861%, -process t 95% interval [-0.3873%, +0.5617%], consistent with the full result. -This is a hardware-matching criterion, **not** a latency-noise cutoff. All 96 -blocks remain in the primary analysis. Selection changes composition and -does not prove constant per-kernel clocks. In the full primary sampling, -baseline SM mean was 2776.84 MHz and candidate 2815.98 MHz; ten baseline -samples reported memory idle-clock 810 MHz. These readings are not hidden. - -In the resident experiment, average sampled SM clocks for first/second -initialized workers were approximately 2776.89/2775.65 MHz. That near equality -does not support blaming the whole initialization-order bias on downclocking. -Dynamic per-round frequency variation still prevents claiming constant clocks. -Outliers were retained; some individual API calls exceeded 30 ms. - -## Same-context setup and validation - -The manual harness first compiles one normal calculator. It then copies the -exact three reviewed kernel definitions and registered backward into the -experiment process, clears the **module-level** SeZM compiled cache, and -compiles the other calculator. Each retains its separate model-local cache. -This is necessary: the shared cache key does not independently prove that the -second experimental mode produced a new graph. - -The harness asserts distinct compiled wrappers and the expected ECS marker, -keeps the first model's cache unchanged, and checks all three reference frames. -Runtime transplantation is **benchmark setup only**; the production proposal -uses ordinary checked-in functions and does not generate or rewrite kernel -source. No checkpoint or production file is modified by the harness. - -To audit this method against the real PR integration, the generated candidate -has the same multiset of 191 embedded function bodies and the same entire -`call` AST after symbolic-size renaming. Normalized call SHA-256: -`2eb42115a595ebef74c6ffd358fcb1a3cb9ef2f39461cc8b91cd9de89b71ebb7`. -This counts source definitions, not runtime launches. The baseline remains the -unmodified commit `668b60cc2a9559f79abb9c33acd959833376344f`. - -Every same-context process passes three-frame energy/force/virial/stress -comparisons against the pure-PyTorch reference and its paired baseline, using -`atol=1e-5, rtol=1e-4`; worst error/tolerance ratio across these runs is 0.376674. -All compared outputs are finite. The reference is one fresh pure-PyTorch -process with three self-repeats, not a new multi-process Torch matrix. -The 60 focused CUDA tests were rerun: 60 passed, 14 deprecation warnings. - -## Bounded specialization attempt: not adopted - -A full single cohort has `count == E`, `start == 0`. Four runtime -`saved_edges=E` / `output_edges=E` operands were experimentally replaced with -the natural-stride sentinel zero. No floating arithmetic, launch count or tile -was changed. Each of two processes checked 21 generated cases: -E=0/1/37/129/8193/10114/16384, random/zero/tiny legal alpha; input gradients -were bitwise equal, finite, and caller-owned inputs unchanged. - -| Small-stack input-backward only | Original P50 ms | Natural stride P50 ms | Paired change | -| --- | ---: | ---: | ---: | -| Process 0 | 0.308923 | 0.309134 | +0.0653% | -| Process 1 | 0.310015 | 0.310216 | +0.0357% | - -Conditional block intervals include zero. The selected binary sets differ, -so it is not established that the compiler erased every difference; nevertheless -there is no measured benefit. This attempt is **not adopted** and is not an -end-to-end speedup. Alpha backward itself was unchanged. - -## Memory and the extent of Megakernels use - -The unchanged implementation retains the earlier independent cold-process -capacity result: **first/complete allocated peak 498.199552 -> 480.156672 MB -(-3.6216%)**, reserved 528.482304 -> 507.510784 MB (-3.9683%). Steady allocated -397.536256 -> 379.493376 MB (-4.5387%) is secondary. JIT/first execution is not -removed from the primary peak. Dual-model resident capacity is not substituted -for this single-model measurement. - -Current ECS borrows bounded-workset/dependency scheduling ideas; it does not -integrate the HazyResearch runtime or retain the reverse chain wholly on chip. -The new representative traces show **657 GPU launches/frame in both arms, -including 32 SO(2) launches**: two stacks, each 8 forward and 8 backward kernels. -There is no mega-kernel launch-count reduction in this proposal. - -The reference [Megakernels implementation](https://github.com/HazyResearch/Megakernels/blob/main/include/megakernel.cuh) -uses device loader/consumer/storer/controller roles and synchronization. Those -mechanisms are not present here. Earlier single-CTA persistent PBS experiments -in this project regressed at E=158002 (baseline backward about 5.047 ms, best of -12 PBS configurations about 8.780 ms); that route was not promoted. Fusion can -lose column parallelism, increase registers/spill and synchronization costs. -Fewer buffers or launches alone do not establish faster execution. - -## Reproduce and inspect - -Use the same checkpoint/environment as the original protocol. Provide absolute -paths for `BASE_ROOT`, `PR_ROOT`, `MODEL`, `TORCH_REFERENCE` and fresh result dirs. -CPU numbers below are specific to the reported dual-socket server; select valid -GPU-local cores on another host. - -```bash -PY=/path/to/cuda-environment/bin/python -TOOLS=$PR_ROOT/source/tests/pt_expt/kernels - -# Initialize a shared cache using normal BASE and ECS benchmark calls first. -# A nonempty explicit cache requires --allow-warm-cache in the original CLI. -$PY "$TOOLS/benchmark_so2_edge_cohort.py" --mode base --source-root "$BASE_ROOT" \ - --gpu 0 --model "$MODEL" --reference "$TORCH_REFERENCE" \ - --cache-dir "$CACHE" --out "$PREP_BASE" -$PY "$TOOLS/benchmark_so2_edge_cohort.py" --mode ecs --source-root "$PR_ROOT" \ - --gpu 0 --model "$MODEL" --reference "$TORCH_REFERENCE" \ - --cache-dir "$CACHE" --allow-warm-cache --out "$PREP_ECS" - -# Main same-context normal API; repeat all four seed/order combinations. -PYTHONHASHSEED=0 $PY "$TOOLS/benchmark_so2_edge_cohort_same_context.py" \ - --baseline-root "$BASE_ROOT" --candidate-root "$PR_ROOT" \ - --model "$MODEL" --reference "$TORCH_REFERENCE" --cache "$CACHE" \ - --gpu 0 --cpu 8 --monitor-cpu 16 --first BASE --blocks 24 --out "$NEW_OUT" -# Other combinations: seed0/ECS-first, seed1/BASE-first, seed1/ECS-first. -$PY "$TOOLS/analyze_so2_edge_cohort_same_context.py" \ - "$OUT0" "$OUT1" "$OUT2" "$OUT3" --out "$SUMMARY" -$PY "$TOOLS/analyze_so2_edge_cohort_clocks.py" \ - "$OUT0" "$OUT1" "$OUT2" "$OUT3" --out "$CLOCK_SENSITIVITY" - -# Secondary serialized control (never two resident contexts on the card). -$PY "$TOOLS/benchmark_so2_edge_cohort_serial.py" \ - --baseline-root "$BASE_ROOT" --candidate-root "$PR_ROOT" \ - --model "$MODEL" --reference "$TORCH_REFERENCE" --cache "$CACHE" \ - --gpu 0 --cpu 8 --monitor-cpu 16 --seeds 0 1 2 --rounds 8 --samples 50 \ - --conditioning-samples 200 --out "$SERIAL_OUT" -$PY "$TOOLS/analyze_so2_edge_cohort_serial.py" "$SERIAL_OUT" -``` - -`benchmark_so2_edge_cohort_paired.py` and its analyzer reproduce the secondary -resident-worker ABBA experiment (`--pairs 6 --blocks 12 --profile`). It is not -the primary estimator because of the observed resident-process bias. -Original standalone CLI behavior remains unchanged when no control hook is -provided; the shared measurement body is AST-identical to the measured copy. - -[Summary JSON](ecs-edge-cohort-controlled-results.json) includes complete -configuration, uncertainty, cache-choice changes and limitations. -[Compressed raw JSON](ecs-edge-cohort-controlled-raw.json.gz) includes per-call -wall/Event samples, conditional experiments, correctness and representative -profile summaries. It excludes private manifests, model weights and compiler -caches. Its SHA-256 is recorded in the summary; decompress with `python -m gzip --d` or read with Python's `gzip.open`. - -Unresolved gates remain: wider model/size matrices, large-edge make_fx/AOT -dispatch and deployment, PT2/C++ acceptance, and the exact low-level cause of -initialization bias. Passing a Dynamo boundary unit test is not an AOT runtime -bucket-dispatch acceptance test. Default-off draft status remains unchanged. diff --git a/doc/development/ecs-edge-cohort-multimodel-accuracy.md b/doc/development/ecs-edge-cohort-multimodel-accuracy.md deleted file mode 100644 index 8d487d6bd6..0000000000 --- a/doc/development/ecs-edge-cohort-multimodel-accuracy.md +++ /dev/null @@ -1,206 +0,0 @@ -# ECS 多模型多尺寸精度测试 - -> 精度优先；未放宽阈值。误差范围为每对输出max_abs的min～max，不是置信区间。基线SHA与模型SHA见JSON。 - -模式：TORCH=纯PyTorch编译；ECS=当前PR候选（非Neo为回退）；ECS_RUNTIME=强制真实8192分批。单位：能量/位力eV，力eV/Å，stress eV/ų。所有结果均不是DFT预测误差。 - -| 模型 | 原子数 | 对照 | 范围 | 完成进程数 | 能量max_abs范围 | 力max_abs范围 | 位力max_abs范围 | stress max_abs范围 | 最差误差/容差 | 状态 | -| --- | ---: | --- | --- | --- | --- | --- | --- | --- | ---: | --- | -| Mini | 512 | TORCH vs TORCH | within_process | 3/3 | 1.788e-07～1.907e-05 | 5.743e-06～7.458e-06 | 1.941e-05～1.008e-04 | 6.477e-09～3.453e-08 | 2.3256 | FAIL | -| Mini | 512 | TORCH vs TORCH | across_process | 3/3 | 0.000e+00～2.682e-05 | 5.574e-06～8.402e-06 | 2.161e-05～1.017e-04 | 7.439e-09～3.501e-08 | 2.0800 | FAIL | -| Mini | 512 | BASE vs BASE | within_process | 3/3 | 8.941e-07～2.909e-05 | 5.665e-06～7.634e-06 | 1.822e-05～8.902e-05 | 5.847e-09～3.065e-08 | 1.3209 | FAIL | -| Mini | 512 | BASE vs BASE | across_process | 3/3 | 2.384e-07～3.576e-05 | 5.394e-06～8.533e-06 | 1.780e-05～9.780e-05 | 6.128e-09～3.367e-08 | 1.5409 | FAIL | -| Mini | 512 | ECS vs ECS | within_process | 3/3 | 2.384e-07～2.784e-05 | 5.507e-06～8.241e-06 | 2.388e-05～1.102e-04 | 7.637e-09～3.793e-08 | 2.3481 | FAIL | -| Mini | 512 | ECS vs ECS | across_process | 3/3 | 2.980e-07～2.706e-05 | 5.538e-06～8.991e-06 | 8.570e-06～1.185e-04 | 2.600e-09～4.078e-08 | 2.7696 | FAIL | -| Mini | 512 | BASE vs TORCH | cross_implementation | 3/3 | 0.000e+00～3.856e-05 | 7.663e-06～1.194e-05 | 8.734e-05～2.392e-04 | 3.007e-08～8.237e-08 | 2.0564 | FAIL | -| Mini | 512 | ECS vs TORCH | cross_implementation | 3/3 | 1.192e-07～3.630e-05 | 7.538e-06～1.362e-05 | 8.123e-05～2.064e-04 | 2.797e-08～7.105e-08 | 2.6359 | FAIL | -| Mini | 512 | ECS vs BASE | cross_implementation | 3/3 | 1.192e-07～3.225e-05 | 5.317e-06～8.227e-06 | 7.091e-06～1.170e-04 | 2.441e-09～4.027e-08 | 2.1640 | FAIL | -| Mini | 1000 | TORCH vs TORCH | within_process | 3/3 | 7.749e-07～3.952e-05 | 5.942e-06～8.479e-06 | 3.820e-05～1.486e-04 | 6.734e-09～2.619e-08 | 0.6828 | PASS | -| Mini | 1000 | TORCH vs TORCH | across_process | 3/3 | 4.172e-07～5.251e-05 | 6.128e-06～8.859e-06 | 2.881e-05～1.862e-04 | 4.861e-09～3.283e-08 | 0.7997 | PASS | -| Mini | 1000 | BASE vs BASE | within_process | 3/3 | 5.364e-07～3.475e-05 | 5.791e-06～8.084e-06 | 2.814e-05～9.627e-05 | 4.864e-09～1.697e-08 | 0.7113 | PASS | -| Mini | 1000 | BASE vs BASE | across_process | 3/3 | 1.788e-07～5.239e-05 | 5.877e-06～9.181e-06 | 1.580e-05～1.545e-04 | 2.721e-09～2.723e-08 | 0.7256 | PASS | -| Mini | 1000 | ECS vs ECS | within_process | 3/3 | 2.384e-07～3.195e-05 | 5.835e-06～8.294e-06 | 3.425e-05～1.184e-04 | 6.019e-09～2.087e-08 | 1.1129 | FAIL | -| Mini | 1000 | ECS vs ECS | across_process | 3/3 | 2.384e-07～4.518e-05 | 5.955e-06～8.708e-06 | 1.679e-05～1.293e-04 | 2.959e-09～2.279e-08 | 1.3280 | FAIL | -| Mini | 1000 | BASE vs TORCH | cross_implementation | 3/3 | 1.192e-07～6.008e-05 | 8.291e-06～1.272e-05 | 1.898e-04～4.217e-04 | 3.346e-08～7.434e-08 | 0.9772 | PASS | -| Mini | 1000 | ECS vs TORCH | cross_implementation | 3/3 | 5.960e-08～4.542e-05 | 7.863e-06～1.166e-05 | 2.127e-04～3.895e-04 | 3.749e-08～6.865e-08 | 1.0280 | FAIL | -| Mini | 1000 | ECS vs BASE | cross_implementation | 3/3 | 2.384e-07～5.299e-05 | 5.502e-06～9.310e-06 | 1.849e-05～1.536e-04 | 3.259e-09～2.707e-08 | 1.2858 | FAIL | -| Mini | 4096 | TORCH vs TORCH | within_process | 3/3 | 5.960e-08～6.229e-05 | 7.276e-06～9.493e-06 | 5.062e-05～4.385e-04 | 2.066e-09～1.887e-08 | 12.0785 | FAIL | -| Mini | 4096 | TORCH vs TORCH | across_process | 3/3 | 5.960e-08～8.333e-05 | 6.791e-06～9.902e-06 | 5.727e-05～3.834e-04 | 2.084e-09～1.650e-08 | 18.0056 | FAIL | -| Mini | 4096 | BASE vs BASE | within_process | 3/3 | 3.099e-06～1.399e-04 | 7.088e-06～8.594e-06 | 4.909e-05～2.918e-04 | 2.043e-09～1.256e-08 | 7.5328 | FAIL | -| Mini | 4096 | BASE vs BASE | across_process | 3/3 | 3.576e-07～1.013e-04 | 6.853e-06～9.191e-06 | 4.477e-05～3.680e-04 | 1.894e-09～1.584e-08 | 8.7236 | FAIL | -| Mini | 4096 | ECS vs ECS | within_process | 3/3 | 8.941e-07～6.741e-05 | 6.751e-06～9.570e-06 | 8.410e-05～3.134e-04 | 3.504e-09～1.342e-08 | 7.6971 | FAIL | -| Mini | 4096 | ECS vs ECS | across_process | 3/3 | 2.980e-07～8.285e-05 | 6.717e-06～1.040e-05 | 3.166e-05～3.210e-04 | 1.362e-09～1.381e-08 | 8.5980 | FAIL | -| Mini | 4096 | BASE vs TORCH | cross_implementation | 3/3 | 1.073e-06～1.630e-04 | 9.423e-06～1.370e-05 | 8.078e-04～1.487e-03 | 3.476e-08～6.401e-08 | 16.3663 | FAIL | -| Mini | 4096 | ECS vs TORCH | cross_implementation | 3/3 | 1.609e-06～1.435e-04 | 9.178e-06～1.420e-05 | 7.984e-04～1.493e-03 | 3.436e-08～6.425e-08 | 14.4638 | FAIL | -| Mini | 4096 | ECS vs BASE | cross_implementation | 3/3 | 0.000e+00～1.230e-04 | 6.839e-06～9.656e-06 | 4.190e-05～3.580e-04 | 1.803e-09～1.541e-08 | 10.4884 | FAIL | -| Nano | 512 | TORCH vs TORCH | within_process | 3/3 | 1.788e-07～5.186e-05 | 8.767e-06～1.729e-05 | 4.055e-05～1.745e-04 | 1.365e-08～6.008e-08 | 5.1961 | FAIL | -| Nano | 512 | TORCH vs TORCH | across_process | 3/3 | 8.941e-07～5.031e-05 | 8.257e-06～1.488e-05 | 3.397e-05～1.774e-04 | 1.067e-08～6.108e-08 | 8.1770 | FAIL | -| Nano | 512 | BASE vs BASE | within_process | 3/3 | 6.557e-07～5.233e-05 | 8.955e-06～1.241e-05 | 4.078e-05～1.411e-04 | 1.404e-08～4.859e-08 | 4.3052 | FAIL | -| Nano | 512 | BASE vs BASE | across_process | 3/3 | 3.576e-07～4.852e-05 | 8.638e-06～1.356e-05 | 2.392e-05～1.993e-04 | 7.193e-09～6.862e-08 | 5.5042 | FAIL | -| Nano | 512 | ECS vs ECS | within_process | 3/3 | 2.384e-07～4.870e-05 | 8.850e-06～1.174e-05 | 2.709e-05～1.370e-04 | 9.077e-09～4.717e-08 | 3.9393 | FAIL | -| Nano | 512 | ECS vs ECS | across_process | 3/3 | 2.980e-07～6.038e-05 | 8.533e-06～1.260e-05 | 2.392e-05～2.191e-04 | 8.234e-09～7.544e-08 | 5.7037 | FAIL | -| Nano | 512 | BASE vs TORCH | cross_implementation | 3/3 | 2.980e-06～1.152e-04 | 8.867e-06～1.386e-05 | 1.153e-05～2.624e-04 | 3.971e-09～9.033e-08 | 9.4910 | FAIL | -| Nano | 512 | ECS vs TORCH | cross_implementation | 3/3 | 4.172e-07～1.128e-04 | 8.495e-06～1.357e-05 | 2.651e-05～2.647e-04 | 9.126e-09～9.113e-08 | 8.6337 | FAIL | -| Nano | 512 | ECS vs BASE | cross_implementation | 3/3 | 4.172e-07～5.412e-05 | 8.595e-06～1.269e-05 | 2.358e-05～2.068e-04 | 8.119e-09～7.121e-08 | 6.5564 | FAIL | -| Nano | 1000 | TORCH vs TORCH | within_process | 3/3 | 1.788e-07～6.706e-05 | 9.814e-06～1.328e-05 | 5.537e-05～2.621e-04 | 9.760e-09～4.621e-08 | 5.8245 | FAIL | -| Nano | 1000 | TORCH vs TORCH | across_process | 3/3 | 8.941e-07～7.910e-05 | 9.717e-06～1.356e-05 | 3.931e-05～2.863e-04 | 6.794e-09～5.047e-08 | 7.5700 | FAIL | -| Nano | 1000 | BASE vs BASE | within_process | 3/3 | 1.073e-06～5.555e-05 | 8.786e-06～1.246e-05 | 4.376e-05～2.458e-04 | 7.638e-09～4.333e-08 | 6.4322 | FAIL | -| Nano | 1000 | BASE vs BASE | across_process | 3/3 | 5.960e-08～7.153e-05 | 9.121e-06～1.302e-05 | 5.281e-05～2.499e-04 | 9.309e-09～4.405e-08 | 9.2287 | FAIL | -| Nano | 1000 | ECS vs ECS | within_process | 3/3 | 1.192e-06～5.585e-05 | 9.348e-06～1.242e-05 | 4.847e-05～2.198e-04 | 8.545e-09～3.874e-08 | 4.9302 | FAIL | -| Nano | 1000 | ECS vs ECS | across_process | 3/3 | 5.960e-08～6.944e-05 | 9.239e-06～1.430e-05 | 2.857e-05～2.319e-04 | 5.036e-09～4.087e-08 | 6.9685 | FAIL | -| Nano | 1000 | BASE vs TORCH | cross_implementation | 3/3 | 1.049e-05～1.621e-04 | 9.189e-06～1.489e-05 | 3.178e-05～2.637e-04 | 5.601e-09～4.649e-08 | 9.2328 | FAIL | -| Nano | 1000 | ECS vs TORCH | cross_implementation | 3/3 | 1.264e-05～1.663e-04 | 9.170e-06～1.386e-05 | 1.395e-05～3.074e-04 | 2.459e-09～5.418e-08 | 8.0526 | FAIL | -| Nano | 1000 | ECS vs BASE | cross_implementation | 3/3 | 0.000e+00～7.570e-05 | 9.041e-06～1.358e-05 | 3.918e-05～2.690e-04 | 6.906e-09～4.742e-08 | 8.1419 | FAIL | -| Nano | 4096 | TORCH vs TORCH | within_process | 3/3 | 3.755e-06～1.802e-04 | 1.138e-05～1.498e-05 | 9.571e-05～6.979e-04 | 4.119e-09～3.003e-08 | 2.3384 | FAIL | -| Nano | 4096 | TORCH vs TORCH | across_process | 3/3 | 5.960e-08～2.024e-04 | 1.065e-05～1.443e-05 | 9.505e-05～6.595e-04 | 3.965e-09～2.838e-08 | 2.8159 | FAIL | -| Nano | 4096 | BASE vs BASE | within_process | 3/3 | 2.384e-06～1.985e-04 | 1.098e-05～1.587e-05 | 1.133e-04～3.956e-04 | 4.860e-09～1.703e-08 | 1.3307 | FAIL | -| Nano | 4096 | BASE vs BASE | across_process | 3/3 | 1.907e-06～2.370e-04 | 1.060e-05～1.419e-05 | 5.914e-05～4.544e-04 | 2.379e-09～1.956e-08 | 2.5569 | FAIL | -| Nano | 4096 | ECS vs ECS | within_process | 3/3 | 3.278e-06～1.050e-04 | 1.101e-05～1.347e-05 | 6.502e-05～7.514e-04 | 2.450e-09～3.233e-08 | 1.2806 | FAIL | -| Nano | 4096 | ECS vs ECS | across_process | 3/3 | 1.788e-07～1.045e-04 | 1.095e-05～1.462e-05 | 8.139e-05～6.386e-04 | 3.503e-09～2.748e-08 | 2.1274 | FAIL | -| Nano | 4096 | BASE vs TORCH | cross_implementation | 3/3 | 9.382e-05～4.325e-04 | 1.070e-05～1.538e-05 | 8.534e-05～1.047e-03 | 3.673e-09～4.504e-08 | 3.3957 | FAIL | -| Nano | 4096 | ECS vs TORCH | cross_implementation | 3/3 | 1.401e-04～4.290e-04 | 1.060e-05～1.602e-05 | 9.420e-05～1.060e-03 | 3.617e-09～4.560e-08 | 3.0524 | FAIL | -| Nano | 4096 | ECS vs BASE | cross_implementation | 3/3 | 5.960e-08～1.978e-04 | 1.056e-05～1.622e-05 | 7.473e-05～6.500e-04 | 3.042e-09～2.797e-08 | 2.4384 | FAIL | -| Neo | 512 | TORCH vs TORCH | within_process | 3/3 | 5.960e-08～1.943e-05 | 3.261e-06～4.747e-06 | 1.320e-05～7.500e-05 | 4.517e-09～2.582e-08 | 0.6232 | PASS | -| Neo | 512 | TORCH vs TORCH | across_process | 3/3 | 4.768e-07～1.884e-05 | 3.108e-06～5.441e-06 | 9.732e-06～7.461e-05 | 2.689e-09～2.569e-08 | 0.6851 | PASS | -| Neo | 512 | BASE vs BASE | within_process | 3/3 | 5.960e-08～2.521e-05 | 3.148e-06～4.934e-06 | 1.098e-05～4.944e-05 | 3.581e-09～1.702e-08 | 0.6621 | PASS | -| Neo | 512 | BASE vs BASE | across_process | 3/3 | 2.384e-07～2.354e-05 | 3.135e-06～5.108e-06 | 9.663e-06～6.807e-05 | 3.327e-09～2.344e-08 | 0.7136 | PASS | -| Neo | 512 | ECS vs ECS | within_process | 3/3 | 1.192e-07～1.627e-05 | 3.263e-06～4.824e-06 | 5.221e-06～4.653e-05 | 1.513e-09～1.602e-08 | 0.7407 | PASS | -| Neo | 512 | ECS vs ECS | across_process | 3/3 | 0.000e+00～1.740e-05 | 3.247e-06～4.922e-06 | 1.290e-05～6.690e-05 | 3.928e-09～2.303e-08 | 0.8633 | PASS | -| Neo | 512 | BASE vs TORCH | cross_implementation | 3/3 | 0.000e+00～3.117e-05 | 4.321e-06～6.981e-06 | 7.577e-05～1.771e-04 | 2.609e-08～6.098e-08 | 1.1932 | FAIL | -| Neo | 512 | ECS vs TORCH | cross_implementation | 3/3 | 1.192e-07～2.927e-05 | 4.556e-06～7.410e-06 | 7.720e-05～1.594e-04 | 2.658e-08～5.487e-08 | 1.0295 | FAIL | -| Neo | 512 | ECS vs BASE | cross_implementation | 3/3 | 0.000e+00～2.331e-05 | 3.149e-06～5.301e-06 | 7.728e-06～7.278e-05 | 2.660e-09～2.506e-08 | 0.9591 | PASS | -| Neo | 512 | ECS_RUNTIME vs ECS_RUNTIME | within_process | 3/3 | 4.172e-07～1.824e-05 | 3.403e-06～4.479e-06 | 1.187e-05～4.710e-05 | 2.963e-09～1.622e-08 | 1.0518 | FAIL | -| Neo | 512 | ECS_RUNTIME vs ECS_RUNTIME | across_process | 3/3 | 5.960e-08～1.431e-05 | 3.251e-06～5.502e-06 | 6.900e-06～5.424e-05 | 2.375e-09～1.867e-08 | 0.9080 | PASS | -| Neo | 512 | ECS_RUNTIME vs BASE | cross_implementation | 3/3 | 5.960e-08～2.301e-05 | 3.042e-06～5.382e-06 | 9.281e-06～6.285e-05 | 2.715e-09～2.164e-08 | 1.0467 | FAIL | -| Neo | 512 | ECS_RUNTIME vs TORCH | cross_implementation | 3/3 | 5.960e-08～2.372e-05 | 4.383e-06～7.113e-06 | 7.607e-05～1.648e-04 | 2.619e-08～5.675e-08 | 1.0368 | FAIL | -| Neo | 512 | ECS_RUNTIME vs ECS | cross_implementation | 3/3 | 0.000e+00～2.110e-05 | 3.024e-06～5.139e-06 | 6.926e-06～6.315e-05 | 2.384e-09～2.174e-08 | 1.1043 | FAIL | -| Neo | 1000 | TORCH vs TORCH | within_process | 3/3 | 5.960e-07～2.939e-05 | 3.633e-06～5.042e-06 | 1.796e-05～8.294e-05 | 2.496e-09～1.462e-08 | 0.4762 | PASS | -| Neo | 1000 | TORCH vs TORCH | across_process | 3/3 | 1.192e-07～2.736e-05 | 3.471e-06～5.588e-06 | 1.483e-05～1.008e-04 | 2.181e-09～1.777e-08 | 0.4978 | PASS | -| Neo | 1000 | BASE vs BASE | within_process | 3/3 | 7.153e-07～2.074e-05 | 3.335e-06～4.864e-06 | 1.662e-05～7.371e-05 | 2.929e-09～1.299e-08 | 0.3498 | PASS | -| Neo | 1000 | BASE vs BASE | across_process | 3/3 | 5.960e-08～1.848e-05 | 3.363e-06～5.305e-06 | 1.574e-05～1.023e-04 | 2.277e-09～1.802e-08 | 0.5508 | PASS | -| Neo | 1000 | ECS vs ECS | within_process | 3/3 | 6.557e-07～3.302e-05 | 3.544e-06～4.635e-06 | 1.599e-05～7.633e-05 | 2.645e-09～1.346e-08 | 0.3679 | PASS | -| Neo | 1000 | ECS vs ECS | across_process | 3/3 | 2.980e-07～3.064e-05 | 3.362e-06～5.389e-06 | 1.043e-05～1.018e-04 | 1.839e-09～1.794e-08 | 0.5019 | PASS | -| Neo | 1000 | BASE vs TORCH | cross_implementation | 3/3 | 0.000e+00～2.736e-05 | 4.921e-06～7.257e-06 | 1.523e-04～2.917e-04 | 2.685e-08～5.141e-08 | 0.6108 | PASS | -| Neo | 1000 | ECS vs TORCH | cross_implementation | 3/3 | 1.192e-07～4.029e-05 | 5.026e-06～8.691e-06 | 1.513e-04～3.357e-04 | 2.667e-08～5.917e-08 | 0.5704 | PASS | -| Neo | 1000 | ECS vs BASE | cross_implementation | 3/3 | 0.000e+00～3.189e-05 | 3.352e-06～5.042e-06 | 1.021e-05～1.058e-04 | 1.644e-09～1.866e-08 | 0.5937 | PASS | -| Neo | 1000 | ECS_RUNTIME vs ECS_RUNTIME | within_process | 3/3 | 2.384e-07～2.861e-05 | 3.570e-06～4.866e-06 | 1.601e-05～1.015e-04 | 2.815e-09～1.790e-08 | 0.4164 | PASS | -| Neo | 1000 | ECS_RUNTIME vs ECS_RUNTIME | across_process | 3/3 | 2.384e-07～2.778e-05 | 3.505e-06～5.059e-06 | 1.429e-05～1.104e-04 | 2.186e-09～1.946e-08 | 0.3538 | PASS | -| Neo | 1000 | ECS_RUNTIME vs BASE | cross_implementation | 3/3 | 5.960e-08～2.718e-05 | 3.289e-06～4.839e-06 | 1.072e-05～1.038e-04 | 1.889e-09～1.829e-08 | 0.6028 | PASS | -| Neo | 1000 | ECS_RUNTIME vs TORCH | cross_implementation | 3/3 | 5.960e-08～3.272e-05 | 4.816e-06～7.409e-06 | 1.576e-04～3.508e-04 | 2.778e-08～6.184e-08 | 0.5441 | PASS | -| Neo | 1000 | ECS_RUNTIME vs ECS | cross_implementation | 3/3 | 5.960e-08～3.481e-05 | 3.344e-06～5.309e-06 | 1.161e-05～1.169e-04 | 1.612e-09～2.061e-08 | 0.5110 | PASS | -| Neo | 4096 | BASE vs BASE | within_process | 3/3 | 2.384e-07～4.643e-05 | 4.007e-06～5.565e-06 | 4.326e-05～1.764e-04 | 1.428e-09～7.592e-09 | 2.0354 | FAIL | -| Neo | 4096 | BASE vs BASE | across_process | 3/3 | 4.172e-07～5.627e-05 | 3.912e-06～5.939e-06 | 2.275e-05～2.513e-04 | 9.788e-10～1.081e-08 | 2.8536 | FAIL | -| Neo | 4096 | ECS vs ECS | within_process | 3/3 | 2.384e-07～5.406e-05 | 4.204e-06～5.183e-06 | 2.174e-05～1.084e-04 | 5.853e-10～4.666e-09 | 2.2326 | FAIL | -| Neo | 4096 | ECS vs ECS | across_process | 3/3 | 3.576e-07～8.285e-05 | 4.080e-06～5.679e-06 | 2.126e-05～1.705e-04 | 5.108e-10～7.337e-09 | 2.8543 | FAIL | -| Neo | 4096 | ECS vs BASE | cross_implementation | 3/3 | 5.960e-08～6.813e-05 | 4.013e-06～5.806e-06 | 2.113e-05～2.482e-04 | 6.362e-10～1.068e-08 | 3.2394 | FAIL | -| Neo | 4096 | ECS_RUNTIME vs ECS_RUNTIME | within_process | 3/3 | 3.576e-07～5.656e-05 | 4.011e-06～5.388e-06 | 2.321e-05～2.127e-04 | 9.989e-10～9.155e-09 | 3.0024 | FAIL | -| Neo | 4096 | ECS_RUNTIME vs ECS_RUNTIME | across_process | 3/3 | 2.384e-07～6.253e-05 | 4.014e-06～6.203e-06 | 2.701e-05～1.597e-04 | 9.890e-10～6.871e-09 | 2.9657 | FAIL | -| Neo | 4096 | ECS_RUNTIME vs BASE | cross_implementation | 3/3 | 5.960e-08～6.562e-05 | 4.056e-06～6.068e-06 | 1.560e-05～2.561e-04 | 6.714e-10～1.102e-08 | 3.5269 | FAIL | -| Neo | 4096 | ECS_RUNTIME vs ECS | cross_implementation | 3/3 | 5.960e-08～7.308e-05 | 3.972e-06～5.791e-06 | 2.029e-05～1.955e-04 | 8.732e-10～8.411e-09 | 3.1677 | FAIL | -| Pro | 64 | TORCH vs TORCH | within_process | 1/1 | 5.960e-08～1.520e-06 | 7.994e-07～1.084e-06 | 1.829e-06～4.709e-06 | 3.323e-09～1.297e-08 | 0.0749 | PASS（样本未齐） | -| Pro | 64 | BASE vs BASE | within_process | 1/1 | 2.086e-07～2.235e-06 | 7.070e-07～9.719e-07 | 1.706e-06～4.243e-06 | 4.698e-09～1.169e-08 | 0.0727 | PASS（样本未齐） | -| Pro | 64 | ECS vs ECS | within_process | 2/2 | 2.980e-08～3.010e-06 | 6.763e-07～9.147e-07 | 2.130e-06～7.250e-06 | 3.415e-09～1.997e-08 | 0.0893 | PASS（样本未齐） | -| Pro | 64 | ECS vs ECS | across_process | 2/2 | 2.980e-08～4.113e-06 | 6.775e-07～1.166e-06 | 1.785e-06～6.365e-06 | 2.901e-09～1.753e-08 | 0.1206 | PASS（样本未齐） | -| Pro | 64 | BASE vs TORCH | cross_implementation | 1/1 | 1.788e-07～3.934e-06 | 1.086e-06～1.643e-06 | 2.214e-06～9.813e-06 | 5.613e-09～2.703e-08 | 0.0992 | PASS（样本未齐） | -| Pro | 64 | ECS vs TORCH | cross_implementation | 2/1 | 2.086e-07～4.351e-06 | 9.742e-07～1.560e-06 | 2.394e-06～1.112e-05 | 5.669e-09～3.062e-08 | 0.1440 | PASS（样本未齐） | -| Pro | 64 | ECS vs BASE | cross_implementation | 2/1 | 5.960e-08～3.636e-06 | 6.614e-07～1.147e-06 | 1.603e-06～6.327e-06 | 2.958e-09～1.743e-08 | 0.0945 | PASS（样本未齐） | - -## 所有进程状态（含OOM/失败，不能略去） - -| 运行 | 状态 | 参数量 | opaque ECS调用 | single路径标记 | -| --- | --- | ---: | ---: | --- | -| Mini_n1000_BASE_p0 | completed | 654988 | 0 | False | -| Mini_n1000_BASE_p1 | completed | 654988 | 0 | False | -| Mini_n1000_BASE_p2 | completed | 654988 | 0 | False | -| Mini_n1000_ECS_p0 | completed | 654988 | 0 | False | -| Mini_n1000_ECS_p1 | completed | 654988 | 0 | False | -| Mini_n1000_ECS_p2 | completed | 654988 | 0 | False | -| Mini_n1000_TORCH_p0 | completed | 654988 | 0 | False | -| Mini_n1000_TORCH_p1 | completed | 654988 | 0 | False | -| Mini_n1000_TORCH_p2 | completed | 654988 | 0 | False | -| Mini_n4096_BASE_p0 | completed | 654988 | 0 | False | -| Mini_n4096_BASE_p1 | completed | 654988 | 0 | False | -| Mini_n4096_BASE_p2 | completed | 654988 | 0 | False | -| Mini_n4096_ECS_p0 | completed | 654988 | 0 | False | -| Mini_n4096_ECS_p1 | completed | 654988 | 0 | False | -| Mini_n4096_ECS_p2 | completed | 654988 | 0 | False | -| Mini_n4096_TORCH_p0 | completed | 654988 | 0 | False | -| Mini_n4096_TORCH_p1 | completed | 654988 | 0 | False | -| Mini_n4096_TORCH_p2 | completed | 654988 | 0 | False | -| Mini_n512_BASE_p0 | completed | 654988 | 0 | False | -| Mini_n512_BASE_p1 | completed | 654988 | 0 | False | -| Mini_n512_BASE_p2 | completed | 654988 | 0 | False | -| Mini_n512_ECS_p0 | completed | 654988 | 0 | False | -| Mini_n512_ECS_p1 | completed | 654988 | 0 | False | -| Mini_n512_ECS_p2 | completed | 654988 | 0 | False | -| Mini_n512_TORCH_p0 | completed | 654988 | 0 | False | -| Mini_n512_TORCH_p1 | completed | 654988 | 0 | False | -| Mini_n512_TORCH_p2 | completed | 654988 | 0 | False | -| Nano_n1000_BASE_p0 | completed | 480268 | 0 | False | -| Nano_n1000_BASE_p1 | completed | 480268 | 0 | False | -| Nano_n1000_BASE_p2 | completed | 480268 | 0 | False | -| Nano_n1000_ECS_p0 | completed | 480268 | 0 | False | -| Nano_n1000_ECS_p1 | completed | 480268 | 0 | False | -| Nano_n1000_ECS_p2 | completed | 480268 | 0 | False | -| Nano_n1000_TORCH_p0 | completed | 480268 | 0 | False | -| Nano_n1000_TORCH_p1 | completed | 480268 | 0 | False | -| Nano_n1000_TORCH_p2 | completed | 480268 | 0 | False | -| Nano_n4096_BASE_p0 | completed | 480268 | 0 | False | -| Nano_n4096_BASE_p1 | completed | 480268 | 0 | False | -| Nano_n4096_BASE_p2 | completed | 480268 | 0 | False | -| Nano_n4096_ECS_p0 | completed | 480268 | 0 | False | -| Nano_n4096_ECS_p1 | completed | 480268 | 0 | False | -| Nano_n4096_ECS_p2 | completed | 480268 | 0 | False | -| Nano_n4096_TORCH_p0 | completed | 480268 | 0 | False | -| Nano_n4096_TORCH_p1 | completed | 480268 | 0 | False | -| Nano_n4096_TORCH_p2 | completed | 480268 | 0 | False | -| Nano_n512_BASE_p0 | completed | 480268 | 0 | False | -| Nano_n512_BASE_p1 | completed | 480268 | 0 | False | -| Nano_n512_BASE_p2 | completed | 480268 | 0 | False | -| Nano_n512_ECS_p0 | completed | 480268 | 0 | False | -| Nano_n512_ECS_p1 | completed | 480268 | 0 | False | -| Nano_n512_ECS_p2 | completed | 480268 | 0 | False | -| Nano_n512_TORCH_p0 | completed | 480268 | 0 | False | -| Nano_n512_TORCH_p1 | completed | 480268 | 0 | False | -| Nano_n512_TORCH_p2 | completed | 480268 | 0 | False | -| Neo_n1000_BASE_p0 | completed | 1124910 | 0 | False | -| Neo_n1000_BASE_p1 | completed | 1124910 | 0 | False | -| Neo_n1000_BASE_p2 | completed | 1124910 | 0 | False | -| Neo_n1000_ECS_RUNTIME_p0 | completed | 1124910 | 2 | False | -| Neo_n1000_ECS_RUNTIME_p1 | completed | 1124910 | 2 | False | -| Neo_n1000_ECS_RUNTIME_p2 | completed | 1124910 | 2 | False | -| Neo_n1000_ECS_p0 | completed | 1124910 | 0 | True | -| Neo_n1000_ECS_p1 | completed | 1124910 | 0 | True | -| Neo_n1000_ECS_p2 | completed | 1124910 | 0 | True | -| Neo_n1000_TORCH_p0 | completed | 1124910 | 0 | False | -| Neo_n1000_TORCH_p1 | completed | 1124910 | 0 | False | -| Neo_n1000_TORCH_p2 | completed | 1124910 | 0 | False | -| Neo_n4096_BASE_p0 | completed | 1124910 | 0 | False | -| Neo_n4096_BASE_p1 | completed | 1124910 | 0 | False | -| Neo_n4096_BASE_p2 | completed | 1124910 | 0 | False | -| Neo_n4096_ECS_RUNTIME_p0 | completed | 1124910 | 2 | False | -| Neo_n4096_ECS_RUNTIME_p1 | completed | 1124910 | 2 | False | -| Neo_n4096_ECS_RUNTIME_p2 | completed | 1124910 | 2 | False | -| Neo_n4096_ECS_p0 | completed | 1124910 | 0 | True | -| Neo_n4096_ECS_p1 | completed | 1124910 | 0 | True | -| Neo_n4096_ECS_p2 | completed | 1124910 | 0 | True | -| Neo_n4096_TORCH_p0 | oom | 1124910 | 0 | False | -| Neo_n4096_TORCH_p1 | oom | 1124910 | 0 | False | -| Neo_n4096_TORCH_p2 | oom | 1124910 | 0 | False | -| Neo_n512_BASE_p0 | completed | 1124910 | 0 | False | -| Neo_n512_BASE_p1 | completed | 1124910 | 0 | False | -| Neo_n512_BASE_p2 | completed | 1124910 | 0 | False | -| Neo_n512_ECS_RUNTIME_p0 | completed | 1124910 | 2 | False | -| Neo_n512_ECS_RUNTIME_p1 | completed | 1124910 | 2 | False | -| Neo_n512_ECS_RUNTIME_p2 | completed | 1124910 | 2 | False | -| Neo_n512_ECS_p0 | completed | 1124910 | 0 | True | -| Neo_n512_ECS_p1 | completed | 1124910 | 0 | True | -| Neo_n512_ECS_p2 | completed | 1124910 | 0 | True | -| Neo_n512_TORCH_p0 | completed | 1124910 | 0 | False | -| Neo_n512_TORCH_p1 | completed | 1124910 | 0 | False | -| Neo_n512_TORCH_p2 | completed | 1124910 | 0 | False | -| Pro_n512_TORCH_p0 | oom | 22678990 | 0 | False | -| Pro_n64_BASE_p0 | completed | 22678990 | 0 | False | -| Pro_n64_ECS_p0 | completed | 22678990 | 0 | False | -| Pro_n64_ECS_p1 | completed | 22678990 | 0 | False | -| Pro_n64_TORCH_p0 | completed | 22678990 | 0 | False | diff --git a/doc/development/ecs-edge-cohort-multimodel-results.json b/doc/development/ecs-edge-cohort-multimodel-results.json deleted file mode 100644 index 5e00d3a703..0000000000 --- a/doc/development/ecs-edge-cohort-multimodel-results.json +++ /dev/null @@ -1,11013 +0,0 @@ -{ - "threshold": { - "atol": 1e-05, - "rtol": 0.0001, - "relative_floor": 1e-08 - }, - "range_definition": "min/max of max_abs over same-frame comparisons; NOT a confidence interval", - "statuses": [ - { - "run": "Mini_n1000_BASE_p0", - "model": "Mini", - "n": 1000, - "mode": "BASE", - "gpu": "1", - "process": "p0", - "status": "completed", - "raw_status": "completed", - "model_sha256": "13edeea63448c0f8a0b38e08fd8ba196fb1e2fad5f12486b1cd23320829da268", - "routes": [ - { - "sha256": 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} - } - }, - { - "model": "Pro", - "n": 512, - "left": "ECS", - "right": "BASE", - "scope": "cross_implementation", - "left_completed_processes": 3, - "right_completed_processes": 3, - "all_passed": true, - "metrics": { - "energy": { - "comparisons": 243, - "failures": 0, - "all_finite": true, - "all_bitwise": false, - "max_abs_range": [ - 2.9802322387695312e-08, - 9.059906005859375e-06 - ], - "worst_mean_abs": 9.059906005859375e-06, - "worst_max_relative": 1.9531790525757608e-09, - "worst_tolerance_ratio": 1.9531369458898076e-05 - }, - "forces": { - "comparisons": 243, - "failures": 0, - "all_finite": true, - "all_bitwise": false, - "max_abs_range": [ - 8.428170579222183e-07, - 1.6497713382079837e-06 - ], - "worst_mean_abs": 2.422898338375736e-07, - "worst_max_relative": 0.005169339914793636, - "worst_tolerance_ratio": 0.10245559618495009 - }, - "virial": { - "comparisons": 243, - "failures": 0, - "all_finite": true, - "all_bitwise": false, - "max_abs_range": [ - 2.4501980202540352e-06, - 2.0536486857736236e-05 - ], - "worst_mean_abs": 8.182635733982529e-06, - "worst_max_relative": 3.856703373854477e-05, - "worst_tolerance_ratio": 0.279305619586037 - }, - "stress": { - "comparisons": 243, - "failures": 0, - "all_finite": true, - "all_bitwise": false, - "max_abs_range": [ - 5.848777862851526e-10, - 7.070283847970815e-09 - ], - "worst_mean_abs": 3.0359155135010474e-09, - "worst_max_relative": 2.8521296126945394e-05, - "worst_tolerance_ratio": 0.0005746928364611155 - } - } - } - ] -} \ No newline at end of file diff --git a/doc/development/ecs-edge-cohort-multimodel-summary.md b/doc/development/ecs-edge-cohort-multimodel-summary.md deleted file mode 100644 index 1653f68ec0..0000000000 --- a/doc/development/ecs-edge-cohort-multimodel-summary.md +++ /dev/null @@ -1,54 +0,0 @@ -> Historical baseline668b60cc, completed117-process archive (102 complete/15 OOM), finalized2026-10-26. This is not the newer master2ba617bc comparison; see ecs-validation-20261025/REPORT.md. - -# ECS 精度验收汇总（2026-10-19） - -> 以精度为先；下表数值为最差误差÷容差，≤1才满足该项既定判据。未满3个进程注明未齐。OOM不是PASS。 - -已记录117个进程，其中102个完整执行；标准矩阵108进程，加真实Neo分批9进程，共计划117。此轮不是性能测试。 - -## 模型与尺寸总表 - -| 模型 | 原子数 | Torch自身 | BASE自身 | PR候选自身 | PR vs BASE | PR vs Torch | PR实际路线 | -| --- | ---: | ---: | ---: | ---: | ---: | ---: | --- | -| Neo | 512 | 0.685 | 0.714 | 0.863 | 0.959 | 1.030 | single（非8192运行时分批） | -| Neo | 1000 | 0.498 | 0.551 | 0.502 | 0.594 | 0.570 | single（非8192运行时分批） | -| Neo | 4096 | OOM/缺参照 | 2.854 | 2.854 | 3.239 | OOM/缺参照 | single（非8192运行时分批） | -| Nano | 512 | 8.177 | 5.504 | 5.704 | 6.556 | 8.634 | 回退原生，不能算ECS验证 | -| Nano | 1000 | 7.570 | 9.229 | 6.968 | 8.142 | 8.053 | 回退原生，不能算ECS验证 | -| Nano | 4096 | 2.816 | 2.557 | 2.127 | 2.438 | 3.052 | 回退原生，不能算ECS验证 | -| Mini | 512 | 2.326 | 1.541 | 2.770 | 2.164 | 2.636 | 回退原生，不能算ECS验证 | -| Mini | 1000 | 0.800 | 0.726 | 1.328 | 1.286 | 1.028 | 回退原生，不能算ECS验证 | -| Mini | 4096 | 18.006 | 8.724 | 8.598 | 10.488 | 14.464 | 回退原生，不能算ECS验证 | -| Pro | 64 | 0.130 | 0.122 | 0.121 | 0.152 | 0.144 | 回退原生，不能算ECS验证 | -| Pro | 512 | OOM/缺参照 | 0.281 | 0.249 | 0.279 | OOM/缺参照 | 回退原生，不能算ECS验证 | -| Pro | 4096 | OOM/缺参照 | OOM/缺参照 | OOM/缺参照 | OOM/缺参照 | OOM/缺参照 | 回退原生，不能算ECS验证 | - -## 真正8192边运行时分批：与PR单批路径分开 - -| 模型 | 原子数 | 自身 | 对BASE | 对Torch | 对PR单批 | 已观察真实cohort | -| --- | ---: | ---: | ---: | ---: | ---: | --- | -| Neo | 512 | 1.052 | 1.047 | 1.037 | 1.104 | [(80898, 8192, 10)] | -| Neo | 1000 | 0.416 | 0.603 | 0.544 | 0.511 | [(158002, 8192, 20)] | -| Neo | 4096 | 3.002 | 3.527 | OOM/缺参照 | 3.168 | [(647170, 8192, 80)] | - -## 4096原子位力最大绝对误差范围（eV） - -范围是各对输出max_abs的min~max，非置信区间；所有比较仅对同一帧进行。 - -| 模型 | Torch自身 | BASE自身 | PR自身 | PR vs BASE | PR vs Torch | -| --- | --- | --- | --- | --- | --- | -| Neo | OOM/缺参照 | 2.275e-05 ~ 2.513e-04 | 2.126e-05 ~ 1.705e-04 | 2.113e-05 ~ 2.482e-04 | OOM/缺参照 | -| Nano | 9.505e-05 ~ 6.979e-04 | 5.914e-05 ~ 4.544e-04 | 6.502e-05 ~ 7.514e-04 | 7.473e-05 ~ 6.500e-04 | 9.420e-05 ~ 1.060e-03 | -| Mini | 5.062e-05 ~ 4.385e-04 | 4.477e-05 ~ 3.680e-04 | 3.166e-05 ~ 3.210e-04 | 4.190e-05 ~ 3.580e-04 | 7.984e-04 ~ 1.493e-03 | -| Pro | OOM/缺参照 | OOM/缺参照 | OOM/缺参照 | OOM/缺参照 | OOM/缺参照 | - -## 必须保留的限制 - -- 判据仍为逐元素atol=1e-5、rtol=1e-4，finite必需；relative分母max(abs(ref),1e-8)。不因BASE自身波动自动放宽。所有输出误差详情见ACCURACY_TABLE.md与pair_details.json。 -- Torch是关闭Triton的编译路径，不是无限精度真值。Neo4096该路径三次OOM，因此不能宣称该规模对Torch参照通过。 -- BASE固定PR原基线668b60cc；当前远端master4f78c2d0不属于本矩阵。原模型权重不改、TF32/AMP关闭；不混合FP16x3。 -- 非Neo模型的PR候选是安全回退，不是多模型ECS性能成果。ECS_RUNTIME实验只在独立进程把分支阈值置0，不改PR源文件；真实调用计数确认实际分批。 -- Pro是官方登记的Pro-MPtrj，不是OMat24-Pro；官方MD5/HF SHA256匹配。旧fitting type只在读取时兼容映射，严格权重检查零缺失；三路同样处理，见models/pro_mptrj/COMPATIBILITY.md。 -- 本轮只有碳晶胞/三帧扰动的实现等价性测试，不是DFT预测准确率、所有化学体系或完整MD轨迹验收。 -- 能量/位力eV、力eV/Å、stress eV/ų。相同数值atol跨单位不是相同物理精度；stress通过不能覆盖virial失败。任何阈值调整需事前约定，本轮未调整。 -- 九次调用包含首次编译，用于误差样本，不报稳态P50或加速比。详见SCIENCE_AND_PROTOCOL.md；原始NPZ逐次保留。 diff --git a/doc/development/ecs-edge-cohort-multimodel-update.md b/doc/development/ecs-edge-cohort-multimodel-update.md deleted file mode 100644 index b011945994..0000000000 --- a/doc/development/ecs-edge-cohort-multimodel-update.md +++ /dev/null @@ -1,64 +0,0 @@ -> **2026-10-26 status update:** the old-base matrix is now complete (117 processes,102 complete,15 OOM); earlier pending-Pro statements below describe the historical publication snapshot. The new135-process master2ba617bc comparison is in [ecs-validation-20261025](ecs-validation-20261025/README.md). Neither matrix establishes general production acceptance. - -# ECS multi-model and N4096 follow-up (2026-10-19) - -This is a measurement update for the default-off Draft PR, not a claim of multi-model production acceptance. The production implementation is unchanged from PR head `9e2e4c5de1b90cf2d6bc8b2706bc103a06778dae`; BASE is pinned to `668b60cc2a9559f79abb9c33acd959833376344f`, not subsequent upstream master. - -## Read the results by execution path - -- **PR candidate:** unmodified current PR code. In the measured Neo make_fx/AOT deployment graphs, the small-edge single-cohort branch is specialized even for N4096. Those runs are **not** evidence of an 8192-edge workspace bound at large E. -- **ECS_RUNTIME diagnostic:** separate processes set `SINGLE_COHORT_MAX_EDGES=0` before compilation, without editing production files. Runtime instrumentation confirms `(E, capacity, batches)` of `(80898,8192,10)`, `(158002,8192,20)` and `(647170,8192,80)`. At N4096 the final batch contains two edges. This override is not a fix submitted in the PR. -- **Nano, Mini, Pro:** the Neo-specific eligibility guard falls back to the native implementation. These are compatibility/repeatability observations, **not evidence of ECS acceleration or of generalized ECS support**. - -The associated [snapshot](ecs-edge-cohort-multimodel-summary.md), [full accuracy table](ecs-edge-cohort-multimodel-accuracy.md), and [machine-readable summary](ecs-edge-cohort-multimodel-results.json) retain incomplete runs and OOM explicitly. Pro measurements are still in progress at this snapshot; no pending cell is a pass. - -## Protocol and scientific gate - -RTX 5090; PyTorch 2.11.0+cu128, Triton 3.6.0; strict level-2 FP32 mixing, TF32/AMP disabled. Normal ASE energy/forces/virial/stress evaluation, diamond carbon at 3.567 Angstrom, seed0 and three 0.02-Angstrom-perturbed frames. Neo/Nano/Mini use N512/1000/4096; Pro uses N64/512/4096. For each model/size/mode: three independent processes, three frames, three repeats per frame. Completed groups therefore retain 27 evaluations per mode. Every comparison uses the same frame; inputs and model identity are checked. All modes for a model/size use the same physical GPU. - -TORCH means the normal compiled path with `DP_TRITON_INFER=0`, not eager execution or an exact reference. BASE uses level2; PR candidate additionally enables `DP_SO2_EDGE_COHORT_INFER=1`. Each model/mode has its own cache reused by subsequent repeats; this is not a latency study with matched autotune selections. The short user-requested pause does not become a timing sample. - -Every output must be finite and satisfy `abs(candidate-reference) <= 1e-5 + 1e-4*abs(reference)`. Max-relative uses `max(abs(reference),1e-8)`. Summary ratios are worst elementwise error/tolerance; <=1 is within the criterion. Full tables include energy, force, virial and stress separately and distinguish within-process repeats from across-process repeats. Ranges are min/max of pairwise max-absolute error, not confidence intervals. BASE or TORCH self-failure does **not** turn a candidate failure into a pass. All-pairs comparisons are descriptive, not independent statistical replicates. - -The N4096 Neo compiled TORCH path OOMed in all three processes. BASE self-repeat worst ratio is 2.854, PR self-repeat 2.854, PR-vs-BASE 3.239; actual runtime batching-vs-BASE is 3.527. Thus **N4096 is not accepted under the fixed criterion**. Existing fluctuations complicate attribution, but do not establish that the candidate is harmless. No threshold was relaxed. - -Energy and virial are in eV, force in eV/Angstrom, stress in eV/Angstrom^3; identical numerical tolerances across units are not equivalent physical tolerances. This is implementation-equivalence testing on perturbed carbon crystals, not DFT predictive-accuracy or long-MD validation. - -## N4096 historical performance, not a current-PR speed claim - -The following is explicitly the **2026-10-18 runtime prototype**, each arm one GPU0 process, 8 warmups and 30 synchronized wall-clock samples through the normal full API, plus separate memory replay. First/JIT execution is excluded from steady timing and included in the full memory window. Decimal GB=1e9 bytes. It is not the controlled N64 latency study and not a new performance measurement of the current PR. - -| Strategy | Frame P50 ms | Time reduction | Steady allocated GB | Steady reduction | Full-window peak including first GB | Full-peak reduction | -| --- | ---: | ---: | ---: | ---: | ---: | ---: | -| Historical BASE | 156.495125 | — | 23.970400 | — | 24.061593 | — | -| Historical runtime ECS prototype | 152.996582 | 2.2356% | 22.256657 | 7.1494% | 22.256657 | 7.5013% | - -BASE/candidate P90: 156.614295/153.179559 ms. Candidate first-call-only allocated peak is 22.255608 GB; the full window is slightly higher at 22.256657 GB and is the primary capacity value above. The historical third-frame virial check failed (error/tolerance 1.394), and TORCH OOMed. These are **unaccepted historical observations**, not a stable speedup claim or a substitute for new precision/latency acceptance. The newer multi-process results further prohibit treating this historical row as accepted. - -[Historical per-call timing, memory and accuracy JSON](ecs-edge-cohort-n4096-historical-results.json) contains both original result objects and the aggregate row. It is deliberately separate from the current accuracy snapshot. - -## Model identity - -- Neo: `DPA4-Neo-OMat24-v20260627.pt`, SHA256 `8d8b2b02b9575cb9e0af420cc1e8b1d9ffcbc04752c2203d29f48ccd0cd5f07d`. -- Nano/Mini: official OMat24 v20260805 checkpoints; exact SHA256 and loaded parameter counts are retained in the machine-readable snapshot. -- Pro: **DPA-4.0.1-Pro-MPtrj v2026.06.05**, not OMat24-Pro. [Official registry](https://github.com/janosh/matbench-discovery/blob/main/models/deepmd/dpa-4.0.1-pro-mptrj.yml), [official record](https://doi.org/10.6084/m9.figshare.32647224). Downloaded mirror revision `452f673645030b1daeda91c75cf6e1b1c346ee5f` of `JinukMoon/oh-my-mlip-mirror-dpa4`; 273318000 bytes, official MD5 `2174bb3095b197489f978d31393c1e06`, SHA256 `446a6e893101fc52190cfccabb9d2328863d651aca36981fd8d3401c71a61221`, both verified. -- Pro's legacy checkpoint fitting type `ener` is unsupported by the current reader. All three modes apply the same **in-memory metadata-only** alias `ener -> dpa4_ener`, referring to the same historical SeZMEnergyFittingNet class; tensor contents and original file are unchanged. Strict CPU state loading has zero missing/unexpected keys. Therefore Pro BASE is the native computation with disclosed reader compatibility, not proof that the unmodified default reader loads this checkpoint directly. - -## Reproduction and limitations of published evidence - -The public benchmark already provides normal API runs for the three primary modes. For example, from the candidate checkout, with a prebuilt compatible CUDA installation, an idle selected GPU and separate output/cache directories: - -```bash -python source/tests/pt_expt/kernels/benchmark_so2_edge_cohort.py \ - --source-root /absolute/path/to/base-checkout --model /absolute/path/to/neo.pt \ - --mode base --gpu 0 --nrep 8 --warmup 8 --samples 30 \ - --out /absolute/path/to/new-base-output --cache-dir /absolute/path/to/new-base-cache -python source/tests/pt_expt/kernels/benchmark_so2_edge_cohort.py \ - --source-root /absolute/path/to/candidate-checkout --model /absolute/path/to/neo.pt \ - --mode ecs --gpu 0 --nrep 8 --warmup 8 --samples 30 \ - --out /absolute/path/to/new-ecs-output --cache-dir /absolute/path/to/new-ecs-cache -``` - -`nrep=2,4,5,8` corresponds to diamond N64,512,1000,4096. Use `--mode torch` for the compiled TORCH reference. Repeat independently with fresh outputs; do not compare across cards or source revisions. These commands exercise the current PR's deployment route and **do not force runtime batching**, and are not an exact replay of the new three-frame/repeat all-pairs matrix. Generated graph route inspection is mandatory. - -This supplement publishes complete aggregate metrics/route counts and the historical per-call result objects, not model weights, compiler caches, full matrix output-array archives or its independent orchestration harness. Therefore it does not claim that the original full matrix can be audited end-to-end from GitHub alone yet. Existing focused unit tests do not replace full-model numerical acceptance or PT2/C++ deployment validation. Keep this PR Draft and default-off; dynamic dispatch correction, unresolved accuracy failures and portable full-matrix reproduction remain gates. diff --git a/doc/development/ecs-edge-cohort-n4096-historical-results.json b/doc/development/ecs-edge-cohort-n4096-historical-results.json deleted file mode 100644 index fc93180ad3..0000000000 --- a/doc/development/ecs-edge-cohort-n4096-historical-results.json +++ /dev/null @@ -1 +0,0 @@ -{"label": "2026-10-18 historical prototype; not current PR accepted performance", "metrics": [{"label": "N4096 GPU0", "bases": ["model_n4096_BASE_r0"], "candidates": ["model_n4096_SPLIT_r0"], "base": {"p50": 156.49512538220733, "p90": 156.6142953466624, "complete": 24061592576, "first": 24061592576, "steady": 23970400256}, "candidate": {"p50": 152.99658162984997, "p90": 153.1795586925, "complete": 22256656896, "first": 22255608320, "steady": 22256656896}, "time_reduction_pct": 2.235560848181617, "throughput_increase_pct": 2.2866809931881393, "peak_reduction_pct": 7.501314280420135}], "base": {"variant": "BASE", "n_atoms": 4096, "status": "completed", "first_ms": 127721.33345692419, "wall_ms": {"median": 156.49512538220733, "p90": 156.6142953466624, "min": 156.24970709905028, "max": 156.7487590946257, "samples": [156.6109189298004, 156.6446830984205, 156.6600319929421, 156.5163000486791, 156.5106299240142, 156.38837916776538, 156.33732313290238, 156.52387496083975, 156.60078101791441, 156.53209085576236, 156.48909914307296, 156.39495220966637, 156.56973188742995, 156.49431897327304, 156.4414391759783, 156.60207299515605, 156.4169730991125, 156.50827600620687, 156.49593179114163, 156.46439278498292, 156.32704296149313, 156.4743521157652, 156.4188168849796, 156.34497697465122, 156.7487590946257, 156.57230583019555, 156.5067518968135, 156.24970709905028, 156.46444307640195, 156.38035396113992]}, "event_elapsed_ms": {"median": 156.46929168701172, "p90": 156.5800567626953, "min": 156.22454833984375, "max": 156.7229766845703, 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"max_abs": 0.0001093739360840118, "mean_abs": 4.6400631288048525e-05, "max_relative": 0.000256288674699523, "max_ratio": 1.5066132082220152, "bitwise_equal": false}, "2_stress": {"passed": true, "finite": true, "max_abs": 4.706895460199201e-09, "mean_abs": 2.3719541324566295e-09, "max_relative": 0.0002549724062461468, "max_ratio": 0.0004017878813312413, "bitwise_equal": false}}, {"0_energy": {"passed": true, "finite": true, "max_abs": 1.7821788787841797e-05, "mean_abs": 1.7821788787841797e-05, "max_relative": 4.802337764282713e-10, "max_ratio": 4.802324823726559e-06, "bitwise_equal": false}, "0_forces": {"passed": true, "finite": true, "max_abs": 5.0092467134277285e-06, "mean_abs": 9.146910072664108e-07, "max_relative": 0.005768027904387839, "max_ratio": 0.25206304738573826, "bitwise_equal": false}, "0_virial": {"passed": true, "finite": true, "max_abs": 8.904238183049529e-05, "mean_abs": 4.9794779707321465e-05, "max_relative": 7.67255975425693e-06, "max_ratio": 0.07576742928305333, "bitwise_equal": false}, "0_stress": {"passed": true, "finite": true, "max_abs": 3.8319292287869455e-09, "mean_abs": 2.51668933997219e-09, "max_relative": 6.952811946488008e-06, "max_ratio": 0.0003273815260532409, "bitwise_equal": false}, "1_energy": {"passed": true, "finite": true, "max_abs": 5.27501106262207e-05, "mean_abs": 5.27501106262207e-05, "max_relative": 1.421428473282084e-09, "max_ratio": 1.42142464304659e-05, "bitwise_equal": false}, "1_forces": {"passed": true, "finite": true, "max_abs": 4.4334826725389576e-06, "mean_abs": 9.03712043306938e-07, "max_relative": 0.021659841146778047, "max_ratio": 0.2560348612406004, "bitwise_equal": false}, "1_virial": {"passed": true, "finite": true, "max_abs": 0.00011154932775525594, "mean_abs": 5.0556706962683824e-05, "max_relative": 1.44654286966944e-05, "max_ratio": 0.1391340540373662, "bitwise_equal": false}, "1_stress": {"passed": true, "finite": true, "max_abs": 4.800513202229961e-09, "mean_abs": 2.6663310544559663e-09, "max_relative": 1.3561270113081683e-05, "max_ratio": 0.0004102277252605286, "bitwise_equal": false}, "2_energy": {"passed": true, "finite": true, "max_abs": 9.715557098388672e-06, "mean_abs": 9.715557098388672e-06, "max_relative": 2.61813238128995e-10, "max_ratio": 2.618125326008791e-06, "bitwise_equal": false}, "2_forces": {"passed": true, "finite": true, "max_abs": 4.431313683994631e-06, "mean_abs": 9.057077641526737e-07, "max_relative": 0.00852032258076752, "max_ratio": 0.290614836431981, "bitwise_equal": false}, "2_virial": {"passed": false, "finite": true, "max_abs": 4.987805443201765e-05, "mean_abs": 2.7012913177230462e-05, "max_relative": 0.0002144051028488081, "max_ratio": 1.2603470335234133, "bitwise_equal": false}, "2_stress": {"passed": true, "finite": true, "max_abs": 2.0668965696954422e-09, "mean_abs": 1.1189062796575815e-09, "max_relative": 0.00019382247305812142, "max_ratio": 0.00020639913063781194, "bitwise_equal": false}}], "wrappers": ["/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/megakernel_20261018/results/model_n4096_SPLIT_r0/inductor/6p/c6psnzostje44djrfofoyjfda4nemxgr37x23mi2ttc3n4hjmz5m.py"], "pbs_calls": 2, "gpu_end": "index, name, uuid, driver_version, clocks.current.sm [MHz], clocks.current.memory [MHz], temperature.gpu, power.draw [W]\n0, NVIDIA GeForce RTX 5090, GPU-8a1f47f6-0655-97e5-8ed5-433be97af788, 595.91.07, 2580 MHz, 13801 MHz, 56, 566.91 W\n1, NVIDIA GeForce RTX 5090, GPU-fcc63da0-f1b3-74c7-f868-681a40ef5f03, 595.91.07, 180 MHz, 405 MHz, 35, 12.92 W\n"}} diff --git a/doc/development/ecs-validation-20261025/ACCURACY_TABLE.md b/doc/development/ecs-validation-20261025/ACCURACY_TABLE.md deleted file mode 100644 index a68839dd9c..0000000000 --- a/doc/development/ecs-validation-20261025/ACCURACY_TABLE.md +++ /dev/null @@ -1,266 +0,0 @@ -# ECS 多模型多尺寸精度测试 - -> 精度优先；未放宽阈值。误差范围为每对输出max_abs的min～max，不是置信区间。基线SHA与模型SHA见JSON。 - -模式：TORCH=纯PyTorch编译；ECS=当前PR候选（非Neo为回退）；ECS_RUNTIME=强制真实8192分批。单位：能量/位力eV，力eV/Å，stress eV/ų。所有结果均不是DFT预测误差。 - -| 模型 | 原子数 | 对照 | 范围 | 完成进程数 | 能量max_abs范围 | 力max_abs范围 | 位力max_abs范围 | stress max_abs范围 | 最差误差/容差 | 状态 | -| --- | ---: | --- | --- | --- | --- | --- | --- | --- | ---: | --- | -| Mini | 96 | TORCH vs TORCH | within_process | 3/3 | 1.788e-07～1.234e-05 | 4.585e-06～8.618e-06 | 1.402e-05～6.538e-05 | 2.375e-08～1.200e-07 | 1.1463 | FAIL | -| Mini | 96 | TORCH vs TORCH | across_process | 3/3 | 0.000e+00～1.246e-05 | 4.587e-06～8.061e-06 | 7.968e-06～6.371e-05 | 1.463e-08～1.170e-07 | 1.5990 | FAIL | -| Mini | 96 | BASE vs BASE | within_process | 3/3 | 1.192e-07～9.894e-06 | 4.567e-06～6.735e-06 | 1.251e-05～4.619e-05 | 2.294e-08～8.481e-08 | 1.8636 | FAIL | -| Mini | 96 | BASE vs BASE | across_process | 3/3 | 5.960e-08～1.049e-05 | 4.451e-06～7.551e-06 | 1.017e-05～4.644e-05 | 1.809e-08～8.526e-08 | 1.7011 | FAIL | -| Mini | 96 | ECS vs ECS | within_process | 3/3 | 3.576e-07～1.740e-05 | 4.543e-06～7.322e-06 | 7.101e-06～7.174e-05 | 1.285e-08～1.317e-07 | 2.1289 | FAIL | -| Mini | 96 | ECS vs ECS | across_process | 3/3 | 5.960e-08～1.699e-05 | 4.675e-06～8.280e-06 | 8.842e-06～5.695e-05 | 1.572e-08～1.046e-07 | 2.1372 | FAIL | -| Mini | 96 | BASE vs TORCH | cross_implementation | 3/3 | 0.000e+00～1.836e-05 | 5.616e-06～1.080e-05 | 1.972e-05～8.237e-05 | 3.388e-08～1.512e-07 | 1.7538 | FAIL | -| Mini | 96 | ECS vs TORCH | cross_implementation | 3/3 | 0.000e+00～1.651e-05 | 5.894e-06～1.135e-05 | 1.419e-05～9.370e-05 | 2.501e-08～1.720e-07 | 1.9425 | FAIL | -| Mini | 96 | ECS vs BASE | cross_implementation | 3/3 | 0.000e+00～1.663e-05 | 4.214e-06～8.067e-06 | 8.232e-06～8.385e-05 | 1.474e-08～1.540e-07 | 1.9484 | FAIL | -| Mini | 512 | TORCH vs TORCH | within_process | 3/3 | 4.172e-07～3.606e-05 | 5.870e-06～7.873e-06 | 2.876e-05～1.708e-04 | 9.493e-09～5.882e-08 | 2.6615 | FAIL | -| Mini | 512 | TORCH vs TORCH | across_process | 3/3 | 1.788e-07～3.493e-05 | 5.543e-06～8.601e-06 | 2.627e-05～1.340e-04 | 9.046e-09～4.613e-08 | 2.1807 | FAIL | -| Mini | 512 | BASE vs BASE | within_process | 3/3 | 7.153e-07～3.594e-05 | 5.392e-06～8.266e-06 | 2.302e-05～9.672e-05 | 7.924e-09～3.330e-08 | 2.4707 | FAIL | -| Mini | 512 | BASE vs BASE | across_process | 3/3 | 1.192e-07～2.855e-05 | 5.616e-06～8.582e-06 | 1.416e-05～1.244e-04 | 4.757e-09～4.283e-08 | 2.6165 | FAIL | -| Mini | 512 | ECS vs ECS | within_process | 3/3 | 5.960e-07～2.813e-05 | 5.659e-06～9.532e-06 | 1.953e-05～1.400e-04 | 6.450e-09～4.821e-08 | 1.9644 | FAIL | -| Mini | 512 | ECS vs ECS | across_process | 3/3 | 0.000e+00～2.646e-05 | 5.619e-06～8.691e-06 | 1.610e-05～1.445e-04 | 5.544e-09～4.975e-08 | 3.1341 | FAIL | -| Mini | 512 | BASE vs TORCH | cross_implementation | 3/3 | 5.960e-08～3.952e-05 | 7.673e-06～1.210e-05 | 9.353e-05～2.595e-04 | 3.220e-08～8.933e-08 | 2.7111 | FAIL | -| Mini | 512 | ECS vs TORCH | cross_implementation | 3/3 | 5.960e-08～3.040e-05 | 7.604e-06～1.289e-05 | 9.723e-05～2.485e-04 | 3.347e-08～8.555e-08 | 3.0424 | FAIL | -| Mini | 512 | ECS vs BASE | cross_implementation | 3/3 | 5.960e-08～3.386e-05 | 5.100e-06～8.801e-06 | 1.883e-05～1.636e-04 | 6.483e-09～5.634e-08 | 3.0773 | FAIL | -| Mini | 4096 | TORCH vs TORCH | within_process | 3/3 | 5.364e-07～7.576e-05 | 6.887e-06～1.004e-05 | 9.010e-05～3.499e-04 | 3.878e-09～1.506e-08 | 10.2623 | FAIL | -| Mini | 4096 | TORCH vs TORCH | across_process | 3/3 | 1.788e-07～7.707e-05 | 6.973e-06～8.997e-06 | 6.730e-05～3.137e-04 | 2.584e-09～1.350e-08 | 8.9322 | FAIL | -| Mini | 4096 | BASE vs BASE | within_process | 3/3 | 2.086e-06～9.507e-05 | 6.874e-06～9.211e-06 | 8.355e-05～2.777e-04 | 3.595e-09～1.195e-08 | 10.3628 | FAIL | -| Mini | 4096 | BASE vs BASE | across_process | 3/3 | 7.749e-07～1.156e-04 | 7.001e-06～9.328e-06 | 5.222e-05～3.119e-04 | 2.040e-09～1.342e-08 | 12.0549 | FAIL | -| Mini | 4096 | ECS vs ECS | within_process | 3/3 | 5.960e-07～7.117e-05 | 7.187e-06～1.007e-05 | 5.141e-05～3.646e-04 | 2.061e-09～1.569e-08 | 6.1755 | FAIL | -| Mini | 4096 | ECS vs ECS | across_process | 3/3 | 5.364e-07～8.416e-05 | 7.002e-06～9.466e-06 | 4.396e-05～3.296e-04 | 1.892e-09～1.418e-08 | 7.3596 | FAIL | -| Mini | 4096 | BASE vs TORCH | cross_implementation | 3/3 | 3.576e-07～1.388e-04 | 9.301e-06～1.306e-05 | 7.796e-04～1.488e-03 | 3.355e-08～6.403e-08 | 12.5994 | FAIL | -| Mini | 4096 | ECS vs TORCH | cross_implementation | 3/3 | 1.311e-06～1.465e-04 | 9.302e-06～1.398e-05 | 8.275e-04～1.521e-03 | 3.561e-08～6.544e-08 | 9.8370 | FAIL | -| Mini | 4096 | ECS vs BASE | cross_implementation | 3/3 | 2.384e-07～1.084e-04 | 6.848e-06～1.005e-05 | 3.919e-05～3.468e-04 | 1.687e-09～1.492e-08 | 11.9793 | FAIL | -| Nano | 96 | TORCH vs TORCH | within_process | 3/3 | 4.768e-07～2.658e-05 | 7.035e-06～1.130e-05 | 1.325e-05～7.839e-05 | 2.318e-08～1.439e-07 | 5.5420 | FAIL | -| Nano | 96 | TORCH vs TORCH | across_process | 3/3 | 0.000e+00～2.420e-05 | 6.704e-06～1.136e-05 | 1.439e-05～7.676e-05 | 2.480e-08～1.409e-07 | 5.4077 | FAIL | -| Nano | 96 | BASE vs BASE | within_process | 3/3 | 1.192e-07～1.299e-05 | 6.957e-06～1.220e-05 | 1.406e-05～7.483e-05 | 2.405e-08～1.374e-07 | 5.7063 | FAIL | -| Nano | 96 | BASE vs BASE | across_process | 3/3 | 1.788e-07～1.878e-05 | 6.726e-06～1.142e-05 | 1.593e-05～8.811e-05 | 2.581e-08～1.618e-07 | 8.3241 | FAIL | -| Nano | 96 | ECS vs ECS | within_process | 3/3 | 0.000e+00～1.794e-05 | 7.266e-06～1.080e-05 | 1.871e-05～8.601e-05 | 3.432e-08～1.579e-07 | 7.3608 | FAIL | -| Nano | 96 | ECS vs ECS | across_process | 3/3 | 1.192e-07～2.122e-05 | 7.020e-06～1.150e-05 | 1.177e-05～8.074e-05 | 2.161e-08～1.482e-07 | 6.4631 | FAIL | -| Nano | 96 | BASE vs TORCH | cross_implementation | 3/3 | 1.788e-07～3.499e-05 | 6.454e-06～1.286e-05 | 1.219e-05～9.567e-05 | 2.155e-08～1.757e-07 | 9.4133 | FAIL | -| Nano | 96 | ECS vs TORCH | cross_implementation | 3/3 | 0.000e+00～3.982e-05 | 6.870e-06～1.269e-05 | 1.227e-05～9.163e-05 | 2.207e-08～1.682e-07 | 8.0976 | FAIL | -| Nano | 96 | ECS vs BASE | cross_implementation | 3/3 | 0.000e+00～2.354e-05 | 6.351e-06～1.155e-05 | 1.137e-05～1.014e-04 | 2.003e-08～1.862e-07 | 9.1350 | FAIL | -| Nano | 512 | TORCH vs TORCH | within_process | 3/3 | 8.941e-07～3.463e-05 | 8.984e-06～1.488e-05 | 4.949e-05～1.978e-04 | 1.683e-08～6.809e-08 | 7.7673 | FAIL | -| Nano | 512 | TORCH vs TORCH | across_process | 3/3 | 3.576e-07～6.020e-05 | 8.737e-06～1.247e-05 | 3.414e-05～2.010e-04 | 1.174e-08～6.921e-08 | 7.8147 | FAIL | -| Nano | 512 | BASE vs BASE | within_process | 3/3 | 1.371e-06～5.305e-05 | 8.899e-06～1.278e-05 | 4.218e-05～1.863e-04 | 1.452e-08～6.415e-08 | 4.2656 | FAIL | -| Nano | 512 | BASE vs BASE | across_process | 3/3 | 8.941e-07～6.390e-05 | 8.700e-06～1.347e-05 | 1.671e-05～1.607e-04 | 5.273e-09～5.534e-08 | 4.6439 | FAIL | -| Nano | 512 | ECS vs ECS | within_process | 3/3 | 1.907e-06～4.596e-05 | 8.712e-06～1.348e-05 | 3.527e-05～1.795e-04 | 1.214e-08～6.181e-08 | 7.0513 | FAIL | -| Nano | 512 | ECS vs ECS | across_process | 3/3 | 1.073e-06～5.358e-05 | 8.399e-06～1.288e-05 | 1.549e-05～2.203e-04 | 5.095e-09～7.585e-08 | 7.1989 | FAIL | -| Nano | 512 | BASE vs TORCH | cross_implementation | 3/3 | 2.980e-07～1.107e-04 | 9.036e-06～1.477e-05 | 2.200e-05～2.575e-04 | 6.814e-09～8.866e-08 | 7.1940 | FAIL | -| Nano | 512 | ECS vs TORCH | cross_implementation | 3/3 | 6.676e-06～1.102e-04 | 8.284e-06～1.325e-05 | 2.255e-05～2.560e-04 | 7.763e-09～8.813e-08 | 9.4950 | FAIL | -| Nano | 512 | ECS vs BASE | cross_implementation | 3/3 | 0.000e+00～6.342e-05 | 8.381e-06～1.570e-05 | 2.904e-05～1.981e-04 | 9.659e-09～6.821e-08 | 6.2237 | FAIL | -| Nano | 4096 | TORCH vs TORCH | within_process | 3/3 | 1.848e-06～1.260e-04 | 1.119e-05～1.436e-05 | 5.544e-05～3.779e-04 | 2.386e-09～1.626e-08 | 2.4958 | FAIL | -| Nano | 4096 | TORCH vs TORCH | across_process | 3/3 | 7.749e-07～1.612e-04 | 1.085e-05～1.484e-05 | 7.050e-05～4.504e-04 | 3.034e-09～1.938e-08 | 2.6872 | FAIL | -| Nano | 4096 | BASE vs BASE | within_process | 3/3 | 1.091e-05～1.924e-04 | 1.079e-05～1.563e-05 | 8.116e-05～4.467e-04 | 3.240e-09～1.922e-08 | 2.2079 | FAIL | -| Nano | 4096 | BASE vs BASE | across_process | 3/3 | 7.749e-07～1.666e-04 | 1.064e-05～1.480e-05 | 9.755e-05～5.898e-04 | 4.198e-09～2.538e-08 | 3.8540 | FAIL | -| Nano | 4096 | ECS vs ECS | within_process | 3/3 | 2.801e-06～1.946e-04 | 1.118e-05～1.424e-05 | 9.154e-05～4.406e-04 | 3.940e-09～1.896e-08 | 2.3048 | FAIL | -| Nano | 4096 | ECS vs ECS | across_process | 3/3 | 1.132e-06～2.133e-04 | 1.075e-05～1.605e-05 | 6.212e-05～5.332e-04 | 2.673e-09～2.294e-08 | 4.1555 | FAIL | -| Nano | 4096 | BASE vs TORCH | cross_implementation | 3/3 | 1.556e-04～4.887e-04 | 1.069e-05～1.613e-05 | 5.668e-05～7.157e-04 | 2.388e-09～3.080e-08 | 4.1304 | FAIL | -| Nano | 4096 | ECS vs TORCH | cross_implementation | 3/3 | 1.753e-04～5.112e-04 | 1.066e-05～1.554e-05 | 7.483e-05～7.943e-04 | 3.220e-09～3.418e-08 | 4.4355 | FAIL | -| Nano | 4096 | ECS vs BASE | cross_implementation | 3/3 | 2.384e-07～2.006e-04 | 1.020e-05～1.522e-05 | 3.754e-05～6.358e-04 | 1.501e-09～2.736e-08 | 4.1590 | FAIL | -| Neo | 96 | TORCH vs TORCH | within_process | 3/3 | 0.000e+00～1.222e-05 | 2.529e-06～4.930e-06 | 4.051e-06～2.011e-05 | 6.975e-09～3.692e-08 | 0.5965 | PASS | -| Neo | 96 | TORCH vs TORCH | across_process | 3/3 | 1.788e-07～1.091e-05 | 2.529e-06～4.594e-06 | 4.727e-06～2.222e-05 | 8.078e-09～4.080e-08 | 0.4535 | PASS | -| Neo | 96 | BASE vs BASE | within_process | 3/3 | 4.768e-07～7.212e-06 | 2.483e-06～3.981e-06 | 3.664e-06～2.443e-05 | 4.932e-09～4.486e-08 | 0.3532 | PASS | -| Neo | 96 | BASE vs BASE | across_process | 3/3 | 5.960e-08～1.073e-05 | 2.437e-06～4.474e-06 | 3.807e-06～1.991e-05 | 6.350e-09～3.656e-08 | 0.4794 | PASS | -| Neo | 96 | ECS vs ECS | within_process | 3/3 | 5.960e-08～6.557e-06 | 2.617e-06～4.197e-06 | 2.986e-06～2.181e-05 | 5.483e-09～4.005e-08 | 0.4760 | PASS | -| Neo | 96 | ECS vs ECS | across_process | 3/3 | 1.192e-07～6.855e-06 | 2.545e-06～4.369e-06 | 3.881e-06～2.690e-05 | 7.126e-09～4.938e-08 | 0.5175 | PASS | -| Neo | 96 | BASE vs TORCH | cross_implementation | 3/3 | 0.000e+00～1.115e-05 | 3.603e-06～7.196e-06 | 1.558e-05～4.824e-05 | 2.862e-08～8.858e-08 | 0.5819 | PASS | -| Neo | 96 | ECS vs TORCH | cross_implementation | 3/3 | 0.000e+00～1.127e-05 | 3.614e-06～7.185e-06 | 1.331e-05～5.232e-05 | 2.154e-08～9.607e-08 | 0.5914 | PASS | -| Neo | 96 | ECS vs BASE | cross_implementation | 3/3 | 0.000e+00～1.037e-05 | 2.304e-06～5.034e-06 | 2.399e-06～2.851e-05 | 2.107e-09～5.235e-08 | 0.5769 | PASS | -| Neo | 512 | TORCH vs TORCH | within_process | 3/3 | 1.192e-07～1.776e-05 | 3.427e-06～4.336e-06 | 1.278e-05～5.254e-05 | 4.298e-09～1.809e-08 | 0.4674 | PASS | -| Neo | 512 | TORCH vs TORCH | across_process | 3/3 | 4.172e-07～1.734e-05 | 3.283e-06～4.901e-06 | 1.086e-05～6.951e-05 | 3.545e-09～2.393e-08 | 0.7340 | PASS | -| Neo | 512 | BASE vs BASE | within_process | 3/3 | 1.192e-07～2.247e-05 | 3.262e-06～4.286e-06 | 7.840e-06～6.508e-05 | 2.699e-09～2.240e-08 | 0.6034 | PASS | -| Neo | 512 | BASE vs BASE | across_process | 3/3 | 5.960e-08～1.889e-05 | 2.996e-06～5.144e-06 | 8.897e-06～6.434e-05 | 2.589e-09～2.215e-08 | 0.5044 | PASS | -| Neo | 512 | ECS vs ECS | within_process | 3/3 | 9.537e-07～1.884e-05 | 3.293e-06～5.012e-06 | 9.156e-06～5.708e-05 | 3.152e-09～1.965e-08 | 0.8473 | PASS | -| Neo | 512 | ECS vs ECS | across_process | 3/3 | 1.192e-07～2.056e-05 | 3.137e-06～4.858e-06 | 6.668e-06～5.049e-05 | 2.046e-09～1.738e-08 | 0.8394 | PASS | -| Neo | 512 | BASE vs TORCH | cross_implementation | 3/3 | 0.000e+00～2.021e-05 | 4.529e-06～7.150e-06 | 7.708e-05～1.673e-04 | 2.654e-08～5.758e-08 | 0.8254 | PASS | -| Neo | 512 | ECS vs TORCH | cross_implementation | 3/3 | 2.980e-07～2.718e-05 | 4.555e-06～7.020e-06 | 7.569e-05～1.572e-04 | 2.606e-08～5.411e-08 | 1.0809 | FAIL | -| Neo | 512 | ECS vs BASE | cross_implementation | 3/3 | 1.192e-07～2.670e-05 | 3.075e-06～5.255e-06 | 4.760e-06～7.743e-05 | 1.186e-09～2.666e-08 | 0.8589 | PASS | -| Neo | 4096 | BASE vs BASE | within_process | 3/3 | 1.788e-07～5.084e-05 | 3.915e-06～5.114e-06 | 2.331e-05～1.037e-04 | 1.003e-09～4.284e-09 | 1.3472 | FAIL | -| Neo | 4096 | BASE vs BASE | across_process | 3/3 | 5.364e-07～5.960e-05 | 4.095e-06～5.564e-06 | 2.725e-05～1.374e-04 | 1.066e-09～5.913e-09 | 1.7886 | FAIL | -| Neo | 4096 | ECS vs ECS | within_process | 3/3 | 2.086e-06～5.770e-05 | 4.087e-06～5.089e-06 | 3.967e-05～2.099e-04 | 1.707e-09～9.031e-09 | 2.6929 | FAIL | -| Neo | 4096 | ECS vs ECS | across_process | 3/3 | 1.788e-07～5.788e-05 | 4.010e-06～5.939e-06 | 2.293e-05～2.537e-04 | 8.575e-10～1.092e-08 | 3.4631 | FAIL | -| Neo | 4096 | ECS vs BASE | cross_implementation | 3/3 | 1.788e-07～6.694e-05 | 4.074e-06～6.208e-06 | 1.792e-05～1.883e-04 | 6.963e-10～8.102e-09 | 2.7953 | FAIL | -| NeoCu | 96 | TORCH vs TORCH | within_process | 3/3 | 1.192e-07～3.338e-06 | 3.603e-07～5.453e-07 | 1.724e-06～1.361e-05 | 1.519e-09～1.200e-08 | 0.1450 | PASS | -| NeoCu | 96 | TORCH vs TORCH | across_process | 3/3 | 0.000e+00～3.636e-06 | 3.280e-07～6.306e-07 | 1.289e-06～1.229e-05 | 1.134e-09～1.083e-08 | 0.1805 | PASS | -| NeoCu | 96 | BASE vs BASE | within_process | 3/3 | 5.960e-08～4.292e-06 | 3.674e-07～6.496e-07 | 1.058e-06～1.315e-05 | 9.327e-10～1.159e-08 | 0.2355 | PASS | -| NeoCu | 96 | BASE vs BASE | across_process | 3/3 | 0.000e+00～4.590e-06 | 3.296e-07～5.808e-07 | 1.086e-06～1.663e-05 | 9.277e-10～1.466e-08 | 0.2466 | PASS | -| NeoCu | 96 | ECS vs ECS | within_process | 3/3 | 1.192e-07～4.292e-06 | 3.467e-07～5.431e-07 | 1.491e-06～1.204e-05 | 1.314e-09～1.061e-08 | 0.2134 | PASS | -| NeoCu | 96 | ECS vs ECS | across_process | 3/3 | 0.000e+00～4.411e-06 | 3.581e-07～6.476e-07 | 9.126e-07～1.739e-05 | 8.043e-10～1.533e-08 | 0.2576 | PASS | -| NeoCu | 96 | BASE vs TORCH | cross_implementation | 3/3 | 0.000e+00～6.855e-06 | 3.360e-07～6.391e-07 | 3.217e-06～2.707e-05 | 2.835e-09～2.386e-08 | 0.2037 | PASS | -| NeoCu | 96 | ECS vs TORCH | cross_implementation | 3/3 | 0.000e+00～5.782e-06 | 3.374e-07～6.297e-07 | 3.805e-06～2.837e-05 | 3.353e-09～2.500e-08 | 0.2452 | PASS | -| NeoCu | 96 | ECS vs BASE | cross_implementation | 3/3 | 0.000e+00～5.841e-06 | 3.036e-07～6.730e-07 | 5.077e-07～1.968e-05 | 4.474e-10～1.734e-08 | 0.2689 | PASS | -| NeoCu | 512 | TORCH vs TORCH | within_process | 3/3 | 0.000e+00～1.115e-05 | 4.107e-07～5.963e-07 | 3.552e-06～3.151e-05 | 5.869e-10～5.207e-09 | 0.2895 | PASS | -| NeoCu | 512 | TORCH vs TORCH | across_process | 3/3 | 3.576e-07～1.138e-05 | 4.138e-07～6.573e-07 | 2.813e-06～4.521e-05 | 4.174e-10～7.471e-09 | 0.2794 | PASS | -| NeoCu | 512 | BASE vs BASE | within_process | 3/3 | 1.192e-07～8.345e-06 | 4.176e-07～5.775e-07 | 4.065e-06～3.114e-05 | 6.577e-10～5.145e-09 | 0.3275 | PASS | -| NeoCu | 512 | BASE vs BASE | across_process | 3/3 | 0.000e+00～1.007e-05 | 4.184e-07～6.110e-07 | 1.907e-06～3.137e-05 | 3.124e-10～5.184e-09 | 0.3759 | PASS | -| NeoCu | 512 | ECS vs ECS | within_process | 3/3 | 6.557e-07～6.557e-06 | 4.320e-07～7.186e-07 | 2.182e-06～4.652e-05 | 3.606e-10～7.687e-09 | 0.2868 | PASS | -| NeoCu | 512 | ECS vs ECS | across_process | 3/3 | 5.960e-08～1.085e-05 | 4.208e-07～6.428e-07 | 2.016e-06～4.702e-05 | 3.332e-10～7.770e-09 | 0.4818 | PASS | -| NeoCu | 512 | BASE vs TORCH | cross_implementation | 3/3 | 0.000e+00～1.919e-05 | 4.175e-07～7.357e-07 | 4.216e-05～1.130e-04 | 6.966e-09～1.867e-08 | 0.3659 | PASS | -| NeoCu | 512 | ECS vs TORCH | cross_implementation | 3/3 | 5.960e-08～1.860e-05 | 4.282e-07～7.158e-07 | 3.795e-05～1.295e-04 | 6.271e-09～2.139e-08 | 0.3641 | PASS | -| NeoCu | 512 | ECS vs BASE | cross_implementation | 3/3 | 0.000e+00～1.073e-05 | 3.929e-07～6.560e-07 | 1.895e-06～4.981e-05 | 2.801e-10～8.231e-09 | 0.4547 | PASS | -| NeoCu | 4096 | TORCH vs TORCH | within_process | 3/3 | 8.345e-07～1.687e-05 | 5.199e-07～6.523e-07 | 7.247e-06～8.839e-05 | 1.497e-10～1.826e-09 | 0.3163 | PASS | -| NeoCu | 4096 | TORCH vs TORCH | across_process | 3/3 | 5.960e-08～2.599e-05 | 5.038e-07～7.152e-07 | 8.798e-06～1.036e-04 | 1.817e-10～2.140e-09 | 0.3102 | PASS | -| NeoCu | 4096 | BASE vs BASE | within_process | 3/3 | 7.153e-07～4.148e-05 | 5.221e-07～7.069e-07 | 5.660e-06～7.616e-05 | 9.913e-11～1.573e-09 | 0.2691 | PASS | -| NeoCu | 4096 | BASE vs BASE | across_process | 3/3 | 1.192e-07～3.791e-05 | 5.172e-07～6.890e-07 | 8.123e-06～1.054e-04 | 1.678e-10～2.178e-09 | 0.3652 | PASS | -| NeoCu | 4096 | ECS vs ECS | within_process | 3/3 | 1.788e-07～2.778e-05 | 5.439e-07～7.169e-07 | 7.803e-06～1.111e-04 | 1.612e-10～2.295e-09 | 0.2754 | PASS | -| NeoCu | 4096 | ECS vs ECS | across_process | 3/3 | 5.960e-08～3.821e-05 | 5.166e-07～7.105e-07 | 7.015e-06～9.401e-05 | 1.449e-10～1.942e-09 | 0.4257 | PASS | -| NeoCu | 4096 | BASE vs TORCH | cross_implementation | 3/3 | 3.046e-05～8.726e-05 | 5.351e-07～8.014e-07 | 4.939e-04～6.932e-04 | 1.020e-08～1.432e-08 | 0.4284 | PASS | -| NeoCu | 4096 | ECS vs TORCH | cross_implementation | 3/3 | 2.074e-05～8.494e-05 | 5.446e-07～7.932e-07 | 4.744e-04～6.950e-04 | 9.798e-09～1.435e-08 | 0.4357 | PASS | -| NeoCu | 4096 | ECS vs BASE | cross_implementation | 3/3 | 5.960e-08～3.868e-05 | 5.229e-07～7.972e-07 | 5.578e-06～1.249e-04 | 9.600e-11～2.581e-09 | 0.3727 | PASS | -| Pro | 96 | TORCH vs TORCH | within_process | 3/3 | 2.980e-08～3.546e-06 | 7.446e-07～1.197e-06 | 1.965e-06～9.613e-06 | 2.423e-09～1.765e-08 | 0.1584 | PASS | -| Pro | 96 | TORCH vs TORCH | across_process | 3/3 | 5.960e-08～4.083e-06 | 7.445e-07～1.667e-06 | 1.298e-06～8.369e-06 | 1.832e-09～1.537e-08 | 0.1908 | PASS | -| Pro | 96 | BASE vs BASE | within_process | 3/3 | 2.980e-08～2.086e-06 | 6.661e-07～1.003e-06 | 1.850e-06～6.390e-06 | 2.572e-09～1.173e-08 | 0.1034 | PASS | -| Pro | 96 | BASE vs BASE | across_process | 3/3 | 2.980e-08～3.368e-06 | 6.060e-07～1.153e-06 | 1.505e-06～8.763e-06 | 2.631e-09～1.609e-08 | 0.1715 | PASS | -| Pro | 96 | ECS vs ECS | within_process | 3/3 | 2.980e-08～2.652e-06 | 7.281e-07～1.056e-06 | 1.903e-06～7.248e-06 | 1.676e-09～1.331e-08 | 0.1771 | PASS | -| Pro | 96 | ECS vs ECS | across_process | 3/3 | 0.000e+00～3.338e-06 | 6.510e-07～1.144e-06 | 1.396e-06～7.585e-06 | 1.931e-09～1.393e-08 | 0.2516 | PASS | -| Pro | 96 | BASE vs TORCH | cross_implementation | 3/3 | 0.000e+00～5.037e-06 | 1.030e-06～1.919e-06 | 6.303e-06～1.613e-05 | 1.023e-08～2.962e-08 | 0.1988 | PASS | -| Pro | 96 | ECS vs TORCH | cross_implementation | 3/3 | 0.000e+00～5.871e-06 | 1.039e-06～1.691e-06 | 5.839e-06～1.643e-05 | 9.968e-09～3.017e-08 | 0.2784 | PASS | -| Pro | 96 | ECS vs BASE | cross_implementation | 3/3 | 0.000e+00～3.725e-06 | 6.266e-07～1.310e-06 | 1.210e-06～9.066e-06 | 1.710e-09～1.665e-08 | 0.2276 | PASS | -| Pro | 512 | BASE vs BASE | within_process | 3/3 | 1.192e-07～7.242e-06 | 8.882e-07～1.212e-06 | 3.637e-06～1.652e-05 | 6.287e-10～5.687e-09 | 0.2266 | PASS | -| Pro | 512 | BASE vs BASE | across_process | 3/3 | 2.980e-08～1.019e-05 | 8.338e-07～1.360e-06 | 3.104e-06～2.018e-05 | 8.452e-10～6.947e-09 | 0.2604 | PASS | -| Pro | 512 | ECS vs ECS | within_process | 3/3 | 2.980e-08～7.421e-06 | 8.405e-07～1.356e-06 | 3.452e-06～1.421e-05 | 1.188e-09～4.893e-09 | 0.2375 | PASS | -| Pro | 512 | ECS vs ECS | across_process | 3/3 | 2.980e-08～8.345e-06 | 8.582e-07～1.497e-06 | 2.475e-06～1.641e-05 | 6.944e-10～5.648e-09 | 0.2399 | PASS | -| Pro | 512 | ECS vs BASE | cross_implementation | 3/3 | 0.000e+00～1.121e-05 | 8.272e-07～1.477e-06 | 2.360e-06～1.753e-05 | 5.223e-10～6.034e-09 | 0.2884 | PASS | - -## 所有进程状态（含OOM/失败，不能略去） - -| 运行 | 状态 | 参数量 | opaque ECS调用 | single路径标记 | -| --- | --- | ---: | ---: | --- | -| Mini_n4096_BASE_p0 | completed | 654988 | 0 | False | -| Mini_n4096_BASE_p1 | completed | 654988 | 0 | False | -| Mini_n4096_BASE_p2 | completed | 654988 | 0 | False | -| Mini_n4096_ECS_p0 | completed | 654988 | 0 | False | -| Mini_n4096_ECS_p1 | completed | 654988 | 0 | False | -| Mini_n4096_ECS_p2 | completed | 654988 | 0 | False | -| Mini_n4096_TORCH_p0 | completed | 654988 | 0 | False | -| Mini_n4096_TORCH_p1 | completed | 654988 | 0 | False | -| Mini_n4096_TORCH_p2 | completed | 654988 | 0 | False | -| Mini_n512_BASE_p0 | completed | 654988 | 0 | False | -| Mini_n512_BASE_p1 | completed | 654988 | 0 | False | -| Mini_n512_BASE_p2 | completed | 654988 | 0 | False | -| Mini_n512_ECS_p0 | completed | 654988 | 0 | False | -| Mini_n512_ECS_p1 | completed | 654988 | 0 | False | -| Mini_n512_ECS_p2 | completed | 654988 | 0 | False | -| Mini_n512_TORCH_p0 | completed | 654988 | 0 | False | -| Mini_n512_TORCH_p1 | completed | 654988 | 0 | False | -| Mini_n512_TORCH_p2 | completed | 654988 | 0 | False | -| Mini_n96_BASE_p0 | completed | 654988 | 0 | False | -| Mini_n96_BASE_p1 | completed | 654988 | 0 | False | -| Mini_n96_BASE_p2 | completed | 654988 | 0 | False | -| Mini_n96_ECS_p0 | completed | 654988 | 0 | False | -| Mini_n96_ECS_p1 | completed | 654988 | 0 | False | -| Mini_n96_ECS_p2 | completed | 654988 | 0 | False | -| Mini_n96_TORCH_p0 | completed | 654988 | 0 | False | -| Mini_n96_TORCH_p1 | completed | 654988 | 0 | False | -| Mini_n96_TORCH_p2 | completed | 654988 | 0 | False | -| Nano_n4096_BASE_p0 | completed | 480268 | 0 | False | -| Nano_n4096_BASE_p1 | completed | 480268 | 0 | False | -| Nano_n4096_BASE_p2 | completed | 480268 | 0 | False | -| Nano_n4096_ECS_p0 | completed | 480268 | 0 | False | -| Nano_n4096_ECS_p1 | completed | 480268 | 0 | False | -| Nano_n4096_ECS_p2 | completed | 480268 | 0 | False | -| Nano_n4096_TORCH_p0 | completed | 480268 | 0 | False | -| Nano_n4096_TORCH_p1 | completed | 480268 | 0 | False | -| Nano_n4096_TORCH_p2 | completed | 480268 | 0 | False | -| Nano_n512_BASE_p0 | completed | 480268 | 0 | False | -| Nano_n512_BASE_p1 | completed | 480268 | 0 | False | -| Nano_n512_BASE_p2 | completed | 480268 | 0 | False | -| Nano_n512_ECS_p0 | completed | 480268 | 0 | False | -| Nano_n512_ECS_p1 | completed | 480268 | 0 | False | -| Nano_n512_ECS_p2 | completed | 480268 | 0 | False | -| Nano_n512_TORCH_p0 | completed | 480268 | 0 | False | -| Nano_n512_TORCH_p1 | completed | 480268 | 0 | False | -| Nano_n512_TORCH_p2 | completed | 480268 | 0 | False | -| Nano_n96_BASE_p0 | completed | 480268 | 0 | False | -| Nano_n96_BASE_p1 | completed | 480268 | 0 | False | -| Nano_n96_BASE_p2 | completed | 480268 | 0 | False | -| Nano_n96_ECS_p0 | completed | 480268 | 0 | False | -| Nano_n96_ECS_p1 | completed | 480268 | 0 | False | -| Nano_n96_ECS_p2 | completed | 480268 | 0 | False | -| Nano_n96_TORCH_p0 | completed | 480268 | 0 | False | -| Nano_n96_TORCH_p1 | completed | 480268 | 0 | False | -| Nano_n96_TORCH_p2 | completed | 480268 | 0 | False | -| NeoCu_n4096_BASE_p0 | completed | 1124910 | 0 | False | -| NeoCu_n4096_BASE_p1 | completed | 1124910 | 0 | False | -| NeoCu_n4096_BASE_p2 | completed | 1124910 | 0 | False | -| NeoCu_n4096_ECS_p0 | completed | 1124910 | 0 | True | -| NeoCu_n4096_ECS_p1 | completed | 1124910 | 0 | True | -| NeoCu_n4096_ECS_p2 | completed | 1124910 | 0 | True | -| NeoCu_n4096_TORCH_p0 | completed | 1124910 | 0 | False | -| NeoCu_n4096_TORCH_p1 | completed | 1124910 | 0 | False | -| NeoCu_n4096_TORCH_p2 | completed | 1124910 | 0 | False | -| NeoCu_n512_BASE_p0 | completed | 1124910 | 0 | False | -| NeoCu_n512_BASE_p1 | completed | 1124910 | 0 | False | -| NeoCu_n512_BASE_p2 | completed | 1124910 | 0 | False | -| NeoCu_n512_ECS_p0 | completed | 1124910 | 0 | True | -| NeoCu_n512_ECS_p1 | completed | 1124910 | 0 | True | -| NeoCu_n512_ECS_p2 | completed | 1124910 | 0 | True | -| NeoCu_n512_TORCH_p0 | completed | 1124910 | 0 | False | -| NeoCu_n512_TORCH_p1 | completed | 1124910 | 0 | False | -| NeoCu_n512_TORCH_p2 | completed | 1124910 | 0 | False | -| NeoCu_n96_BASE_p0 | completed | 1124910 | 0 | False | -| NeoCu_n96_BASE_p1 | completed | 1124910 | 0 | False | -| NeoCu_n96_BASE_p2 | completed | 1124910 | 0 | False | -| NeoCu_n96_ECS_p0 | completed | 1124910 | 0 | True | -| NeoCu_n96_ECS_p1 | completed | 1124910 | 0 | True | -| NeoCu_n96_ECS_p2 | completed | 1124910 | 0 | True | -| NeoCu_n96_TORCH_p0 | completed | 1124910 | 0 | False | -| NeoCu_n96_TORCH_p1 | completed | 1124910 | 0 | False | -| NeoCu_n96_TORCH_p2 | completed | 1124910 | 0 | False | -| Neo_n4096_BASE_p0 | completed | 1124910 | 0 | False | -| Neo_n4096_BASE_p1 | completed | 1124910 | 0 | False | -| Neo_n4096_BASE_p2 | completed | 1124910 | 0 | False | -| Neo_n4096_ECS_p0 | completed | 1124910 | 0 | True | -| Neo_n4096_ECS_p1 | completed | 1124910 | 0 | True | -| Neo_n4096_ECS_p2 | completed | 1124910 | 0 | True | -| Neo_n4096_TORCH_p0 | oom | 1124910 | 0 | False | -| Neo_n4096_TORCH_p1 | oom | 1124910 | 0 | False | -| Neo_n4096_TORCH_p2 | oom | 1124910 | 0 | False | -| Neo_n512_BASE_p0 | completed | 1124910 | 0 | False | -| Neo_n512_BASE_p1 | completed | 1124910 | 0 | False | -| Neo_n512_BASE_p2 | completed | 1124910 | 0 | False | -| Neo_n512_ECS_p0 | completed | 1124910 | 0 | True | -| Neo_n512_ECS_p1 | completed | 1124910 | 0 | True | -| Neo_n512_ECS_p2 | completed | 1124910 | 0 | True | -| Neo_n512_TORCH_p0 | completed | 1124910 | 0 | False | -| Neo_n512_TORCH_p1 | completed | 1124910 | 0 | False | -| Neo_n512_TORCH_p2 | completed | 1124910 | 0 | False | -| Neo_n96_BASE_p0 | completed | 1124910 | 0 | False | -| Neo_n96_BASE_p1 | completed | 1124910 | 0 | False | -| Neo_n96_BASE_p2 | completed | 1124910 | 0 | False | -| Neo_n96_ECS_p0 | completed | 1124910 | 0 | True | -| Neo_n96_ECS_p1 | completed | 1124910 | 0 | True | -| Neo_n96_ECS_p2 | completed | 1124910 | 0 | True | -| Neo_n96_TORCH_p0 | completed | 1124910 | 0 | False | -| Neo_n96_TORCH_p1 | completed | 1124910 | 0 | False | -| Neo_n96_TORCH_p2 | completed | 1124910 | 0 | False | -| Pro_n4096_BASE_p0 | oom | 22678990 | 0 | False | -| Pro_n4096_BASE_p1 | oom | 22678990 | 0 | False | -| Pro_n4096_BASE_p2 | oom | 22678990 | 0 | False | -| Pro_n4096_ECS_p0 | oom | 22678990 | 0 | False | -| Pro_n4096_ECS_p1 | oom | 22678990 | 0 | False | -| Pro_n4096_ECS_p2 | oom | 22678990 | 0 | False | -| Pro_n4096_TORCH_p0 | oom | 22678990 | 0 | False | -| Pro_n4096_TORCH_p1 | oom | 22678990 | 0 | False | -| Pro_n4096_TORCH_p2 | oom | 22678990 | 0 | False | -| Pro_n512_BASE_p0 | completed | 22678990 | 0 | False | -| Pro_n512_BASE_p1 | completed | 22678990 | 0 | False | -| Pro_n512_BASE_p2 | completed | 22678990 | 0 | False | -| Pro_n512_ECS_p0 | completed | 22678990 | 0 | False | -| Pro_n512_ECS_p1 | completed | 22678990 | 0 | False | -| Pro_n512_ECS_p2 | completed | 22678990 | 0 | False | -| Pro_n512_TORCH_p0 | oom | 22678990 | 0 | False | -| Pro_n512_TORCH_p1 | oom | 22678990 | 0 | False | -| Pro_n512_TORCH_p2 | oom | 22678990 | 0 | False | -| Pro_n96_BASE_p0 | completed | 22678990 | 0 | False | -| Pro_n96_BASE_p1 | completed | 22678990 | 0 | False | -| Pro_n96_BASE_p2 | completed | 22678990 | 0 | False | -| Pro_n96_ECS_p0 | completed | 22678990 | 0 | False | -| Pro_n96_ECS_p1 | completed | 22678990 | 0 | False | -| Pro_n96_ECS_p2 | completed | 22678990 | 0 | False | -| Pro_n96_TORCH_p0 | completed | 22678990 | 0 | False | -| Pro_n96_TORCH_p1 | completed | 22678990 | 0 | False | -| Pro_n96_TORCH_p2 | completed | 22678990 | 0 | False | diff --git a/doc/development/ecs-validation-20261025/README.md b/doc/development/ecs-validation-20261025/README.md deleted file mode 100644 index 529ec8ab0a..0000000000 --- a/doc/development/ecs-validation-20261025/README.md +++ /dev/null @@ -1,74 +0,0 @@ -# Manual ECS validation artifact (2026-10-25 measurements; 2026-10-26 publication) - -## What is being compared - -This artifact records **135 process outcomes: 120 completed, 15 OOM**, not 135 numerical passes. Each complete arm has three fresh processes, three identical perturbed frames repeated three times per process. The three columns are compiled pure TORCH, native master, and the isolated candidate port. Sources are pinned; raw results are not mixed across trees. - -The master/candidate port uses `2ba617bc3db67adee2b8ba00c71e9fa1c2d2bb8c`, which includes merged #6058 residual-inplace. The existing personal PR still has the original668 base; **this evidence update does not silently rebase that PR or replace its production source**. `port-to-2ba617bc.patch` reproduces the tested three-file port on the pinned master. Generated source hashes are checked against the measurement manifests. - -## Files - -- `REPORT.md`: explicit three-column strict and empirical-band PASS/FAIL, per-output errors, P50/P90, CUDA Event time, first/full/steady allocated peaks, changes and route limits. -- `ACCURACY_TABLE.md`: separate within/across-process and implementation comparisons, strict failures and ranges. -- `summary.json`: complete aggregate metrics including mean_abs/max_relative, finite status, route counts, checkpoint identity and per-process peaks. -- `runs.json`: all135 original worker result/manifest records, including per-call timing samples and exceptions. OOM is retained. -- `provenance.json`: pinned sources, per-run NPZ hashes, source-consistency checks and publication-time environment snapshot. -- `validation.json`: prior60-test focused CUDA result and the published worker's separate complete Neo96/GPU1 smoke (3warmups/10samples,finite). The GPU0 attempt correctly stopped on a busy GPU. Smoke is not pooled into the135-run matrix and is not a new cross-reference accuracy acceptance. -- `worker.py`, `run_matrix.py`, `analyze.py`, `report.py`: standalone manual tools, not added to routine GPU CI. `pro_cpu_load_check.json` is the historical metadata-compatibility proof for the verified Pro checkpoint, not a new numerical reference. - -The original input/output NPZ archives total approximately47MiB and remain in the local experiment archive; they are **not committed here**. JSON timing/config/error summaries are inspectable, and the published harness permits fresh reproduction, but the published subset does not itself permit recomputing every original output comparison. NPZ hashes in provenance support later transfer/integrity checking; a hash is not a substitute for the arrays. - -## Checkpoints and scope - -Neo: DPA4-Neo-OMat24-v20260627.pt, SHA256 `8d8b2b02b9575cb9e0af420cc1e8b1d9ffcbc04752c2203d29f48ccd0cd5f07d`. Nano/Mini: official OMat24 v20260805; SHA256 and loaded counts in manifests. Pro: DPA-4.0.1-Pro-MPtrj v2026.06.05, **not OMat24-Pro**; SHA256 `446a6e893101fc52190cfccabb9d2328863d651aca36981fd8d3401c71a61221`, official MD5 `2174bb3095b197489f978d31393c1e06`,273318000 bytes. [Official Pro record](https://doi.org/10.6084/m9.figshare.32647224). - -Pro's historical fitting type alias is mapped in memory only (`ener -> dpa4_ener`), equally for all arms. Copy the included proof as `cpu_load_check.json` beside the verified Pro checkpoint before using the worker. This proof records strict CPU load on the historical reader; it is not evidence of passing inference on every current environment. The worker requires the exact verified Pro SHA; it does not patch arbitrary checkpoint files. No checkpoint tensors are changed or uploaded. - -The candidate guard supports the fixed Neo layout. Nano/Mini/Pro execute **native fallback**, not optimized ECS. Their timing variation cannot be credited to ECS. Neo's measured AOT graphs specialize the single-cohort path, including4096; this is not evidence of actual bounded8192-edge runtime batches. No deployment fix is included in this evidence publication. - -## Measurement protocol and limits - -- RTX5090 x2, PyTorch2.11.0+cu128, Triton3.6.0, CUDA runtime12.8. Driver595.91.07 observed on the same server at publication. The supplied environment snapshot is **post-run**, not a clock/temperature trace of the timed window. This matrix did not collect continuous GPU telemetry, unlike the earlier controlled N64 study. -- Each worker checks for existing GPU compute clients before initialization. Same physical GPU for all arms/repeats of a model/system/size: Neo/Pro GPU0, Nano/Mini GPU1. Two GPUs may run distinct cases simultaneously; CPU/package contention is not excluded. No global clock/driver changes, no killed foreign process. -- FP32 mixing, TF32/AMP off, no FP16x3. Preserve original non-mixing model precision. Normal ASE energy/forces/virial/stress API includes neighbor construction. -- Diamond C3.567Angstrom; bccCu2.87Angstrom. Seed0, sigma0.02Angstrom, three exact same frames checked across arms. Diamond N96 repeats(2,2,3), N512(4,4,4), N4096(8,8,8). Cu96(4,4,3),512(8,8,4),4096(16,16,8). -- Accuracy9calls includes first compilation but those calls are not latency samples. Then8 warmups and30 synchronized wall/Event samples; separate30call steady-memory replay. Full peak takes the maximum including first execution and subsequent phases; steady reset is never substituted for first capacity. Allocated/reserved bytes retained; decimalGB=1e9B. -- Explicit cache per model/system/mode is reused in later independent repeats. The first worker was cold; later repeats benefit from that cache. All generic autotune choices are **not** frozen across arms. Therefore these independent-process quantiles do not establish a stable isolated sub-percent ECS cost/benefit. Earlier controlled N64 evidence remains a different experiment. -- Median of three process P50/P90 values, worst process capacity. Timing/accuracy/profile are distinct; no profiled timings are included. No DFT/long-MD accuracy, H20,10k-atoms, PT2/C++ or new training acceptance claim. - -## Numerical criteria - -Strict elementwise criterion: finite and `abs(got-ref)<=1e-5+1e-4*abs(ref)`. Max-relative denominator `max(abs(ref),1e-8)`. Energy/virial in eV, force eV/Angstrom, stress eV/Angstrom^3; same numeric tolerance across units does not imply same physical accuracy. - -Meeting-band diagnostic: separately per output/model/system/size, worst same-frame cross-implementation max_abs divided by pure-TORCH's own worst repeat max_abs (within and across processes). <=3 is marked band PASS; it is an empirical sampled repeat band, not a formal error bound. Strict failures remain failures; zero self-band requires zero deviation. Missing/OOM reference is N/A, not PASS. A maximum over more repeat samples may increase; this protocol does not allow enlarging the sample set only to inflate tolerance. - -## Reproduce on explicit source trees - -Use a compatible CUDA/native DeePMD installation. Set source roots explicitly; the worker checks imported package provenance and avoids silently using an unrelated editable checkout. Obtain checkpoints separately under their licenses. Start with idle GPUs and new output/cache directories. - -```bash -git clone https://github.com/deepmodeling/deepmd-kit.git base -git -C base checkout --detach 2ba617bc3db67adee2b8ba00c71e9fa1c2d2bb8c -git clone --shared base candidate -git -C candidate apply /absolute/path/to/this-artifact/port-to-2ba617bc.patch -``` - -Create a local model-map JSON with Neo/Nano/Mini/Pro mapped to absolute checkpoint paths (not committed weights). For Pro, place its checked compatibility proof beside the checkpoint as described above. Then, using the appropriate Python environment: - -```bash -python run_matrix.py --base-root /absolute/base --candidate-root /absolute/candidate \ - --model-map /absolute/models.json --out-root /absolute/new-results \ - --gpu 0 --models Neo Pro -python run_matrix.py --base-root /absolute/base --candidate-root /absolute/candidate \ - --model-map /absolute/models.json --out-root /absolute/new-results \ - --gpu 1 --models Nano Mini -python report.py --root /absolute/new-results -``` - -The two commands can use different GPUs concurrently. Do not run two workers on one GPU. Default is all three sizes and three independent processes; `--sizes 96` permits an initial smoke, not full acceptance. Existing outcomes are not overwritten; explicit `--skip-existing` only resumes, not a new rerun. The public runner reproduces the calculation/protocol with explicit paths; orchestration/path handling is adapted from the local runner. Validate it on the target environment rather than claiming a new135-process run from `--help` success. - -## Conclusion and open gates - -Current-master Neo4096: P50 **156.512587->156.594862ms**, full allocated peak **22.313645->22.806378GB** (increase2.2082%). Neo96: **17.992521->18.035012ms**, full peak **0.646372->0.658145GB** (increase1.8213%). Small timing differences lack matched-autotune/process uncertainty; the observed memory increases are retained. No new positive ECS performance claim is made. - -Remaining: fix real dynamic deployment dispatch; resolve scientific acceptance/OOM reference limitations; replace the fine-grained development switch with an agreed existing-level/internal policy and tuning integration where appropriate; retain optional-Triton CPU safety; add proportionate actual engagement/guard/alias tests; obtain real upstream CI approval. Keep personal PR Draft/default-off. This update is evidence for human review, **not an upstream submission or ready-to-merge declaration**. diff --git a/doc/development/ecs-validation-20261025/REPORT.md b/doc/development/ecs-validation-20261025/REPORT.md deleted file mode 100644 index 611ab06412..0000000000 --- a/doc/development/ecs-validation-20261025/REPORT.md +++ /dev/null @@ -1,171 +0,0 @@ -# ECS current-master comparison — measured 2026-10-25, finalized 2026-10-26 - -BASE: 2ba617bc3db67adee2b8ba00c71e9fa1c2d2bb8c, including merged residual-inplace #6058. Old668-base numbers are not substituted. Candidate is the isolated port of PR#2; its AOT branch-specialization issue remains unresolved. - -Recorded 135/135 process results: {'completed': 120, 'oom': 15}. Missing cells are NOT passes. Three processes ×3 frames ×3 accuracy repeats per arm; 8 extra warmups/30 wall+Event samples, separate steady replay. Same physical GPU within each case. TF32/AMP off. - -## Three-column PASS/FAIL overview - -Strict columns cover all four fields: Torch self-repeat; master self-repeat and versus Torch; candidate self-repeat, versus Torch and versus master. A missing Torch reference is N/A, never PASS. The meeting band is a separately reported empirical diagnostic, not a rigorous error bound or a waiver of strict failure. - -| Model/system | Atoms | Compiled Torch | Current master | Candidate | -| --- | ---: | --- | --- | --- | -| Neo | 96 | strict self=PASS | strict self=PASS; vs Torch=PASS; band=PASS (2.171x) | strict self=PASS; vs Torch=PASS; vs master=PASS; band=PASS (2.355x) | -| Neo | 512 | strict self=PASS | strict self=PASS; vs Torch=PASS; band=PASS (2.406x) | strict self=PASS; vs Torch=FAIL; vs master=PASS; band=PASS (2.261x) | -| Neo | 4096 | strict self=N/A | strict self=FAIL; vs Torch=N/A; band=N/A | strict self=FAIL; vs Torch=N/A; vs master=FAIL; band=N/A | -| NeoCu | 96 | strict self=PASS | strict self=PASS; vs Torch=PASS; band=PASS (1.989x) | strict self=PASS; vs Torch=PASS; vs master=PASS; band=PASS (2.084x) | -| NeoCu | 512 | strict self=PASS | strict self=PASS; vs Torch=PASS; band=PASS (2.499x) | strict self=PASS; vs Torch=PASS; vs master=PASS; band=PASS (2.863x) | -| NeoCu | 4096 | strict self=PASS | strict self=PASS; vs Torch=PASS; band=FAIL (6.690x) | strict self=PASS; vs Torch=PASS; vs master=PASS; band=FAIL (6.707x) | -| Nano | 96 | strict self=FAIL | strict self=FAIL; vs Torch=FAIL; band=PASS (1.316x) | strict self=FAIL; vs Torch=FAIL; vs master=FAIL; band=PASS (1.498x) | -| Nano | 512 | strict self=FAIL | strict self=FAIL; vs Torch=FAIL; band=PASS (1.839x) | strict self=FAIL; vs Torch=FAIL; vs master=FAIL; band=PASS (1.831x) | -| Nano | 4096 | strict self=FAIL | strict self=FAIL; vs Torch=FAIL; band=FAIL (3.031x) | strict self=FAIL; vs Torch=FAIL; vs master=FAIL; band=FAIL (3.171x) | -| Mini | 96 | strict self=FAIL | strict self=FAIL; vs Torch=FAIL; band=PASS (1.474x) | strict self=FAIL; vs Torch=FAIL; vs master=FAIL; band=PASS (1.433x) | -| Mini | 512 | strict self=FAIL | strict self=FAIL; vs Torch=FAIL; band=PASS (1.519x) | strict self=FAIL; vs Torch=FAIL; vs master=FAIL; band=PASS (1.498x) | -| Mini | 4096 | strict self=FAIL | strict self=FAIL; vs Torch=FAIL; band=FAIL (4.252x) | strict self=FAIL; vs Torch=FAIL; vs master=FAIL; band=FAIL (4.346x) | -| Pro | 96 | strict self=PASS | strict self=PASS; vs Torch=PASS; band=PASS (1.678x) | strict self=PASS; vs Torch=PASS; vs master=PASS; band=PASS (1.709x) | -| Pro | 512 | strict self=N/A | strict self=PASS; vs Torch=N/A; band=N/A | strict self=PASS; vs Torch=N/A; vs master=PASS; band=N/A | -| Pro | 4096 | strict self=N/A | strict self=N/A; vs Torch=N/A; band=N/A | strict self=N/A; vs Torch=N/A; vs master=N/A; band=N/A | - -## Three accuracy columns - -Each field: worst pairwise max-absolute difference over same-frame outputs; self includes within/across-process repeats. Scientific band multiplier = worst deviation from compiled TORCH / TORCH own worst repeat max_abs, separately per field/model/size/system. <=3 is the meeting criterion, not a replacement for the fixed strict elementwise gate. Zero repeat band requires zero cross difference. Energy/virial eV, force eV/Angstrom, stress eV/Angstrom^3. - -| Model/system | Atoms | Output | Compiled Torch | Current master | Candidate | -| --- | ---: | --- | --- | --- | --- | -| Neo | 96 | energy | self max_abs=1.222e-05 | self max_abs=1.073e-05; vs Torch=1.115e-05; band×0.912 | self max_abs=6.855e-06; vs Torch=1.127e-05; band×0.922 | -| Neo | 96 | forces | self max_abs=4.93e-06 | self max_abs=4.474e-06; vs Torch=7.196e-06; band×1.46 | self max_abs=4.369e-06; vs Torch=7.185e-06; band×1.46 | -| Neo | 96 | virial | self max_abs=2.222e-05 | self max_abs=2.443e-05; vs Torch=4.824e-05; band×2.17 | self max_abs=2.69e-05; vs Torch=5.232e-05; band×2.35 | -| Neo | 96 | stress | self max_abs=4.08e-08 | self max_abs=4.486e-08; vs Torch=8.858e-08; band×2.17 | self max_abs=4.938e-08; vs Torch=9.607e-08; band×2.35 | -| Neo | 512 | energy | self max_abs=1.776e-05 | self max_abs=2.247e-05; vs Torch=2.021e-05; band×1.14 | self max_abs=2.056e-05; vs Torch=2.718e-05; band×1.53 | -| Neo | 512 | forces | self max_abs=4.901e-06 | self max_abs=5.144e-06; vs Torch=7.15e-06; band×1.46 | self max_abs=5.012e-06; vs Torch=7.02e-06; band×1.43 | -| Neo | 512 | virial | self max_abs=6.951e-05 | self max_abs=6.508e-05; vs Torch=0.0001673; band×2.41 | self max_abs=5.708e-05; vs Torch=0.0001572; band×2.26 | -| Neo | 512 | stress | self max_abs=2.393e-08 | self max_abs=2.24e-08; vs Torch=5.758e-08; band×2.41 | self max_abs=1.965e-08; vs Torch=5.411e-08; band×2.26 | -| Neo | 4096 | energy | OOM | self max_abs=5.96e-05; Torch ref unavailable | self max_abs=5.788e-05; Torch ref unavailable | -| Neo | 4096 | forces | OOM | self max_abs=5.564e-06; Torch ref unavailable | self max_abs=5.939e-06; Torch ref unavailable | -| Neo | 4096 | virial | OOM | self max_abs=0.0001374; Torch ref unavailable | self max_abs=0.0002537; Torch ref unavailable | -| Neo | 4096 | stress | OOM | self max_abs=5.913e-09; Torch ref unavailable | self max_abs=1.092e-08; Torch ref unavailable | -| NeoCu | 96 | energy | self max_abs=3.636e-06 | self max_abs=4.59e-06; vs Torch=6.855e-06; band×1.89 | self max_abs=4.411e-06; vs Torch=5.782e-06; band×1.59 | -| NeoCu | 96 | forces | self max_abs=6.306e-07 | self max_abs=6.496e-07; vs Torch=6.391e-07; band×1.01 | self max_abs=6.476e-07; vs Torch=6.297e-07; band×0.999 | -| NeoCu | 96 | virial | self max_abs=1.361e-05 | self max_abs=1.663e-05; vs Torch=2.707e-05; band×1.99 | self max_abs=1.739e-05; vs Torch=2.837e-05; band×2.08 | -| NeoCu | 96 | stress | self max_abs=1.2e-08 | self max_abs=1.466e-08; vs Torch=2.386e-08; band×1.99 | self max_abs=1.533e-08; vs Torch=2.5e-08; band×2.08 | -| NeoCu | 512 | energy | self max_abs=1.138e-05 | self max_abs=1.007e-05; vs Torch=1.919e-05; band×1.69 | self max_abs=1.085e-05; vs Torch=1.86e-05; band×1.63 | -| NeoCu | 512 | forces | self max_abs=6.573e-07 | self max_abs=6.11e-07; vs Torch=7.357e-07; band×1.12 | self max_abs=7.186e-07; vs Torch=7.158e-07; band×1.09 | -| NeoCu | 512 | virial | self max_abs=4.521e-05 | self max_abs=3.137e-05; vs Torch=0.000113; band×2.5 | self max_abs=4.702e-05; vs Torch=0.0001295; band×2.86 | -| NeoCu | 512 | stress | self max_abs=7.471e-09 | self max_abs=5.184e-09; vs Torch=1.867e-08; band×2.5 | self max_abs=7.77e-09; vs Torch=2.139e-08; band×2.86 | -| NeoCu | 4096 | energy | self max_abs=2.599e-05 | self max_abs=4.148e-05; vs Torch=8.726e-05; band×3.36 | self max_abs=3.821e-05; vs Torch=8.494e-05; band×3.27 | -| NeoCu | 4096 | forces | self max_abs=7.152e-07 | self max_abs=7.069e-07; vs Torch=8.014e-07; band×1.12 | self max_abs=7.169e-07; vs Torch=7.932e-07; band×1.11 | -| NeoCu | 4096 | virial | self max_abs=0.0001036 | self max_abs=0.0001054; vs Torch=0.0006932; band×6.69 | self max_abs=0.0001111; vs Torch=0.000695; band×6.71 | -| NeoCu | 4096 | stress | self max_abs=2.14e-09 | self max_abs=2.178e-09; vs Torch=1.432e-08; band×6.69 | self max_abs=2.295e-09; vs Torch=1.435e-08; band×6.71 | -| Nano | 96 | energy | self max_abs=2.658e-05 | self max_abs=1.878e-05; vs Torch=3.499e-05; band×1.32 | self max_abs=2.122e-05; vs Torch=3.982e-05; band×1.5 | -| Nano | 96 | forces | self max_abs=1.136e-05 | self max_abs=1.22e-05; vs Torch=1.286e-05; band×1.13 | self max_abs=1.15e-05; vs Torch=1.269e-05; band×1.12 | -| Nano | 96 | virial | self max_abs=7.839e-05 | self max_abs=8.811e-05; vs Torch=9.567e-05; band×1.22 | self max_abs=8.601e-05; vs Torch=9.163e-05; band×1.17 | -| Nano | 96 | stress | self max_abs=1.439e-07 | self max_abs=1.618e-07; vs Torch=1.757e-07; band×1.22 | self max_abs=1.579e-07; vs Torch=1.682e-07; band×1.17 | -| Nano | 512 | energy | self max_abs=6.02e-05 | self max_abs=6.39e-05; vs Torch=0.0001107; band×1.84 | self max_abs=5.358e-05; vs Torch=0.0001102; band×1.83 | -| Nano | 512 | forces | self max_abs=1.488e-05 | self max_abs=1.347e-05; vs Torch=1.477e-05; band×0.992 | self max_abs=1.348e-05; vs Torch=1.325e-05; band×0.89 | -| Nano | 512 | virial | self max_abs=0.000201 | self max_abs=0.0001863; vs Torch=0.0002575; band×1.28 | self max_abs=0.0002203; vs Torch=0.000256; band×1.27 | -| Nano | 512 | stress | self max_abs=6.921e-08 | self max_abs=6.415e-08; vs Torch=8.866e-08; band×1.28 | self max_abs=7.585e-08; vs Torch=8.813e-08; band×1.27 | -| Nano | 4096 | energy | self max_abs=0.0001612 | self max_abs=0.0001924; vs Torch=0.0004887; band×3.03 | self max_abs=0.0002133; vs Torch=0.0005112; band×3.17 | -| Nano | 4096 | forces | self max_abs=1.484e-05 | self max_abs=1.563e-05; vs Torch=1.613e-05; band×1.09 | self max_abs=1.605e-05; vs Torch=1.554e-05; band×1.05 | -| Nano | 4096 | virial | self max_abs=0.0004504 | self max_abs=0.0005898; vs Torch=0.0007157; band×1.59 | self max_abs=0.0005332; vs Torch=0.0007943; band×1.76 | -| Nano | 4096 | stress | self max_abs=1.938e-08 | self max_abs=2.538e-08; vs Torch=3.08e-08; band×1.59 | self max_abs=2.294e-08; vs Torch=3.418e-08; band×1.76 | -| Mini | 96 | energy | self max_abs=1.246e-05 | self max_abs=1.049e-05; vs Torch=1.836e-05; band×1.47 | self max_abs=1.74e-05; vs Torch=1.651e-05; band×1.33 | -| Mini | 96 | forces | self max_abs=8.618e-06 | self max_abs=7.551e-06; vs Torch=1.08e-05; band×1.25 | self max_abs=8.28e-06; vs Torch=1.135e-05; band×1.32 | -| Mini | 96 | virial | self max_abs=6.538e-05 | self max_abs=4.644e-05; vs Torch=8.237e-05; band×1.26 | self max_abs=7.174e-05; vs Torch=9.37e-05; band×1.43 | -| Mini | 96 | stress | self max_abs=1.2e-07 | self max_abs=8.526e-08; vs Torch=1.512e-07; band×1.26 | self max_abs=1.317e-07; vs Torch=1.72e-07; band×1.43 | -| Mini | 512 | energy | self max_abs=3.606e-05 | self max_abs=3.594e-05; vs Torch=3.952e-05; band×1.1 | self max_abs=2.813e-05; vs Torch=3.04e-05; band×0.843 | -| Mini | 512 | forces | self max_abs=8.601e-06 | self max_abs=8.582e-06; vs Torch=1.21e-05; band×1.41 | self max_abs=9.532e-06; vs Torch=1.289e-05; band×1.5 | -| Mini | 512 | virial | self max_abs=0.0001708 | self max_abs=0.0001244; vs Torch=0.0002595; band×1.52 | self max_abs=0.0001445; vs Torch=0.0002485; band×1.45 | -| Mini | 512 | stress | self max_abs=5.882e-08 | self max_abs=4.283e-08; vs Torch=8.933e-08; band×1.52 | self max_abs=4.975e-08; vs Torch=8.555e-08; band×1.45 | -| Mini | 4096 | energy | self max_abs=7.707e-05 | self max_abs=0.0001156; vs Torch=0.0001388; band×1.8 | self max_abs=8.416e-05; vs Torch=0.0001465; band×1.9 | -| Mini | 4096 | forces | self max_abs=1.004e-05 | self max_abs=9.328e-06; vs Torch=1.306e-05; band×1.3 | self max_abs=1.007e-05; vs Torch=1.398e-05; band×1.39 | -| Mini | 4096 | virial | self max_abs=0.0003499 | self max_abs=0.0003119; vs Torch=0.001488; band×4.25 | self max_abs=0.0003646; vs Torch=0.001521; band×4.35 | -| Mini | 4096 | stress | self max_abs=1.506e-08 | self max_abs=1.342e-08; vs Torch=6.403e-08; band×4.25 | self max_abs=1.569e-08; vs Torch=6.544e-08; band×4.35 | -| Pro | 96 | energy | self max_abs=4.083e-06 | self max_abs=3.368e-06; vs Torch=5.037e-06; band×1.23 | self max_abs=3.338e-06; vs Torch=5.871e-06; band×1.44 | -| Pro | 96 | forces | self max_abs=1.667e-06 | self max_abs=1.153e-06; vs Torch=1.919e-06; band×1.15 | self max_abs=1.144e-06; vs Torch=1.691e-06; band×1.01 | -| Pro | 96 | virial | self max_abs=9.613e-06 | self max_abs=8.763e-06; vs Torch=1.613e-05; band×1.68 | self max_abs=7.585e-06; vs Torch=1.643e-05; band×1.71 | -| Pro | 96 | stress | self max_abs=1.765e-08 | self max_abs=1.609e-08; vs Torch=2.962e-08; band×1.68 | self max_abs=1.393e-08; vs Torch=3.017e-08; band×1.71 | -| Pro | 512 | energy | OOM | self max_abs=1.019e-05; Torch ref unavailable | self max_abs=8.345e-06; Torch ref unavailable | -| Pro | 512 | forces | OOM | self max_abs=1.36e-06; Torch ref unavailable | self max_abs=1.497e-06; Torch ref unavailable | -| Pro | 512 | virial | OOM | self max_abs=2.018e-05; Torch ref unavailable | self max_abs=1.641e-05; Torch ref unavailable | -| Pro | 512 | stress | OOM | self max_abs=6.947e-09; Torch ref unavailable | self max_abs=5.648e-09; Torch ref unavailable | -| Pro | 4096 | energy | OOM | OOM | OOM | -| Pro | 4096 | forces | OOM | OOM | OOM | -| Pro | 4096 | virial | OOM | OOM | OOM | -| Pro | 4096 | stress | OOM | OOM | OOM | - -Strict per-element atol1e-5/rtol1e-4 and finite results, mean_abs/max_relative and within/across detail remain in ACCURACY_TABLE.md and summary.json. Pure-reference OOM prevents band acceptance. Band PASS does not silently erase strict FAIL. - -## Timing and memory — process medians and worst capacity - -Compilation excluded from timing, included in first/full memory. This independent-process protocol does not freeze all generated autotune choices: no stable isolated speedup is inferred solely from these percentages. GB=1e9B. Both allocated/reserved raw bytes are retained in result.json. Numbers are preliminary until 3 processes per arm complete. - -| Model/system | Atoms | Mode | Processes | Wall P50 ms | Wall P90 ms | Event P50 ms | First alloc GB | Full peak incl first GB | Steady alloc GB | -| --- | ---: | --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | -| Neo | 96 | TORCH | 3/3 | 13.075581 | 13.151851 | 13.030096 | 1.118502 | 1.118502 | 1.017839 | -| Neo | 96 | BASE | 3/3 | 17.992521 | 18.385345 | 17.950175 | 0.646372 | 0.646372 | 0.545709 | -| Neo | 96 | ECS | 3/3 | 18.035012 | 18.224658 | 17.991696 | 0.658145 | 0.658145 | 0.557481 | -| Neo | 512 | TORCH | 3/3 | 46.979462 | 47.018433 | 46.936768 | 5.331018 | 5.331018 | 5.331018 | -| Neo | 512 | BASE | 3/3 | 23.472956 | 23.531686 | 23.427423 | 2.809001 | 2.809001 | 2.809001 | -| Neo | 512 | ECS | 3/3 | 23.494863 | 23.648258 | 23.449760 | 2.871368 | 2.871368 | 2.871368 | -| Neo | 4096 | BASE | 3/3 | 156.512587 | 156.684338 | 156.465652 | 22.313645 | 22.313645 | 22.313645 | -| Neo | 4096 | ECS | 3/3 | 156.594862 | 156.777619 | 156.533646 | 22.806378 | 22.806378 | 22.806378 | -| NeoCu | 96 | TORCH | 3/3 | 12.232151 | 12.297081 | 12.188800 | 0.541385 | 0.541385 | 0.440721 | -| NeoCu | 96 | BASE | 3/3 | 18.154909 | 18.366497 | 18.111904 | 0.353757 | 0.353757 | 0.253004 | -| NeoCu | 96 | ECS | 3/3 | 18.259421 | 18.342752 | 18.215392 | 0.355033 | 0.355033 | 0.254369 | -| NeoCu | 512 | TORCH | 3/3 | 22.897386 | 22.935968 | 22.853473 | 2.253040 | 2.253040 | 2.253040 | -| NeoCu | 512 | BASE | 3/3 | 18.317986 | 18.392029 | 18.274000 | 1.249555 | 1.249555 | 1.249555 | -| NeoCu | 512 | ECS | 3/3 | 18.244627 | 18.391290 | 18.201713 | 1.254748 | 1.254748 | 1.254748 | -| NeoCu | 4096 | TORCH | 3/3 | 160.911316 | 161.083811 | 160.864113 | 17.866908 | 17.866908 | 17.866908 | -| NeoCu | 4096 | BASE | 3/3 | 80.131557 | 80.263306 | 80.085968 | 9.842755 | 9.842755 | 9.842755 | -| NeoCu | 4096 | ECS | 3/3 | 80.314757 | 80.400489 | 80.269539 | 9.873422 | 9.873422 | 9.873422 | -| Nano | 96 | TORCH | 3/3 | 10.842765 | 10.894773 | 10.799536 | 0.267505 | 0.267505 | 0.168914 | -| Nano | 96 | BASE | 3/3 | 10.827782 | 10.899461 | 10.785792 | 0.204831 | 0.204831 | 0.104168 | -| Nano | 96 | ECS | 3/3 | 10.780998 | 10.826602 | 10.738672 | 0.204831 | 0.204831 | 0.104168 | -| Nano | 512 | TORCH | 3/3 | 11.856695 | 11.910365 | 11.813136 | 0.845872 | 0.845872 | 0.845872 | -| Nano | 512 | BASE | 3/3 | 11.192140 | 11.325888 | 11.147888 | 0.500028 | 0.500028 | 0.500028 | -| Nano | 512 | ECS | 3/3 | 11.160740 | 11.249917 | 11.116624 | 0.500028 | 0.500028 | 0.500028 | -| Nano | 4096 | TORCH | 3/3 | 56.467913 | 56.592532 | 56.422001 | 6.678115 | 6.678115 | 6.678115 | -| Nano | 4096 | BASE | 3/3 | 33.593906 | 33.720488 | 33.547775 | 3.910505 | 3.910505 | 3.910505 | -| Nano | 4096 | ECS | 3/3 | 33.589447 | 33.682449 | 33.544529 | 3.910505 | 3.910505 | 3.910505 | -| Mini | 96 | TORCH | 3/3 | 11.962016 | 12.010488 | 11.917488 | 0.400473 | 0.400473 | 0.299810 | -| Mini | 96 | BASE | 3/3 | 11.099674 | 11.319123 | 11.057840 | 0.286303 | 0.286303 | 0.190500 | -| Mini | 96 | ECS | 3/3 | 11.180259 | 11.242529 | 11.135952 | 0.286303 | 0.286303 | 0.190500 | -| Mini | 512 | TORCH | 3/3 | 18.303323 | 18.349090 | 18.260481 | 1.528662 | 1.528662 | 1.528662 | -| Mini | 512 | BASE | 3/3 | 12.184330 | 12.276598 | 12.140736 | 0.944773 | 0.945822 | 0.945822 | -| Mini | 512 | ECS | 3/3 | 12.208581 | 12.249058 | 12.166288 | 0.944773 | 0.945822 | 0.945822 | -| Mini | 4096 | TORCH | 3/3 | 122.270842 | 122.379773 | 122.217281 | 12.115697 | 12.115697 | 12.115697 | -| Mini | 4096 | BASE | 3/3 | 61.097768 | 61.183442 | 61.052145 | 7.452142 | 7.453190 | 7.453190 | -| Mini | 4096 | ECS | 3/3 | 60.989576 | 61.053372 | 60.944431 | 7.452142 | 7.453190 | 7.453190 | -| Pro | 96 | TORCH | 3/3 | 125.952058 | 126.149409 | 125.908112 | 11.443602 | 11.443602 | 11.443602 | -| Pro | 96 | BASE | 3/3 | 66.992101 | 67.231234 | 66.948528 | 5.139665 | 5.139665 | 5.039002 | -| Pro | 96 | ECS | 3/3 | 67.021606 | 67.312684 | 66.977631 | 5.139665 | 5.139665 | 5.039002 | -| Pro | 512 | BASE | 3/3 | 330.464768 | 331.377085 | 330.418015 | 25.395441 | 25.395441 | 25.395441 | -| Pro | 512 | ECS | 3/3 | 330.612567 | 331.352630 | 330.561584 | 25.395441 | 25.395441 | 25.395441 | - -## Route and acceptance limits - -Nano/Mini/Pro candidate is native fallback, not demonstrated ECS acceleration. Neo dispatch must be checked against generated wrappers; current single-cohort specialization must not be described as 80 runtime batches. Pro-MPtrj is not OMat24-Pro; original checkpoint unmodified, reader-only legacy metadata alias applies equally to all arms. H20/PT2/C++/training and unmeasured larger models are not accepted by this matrix. - -Upstream review also requires focused guard/alias tests, no new public fine-grained development switch for a merge-ready path, freeze-time policy/selection where applicable, optional-Triton CPU safety and real CI approval. Current port keeps the old default-off experimental switch pending policy resolution. Do not submit as ready-to-merge. - -## Candidate vs current-master changes - -Positive percentage means reduction/improvement; negative means increase. Medians of independent process quantiles, not a controlled paired significance estimate. Fallback timing variation is not attributed to ECS. - -| Model/system | Atoms | Time reduction % | First-inclusive full alloc reduction % | Steady alloc reduction % | Candidate route | -| --- | ---: | ---: | ---: | ---: | --- | -| Neo | 96 | -0.2362 | -1.8213 | -2.1573 | single-cohort specialized, not bounded runtime batching | -| Neo | 512 | -0.0933 | -2.2202 | -2.2202 | single-cohort specialized, not bounded runtime batching | -| Neo | 4096 | -0.0526 | -2.2082 | -2.2082 | single-cohort specialized, not bounded runtime batching | -| NeoCu | 96 | -0.5757 | -0.3605 | -0.5395 | single-cohort specialized, not bounded runtime batching | -| NeoCu | 512 | 0.4005 | -0.4156 | -0.4156 | single-cohort specialized, not bounded runtime batching | -| NeoCu | 4096 | -0.2286 | -0.3116 | -0.3116 | single-cohort specialized, not bounded runtime batching | -| Nano | 96 | 0.4321 | 0.0000 | 0.0000 | native fallback | -| Nano | 512 | 0.2806 | 0.0000 | 0.0000 | native fallback | -| Nano | 4096 | 0.0133 | 0.0000 | 0.0000 | native fallback | -| Mini | 96 | -0.7260 | 0.0000 | 0.0000 | native fallback | -| Mini | 512 | -0.1990 | 0.0000 | 0.0000 | native fallback | -| Mini | 4096 | 0.1771 | 0.0000 | 0.0000 | native fallback | -| Pro | 96 | -0.0440 | 0.0000 | 0.0000 | native fallback | -| Pro | 512 | -0.0447 | 0.0000 | 0.0000 | native fallback | -| Pro | 4096 | N/A | N/A | N/A | incomplete/OOM | diff --git a/doc/development/ecs-validation-20261025/analyze.py b/doc/development/ecs-validation-20261025/analyze.py deleted file mode 100644 index c8db191f33..0000000000 --- a/doc/development/ecs-validation-20261025/analyze.py +++ /dev/null @@ -1,250 +0,0 @@ -"""All-pairs scientific-accuracy ranges; never relax thresholds.""" -# ruff: noqa: T201, ANN001, ANN201, RUF001 -- standalone manual experiment, not imported package code - -import argparse -from itertools import combinations, product -import json -from pathlib import Path - -import numpy as np - -P = argparse.ArgumentParser() -P.add_argument("--root", type=Path, default=Path(__file__).resolve().parent) -a = P.parse_args() -root = a.root -all_results = [] -cases = {} -for d in sorted((root / "results").glob("*")): - if not (d / "result.json").exists(): - continue - result = json.loads((d / "result.json").read_text()) - manifest = ( - json.loads((d / "manifest.json").read_text()) - if (d / "manifest.json").exists() - else {} - ) - pieces = d.name.split("_") - model, n, mode, proc = pieces[0], pieces[1], "_".join(pieces[2:-1]), pieces[-1] - entry = { - "run": d.name, - "model": model, - "n": int(n[1:]), - "mode": mode, - "gpu": manifest.get("args", {}).get("gpu"), - "process": proc, - "status": "oom" - if "deepmd.utils.errors.OutOfMemoryError:" in result.get("exception", "") - else result["status"], - "raw_status": result["status"], - "model_sha256": manifest.get("model_sha256"), - "routes": result.get("routes"), - "runtime_cohort_calls": result.get("runtime_cohort_calls", []), - "finite": result.get("finite"), - "parameter_count": result.get("parameter_count"), - "first_peak": result.get("first_peak"), - "complete_peak": result.get("complete_peak"), - } - all_results.append(entry) - if result["status"] != "completed": - continue - with np.load(d / "outputs.npz") as z: - arrays = {k: z[k] for k in z.files} - with np.load(d / "inputs.npz") as z: - inputs = {k: z[k] for k in z.files} - key = (model, int(n[1:])) - group = cases.setdefault(key, {"inputs": inputs, "modes": {}}) - for k, v in inputs.items(): - if not np.array_equal(group["inputs"][k], v): - raise ValueError(f"inputs differ: {d}/{k}") - group["modes"].setdefault(mode, []).append((proc, arrays)) - - -for model in {r["model"] for r in all_results}: - hashes = { - r["model_sha256"] - for r in all_results - if r["model"] == model and r["model_sha256"] - } - assert len(hashes) == 1, f"checkpoint changed inside the matrix: {model}" -for model, n in {(r["model"], r["n"]) for r in all_results}: - devices = { - r["gpu"] - for r in all_results - if (r["model"], r["n"]) == (model, n) and r["gpu"] is not None - } - assert len(devices) == 1, f"cross-GPU case mixing: {model}/{n}" - - -def error(got, ref): - finite = bool(np.isfinite(got).all() and np.isfinite(ref).all()) - if not finite: - return {"finite": False, "passed": False} - diff = np.abs(got - ref) - ratio = diff / (1e-5 + 1e-4 * np.abs(ref)) - worst = int(np.argmax(ratio)) - return { - "finite": True, - "passed": bool((ratio <= 1).all()), - "max_abs": float(diff.max()), - "mean_abs": float(diff.mean()), - "max_relative": float((diff / np.maximum(np.abs(ref), 1e-8)).max()), - "max_ratio": float(ratio.max()), - "bitwise": bool(np.array_equal(got, ref)), - "worst_ratio_element": { - "index": [int(v) for v in np.unravel_index(worst, ratio.shape)], - "got": float(got.flat[worst]), - "reference": float(ref.flat[worst]), - "abs_error": float(diff.flat[worst]), - "tolerance": float(1e-5 + 1e-4 * abs(ref.flat[worst])), - }, - } - - -groups = [] -details = [] -for (model, n), case in sorted(cases.items()): - modes = case["modes"] - for left, right in [ - ("TORCH", "TORCH"), - ("BASE", "BASE"), - ("ECS", "ECS"), - ("BASE", "TORCH"), - ("ECS", "TORCH"), - ("ECS", "BASE"), - ("ECS_RUNTIME", "ECS_RUNTIME"), - ("ECS_RUNTIME", "BASE"), - ("ECS_RUNTIME", "TORCH"), - ("ECS_RUNTIME", "ECS"), - ]: - if left not in modes or right not in modes: - continue - for scope in ( - ["within_process", "across_process"] - if left == right - else ["cross_implementation"] - ): - raw = [] - for frame in range(3): - la = [(p, r, arr) for p, arr in modes[left] for r in range(3)] - ra = [(p, r, arr) for p, arr in modes[right] for r in range(3)] - pairs = combinations(la, 2) if left == right else product(la, ra) - for (lp, lr, lv), (rp, rr, rv) in pairs: - if scope == "within_process" and lp != rp: - continue - if scope == "across_process" and lp == rp: - continue - for out in ("energy", "forces", "virial", "stress"): - raw.append( - { - "frame": frame, - "left_process": lp, - "left_repeat": lr, - "right_process": rp, - "right_repeat": rr, - "output": out, - **error( - lv[f"{lr}_{frame}_{out}"], rv[f"{rr}_{frame}_{out}"] - ), - } - ) - if not raw: - continue - record = { - "model": model, - "n": n, - "left": left, - "right": right, - "scope": scope, - "left_completed_processes": len(modes[left]), - "right_completed_processes": len(modes[right]), - "all_passed": all(v["passed"] for v in raw), - "metrics": {}, - } - for out in ("energy", "forces", "virial", "stress"): - rows = [r for r in raw if r["output"] == out] - finite = [r for r in rows if r["finite"]] - record["metrics"][out] = { - "comparisons": len(rows), - "failures": sum(not r["passed"] for r in rows), - "all_finite": len(finite) == len(rows), - "all_bitwise": all(r.get("bitwise", False) for r in rows), - "max_abs_range": [ - min(r["max_abs"] for r in finite), - max(r["max_abs"] for r in finite), - ] - if finite - else None, - "worst_mean_abs": max( - (r["mean_abs"] for r in finite), default=None - ), - "worst_max_relative": max( - (r["max_relative"] for r in finite), default=None - ), - "worst_tolerance_ratio": max( - (r["max_ratio"] for r in finite), default=None - ), - } - groups.append(record) - details.append( - { - "model": model, - "n": n, - "left": left, - "right": right, - "scope": scope, - "pairs": raw, - } - ) -summary = { - "threshold": {"atol": 1e-5, "rtol": 1e-4, "relative_floor": 1e-8}, - "range_definition": "min/max of max_abs over same-frame comparisons; NOT a confidence interval", - "statuses": all_results, - "comparisons": groups, -} -(root / "summary.json").write_text(json.dumps(summary, indent=2)) -(root / "pair_details.json").write_text(json.dumps(details, indent=2)) -lines = [ - "# ECS 多模型多尺寸精度测试", - "", - "> 精度优先；未放宽阈值。误差范围为每对输出max_abs的min～max，不是置信区间。基线SHA与模型SHA见JSON。", - "", - "模式：TORCH=纯PyTorch编译；ECS=当前PR候选（非Neo为回退）；ECS_RUNTIME=强制真实8192分批。单位：能量/位力eV，力eV/Å，stress eV/ų。所有结果均不是DFT预测误差。", - "", - "| 模型 | 原子数 | 对照 | 范围 | 完成进程数 | 能量max_abs范围 | 力max_abs范围 | 位力max_abs范围 | stress max_abs范围 | 最差误差/容差 | 状态 |", - "| --- | ---: | --- | --- | --- | --- | --- | --- | --- | ---: | --- |", -] - - -def fmt(r): - return "～".join(f"{v:.3e}" for v in r) if r else "nonfinite" - - -for g in groups: - m = g["metrics"] - worst = max(v["worst_tolerance_ratio"] or 0 for v in m.values()) - lines.append( - f"| {g['model']} | {g['n']} | {g['left']} vs {g['right']} | {g['scope']} | {g['left_completed_processes']}/{g['right_completed_processes']} | {fmt(m['energy']['max_abs_range'])} | {fmt(m['forces']['max_abs_range'])} | {fmt(m['virial']['max_abs_range'])} | {fmt(m['stress']['max_abs_range'])} | {worst:.4f} | {'PASS' if g['all_passed'] else 'FAIL'}{'（样本未齐）' if min(g['left_completed_processes'], g['right_completed_processes']) < 3 else ''} |" - ) -lines += [ - "", - "## 所有进程状态（含OOM/失败，不能略去）", - "", - "| 运行 | 状态 | 参数量 | opaque ECS调用 | single路径标记 |", - "| --- | --- | ---: | ---: | --- |", -] -for r in all_results: - routes = r["routes"] or [] - lines.append( - f"| {r['run']} | {r['status']} | {r['parameter_count']} | {sum(x['opaque_cohort_calls'] for x in routes)} | {any(x['single_cohort_marker'] for x in routes)} |" - ) -(root / "ACCURACY_TABLE.md").write_text("\n".join(lines) + "\n") -print( - "runs", - len(all_results), - "completed", - sum(r["status"] == "completed" for r in all_results), - "comparisons", - len(groups), - "failed_groups", - sum(not g["all_passed"] for g in groups), -) diff --git a/doc/development/ecs-validation-20261025/port-to-2ba617bc.patch b/doc/development/ecs-validation-20261025/port-to-2ba617bc.patch deleted file mode 100644 index 25d65b4d65..0000000000 --- a/doc/development/ecs-validation-20261025/port-to-2ba617bc.patch +++ /dev/null @@ -1,720 +0,0 @@ -diff --git a/deepmd/pt_expt/kernels/triton/sezm/so2_value_path.py b/deepmd/pt_expt/kernels/triton/sezm/so2_value_path.py -index a02f4288..d874c8ed 100644 ---- a/deepmd/pt_expt/kernels/triton/sezm/so2_value_path.py -+++ b/deepmd/pt_expt/kernels/triton/sezm/so2_value_path.py -@@ -91,6 +91,7 @@ from __future__ import ( - annotations, - ) - -+import os - from typing import ( - TYPE_CHECKING, - NamedTuple, -@@ -161,6 +162,10 @@ _MAX_MIXER_RANK = 4 - - _ROTATE_MIX_BWD_CONFIG = (1, 2) # per-edge backward (warps, stages) - -+# Experimental, frozen first-order inference only. Read before compilation; -+# changing the switch requires a new process/graph. Disabled by default. -+_EDGE_COHORT_INFER = os.environ.get("DP_SO2_EDGE_COHORT_INFER", "0") == "1" -+ - - # ====================================================================== - # Eager reference / fallback implementations -@@ -1369,6 +1374,7 @@ if SO2_VALUE_PATH_TRITON_AVAILABLE: - L: tl.constexpr, - CF: tl.constexpr, - BLOCK_M: tl.constexpr, -+ saved_edges=0, - ): - """Recompute the gate sigmoids from the saved pre-activation (backward).""" - ROW: tl.constexpr = (3 * L + 1) * CF -@@ -1386,7 +1392,8 @@ if SO2_VALUE_PATH_TRITON_AVAILABLE: - cm = mm & (nc < CF)[None, :] - wm = ((nc < CF)[:, None]) & ((nc < CF)[None, :]) - -- z_row = z_ptr + (layer * n_focus + fid) * n_edge * ROW + offs_m * ROW -+ z_stride = tl.where(saved_edges > 0, saved_edges, n_edge).to(tl.int64) -+ z_row = z_ptr + (layer * n_focus + fid) * z_stride * ROW + offs_m * ROW - z_s = tl.load(z_row[:, None] + nc[None, :], mask=cm, other=0.0).to(tl.float32) - sig_row = sig_ptr + (fid * n_edge + offs_m) * LG - for g in tl.static_range(L): -@@ -1420,6 +1427,7 @@ if SO2_VALUE_PATH_TRITON_AVAILABLE: - RECOMPUTE_SIG: tl.constexpr, - RECOVER_INPUT: tl.constexpr, - BLOCK_M: tl.constexpr, -+ saved_edges=0, - ): - """Pointwise part of the gated-layer backward. - -@@ -1456,7 +1464,8 @@ if SO2_VALUE_PATH_TRITON_AVAILABLE: - wm = ((nc < CF)[:, None]) & ((nc < CF)[None, :]) - - g_row = g_ptr + fid * n_edge * ROW + offs_m * ROW -- z_row = z_ptr + (layer * n_focus + fid) * n_edge * ROW + offs_m * ROW -+ z_stride = tl.where(saved_edges > 0, saved_edges, n_edge).to(tl.int64) -+ z_row = z_ptr + (layer * n_focus + fid) * z_stride * ROW + offs_m * ROW - gz_row = gz_ptr + fid * n_edge * ROW + offs_m * ROW - sig_row = sig_ptr + (fid * n_edge + offs_m) * LG - gl_row = gl_ptr + (fid * n_edge + offs_m) * LG -@@ -2014,6 +2023,7 @@ if SO2_VALUE_PATH_TRITON_AVAILABLE: - BLOCK_M: tl.constexpr, - BLOCK_N: tl.constexpr, - BLOCK_K: tl.constexpr, -+ output_edges=0, - ): - """Backward block GEMM ``g_u = residual + gz @ W^T`` over both blocks. - -@@ -2044,7 +2054,8 @@ if SO2_VALUE_PATH_TRITON_AVAILABLE: - gz_row = gz_ptr + fid * ROW + offs_m * (n_focus * ROW) - else: - gz_row = gz_ptr + fid * n_edge * ROW + offs_m * ROW -- gu_row = gu_ptr + fid * n_edge * ROW + offs_m * ROW -+ output_stride = tl.where(output_edges > 0, output_edges, n_edge).to(tl.int64) -+ gu_row = gu_ptr + fid * output_stride * ROW + offs_m * ROW - if FOLD_ALPHA: - alpha = tl.load(alpha_ptr + offs_m * n_focus + fid, mask=m_mask, other=0.0) - -@@ -2435,6 +2446,7 @@ def _launch_stack_point_backward( - L=lmax, - CF=focus_dim, - BLOCK_M=block_m, -+ saved_edges=0, - num_warps=warps, - num_stages=stages, - ) -@@ -2458,6 +2470,7 @@ def _launch_stack_point_backward( - RECOMPUTE_SIG=recompute_sigmoid, - RECOVER_INPUT=recover, - BLOCK_M=block_m, -+ saved_edges=0, - num_warps=warps, - num_stages=stages, - ) -@@ -3222,6 +3235,7 @@ def _stack_backward_traversal( - BLOCK_M=block_m, - BLOCK_N=block_n, - BLOCK_K=block_k, -+ output_edges=0, - num_warps=warps, - num_stages=stages, - ) -@@ -3406,6 +3420,7 @@ def _stack_backward_traversal( - BLOCK_M=block_m, - BLOCK_N=block_n, - BLOCK_K=block_k, -+ output_edges=0, - num_warps=warps, - num_stages=stages, - ) -@@ -4960,7 +4975,18 @@ def _mixing_stack_backward(ctx, grad_out, grad_z, grad_u): - None, - None, - ) -- grad_u0, grad_alpha = _mixing_stack_bwd_op( -+ backward_op = _mixing_stack_bwd_op -+ if _EDGE_COHORT_INFER: -+ from .so2_edge_cohort import ( -+ cohort_backward, -+ supports_edge_cohort, -+ ) -+ -+ if supports_edge_cohort( -+ grad_out, z_all, ctx.lmax, ctx.focus_dim, ctx.apply_alpha -+ ): -+ backward_op = cohort_backward -+ grad_u0, grad_alpha = backward_op( - grad_out, - x_local, - z_all, -diff --git a/deepmd/pt_expt/kernels/triton/sezm/so2_edge_cohort.py b/deepmd/pt_expt/kernels/triton/sezm/so2_edge_cohort.py -new file mode 100644 ---- /dev/null -+++ b/deepmd/pt_expt/kernels/triton/sezm/so2_edge_cohort.py -@@ -0,0 +1,380 @@ -+# SPDX-License-Identifier: LGPL-3.0-or-later -+"""Experimental edge-cohort backward for frozen, narrow SO(2) inference. -+ -+Keep native GEMM column parallelism and arithmetic, but traverse all layers -+of one cohort before advancing to the next cohort. Scratch scales with the -+cohort, not the entire graph. The saved activations and returned input gradient -+still have full edge extent. This Python runtime launcher is an online-compile -+prototype; standalone AOTInductor/C++ deployment is not validated. -+""" -+ -+from __future__ import annotations -+ -+import torch -+import triton -+from torch import Tensor -+from torch.library import wrap_triton -+ -+from . import so2_value_path as sv -+ -+COHORT_EDGES = 8192 -+SINGLE_COHORT_MAX_EDGES = 16384 -+ -+ -+def supports_edge_cohort( -+ grad_out: Tensor, z_all: Tensor, lmax: int, focus_dim: int, apply_alpha: bool -+) -> bool: -+ """Restrict opt-in dispatch to the experimentally checked Neo layout.""" -+ return ( -+ grad_out.is_cuda -+ and z_all.device == grad_out.device -+ and grad_out.dtype == torch.float32 -+ and z_all.dtype == torch.float32 -+ and grad_out.is_contiguous() -+ and z_all.is_contiguous() -+ and lmax == 3 -+ and focus_dim == 32 -+ and apply_alpha -+ and grad_out.ndim == 3 -+ and z_all.ndim == 4 -+ and grad_out.shape[1:] == (2, 320) -+ and z_all.shape[:2] == (2, 2) -+ and z_all.shape[2] == grad_out.shape[0] -+ and z_all.shape[3] == 320 -+ ) -+ -+ -+def cohort_input_backward_impl( -+ grad_out: Tensor, -+ z_all: Tensor, -+ alpha: Tensor, -+ w0t: Tensor, -+ w1t: Tensor, -+ gw: Tensor, -+ gwt: Tensor, -+ cohort_edges: int, -+) -> Tensor: -+ """Compute input gradients without retaining unrelated forward outputs. -+ -+ Kernels infer scratch focus strides from the cohort edge count. Independent -+ saved/output strides retain full-graph layout, including partial cohorts. -+ Only an internally owned residual is overwritten: matrix input ``gz`` and -+ GEMM output never alias, and caller-owned tensors remain read-only. -+ """ -+ if not supports_edge_cohort(grad_out, z_all, 3, 32, True): -+ raise ValueError("edge cohorts require contiguous FP32 Neo inference tensors") -+ if cohort_edges <= 0: -+ raise ValueError("cohort_edges must be positive") -+ if not all(t.is_contiguous() for t in (alpha, w0t, w1t, gw, gwt)): -+ raise ValueError("edge-cohort parameters must be contiguous") -+ n_gated, n_focus, n_edge, row = z_all.shape -+ result = grad_out.new_empty((n_focus, n_edge, row)) -+ if n_edge == 0: -+ return result -+ cap = min(n_edge, cohort_edges) -+ g = grad_out.new_empty((n_focus, cap, row)) -+ gz = torch.empty_like(g) -+ sig = grad_out.new_empty((n_focus, cap, 96)) -+ bm, bn, bk, warps, stages = sv.stack_fp32_configs(32, 3)[2] -+ n_tiles = triton.cdiv(128, bn) + triton.cdiv(192, bn) -+ inside, point_config, recompute_config = sv._point_backward_schedule( -+ 32, 3, train=False -+ ) -+ for start in range(0, n_edge, cap): -+ count = min(cap, n_edge - start) -+ go = grad_out[start:] -+ a = alpha[start:] -+ z = z_all.view(-1)[start * row :] -+ grid = (triton.cdiv(count, bm) * n_tiles, n_focus) -+ sv._stack_gemm_bwd_kernel[grid]( -+ go, -+ go, -+ w0t, -+ w1t, -+ a, -+ g, -+ count, -+ n_gated, -+ L=3, -+ CF=32, -+ G_EDGE_MAJOR=True, -+ FOLD_ALPHA=True, -+ RES_IS_GZ=True, -+ BLOCK_M=bm, -+ BLOCK_N=bn, -+ BLOCK_K=bk, -+ num_warps=warps, -+ num_stages=stages, -+ ) -+ for layer in range(n_gated - 1, -1, -1): -+ if not inside: -+ rbm, rw, rs = recompute_config -+ sv._stack_recompute_kernel[(triton.cdiv(count, rbm), n_focus)]( -+ z, -+ gw, -+ sig, -+ count, -+ layer, -+ L=3, -+ CF=32, -+ BLOCK_M=rbm, -+ saved_edges=n_edge, -+ num_warps=rw, -+ num_stages=rs, -+ ) -+ pbm, pw, ps = point_config -+ sv._stack_point_bwd_kernel[(triton.cdiv(count, pbm), n_focus)]( -+ g, -+ z, -+ sig, -+ gwt, -+ gz, -+ sig, -+ g, -+ g, -+ count, -+ layer, -+ L=3, -+ CF=32, -+ GLOGIT_OUT=False, -+ GLOGIT_STORE=False, -+ RECOMPUTE_SIG=inside, -+ RECOVER_INPUT=False, -+ BLOCK_M=pbm, -+ saved_edges=n_edge, -+ num_warps=pw, -+ num_stages=ps, -+ ) -+ # Residual elements are loaded before their matching output store. -+ # Distinct (edge, focus, column) tiles own disjoint residual elements. -+ target = result.view(-1)[start * row :] if layer == 0 else g -+ sv._stack_gemm_bwd_kernel[grid]( -+ gz, -+ g, -+ w0t, -+ w1t, -+ gz, -+ target, -+ count, -+ layer, -+ L=3, -+ CF=32, -+ G_EDGE_MAJOR=False, -+ FOLD_ALPHA=False, -+ RES_IS_GZ=False, -+ BLOCK_M=bm, -+ BLOCK_N=bn, -+ BLOCK_K=bk, -+ output_edges=n_edge if layer == 0 else count, -+ num_warps=warps, -+ num_stages=stages, -+ ) -+ return result -+ -+ -+# Small edge sets must not pay the opaque multi-cohort launcher overhead. -+# The residual and returned result share internally owned storage; gz never -+# aliases GEMM output. See the inference-only dispatcher below. -+@torch.library.triton_op("sezm_triton::so2_single_cohort_input_bwd", mutates_args=()) -+def _single_cohort_input_backward( -+ grad_out: Tensor, -+ z_all: Tensor, -+ alpha: Tensor, -+ w0t: Tensor, -+ w1t: Tensor, -+ gw: Tensor, -+ gwt: Tensor, -+ cohort_edges: int, -+) -> Tensor: -+ """Compute one small cohort with compiled launchers and owned residual reuse.""" -+ if not supports_edge_cohort(grad_out, z_all, 3, 32, True): -+ raise ValueError("edge cohorts require contiguous FP32 Neo inference tensors") -+ if cohort_edges <= 0: -+ raise ValueError("cohort_edges must be positive") -+ if not all(t.is_contiguous() for t in (alpha, w0t, w1t, gw, gwt)): -+ raise ValueError("edge-cohort parameters must be contiguous") -+ n_gated, n_focus, n_edge, row = z_all.shape -+ result = grad_out.new_empty((n_focus, n_edge, row)) -+ if n_edge == 0: -+ return result -+ cap = n_edge -+ g = result -+ gz = torch.empty_like(g) -+ sig = grad_out.new_empty((n_focus, cap, 96)) -+ bm, bn, bk, warps, stages = sv.stack_fp32_configs(32, 3)[2] -+ n_tiles = triton.cdiv(128, bn) + triton.cdiv(192, bn) -+ inside, point_config, recompute_config = sv._point_backward_schedule( -+ 32, 3, train=False -+ ) -+ start = 0 -+ count = min(cap, n_edge - start) -+ go = grad_out[start:] -+ a = alpha[start:] -+ z = z_all.view(-1)[start * row :] -+ grid = (triton.cdiv(count, bm) * n_tiles, n_focus) -+ wrap_triton(sv._stack_gemm_bwd_kernel)[grid]( -+ go, -+ go, -+ w0t, -+ w1t, -+ a, -+ g, -+ count, -+ n_gated, -+ L=3, -+ CF=32, -+ G_EDGE_MAJOR=True, -+ FOLD_ALPHA=True, -+ RES_IS_GZ=True, -+ output_edges=n_edge, -+ BLOCK_M=bm, -+ BLOCK_N=bn, -+ BLOCK_K=bk, -+ num_warps=warps, -+ num_stages=stages, -+ ) -+ for layer in range(n_gated - 1, -1, -1): -+ if not inside: -+ rbm, rw, rs = recompute_config -+ wrap_triton(sv._stack_recompute_kernel)[triton.cdiv(count, rbm), n_focus]( -+ z, -+ gw, -+ sig, -+ count, -+ layer, -+ L=3, -+ CF=32, -+ BLOCK_M=rbm, -+ saved_edges=n_edge, -+ num_warps=rw, -+ num_stages=rs, -+ ) -+ pbm, pw, ps = point_config -+ wrap_triton(sv._stack_point_bwd_kernel)[triton.cdiv(count, pbm), n_focus]( -+ g, -+ z, -+ sig, -+ gwt, -+ gz, -+ sig, -+ g, -+ g, -+ count, -+ layer, -+ L=3, -+ CF=32, -+ GLOGIT_OUT=False, -+ GLOGIT_STORE=False, -+ RECOMPUTE_SIG=inside, -+ RECOVER_INPUT=False, -+ BLOCK_M=pbm, -+ saved_edges=n_edge, -+ num_warps=pw, -+ num_stages=ps, -+ ) -+ target = result.view(-1)[start * row :] if layer == 0 else g -+ wrap_triton(sv._stack_gemm_bwd_kernel)[grid]( -+ gz, -+ g, -+ w0t, -+ w1t, -+ gz, -+ target, -+ count, -+ layer, -+ L=3, -+ CF=32, -+ G_EDGE_MAJOR=False, -+ FOLD_ALPHA=False, -+ RES_IS_GZ=False, -+ BLOCK_M=bm, -+ BLOCK_N=bn, -+ BLOCK_K=bk, -+ output_edges=n_edge if layer == 0 else count, -+ num_warps=warps, -+ num_stages=stages, -+ ) -+ return result -+ -+ -+@torch.library.custom_op("sezm_triton::so2_cohort_input_bwd", mutates_args=()) -+def _cohort_input_backward( -+ grad_out: Tensor, -+ z_all: Tensor, -+ alpha: Tensor, -+ w0t: Tensor, -+ w1t: Tensor, -+ gw: Tensor, -+ gwt: Tensor, -+ cohort_edges: int, -+) -> Tensor: -+ """Keep the runtime edge loop opaque without retaining x_local/u_final.""" -+ return cohort_input_backward_impl( -+ grad_out, z_all, alpha, w0t, w1t, gw, gwt, cohort_edges -+ ) -+ -+ -+@_cohort_input_backward.register_fake -+def _cohort_input_backward_fake( -+ grad_out: Tensor, -+ z_all: Tensor, -+ alpha: Tensor, -+ w0t: Tensor, -+ w1t: Tensor, -+ gw: Tensor, -+ gwt: Tensor, -+ cohort_edges: int, -+) -> Tensor: -+ """Preserve full input-gradient shape under FakeTensor tracing.""" -+ return grad_out.new_empty(z_all.shape[1:]) -+ -+ -+@torch.library.triton_op("sezm_triton::so2_cohort_alpha_bwd", mutates_args=()) -+def _cohort_alpha_backward(grad_out: Tensor, x_local: Tensor, alpha: Tensor) -> Tensor: -+ """Expose alpha's short-lived output dependency to the graph scheduler.""" -+ n_edge, n_focus = alpha.shape -+ result = torch.empty_like(alpha) -+ if sv._has_no_edges(n_edge): -+ return result -+ bm, warps, stages = sv.gate_config(32, 3) -+ wrap_triton(sv._stack_grad_alpha_kernel)[(triton.cdiv(n_edge, bm), n_focus)]( -+ grad_out, -+ x_local, -+ alpha, -+ result, -+ n_edge, -+ L=3, -+ CF=32, -+ BLOCK_M=bm, -+ num_warps=warps, -+ num_stages=stages, -+ ) -+ return result -+ -+ -+def cohort_backward( -+ grad_out: Tensor, -+ x_local: Tensor, -+ z_all: Tensor, -+ u_final: Tensor, -+ alpha: Tensor, -+ w0t: Tensor, -+ w1t: Tensor, -+ gw: Tensor, -+ gwt: Tensor, -+ lmax: int, -+ focus_dim: int, -+ apply_alpha: bool, -+) -> tuple[Tensor, Tensor]: -+ """First-order dispatcher; training/higher derivatives never enter here.""" -+ del u_final -+ if not supports_edge_cohort(grad_out, z_all, lmax, focus_dim, apply_alpha): -+ raise ValueError("unsupported edge-cohort layout") -+ grad_alpha = _cohort_alpha_backward(grad_out, x_local, alpha) -+ input_backward = ( -+ _single_cohort_input_backward -+ if grad_out.shape[0] <= SINGLE_COHORT_MAX_EDGES -+ else _cohort_input_backward -+ ) -+ grad_u0 = input_backward(grad_out, z_all, alpha, w0t, w1t, gw, gwt, COHORT_EDGES) -+ return grad_u0, grad_alpha -diff --git a/source/tests/pt_expt/kernels/test_so2_edge_cohort.py b/source/tests/pt_expt/kernels/test_so2_edge_cohort.py -new file mode 100644 ---- /dev/null -+++ b/source/tests/pt_expt/kernels/test_so2_edge_cohort.py -@@ -0,0 +1,201 @@ -+# SPDX-License-Identifier: LGPL-3.0-or-later -+"""Generated-input checks for the opt-in frozen Neo edge-cohort prototype.""" -+ -+from __future__ import annotations -+ -+from types import SimpleNamespace -+ -+import pytest -+import torch -+ -+pytest.importorskip("triton") -+ -+from deepmd.pt_expt.kernels.triton.sezm import so2_edge_cohort as ecs -+from deepmd.pt_expt.kernels.triton.sezm import so2_value_path as sv -+ -+pytestmark = pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA required") -+ -+ -+def operands(edges: int, mode: str = "random") -> tuple: -+ """Generate well-conditioned operands without a downloaded checkpoint.""" -+ torch.manual_seed(719) -+ u = torch.randn(2, edges, 320, device="cuda") * 0.1 -+ if mode == "zero": -+ u.zero_() -+ alpha = torch.rand(edges, 2, device="cuda") * 0.8 + 0.1 -+ if mode == "tiny": -+ alpha.fill_(1e-5) -+ w0 = torch.randn(3, 2, 128, 128, device="cuda") * 0.01 -+ w1 = torch.randn(3, 2, 192, 192, device="cuda") * 0.01 -+ gw = torch.randn(2, 2, 32, 96, device="cuda") * 0.01 -+ out, z, uf = sv._mixing_stack_impl(u, alpha, w0, w1, gw, 3, 32, True) -+ go = torch.randn_like(out) * 0.1 -+ return ( -+ go, -+ out, -+ z, -+ uf, -+ alpha, -+ w0.transpose(2, 3).contiguous(), -+ w1.transpose(2, 3).contiguous(), -+ gw, -+ gw.transpose(2, 3).contiguous(), -+ 3, -+ 32, -+ True, -+ ) -+ -+ -+@pytest.mark.parametrize("edges", [0, 1, 37, 129, 1025, 8193]) -+@pytest.mark.parametrize("cap", [64, 8192]) -+@pytest.mark.parametrize("mode", ["random", "zero", "tiny"]) -+def test_cohort_matches_native(edges: int, cap: int, mode: str) -> None: -+ """Cover tail cohorts, two focus strides, legal tiny alpha and no mutation.""" -+ args = operands(edges, mode) -+ copies = [x.clone() for x in args[:9]] -+ expected_u, expected_a = sv._mixing_stack_bwd_impl(*args) -+ go, out, z, _, alpha, w0t, w1t, gw, gwt, *_ = args -+ actual_u = ecs.cohort_input_backward_impl(go, z, alpha, w0t, w1t, gw, gwt, cap) -+ actual_a = ecs._cohort_alpha_backward(go, out, alpha) -+ for actual, expected in ((actual_u, expected_u), (actual_a, expected_a)): -+ assert torch.isfinite(actual).all() -+ torch.testing.assert_close(actual, expected, rtol=0, atol=0) -+ for before, after in zip(copies, args[:9], strict=True): -+ assert torch.equal(before, after) -+ -+ -+def test_compiled_dynamic_cohort() -> None: -+ """The opaque runtime edge loop supports online dynamic graph execution.""" -+ compiled = torch.compile(ecs.cohort_backward, fullgraph=True, dynamic=True) -+ for edges in (37, 129): -+ args = operands(edges) -+ actual = compiled(*args) -+ expected = sv._mixing_stack_bwd_impl(*args) -+ for a, b in zip(actual, expected, strict=True): -+ torch.testing.assert_close(a, b, atol=0, rtol=0) -+ -+ -+@pytest.mark.parametrize("dynamic", [False, True]) -+def test_compiled_native_backward(dynamic: bool) -> None: -+ """Optional strides must preserve the default native compilation path.""" -+ compiled = torch.compile(sv._mixing_stack_bwd_op, fullgraph=True, dynamic=dynamic) -+ for edges in (37, 129): -+ args = operands(edges) -+ expected = sv._mixing_stack_bwd_impl(*args) -+ actual = compiled(*args) -+ for a, b in zip(actual, expected, strict=True): -+ torch.testing.assert_close(a, b, atol=0, rtol=0) -+ -+ -+@pytest.mark.parametrize("edges", [0, 37, 8193, 16384]) -+@pytest.mark.parametrize("mode", ["random", "zero", "tiny"]) -+def test_single_cohort_owned_output(edges: int, mode: str) -> None: -+ """Small-cohort reuse changes neither gradients nor caller-owned storage.""" -+ args = operands(edges, mode) -+ copies = [t.clone() for t in args[:9]] -+ go, out, z, _, alpha, w0t, w1t, gw, gwt, *_ = args -+ expected_u, expected_a = sv._mixing_stack_bwd_impl(*args) -+ actual_u = ecs._single_cohort_input_backward( -+ go, z, alpha, w0t, w1t, gw, gwt, ecs.COHORT_EDGES -+ ) -+ actual_a = ecs._cohort_alpha_backward(go, out, alpha) -+ for a, b in ((actual_u, expected_u), (actual_a, expected_a)): -+ assert torch.isfinite(a).all() -+ torch.testing.assert_close(a, b, atol=0, rtol=0) -+ for before, after in zip(copies, args[:9], strict=True): -+ assert torch.equal(before, after) -+ -+ -+def test_compiled_cohort_bucket_boundary() -> None: -+ """A dynamic compiled graph handles both sides of the small-E boundary.""" -+ compiled = torch.compile(ecs.cohort_backward, fullgraph=True, dynamic=True) -+ for edges in (16384, 16385): -+ args = operands(edges) -+ expected = sv._mixing_stack_bwd_impl(*args) -+ actual = compiled(*args) -+ for a, b in zip(actual, expected, strict=True): -+ torch.testing.assert_close(a, b, atol=0, rtol=0) -+ -+ -+@pytest.mark.parametrize( -+ "lmax,cf,apply", [(2, 32, True), (3, 64, True), (3, 32, False)] -+) -+def test_unsupported_layout(lmax: int, cf: int, apply: bool) -> None: -+ """Untested layouts must not enter the experimental implementation.""" -+ args = operands(1) -+ assert not ecs.supports_edge_cohort(args[0], args[2], lmax, cf, apply) -+ assert not ecs.supports_edge_cohort(args[0].cpu(), args[2].cpu(), 3, 32, True) -+ -+ -+@pytest.mark.parametrize("reason", ["weights", "saved_z", "saved_u", "create_graph"]) -+def test_training_dispatch_preserved(monkeypatch, reason: str) -> None: -+ """Weight/state/higher-order requests bypass ECS even when opted in.""" -+ args = operands(1) -+ go, out, z, uf, alpha, w0t, w1t, gw, *_ = args -+ u = torch.zeros_like(uf) -+ w0 = w0t.transpose(2, 3).contiguous() -+ w1 = w1t.transpose(2, 3).contiguous() -+ ctx = SimpleNamespace( -+ saved_tensors=(u, alpha, out, z, uf, w0, w1, gw), -+ needs_input_grad=( -+ True, -+ True, -+ reason == "weights", -+ False, -+ False, -+ False, -+ False, -+ False, -+ ), -+ lmax=3, -+ focus_dim=32, -+ apply_alpha=True, -+ ) -+ calls = [] -+ -+ def training(*unused): -+ calls.append(True) -+ return torch.zeros_like(u), torch.zeros_like(alpha), w0, w1, gw -+ -+ def forbidden(*unused): -+ raise AssertionError("ECS entered a training/higher-order request") -+ -+ monkeypatch.setattr(sv, "_EDGE_COHORT_INFER", True) -+ monkeypatch.setattr(sv, "_mixing_stack_train_bwd_op", training) -+ monkeypatch.setattr(ecs, "cohort_backward", forbidden) -+ with torch.set_grad_enabled(reason == "create_graph"): -+ sv._mixing_stack_backward( -+ ctx, -+ go, -+ torch.zeros_like(z) if reason == "saved_z" else None, -+ torch.zeros_like(uf) if reason == "saved_u" else None, -+ ) -+ assert calls == [True] -+ -+ -+def test_frozen_dispatch(monkeypatch) -> None: -+ """Enabling the switch actually changes the frozen inference dispatch.""" -+ args = operands(37) -+ go, out, z, uf, alpha, w0t, w1t, gw, *_ = args -+ ctx = SimpleNamespace( -+ saved_tensors=( -+ uf, -+ alpha, -+ out, -+ z, -+ uf, -+ w0t.transpose(2, 3).contiguous(), -+ w1t.transpose(2, 3).contiguous(), -+ gw, -+ ), -+ needs_input_grad=(True, True, False, False, False, False, False, False), -+ lmax=3, -+ focus_dim=32, -+ apply_alpha=True, -+ ) -+ monkeypatch.setattr(sv, "_EDGE_COHORT_INFER", True) -+ expected = sv._mixing_stack_bwd_impl(*args) -+ with torch.no_grad(): -+ actual = sv._mixing_stack_backward(ctx, go, None, None) -+ for a, b in zip(actual[:2], expected, strict=True): -+ torch.testing.assert_close(a, b, atol=0, rtol=0) diff --git a/doc/development/ecs-validation-20261025/pro_cpu_load_check.json b/doc/development/ecs-validation-20261025/pro_cpu_load_check.json deleted file mode 100644 index 68ef6863b6..0000000000 --- a/doc/development/ecs-validation-20261025/pro_cpu_load_check.json +++ /dev/null @@ -1,318 +0,0 @@ -{ - "status": "loaded", - "device": "cpu", - "strict_missing": [], - "strict_unexpected": [], - "compatibility": "in-memory historical fitting type alias only: ener -> dpa4_ener; original file 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type=Path, default=SCRIPT_DIR) -HERE = P.parse_args().root.resolve() -subprocess.run( - [sys.executable, str(SCRIPT_DIR / "analyze.py"), "--root", str(HERE)], check=True -) -data = json.loads((HERE / "summary.json").read_text()) -states = data["statuses"] -groups = data["comparisons"] - - -def select(model, n, left, right): - return [ - g - for g in groups - if (g["model"], g["n"], g["left"], g["right"]) == (model, n, left, right) - ] - - -def band(model, n, left, right, field): - gs = select(model, n, left, right) - if not gs: - return None - return max(g["metrics"][field]["max_abs_range"][1] for g in gs) - - -def strict_status(model, n, left, right): - gs = select(model, n, left, right) - if not gs: - return "N/A" - if ( - min( - min(g["left_completed_processes"], g["right_completed_processes"]) - for g in gs - ) - < 3 - ): - return "INCOMPLETE" - return "PASS" if all(g["all_passed"] for g in gs) else "FAIL" - - -def band_status(model, n, mode): - ratios = [] - for field in ("energy", "forces", "virial", "stress"): - cross = band(model, n, mode, "TORCH", field) - own = band(model, n, "TORCH", "TORCH", field) - if cross is None or own is None: - return "N/A" - ratios.append(cross / own if own else (0 if cross == 0 else float("inf"))) - return ("PASS" if max(ratios) <= 3 else "FAIL") + f" ({max(ratios):.3f}x)" - - -def summary_cell(model, n, mode, field): - ss = [s for s in states if (s["model"], s["n"], s["mode"]) == (model, n, mode)] - good = sum(s["status"] == "completed" for s in ss) - if not good: - return "OOM" if any(s["status"] == "oom" for s in ss) else "pending/failed" - own = band(model, n, mode, mode, field) - text = f"self max_abs={own:.4g}" if own is not None else "self pending" - if mode != "TORCH": - cross = band(model, n, mode, "TORCH", field) - pure = band(model, n, "TORCH", "TORCH", field) - text += ( - f"; vs Torch={cross:.4g}" - if cross is not None - else "; Torch ref unavailable" - ) - if cross is not None and pure is not None: - ratio = cross / pure if pure else (0 if cross == 0 else float("inf")) - text += f"; band×{ratio:.3g}" - return text + (f" ({good}/3 processes)" if good < 3 else "") - - -lines = [ - "# ECS current-master comparison — measured 2026-10-25, finalized 2026-10-26", - "", - "BASE: 2ba617bc3db67adee2b8ba00c71e9fa1c2d2bb8c, including merged residual-inplace #6058. Old668-base numbers are not substituted. Candidate is the isolated port of PR#2; its AOT branch-specialization issue remains unresolved.", - "", - f"Recorded {len(states)}/135 process results: {dict(Counter(s['status'] for s in states))}. Missing cells are NOT passes. Three processes ×3 frames ×3 accuracy repeats per arm; 8 extra warmups/30 wall+Event samples, separate steady replay. Same physical GPU within each case. TF32/AMP off.", - "", - "## Three accuracy columns", - "", - "Each field: worst pairwise max-absolute difference over same-frame outputs; self includes within/across-process repeats. Scientific band multiplier = worst deviation from compiled TORCH / TORCH own worst repeat max_abs, separately per field/model/size/system. <=3 is the meeting criterion, not a replacement for the fixed strict elementwise gate. Zero repeat band requires zero cross difference. Energy/virial eV, force eV/Angstrom, stress eV/Angstrom^3.", - "", - "| Model/system | Atoms | Output | Compiled Torch | Current master | Candidate |", - "| --- | ---: | --- | --- | --- | --- |", -] -cases = [ - (m, n) for m in ("Neo", "NeoCu", "Nano", "Mini", "Pro") for n in (96, 512, 4096) -] -gate_lines = [ - "## Three-column PASS/FAIL overview", - "", - "Strict columns cover all four fields: Torch self-repeat; master self-repeat and versus Torch; candidate self-repeat, versus Torch and versus master. A missing Torch reference is N/A, never PASS. The meeting band is a separately reported empirical diagnostic, not a rigorous error bound or a waiver of strict failure.", - "", - "| Model/system | Atoms | Compiled Torch | Current master | Candidate |", - "| --- | ---: | --- | --- | --- |", -] -for model, n in cases: - torch_gate = strict_status(model, n, "TORCH", "TORCH") - master_gate = f"strict self={strict_status(model, n, 'BASE', 'BASE')}; vs Torch={strict_status(model, n, 'BASE', 'TORCH')}; band={band_status(model, n, 'BASE')}" - candidate_gate = f"strict self={strict_status(model, n, 'ECS', 'ECS')}; vs Torch={strict_status(model, n, 'ECS', 'TORCH')}; vs master={strict_status(model, n, 'ECS', 'BASE')}; band={band_status(model, n, 'ECS')}" - gate_lines.append( - f"| {model} | {n} | strict self={torch_gate} | {master_gate} | {candidate_gate} |" - ) -first_table = lines.index("## Three accuracy columns") -lines[first_table:first_table] = [*gate_lines, ""] -for model, n in cases: - for field in ("energy", "forces", "virial", "stress"): - cells = [ - summary_cell(model, n, mode, field) for mode in ("TORCH", "BASE", "ECS") - ] - lines.append(f"| {model} | {n} | {field} | " + " | ".join(cells) + " |") -lines += [ - "", - "Strict per-element atol1e-5/rtol1e-4 and finite results, mean_abs/max_relative and within/across detail remain in ACCURACY_TABLE.md and summary.json. Pure-reference OOM prevents band acceptance. Band PASS does not silently erase strict FAIL.", - "", - "## Timing and memory — process medians and worst capacity", - "", - "Compilation excluded from timing, included in first/full memory. This independent-process protocol does not freeze all generated autotune choices: no stable isolated speedup is inferred solely from these percentages. GB=1e9B. Both allocated/reserved raw bytes are retained in result.json. Numbers are preliminary until 3 processes per arm complete.", - "", - "| Model/system | Atoms | Mode | Processes | Wall P50 ms | Wall P90 ms | Event P50 ms | First alloc GB | Full peak incl first GB | Steady alloc GB |", - "| --- | ---: | --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: |", -] -for model, n in cases: - for mode in ("TORCH", "BASE", "ECS"): - ss = [ - s - for s in states - if (s["model"], s["n"], s["mode"], s["status"]) - == (model, n, mode, "completed") - ] - if not ss: - continue - raw = [ - json.loads((HERE / "results" / s["run"] / "result.json").read_text()) - for s in ss - ] - vals = [ - np.median([r["steady_wall_ms"][k] for r in raw]) for k in ("median", "p90") - ] - vals += [np.median([r["steady_event_ms"]["median"] for r in raw])] - vals += [ - max(r[k]["allocated"] for r in raw) / 1e9 - for k in ("first_peak", "complete_peak", "steady_peak") - ] - lines.append( - f"| {model} | {n} | {mode} | {len(raw)}/3 | " - + " | ".join(f"{v:.6f}" for v in vals) - + " |" - ) -lines += [ - "", - "## Route and acceptance limits", - "", - "Nano/Mini/Pro candidate is native fallback, not demonstrated ECS acceleration. Neo dispatch must be checked against generated wrappers; current single-cohort specialization must not be described as 80 runtime batches. Pro-MPtrj is not OMat24-Pro; original checkpoint unmodified, reader-only legacy metadata alias applies equally to all arms. H20/PT2/C++/training and unmeasured larger models are not accepted by this matrix.", - "", - "Upstream review also requires focused guard/alias tests, no new public fine-grained development switch for a merge-ready path, freeze-time policy/selection where applicable, optional-Triton CPU safety and real CI approval. Current port keeps the old default-off experimental switch pending policy resolution. Do not submit as ready-to-merge.", -] -lines += [ - "", - "## Candidate vs current-master changes", - "", - "Positive percentage means reduction/improvement; negative means increase. Medians of independent process quantiles, not a controlled paired significance estimate. Fallback timing variation is not attributed to ECS.", - "", - "| Model/system | Atoms | Time reduction % | First-inclusive full alloc reduction % | Steady alloc reduction % | Candidate route |", - "| --- | ---: | ---: | ---: | ---: | --- |", -] -for model, n in cases: - by_mode = {} - for mode in ("BASE", "ECS"): - rows = [ - json.loads((HERE / "results" / s["run"] / "result.json").read_text()) - for s in states - if (s["model"], s["n"], s["mode"], s["status"]) - == (model, n, mode, "completed") - ] - if len(rows) == 3: - by_mode[mode] = ( - float(np.median([r["steady_wall_ms"]["median"] for r in rows])), - max(r["complete_peak"]["allocated"] for r in rows), - max(r["steady_peak"]["allocated"] for r in rows), - ) - if len(by_mode) != 2: - lines.append(f"| {model} | {n} | N/A | N/A | N/A | incomplete/OOM |") - continue - changes = [ - 100 * (b - c) / b for b, c in zip(by_mode["BASE"], by_mode["ECS"], strict=True) - ] - route = ( - "single-cohort specialized, not bounded runtime batching" - if model.startswith("Neo") - else "native fallback" - ) - lines.append( - f"| {model} | {n} | " - + " | ".join(f"{v:.4f}" for v in changes) - + f" | {route} |" - ) -(HERE / "REPORT.md").write_text("\n".join(lines) + "\n") -# Generated public evidence only; never copy advisor or private handoff notes. -public_dir = HERE / "candidate/doc/development" -if public_dir.is_dir(): - (public_dir / "ecs-current-master-validation.md").write_text( - "\n".join(lines) + "\n" - ) - (public_dir / "ecs-current-master-validation.json").write_text( - json.dumps(data, indent=2, ensure_ascii=False, allow_nan=False) + "\n" - ) -print("REPORT updated", len(states)) diff --git a/doc/development/ecs-validation-20261025/run_matrix.py b/doc/development/ecs-validation-20261025/run_matrix.py deleted file mode 100644 index fb4214b4f1..0000000000 --- a/doc/development/ecs-validation-20261025/run_matrix.py +++ /dev/null @@ -1,119 +0,0 @@ -"""Run the disclosed three-arm manual CUDA matrix with explicit source/model paths.""" - -# ruff: noqa: T201 -- manual benchmark -import argparse -import json -import os -from pathlib import Path -import subprocess -import sys - -P = argparse.ArgumentParser(description=__doc__) -P.add_argument("--base-root", type=Path, required=True) -P.add_argument("--candidate-root", type=Path, required=True) -P.add_argument("--model-map", type=Path, required=True) -P.add_argument("--out-root", type=Path, required=True) -P.add_argument("--gpu", type=int, required=True) -P.add_argument( - "--models", nargs="+", choices=["Neo", "Nano", "Mini", "Pro"], required=True -) -P.add_argument( - "--sizes", type=int, nargs="+", choices=[96, 512, 4096], default=[96, 512, 4096] -) -P.add_argument("--repeats", type=int, default=3) -P.add_argument("--skip-existing", action="store_true") -a = P.parse_args() -models = json.loads(a.model_map.read_text()) -assert a.repeats >= 3 -for root in (a.base_root, a.candidate_root): - assert (root / "deepmd").is_dir(), root -records = [] -for model in a.models: - checkpoint = Path(models[model]).resolve() - assert checkpoint.is_file(), checkpoint - for system in ["diamond", "bcccu"] if model == "Neo" else ["diamond"]: - dims = ( - {96: (2, 2, 3), 512: (4, 4, 4), 4096: (8, 8, 8)} - if system == "diamond" - else {96: (4, 4, 3), 512: (8, 8, 4), 4096: (16, 16, 8)} - ) - label = model if system == "diamond" else model + "Cu" - for n in a.sizes: - for repeat in range(a.repeats): - for mode in ( - ["TORCH", "BASE", "ECS"] - if repeat % 2 == 0 - else ["ECS", "BASE", "TORCH"] - ): - out = ( - a.out_root.resolve() - / "results" - / f"{label}_n{n}_{mode}_p{repeat}" - ) - if (out / "result.json").exists(): - if a.skip_existing: - continue - raise FileExistsError( - f"Use a fresh output root; not overwriting {out}" - ) - busy = subprocess.check_output( - [ - "nvidia-smi", - "-i", - str(a.gpu), - "--query-compute-apps=pid", - "--format=csv,noheader", - ], - text=True, - ).strip() - if busy: - raise RuntimeError( - f"GPU busy; do not kill other processes: {busy}" - ) - out.parent.mkdir(parents=True, exist_ok=True) - command = [ - sys.executable, - str(Path(__file__).with_name("worker.py")), - "--root", - str( - ( - a.candidate_root if mode == "ECS" else a.base_root - ).resolve() - ), - "--model", - str(checkpoint), - "--out", - str(out), - "--cache", - str(a.out_root.resolve() / "cache" / f"{label}_{mode}"), - "--gpu", - str(a.gpu), - "--mode", - mode, - "--nrep", - "2", - "--repeat-cell", - *map(str, dims[n]), - "--system", - system, - "--warmup", - "8", - "--samples", - "30", - ] - print("START", out.name, flush=True) - with out.with_suffix(".log").open("w") as stream: - proc = subprocess.run( - command, - stdout=stream, - stderr=subprocess.STDOUT, - env=dict(os.environ, PYTHONHASHSEED=str(repeat % 2)), - check=False, - ) - records.append( - {"run": out.name, "exit": proc.returncode, "command": command} - ) - (a.out_root / f"commands_gpu{a.gpu}.json").write_text( - json.dumps(records, indent=2) - ) - print("DONE", 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"threshold": { - "atol": 1e-05, - "rtol": 0.0001, - "relative_denominator_floor": 1e-08 - }, - "system": "diamond", - "input_sha256": "eda961dd45b711e42fad51b8dfde87d6ca736b34ac729d646c98f67481308f83", - "parameter_count": 1124910, - "exception": "Traceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 156, in execute\n n_batch, result = callable(batch_nframes, start_index)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 243, in execute_with_batch_size\n return (end_index - start_index), callable(\n ^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 730, in _eval_model\n batch_output = model(\n ^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/train/wrapper.py\", line 201, in forward\n model_pred = self._forward_without_loss(task_key, input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/train/wrapper.py\", line 249, in _forward_without_loss\n model_pred = self.model[task_key](**input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 839, in forward\n model_ret = self.forward_common(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 1053, in forward_common\n return self.forward_common_lower(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 1217, in forward_common_lower\n model_predict = compiled_core_compute(\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 2399, in compiled\n return dict(zip(_keys, _fn(*args)))\n ^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/aot_autograd.py\", line 1200, in forward\n return compiled_fn(full_args)\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 580, in runtime_wrapper\n all_outs = call_func_at_runtime_with_args(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/utils.py\", line 138, in call_func_at_runtime_with_args\n out = normalize_as_list(f(args))\n ^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 783, in wrapper\n return compiled_fn(runtime_args)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 1011, in inner_fn\n outs = compiled_fn(args)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/output_code.py\", line 656, in __call__\n return self.current_callable(inputs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/utils.py\", line 3401, in run\n out = model(new_inputs)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Neo_TORCH/inductor/7p/c7pndtfxe3mrtfgixzsmsf3r355pce3qwesz5k6bbekdcu6zibmr.py\", line 21401, in call\n buf200 = empty_strided_cuda((2, s29, 10, 32), (320*s29, 320, 32, 1), torch.float32)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\ntorch.OutOfMemoryError: CUDA out of memory. Tried to allocate 1.54 GiB. GPU 0 has a total capacity of 31.36 GiB of which 441.06 MiB is free. Including non-PyTorch memory, this process has 30.89 GiB memory in use. Of the allocated memory 30.08 GiB is allocated by PyTorch, and 131.51 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://docs.pytorch.org/docs/stable/notes/cuda.html#optimizing-memory-usage-with-pytorch-cuda-alloc-conf)\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 243, in \n out = evaluate(pos)\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 224, in evaluate\n forces = sample.get_forces().copy()\n ^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/atoms.py\", line 2015, in get_forces\n forces = self._calc.get_forces(self)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/abc.py\", line 31, in get_forces\n return self.get_property('forces', atoms)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/calculator.py\", line 517, in get_property\n self.calculate(atoms, [name], system_changes)\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/calculator.py\", line 143, in calculate\n e, f, v = self.dp.eval(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/infer/deep_pot.py\", line 206, in eval\n results = self.deep_eval.eval(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 556, in eval\n out = self._eval_func(self._eval_model, numb_test, natoms)(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 637, in eval_func\n return self.auto_batch_size.execute_all(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 272, in execute_all\n n_batch, result = self.execute(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 169, in execute\n raise OutOfMemoryError(\ndeepmd.utils.errors.OutOfMemoryError: The callable still throws an out-of-memory (OOM) error even when batch size is 1!\n", - "partial_arrays": 0, - "complete_peak": { - "allocated": 32302947328, - "reserved": 32440844288 - }, - "elapsed_s": 73.36892628669739 - }, - "manifest": { - "args": { - "root": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base", - "model": "/home/kmpl/mkl/project/DPA4-Neo-OMat24-v20260627.pt", - "out": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/results/Neo_n4096_TORCH_p0", - "cache": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Neo_TORCH", - "gpu": "0", - "mode": "TORCH", - "nrep": "2", - "seed": "0", - "repeat_cell": "[8, 8, 8]", - "system": "diamond", - "samples": "30", - "warmup": "8" - }, - "model_sha256": "8d8b2b02b9575cb9e0af420cc1e8b1d9ffcbc04752c2203d29f48ccd0cd5f07d", - "torch": "2.11.0+cu128", - "triton": "3.6.0", - "cuda": "12.8", - "baseline_revision": "2ba617bc3db67adee2b8ba00c71e9fa1c2d2bb8c", - "source_hashes": { - "deepmd/pt_expt/kernels/triton/sezm/so2_value_path.py": "9c8b01082cf01fdcd41fe58e0884364e2166d7acbd4c41f75dda625829a64dbf" - }, - "flags": { - "PYTHONHASHSEED": "0", - "DP_COMPILE_INFER": "1", - "DP_COMPILE": "0", - "DP_TRITON_INFER": "0", - "DP_CUDA_INFER": "0", - "DP_TF32_INFER": "0", - "DP_AMP_INFER": "0", - "DP_SO2_EDGE_COHORT_INFER": "0", - "DP_INTRA_OP_PARALLELISM_THREADS": "1", - "DP_INTER_OP_PARALLELISM_THREADS": "1", - "PYTORCH_ALLOC_CONF": "expandable_segments:True", - "TORCHINDUCTOR_COMPILE_THREADS": "1", - "TORCHINDUCTOR_CACHE_DIR": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Neo_TORCH/inductor", - "TRITON_CACHE_DIR": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Neo_TORCH/triton" - } - } - }, - { - "run": "Neo_n4096_TORCH_p1", - "result": { - "status": "oom", - "mode": "TORCH", - "n_atoms": 4096, - "started": 1792866073.2576334, - "threshold": { - "atol": 1e-05, - "rtol": 0.0001, - "relative_denominator_floor": 1e-08 - }, - "system": "diamond", - "input_sha256": "eda961dd45b711e42fad51b8dfde87d6ca736b34ac729d646c98f67481308f83", - "parameter_count": 1124910, - "exception": "Traceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 156, in execute\n n_batch, result = callable(batch_nframes, start_index)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 243, in execute_with_batch_size\n return (end_index - start_index), callable(\n ^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 730, in _eval_model\n batch_output = model(\n ^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/train/wrapper.py\", line 201, in forward\n model_pred = self._forward_without_loss(task_key, input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/train/wrapper.py\", line 249, in _forward_without_loss\n model_pred = self.model[task_key](**input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 839, in forward\n model_ret = self.forward_common(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 1053, in forward_common\n return self.forward_common_lower(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 1217, in forward_common_lower\n model_predict = compiled_core_compute(\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 2399, in compiled\n return dict(zip(_keys, _fn(*args)))\n ^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/aot_autograd.py\", line 1200, in forward\n return compiled_fn(full_args)\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 580, in runtime_wrapper\n all_outs = call_func_at_runtime_with_args(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/utils.py\", line 138, in call_func_at_runtime_with_args\n out = normalize_as_list(f(args))\n ^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 783, in wrapper\n return compiled_fn(runtime_args)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 1011, in inner_fn\n outs = compiled_fn(args)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/output_code.py\", line 656, in __call__\n return self.current_callable(inputs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/utils.py\", line 3401, in run\n out = model(new_inputs)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Neo_TORCH/inductor/7p/c7pndtfxe3mrtfgixzsmsf3r355pce3qwesz5k6bbekdcu6zibmr.py\", line 21401, in call\n buf200 = empty_strided_cuda((2, s29, 10, 32), (320*s29, 320, 32, 1), torch.float32)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\ntorch.OutOfMemoryError: CUDA out of memory. Tried to allocate 1.54 GiB. GPU 0 has a total capacity of 31.36 GiB of which 441.06 MiB is free. Including non-PyTorch memory, this process has 30.89 GiB memory in use. Of the allocated memory 30.08 GiB is allocated by PyTorch, and 131.51 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://docs.pytorch.org/docs/stable/notes/cuda.html#optimizing-memory-usage-with-pytorch-cuda-alloc-conf)\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 243, in \n out = evaluate(pos)\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 224, in evaluate\n forces = sample.get_forces().copy()\n ^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/atoms.py\", line 2015, in get_forces\n forces = self._calc.get_forces(self)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/abc.py\", line 31, in get_forces\n return self.get_property('forces', atoms)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/calculator.py\", line 517, in get_property\n self.calculate(atoms, [name], system_changes)\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/calculator.py\", line 143, in calculate\n e, f, v = self.dp.eval(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/infer/deep_pot.py\", line 206, in eval\n results = self.deep_eval.eval(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 556, in eval\n out = self._eval_func(self._eval_model, numb_test, natoms)(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 637, in eval_func\n return self.auto_batch_size.execute_all(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 272, in execute_all\n n_batch, result = self.execute(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 169, in execute\n raise OutOfMemoryError(\ndeepmd.utils.errors.OutOfMemoryError: The callable still throws an out-of-memory (OOM) error even when batch size is 1!\n", - "partial_arrays": 0, - "complete_peak": { - "allocated": 32302947328, - "reserved": 32440844288 - }, - "elapsed_s": 73.83922123908997 - }, - "manifest": { - "args": { - "root": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base", - "model": "/home/kmpl/mkl/project/DPA4-Neo-OMat24-v20260627.pt", - "out": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/results/Neo_n4096_TORCH_p1", - "cache": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Neo_TORCH", - "gpu": "0", - "mode": "TORCH", - "nrep": "2", - "seed": "0", - "repeat_cell": "[8, 8, 8]", - "system": "diamond", - "samples": "30", - "warmup": "8" - }, - "model_sha256": "8d8b2b02b9575cb9e0af420cc1e8b1d9ffcbc04752c2203d29f48ccd0cd5f07d", - "torch": "2.11.0+cu128", - "triton": "3.6.0", - "cuda": "12.8", - "baseline_revision": "2ba617bc3db67adee2b8ba00c71e9fa1c2d2bb8c", - "source_hashes": { - "deepmd/pt_expt/kernels/triton/sezm/so2_value_path.py": "9c8b01082cf01fdcd41fe58e0884364e2166d7acbd4c41f75dda625829a64dbf" - }, - "flags": { - "PYTHONHASHSEED": "1", - "DP_COMPILE_INFER": "1", - "DP_COMPILE": "0", - "DP_TRITON_INFER": "0", - "DP_CUDA_INFER": "0", - "DP_TF32_INFER": "0", - "DP_AMP_INFER": "0", - "DP_SO2_EDGE_COHORT_INFER": "0", - "DP_INTRA_OP_PARALLELISM_THREADS": "1", - "DP_INTER_OP_PARALLELISM_THREADS": "1", - "PYTORCH_ALLOC_CONF": "expandable_segments:True", - "TORCHINDUCTOR_COMPILE_THREADS": "1", - "TORCHINDUCTOR_CACHE_DIR": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Neo_TORCH/inductor", - "TRITON_CACHE_DIR": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Neo_TORCH/triton" - } - } - }, - { - "run": "Neo_n4096_TORCH_p2", - "result": { - "status": "oom", - "mode": "TORCH", - "n_atoms": 4096, - "started": 1792866152.7280385, - "threshold": { - "atol": 1e-05, - "rtol": 0.0001, - "relative_denominator_floor": 1e-08 - }, - "system": "diamond", - "input_sha256": "eda961dd45b711e42fad51b8dfde87d6ca736b34ac729d646c98f67481308f83", - "parameter_count": 1124910, - "exception": "Traceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 156, in execute\n n_batch, result = callable(batch_nframes, start_index)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 243, in execute_with_batch_size\n return (end_index - start_index), callable(\n ^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 730, in _eval_model\n batch_output = model(\n ^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/train/wrapper.py\", line 201, in forward\n model_pred = self._forward_without_loss(task_key, input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/train/wrapper.py\", line 249, in _forward_without_loss\n model_pred = self.model[task_key](**input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 839, in forward\n model_ret = self.forward_common(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 1053, in forward_common\n return self.forward_common_lower(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 1217, in forward_common_lower\n model_predict = compiled_core_compute(\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 2399, in compiled\n return dict(zip(_keys, _fn(*args)))\n ^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/aot_autograd.py\", line 1200, in forward\n return compiled_fn(full_args)\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 580, in runtime_wrapper\n all_outs = call_func_at_runtime_with_args(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/utils.py\", line 138, in call_func_at_runtime_with_args\n out = normalize_as_list(f(args))\n ^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 783, in wrapper\n return compiled_fn(runtime_args)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 1011, in inner_fn\n outs = compiled_fn(args)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/output_code.py\", line 656, in __call__\n return self.current_callable(inputs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/utils.py\", line 3401, in run\n out = model(new_inputs)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Neo_TORCH/inductor/7p/c7pndtfxe3mrtfgixzsmsf3r355pce3qwesz5k6bbekdcu6zibmr.py\", line 21401, in call\n buf200 = empty_strided_cuda((2, s29, 10, 32), (320*s29, 320, 32, 1), torch.float32)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\ntorch.OutOfMemoryError: CUDA out of memory. Tried to allocate 1.54 GiB. GPU 0 has a total capacity of 31.36 GiB of which 441.06 MiB is free. Including non-PyTorch memory, this process has 30.89 GiB memory in use. Of the allocated memory 30.08 GiB is allocated by PyTorch, and 131.51 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://docs.pytorch.org/docs/stable/notes/cuda.html#optimizing-memory-usage-with-pytorch-cuda-alloc-conf)\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 243, in \n out = evaluate(pos)\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 224, in evaluate\n forces = sample.get_forces().copy()\n ^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/atoms.py\", line 2015, in get_forces\n forces = self._calc.get_forces(self)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/abc.py\", line 31, in get_forces\n return self.get_property('forces', atoms)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/calculator.py\", line 517, in get_property\n self.calculate(atoms, [name], system_changes)\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/calculator.py\", line 143, in calculate\n e, f, v = self.dp.eval(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/infer/deep_pot.py\", line 206, in eval\n results = self.deep_eval.eval(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 556, in eval\n out = self._eval_func(self._eval_model, numb_test, natoms)(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 637, in eval_func\n return self.auto_batch_size.execute_all(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 272, in execute_all\n n_batch, result = self.execute(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 169, in execute\n raise OutOfMemoryError(\ndeepmd.utils.errors.OutOfMemoryError: The callable still throws an out-of-memory (OOM) error even when batch size is 1!\n", - "partial_arrays": 0, - "complete_peak": { - "allocated": 32302947328, - "reserved": 32440844288 - }, - "elapsed_s": 71.68084454536438 - }, - "manifest": { - "args": { - "root": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base", - "model": "/home/kmpl/mkl/project/DPA4-Neo-OMat24-v20260627.pt", - "out": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/results/Neo_n4096_TORCH_p2", - "cache": 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156, in execute\n n_batch, result = callable(batch_nframes, start_index)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 243, in execute_with_batch_size\n return (end_index - start_index), callable(\n ^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 730, in _eval_model\n batch_output = model(\n ^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/train/wrapper.py\", line 201, in forward\n model_pred = self._forward_without_loss(task_key, input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/train/wrapper.py\", line 249, in _forward_without_loss\n model_pred = self.model[task_key](**input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 839, in forward\n model_ret = self.forward_common(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 1053, in forward_common\n return self.forward_common_lower(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 1217, in forward_common_lower\n model_predict = compiled_core_compute(\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 2399, in compiled\n return dict(zip(_keys, _fn(*args)))\n ^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/aot_autograd.py\", line 1200, in forward\n return compiled_fn(full_args)\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 580, in runtime_wrapper\n all_outs = call_func_at_runtime_with_args(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/utils.py\", line 138, in call_func_at_runtime_with_args\n out = normalize_as_list(f(args))\n ^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 783, in wrapper\n return compiled_fn(runtime_args)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 1011, in inner_fn\n outs = compiled_fn(args)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/output_code.py\", line 656, in __call__\n return self.current_callable(inputs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/utils.py\", line 3401, in run\n out = model(new_inputs)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_BASE/inductor/am/camspdo6kx5gnjlafl7iexnzw6e5snfddhd3trsr3e3n2cztlycx.py\", line 20919, in call\n buf64 = empty_strided_cuda((s46, 6, 64), (384, 64, 1), torch.float32)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\ntorch.OutOfMemoryError: CUDA out of memory. Tried to allocate 950.00 MiB. GPU 0 has a total capacity of 31.36 GiB of which 871.06 MiB is free. Including non-PyTorch memory, this process has 30.47 GiB memory in use. Of the allocated memory 29.74 GiB is allocated by PyTorch, and 57.85 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://docs.pytorch.org/docs/stable/notes/cuda.html#optimizing-memory-usage-with-pytorch-cuda-alloc-conf)\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 243, in \n out = evaluate(pos)\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 224, in evaluate\n forces = sample.get_forces().copy()\n ^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/atoms.py\", line 2015, in get_forces\n forces = self._calc.get_forces(self)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/abc.py\", line 31, in get_forces\n return self.get_property('forces', atoms)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/calculator.py\", line 517, in get_property\n self.calculate(atoms, [name], system_changes)\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/calculator.py\", line 143, in calculate\n e, f, v = self.dp.eval(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/infer/deep_pot.py\", line 206, in eval\n results = self.deep_eval.eval(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 556, in eval\n out = self._eval_func(self._eval_model, numb_test, natoms)(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 637, in eval_func\n return self.auto_batch_size.execute_all(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 272, in execute_all\n n_batch, result = self.execute(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 169, in execute\n raise OutOfMemoryError(\ndeepmd.utils.errors.OutOfMemoryError: The callable still throws an out-of-memory (OOM) error even when batch size is 1!\n", - "partial_arrays": 0, - "complete_peak": { - "allocated": 31929301504, - "reserved": 31989956608 - }, - "elapsed_s": 188.92400097846985 - }, - "manifest": { - "args": { - "root": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base", - "model": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_accuracy_20261019/models/pro_mptrj/pro_download.pt", - "out": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/results/Pro_n4096_BASE_p0", - "cache": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_BASE", - "gpu": "0", - "mode": "BASE", - "nrep": "2", - "seed": "0", - "repeat_cell": "[8, 8, 8]", - "system": "diamond", - "samples": "30", - "warmup": "8" - }, - "model_sha256": "446a6e893101fc52190cfccabb9d2328863d651aca36981fd8d3401c71a61221", - "torch": "2.11.0+cu128", - "triton": "3.6.0", - "cuda": "12.8", - "baseline_revision": "2ba617bc3db67adee2b8ba00c71e9fa1c2d2bb8c", - "source_hashes": { - "deepmd/pt_expt/kernels/triton/sezm/so2_value_path.py": "9c8b01082cf01fdcd41fe58e0884364e2166d7acbd4c41f75dda625829a64dbf" - }, - "flags": { - "PYTHONHASHSEED": "0", - "DP_COMPILE_INFER": "1", - "DP_COMPILE": "0", - "DP_TRITON_INFER": "2", - "DP_CUDA_INFER": "0", - "DP_TF32_INFER": "0", - "DP_AMP_INFER": "0", - "DP_SO2_EDGE_COHORT_INFER": "0", - "DP_INTRA_OP_PARALLELISM_THREADS": "1", - "DP_INTER_OP_PARALLELISM_THREADS": "1", - "PYTORCH_ALLOC_CONF": "expandable_segments:True", - "TORCHINDUCTOR_COMPILE_THREADS": "1", - "TORCHINDUCTOR_CACHE_DIR": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_BASE/inductor", - "TRITON_CACHE_DIR": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_BASE/triton" - } - } - }, - { - "run": "Pro_n4096_BASE_p1", - "result": { - "status": "oom", - "mode": "BASE", - "n_atoms": 4096, - "started": 1792873213.2061002, - "threshold": { - "atol": 1e-05, - "rtol": 0.0001, - "relative_denominator_floor": 1e-08 - }, - "system": "diamond", - "input_sha256": "eda961dd45b711e42fad51b8dfde87d6ca736b34ac729d646c98f67481308f83", - "legacy_compatibility": "Pro-only in-memory fitting alias ener -> dpa4_ener; strict CPU state check passed; original checkpoint unchanged", - "parameter_count": 22678990, - "exception": "Traceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 156, in execute\n n_batch, result = callable(batch_nframes, start_index)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 243, in execute_with_batch_size\n return (end_index - start_index), callable(\n ^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 730, in _eval_model\n batch_output = model(\n ^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/train/wrapper.py\", line 201, in forward\n model_pred = self._forward_without_loss(task_key, input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/train/wrapper.py\", line 249, in _forward_without_loss\n model_pred = self.model[task_key](**input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 839, in forward\n model_ret = self.forward_common(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 1053, in forward_common\n return self.forward_common_lower(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 1217, in forward_common_lower\n model_predict = compiled_core_compute(\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 2399, in compiled\n return dict(zip(_keys, _fn(*args)))\n ^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/aot_autograd.py\", line 1200, in forward\n return compiled_fn(full_args)\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 580, in runtime_wrapper\n all_outs = call_func_at_runtime_with_args(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/utils.py\", line 138, in call_func_at_runtime_with_args\n out = normalize_as_list(f(args))\n ^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 783, in wrapper\n return compiled_fn(runtime_args)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 1011, in inner_fn\n outs = compiled_fn(args)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/output_code.py\", line 656, in __call__\n return self.current_callable(inputs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/utils.py\", line 3401, in run\n out = model(new_inputs)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_BASE/inductor/am/camspdo6kx5gnjlafl7iexnzw6e5snfddhd3trsr3e3n2cztlycx.py\", line 20919, in call\n buf64 = empty_strided_cuda((s46, 6, 64), (384, 64, 1), torch.float32)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\ntorch.OutOfMemoryError: CUDA out of memory. Tried to allocate 950.00 MiB. GPU 0 has a total capacity of 31.36 GiB of which 871.06 MiB is free. Including non-PyTorch memory, this process has 30.47 GiB memory in use. Of the allocated memory 29.74 GiB is allocated by PyTorch, and 57.85 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://docs.pytorch.org/docs/stable/notes/cuda.html#optimizing-memory-usage-with-pytorch-cuda-alloc-conf)\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 243, in \n out = evaluate(pos)\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 224, in evaluate\n forces = sample.get_forces().copy()\n ^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/atoms.py\", line 2015, in get_forces\n forces = self._calc.get_forces(self)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/abc.py\", line 31, in get_forces\n return self.get_property('forces', atoms)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/calculator.py\", line 517, in get_property\n self.calculate(atoms, [name], system_changes)\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/calculator.py\", line 143, in calculate\n e, f, v = self.dp.eval(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/infer/deep_pot.py\", line 206, in eval\n results = self.deep_eval.eval(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 556, in eval\n out = self._eval_func(self._eval_model, numb_test, natoms)(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 637, in eval_func\n return self.auto_batch_size.execute_all(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 272, in execute_all\n n_batch, result = self.execute(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 169, in execute\n raise OutOfMemoryError(\ndeepmd.utils.errors.OutOfMemoryError: The callable still throws an out-of-memory (OOM) error even when batch size is 1!\n", - "partial_arrays": 0, - "complete_peak": { - "allocated": 31929301504, - "reserved": 31989956608 - }, - "elapsed_s": 186.5676712989807 - }, - "manifest": { - "args": { - "root": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base", - "model": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_accuracy_20261019/models/pro_mptrj/pro_download.pt", - "out": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/results/Pro_n4096_BASE_p1", - "cache": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_BASE", - "gpu": "0", - "mode": "BASE", - "nrep": "2", - "seed": "0", - "repeat_cell": "[8, 8, 8]", - "system": "diamond", - "samples": "30", - "warmup": "8" - }, - "model_sha256": "446a6e893101fc52190cfccabb9d2328863d651aca36981fd8d3401c71a61221", - "torch": "2.11.0+cu128", - "triton": "3.6.0", - "cuda": "12.8", - "baseline_revision": "2ba617bc3db67adee2b8ba00c71e9fa1c2d2bb8c", - "source_hashes": { - "deepmd/pt_expt/kernels/triton/sezm/so2_value_path.py": "9c8b01082cf01fdcd41fe58e0884364e2166d7acbd4c41f75dda625829a64dbf" - }, - "flags": { - "PYTHONHASHSEED": "1", - "DP_COMPILE_INFER": "1", - "DP_COMPILE": "0", - "DP_TRITON_INFER": "2", - "DP_CUDA_INFER": "0", - "DP_TF32_INFER": "0", - "DP_AMP_INFER": "0", - "DP_SO2_EDGE_COHORT_INFER": "0", - "DP_INTRA_OP_PARALLELISM_THREADS": "1", - "DP_INTER_OP_PARALLELISM_THREADS": "1", - "PYTORCH_ALLOC_CONF": "expandable_segments:True", - "TORCHINDUCTOR_COMPILE_THREADS": "1", - "TORCHINDUCTOR_CACHE_DIR": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_BASE/inductor", - "TRITON_CACHE_DIR": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_BASE/triton" - } - } - }, - { - "run": "Pro_n4096_BASE_p2", - "result": { - "status": "oom", - "mode": "BASE", - "n_atoms": 4096, - "started": 1792873698.0892181, - "threshold": { - "atol": 1e-05, - "rtol": 0.0001, - "relative_denominator_floor": 1e-08 - }, - "system": "diamond", - "input_sha256": "eda961dd45b711e42fad51b8dfde87d6ca736b34ac729d646c98f67481308f83", - "legacy_compatibility": "Pro-only in-memory fitting alias ener -> dpa4_ener; strict CPU state check passed; original checkpoint unchanged", - "parameter_count": 22678990, - "exception": "Traceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 156, in execute\n n_batch, result = callable(batch_nframes, start_index)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 243, in execute_with_batch_size\n return (end_index - start_index), callable(\n ^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 730, in _eval_model\n batch_output = model(\n ^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/train/wrapper.py\", line 201, in forward\n model_pred = self._forward_without_loss(task_key, input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/train/wrapper.py\", line 249, in _forward_without_loss\n model_pred = self.model[task_key](**input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 839, in forward\n model_ret = self.forward_common(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 1053, in forward_common\n return self.forward_common_lower(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 1217, in forward_common_lower\n model_predict = compiled_core_compute(\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 2399, in compiled\n return dict(zip(_keys, _fn(*args)))\n ^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/aot_autograd.py\", line 1200, in forward\n return compiled_fn(full_args)\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 580, in runtime_wrapper\n all_outs = call_func_at_runtime_with_args(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/utils.py\", line 138, in call_func_at_runtime_with_args\n out = normalize_as_list(f(args))\n ^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 783, in wrapper\n return compiled_fn(runtime_args)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 1011, in inner_fn\n outs = compiled_fn(args)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/output_code.py\", line 656, in __call__\n return self.current_callable(inputs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/utils.py\", line 3401, in run\n out = model(new_inputs)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_BASE/inductor/am/camspdo6kx5gnjlafl7iexnzw6e5snfddhd3trsr3e3n2cztlycx.py\", line 20919, in call\n buf64 = empty_strided_cuda((s46, 6, 64), (384, 64, 1), torch.float32)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\ntorch.OutOfMemoryError: CUDA out of memory. Tried to allocate 950.00 MiB. GPU 0 has a total capacity of 31.36 GiB of which 871.06 MiB is free. Including non-PyTorch memory, this process has 30.47 GiB memory in use. Of the allocated memory 29.74 GiB is allocated by PyTorch, and 57.85 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://docs.pytorch.org/docs/stable/notes/cuda.html#optimizing-memory-usage-with-pytorch-cuda-alloc-conf)\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 243, in \n out = evaluate(pos)\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 224, in evaluate\n forces = sample.get_forces().copy()\n ^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/atoms.py\", line 2015, in get_forces\n forces = self._calc.get_forces(self)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/abc.py\", line 31, in get_forces\n return self.get_property('forces', atoms)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/calculator.py\", line 517, in get_property\n self.calculate(atoms, [name], system_changes)\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/calculator.py\", line 143, in calculate\n e, f, v = self.dp.eval(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/infer/deep_pot.py\", line 206, in eval\n results = self.deep_eval.eval(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 556, in eval\n out = self._eval_func(self._eval_model, numb_test, natoms)(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 637, in eval_func\n return self.auto_batch_size.execute_all(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 272, in execute_all\n n_batch, result = self.execute(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 169, in execute\n raise OutOfMemoryError(\ndeepmd.utils.errors.OutOfMemoryError: The callable still throws an out-of-memory (OOM) error even when batch size is 1!\n", - "partial_arrays": 0, - "complete_peak": { - "allocated": 31929301504, - "reserved": 31989956608 - }, - "elapsed_s": 188.6032009124756 - }, - "manifest": { - "args": { - "root": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base", - "model": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_accuracy_20261019/models/pro_mptrj/pro_download.pt", - "out": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/results/Pro_n4096_BASE_p2", - "cache": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_BASE", - "gpu": "0", - "mode": "BASE", - "nrep": "2", - "seed": "0", - "repeat_cell": "[8, 8, 8]", - "system": "diamond", - "samples": "30", - "warmup": "8" - }, - "model_sha256": "446a6e893101fc52190cfccabb9d2328863d651aca36981fd8d3401c71a61221", - "torch": "2.11.0+cu128", - "triton": "3.6.0", - "cuda": "12.8", - "baseline_revision": "2ba617bc3db67adee2b8ba00c71e9fa1c2d2bb8c", - "source_hashes": { - "deepmd/pt_expt/kernels/triton/sezm/so2_value_path.py": "9c8b01082cf01fdcd41fe58e0884364e2166d7acbd4c41f75dda625829a64dbf" - }, - "flags": { - "PYTHONHASHSEED": "0", - "DP_COMPILE_INFER": "1", - "DP_COMPILE": "0", - "DP_TRITON_INFER": "2", - "DP_CUDA_INFER": "0", - "DP_TF32_INFER": "0", - "DP_AMP_INFER": "0", - "DP_SO2_EDGE_COHORT_INFER": "0", - "DP_INTRA_OP_PARALLELISM_THREADS": "1", - "DP_INTER_OP_PARALLELISM_THREADS": "1", - "PYTORCH_ALLOC_CONF": "expandable_segments:True", - "TORCHINDUCTOR_COMPILE_THREADS": "1", - "TORCHINDUCTOR_CACHE_DIR": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_BASE/inductor", - "TRITON_CACHE_DIR": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_BASE/triton" - } - } - }, - { - "run": "Pro_n4096_ECS_p0", - "result": { - "status": "oom", - "mode": "ECS", - "n_atoms": 4096, - "started": 1792872829.7394366, - "threshold": { - "atol": 1e-05, - "rtol": 0.0001, - "relative_denominator_floor": 1e-08 - }, - "system": "diamond", - "input_sha256": "eda961dd45b711e42fad51b8dfde87d6ca736b34ac729d646c98f67481308f83", - "legacy_compatibility": "Pro-only in-memory fitting alias ener -> dpa4_ener; strict CPU state check passed; original checkpoint unchanged", - "parameter_count": 22678990, - "exception": "Traceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/utils/batch_size.py\", line 156, in execute\n n_batch, result = callable(batch_nframes, start_index)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/utils/batch_size.py\", line 243, in execute_with_batch_size\n return (end_index - start_index), callable(\n ^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/infer/deep_eval.py\", line 730, in _eval_model\n batch_output = model(\n ^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/train/wrapper.py\", line 201, in forward\n model_pred = self._forward_without_loss(task_key, input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/train/wrapper.py\", line 249, in _forward_without_loss\n model_pred = self.model[task_key](**input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/model/model/sezm_model.py\", line 839, in forward\n model_ret = self.forward_common(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/model/model/sezm_model.py\", line 1053, in forward_common\n return self.forward_common_lower(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/model/model/sezm_model.py\", line 1217, in forward_common_lower\n model_predict = compiled_core_compute(\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/model/model/sezm_model.py\", line 2399, in compiled\n return dict(zip(_keys, _fn(*args)))\n ^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/aot_autograd.py\", line 1200, in forward\n return compiled_fn(full_args)\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 580, in runtime_wrapper\n all_outs = call_func_at_runtime_with_args(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/utils.py\", line 138, in call_func_at_runtime_with_args\n out = normalize_as_list(f(args))\n ^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 783, in wrapper\n return compiled_fn(runtime_args)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 1011, in inner_fn\n outs = compiled_fn(args)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/output_code.py\", line 656, in __call__\n return self.current_callable(inputs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/utils.py\", line 3401, in run\n out = model(new_inputs)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_ECS/inductor/wu/cwu5rkg3ws24k2ch22id4pkjawrgdklubhesfbxm3qaciqae7gpf.py\", line 20939, in call\n buf64 = empty_strided_cuda((s46, 6, 64), (384, 64, 1), torch.float32)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\ntorch.OutOfMemoryError: CUDA out of memory. Tried to allocate 950.00 MiB. GPU 0 has a total capacity of 31.36 GiB of which 871.06 MiB is free. Including non-PyTorch memory, this process has 30.47 GiB memory in use. Of the allocated memory 29.74 GiB is allocated by PyTorch, and 57.85 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://docs.pytorch.org/docs/stable/notes/cuda.html#optimizing-memory-usage-with-pytorch-cuda-alloc-conf)\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 243, in \n out = evaluate(pos)\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 224, in evaluate\n forces = sample.get_forces().copy()\n ^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/atoms.py\", line 2015, in get_forces\n forces = self._calc.get_forces(self)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/abc.py\", line 31, in get_forces\n return self.get_property('forces', atoms)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/calculator.py\", line 517, in get_property\n self.calculate(atoms, [name], system_changes)\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/calculator.py\", line 143, in calculate\n e, f, v = self.dp.eval(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/infer/deep_pot.py\", line 206, in eval\n results = self.deep_eval.eval(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/infer/deep_eval.py\", line 556, in eval\n out = self._eval_func(self._eval_model, numb_test, natoms)(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/infer/deep_eval.py\", line 637, in eval_func\n return self.auto_batch_size.execute_all(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/utils/batch_size.py\", line 272, in execute_all\n n_batch, result = self.execute(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/utils/batch_size.py\", line 169, in execute\n raise OutOfMemoryError(\ndeepmd.utils.errors.OutOfMemoryError: The callable still throws an out-of-memory (OOM) error even when batch size is 1!\n", - "partial_arrays": 0, - "complete_peak": { - "allocated": 31929301504, - "reserved": 31989956608 - }, - "elapsed_s": 186.17430138587952 - }, - "manifest": { - "args": { - "root": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate", - "model": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_accuracy_20261019/models/pro_mptrj/pro_download.pt", - "out": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/results/Pro_n4096_ECS_p0", - "cache": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_ECS", - "gpu": "0", - "mode": "ECS", - "nrep": "2", - "seed": "0", - "repeat_cell": "[8, 8, 8]", - "system": "diamond", - "samples": "30", - "warmup": "8" - }, - "model_sha256": "446a6e893101fc52190cfccabb9d2328863d651aca36981fd8d3401c71a61221", - "torch": "2.11.0+cu128", - "triton": "3.6.0", - "cuda": "12.8", - "baseline_revision": "2ba617bc3db67adee2b8ba00c71e9fa1c2d2bb8c", - "source_hashes": { - "deepmd/pt_expt/kernels/triton/sezm/so2_value_path.py": "0fb6ae06e62af5dfafe8b7c9cfdce975714a9ffb577dc4f3581ed13706735462", - "deepmd/pt_expt/kernels/triton/sezm/so2_edge_cohort.py": "08c67a81883ff70f19ce9dad8809037585e0c5b0949ecceab747bb4179c44474" - }, - "flags": { - "PYTHONHASHSEED": "0", - "DP_COMPILE_INFER": "1", - "DP_COMPILE": "0", - "DP_TRITON_INFER": "2", - "DP_CUDA_INFER": "0", - "DP_TF32_INFER": "0", - "DP_AMP_INFER": "0", - "DP_SO2_EDGE_COHORT_INFER": "1", - "DP_INTRA_OP_PARALLELISM_THREADS": "1", - "DP_INTER_OP_PARALLELISM_THREADS": "1", - "PYTORCH_ALLOC_CONF": "expandable_segments:True", - "TORCHINDUCTOR_COMPILE_THREADS": "1", - "TORCHINDUCTOR_CACHE_DIR": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_ECS/inductor", - "TRITON_CACHE_DIR": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_ECS/triton" - } - } - }, - { - "run": "Pro_n4096_ECS_p1", - "result": { - "status": "oom", - "mode": "ECS", - "n_atoms": 4096, - "started": 1792873021.7048163, - "threshold": { - "atol": 1e-05, - "rtol": 0.0001, - "relative_denominator_floor": 1e-08 - }, - "system": "diamond", - "input_sha256": "eda961dd45b711e42fad51b8dfde87d6ca736b34ac729d646c98f67481308f83", - "legacy_compatibility": "Pro-only in-memory fitting alias ener -> dpa4_ener; strict CPU state check passed; original checkpoint unchanged", - "parameter_count": 22678990, - "exception": "Traceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/utils/batch_size.py\", line 156, in execute\n n_batch, result = callable(batch_nframes, start_index)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/utils/batch_size.py\", line 243, in execute_with_batch_size\n return (end_index - start_index), callable(\n ^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/infer/deep_eval.py\", line 730, in _eval_model\n batch_output = model(\n ^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/train/wrapper.py\", line 201, in forward\n model_pred = self._forward_without_loss(task_key, input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/train/wrapper.py\", line 249, in _forward_without_loss\n model_pred = self.model[task_key](**input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/model/model/sezm_model.py\", line 839, in forward\n model_ret = self.forward_common(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/model/model/sezm_model.py\", line 1053, in forward_common\n return self.forward_common_lower(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/model/model/sezm_model.py\", line 1217, in forward_common_lower\n model_predict = compiled_core_compute(\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/model/model/sezm_model.py\", line 2399, in compiled\n return dict(zip(_keys, _fn(*args)))\n ^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/aot_autograd.py\", line 1200, in forward\n return compiled_fn(full_args)\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 580, in runtime_wrapper\n all_outs = call_func_at_runtime_with_args(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/utils.py\", line 138, in call_func_at_runtime_with_args\n out = normalize_as_list(f(args))\n ^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 783, in wrapper\n return compiled_fn(runtime_args)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 1011, in inner_fn\n outs = compiled_fn(args)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/output_code.py\", line 656, in __call__\n return self.current_callable(inputs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/utils.py\", line 3401, in run\n out = model(new_inputs)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_ECS/inductor/wu/cwu5rkg3ws24k2ch22id4pkjawrgdklubhesfbxm3qaciqae7gpf.py\", line 20939, in call\n buf64 = empty_strided_cuda((s46, 6, 64), (384, 64, 1), torch.float32)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\ntorch.OutOfMemoryError: CUDA out of memory. Tried to allocate 950.00 MiB. GPU 0 has a total capacity of 31.36 GiB of which 871.06 MiB is free. Including non-PyTorch memory, this process has 30.47 GiB memory in use. Of the allocated memory 29.74 GiB is allocated by PyTorch, and 57.85 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://docs.pytorch.org/docs/stable/notes/cuda.html#optimizing-memory-usage-with-pytorch-cuda-alloc-conf)\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 243, in \n out = evaluate(pos)\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 224, in evaluate\n forces = sample.get_forces().copy()\n ^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/atoms.py\", line 2015, in get_forces\n forces = self._calc.get_forces(self)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/abc.py\", line 31, in get_forces\n return self.get_property('forces', atoms)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/calculator.py\", line 517, in get_property\n self.calculate(atoms, [name], system_changes)\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/calculator.py\", line 143, in calculate\n e, f, v = self.dp.eval(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/infer/deep_pot.py\", line 206, in eval\n results = self.deep_eval.eval(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/infer/deep_eval.py\", line 556, in eval\n out = self._eval_func(self._eval_model, numb_test, natoms)(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/infer/deep_eval.py\", line 637, in eval_func\n return self.auto_batch_size.execute_all(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/utils/batch_size.py\", line 272, in execute_all\n n_batch, result = self.execute(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/utils/batch_size.py\", line 169, in execute\n raise OutOfMemoryError(\ndeepmd.utils.errors.OutOfMemoryError: The callable still throws an out-of-memory (OOM) error even when batch size is 1!\n", - "partial_arrays": 0, - "complete_peak": { - "allocated": 31929301504, - "reserved": 31989956608 - }, - "elapsed_s": 185.50847601890564 - }, - "manifest": { - "args": { - "root": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate", - "model": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_accuracy_20261019/models/pro_mptrj/pro_download.pt", - "out": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/results/Pro_n4096_ECS_p1", - "cache": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_ECS", - "gpu": "0", - "mode": "ECS", - "nrep": "2", - "seed": "0", - "repeat_cell": "[8, 8, 8]", - "system": "diamond", - "samples": "30", - "warmup": "8" - }, - "model_sha256": "446a6e893101fc52190cfccabb9d2328863d651aca36981fd8d3401c71a61221", - "torch": "2.11.0+cu128", - "triton": "3.6.0", - "cuda": "12.8", - "baseline_revision": "2ba617bc3db67adee2b8ba00c71e9fa1c2d2bb8c", - "source_hashes": { - "deepmd/pt_expt/kernels/triton/sezm/so2_value_path.py": "0fb6ae06e62af5dfafe8b7c9cfdce975714a9ffb577dc4f3581ed13706735462", - "deepmd/pt_expt/kernels/triton/sezm/so2_edge_cohort.py": "08c67a81883ff70f19ce9dad8809037585e0c5b0949ecceab747bb4179c44474" - }, - "flags": { - "PYTHONHASHSEED": "1", - "DP_COMPILE_INFER": "1", - "DP_COMPILE": "0", - "DP_TRITON_INFER": "2", - "DP_CUDA_INFER": "0", - "DP_TF32_INFER": "0", - "DP_AMP_INFER": "0", - "DP_SO2_EDGE_COHORT_INFER": "1", - "DP_INTRA_OP_PARALLELISM_THREADS": "1", - "DP_INTER_OP_PARALLELISM_THREADS": "1", - "PYTORCH_ALLOC_CONF": "expandable_segments:True", - "TORCHINDUCTOR_COMPILE_THREADS": "1", - "TORCHINDUCTOR_CACHE_DIR": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_ECS/inductor", - "TRITON_CACHE_DIR": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_ECS/triton" - } - } - }, - { - "run": "Pro_n4096_ECS_p2", - "result": { - "status": "oom", - "mode": "ECS", - "n_atoms": 4096, - "started": 1792873892.4796147, - "threshold": { - "atol": 1e-05, - "rtol": 0.0001, - "relative_denominator_floor": 1e-08 - }, - "system": "diamond", - "input_sha256": "eda961dd45b711e42fad51b8dfde87d6ca736b34ac729d646c98f67481308f83", - "legacy_compatibility": "Pro-only in-memory fitting alias ener -> dpa4_ener; strict CPU state check passed; original checkpoint unchanged", - "parameter_count": 22678990, - "exception": "Traceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/utils/batch_size.py\", line 156, in execute\n n_batch, result = callable(batch_nframes, start_index)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/utils/batch_size.py\", line 243, in execute_with_batch_size\n return (end_index - start_index), callable(\n ^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/infer/deep_eval.py\", line 730, in _eval_model\n batch_output = model(\n ^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/train/wrapper.py\", line 201, in forward\n model_pred = self._forward_without_loss(task_key, input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/train/wrapper.py\", line 249, in _forward_without_loss\n model_pred = self.model[task_key](**input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/model/model/sezm_model.py\", line 839, in forward\n model_ret = self.forward_common(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/model/model/sezm_model.py\", line 1053, in forward_common\n return self.forward_common_lower(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/model/model/sezm_model.py\", line 1217, in forward_common_lower\n model_predict = compiled_core_compute(\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/model/model/sezm_model.py\", line 2399, in compiled\n return dict(zip(_keys, _fn(*args)))\n ^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/aot_autograd.py\", line 1200, in forward\n return compiled_fn(full_args)\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 580, in runtime_wrapper\n all_outs = call_func_at_runtime_with_args(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/utils.py\", line 138, in call_func_at_runtime_with_args\n out = normalize_as_list(f(args))\n ^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 783, in wrapper\n return compiled_fn(runtime_args)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 1011, in inner_fn\n outs = compiled_fn(args)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/output_code.py\", line 656, in __call__\n return self.current_callable(inputs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/utils.py\", line 3401, in run\n out = model(new_inputs)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_ECS/inductor/wu/cwu5rkg3ws24k2ch22id4pkjawrgdklubhesfbxm3qaciqae7gpf.py\", line 20939, in call\n buf64 = empty_strided_cuda((s46, 6, 64), (384, 64, 1), torch.float32)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\ntorch.OutOfMemoryError: CUDA out of memory. Tried to allocate 950.00 MiB. GPU 0 has a total capacity of 31.36 GiB of which 871.06 MiB is free. Including non-PyTorch memory, this process has 30.47 GiB memory in use. Of the allocated memory 29.74 GiB is allocated by PyTorch, and 57.85 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://docs.pytorch.org/docs/stable/notes/cuda.html#optimizing-memory-usage-with-pytorch-cuda-alloc-conf)\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 243, in \n out = evaluate(pos)\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 224, in evaluate\n forces = sample.get_forces().copy()\n ^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/atoms.py\", line 2015, in get_forces\n forces = self._calc.get_forces(self)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/abc.py\", line 31, in get_forces\n return self.get_property('forces', atoms)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/calculator.py\", line 517, in get_property\n self.calculate(atoms, [name], system_changes)\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/calculator.py\", line 143, in calculate\n e, f, v = self.dp.eval(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/infer/deep_pot.py\", line 206, in eval\n results = self.deep_eval.eval(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/infer/deep_eval.py\", line 556, in eval\n out = self._eval_func(self._eval_model, numb_test, natoms)(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/pt/infer/deep_eval.py\", line 637, in eval_func\n return self.auto_batch_size.execute_all(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/utils/batch_size.py\", line 272, in execute_all\n n_batch, result = self.execute(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate/deepmd/utils/batch_size.py\", line 169, in execute\n raise OutOfMemoryError(\ndeepmd.utils.errors.OutOfMemoryError: The callable still throws an out-of-memory (OOM) error even when batch size is 1!\n", - "partial_arrays": 0, - "complete_peak": { - "allocated": 31929301504, - "reserved": 31989956608 - }, - "elapsed_s": 187.12241053581238 - }, - "manifest": { - "args": { - "root": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/candidate", - "model": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_accuracy_20261019/models/pro_mptrj/pro_download.pt", - "out": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/results/Pro_n4096_ECS_p2", - "cache": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_ECS", - "gpu": "0", - "mode": "ECS", - "nrep": "2", - "seed": "0", - "repeat_cell": "[8, 8, 8]", - "system": "diamond", - "samples": "30", - "warmup": "8" - }, - "model_sha256": "446a6e893101fc52190cfccabb9d2328863d651aca36981fd8d3401c71a61221", - "torch": "2.11.0+cu128", - "triton": "3.6.0", - "cuda": "12.8", - "baseline_revision": "2ba617bc3db67adee2b8ba00c71e9fa1c2d2bb8c", - "source_hashes": { - "deepmd/pt_expt/kernels/triton/sezm/so2_value_path.py": "0fb6ae06e62af5dfafe8b7c9cfdce975714a9ffb577dc4f3581ed13706735462", - "deepmd/pt_expt/kernels/triton/sezm/so2_edge_cohort.py": "08c67a81883ff70f19ce9dad8809037585e0c5b0949ecceab747bb4179c44474" - }, - "flags": { - "PYTHONHASHSEED": "0", - "DP_COMPILE_INFER": "1", - "DP_COMPILE": "0", - "DP_TRITON_INFER": "2", - "DP_CUDA_INFER": "0", - "DP_TF32_INFER": "0", - "DP_AMP_INFER": "0", - "DP_SO2_EDGE_COHORT_INFER": "1", - "DP_INTRA_OP_PARALLELISM_THREADS": "1", - "DP_INTER_OP_PARALLELISM_THREADS": "1", - "PYTORCH_ALLOC_CONF": "expandable_segments:True", - "TORCHINDUCTOR_COMPILE_THREADS": "1", - "TORCHINDUCTOR_CACHE_DIR": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_ECS/inductor", - "TRITON_CACHE_DIR": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_ECS/triton" - } - } - }, - { - "run": "Pro_n4096_TORCH_p0", - "result": { - "status": "oom", - "mode": "TORCH", - "n_atoms": 4096, - "started": 1792872488.735287, - "threshold": { - "atol": 1e-05, - "rtol": 0.0001, - "relative_denominator_floor": 1e-08 - }, - "system": "diamond", - "input_sha256": "eda961dd45b711e42fad51b8dfde87d6ca736b34ac729d646c98f67481308f83", - "legacy_compatibility": "Pro-only in-memory fitting alias ener -> dpa4_ener; strict CPU state check passed; original checkpoint unchanged", - "parameter_count": 22678990, - "exception": "Traceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 156, in execute\n n_batch, result = callable(batch_nframes, start_index)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 243, in execute_with_batch_size\n return (end_index - start_index), callable(\n ^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 730, in _eval_model\n batch_output = model(\n ^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/train/wrapper.py\", line 201, in forward\n model_pred = self._forward_without_loss(task_key, input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/train/wrapper.py\", line 249, in _forward_without_loss\n model_pred = self.model[task_key](**input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 839, in forward\n model_ret = self.forward_common(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 1053, in forward_common\n return self.forward_common_lower(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 1217, in forward_common_lower\n model_predict = compiled_core_compute(\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 2399, in compiled\n return dict(zip(_keys, _fn(*args)))\n ^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/aot_autograd.py\", line 1200, in forward\n return compiled_fn(full_args)\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 580, in runtime_wrapper\n all_outs = call_func_at_runtime_with_args(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/utils.py\", line 138, in call_func_at_runtime_with_args\n out = normalize_as_list(f(args))\n ^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 783, in wrapper\n return compiled_fn(runtime_args)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 1011, in inner_fn\n outs = compiled_fn(args)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/output_code.py\", line 656, in __call__\n return self.current_callable(inputs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/utils.py\", line 3401, in run\n out = model(new_inputs)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_TORCH/inductor/db/cdbxhl4ag2mms4oooq2iu5tx2nwa73j2ckthin7jon7hvaejblzz.py\", line 21624, in call\n buf83 = empty_strided_cuda((s29, 6, 64), (384, 64, 1), torch.float32)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\ntorch.OutOfMemoryError: CUDA out of memory. Tried to allocate 950.00 MiB. GPU 0 has a total capacity of 31.36 GiB of which 631.06 MiB is free. Including non-PyTorch memory, this process has 30.70 GiB memory in use. Of the allocated memory 29.98 GiB is allocated by PyTorch, and 49.85 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://docs.pytorch.org/docs/stable/notes/cuda.html#optimizing-memory-usage-with-pytorch-cuda-alloc-conf)\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 243, in \n out = evaluate(pos)\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 224, in evaluate\n forces = sample.get_forces().copy()\n ^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/atoms.py\", line 2015, in get_forces\n forces = self._calc.get_forces(self)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/abc.py\", line 31, in get_forces\n return self.get_property('forces', atoms)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/calculator.py\", line 517, in get_property\n self.calculate(atoms, [name], system_changes)\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/calculator.py\", line 143, in calculate\n e, f, v = self.dp.eval(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/infer/deep_pot.py\", line 206, in eval\n results = self.deep_eval.eval(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 556, in eval\n out = self._eval_func(self._eval_model, numb_test, natoms)(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 637, in eval_func\n return self.auto_batch_size.execute_all(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 272, in execute_all\n n_batch, result = self.execute(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 169, in execute\n raise OutOfMemoryError(\ndeepmd.utils.errors.OutOfMemoryError: The callable still throws an out-of-memory (OOM) error even when batch size is 1!\n", - "partial_arrays": 0, - "complete_peak": { - "allocated": 32189347328, - "reserved": 32598130688 - }, - "elapsed_s": 137.5577781200409 - }, - "manifest": { - "args": { - "root": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base", - "model": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_accuracy_20261019/models/pro_mptrj/pro_download.pt", - "out": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/results/Pro_n4096_TORCH_p0", - "cache": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_TORCH", - "gpu": "0", - "mode": "TORCH", - "nrep": "2", - "seed": "0", - "repeat_cell": "[8, 8, 8]", - "system": "diamond", - "samples": "30", - "warmup": "8" - }, - "model_sha256": "446a6e893101fc52190cfccabb9d2328863d651aca36981fd8d3401c71a61221", - "torch": "2.11.0+cu128", - "triton": "3.6.0", - "cuda": "12.8", - "baseline_revision": "2ba617bc3db67adee2b8ba00c71e9fa1c2d2bb8c", - "source_hashes": { - "deepmd/pt_expt/kernels/triton/sezm/so2_value_path.py": "9c8b01082cf01fdcd41fe58e0884364e2166d7acbd4c41f75dda625829a64dbf" - }, - "flags": { - "PYTHONHASHSEED": "0", - "DP_COMPILE_INFER": "1", - "DP_COMPILE": "0", - "DP_TRITON_INFER": "0", - "DP_CUDA_INFER": "0", - "DP_TF32_INFER": "0", - "DP_AMP_INFER": "0", - "DP_SO2_EDGE_COHORT_INFER": "0", - "DP_INTRA_OP_PARALLELISM_THREADS": "1", - "DP_INTER_OP_PARALLELISM_THREADS": "1", - "PYTORCH_ALLOC_CONF": "expandable_segments:True", - "TORCHINDUCTOR_COMPILE_THREADS": "1", - "TORCHINDUCTOR_CACHE_DIR": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_TORCH/inductor", - "TRITON_CACHE_DIR": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_TORCH/triton" - } - } - }, - { - "run": "Pro_n4096_TORCH_p1", - "result": { - "status": "oom", - "mode": "TORCH", - "n_atoms": 4096, - "started": 1792873405.5164213, - "threshold": { - "atol": 1e-05, - "rtol": 0.0001, - "relative_denominator_floor": 1e-08 - }, - "system": "diamond", - "input_sha256": "eda961dd45b711e42fad51b8dfde87d6ca736b34ac729d646c98f67481308f83", - "legacy_compatibility": "Pro-only in-memory fitting alias ener -> dpa4_ener; strict CPU state check passed; original checkpoint unchanged", - "parameter_count": 22678990, - "exception": "Traceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 156, in execute\n n_batch, result = callable(batch_nframes, start_index)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 243, in execute_with_batch_size\n return (end_index - start_index), callable(\n ^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 730, in _eval_model\n batch_output = model(\n ^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/train/wrapper.py\", line 201, in forward\n model_pred = self._forward_without_loss(task_key, input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/train/wrapper.py\", line 249, in _forward_without_loss\n model_pred = self.model[task_key](**input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 839, in forward\n model_ret = self.forward_common(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 1053, in forward_common\n return self.forward_common_lower(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 1217, in forward_common_lower\n model_predict = compiled_core_compute(\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 2399, in compiled\n return dict(zip(_keys, _fn(*args)))\n ^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/aot_autograd.py\", line 1200, in forward\n return compiled_fn(full_args)\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 580, in runtime_wrapper\n all_outs = call_func_at_runtime_with_args(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/utils.py\", line 138, in call_func_at_runtime_with_args\n out = normalize_as_list(f(args))\n ^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 783, in wrapper\n return compiled_fn(runtime_args)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 1011, in inner_fn\n outs = compiled_fn(args)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/output_code.py\", line 656, in __call__\n return self.current_callable(inputs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/utils.py\", line 3401, in run\n out = model(new_inputs)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_TORCH/inductor/db/cdbxhl4ag2mms4oooq2iu5tx2nwa73j2ckthin7jon7hvaejblzz.py\", line 21624, in call\n buf83 = empty_strided_cuda((s29, 6, 64), (384, 64, 1), torch.float32)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\ntorch.OutOfMemoryError: CUDA out of memory. Tried to allocate 950.00 MiB. GPU 0 has a total capacity of 31.36 GiB of which 631.06 MiB is free. Including non-PyTorch memory, this process has 30.70 GiB memory in use. Of the allocated memory 29.98 GiB is allocated by PyTorch, and 49.85 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://docs.pytorch.org/docs/stable/notes/cuda.html#optimizing-memory-usage-with-pytorch-cuda-alloc-conf)\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 243, in \n out = evaluate(pos)\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 224, in evaluate\n forces = sample.get_forces().copy()\n ^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/atoms.py\", line 2015, in get_forces\n forces = self._calc.get_forces(self)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/abc.py\", line 31, in get_forces\n return self.get_property('forces', atoms)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/calculator.py\", line 517, in get_property\n self.calculate(atoms, [name], system_changes)\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/calculator.py\", line 143, in calculate\n e, f, v = self.dp.eval(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/infer/deep_pot.py\", line 206, in eval\n results = self.deep_eval.eval(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 556, in eval\n out = self._eval_func(self._eval_model, numb_test, natoms)(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 637, in eval_func\n return self.auto_batch_size.execute_all(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 272, in execute_all\n n_batch, result = self.execute(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 169, in execute\n raise OutOfMemoryError(\ndeepmd.utils.errors.OutOfMemoryError: The callable still throws an out-of-memory (OOM) error even when batch size is 1!\n", - "partial_arrays": 0, - "complete_peak": { - "allocated": 32189347328, - "reserved": 32598130688 - }, - "elapsed_s": 138.13616728782654 - }, - "manifest": { - "args": { - "root": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base", - "model": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_accuracy_20261019/models/pro_mptrj/pro_download.pt", - "out": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/results/Pro_n4096_TORCH_p1", - "cache": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_TORCH", - "gpu": "0", - "mode": "TORCH", - "nrep": "2", - "seed": "0", - "repeat_cell": "[8, 8, 8]", - "system": "diamond", - "samples": "30", - "warmup": "8" - }, - "model_sha256": "446a6e893101fc52190cfccabb9d2328863d651aca36981fd8d3401c71a61221", - "torch": "2.11.0+cu128", - "triton": "3.6.0", - "cuda": "12.8", - "baseline_revision": "2ba617bc3db67adee2b8ba00c71e9fa1c2d2bb8c", - "source_hashes": { - "deepmd/pt_expt/kernels/triton/sezm/so2_value_path.py": "9c8b01082cf01fdcd41fe58e0884364e2166d7acbd4c41f75dda625829a64dbf" - }, - "flags": { - "PYTHONHASHSEED": "1", - "DP_COMPILE_INFER": "1", - "DP_COMPILE": "0", - "DP_TRITON_INFER": "0", - "DP_CUDA_INFER": "0", - "DP_TF32_INFER": "0", - "DP_AMP_INFER": "0", - "DP_SO2_EDGE_COHORT_INFER": "0", - "DP_INTRA_OP_PARALLELISM_THREADS": "1", - "DP_INTER_OP_PARALLELISM_THREADS": "1", - "PYTORCH_ALLOC_CONF": "expandable_segments:True", - "TORCHINDUCTOR_COMPILE_THREADS": "1", - "TORCHINDUCTOR_CACHE_DIR": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_TORCH/inductor", - "TRITON_CACHE_DIR": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_TORCH/triton" - } - } - }, - { - "run": "Pro_n4096_TORCH_p2", - "result": { - "status": "oom", - "mode": "TORCH", - "n_atoms": 4096, - "started": 1792873552.185923, - "threshold": { - "atol": 1e-05, - "rtol": 0.0001, - "relative_denominator_floor": 1e-08 - }, - "system": "diamond", - "input_sha256": "eda961dd45b711e42fad51b8dfde87d6ca736b34ac729d646c98f67481308f83", - "legacy_compatibility": "Pro-only in-memory fitting alias ener -> dpa4_ener; strict CPU state check passed; original checkpoint unchanged", - "parameter_count": 22678990, - "exception": "Traceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 156, in execute\n n_batch, result = callable(batch_nframes, start_index)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 243, in execute_with_batch_size\n return (end_index - start_index), callable(\n ^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 730, in _eval_model\n batch_output = model(\n ^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/train/wrapper.py\", line 201, in forward\n model_pred = self._forward_without_loss(task_key, input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/train/wrapper.py\", line 249, in _forward_without_loss\n model_pred = self.model[task_key](**input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 839, in forward\n model_ret = self.forward_common(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 1053, in forward_common\n return self.forward_common_lower(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 1217, in forward_common_lower\n model_predict = compiled_core_compute(\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 2399, in compiled\n return dict(zip(_keys, _fn(*args)))\n ^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/aot_autograd.py\", line 1200, in forward\n return compiled_fn(full_args)\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 580, in runtime_wrapper\n all_outs = call_func_at_runtime_with_args(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/utils.py\", line 138, in call_func_at_runtime_with_args\n out = normalize_as_list(f(args))\n ^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 783, in wrapper\n return compiled_fn(runtime_args)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 1011, in inner_fn\n outs = compiled_fn(args)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/output_code.py\", line 656, in __call__\n return self.current_callable(inputs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/utils.py\", line 3401, in run\n out = model(new_inputs)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_TORCH/inductor/db/cdbxhl4ag2mms4oooq2iu5tx2nwa73j2ckthin7jon7hvaejblzz.py\", line 21624, in call\n buf83 = empty_strided_cuda((s29, 6, 64), (384, 64, 1), torch.float32)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\ntorch.OutOfMemoryError: CUDA out of memory. Tried to allocate 950.00 MiB. GPU 0 has a total capacity of 31.36 GiB of which 631.06 MiB is free. Including non-PyTorch memory, this process has 30.70 GiB memory in use. Of the allocated memory 29.98 GiB is allocated by PyTorch, and 49.85 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://docs.pytorch.org/docs/stable/notes/cuda.html#optimizing-memory-usage-with-pytorch-cuda-alloc-conf)\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 243, in \n out = evaluate(pos)\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 224, in evaluate\n forces = sample.get_forces().copy()\n ^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/atoms.py\", line 2015, in get_forces\n forces = self._calc.get_forces(self)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/abc.py\", line 31, in get_forces\n return self.get_property('forces', atoms)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/calculator.py\", line 517, in get_property\n self.calculate(atoms, [name], system_changes)\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/calculator.py\", line 143, in calculate\n e, f, v = self.dp.eval(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/infer/deep_pot.py\", line 206, in eval\n results = self.deep_eval.eval(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 556, in eval\n out = self._eval_func(self._eval_model, numb_test, natoms)(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 637, in eval_func\n return self.auto_batch_size.execute_all(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 272, in execute_all\n n_batch, result = self.execute(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 169, in execute\n raise OutOfMemoryError(\ndeepmd.utils.errors.OutOfMemoryError: The callable still throws an out-of-memory (OOM) error even when batch size is 1!\n", - "partial_arrays": 0, - "complete_peak": { - "allocated": 32189347328, - "reserved": 32598130688 - }, - "elapsed_s": 137.1274483203888 - }, - "manifest": { - "args": { - "root": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base", - "model": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_accuracy_20261019/models/pro_mptrj/pro_download.pt", - "out": 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"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_ECS/inductor", - "TRITON_CACHE_DIR": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_ECS/triton" - } - } - }, - { - "run": "Pro_n512_TORCH_p0", - "result": { - "status": "oom", - "mode": "TORCH", - "n_atoms": 512, - "started": 1792870737.0522878, - "threshold": { - "atol": 1e-05, - "rtol": 0.0001, - "relative_denominator_floor": 1e-08 - }, - "system": "diamond", - "input_sha256": "bf70d484ff4dc5117009b05a348a5f4a7a201d16571d4aa92768d0110eb584e1", - "legacy_compatibility": "Pro-only in-memory fitting alias ener -> dpa4_ener; strict CPU state check passed; original checkpoint unchanged", - "parameter_count": 22678990, - "exception": "Traceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 156, in execute\n n_batch, result = callable(batch_nframes, start_index)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 243, in execute_with_batch_size\n return (end_index - start_index), callable(\n ^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 730, in _eval_model\n batch_output = model(\n ^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/train/wrapper.py\", line 201, in forward\n model_pred = self._forward_without_loss(task_key, input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/train/wrapper.py\", line 249, in _forward_without_loss\n model_pred = self.model[task_key](**input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 839, in forward\n model_ret = self.forward_common(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 1053, in forward_common\n return self.forward_common_lower(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 1217, in forward_common_lower\n model_predict = compiled_core_compute(\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 2399, in compiled\n return dict(zip(_keys, _fn(*args)))\n ^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/aot_autograd.py\", line 1200, in forward\n return compiled_fn(full_args)\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 580, in runtime_wrapper\n all_outs = call_func_at_runtime_with_args(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/utils.py\", line 138, in call_func_at_runtime_with_args\n out = normalize_as_list(f(args))\n ^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 783, in wrapper\n return compiled_fn(runtime_args)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 1011, in inner_fn\n outs = compiled_fn(args)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/output_code.py\", line 656, in __call__\n return self.current_callable(inputs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/utils.py\", line 3401, in run\n out = model(new_inputs)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_TORCH/inductor/b6/cb6y74z5wjdb23bsj7sgwz7n4l6tpvrsmehqafsc5rp6ytf6afxd.py\", line 23148, in call\n buf476 = empty_strided_cuda((1, 16*s29, 128), (2048*s29, 128, 1), torch.float32)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\ntorch.OutOfMemoryError: CUDA out of memory. Tried to allocate 634.00 MiB. GPU 0 has a total capacity of 31.36 GiB of which 153.06 MiB is free. Including non-PyTorch memory, this process has 31.17 GiB memory in use. Of the allocated memory 30.43 GiB is allocated by PyTorch, and 62.92 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://docs.pytorch.org/docs/stable/notes/cuda.html#optimizing-memory-usage-with-pytorch-cuda-alloc-conf)\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 243, in \n out = evaluate(pos)\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 224, in evaluate\n forces = sample.get_forces().copy()\n ^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/atoms.py\", line 2015, in get_forces\n forces = self._calc.get_forces(self)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/abc.py\", line 31, in get_forces\n return self.get_property('forces', atoms)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/calculator.py\", line 517, in get_property\n self.calculate(atoms, [name], system_changes)\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/calculator.py\", line 143, in calculate\n e, f, v = self.dp.eval(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/infer/deep_pot.py\", line 206, in eval\n results = self.deep_eval.eval(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 556, in eval\n out = self._eval_func(self._eval_model, numb_test, natoms)(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 637, in eval_func\n return self.auto_batch_size.execute_all(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 272, in execute_all\n n_batch, result = self.execute(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 169, in execute\n raise OutOfMemoryError(\ndeepmd.utils.errors.OutOfMemoryError: The callable still throws an out-of-memory (OOM) error even when batch size is 1!\n", - "partial_arrays": 0, - "complete_peak": { - "allocated": 32676858368, - "reserved": 32847691776 - }, - "elapsed_s": 138.26069927215576 - }, - "manifest": { - "args": { - "root": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base", - "model": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_accuracy_20261019/models/pro_mptrj/pro_download.pt", - "out": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/results/Pro_n512_TORCH_p0", - "cache": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_TORCH", - "gpu": "0", - "mode": "TORCH", - "nrep": "2", - "seed": "0", - "repeat_cell": "[4, 4, 4]", - "system": "diamond", - "samples": "30", - "warmup": "8" - }, - "model_sha256": "446a6e893101fc52190cfccabb9d2328863d651aca36981fd8d3401c71a61221", - "torch": "2.11.0+cu128", - "triton": "3.6.0", - "cuda": "12.8", - "baseline_revision": "2ba617bc3db67adee2b8ba00c71e9fa1c2d2bb8c", - "source_hashes": { - "deepmd/pt_expt/kernels/triton/sezm/so2_value_path.py": "9c8b01082cf01fdcd41fe58e0884364e2166d7acbd4c41f75dda625829a64dbf" - }, - "flags": { - "PYTHONHASHSEED": "0", - "DP_COMPILE_INFER": "1", - "DP_COMPILE": "0", - "DP_TRITON_INFER": "0", - "DP_CUDA_INFER": "0", - "DP_TF32_INFER": "0", - "DP_AMP_INFER": "0", - "DP_SO2_EDGE_COHORT_INFER": "0", - "DP_INTRA_OP_PARALLELISM_THREADS": "1", - "DP_INTER_OP_PARALLELISM_THREADS": "1", - "PYTORCH_ALLOC_CONF": "expandable_segments:True", - "TORCHINDUCTOR_COMPILE_THREADS": "1", - "TORCHINDUCTOR_CACHE_DIR": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_TORCH/inductor", - "TRITON_CACHE_DIR": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_TORCH/triton" - } - } - }, - { - "run": "Pro_n512_TORCH_p1", - "result": { - "status": "oom", - "mode": "TORCH", - "n_atoms": 512, - "started": 1792871760.549256, - "threshold": { - "atol": 1e-05, - "rtol": 0.0001, - "relative_denominator_floor": 1e-08 - }, - "system": "diamond", - "input_sha256": "bf70d484ff4dc5117009b05a348a5f4a7a201d16571d4aa92768d0110eb584e1", - "legacy_compatibility": "Pro-only in-memory fitting alias ener -> dpa4_ener; strict CPU state check passed; original checkpoint unchanged", - "parameter_count": 22678990, - "exception": "Traceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 156, in execute\n n_batch, result = callable(batch_nframes, start_index)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 243, in execute_with_batch_size\n return (end_index - start_index), callable(\n ^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 730, in _eval_model\n batch_output = model(\n ^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/train/wrapper.py\", line 201, in forward\n model_pred = self._forward_without_loss(task_key, input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/train/wrapper.py\", line 249, in _forward_without_loss\n model_pred = self.model[task_key](**input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 839, in forward\n model_ret = self.forward_common(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 1053, in forward_common\n return self.forward_common_lower(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 1217, in forward_common_lower\n model_predict = compiled_core_compute(\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 2399, in compiled\n return dict(zip(_keys, _fn(*args)))\n ^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/aot_autograd.py\", line 1200, in forward\n return compiled_fn(full_args)\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 580, in runtime_wrapper\n all_outs = call_func_at_runtime_with_args(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/utils.py\", line 138, in call_func_at_runtime_with_args\n out = normalize_as_list(f(args))\n ^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 783, in wrapper\n return compiled_fn(runtime_args)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 1011, in inner_fn\n outs = compiled_fn(args)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/output_code.py\", line 656, in __call__\n return self.current_callable(inputs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/utils.py\", line 3401, in run\n out = model(new_inputs)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_TORCH/inductor/b6/cb6y74z5wjdb23bsj7sgwz7n4l6tpvrsmehqafsc5rp6ytf6afxd.py\", line 23148, in call\n buf476 = empty_strided_cuda((1, 16*s29, 128), (2048*s29, 128, 1), torch.float32)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\ntorch.OutOfMemoryError: CUDA out of memory. Tried to allocate 634.00 MiB. GPU 0 has a total capacity of 31.36 GiB of which 153.06 MiB is free. Including non-PyTorch memory, this process has 31.17 GiB memory in use. Of the allocated memory 30.43 GiB is allocated by PyTorch, and 62.92 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://docs.pytorch.org/docs/stable/notes/cuda.html#optimizing-memory-usage-with-pytorch-cuda-alloc-conf)\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 243, in \n out = evaluate(pos)\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 224, in evaluate\n forces = sample.get_forces().copy()\n ^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/atoms.py\", line 2015, in get_forces\n forces = self._calc.get_forces(self)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/abc.py\", line 31, in get_forces\n return self.get_property('forces', atoms)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/calculator.py\", line 517, in get_property\n self.calculate(atoms, [name], system_changes)\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/calculator.py\", line 143, in calculate\n e, f, v = self.dp.eval(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/infer/deep_pot.py\", line 206, in eval\n results = self.deep_eval.eval(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 556, in eval\n out = self._eval_func(self._eval_model, numb_test, natoms)(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 637, in eval_func\n return self.auto_batch_size.execute_all(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 272, in execute_all\n n_batch, result = self.execute(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 169, in execute\n raise OutOfMemoryError(\ndeepmd.utils.errors.OutOfMemoryError: The callable still throws an out-of-memory (OOM) error even when batch size is 1!\n", - "partial_arrays": 0, - "complete_peak": { - "allocated": 32676858368, - "reserved": 32847691776 - }, - "elapsed_s": 136.69677186012268 - }, - "manifest": { - "args": { - "root": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base", - "model": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_accuracy_20261019/models/pro_mptrj/pro_download.pt", - "out": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/results/Pro_n512_TORCH_p1", - "cache": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_TORCH", - "gpu": "0", - "mode": "TORCH", - "nrep": "2", - "seed": "0", - "repeat_cell": "[4, 4, 4]", - "system": "diamond", - "samples": "30", - "warmup": "8" - }, - "model_sha256": "446a6e893101fc52190cfccabb9d2328863d651aca36981fd8d3401c71a61221", - "torch": "2.11.0+cu128", - "triton": "3.6.0", - "cuda": "12.8", - "baseline_revision": "2ba617bc3db67adee2b8ba00c71e9fa1c2d2bb8c", - "source_hashes": { - "deepmd/pt_expt/kernels/triton/sezm/so2_value_path.py": "9c8b01082cf01fdcd41fe58e0884364e2166d7acbd4c41f75dda625829a64dbf" - }, - "flags": { - "PYTHONHASHSEED": "1", - "DP_COMPILE_INFER": "1", - "DP_COMPILE": "0", - "DP_TRITON_INFER": "0", - "DP_CUDA_INFER": "0", - "DP_TF32_INFER": "0", - "DP_AMP_INFER": "0", - "DP_SO2_EDGE_COHORT_INFER": "0", - "DP_INTRA_OP_PARALLELISM_THREADS": "1", - "DP_INTER_OP_PARALLELISM_THREADS": "1", - "PYTORCH_ALLOC_CONF": "expandable_segments:True", - "TORCHINDUCTOR_COMPILE_THREADS": "1", - "TORCHINDUCTOR_CACHE_DIR": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_TORCH/inductor", - "TRITON_CACHE_DIR": "/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_TORCH/triton" - } - } - }, - { - "run": "Pro_n512_TORCH_p2", - "result": { - "status": "oom", - "mode": "TORCH", - "n_atoms": 512, - "started": 1792871905.773682, - "threshold": { - "atol": 1e-05, - "rtol": 0.0001, - "relative_denominator_floor": 1e-08 - }, - "system": "diamond", - "input_sha256": "bf70d484ff4dc5117009b05a348a5f4a7a201d16571d4aa92768d0110eb584e1", - "legacy_compatibility": "Pro-only in-memory fitting alias ener -> dpa4_ener; strict CPU state check passed; original checkpoint unchanged", - "parameter_count": 22678990, - "exception": "Traceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 156, in execute\n n_batch, result = callable(batch_nframes, start_index)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 243, in execute_with_batch_size\n return (end_index - start_index), callable(\n ^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 730, in _eval_model\n batch_output = model(\n ^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/train/wrapper.py\", line 201, in forward\n model_pred = self._forward_without_loss(task_key, input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/train/wrapper.py\", line 249, in _forward_without_loss\n model_pred = self.model[task_key](**input_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1779, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1790, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 839, in forward\n model_ret = self.forward_common(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 1053, in forward_common\n return self.forward_common_lower(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 1217, in forward_common_lower\n model_predict = compiled_core_compute(\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/model/model/sezm_model.py\", line 2399, in compiled\n return dict(zip(_keys, _fn(*args)))\n ^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/aot_autograd.py\", line 1200, in forward\n return compiled_fn(full_args)\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 580, in runtime_wrapper\n all_outs = call_func_at_runtime_with_args(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/utils.py\", line 138, in call_func_at_runtime_with_args\n out = normalize_as_list(f(args))\n ^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 783, in wrapper\n return compiled_fn(runtime_args)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py\", line 1011, in inner_fn\n outs = compiled_fn(args)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/output_code.py\", line 656, in __call__\n return self.current_callable(inputs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/torch/_inductor/utils.py\", line 3401, in run\n out = model(new_inputs)\n ^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/cache/Pro_TORCH/inductor/b6/cb6y74z5wjdb23bsj7sgwz7n4l6tpvrsmehqafsc5rp6ytf6afxd.py\", line 23148, in call\n buf476 = empty_strided_cuda((1, 16*s29, 128), (2048*s29, 128, 1), torch.float32)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\ntorch.OutOfMemoryError: CUDA out of memory. Tried to allocate 634.00 MiB. GPU 0 has a total capacity of 31.36 GiB of which 153.06 MiB is free. Including non-PyTorch memory, this process has 31.17 GiB memory in use. Of the allocated memory 30.43 GiB is allocated by PyTorch, and 62.92 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://docs.pytorch.org/docs/stable/notes/cuda.html#optimizing-memory-usage-with-pytorch-cuda-alloc-conf)\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 243, in \n out = evaluate(pos)\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/worker.py\", line 224, in evaluate\n forces = sample.get_forces().copy()\n ^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/atoms.py\", line 2015, in get_forces\n forces = self._calc.get_forces(self)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/abc.py\", line 31, in get_forces\n return self.get_property('forces', atoms)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/anaconda3/envs/p11/lib/python3.11/site-packages/ase/calculators/calculator.py\", line 517, in get_property\n self.calculate(atoms, [name], system_changes)\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/calculator.py\", line 143, in calculate\n e, f, v = self.dp.eval(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/infer/deep_pot.py\", line 206, in eval\n results = self.deep_eval.eval(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 556, in eval\n out = self._eval_func(self._eval_model, numb_test, natoms)(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/pt/infer/deep_eval.py\", line 637, in eval_func\n return self.auto_batch_size.execute_all(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 272, in execute_all\n n_batch, result = self.execute(\n ^^^^^^^^^^^^^\n File \"/home/kmpl/mkl/project/deepmd-kit/labs/dpa4_analysis/ecs_submission_20261025/base/deepmd/utils/batch_size.py\", line 169, in execute\n raise OutOfMemoryError(\ndeepmd.utils.errors.OutOfMemoryError: The callable still throws an out-of-memory (OOM) error even when batch size is 1!\n", - 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}, - "gpu0_probe": { - "status": "blocked_busy", - "pids": "2030231" - }, - "lint": "candidate full-repository Ruff check/format/diff-check passed; published Python whitelist Ruff/format passed. Generated compiler-cache files are not source lint targets.", - "artifact_patch": "git apply --check passed against clean2ba617bc" -} diff --git a/doc/development/ecs-validation-20261025/worker.py b/doc/development/ecs-validation-20261025/worker.py deleted file mode 100644 index bf32b9fddf..0000000000 --- a/doc/development/ecs-validation-20261025/worker.py +++ /dev/null @@ -1,370 +0,0 @@ -"""Accuracy-first full API matrix. No production source mutation.""" -# ruff: noqa: T201, TID253, ANN001, ANN002, ANN003, ANN201 -- standalone manual experiment, not imported package code - -import argparse -import copy -import hashlib -import json -import os -from pathlib import Path -import subprocess -import sys -import time -import traceback - -P = argparse.ArgumentParser() -P.add_argument("--root", type=Path, required=True) -P.add_argument("--model", type=Path, required=True) -P.add_argument("--out", type=Path, required=True) -P.add_argument("--cache", type=Path, required=True) -P.add_argument("--gpu", type=int, required=True) -P.add_argument( - "--mode", - choices=["TORCH", "BASE", "ECS", "ECS_RUNTIME", "TORCH_EAGER"], - required=True, -) -P.add_argument("--nrep", type=int, required=True) -P.add_argument("--seed", type=int, default=0) -P.add_argument("--repeat-cell", type=int, nargs=3) -P.add_argument("--system", choices=["diamond", "bcccu"], default="diamond") -P.add_argument("--samples", type=int, default=30) -P.add_argument("--warmup", type=int, default=8) -a = P.parse_args() -a.out.mkdir(parents=True, exist_ok=False) - - -def write(name, obj): - (a.out / name).write_text( - json.dumps(obj, indent=2, ensure_ascii=False, allow_nan=False) - ) - - -def sha(p): - return hashlib.sha256(p.read_bytes()).hexdigest() - - -busy = subprocess.check_output( - [ - "nvidia-smi", - "-i", - str(a.gpu), - "--query-compute-apps=pid", - "--format=csv,noheader", - ], - text=True, -).strip() -if busy: - write("result.json", {"status": "blocked_busy", "pids": busy}) - raise SystemExit(3) -for k in list(os.environ): - if k.startswith("DP_"): - del os.environ[k] -os.environ.update( - CUDA_VISIBLE_DEVICES=str(a.gpu), - DP_COMPILE_INFER="0" if a.mode == "TORCH_EAGER" else "1", - DP_COMPILE="0", - DP_TRITON_INFER="0" if a.mode.startswith("TORCH") else "2", - DP_CUDA_INFER="0", - DP_TF32_INFER="0", - DP_AMP_INFER="0", - DP_SO2_EDGE_COHORT_INFER="1" if a.mode.startswith("ECS") else "0", - OMP_NUM_THREADS="1", - DP_INTRA_OP_PARALLELISM_THREADS="1", - DP_INTER_OP_PARALLELISM_THREADS="1", - PYTORCH_ALLOC_CONF="expandable_segments:True", - TORCH_FORCE_NO_WEIGHTS_ONLY_LOAD="1", - TORCHINDUCTOR_COMPILE_THREADS="1", - TORCHINDUCTOR_CACHE_DIR=str(a.cache / "inductor"), - TRITON_CACHE_DIR=str(a.cache / "triton"), -) -sys.path.insert(0, str(a.root)) -sys.meta_path[:] = [ - f - for f in sys.meta_path - if not type(f).__module__.startswith("_editable_skbc_deepmd_kit") -] -import site -import deepmd - -for installed in site.getsitepackages(): - p = Path(installed) / "deepmd" - if (p / "lib").is_dir(): - deepmd.__path__.append(str(p)) -assert Path(deepmd.__file__).resolve().parent.parent == a.root.resolve() -import numpy as np -import torch -import triton - -torch.set_num_threads(1) -torch.set_float32_matmul_precision("highest") -torch.backends.cuda.matmul.allow_tf32 = False -torch.backends.cudnn.allow_tf32 = False -torch.cuda.reset_peak_memory_stats() -manifest = { - "args": {k: str(v) for k, v in vars(a).items()}, - "model_sha256": sha(a.model), - "torch": torch.__version__, - "triton": triton.__version__, - "cuda": torch.version.cuda, - "baseline_revision": subprocess.check_output( - ["git", "rev-parse", "HEAD"], cwd=a.root, text=True - ).strip(), - "source_hashes": { - str(p.relative_to(a.root)): sha(p) - for p in [ - a.root / "deepmd/pt_expt/kernels/triton/sezm/so2_value_path.py", - a.root / "deepmd/pt_expt/kernels/triton/sezm/so2_edge_cohort.py", - ] - if p.exists() - }, - "flags": { - k: v - for k, v in os.environ.items() - if k.startswith( - ("DP_", "TORCHINDUCTOR", "TRITON_CACHE", "PYTORCH_ALLOC", "PYTHONHASH") - ) - }, -} -write("manifest.json", manifest) -result = { - "status": "running", - "mode": a.mode, - "n_atoms": (8 if a.system == "diamond" else 2) - * int(np.prod(a.repeat_cell if a.repeat_cell else [a.nrep] * 3)), - "started": time.time(), - "threshold": {"atol": 1e-5, "rtol": 1e-4, "relative_denominator_floor": 1e-8}, -} -arrays = {} -runtime_calls = [] -try: - from ase.build import bulk - from deepmd.calculator import DP - - repeat_cell = tuple(a.repeat_cell) if a.repeat_cell else (a.nrep,) * 3 - atoms = ( - bulk("C", "diamond", a=3.567, cubic=True) - if a.system == "diamond" - else bulk("Cu", "bcc", a=2.87, cubic=True) - ).repeat(repeat_cell) - result["n_atoms"] = len(atoms) - result["system"] = a.system - rng = np.random.default_rng(a.seed) - frames = np.stack( - [ - atoms.positions + 0.02 * rng.standard_normal(atoms.positions.shape) - for _ in range(3) - ] - ) - np.savez_compressed( - a.out / "inputs.npz", - positions=frames, - cell=np.asarray(atoms.cell), - numbers=atoms.numbers, - ) - result["input_sha256"] = sha(a.out / "inputs.npz") - if a.mode == "ECS_RUNTIME": - from deepmd.pt_expt.kernels.triton.sezm import so2_edge_cohort as ec - - ec.SINGLE_COHORT_MAX_EDGES = 0 - original_runtime = ec.cohort_input_backward_impl - - def traced_runtime(*args, **kwargs): - edges = args[0].shape[0] - cap = args[7] if len(args) > 7 else kwargs["cohort_edges"] - runtime_calls.append( - {"edges": edges, "cohort": cap, "chunks": (edges + cap - 1) // cap} - ) - return original_runtime(*args, **kwargs) - - ec.cohort_input_backward_impl = traced_runtime - original_load = torch.load - if ( - manifest["model_sha256"] - == "446a6e893101fc52190cfccabb9d2328863d651aca36981fd8d3401c71a61221" - ): - assert ( - manifest["model_sha256"] - == "446a6e893101fc52190cfccabb9d2328863d651aca36981fd8d3401c71a61221" - ) - proof = json.loads((a.model.parent / "cpu_load_check.json").read_text()) - assert proof["strict_missing"] == proof["strict_unexpected"] == [] - - def legacy_load(file, *args, **kwargs): - obj = original_load(file, *args, **kwargs) - if ( - isinstance(file, (str, Path)) - and Path(file).resolve() == a.model.resolve() - ): - state = obj["model"] if "model" in obj else obj - state["_extra_state"] = copy.deepcopy(state["_extra_state"]) - fit = state["_extra_state"]["model_params"]["fitting_net"] - assert fit["type"] == "ener" - fit["type"] = "dpa4_ener" - return obj - - torch.load = legacy_load - result["legacy_compatibility"] = ( - "Pro-only in-memory fitting alias ener -> dpa4_ener; strict CPU state check passed; original checkpoint unchanged" - ) - try: - calc = DP(model=str(a.model)) - finally: - torch.load = original_load - inner = calc.dp.get_model() - assert not inner.training - definition = calc.dp.get_model_def_script() - write("model_definition.json", definition) - result["parameter_count"] = sum(p.numel() for p in inner.parameters()) - - def evaluate(pos): - calc.reset() - sample = atoms.copy() - sample.positions = pos - sample.calc = calc - forces = sample.get_forces().copy() - return { - "energy": np.asarray(sample.get_potential_energy()), - "forces": forces, - "virial": np.asarray(calc.results["virial"]).copy(), - "stress": sample.get_stress().copy(), - } - - wall = [] - events = [] - for repeat in range(3): - for frame, pos in enumerate(frames): - torch.cuda.synchronize() - s, e = ( - torch.cuda.Event(enable_timing=True), - torch.cuda.Event(enable_timing=True), - ) - start = time.perf_counter() - s.record() - out = evaluate(pos) - e.record() - torch.cuda.synchronize() - elapsed = 1000 * (time.perf_counter() - start) - wall.append(elapsed) - events.append(s.elapsed_time(e)) - for k, v in out.items(): - arrays[f"{repeat}_{frame}_{k}"] = v - np.savez_compressed(a.out / "outputs.npz", **arrays) - if repeat == 0 and frame == 0: - result["first_peak"] = { - "allocated": torch.cuda.max_memory_allocated(), - "reserved": torch.cuda.max_memory_reserved(), - } - write( - "progress.json", - { - "repeat": repeat, - "frame": frame, - "elapsed_ms": elapsed, - "finite": all(bool(np.isfinite(v).all()) for v in out.values()), - }, - ) - print(a.mode, len(atoms), repeat, frame, elapsed, flush=True) - # Accuracy collection is separate from warmed timing and steady memory. - assert a.warmup >= 3 and a.samples >= 10 - for i in range(a.warmup): - evaluate(frames[i % len(frames)]) - timed_wall, timed_event = [], [] - for i in range(a.samples): - torch.cuda.synchronize() - begin = torch.cuda.Event(enable_timing=True) - end = torch.cuda.Event(enable_timing=True) - tick = time.perf_counter() - begin.record() - measured = evaluate(frames[i % len(frames)]) - end.record() - torch.cuda.synchronize() - assert all(bool(np.isfinite(v).all()) for v in measured.values()) - timed_wall.append(1000 * (time.perf_counter() - tick)) - timed_event.append(begin.elapsed_time(end)) - full_peak = { - "allocated": torch.cuda.max_memory_allocated(), - "reserved": torch.cuda.max_memory_reserved(), - } - torch.cuda.reset_peak_memory_stats() - for i in range(a.samples): - evaluate(frames[i % len(frames)]) - torch.cuda.synchronize() - result["steady_peak"] = { - "allocated": torch.cuda.max_memory_allocated(), - "reserved": torch.cuda.max_memory_reserved(), - } - result["full_window_peak"] = full_peak - result["steady_wall_ms"] = { - "median": float(np.median(timed_wall)), - "p90": float(np.percentile(timed_wall, 90)), - "min": min(timed_wall), - "max": max(timed_wall), - "samples": timed_wall, - } - result["steady_event_ms"] = { - "median": float(np.median(timed_event)), - "p90": float(np.percentile(timed_event, 90)), - "samples": timed_event, - } - from torch._inductor.codecache import PyCodeCache - - wrappers = [] - route = [] - for m in PyCodeCache.modules: - path = Path(m.__file__) - text = path.read_text() - if "def call(" not in text: - continue - wrappers.append(str(path)) - route.append( - { - "sha256": sha(path), - "opaque_cohort_calls": text.count("so2_cohort_input_bwd.default("), - "single_cohort_named_calls": text.count("so2_single_cohort_input_bwd"), - "stack_kernel_definitions": text.count("def _stack_"), - "single_cohort_marker": "so2_single_cohort_input_bwd" in text, - } - ) - if a.mode != "TORCH_EAGER": - assert wrappers, "compiled path missing" - assert torch.get_float32_matmul_precision() == "highest" - assert not torch.backends.cuda.matmul.allow_tf32 - assert not torch.backends.cudnn.allow_tf32 - assert not torch.is_autocast_enabled("cuda") - result.update( - status="completed", - finite=all(bool(np.isfinite(v).all()) for v in arrays.values()), - evaluations=9, - runtime_cohort_calls=runtime_calls, - precision_runtime={ - "matmul_precision": torch.get_float32_matmul_precision(), - "matmul_tf32": torch.backends.cuda.matmul.allow_tf32, - "cudnn_tf32": torch.backends.cudnn.allow_tf32, - "outer_autocast": torch.is_autocast_enabled("cuda"), - }, - wall_ms_including_first=wall, - event_ms_including_first=events, - wrappers=wrappers, - routes=route, - complete_peak={ - k: max(full_peak[k], result["steady_peak"][k]) for k in full_peak - }, - ) -except Exception as exc: - result.update( - status="oom" - if isinstance(exc, torch.OutOfMemoryError) - or type(exc).__name__ == "OutOfMemoryError" - else "failed", - exception=traceback.format_exc(), - partial_arrays=len(arrays), - complete_peak={ - "allocated": torch.cuda.max_memory_allocated(), - "reserved": torch.cuda.max_memory_reserved(), - }, - ) - print(result["exception"], flush=True) -finally: - result["elapsed_s"] = time.time() - result["started"] - write("result.json", result) -raise SystemExit(0 if result["status"] == "completed" else 1) diff --git a/source/tests/pt_expt/kernels/_so2_edge_cohort_benchmark_common.py b/source/tests/pt_expt/kernels/_so2_edge_cohort_benchmark_common.py deleted file mode 100644 index d0303669b4..0000000000 --- a/source/tests/pt_expt/kernels/_so2_edge_cohort_benchmark_common.py +++ /dev/null @@ -1,131 +0,0 @@ -# SPDX-License-Identifier: LGPL-3.0-or-later -"""Isolated-process setup and serialization for the SO(2) benchmark.""" - -import hashlib -import json -import os -from pathlib import Path -import site -import subprocess -import sys - -LAB = Path(__file__).resolve().parent -ROOT = Path(__file__).resolve().parents[4] - - -def setup(gpu, out, level="2"): - out.mkdir(parents=True, exist_ok=False) - busy = subprocess.check_output( - [ - "nvidia-smi", - "-i", - str(gpu), - "--query-compute-apps=pid", - "--format=csv,noheader", - ], - text=True, - ).strip() - if busy: - raise RuntimeError(f"GPU {gpu} has compute processes: {busy}") - for key in list(os.environ): - if key.startswith("DP_"): - del os.environ[key] - os.environ.update( - CUDA_VISIBLE_DEVICES=str(gpu), - DP_COMPILE_INFER="1", - DP_COMPILE="0", - DP_TRITON_INFER=level, - DP_CUDA_INFER="0", - DP_TF32_INFER="0", - DP_AMP_INFER="0", - OMP_NUM_THREADS="1", - DP_INTRA_OP_PARALLELISM_THREADS="1", - DP_INTER_OP_PARALLELISM_THREADS="1", - PYTORCH_ALLOC_CONF=os.getenv("LAB_ALLOC_CONF", "expandable_segments:True"), - TORCH_FORCE_NO_WEIGHTS_ONLY_LOAD="1", - TORCHINDUCTOR_COMPILE_THREADS="1", - TORCHINDUCTOR_CACHE_DIR=str(out.resolve() / "inductor"), - TRITON_CACHE_DIR=str(out.resolve() / "triton"), - ) - sys.path.insert(0, str(ROOT)) - sys.meta_path[:] = [ - f - for f in sys.meta_path - if not type(f).__module__.startswith("_editable_skbc_deepmd_kit") - ] - import deepmd - - for installed in site.getsitepackages(): - p = Path(installed) / "deepmd" - if (p / "lib").is_dir(): - deepmd.__path__.append(str(p)) - assert Path(deepmd.__file__).resolve().parent.parent == ROOT - import torch - - torch.set_num_threads(1) - torch.set_float32_matmul_precision("highest") - torch.backends.cuda.matmul.allow_tf32 = False - torch.backends.cudnn.allow_tf32 = False - - -def write(path, obj): - Path(path).write_text( - json.dumps(obj, indent=2, ensure_ascii=False, allow_nan=False) - ) - - -def sha(path): - return hashlib.sha256(Path(path).read_bytes()).hexdigest() - - -def environment(): - import torch - import triton - - return { - "python": sys.version, - "torch": torch.__version__, - "triton": triton.__version__, - "cuda": torch.version.cuda, - "gpu": subprocess.check_output( - [ - "nvidia-smi", - "--query-gpu=index,name,uuid,driver_version,clocks.sm,clocks.mem,temperature.gpu,power.draw", - "--format=csv", - ], - text=True, - ), - "revision": subprocess.check_output( - ["git", "rev-parse", "HEAD"], cwd=ROOT, text=True - ).strip(), - "flags": { - k: v - for k, v in os.environ.items() - if k.startswith( - ( - "DP_", - "PBS_", - "COHORT_", - "TORCHINDUCTOR", - "TRITON_CACHE", - "CUDA_VISIBLE", - "PYTORCH_ALLOC", - ) - ) - }, - "harness_hashes": { - p.name: sha(p) for p in LAB.glob("*so2_edge_cohort*benchmark*.py") - }, - } - - -def stats(values): - import numpy as np - - return { - "median": float(np.median(values)), - "p90": float(np.percentile(values, 90)), - "min": float(min(values)), - "max": float(max(values)), - "samples": values, - } diff --git a/source/tests/pt_expt/kernels/_so2_edge_cohort_benchmark_impl.py b/source/tests/pt_expt/kernels/_so2_edge_cohort_benchmark_impl.py deleted file mode 100644 index 2728d023b4..0000000000 --- a/source/tests/pt_expt/kernels/_so2_edge_cohort_benchmark_impl.py +++ /dev/null @@ -1,209 +0,0 @@ -# SPDX-License-Identifier: LGPL-3.0-or-later -# ruff: noqa: T201 -- manual benchmark CLI output -"""Measurement body executed in an isolated module namespace by the CLI.""" - -import argparse -from pathlib import Path -import time -import traceback - -from _so2_edge_cohort_benchmark_common import environment, setup, sha, stats, write - -p = argparse.ArgumentParser() -p.add_argument("--out", type=Path, required=True) -p.add_argument("--gpu", default="0") -p.add_argument("--variant", choices=("BASE", "TORCH"), default="BASE") -p.add_argument("--nrep", type=int, default=5) -p.add_argument( - "--model", - type=Path, - required=True, -) -p.add_argument("--reference", type=Path) -p.add_argument("--warmup", type=int, default=8) -p.add_argument("--samples", type=int, default=30) -p.add_argument("--system", choices=("diamond", "water"), default="diamond") -a = p.parse_args() -setup(a.gpu, a.out, "0" if a.variant == "TORCH" else "2") - -import numpy as np -import torch -from ase.build import bulk -from deepmd.calculator import DP -from deepmd.pt_expt.kernels.triton.sezm import so2_value_path as sv - -assert a.warmup >= 3 and a.samples >= 10 -manifest = environment() -manifest.update(model_sha256=sha(a.model), args={k: str(v) for k, v in vars(a).items()}) -manifest["neo_stack_fp32_configs"] = sv.stack_fp32_configs(32, 3) -write(a.out / "manifest.json", manifest) - -if a.system == "diamond": - base = bulk("C", "diamond", a=3.567, cubic=True).repeat((a.nrep,) * 3) -else: - from ase.build import molecule - - base = molecule("H2O") - base.set_cell([4.0, 4.0, 4.0]) - base.center() - base.pbc = True - base = base.repeat((a.nrep,) * 3) -rng = np.random.default_rng(0) -frames = [ - base.positions + 0.02 * rng.standard_normal(base.positions.shape) - for _ in range(max(3, a.warmup + a.samples)) -] -calc = DP(model=str(a.model)) -inner = calc.dp.get_model() -assert not inner.training, "Inference must not enable model AMP through training mode" -write(a.out / "model_definition.json", calc.dp.get_model_def_script()) - - -def evaluate(pos): - calc.reset() - atoms = base.copy() - atoms.positions = pos - atoms.calc = calc - forces = atoms.get_forces().copy() - return { - "energy": np.asarray(atoms.get_potential_energy()), - "forces": forces, - "virial": np.asarray(calc.results["virial"]).copy(), - "stress": atoms.get_stress().copy(), - } - - -def compare(got, ref): - result = {} - for key, value in got.items(): - expected = ref[key] - assert value.shape == expected.shape - finite = bool(np.isfinite(value).all() and np.isfinite(expected).all()) - if finite: - d = np.abs(value - expected) - ratio = d / (1e-5 + 1e-4 * np.abs(expected)) - result[key] = { - "passed": bool((ratio <= 1).all()), - "finite": True, - "max_abs": float(d.max()), - "mean_abs": float(d.mean()), - "max_relative": float((d / np.maximum(np.abs(expected), 1e-8)).max()), - "max_ratio": float(ratio.max()), - "bitwise_equal": bool(np.array_equal(value, expected)), - } - else: - result[key] = {"passed": False, "finite": False} - return result - - -result = {"variant": a.variant, "n_atoms": len(base), "status": "running"} -try: - torch.cuda.synchronize() - before = { - "allocated": torch.cuda.memory_allocated(), - "reserved": torch.cuda.memory_reserved(), - } - start = time.perf_counter() - evaluate(frames[0]) - torch.cuda.synchronize() - first_ms = 1000 * (time.perf_counter() - start) - first = { - "allocated": torch.cuda.max_memory_allocated(), - "reserved": torch.cuda.max_memory_reserved(), - } - print("first", first_ms, first, flush=True) - write(a.out / "first.json", {"first_ms": first_ms, "memory": first}) - for pos in frames[1 : a.warmup]: - evaluate(pos) - wall, gpu = [], [] - for pos in frames[a.warmup : a.warmup + a.samples]: - torch.cuda.synchronize() - s, e = ( - torch.cuda.Event(enable_timing=True), - torch.cuda.Event(enable_timing=True), - ) - start = time.perf_counter() - s.record() - evaluate(pos) - e.record() - torch.cuda.synchronize() - wall.append(1000 * (time.perf_counter() - start)) - gpu.append(s.elapsed_time(e)) - if globals().get("_controlled_rounds") is not None: - globals()["_controlled_rounds"](globals()) - complete = { - "allocated": torch.cuda.max_memory_allocated(), - "reserved": torch.cuda.max_memory_reserved(), - } - torch.cuda.reset_peak_memory_stats() - for pos in frames[a.warmup : a.warmup + a.samples]: - evaluate(pos) - torch.cuda.synchronize() - steady = { - "allocated": torch.cuda.max_memory_allocated(), - "reserved": torch.cuda.max_memory_reserved(), - } - arrays = { - f"{i}_{k}": v - for i, pos in enumerate(frames[:3]) - for k, v in evaluate(pos).items() - } - np.savez_compressed(a.out / "outputs.npz", **arrays) - accuracy = {} - if a.reference: - with np.load(a.reference) as ref: - accuracy = compare(arrays, ref) - repeats = [] - for _ in range(3): - repeated = { - f"{i}_{k}": v - for i, pos in enumerate(frames[:3]) - for k, v in evaluate(pos).items() - } - repeats.append(compare(repeated, arrays)) - from torch._inductor.codecache import PyCodeCache - - wrappers = [ - str(m.__file__) - for m in PyCodeCache.modules - if "def call(" in Path(m.__file__).read_text() - ] - assert wrappers, "No compiled wrapper; eager must not be reported as compiled" - result.update( - first_ms=first_ms, - wall_ms=stats(wall), - event_elapsed_ms=stats(gpu), - event_scope="entire API call on default stream; includes host gaps, not kernel-sum", - throughput_atoms_per_s=len(base) / (np.median(wall) / 1000), - memory={ - "before": before, - "first": first, - "complete": complete, - "steady": steady, - }, - accuracy=accuracy, - precision_pass=all(v["passed"] for v in accuracy.values()) - if accuracy - else None, - finite=all(bool(np.isfinite(v).all()) for v in arrays.values()), - repeat_noise=repeats, - wrappers=wrappers, - gpu_end=environment()["gpu"], - status="completed", - ) -except Exception: - result["status"] = "failed" - result["exception"] = traceback.format_exc() - print(result["exception"], flush=True) -finally: - write(a.out / "result.json", result) - print( - { - k: v - for k, v in result.items() - if k not in ("repeat_noise", "accuracy", "wrappers") - }, - flush=True, - ) -if result["status"] != "completed": - raise SystemExit(1) diff --git a/source/tests/pt_expt/kernels/_so2_edge_cohort_controlled_worker.py b/source/tests/pt_expt/kernels/_so2_edge_cohort_controlled_worker.py deleted file mode 100644 index 3b21737f42..0000000000 --- a/source/tests/pt_expt/kernels/_so2_edge_cohort_controlled_worker.py +++ /dev/null @@ -1,245 +0,0 @@ -# SPDX-License-Identifier: LGPL-3.0-or-later -# ruff: noqa: T201 -- manual benchmark CLI output -"""Resident worker preserving the public benchmark's pre-validation entry.""" - -import argparse -import ast -import hashlib -import json -import os -from pathlib import Path -import runpy -import subprocess -import sys -import time - -HERE = Path(__file__).resolve().parent -import _so2_edge_cohort_benchmark_common as common - -p = argparse.ArgumentParser() -p.add_argument("--baseline-root", type=Path, required=True) -p.add_argument("--candidate-root", type=Path, required=True) -p.add_argument("--model", type=Path, required=True) -p.add_argument("--reference", type=Path, required=True) -p.add_argument( - "--variant", choices=("BASE", "BASE2", "NATIVE", "ECS", "ECS2"), required=True -) -p.add_argument("--out", type=Path, required=True) -p.add_argument("--cache", type=Path, required=True) -p.add_argument("--gpu", default="0") -p.add_argument("--cpu", type=int, default=8) -a = p.parse_args() -os.sched_setaffinity(0, {a.cpu}) -init = json.loads(sys.stdin.readline()) -allowed = set(init["pids"]) -original_check = subprocess.check_output - - -def checked(cmd, *args, **kwargs): - value = original_check(cmd, *args, **kwargs) - if isinstance(cmd, list) and "--query-compute-apps=pid" in cmd: - foreign = {int(x) for x in value.split()} - allowed - if foreign: - raise RuntimeError(f"foreign compute processes: {foreign}") - return "" - return value - - -common.ROOT = ( - a.baseline_root.resolve() - if a.variant in ("BASE", "BASE2") - else a.candidate_root.resolve() -) -original_setup = common.setup - - -def setup(*args, **kwargs): - subprocess.check_output = checked - try: - original_setup(*args, **kwargs) - finally: - subprocess.check_output = original_check - os.environ.update( - DP_SO2_EDGE_COHORT_INFER="1" if a.variant in ("ECS", "ECS2") else "0", - TORCHINDUCTOR_CACHE_DIR=str(a.cache / "inductor"), - TRITON_CACHE_DIR=str(a.cache / "triton"), - ) - - -common.setup = setup - - -def emit(value): - print("@CONTROL@" + json.dumps(value), flush=True) - - -def callback(ns): - import torch - from torch._dynamo.utils import counters - from torch._inductor.codecache import PyCodeCache - - wrappers, kernels = [], [] - for module in PyCodeCache.modules: - path = Path(module.__file__) - if "def call(" in path.read_text(): - wrappers.append({"path": str(path), "sha256": common.sha(path)}) - for name, kernel in vars(module).items(): - if not hasattr(kernel, "launchers") or not hasattr( - kernel, "triton_meta" - ): - continue - source = ast.parse(kernel.fn.src) - for node in ast.walk(source): - if isinstance(node, ast.FunctionDef): - node.name = "kernel" - identity = { - "body": ast.dump(source, include_attributes=False), - "signature": { - k: str(v) - for k, v in kernel.triton_meta.get("signature", {}).items() - }, - "constants": kernel.triton_meta.get("constants", {}), - } - launches = [] - for launch in kernel.launchers: - compiled = next( - ( - r.kernel - for r in kernel.compile_results - if str(r.config) == str(launch.config) - ), - None, - ) - binary = ( - getattr(compiled, "asm", {}).get("cubin") - if compiled is not None - else None - ) - launches.append( - { - "config": str(launch.config), - "cubin_sha256": hashlib.sha256(binary).hexdigest() - if binary is not None - else None, - "n_regs": getattr(launch, "n_regs", None), - "n_spills": getattr(launch, "n_spills", None), - "shared": getattr(launch, "shared", None), - } - ) - kernels.append( - { - "name": name, - "semantic_sha256": hashlib.sha256( - json.dumps(identity, sort_keys=True, default=str).encode() - ).hexdigest(), - "launchers": launches, - } - ) - first = { - "variant": a.variant, - "pid": os.getpid(), - "entry_wall_ms": common.stats(ns["wall"]), - "entry_event_ms": common.stats(ns["gpu"]), - "first_ms": ns["first_ms"], - "first_peak": ns["first"], - "wrappers": wrappers, - "kernels": kernels, - "source_sha256": { - str(path.relative_to(common.ROOT)): common.sha(path) - for path in (common.ROOT / "deepmd/pt_expt/kernels/triton/sezm").glob( - "so2_*cohort.py" - ) - }, - "counters": {k: dict(v) for k, v in counters.items()}, - } - common.write(a.out / "ready.json", first) - emit(dict(status="ready", **first)) - rounds = [] - evaluate, frames = ns["evaluate"], ns["frames"] - for line in sys.stdin: - command = json.loads(line) - if command["action"] == "stop": - break - if command["action"] == "profile": - from torch.profiler import ProfilerActivity, profile, record_function - - for pos in frames[:8]: - evaluate(pos) - torch.cuda.synchronize() - with profile( - activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA] - ) as prof: - for i in range(5): - torch.cuda.synchronize() - with record_function(f"CONTROLLED_FRAME_{i}"): - evaluate(frames[8 + i]) - torch.cuda.synchronize() - trace = a.out / "trace.json" - prof.export_chrome_trace(str(trace)) - emit({"status": "profiled", "variant": a.variant, "trace": str(trace)}) - continue - if command.get("cpu") is not None: - os.sched_setaffinity(0, {command["cpu"]}) - for i in range(command.get("warmup", 8)): - evaluate(frames[i % len(frames)]) - torch.cuda.synchronize() - torch.cuda.reset_peak_memory_stats() - pre_counters = {k: dict(v) for k, v in counters.items()} - start_ns = time.monotonic_ns() - wall, events = [], [] - for i in range(command.get("samples", 50)): - torch.cuda.synchronize() - s, e = ( - torch.cuda.Event(enable_timing=True), - torch.cuda.Event(enable_timing=True), - ) - start = time.perf_counter() - s.record() - evaluate(frames[8 + i % 30]) - e.record() - torch.cuda.synchronize() - wall.append(1000 * (time.perf_counter() - start)) - events.append(s.elapsed_time(e)) - record = { - "variant": a.variant, - "pid": os.getpid(), - "command": command, - "start_monotonic_ns": start_ns, - "end_monotonic_ns": time.monotonic_ns(), - "cpu": sorted(os.sched_getaffinity(0)), - "wall_ms": common.stats(wall), - "event_ms": common.stats(events), - "peak": { - "allocated": torch.cuda.max_memory_allocated(), - "reserved": torch.cuda.max_memory_reserved(), - }, - "counters_before": pre_counters, - "counters_after": {k: dict(v) for k, v in counters.items()}, - } - rounds.append(record) - common.write(a.out / "rounds.json", rounds) - emit(record) - - -sys.argv = [ - str(HERE / "_so2_edge_cohort_benchmark_impl.py"), - "--gpu", - a.gpu, - "--out", - str(a.out), - "--model", - str(a.model), - "--nrep", - "2", - "--reference", - str(a.reference), -] -ns = runpy.run_path( - str(HERE / "_so2_edge_cohort_benchmark_impl.py"), - init_globals={"_controlled_rounds": callback}, - run_name="__main__", -) -result = ns["result"] -if not result["precision_pass"] or not result["finite"]: - raise SystemExit("accuracy failure") -emit({"status": "completed", "variant": a.variant, "precision_pass": True}) diff --git a/source/tests/pt_expt/kernels/analyze_so2_edge_cohort_clocks.py b/source/tests/pt_expt/kernels/analyze_so2_edge_cohort_clocks.py deleted file mode 100644 index 710163c1cc..0000000000 --- a/source/tests/pt_expt/kernels/analyze_so2_edge_cohort_clocks.py +++ /dev/null @@ -1,76 +0,0 @@ -# SPDX-License-Identifier: LGPL-3.0-or-later -# ruff: noqa: T201 -- manual benchmark CLI output -"""Supplement only: match observed clock/temperature bins, never replace raw trials.""" - -import argparse -import json -from pathlib import Path - -import numpy as np -from scipy.stats import t - -p = argparse.ArgumentParser(description=__doc__) -p.add_argument("directories", type=Path, nargs="+") -p.add_argument("--out", type=Path, required=True) -p.add_argument("--memory-clock", type=int, default=13801) -a = p.parse_args() -result = [] -for root in a.directories: - rows = json.loads((root / "same_context_rounds.json").read_text()) - monitor = json.loads((root / "same_context_monitor.json").read_text()) - kept = [] - for block in sorted({r["command"]["block"] for r in rows}): - group = [r for r in rows if r["command"]["block"] == block] - clocks = {} - temperatures = {} - memory = {} - effects = {} - for arm in ("BASE", "ECS"): - rounds = [r for r in group if r["variant"] == arm] - samples = [ - m - for r in rounds - for m in monitor - if r["start_monotonic_ns"] <= m["t_ns"] <= r["end_monotonic_ns"] - ] - clocks[arm] = np.mean([m["sm_mhz"] for m in samples]) - temperatures[arm] = np.mean([m["temperature"] for m in samples]) - memory[arm] = {m["mem_mhz"] for m in samples} - effects[arm] = np.mean(np.log([r["wall_ms"]["median"] for r in rounds])) - if ( - abs(clocks["ECS"] / clocks["BASE"] - 1) <= 0.005 - and memory["BASE"] == memory["ECS"] == {a.memory_clock} - and abs(temperatures["ECS"] - temperatures["BASE"]) <= 2 - ): - kept.append( - { - "block": block, - "logratio": float(effects["ECS"] - effects["BASE"]), - "sm_mhz": {k: float(v) for k, v in clocks.items()}, - } - ) - result.append( - { - "run": root.name, - "retained_blocks": len(kept), - "blocks": kept, - "mean_logratio": float(np.mean([r["logratio"] for r in kept])) - if kept - else None, - } - ) -logs = np.array([r["mean_logratio"] for r in result if r["mean_logratio"] is not None]) -radius = t.ppf(0.975, len(logs) - 1) * logs.std(ddof=1) / np.sqrt(len(logs)) -summary = { - "criteria": f"block mean SM clock difference <=0.5%, both memory clocks {a.memory_clock}MHz, mean temperature difference <=2C", - "primary": False, - "caveat": "post-stratified sensitivity analysis; conditioning can change composition and does not prove constant clocks or replace full results", - "processes": result, - "mean_change_percent": float(100 * np.expm1(logs.mean())), - "process_t95_percent": ( - 100 * np.expm1(logs.mean() + np.array([-1, 1]) * radius) - ).tolist(), -} -a.out.write_text(json.dumps(summary, indent=2)) -print(json.dumps({k: v for k, v in summary.items() if k != "processes"}, indent=2)) -print([(r["run"], r["retained_blocks"]) for r in result]) diff --git a/source/tests/pt_expt/kernels/analyze_so2_edge_cohort_paired.py b/source/tests/pt_expt/kernels/analyze_so2_edge_cohort_paired.py deleted file mode 100644 index bd72c76f2e..0000000000 --- a/source/tests/pt_expt/kernels/analyze_so2_edge_cohort_paired.py +++ /dev/null @@ -1,229 +0,0 @@ -# SPDX-License-Identifier: LGPL-3.0-or-later -# ruff: noqa: T201 -- manual benchmark CLI output -"""Analyze ABBA blocks using process/temporal clusters, not iid frame counts.""" - -import argparse -import ast -from collections import Counter, defaultdict -import json -from pathlib import Path - -import numpy as np -from scipy.stats import t as student_t - -p = argparse.ArgumentParser() -p.add_argument("directory", type=Path) -a = p.parse_args() -root = a.directory -records = json.loads((root / "rounds.json").read_text()) -control = json.loads((root / "controller.json").read_text()) -assert control["status"] == "completed", ( - "incomplete/failed trials cannot be final evidence" -) -arms = ast.literal_eval(control["args"]["variants"]) -blocks = defaultdict(list) -for row in records: - blocks[(row["pair"], row["command"]["block"])].append(row) -effects, absolute, by_pair = [], [], defaultdict(list) -for (pair, block), rows in sorted(blocks.items()): - assert len(rows) == 4 - per_arm = { - arm: [r["wall_ms"]["median"] for r in rows if r["variant"] == arm] - for arm in arms - } - assert all(len(v) == 2 for v in per_arm.values()) - logratio = float( - np.mean(np.log(per_arm[arms[1]])) - np.mean(np.log(per_arm[arms[0]])) - ) - difference = float(np.mean(per_arm[arms[1]]) - np.mean(per_arm[arms[0]])) - effects.append( - { - "pair": pair, - "block": block, - "logratio": logratio, - "percent": 100 * np.expm1(logratio), - "difference_ms": difference, - } - ) - by_pair[pair].append(logratio) - absolute.append(difference) -rng = np.random.default_rng(20261019) -matrix = np.asarray(list(by_pair.values())) -draws = [] -for _ in range(20000): - pair_indices = rng.integers(0, len(matrix), size=len(matrix)) - resampled = [ - rng.choice(matrix[i], size=len(matrix[i]), replace=True).mean() - for i in pair_indices - ] - draws.append(np.mean(resampled)) -ci = 100 * np.expm1(np.percentile(draws, [2.5, 97.5])) -pair_means = matrix.mean(axis=1) -process_t_ci = None -upper95 = None -if len(pair_means) > 1: - se = pair_means.std(ddof=1) / np.sqrt(len(pair_means)) - radius = student_t.ppf(0.975, len(pair_means) - 1) * se - process_t_ci = ( - 100 * np.expm1([pair_means.mean() - radius, pair_means.mean() + radius]) - ).tolist() - upper95 = float( - 100 - * np.expm1(pair_means.mean() + student_t.ppf(0.95, len(pair_means) - 1) * se) - ) -stats = {} -for arm in arms: - rows = [r for r in records if r["variant"] == arm] - samples = [v for r in rows for v in r["wall_ms"]["samples"]] - stats[arm] = { - "rounds": len(rows), - "samples": len(samples), - "median_of_round_medians_ms": float( - np.median([r["wall_ms"]["median"] for r in rows]) - ), - "pooled_p50_ms": float(np.median(samples)), - "pooled_p90_ms": float(np.percentile(samples, 90)), - "min_ms": float(min(samples)), - "max_ms": float(max(samples)), - "event_p50_ms": float( - np.median([v for r in rows for v in r["event_ms"]["samples"]]) - ), - } -monitor = json.loads((root / "monitor.json").read_text()) -telemetry = [] -for row in records: - values = [ - m - for m in monitor - if row["start_monotonic_ns"] <= m["t_ns"] <= row["end_monotonic_ns"] - ] - telemetry.append( - { - "pair": row["pair"], - "block": row["command"]["block"], - "position": row["command"]["position"], - "arm": row["variant"], - "count": len(values), - "sm_mhz": { - str(k): v for k, v in Counter(m["sm_mhz"] for m in values).items() - }, - "mem_mhz": { - str(k): v for k, v in Counter(m["mem_mhz"] for m in values).items() - }, - "temperature_range": [ - min(m["temperature"] for m in values), - max(m["temperature"] for m in values), - ] - if values - else None, - "mean_power_w": float(np.mean([m["power_mw"] / 1000 for m in values])) - if values - else None, - } - ) -ready = json.loads((root / "ready.json").read_text()) -kernel_audits = [] -for pair in by_pair: - arm_kernels = [] - for arm in arms: - entry = next(r for r in ready if r["pair"] == pair and r["variant"] == arm) - grouped = defaultdict(list) - for kernel in entry.get("kernels", []): - grouped[kernel["semantic_sha256"]].extend(kernel["launchers"]) - arm_kernels.append(grouped) - shared = set(arm_kernels[0]) & set(arm_kernels[1]) - different = [] - identical_binaries = 0 - for identity in shared: - configs = [{v["config"] for v in group[identity]} for group in arm_kernels] - if configs[0] != configs[1]: - different.append( - {"semantic_sha256": identity, "configs": [sorted(v) for v in configs]} - ) - bins = [ - {v["cubin_sha256"] for v in group[identity] if v["cubin_sha256"]} - for group in arm_kernels - ] - if bins[0] and bins[0] == bins[1]: - identical_binaries += 1 - before = json.loads((root / f"cache_pair{pair}_before.json").read_text()) - after = json.loads((root / f"cache_pair{pair}_after.json").read_text()) - kernel_audits.append( - { - "pair": pair, - "shared_semantic_programs": len(shared), - "common_programs_identical_cubin": identical_binaries, - "different_configs": different, - "all_compiler_artifact_hashes_unchanged": before == after, - "fingerprinted_artifacts": len(before), - } - ) -profiles = [] -for path in sorted(root.glob("pair*_*/*.json")): - if path.name != "trace.json": - continue - events = json.loads(path.read_text())["traceEvents"] - frames = [ - v - for v in events - if v.get("name", "").startswith("CONTROLLED_FRAME_") and v.get("ph") == "X" - ] - for frame in frames: - kernels = [ - v - for v in events - if v.get("cat") == "kernel" - and v.get("ph") == "X" - and frame["ts"] <= v["ts"] - and v["ts"] + v["dur"] <= frame["ts"] + frame["dur"] - ] - mixed = [v for v in kernels if v["name"].startswith("_stack_")] - profiles.append( - { - "run": path.parent.name, - "frame": frame["name"], - "kernel_count": len(kernels), - "kernel_sum_ms": sum(v["dur"] for v in kernels) / 1000, - "mixing_kernel_count": len(mixed), - "mixing_kernel_sum_ms": sum(v["dur"] for v in mixed) / 1000, - "mixing_names": dict(Counter(v["name"] for v in mixed)), - } - ) -summary = { - "arms": arms, - "process_pairs": len(by_pair), - "blocks": len(effects), - "paired_logmean_change_percent": float(100 * np.expm1(matrix.mean())), - "mean_block_difference_ms": float(np.mean(absolute)), - "hierarchical_bootstrap_95_percent": ci.tolist(), - "process_pair_t_95_percent": process_t_ci, - "process_pair_t_one_sided_upper95_percent": upper95, - "t_interval_caveat": "t interval over independent process-pair means; n=2 is calibration only, not a precise population estimate", - "bootstrap_draws": 20000, - "bootstrap_seed": 20261019, - "interval_method": "resample independent process pairs, then ABBA blocks within each selected pair; frame samples are not iid replicates", - "per_pair_change_percent": [float(100 * np.expm1(row.mean())) for row in matrix], - "interpretation": "positive interval: repeatable cost in this controlled setup" - if ci[0] > 0 - else "negative interval: repeatable improvement in this controlled setup" - if ci[1] < 0 - else "interval crosses zero: unresolved sign, not proof of zero cost", - "stats": stats, - "block_effects": effects, - "interval_telemetry": telemetry, - "kernel_audits": kernel_audits, - "profiles": profiles, - "monitor_errors": control["errors"], - "memory_scope": "resident timing only; retain independent cold-first/full-window capacity measurements separately", -} -(root / "summary.json").write_text(json.dumps(summary, indent=2)) -print( - json.dumps( - { - k: v - for k, v in summary.items() - if k not in ("block_effects", "interval_telemetry", "profiles") - }, - indent=2, - ) -) diff --git a/source/tests/pt_expt/kernels/analyze_so2_edge_cohort_same_context.py b/source/tests/pt_expt/kernels/analyze_so2_edge_cohort_same_context.py deleted file mode 100644 index 43fb585ffc..0000000000 --- a/source/tests/pt_expt/kernels/analyze_so2_edge_cohort_same_context.py +++ /dev/null @@ -1,104 +0,0 @@ -# SPDX-License-Identifier: LGPL-3.0-or-later -# ruff: noqa: T201 -- manual benchmark CLI output -"""Cluster normal same-context API contrasts by independent process.""" - -import argparse -import json -from pathlib import Path - -import numpy as np -from scipy.stats import t - -p = argparse.ArgumentParser() -p.add_argument("directories", type=Path, nargs="+") -p.add_argument("--out", type=Path, required=True) -a = p.parse_args() -rng = np.random.default_rng(20261019) -processes = [] -all_rows = [] -for root in a.directories: - status = json.loads((root / "same_context_status.json").read_text()) - assert status["status"] == "completed" - rows = json.loads((root / "same_context_rounds.json").read_text()) - all_rows.extend(rows) - effects = [] - for block in sorted({r["command"]["block"] for r in rows}): - group = [r for r in rows if r["command"]["block"] == block] - m = { - arm: np.mean( - np.log([r["wall_ms"]["median"] for r in group if r["variant"] == arm]) - ) - for arm in ("BASE", "ECS") - } - effects.append(float(m["ECS"] - m["BASE"])) - draws = np.mean(rng.choice(effects, (20000, len(effects))), axis=1) - meta = json.loads((root / "same_context_manifest.json").read_text()) - processes.append( - { - "run": root.name, - "first": meta["first"], - "blocks": len(effects), - "logmean": float(np.mean(effects)), - "change_percent": float(100 * np.expm1(np.mean(effects))), - "conditional_block_ci95_percent": ( - 100 * np.expm1(np.percentile(draws, [2.5, 97.5])) - ).tolist(), - "worst_error_ratio": max( - v["max_ratio"] - for case in meta["accuracy"].values() - for v in case.values() - ), - "wrappers": { - arm: entry["sha256"] for arm, entry in meta["wrappers"].items() - }, - } - ) -logs = np.array([p["logmean"] for p in processes]) -se = logs.std(ddof=1) / np.sqrt(len(logs)) if len(logs) > 1 else None -interval = ( - ( - 100 - * np.expm1(logs.mean() + np.array([-1, 1]) * t.ppf(0.975, len(logs) - 1) * se) - ).tolist() - if se is not None - else None -) -summary = { - "processes": processes, - "process_count": len(processes), - "mean_change_percent": float(100 * np.expm1(logs.mean())), - "process_t95_percent": interval, - "confidence_note": "processes, not frames, are independent replicates; conditional block intervals do not establish population equivalence", - "stats": { - arm: { - "samples": sum( - len(r["wall_ms"]["samples"]) for r in all_rows if r["variant"] == arm - ), - "p50_ms": float( - np.median( - [ - v - for r in all_rows - if r["variant"] == arm - for v in r["wall_ms"]["samples"] - ] - ) - ), - "p90_ms": float( - np.percentile( - [ - v - for r in all_rows - if r["variant"] == arm - for v in r["wall_ms"]["samples"] - ], - 90, - ) - ), - } - for arm in ("BASE", "ECS") - }, - "scope": "normal full ASE API, both implementations in one CUDA context; two calculators, actual master and exact PR definitions; no Dynamo inference frontend", -} -a.out.write_text(json.dumps(summary, indent=2)) -print(json.dumps(summary, indent=2)) diff --git a/source/tests/pt_expt/kernels/analyze_so2_edge_cohort_serial.py b/source/tests/pt_expt/kernels/analyze_so2_edge_cohort_serial.py deleted file mode 100644 index 638ea9f6fc..0000000000 --- a/source/tests/pt_expt/kernels/analyze_so2_edge_cohort_serial.py +++ /dev/null @@ -1,112 +0,0 @@ -# SPDX-License-Identifier: LGPL-3.0-or-later -# ruff: noqa: T201 -- manual benchmark CLI output -"""Matched-seed independent-process analysis of serialized normal API runs.""" - -import argparse -import json -from pathlib import Path - -import numpy as np -from scipy.stats import t - -p = argparse.ArgumentParser() -p.add_argument("directory", type=Path) -a = p.parse_args() -root = a.directory -assert json.loads((root / "controller.json").read_text())["status"] == "completed" -rows = json.loads((root / "rounds.json").read_text()) -formal = [r for r in rows if r["command"]["phase"] == "formal"] -ready = json.loads((root / "ready.json").read_text()) -seeds = sorted({r["seed"] for r in formal}) -pairs = [] -for seed in seeds: - stats = {} - for arm in ("BASE", "ECS"): - group = [r for r in formal if r["seed"] == seed and r["variant"] == arm] - values = [v for r in group for v in r["wall_ms"]["samples"]] - stats[arm] = { - "p50_ms": float(np.median(values)), - "p90_ms": float(np.percentile(values, 90)), - "round_logmean_ms": float( - np.exp(np.mean(np.log([r["wall_ms"]["median"] for r in group]))) - ), - "round_medians_ms": [r["wall_ms"]["median"] for r in group], - } - change = float( - np.log(stats["ECS"]["round_logmean_ms"]) - - np.log(stats["BASE"]["round_logmean_ms"]) - ) - pairs.append( - { - "seed": seed, - "arms": stats, - "logratio": change, - "percent": float(100 * np.expm1(change)), - } - ) -logs = np.array([p["logratio"] for p in pairs]) -se = logs.std(ddof=1) / np.sqrt(len(logs)) if len(logs) > 1 else None -ci = ( - ( - 100 - * np.expm1(logs.mean() + np.array([-1, 1]) * t.ppf(0.975, len(logs) - 1) * se) - ).tolist() - if se is not None - else None -) -monitor = json.loads((root / "monitor.json").read_text()) -telemetry = [] -for r in formal: - vals = [ - m - for m in monitor - if r["start_monotonic_ns"] <= m["t_ns"] <= r["end_monotonic_ns"] - ] - telemetry.append( - { - "run": r["run"], - "seed": r["seed"], - "arm": r["variant"], - "block": r["command"]["block"], - "mean_sm_mhz": float(np.mean([m["sm_mhz"] for m in vals])), - "mem_mhz": sorted({m["mem_mhz"] for m in vals}), - "temperature_range": [ - min(m["temperature"] for m in vals), - max(m["temperature"] for m in vals), - ], - "clock_reason_masks": sorted({m["clock_reasons"] for m in vals}), - } - ) -artifacts = [] -for entry in ready: - before = json.loads((root / f"cache_{entry['run']}_before.json").read_text()) - after = json.loads((root / f"cache_{entry['run']}_after.json").read_text()) - assert before == after - artifacts.append({"run": entry["run"], "unchanged": True, "count": len(before)}) -stats = {} -for arm in ("BASE", "ECS"): - values = [v for r in formal if r["variant"] == arm for v in r["wall_ms"]["samples"]] - stats[arm] = { - "samples": len(values), - "p50_ms": float(np.median(values)), - "p90_ms": float(np.percentile(values, 90)), - "min_ms": float(min(values)), - "max_ms": float(max(values)), - } -summary = { - "scope": "normal API; one CUDA process at a time; matched Python hash seeds; see controller arguments", - "pairs": pairs, - "mean_change_percent": float(100 * np.expm1(logs.mean())), - "process_pair_t_95_percent": ci, - "stats": stats, - "interval_telemetry": telemetry, - "compiler_artifacts": artifacts, - "interpretation": "finite independent process pairs; a confidence interval is not proof of zero cost", - "cold_peak": "separate existing cold-process measurement, not replaced", -} -(root / "summary.json").write_text(json.dumps(summary, indent=2)) -print( - json.dumps( - {k: v for k, v in summary.items() if k != "interval_telemetry"}, indent=2 - ) -) diff --git a/source/tests/pt_expt/kernels/benchmark_so2_edge_cohort.py b/source/tests/pt_expt/kernels/benchmark_so2_edge_cohort.py deleted file mode 100644 index 90b579489d..0000000000 --- a/source/tests/pt_expt/kernels/benchmark_so2_edge_cohort.py +++ /dev/null @@ -1,131 +0,0 @@ -# SPDX-License-Identifier: LGPL-3.0-or-later -# ruff: noqa: T201 -- manual benchmark CLI output -"""Reproduce full-model first-call, global and steady SO(2) memory results. - -This manual CUDA benchmark is not collected by pytest. Checkpoint files and -generated compiler caches are intentionally not included in the repository. -""" - -import argparse -import os -from pathlib import Path -import runpy -import sys - -import _so2_edge_cohort_benchmark_common as common - -parser = argparse.ArgumentParser(description=__doc__) -parser.add_argument("--model", required=True, type=Path) -parser.add_argument("--out", required=True, type=Path) -parser.add_argument( - "--source-root", type=Path, default=Path(__file__).resolve().parents[4] -) -parser.add_argument("--mode", choices=("base", "ecs", "torch"), default="base") -parser.add_argument("--gpu", default="0") -parser.add_argument("--nrep", type=int, default=2) -parser.add_argument("--warmup", type=int, default=8) -parser.add_argument("--samples", type=int, default=30) -parser.add_argument("--reference", type=Path) -parser.add_argument("--cache-dir", type=Path) -parser.add_argument("--allow-warm-cache", action="store_true") -args = parser.parse_args() -common.ROOT = args.source_root.resolve() -original_setup = common.setup - - -def setup(gpu, out, level="2"): - """Select code and precision before importing the model implementation.""" - original_setup(gpu, out, level) - os.environ["DP_SO2_EDGE_COHORT_INFER"] = "1" if args.mode == "ecs" else "0" - if args.cache_dir is not None: - cache = args.cache_dir.resolve() - if cache.exists() and any(cache.iterdir()) and not args.allow_warm_cache: - raise ValueError( - "nonempty cache: pass --allow-warm-cache or use a new directory" - ) - os.environ["TORCHINDUCTOR_CACHE_DIR"] = str(cache / "inductor") - os.environ["TRITON_CACHE_DIR"] = str(cache / "triton") - - -common.setup = setup -implementation = Path(__file__).with_name("_so2_edge_cohort_benchmark_impl.py") -sys.argv = [ - str(implementation), - "--model", - str(args.model), - "--out", - str(args.out), - "--gpu", - args.gpu, - "--nrep", - str(args.nrep), - "--warmup", - str(args.warmup), - "--samples", - str(args.samples), - "--variant", - "TORCH" if args.mode == "torch" else "BASE", -] -if args.reference is not None: - sys.argv += ["--reference", str(args.reference)] -namespace = runpy.run_path(str(implementation), run_name="__main__") -import torch - -result = namespace["result"] -result["variant"] = args.mode -memory = result["memory"] -# The implementation resets counters only for its separate steady replay. -# Preserve the preceding high-water mark, including model load and first JIT. -memory["after_validation"] = { - "allocated": torch.cuda.max_memory_allocated(), - "reserved": torch.cuda.max_memory_reserved(), -} -memory["global"] = { - key: max( - memory[phase][key] - for phase in ("first", "complete", "steady", "after_validation") - ) - for key in ("allocated", "reserved") -} -memory["global_first_observed_phase"] = next( - phase - for phase in ("first", "complete", "steady", "after_validation") - if memory[phase]["allocated"] == memory["global"]["allocated"] -) -result["protocol"] = { - "seed": 0, - "coordinate_std_angstrom": 0.02, - "cache": "warm_allowed" - if args.allow_warm_cache - else "new_inductor_and_triton_cache", - "primary_memory": "global high-water mark including model load and first JIT; never substitute steady", - "tf32": False, - "amp": False, - "weight_gradients": False, - "cold_scope": "new process and application compiler caches; driver/system caches are not purged", -} -result["source_hashes"] = { - str(p.relative_to(common.ROOT)): common.sha(p) - for p in [ - common.ROOT / "deepmd/pt_expt/kernels/triton/sezm/so2_value_path.py", - common.ROOT / "deepmd/pt_expt/kernels/triton/sezm/so2_edge_cohort.py", - ] - if p.exists() -} -assert torch.get_float32_matmul_precision() == "highest" -assert not torch.backends.cuda.matmul.allow_tf32 -assert not torch.backends.cudnn.allow_tf32 -common.write(args.out / "result.json", result) -if args.reference is not None and not result["precision_pass"]: - raise SystemExit("accuracy check failed; thresholds were not relaxed") -if not all(v["passed"] for repeat in result["repeat_noise"] for v in repeat.values()): - raise SystemExit("within-process reproducibility check failed") -print( - { - "mode": args.mode, - "p50_ms": result["wall_ms"]["median"], - "first_peak": memory["first"], - "global_peak": memory["global"], - "steady_peak": memory["steady"], - } -) diff --git a/source/tests/pt_expt/kernels/benchmark_so2_edge_cohort_paired.py b/source/tests/pt_expt/kernels/benchmark_so2_edge_cohort_paired.py deleted file mode 100644 index 7b651909d6..0000000000 --- a/source/tests/pt_expt/kernels/benchmark_so2_edge_cohort_paired.py +++ /dev/null @@ -1,228 +0,0 @@ -# SPDX-License-Identifier: LGPL-3.0-or-later -# ruff: noqa: T201 -- manual benchmark CLI output -"""Sequential resident-worker ABBA trials with fixed caches and interval NVML.""" - -import argparse -import hashlib -import json -import os -from pathlib import Path -import subprocess -import sys -import threading -import time - -import pynvml as nv - -HERE = Path(__file__).resolve().parent -p = argparse.ArgumentParser() -p.add_argument("--out", type=Path, required=True) -p.add_argument("--baseline-root", type=Path, required=True) -p.add_argument("--candidate-root", type=Path, required=True) -p.add_argument("--model", type=Path, required=True) -p.add_argument("--reference", type=Path, required=True) -p.add_argument("--cache", type=Path, required=True) -p.add_argument("--variants", nargs=2, default=["BASE", "ECS"]) -p.add_argument("--pairs", type=int, default=3) -p.add_argument("--blocks", type=int, default=12) -p.add_argument("--samples", type=int, default=50) -p.add_argument("--cpu", type=int, default=8) -p.add_argument("--gpu", default="0") -p.add_argument("--monitor-cpu", type=int, default=16) -p.add_argument("--profile", action="store_true") -a = p.parse_args() -out = a.out.resolve() -out.mkdir(parents=True, exist_ok=False) -a.cache = a.cache.resolve() -a.cache.mkdir(parents=True, exist_ok=True) -os.sched_setaffinity(0, {a.monitor_cpu}) -nv.nvmlInit() -h = nv.nvmlDeviceGetHandleByIndex(int(a.gpu)) -assert not nv.nvmlDeviceGetComputeRunningProcesses(h), f"GPU{a.gpu} not idle" -workers = {} -stop = threading.Event() -monitor, errors, records, ready = [], [], [], [] - - -def watch(): - while not stop.wait(0.05): - try: - pids = {v.pid for v in nv.nvmlDeviceGetComputeRunningProcesses(h)} - foreign = pids - {w.pid for w in workers.values()} - if foreign: - errors.append(f"foreign compute pids: {sorted(foreign)}") - for w in list(workers.values()): - if w.poll() is None: - w.terminate() - return - monitor.append( - { - "t_ns": time.monotonic_ns(), - "pids": sorted(pids), - "sm_mhz": nv.nvmlDeviceGetClockInfo(h, nv.NVML_CLOCK_SM), - "mem_mhz": nv.nvmlDeviceGetClockInfo(h, nv.NVML_CLOCK_MEM), - "temperature": nv.nvmlDeviceGetTemperature( - h, nv.NVML_TEMPERATURE_GPU - ), - "power_mw": nv.nvmlDeviceGetPowerUsage(h), - "utilization": nv.nvmlDeviceGetUtilizationRates(h).gpu, - "pstate": nv.nvmlDeviceGetPerformanceState(h), - } - ) - except Exception as exc: - errors.append(repr(exc)) - return - - -def write(name, value): - (out / name).write_text(json.dumps(value, indent=2)) - - -def fingerprint(include_binaries=False): - patterns = ["*.best_config"] + (["*.cubin", "*.ptx"] if include_binaries else []) - return { - str(path.relative_to(a.cache)): hashlib.sha256(path.read_bytes()).hexdigest() - for pattern in patterns - for path in sorted(a.cache.rglob(pattern)) - } - - -def message(worker, log): - while True: - line = worker.stdout.readline() - if not line: - raise RuntimeError(f"worker exited: {worker.poll()} {errors}") - log.write(line) - log.flush() - if line.startswith("@CONTROL@"): - return json.loads(line[len("@CONTROL@") :]) - - -thread = threading.Thread(target=watch, daemon=True) -thread.start() -logs = [] -try: - for pair in range(a.pairs): - pair_ready = [] - init_order = a.variants if pair % 2 == 0 else list(reversed(a.variants)) - for variant in init_order: - log = (out / f"pair{pair}_{variant}.log").open("x") - logs.append(log) - worker = subprocess.Popen( - [ - sys.executable, - str(HERE / "_so2_edge_cohort_controlled_worker.py"), - "--baseline-root", - str(a.baseline_root.resolve()), - "--candidate-root", - str(a.candidate_root.resolve()), - "--model", - str(a.model.resolve()), - "--reference", - str(a.reference.resolve()), - "--variant", - variant, - "--out", - str(out / f"pair{pair}_{variant}"), - "--cache", - str(a.cache), - "--cpu", - str(a.cpu), - "--gpu", - a.gpu, - ], - stdin=subprocess.PIPE, - stdout=subprocess.PIPE, - stderr=log, - text=True, - bufsize=1, - ) - workers[variant] = worker - worker.stdin.write( - json.dumps({"pids": [w.pid for w in workers.values()]}) + "\n" - ) - worker.stdin.flush() - r = message(worker, log) - assert r["status"] == "ready" - r["pair"] = pair - ready.append(r) - pair_ready.append(r) - write("ready.json", ready) - print("READY", pair, variant, r["entry_wall_ms"]["median"], flush=True) - frozen = fingerprint() - binary_before = fingerprint(True) - write(f"cache_pair{pair}_before.json", binary_before) - for block in range(a.blocks): - left, right = a.variants if block % 2 == 0 else list(reversed(a.variants)) - for position, variant in enumerate((left, right, right, left)): - worker = workers[variant] - command = { - "action": "measure", - "pair": pair, - "block": block, - "position": position, - "warmup": 8, - "samples": a.samples, - "cpu": a.cpu, - } - assert fingerprint() == frozen, "autotune cache changed before timing" - worker.stdin.write(json.dumps(command) + "\n") - worker.stdin.flush() - log = logs[-2 + init_order.index(variant)] - r = message(worker, log) - assert fingerprint() == frozen, "autotune cache changed during timing" - r["pair"] = pair - records.append(r) - write("rounds.json", records) - print( - "ROUND", - pair, - block, - position, - variant, - r["wall_ms"]["median"], - flush=True, - ) - if a.profile: - for variant in init_order: - worker = workers[variant] - worker.stdin.write('{"action":"profile"}\n') - worker.stdin.flush() - profiled = message(worker, logs[-2 + init_order.index(variant)]) - assert profiled["status"] == "profiled" - write(f"profile_pair{pair}_{variant}.json", profiled) - after = fingerprint(True) - write(f"cache_pair{pair}_after.json", after) - assert after == binary_before, ( - "compiled binaries changed during measured blocks" - ) - for variant in init_order: - worker = workers[variant] - worker.stdin.write('{"action":"stop"}\n') - worker.stdin.flush() - r = message(worker, logs[-2 + init_order.index(variant)]) - assert r["status"] == "completed" and r["precision_pass"] - worker.wait(timeout=30) - assert worker.returncode == 0 - workers.clear() -finally: - for worker in workers.values(): - if worker.poll() is None: - worker.terminate() - worker.wait(timeout=30) - stop.set() - thread.join(timeout=5) - write("monitor.json", monitor) - write( - "controller.json", - { - "args": {k: str(v) for k, v in vars(a).items()}, - "errors": errors, - "status": "completed" - if not errors and len(records) == a.pairs * a.blocks * 4 - else "failed", - }, - ) - for log in logs: - log.close() - nv.nvmlShutdown() diff --git a/source/tests/pt_expt/kernels/benchmark_so2_edge_cohort_same_context.py b/source/tests/pt_expt/kernels/benchmark_so2_edge_cohort_same_context.py deleted file mode 100644 index 0e623a7b10..0000000000 --- a/source/tests/pt_expt/kernels/benchmark_so2_edge_cohort_same_context.py +++ /dev/null @@ -1,326 +0,0 @@ -# SPDX-License-Identifier: LGPL-3.0-or-later -# ruff: noqa: T201 -- manual benchmark CLI output -"""Normal ASE API A/B in one CUDA context, preserving the original baseline. - -Experimental setup copies exactly the three reviewed kernel definitions and -registered backward into memory *after* compiling the baseline. No production -file, kernel arithmetic, or checkpoint is edited. Each model keeps its own -compiled cache. Inference bypasses Dynamo; stale module-level cache reuse is -explicitly prohibited and compiled graph provenance is checked. -""" - -import argparse -import ast -import hashlib -import importlib.util -import os -from pathlib import Path -import runpy -import sys -import threading -import time - -HERE = Path(__file__).resolve().parent -import _so2_edge_cohort_benchmark_common as common - -p = argparse.ArgumentParser() -p.add_argument("--baseline-root", type=Path, required=True) -p.add_argument("--candidate-root", type=Path, required=True) -p.add_argument("--model", type=Path, required=True) -p.add_argument("--reference", type=Path, required=True) -p.add_argument("--cache", type=Path, required=True) -p.add_argument("--out", type=Path, required=True) -p.add_argument("--first", choices=("BASE", "ECS"), default="BASE") -p.add_argument("--gpu", default="0") -p.add_argument("--cpu", type=int, default=8) -p.add_argument("--monitor-cpu", type=int, default=16) -p.add_argument("--blocks", type=int, default=24) -a = p.parse_args() -os.sched_setaffinity(0, {a.cpu}) -PUBLIC = a.candidate_root.resolve() -common.ROOT = a.baseline_root.resolve() -original_setup = common.setup -state = {} - - -def install_candidate(): - from deepmd.pt_expt.kernels.triton.sezm import so2_value_path as sv - - if not state: - state.update( - sv=sv, - backward=sv._mixing_stack_backward, - kernels={ - name: getattr(sv, name) - for name in ( - "_stack_gemm_bwd_kernel", - "_stack_point_bwd_kernel", - "_stack_recompute_kernel", - ) - }, - ) - source = PUBLIC / "deepmd/pt_expt/kernels/triton/sezm/so2_value_path.py" - tree = ast.parse(source.read_text()) - names = set(state["kernels"]) | {"_mixing_stack_backward"} - definitions = [ - n - for n in ast.walk(tree) - if isinstance(n, ast.FunctionDef) and n.name in names - ] - assert len(definitions) == 4 - scope = dict(vars(sv), _EDGE_COHORT_INFER=True) - exec( - compile(ast.Module(body=definitions, type_ignores=[]), str(source), "exec"), - scope, - ) - state["candidate_definitions"] = {name: scope[name] for name in names} - name = "deepmd.pt_expt.kernels.triton.sezm.so2_edge_cohort" - path = PUBLIC / "deepmd/pt_expt/kernels/triton/sezm/so2_edge_cohort.py" - spec = importlib.util.spec_from_file_location(name, path) - module = importlib.util.module_from_spec(spec) - sys.modules[name] = module - spec.loader.exec_module(module) - for name in state["kernels"]: - setattr(sv, name, state["candidate_definitions"][name]) - sv._mixing_stack_op.register_autograd( - state["candidate_definitions"]["_mixing_stack_backward"], - setup_context=sv._mixing_stack_setup_context, - ) - - -def restore_baseline(): - sv = state["sv"] - for name, kernel in state["kernels"].items(): - setattr(sv, name, kernel) - sv._mixing_stack_op.register_autograd( - state["backward"], setup_context=sv._mixing_stack_setup_context - ) - - -def setup(*args, **kwargs): - original_setup(*args, **kwargs) - os.environ.update( - TORCHINDUCTOR_CACHE_DIR=str(a.cache.resolve() / "inductor"), - TRITON_CACHE_DIR=str(a.cache.resolve() / "triton"), - ) - if a.first == "ECS": - install_candidate() - - -common.setup = setup - - -def callback(ns): - import numpy as np - import torch - import pynvml as nv - from deepmd.calculator import DP - from deepmd.pt.model.model import sezm_model - from torch._inductor.codecache import PyCodeCache - - first = ns["calc"] - model = ns["inner"] - assert model.compiled_core_compute_cache - first_cache = dict(model.compiled_core_compute_cache) - old_modules = { - str(m.__file__) - for m in PyCodeCache.modules - if "def call(" in Path(m.__file__).read_text() - } - assert len(old_modules) == 1 - if a.first == "BASE": - install_candidate() - else: - restore_baseline() - sezm_model._SEZM_COMPILE_CACHE.clear() - sezm_model._SEZM_TASK_BUF_ORDER.clear() - second = DP(model=str(a.model)) - assert not second.dp.get_model().compiled_core_compute_cache - calculators = {a.first: first, ("ECS" if a.first == "BASE" else "BASE"): second} - base, frames = ns["base"], ns["frames"] - - def evaluate(calc, pos): - calc.reset() - atoms = base.copy() - atoms.positions = pos - atoms.calc = calc - forces = atoms.get_forces().copy() - return { - "energy": np.asarray(atoms.get_potential_energy()), - "forces": forces, - "virial": np.asarray(calc.results["virial"]).copy(), - "stress": atoms.get_stress().copy(), - } - - started = time.perf_counter() - for pos in frames[:8]: - evaluate(second, pos) - torch.cuda.synchronize() - assert model.compiled_core_compute_cache == first_cache, "first graph cache changed" - modules = { - str(m.__file__) - for m in PyCodeCache.modules - if "def call(" in Path(m.__file__).read_text() - } - new_modules = modules - old_modules - assert len(new_modules) == 1, ( - "second mode silently reused a graph or compiled multiple entries" - ) - wrappers = { - a.first: next(iter(old_modules)), - ("ECS" if a.first == "BASE" else "BASE"): next(iter(new_modules)), - } - assert "so2_single_cohort" in Path(wrappers["ECS"]).read_text() - assert "so2_single_cohort" not in Path(wrappers["BASE"]).read_text() - with np.load(a.reference) as ref: - accuracy = {} - outputs = {} - for arm, calc in calculators.items(): - values = { - f"{i}_{k}": v - for i, pos in enumerate(frames[:3]) - for k, v in evaluate(calc, pos).items() - } - outputs[arm] = values - accuracy[arm] = ns["compare"](values, ref) - assert all(v["passed"] and v["finite"] for v in accuracy[arm].values()) - np.savez_compressed(a.out / f"{arm}_outputs.npz", **values) - accuracy["paired"] = ns["compare"](outputs["ECS"], outputs["BASE"]) - assert all(v["passed"] for v in accuracy["paired"].values()) - cache = a.cache.resolve() - - def artifacts(): - return { - str(p.relative_to(cache)): hashlib.sha256(p.read_bytes()).hexdigest() - for pattern in ("*.best_config", "*.cubin", "*.ptx") - for p in cache.rglob(pattern) - } - - frozen = artifacts() - common.write( - a.out / "same_context_manifest.json", - { - "first": a.first, - "second_compile_s": time.perf_counter() - started, - "wrappers": { - k: {"path": v, "sha256": common.sha(Path(v))} - for k, v in wrappers.items() - }, - "accuracy": accuracy, - "original_entry_wall_ms": ns["wall"], - "seed": os.environ.get("PYTHONHASHSEED"), - "method": "two normal ASE calculators; distinct AOT compiled caches; same process/context; exact candidate kernel/backward definitions transplanted only for tracing", - }, - ) - nv.nvmlInit() - h = nv.nvmlDeviceGetHandleByIndex(int(a.gpu)) - stop = threading.Event() - monitor = [] - monitor_errors = [] - - def watch(): - os.sched_setaffinity(0, {a.monitor_cpu}) - while not stop.wait(0.05): - pids = {p.pid for p in nv.nvmlDeviceGetComputeRunningProcesses(h)} - if pids != {os.getpid()}: - monitor_errors.append(f"foreign CUDA pid {pids}") - return - monitor.append( - { - "t_ns": time.monotonic_ns(), - "sm_mhz": nv.nvmlDeviceGetClockInfo(h, nv.NVML_CLOCK_SM), - "mem_mhz": nv.nvmlDeviceGetClockInfo(h, nv.NVML_CLOCK_MEM), - "temperature": nv.nvmlDeviceGetTemperature( - h, nv.NVML_TEMPERATURE_GPU - ), - "clock_reasons": nv.nvmlDeviceGetCurrentClocksThrottleReasons(h), - } - ) - - thread = threading.Thread(target=watch, daemon=True) - thread.start() - records = [] - try: - for block in range(a.blocks): - left, right = ("BASE", "ECS") if block % 2 == 0 else ("ECS", "BASE") - for position, arm in enumerate((left, right, right, left)): - calc = calculators[arm] - for pos in frames[:8]: - evaluate(calc, pos) - torch.cuda.synchronize() - wall = [] - events = [] - start_ns = time.monotonic_ns() - for i in range(50): - torch.cuda.synchronize() - s = torch.cuda.Event(enable_timing=True) - e = torch.cuda.Event(enable_timing=True) - start = time.perf_counter() - s.record() - evaluate(calc, frames[8 + i % 30]) - e.record() - torch.cuda.synchronize() - wall.append(1000 * (time.perf_counter() - start)) - events.append(s.elapsed_time(e)) - records.append( - { - "pair": 0, - "variant": arm, - "command": {"block": block, "position": position}, - "wall_ms": common.stats(wall), - "event_ms": common.stats(events), - "start_monotonic_ns": start_ns, - "end_monotonic_ns": time.monotonic_ns(), - } - ) - common.write(a.out / "same_context_rounds.json", records) - assert not monitor_errors, str(monitor_errors) - print( - "CONTEXT_API", - block, - position, - arm, - records[-1]["wall_ms"]["median"], - flush=True, - ) - finally: - stop.set() - thread.join(timeout=5) - nv.nvmlShutdown() - common.write(a.out / "same_context_monitor.json", monitor) - assert not monitor_errors, str(monitor_errors) - assert artifacts() == frozen - assert model.compiled_core_compute_cache == first_cache - common.write( - a.out / "same_context_status.json", - { - "status": "completed", - "accuracy_passed": True, - "cache_unchanged": True, - "first": a.first, - "blocks": a.blocks, - "samples_per_arm": a.blocks * 2 * 50, - }, - ) - restore_baseline() - - -sys.argv = [ - str(HERE / "_so2_edge_cohort_benchmark_impl.py"), - "--out", - str(a.out), - "--gpu", - a.gpu, - "--nrep", - "2", - "--model", - str(a.model), - "--reference", - str(a.reference), -] -ns = runpy.run_path( - str(HERE / "_so2_edge_cohort_benchmark_impl.py"), - init_globals={"_controlled_rounds": callback}, - run_name="__main__", -) -assert ns["result"]["precision_pass"] and ns["result"]["finite"] diff --git a/source/tests/pt_expt/kernels/benchmark_so2_edge_cohort_serial.py b/source/tests/pt_expt/kernels/benchmark_so2_edge_cohort_serial.py deleted file mode 100644 index 23dc31fc47..0000000000 --- a/source/tests/pt_expt/kernels/benchmark_so2_edge_cohort_serial.py +++ /dev/null @@ -1,191 +0,0 @@ -# SPDX-License-Identifier: LGPL-3.0-or-later -# ruff: noqa: T201 -- manual benchmark CLI output -"""One CUDA process at a time: remove resident-context initialization bias.""" - -import argparse -import hashlib -import json -import os -from pathlib import Path -import subprocess -import sys -import threading -import time - -import pynvml as nv - -HERE = Path(__file__).resolve().parent -p = argparse.ArgumentParser(description=__doc__) -p.add_argument("--out", type=Path, required=True) -p.add_argument("--cache", type=Path, required=True) -p.add_argument("--baseline-root", type=Path, required=True) -p.add_argument("--candidate-root", type=Path, required=True) -p.add_argument("--model", type=Path, required=True) -p.add_argument("--reference", type=Path, required=True) -p.add_argument("--gpu", default="0") -p.add_argument("--cpu", type=int, default=8) -p.add_argument("--monitor-cpu", type=int, default=16) -p.add_argument("--seeds", type=int, nargs="+", default=[0, 1, 2]) -p.add_argument("--rounds", type=int, default=8) -p.add_argument("--samples", type=int, default=50) -p.add_argument("--conditioning-samples", type=int, default=200) -a = p.parse_args() -assert a.samples >= 10 and a.rounds >= 1 -out = a.out.resolve() -out.mkdir(parents=True, exist_ok=False) -cache = a.cache.resolve() -os.sched_setaffinity(0, {a.monitor_cpu}) -nv.nvmlInit() -handle = nv.nvmlDeviceGetHandleByIndex(int(a.gpu)) -cases = [ - (arm, seed) - for i, seed in enumerate(a.seeds) - for arm in (("BASE", "ECS") if i % 2 == 0 else ("ECS", "BASE")) -] -worker = None -stop = threading.Event() -monitor, errors, ready, records = [], [], [], [] - - -def write(name, value): - (out / name).write_text(json.dumps(value, indent=2)) - - -def watch(): - while not stop.wait(0.05): - pids = {v.pid for v in nv.nvmlDeviceGetComputeRunningProcesses(handle)} - allowed = {worker.pid} if worker is not None else set() - if pids - allowed: - errors.append(f"foreign GPU0 pids: {pids - allowed}") - if worker is not None and worker.poll() is None: - worker.terminate() - return - monitor.append( - { - "t_ns": time.monotonic_ns(), - "pids": sorted(pids), - "sm_mhz": nv.nvmlDeviceGetClockInfo(handle, nv.NVML_CLOCK_SM), - "mem_mhz": nv.nvmlDeviceGetClockInfo(handle, nv.NVML_CLOCK_MEM), - "temperature": nv.nvmlDeviceGetTemperature( - handle, nv.NVML_TEMPERATURE_GPU - ), - "power_mw": nv.nvmlDeviceGetPowerUsage(handle), - "clock_reasons": nv.nvmlDeviceGetCurrentClocksThrottleReasons(handle), - } - ) - - -def fingerprint(): - return { - str(p.relative_to(cache)): hashlib.sha256(p.read_bytes()).hexdigest() - for pattern in ("*.best_config", "*.cubin", "*.ptx") - for p in cache.rglob(pattern) - } - - -def message(log): - while True: - line = worker.stdout.readline() - if not line: - raise RuntimeError(f"worker ended {worker.poll()}") - log.write(line) - log.flush() - if line.startswith("@CONTROL@"): - return json.loads(line[len("@CONTROL@") :]) - - -assert not nv.nvmlDeviceGetComputeRunningProcesses(handle) -thread = threading.Thread(target=watch, daemon=True) -thread.start() -try: - for run, (variant, seed) in enumerate(cases): - assert not nv.nvmlDeviceGetComputeRunningProcesses(handle) - env = dict(os.environ, PYTHONHASHSEED=str(seed)) - with (out / f"{run}_{variant}.log").open("x") as log: - worker = subprocess.Popen( - [ - sys.executable, - str(HERE / "_so2_edge_cohort_controlled_worker.py"), - "--baseline-root", - str(a.baseline_root.resolve()), - "--candidate-root", - str(a.candidate_root.resolve()), - "--model", - str(a.model.resolve()), - "--reference", - str(a.reference.resolve()), - "--variant", - variant, - "--out", - str(out / f"{run}_{variant}"), - "--cache", - str(cache), - "--gpu", - a.gpu, - "--cpu", - str(a.cpu), - ], - stdin=subprocess.PIPE, - stdout=subprocess.PIPE, - stderr=log, - text=True, - bufsize=1, - env=env, - ) - worker.stdin.write(json.dumps({"pids": [worker.pid]}) + "\n") - worker.stdin.flush() - r = message(log) - assert r["status"] == "ready" - r.update(run=run, seed=seed) - ready.append(r) - write("ready.json", ready) - print("READY", run, variant, seed, r["entry_wall_ms"]["median"], flush=True) - frozen = fingerprint() - write(f"cache_{run}_before.json", frozen) - for block in range(a.rounds + 1): - command = { - "action": "measure", - "pair": seed, - "block": block, - "position": run % 2, - "samples": a.conditioning_samples if block == 0 else a.samples, - "warmup": 8, - "cpu": a.cpu, - "phase": "conditioning" if block == 0 else "formal", - } - worker.stdin.write(json.dumps(command) + "\n") - worker.stdin.flush() - r = message(log) - r.update(run=run, seed=seed, pair=seed) - records.append(r) - write("rounds.json", records) - assert fingerprint() == frozen, "autotune/cache changed in timed runs" - print("ROUND", run, variant, block, r["wall_ms"]["median"], flush=True) - write(f"cache_{run}_after.json", fingerprint()) - worker.stdin.write('{"action":"stop"}\n') - worker.stdin.flush() - r = message(log) - assert r["status"] == "completed" and r["precision_pass"] - worker.wait(timeout=30) - assert worker.returncode == 0 - worker = None -finally: - if worker is not None: - if worker.poll() is None: - worker.terminate() - worker.wait(timeout=30) - stop.set() - thread.join(timeout=5) - write("monitor.json", monitor) - write( - "controller.json", - { - "errors": errors, - "status": "completed" - if len(records) == len(cases) * (a.rounds + 1) and not errors - else "failed", - "args": {k: str(v) for k, v in vars(a).items()}, - "protocol": "one CUDA process at a time; matched hash seeds; conditioning separated from formal rounds", - }, - ) - nv.nvmlShutdown() From 7b27c9b3dc80997fb7c1d19ef6879bcb58e07f4b Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 26 Oct 2026 13:46:25 +0000 Subject: [PATCH 6/7] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- .../pt_expt/kernels/triton/sezm/so2_edge_cohort.py | 12 +++++++++--- source/tests/pt_expt/kernels/test_so2_edge_cohort.py | 8 ++++++-- 2 files changed, 15 insertions(+), 5 deletions(-) diff --git a/deepmd/pt_expt/kernels/triton/sezm/so2_edge_cohort.py b/deepmd/pt_expt/kernels/triton/sezm/so2_edge_cohort.py index 1bee615190..57cd95f342 100644 --- a/deepmd/pt_expt/kernels/triton/sezm/so2_edge_cohort.py +++ b/deepmd/pt_expt/kernels/triton/sezm/so2_edge_cohort.py @@ -8,12 +8,18 @@ prototype; standalone AOTInductor/C++ deployment is not validated. """ -from __future__ import annotations +from __future__ import ( + annotations, +) import torch import triton -from torch import Tensor -from torch.library import wrap_triton +from torch import ( + Tensor, +) +from torch.library import ( + wrap_triton, +) from . import so2_value_path as sv diff --git a/source/tests/pt_expt/kernels/test_so2_edge_cohort.py b/source/tests/pt_expt/kernels/test_so2_edge_cohort.py index 087ab83624..bc488a052d 100644 --- a/source/tests/pt_expt/kernels/test_so2_edge_cohort.py +++ b/source/tests/pt_expt/kernels/test_so2_edge_cohort.py @@ -1,9 +1,13 @@ # SPDX-License-Identifier: LGPL-3.0-or-later """Generated-input checks for the opt-in frozen Neo edge-cohort prototype.""" -from __future__ import annotations +from __future__ import ( + annotations, +) -from types import SimpleNamespace +from types import ( + SimpleNamespace, +) import pytest import torch From 98a65e46d0076319f2fcb49ded77e6a93ecab6be Mon Sep 17 00:00:00 2001 From: Macqueen Ma-hnu Date: Mon, 26 Oct 2026 22:33:17 +0800 Subject: [PATCH 7/7] fix(pt): preserve runtime SO2 edge cohorts through AOT tracing --- .../kernels/triton/sezm/so2_edge_cohort.py | 10 +++---- .../pt_expt/kernels/test_so2_edge_cohort.py | 30 +++++++++++++++++++ 2 files changed, 35 insertions(+), 5 deletions(-) diff --git a/deepmd/pt_expt/kernels/triton/sezm/so2_edge_cohort.py b/deepmd/pt_expt/kernels/triton/sezm/so2_edge_cohort.py index 57cd95f342..f811eee328 100644 --- a/deepmd/pt_expt/kernels/triton/sezm/so2_edge_cohort.py +++ b/deepmd/pt_expt/kernels/triton/sezm/so2_edge_cohort.py @@ -377,10 +377,10 @@ def cohort_backward( if not supports_edge_cohort(grad_out, z_all, lmax, focus_dim, apply_alpha): raise ValueError("unsupported edge-cohort layout") grad_alpha = _cohort_alpha_backward(grad_out, x_local, alpha) - input_backward = ( - _single_cohort_input_backward - if grad_out.shape[0] <= SINGLE_COHORT_MAX_EDGES - else _cohort_input_backward + # make_fx/AOT may trace with a small example and retain that route for + # larger inputs. Keep edge partitioning inside the opaque runtime operator + # so its loop always uses the actual edge count, not the tracing example. + grad_u0 = _cohort_input_backward( + grad_out, z_all, alpha, w0t, w1t, gw, gwt, COHORT_EDGES ) - grad_u0 = input_backward(grad_out, z_all, alpha, w0t, w1t, gw, gwt, COHORT_EDGES) return grad_u0, grad_alpha diff --git a/source/tests/pt_expt/kernels/test_so2_edge_cohort.py b/source/tests/pt_expt/kernels/test_so2_edge_cohort.py index bc488a052d..d390c20ad1 100644 --- a/source/tests/pt_expt/kernels/test_so2_edge_cohort.py +++ b/source/tests/pt_expt/kernels/test_so2_edge_cohort.py @@ -79,6 +79,36 @@ def test_compiled_dynamic_cohort() -> None: torch.testing.assert_close(a, b, atol=0, rtol=0) +def test_make_fx_retains_runtime_cohorts(monkeypatch) -> None: + """A small tracing example must not disable batching on larger inputs.""" + from torch.fx.experimental.proxy_tensor import ( + make_fx, + ) + + graph = make_fx(ecs.cohort_backward, tracing_mode="symbolic")(*operands(37)) + assert any( + node.op == "call_function" + and node.target == torch.ops.sezm_triton.so2_cohort_input_bwd.default + for node in graph.graph.nodes + ) + calls = [] + original = ecs.cohort_input_backward_impl + + def observed(go, z, alpha, w0t, w1t, gw, gwt, cap): + calls.append((go.shape[0], cap)) + return original(go, z, alpha, w0t, w1t, gw, gwt, cap) + + monkeypatch.setattr(ecs, "cohort_input_backward_impl", observed) + compiled = torch.compile(graph, fullgraph=True, dynamic=True) + for edges in (37, 8193, 16385): + args = operands(edges) + actual = compiled(*args) + expected = sv._mixing_stack_bwd_impl(*args) + assert calls[-1] == (edges, ecs.COHORT_EDGES) + for got, ref in zip(actual, expected, strict=True): + torch.testing.assert_close(got, ref, atol=0, rtol=0) + + @pytest.mark.parametrize("dynamic", [False, True]) def test_compiled_native_backward(dynamic: bool) -> None: """Optional strides must preserve the default native compilation path."""

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