From b4658349fa11bc61bb67a3538eb870d994f4e264 Mon Sep 17 00:00:00 2001 From: Duo <50307526+iProzd@users.noreply.github.com> Date: Sat, 12 Sep 2026 05:30:19 -0400 Subject: [PATCH 01/34] feat(dpmodel): add Uni-Mol v1 encoder blocks and an exact GELU Ports the Uni-Mol v1 transformer backbone to the array-API dpmodel layer: self-attention that returns its pre-softmax logits, the pre-LN encoder layer, the pair-carrying encoder stack with both norm regularisers, the Gaussian distance basis and the two-layer head. Sources are Uni-Mol 90f52c4 and Uni-Core ace6fae, both MIT licensed; the file header records the provenance per class. Adds "gelu_erf", the exact error-function GELU that Uni-Mol uses, together with an xp_erf backend dispatch. The existing "gelu" and "gelu_tf" keep their current meaning, the tanh approximation, which differs from the exact form by up to 4.7e-4 per element. Verified against tensors dumped from upstream running on the same inputs: with upstream's own attention bias the encoder agrees to 7e-16 relative in fp64. Including the Gaussian basis the agreement is 1e-7 relative, which is one fp32 unit in the last place: upstream evaluates the basis in fp32 because it pretrains an fp16 model, and NumPy and Torch round that last place differently. That behaviour is reproduced by default and can be switched off. No existing code path changes: the new modules are not imported anywhere yet. --- deepmd/dpmodel/array_api.py | 28 + .../dpmodel/descriptor/unimol_nn/__init__.py | 18 + .../dpmodel/descriptor/unimol_nn/encoder.py | 586 ++++++++++++++++++ deepmd/dpmodel/utils/network.py | 14 + 4 files changed, 646 insertions(+) create mode 100644 deepmd/dpmodel/descriptor/unimol_nn/__init__.py create mode 100644 deepmd/dpmodel/descriptor/unimol_nn/encoder.py diff --git a/deepmd/dpmodel/array_api.py b/deepmd/dpmodel/array_api.py index 6d3c89d584..52f3bf1a6c 100644 --- a/deepmd/dpmodel/array_api.py +++ b/deepmd/dpmodel/array_api.py @@ -516,6 +516,34 @@ def xp_sigmoid(x: Array) -> Array: return 1 / (1 + xp.exp(-x)) +def xp_erf(x: Array) -> Array: + """Compute the error function. + + Used by the exact (non-approximated) GELU. The array API has no ``erf``, so + each backend's own implementation is used; NumPy goes through SciPy, which + is already a core dependency. + """ + if array_api_compat.is_jax_array(x): + from deepmd.jax.env import ( + jax, + ) + + return jax.scipy.special.erf(x) + elif array_api_compat.is_torch_array(x): + import torch + + return torch.special.erf(x) + from scipy.special import ( + erf, + ) + + if array_api_compat.is_numpy_array(x): + return erf(x) + # array-api-strict and friends: round-trip through NumPy. + xp = array_api_compat.array_namespace(x) + return xp.asarray(erf(np.asarray(x)), dtype=x.dtype) + + def xp_setitem_at(x: Array, mask: Array, values: Array) -> Array: """Set items at boolean mask indices. diff --git a/deepmd/dpmodel/descriptor/unimol_nn/__init__.py b/deepmd/dpmodel/descriptor/unimol_nn/__init__.py new file mode 100644 index 0000000000..179bbe0b51 --- /dev/null +++ b/deepmd/dpmodel/descriptor/unimol_nn/__init__.py @@ -0,0 +1,18 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +"""Neural-network building blocks of the Uni-Mol v1 backbone.""" + +from .encoder import ( + GaussianLayer, + NonLinearHead, + SelfMultiheadAttention, + TransformerEncoderLayer, + TransformerEncoderWithPair, +) + +__all__ = [ + "GaussianLayer", + "NonLinearHead", + "SelfMultiheadAttention", + "TransformerEncoderLayer", + "TransformerEncoderWithPair", +] diff --git a/deepmd/dpmodel/descriptor/unimol_nn/encoder.py b/deepmd/dpmodel/descriptor/unimol_nn/encoder.py new file mode 100644 index 0000000000..7054ea215a --- /dev/null +++ b/deepmd/dpmodel/descriptor/unimol_nn/encoder.py @@ -0,0 +1,586 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +"""Uni-Mol v1 transformer encoder, ported to the array-API dpmodel layer. + +The maths follows Uni-Mol (https://github.com/deepmodeling/Uni-Mol) at commit +90f52c4 and the Uni-Core modules it builds on +(https://github.com/dptech-corp/Uni-Core) at commit ace6fae, both MIT licensed: + + Copyright (c) DP Technology + This source code is licensed under the MIT license found in the LICENSE + file in the root directory of that source tree. + +Parts of Uni-Core derive in turn from fairseq (Copyright (c) Facebook, Inc. and +its affiliates, MIT licensed). + +Ported files and the classes taken from them: + +======================================== ========================================== +upstream classes +======================================== ========================================== +unicore/modules/multihead_attention.py :class:`SelfMultiheadAttention` +unicore/modules/transformer_encoder_layer.py :class:`TransformerEncoderLayer` +unimol/models/transformer_encoder_with_pair.py :class:`TransformerEncoderWithPair` +unimol/models/unimol.py :class:`GaussianLayer`, :class:`NonLinearHead` +======================================== ========================================== + +The port keeps upstream's exact order of operations, so that a checkpoint +converted from Uni-Mol reproduces upstream outputs to floating-point rounding. +Two upstream quirks are reproduced deliberately and are marked in the code: the +truncated ``pi`` in the Gaussian basis, and the fp32 cast inside the norm +regularisers. +""" + +import array_api_compat + +from deepmd.dpmodel.common import ( + NativeOP, +) +from deepmd.dpmodel.utils.network import ( + LayerNorm, + NativeLayer, +) + +# Uni-Mol's Gaussian basis uses a truncated pi (unimol/models/unimol.py:393-397). +# Keeping it is required for bitwise agreement with the released weights. +UNIMOL_PI = 3.14159 + + +def softmax(x, axis: int = -1): # noqa: ANN001, ANN201 + """Numerically stable softmax over ``axis``. + + Rows that are entirely ``-inf`` would produce NaN, as they do upstream. + Uni-Mol never builds such a row: the BOS column is never masked. + """ + xp = array_api_compat.array_namespace(x) + x_max = xp.max(x, axis=axis, keepdims=True) + e = xp.exp(x - x_max) + return e / xp.sum(e, axis=axis, keepdims=True) + + +def norm_loss(x, eps: float = 1e-10, tolerance: float = 1.0): # noqa: ANN001, ANN201 + """Hinge on the deviation of the row norm from ``sqrt(dim)``. + + Mirrors ``norm_loss`` in unimol/models/transformer_encoder_with_pair.py:101. + Upstream evaluates this in fp32 because it pretrains a pure-fp16 model; the + cast is reproduced so that the value matches upstream exactly. + """ + xp = array_api_compat.array_namespace(x) + x = xp.astype(x, xp.float32) + max_norm = x.shape[-1] ** 0.5 + norm = xp.sqrt(xp.sum(x**2, axis=-1) + eps) + error = xp.abs(norm - max_norm) - tolerance + return xp.where(error > 0, error, xp.zeros_like(error)) + + +def masked_mean(mask, value, axis=-1, eps: float = 1e-10): # noqa: ANN001, ANN201 + """Mean of ``value`` over ``mask``, then mean over what is left. + + Mirrors ``masked_mean`` in transformer_encoder_with_pair.py:108. The ``eps`` + in the denominator is what makes an all-padding row return 0 rather than NaN. + """ + xp = array_api_compat.array_namespace(value) + mask = xp.astype(mask, value.dtype) + num = xp.sum(mask * value, axis=axis) + den = eps + xp.sum(mask, axis=axis) + return xp.mean(num / den) + + +class NonLinearHead(NativeOP): + """Two-layer head, ``linear1 -> activation -> linear2``. + + Mirrors unimol/models/unimol.py:347. + """ + + def __init__( + self, + input_dim: int, + out_dim: int, + activation_function: str = "gelu_erf", + hidden: int | None = None, + precision: str = "float64", + seed: int | list[int] | None = None, + ) -> None: + hidden = hidden or input_dim + self.input_dim = input_dim + self.out_dim = out_dim + self.activation_function = activation_function + self.hidden = hidden + self.precision = precision + self.linear1 = NativeLayer( + input_dim, + hidden, + activation_function=activation_function, + precision=precision, + seed=seed, + ) + self.linear2 = NativeLayer( + hidden, out_dim, activation_function=None, precision=precision, seed=seed + ) + + def call(self, x): # noqa: ANN001, ANN201 + return self.linear2(self.linear1(x)) + + def serialize(self) -> dict: + return { + "input_dim": self.input_dim, + "out_dim": self.out_dim, + "hidden": self.hidden, + "activation_function": self.activation_function, + "precision": self.precision, + "linear1": self.linear1.serialize(), + "linear2": self.linear2.serialize(), + } + + @classmethod + def deserialize(cls, data: dict) -> "NonLinearHead": + data = data.copy() + linear1 = data.pop("linear1") + linear2 = data.pop("linear2") + obj = cls(**data) + obj.linear1 = NativeLayer.deserialize(linear1) + obj.linear2 = NativeLayer.deserialize(linear2) + return obj + + +class GaussianLayer(NativeOP): + """Gaussian radial basis with a per-edge-type affine on the distance. + + Mirrors unimol/models/unimol.py:400. ``means`` and ``stds`` are shared by all + edge types; ``mul`` and ``bias`` hold one scalar per ordered element pair. + """ + + def __init__( + self, + k: int = 128, + edge_types: int = 1024, + single_precision_basis: bool = True, + precision: str = "float64", + seed: int | list[int] | None = None, + ) -> None: + import numpy as np + + self.k = k + self.edge_types = edge_types + # Upstream evaluates the basis in fp32 because it pretrains an fp16 + # model (unimol/models/unimol.py:418-420). Keeping that is what makes + # the released weights reproduce; set False to keep the working dtype. + self.single_precision_basis = single_precision_basis + self.precision = precision + rng = np.random.default_rng(seed if isinstance(seed, int) else None) + # Upstream initialises means/stds as U(0, 3) and mul/bias as 1/0, but + # init_bert_params then overwrites all four with N(0, 0.02); see + # unicore/modules/transformer_encoder.py:16 and the note in the spec. + self.means = rng.uniform(0.0, 3.0, size=(1, k)) + self.stds = rng.uniform(0.0, 3.0, size=(1, k)) + self.mul = np.ones((edge_types, 1)) + self.bias = np.zeros((edge_types, 1)) + + def call(self, dist, edge_type): # noqa: ANN001, ANN201 + """Expand ``dist`` (nf x nt x nt) into ``k`` Gaussians per atom pair.""" + xp = array_api_compat.array_namespace(dist) + mul = xp.reshape( + xp.take(xp.asarray(self.mul), xp.reshape(edge_type, (-1,)), axis=0), + (*edge_type.shape, 1), + ) + bias = xp.reshape( + xp.take(xp.asarray(self.bias), xp.reshape(edge_type, (-1,)), axis=0), + (*edge_type.shape, 1), + ) + mul = xp.astype(mul, dist.dtype) + bias = xp.astype(bias, dist.dtype) + x = mul * dist[..., None] + bias + x = xp.repeat(x, self.k, axis=-1) + work = xp.float32 if self.single_precision_basis else x.dtype + x = xp.astype(x, work) + mean = xp.astype(xp.reshape(xp.asarray(self.means), (-1,)), work) + std = xp.abs(xp.astype(xp.reshape(xp.asarray(self.stds), (-1,)), work)) + 1e-5 + a = (2 * UNIMOL_PI) ** 0.5 + out = xp.exp(-0.5 * (((x - mean) / std) ** 2)) / (a * std) + return xp.astype(out, dist.dtype) + + def serialize(self) -> dict: + from deepmd.dpmodel.common import ( + to_numpy_array, + ) + + return { + "k": self.k, + "edge_types": self.edge_types, + "single_precision_basis": self.single_precision_basis, + "precision": self.precision, + "@variables": { + "means": to_numpy_array(self.means), + "stds": to_numpy_array(self.stds), + "mul": to_numpy_array(self.mul), + "bias": to_numpy_array(self.bias), + }, + } + + @classmethod + def deserialize(cls, data: dict) -> "GaussianLayer": + data = data.copy() + variables = data.pop("@variables") + obj = cls(**data) + for key, value in variables.items(): + setattr(obj, key, value) + return obj + + +class SelfMultiheadAttention(NativeOP): + """Self-attention with a fused QKV projection and an additive pair bias. + + Mirrors unicore/modules/multihead_attention.py:12. Only the ``return_attn`` + path of upstream is kept, because Uni-Mol always takes it: the pre-softmax + logits are the pair representation that the next layer biases with. + """ + + def __init__( + self, + embed_dim: int, + num_heads: int, + dropout: float = 0.1, + bias: bool = True, + scaling_factor: float = 1.0, + precision: str = "float64", + seed: int | list[int] | None = None, + ) -> None: + assert embed_dim % num_heads == 0, "embed_dim must be divisible by num_heads" + self.embed_dim = embed_dim + self.num_heads = num_heads + self.dropout = dropout + self.head_dim = embed_dim // num_heads + self.scaling = (self.head_dim * scaling_factor) ** -0.5 + self.precision = precision + self.in_proj = NativeLayer( + embed_dim, embed_dim * 3, bias=bias, precision=precision, seed=seed + ) + self.out_proj = NativeLayer( + embed_dim, embed_dim, bias=bias, precision=precision, seed=seed + ) + + def call(self, query, attn_bias): # noqa: ANN001, ANN201 + """Return the attended values and the biased pre-softmax logits. + + Parameters + ---------- + query + nf x nt x embed_dim. + attn_bias + (nf * num_heads) x nt x nt, already carrying ``-inf`` on padded keys. + + Returns + ------- + tuple + ``(output, attn_weights)``, shaped nf x nt x embed_dim and + (nf * num_heads) x nt x nt. + """ + xp = array_api_compat.array_namespace(query) + nf, nt, _ = query.shape + qkv = self.in_proj(query) + q, k, v = ( + qkv[..., i * self.embed_dim : (i + 1) * self.embed_dim] for i in range(3) + ) + + def split_heads(t): # noqa: ANN001, ANN202 + t = xp.reshape(t, (nf, nt, self.num_heads, self.head_dim)) + t = xp.permute_dims(t, (0, 2, 1, 3)) + return xp.reshape(t, (nf * self.num_heads, nt, self.head_dim)) + + q = split_heads(q) * self.scaling + k = split_heads(k) + v = split_heads(v) + + attn_weights = q @ xp.permute_dims(k, (0, 2, 1)) + # Upstream adds the bias in place and returns the biased logits, which + # become the next layer's bias (multihead_attention.py:100-103). + attn_weights = attn_weights + attn_bias + attn = softmax(attn_weights, axis=-1) + o = attn @ v + o = xp.reshape(o, (nf, self.num_heads, nt, self.head_dim)) + o = xp.permute_dims(o, (0, 2, 1, 3)) + o = xp.reshape(o, (nf, nt, self.embed_dim)) + return self.out_proj(o), attn_weights + + def serialize(self) -> dict: + return { + "embed_dim": self.embed_dim, + "num_heads": self.num_heads, + "dropout": self.dropout, + "precision": self.precision, + "in_proj": self.in_proj.serialize(), + "out_proj": self.out_proj.serialize(), + } + + @classmethod + def deserialize(cls, data: dict) -> "SelfMultiheadAttention": + data = data.copy() + in_proj = data.pop("in_proj") + out_proj = data.pop("out_proj") + obj = cls(**data) + obj.in_proj = NativeLayer.deserialize(in_proj) + obj.out_proj = NativeLayer.deserialize(out_proj) + return obj + + +class TransformerEncoderLayer(NativeOP): + """Pre-layer-norm transformer block that also returns attention logits. + + Mirrors unicore/modules/transformer_encoder_layer.py:15. Uni-Mol never sets + ``post_ln``, so only the pre-LN order is implemented. + """ + + def __init__( + self, + embed_dim: int = 768, + ffn_embed_dim: int = 3072, + attention_heads: int = 8, + dropout: float = 0.1, + attention_dropout: float = 0.1, + activation_dropout: float = 0.0, + activation_function: str = "gelu_erf", + precision: str = "float64", + seed: int | list[int] | None = None, + ) -> None: + self.embed_dim = embed_dim + self.ffn_embed_dim = ffn_embed_dim + self.attention_heads = attention_heads + self.dropout = dropout + self.attention_dropout = attention_dropout + self.activation_dropout = activation_dropout + self.activation_function = activation_function + self.precision = precision + self.self_attn = SelfMultiheadAttention( + embed_dim, + attention_heads, + dropout=attention_dropout, + precision=precision, + seed=seed, + ) + self.self_attn_layer_norm = LayerNorm(embed_dim, precision=precision, seed=seed) + self.fc1 = NativeLayer( + embed_dim, + ffn_embed_dim, + activation_function=activation_function, + precision=precision, + seed=seed, + ) + self.fc2 = NativeLayer( + ffn_embed_dim, + embed_dim, + activation_function=None, + precision=precision, + seed=seed, + ) + self.final_layer_norm = LayerNorm(embed_dim, precision=precision, seed=seed) + + def call(self, x, attn_bias): # noqa: ANN001, ANN201 + residual = x + x = self.self_attn_layer_norm(x) + x, attn_weights = self.self_attn(x, attn_bias=attn_bias) + x = residual + x + + residual = x + x = self.final_layer_norm(x) + x = self.fc2(self.fc1(x)) + x = residual + x + return x, attn_weights + + def serialize(self) -> dict: + return { + "embed_dim": self.embed_dim, + "ffn_embed_dim": self.ffn_embed_dim, + "attention_heads": self.attention_heads, + "dropout": self.dropout, + "attention_dropout": self.attention_dropout, + "activation_dropout": self.activation_dropout, + "activation_function": self.activation_function, + "precision": self.precision, + "self_attn": self.self_attn.serialize(), + "self_attn_layer_norm": self.self_attn_layer_norm.serialize(), + "fc1": self.fc1.serialize(), + "fc2": self.fc2.serialize(), + "final_layer_norm": self.final_layer_norm.serialize(), + } + + @classmethod + def deserialize(cls, data: dict) -> "TransformerEncoderLayer": + data = data.copy() + parts = { + key: data.pop(key) + for key in ( + "self_attn", + "self_attn_layer_norm", + "fc1", + "fc2", + "final_layer_norm", + ) + } + obj = cls(**data) + obj.self_attn = SelfMultiheadAttention.deserialize(parts["self_attn"]) + obj.self_attn_layer_norm = LayerNorm.deserialize(parts["self_attn_layer_norm"]) + obj.fc1 = NativeLayer.deserialize(parts["fc1"]) + obj.fc2 = NativeLayer.deserialize(parts["fc2"]) + obj.final_layer_norm = LayerNorm.deserialize(parts["final_layer_norm"]) + return obj + + +class TransformerEncoderWithPair(NativeOP): + """Stack of pre-LN blocks that carries a pair representation. + + Mirrors unimol/models/transformer_encoder_with_pair.py:14. Each block's + pre-softmax logits become the next block's bias, so the pair representation + is the running sum of per-layer logits. Returns the node representation, the + final pair representation, the pair delta, and the two norm regularisers. + """ + + def __init__( + self, + encoder_layers: int = 6, + embed_dim: int = 768, + ffn_embed_dim: int = 3072, + attention_heads: int = 8, + emb_dropout: float = 0.1, + dropout: float = 0.1, + attention_dropout: float = 0.1, + activation_dropout: float = 0.0, + max_seq_len: int = 256, + activation_function: str = "gelu_erf", + no_final_head_layer_norm: bool = False, + precision: str = "float64", + seed: int | list[int] | None = None, + ) -> None: + self.encoder_layers = encoder_layers + self.embed_dim = embed_dim + self.ffn_embed_dim = ffn_embed_dim + self.attention_heads = attention_heads + self.emb_dropout = emb_dropout + self.dropout = dropout + self.attention_dropout = attention_dropout + self.activation_dropout = activation_dropout + self.max_seq_len = max_seq_len + self.activation_function = activation_function + self.no_final_head_layer_norm = no_final_head_layer_norm + self.precision = precision + self.emb_layer_norm = LayerNorm(embed_dim, precision=precision, seed=seed) + self.final_layer_norm = LayerNorm(embed_dim, precision=precision, seed=seed) + self.final_head_layer_norm = ( + None + if no_final_head_layer_norm + else LayerNorm(attention_heads, precision=precision, seed=seed) + ) + self.layers = [ + TransformerEncoderLayer( + embed_dim=embed_dim, + ffn_embed_dim=ffn_embed_dim, + attention_heads=attention_heads, + dropout=dropout, + attention_dropout=attention_dropout, + activation_dropout=activation_dropout, + activation_function=activation_function, + precision=precision, + seed=seed, + ) + for _ in range(encoder_layers) + ] + + def call(self, emb, attn_mask, padding_mask): # noqa: ANN001, ANN201 + """Run the stack. + + Parameters + ---------- + emb + nf x nt x embed_dim token embedding. + attn_mask + (nf * heads) x nt x nt bias from the Gaussian basis. + padding_mask + nf x nt, 1 on padded positions. + + Returns + ------- + tuple + ``(x, pair_rep, delta_pair_rep, x_norm, delta_pair_rep_norm)``. + """ + xp = array_api_compat.array_namespace(emb) + nf, nt = emb.shape[0], emb.shape[1] + x = self.emb_layer_norm(emb) + pad = xp.astype(padding_mask, x.dtype) + x = x * (1 - pad[..., None]) + + # Upstream merges padding into the bias as -inf on padded key columns, + # so the attention itself never sees a key_padding_mask. It does this + # in place, which aliases the saved input bias; the delta below is + # therefore taken against the already-filled bias, and the resulting + # NaN on padded pairs is overwritten with 0 right after. + neg_inf = xp.asarray(float("-inf"), dtype=attn_mask.dtype) + key_pad = xp.astype(padding_mask, xp.bool)[:, None, None, :] + attn_mask = xp.reshape(attn_mask, (nf, -1, nt, nt)) + attn_mask = xp.where(key_pad, neg_inf, attn_mask) + input_attn_mask = attn_mask + attn_mask = xp.reshape(attn_mask, (-1, nt, nt)) + + for layer in self.layers: + x, attn_mask = layer(x, attn_bias=attn_mask) + + token_mask = 1.0 - pad + x_norm = masked_mean(token_mask, norm_loss(x)) + + x = self.final_layer_norm(x) + + # Padded pairs are dropped from the delta anyway. Upstream reaches that + # by computing -inf minus -inf, getting NaN, and overwriting it with 0; + # zeroing both operands first gives the same values without the NaN. + out_pair = xp.reshape(attn_mask, (nf, -1, nt, nt)) + zero = xp.zeros_like(out_pair) + delta_pair_repr = xp.where(key_pad, zero, out_pair) - xp.where( + key_pad, zero, input_attn_mask + ) + pair_rep = xp.permute_dims(out_pair, (0, 2, 3, 1)) + delta_pair_repr = xp.permute_dims(delta_pair_repr, (0, 2, 3, 1)) + + pair_mask = token_mask[..., None] * token_mask[..., None, :] + delta_pair_repr_norm = masked_mean( + pair_mask, norm_loss(delta_pair_repr), axis=(-1, -2) + ) + + if self.final_head_layer_norm is not None: + delta_pair_repr = self.final_head_layer_norm(delta_pair_repr) + + return x, pair_rep, delta_pair_repr, x_norm, delta_pair_repr_norm + + def serialize(self) -> dict: + return { + "encoder_layers": self.encoder_layers, + "embed_dim": self.embed_dim, + "ffn_embed_dim": self.ffn_embed_dim, + "attention_heads": self.attention_heads, + "emb_dropout": self.emb_dropout, + "dropout": self.dropout, + "attention_dropout": self.attention_dropout, + "activation_dropout": self.activation_dropout, + "max_seq_len": self.max_seq_len, + "activation_function": self.activation_function, + "no_final_head_layer_norm": self.no_final_head_layer_norm, + "precision": self.precision, + "emb_layer_norm": self.emb_layer_norm.serialize(), + "final_layer_norm": self.final_layer_norm.serialize(), + "final_head_layer_norm": None + if self.final_head_layer_norm is None + else self.final_head_layer_norm.serialize(), + "layers": [layer.serialize() for layer in self.layers], + } + + @classmethod + def deserialize(cls, data: dict) -> "TransformerEncoderWithPair": + data = data.copy() + emb_ln = data.pop("emb_layer_norm") + final_ln = data.pop("final_layer_norm") + head_ln = data.pop("final_head_layer_norm") + layers = data.pop("layers") + obj = cls(**data) + obj.emb_layer_norm = LayerNorm.deserialize(emb_ln) + obj.final_layer_norm = LayerNorm.deserialize(final_ln) + obj.final_head_layer_norm = ( + None if head_ln is None else LayerNorm.deserialize(head_ln) + ) + obj.layers = [TransformerEncoderLayer.deserialize(layer) for layer in layers] + return obj diff --git a/deepmd/dpmodel/utils/network.py b/deepmd/dpmodel/utils/network.py index 18b89730eb..0fbda20a57 100644 --- a/deepmd/dpmodel/utils/network.py +++ b/deepmd/dpmodel/utils/network.py @@ -27,6 +27,7 @@ xp_bincount, xp_setitem_at, xp_sigmoid, + xp_erf, ) from deepmd.dpmodel.common import ( to_numpy_array, @@ -350,6 +351,19 @@ def fn(x): # noqa: ANN001, ANN202 * (1 + xp.tanh(xp.sqrt(xp.asarray(2 / xp.pi)) * (x + 0.044715 * x**3))) ) + return fn + elif activation_function == "gelu_erf": + + def fn(x): # noqa: ANN001, ANN202 + xp = array_api_compat.array_namespace(x) + # Exact GELU, x * Phi(x). deepmd's "gelu"/"gelu_tf" are the tanh + # approximation, which differs from this by up to 4.7e-4 per element. + return ( + 0.5 + * x + * (1 + xp_erf(x / xp.sqrt(xp.asarray(2.0, dtype=x.dtype)))) + ) + return fn elif activation_function == "relu6": From a6a196fa3e36c11a4bfe8482eefd5eb54935aa31 Mon Sep 17 00:00:00 2001 From: Duo <50307526+iProzd@users.noreply.github.com> Date: Sat, 12 Sep 2026 05:36:06 -0400 Subject: [PATCH 02/34] feat(dpmodel): add the Uni-Mol v1 data-side transforms Ports the masking and coordinate-noise pipeline of Uni-Mol molecular pretraining from Uni-Mol 90f52c4 (MIT): conformer sampling, the hydrogen policy, cropping, centring, the 90/5/5 corruption, BOS/EOS insertion, and the distance and edge-type construction. Upstream expresses each step as a lazy dataset wrapper; these are plain functions over one frame, which is what a deepmd data loader can call. Corruption belongs on the data side rather than inside a loss because the PyTorch-Exportable backend runs the model before the loss sees a frame, which is also how upstream does it. The legacy numpy.random interface is used deliberately and every call carries a noqa with the reason: upstream seeds the global legacy PRNG, and a Generator would draw a different stream, giving different masks and different noise for the same seed. Verified against tensors dumped from upstream at seed 1, epoch 1, molecules 0-3 of the bundled example data: tokens, loss targets, edge types and both coordinate arrays are bitwise identical, which means the whole random stream is reproduced, down to which atoms are masked and what noise each one gets. The distance matrices differ by 1.9e-6 absolute, the float32 rounding between scipy's distance_matrix and a sqrt of summed squares. --- deepmd/dpmodel/utils/unimol_transform.py | 366 +++++++++++++++++++++++ 1 file changed, 366 insertions(+) create mode 100644 deepmd/dpmodel/utils/unimol_transform.py diff --git a/deepmd/dpmodel/utils/unimol_transform.py b/deepmd/dpmodel/utils/unimol_transform.py new file mode 100644 index 0000000000..87c314b911 --- /dev/null +++ b/deepmd/dpmodel/utils/unimol_transform.py @@ -0,0 +1,366 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +"""Data-side transforms of Uni-Mol v1 molecular pretraining. + +Ported from Uni-Mol (https://github.com/deepmodeling/Uni-Mol) at commit 90f52c4, +MIT licensed: + + Copyright (c) DP Technology + This source code is licensed under the MIT license found in the LICENSE + file in the root directory of that source tree. + +Upstream expresses each step as a lazy dataset wrapper +(``unimol/data/*_dataset.py``); here they are plain functions over one frame, so +that a deepmd data loader can call them. The random draws keep upstream's order +and its per-sample seeding, ``hash((seed, epoch, index)) % 1e6``, so a frame +comes out corrupted exactly as upstream would corrupt it. + +Corruption has to happen here rather than inside a loss, because the +PyTorch-Exportable backend runs the model before the loss ever sees a frame. + +The legacy ``numpy.random`` interface is used on purpose, against deepmd's +usual preference for ``np.random.Generator``: upstream seeds the global +legacy PRNG, and a Generator draws a different stream, which would give +different masks and different noise for the same seed. Every such call is +marked with ``# noqa: NPY002``. +""" + +import contextlib +from collections.abc import ( + Iterator, + Sequence, +) + +import numpy as np + +__all__ = [ + "add_bos_eos", + "center_coordinates", + "crop_atoms", + "edge_type", + "mask_points", + "numpy_seed", + "pair_distance", + "remove_hydrogen", + "sample_conformer", + "unimol_frame_transform", +] + + +@contextlib.contextmanager +def numpy_seed(seed: int | None, *addl_seeds: int) -> Iterator[None]: + """Seed the legacy NumPy PRNG, then restore the previous state. + + Mirrors ``unimol/data/data_utils.py:9``. Note the modulus is ``1e6`` here, + while Uni-Core's own copy uses ``1e8``; the Uni-Mol transforms call this one. + Hashing a tuple of ints is stable across ``PYTHONHASHSEED``. + """ + if seed is None: + yield + return + if len(addl_seeds) > 0: + seed = int(hash((seed, *addl_seeds)) % 1e6) + state = np.random.get_state() # noqa: NPY002 + np.random.seed(seed) # noqa: NPY002 + try: + yield + finally: + np.random.set_state(state) # noqa: NPY002 + + +def sample_conformer( + conformers: Sequence[np.ndarray], seed: int, epoch: int, index: int +) -> np.ndarray: + """Draw one conformer from the pool. + + Mirrors ``ConformerSampleDataset``. Upstream appends an RDKit 2D conformer to + the pool while loading; in deepmd that belongs to the offline converter, so + the pool arrives here already complete and this step needs no RDKit. + """ + with numpy_seed(seed, epoch, index): + sample_idx = np.random.randint(len(conformers)) # noqa: NPY002 + return np.asarray(conformers[sample_idx], dtype=np.float32) + + +def remove_hydrogen( + atoms: np.ndarray, + coordinates: np.ndarray, + remove_hydrogen: bool = False, + remove_polar_hydrogen: bool = False, +) -> tuple[np.ndarray, np.ndarray]: + """Apply the hydrogen policy. + + Mirrors ``RemoveHydrogenDataset``. ``remove_hydrogen`` drops every H; + ``remove_polar_hydrogen`` drops only the trailing run of H, which is what + Uni-Mol calls polar hydrogens. Upstream maps ``only_polar`` to this pair: + -1 keeps all, 0 removes all, 1 removes the trailing run. + """ + atoms = np.asarray(atoms) + if remove_hydrogen: + keep = atoms != "H" + atoms, coordinates = atoms[keep], coordinates[keep] + if not remove_hydrogen and remove_polar_hydrogen: + end_idx = 0 + for i, atom in enumerate(atoms[::-1]): + if atom != "H": + break + end_idx = i + 1 + if end_idx != 0: + atoms, coordinates = atoms[:-end_idx], coordinates[:-end_idx] + return atoms, coordinates.astype(np.float32) + + +def crop_atoms( + atoms: np.ndarray, + coordinates: np.ndarray, + seed: int, + epoch: int, + index: int, + max_atoms: int = 256, +) -> tuple[np.ndarray, np.ndarray]: + """Randomly keep at most ``max_atoms`` atoms. + + Mirrors ``CroppingDataset``. The subset is drawn without replacement and + without regard to where the atoms are in space. + """ + if max_atoms and len(atoms) > max_atoms: + with numpy_seed(seed, epoch, index): + keep = np.random.choice(len(atoms), max_atoms, replace=False) # noqa: NPY002 + atoms, coordinates = np.asarray(atoms)[keep], coordinates[keep] + return atoms, coordinates.astype(np.float32) + + +def center_coordinates(coordinates: np.ndarray) -> np.ndarray: + """Move the centroid to the origin. Mirrors ``NormalizeDataset``.""" + return (coordinates - coordinates.mean(axis=0)).astype(np.float32) + + +def mask_points( + tokens: np.ndarray, + coordinates: np.ndarray, + *, + num_types: int, + special_indices: Sequence[int], + pad_idx: int, + mask_idx: int, + seed: int, + epoch: int, + index: int, + mask_prob: float = 0.15, + leave_unmasked_prob: float = 0.05, + random_token_prob: float = 0.05, + noise_type: str = "uniform", + noise: float = 1.0, +) -> dict[str, np.ndarray]: + """Corrupt a frame the way Uni-Mol pretraining does. + + Mirrors ``MaskPointsDataset.__getitem_cached__``. Of the selected atoms, 90% + become ``[MASK]``, 5% become a random element and 5% are left alone; all + three go into the loss targets. Coordinate noise lands on the masked and the + randomly replaced atoms, not on the ones left alone. + + The order of the random draws matters and is kept: the rounding draw, the + selection, the two split draws, the noise, then the random elements. + + Returns + ------- + dict + ``tokens`` and ``coordinates`` are the corrupted inputs; ``targets`` + holds the true element at every selected position and ``pad_idx`` + elsewhere. + """ + assert 0.0 < mask_prob < 1.0 + assert 0.0 <= random_token_prob <= 1.0 + assert 0.0 <= leave_unmasked_prob <= 1.0 + assert random_token_prob + leave_unmasked_prob <= 1.0 + + weights = None + if random_token_prob > 0.0: + weights = np.ones(num_types) + weights[list(special_indices)] = 0 + weights = weights / weights.sum() + + if noise_type == "trunc_normal": + + def noise_f(n): # noqa: ANN001, ANN202 + return np.clip( + np.random.randn(n, 3) * noise, a_min=-noise * 2.0, a_max=noise * 2.0 # noqa: NPY002 + ) + elif noise_type == "normal": + + def noise_f(n): # noqa: ANN001, ANN202 + return np.random.randn(n, 3) * noise # noqa: NPY002 + elif noise_type == "uniform": + + def noise_f(n): # noqa: ANN001, ANN202 + return np.random.uniform(low=-noise, high=noise, size=(n, 3)) # noqa: NPY002 + else: + + def noise_f(n): # noqa: ANN001, ANN202 + return 0.0 + + with numpy_seed(seed, epoch, index): + sz = len(tokens) + assert sz > 0 + # A random addend rounds the count probabilistically, so a small + # molecule can end up with no masked atom at all. + num_mask = int(mask_prob * sz + np.random.rand()) # noqa: NPY002 + mask_idc = np.random.choice(sz, num_mask, replace=False) # noqa: NPY002 + mask = np.full(sz, False) + mask[mask_idc] = True + + targets = np.full(len(mask), pad_idx) + targets[mask] = np.asarray(tokens)[mask] + + rand_or_unmask_prob = random_token_prob + leave_unmasked_prob + if rand_or_unmask_prob > 0.0: + rand_or_unmask = mask & (np.random.rand(sz) < rand_or_unmask_prob) # noqa: NPY002 + if random_token_prob == 0.0: + unmask, rand_mask = rand_or_unmask, None + elif leave_unmasked_prob == 0.0: + unmask, rand_mask = None, rand_or_unmask + else: + unmask_prob = leave_unmasked_prob / rand_or_unmask_prob + decision = np.random.rand(sz) < unmask_prob # noqa: NPY002 + unmask = rand_or_unmask & decision + rand_mask = rand_or_unmask & (~decision) + else: + unmask = rand_mask = None + + if unmask is not None: + mask = mask ^ unmask + + new_tokens = np.copy(np.asarray(tokens)) + new_tokens[mask] = mask_idx + + num_mask = mask.astype(np.int32).sum() + new_coord = np.copy(coordinates) + new_coord[mask, :] += noise_f(num_mask) + + if rand_mask is not None: + num_rand = rand_mask.sum() + if num_rand > 0: + new_tokens[rand_mask] = np.random.choice(num_types, num_rand, p=weights) # noqa: NPY002 + + return { + "tokens": new_tokens.astype(np.int64), + "targets": targets.astype(np.int64), + "coordinates": new_coord.astype(np.float32), + } + + +def add_bos_eos( + tokens: np.ndarray, + coordinates: np.ndarray, + targets: np.ndarray, + bos_idx: int, + eos_idx: int, + pad_idx: int, +) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + """Wrap a frame in the two virtual tokens. + + Mirrors ``PrependTokenDataset``/``AppendTokenDataset`` as Uni-Mol calls them + (``tasks/unimol.py:192-206``). Both sit at the origin, which after centering + is the centroid; both take ``pad_idx`` as target, so neither enters the loss. + """ + tokens = np.concatenate([[bos_idx], tokens, [eos_idx]]).astype(np.int64) + targets = np.concatenate([[pad_idx], targets, [pad_idx]]).astype(np.int64) + zero = np.zeros((1, 3), dtype=coordinates.dtype) + coordinates = np.concatenate([zero, coordinates, zero], axis=0) + return tokens, coordinates, targets + + +def pair_distance(coordinates: np.ndarray) -> np.ndarray: + """All-pairs Euclidean distance. Mirrors ``DistanceDataset``.""" + diff = coordinates[:, None, :] - coordinates[None, :, :] + return np.sqrt((diff**2).sum(axis=-1)).astype(np.float32) + + +def edge_type(tokens: np.ndarray, num_types: int) -> np.ndarray: + """Ordered element-pair id, ``t_i * num_types + t_j``. + + Mirrors ``EdgeTypeDataset``. Special tokens take part, so the table has + ``num_types ** 2`` entries. + """ + tokens = np.asarray(tokens) + return (tokens[:, None] * num_types + tokens[None, :]).astype(np.int64) + + +def unimol_frame_transform( + atoms: Sequence[str], + conformers: Sequence[np.ndarray], + *, + vocab: dict[str, int], + num_types: int, + special_indices: Sequence[int], + pad_idx: int, + bos_idx: int, + eos_idx: int, + mask_idx: int, + unk_idx: int, + seed: int, + epoch: int, + index: int, + remove_hydrogen_: bool = False, + remove_polar_hydrogen: bool = False, + max_atoms: int = 256, + max_seq_len: int = 512, + **mask_kwargs: float | str, +) -> dict[str, np.ndarray]: + """Run the whole Uni-Mol pretraining chain for one molecule. + + Mirrors ``UniMolTask.load_dataset`` (``tasks/unimol.py:140-245``): sample a + conformer, apply the hydrogen policy, crop, centre, tokenize, corrupt, then + wrap in BOS/EOS and build the distance matrix and edge types. The inputs are + built from the corrupted coordinates and the targets from the clean ones. + """ + coordinates = sample_conformer(conformers, seed, epoch, index) + atoms_arr = np.asarray(atoms) + atoms_arr, coordinates = remove_hydrogen( + atoms_arr, coordinates, remove_hydrogen_, remove_polar_hydrogen + ) + atoms_arr, coordinates = crop_atoms( + atoms_arr, coordinates, seed, epoch, index, max_atoms + ) + coordinates = center_coordinates(coordinates) + + tokens = np.asarray([vocab.get(str(a), unk_idx) for a in atoms_arr], dtype=np.int64) + assert 0 < len(tokens) < max_seq_len + + corrupted = mask_points( + tokens, + coordinates, + num_types=num_types, + special_indices=special_indices, + pad_idx=pad_idx, + mask_idx=mask_idx, + seed=seed, + epoch=epoch, + index=index, + **mask_kwargs, + ) + + src_tokens, src_coord, tokens_target = add_bos_eos( + corrupted["tokens"], + corrupted["coordinates"], + corrupted["targets"], + bos_idx, + eos_idx, + pad_idx, + ) + clean_coord = np.concatenate( + [ + np.zeros((1, 3), dtype=coordinates.dtype), + coordinates, + np.zeros((1, 3), dtype=coordinates.dtype), + ], + axis=0, + ) + return { + "src_tokens": src_tokens, + "src_coord": src_coord, + "src_distance": pair_distance(src_coord), + "src_edge_type": edge_type(src_tokens, num_types), + "tokens_target": tokens_target, + "coord_target": clean_coord, + "distance_target": pair_distance(clean_coord), + } From 45910351ab33e586a462eb03e950df4eff08e3c2 Mon Sep 17 00:00:00 2001 From: Duo <50307526+iProzd@users.noreply.github.com> Date: Sat, 12 Sep 2026 05:38:17 -0400 Subject: [PATCH 03/34] feat: register the exact GELU in every backend activation table "gelu_erf" was added to the dpmodel table in the previous commit. The name also has to reach the whitelist in deepmd/common.py, because that is what argcheck validates a configuration against, and every backend table has to answer to it: TensorFlow asserts at import that the whitelist is a subset of its own table, so registering the name without a TF entry would break importing deepmd.tf.common. PyTorch, PyTorch-Exportable and Paddle would each raise at runtime instead. All four array backends resolve "gelu_erf" to the exact error-function GELU and agree with torch's own to rounding: 0 for pt and pt_expt, 2.2e-16 for dpmodel. "gelu" and "gelu_tf" keep their current meaning everywhere. --- deepmd/common.py | 1 + deepmd/pd/utils/utils.py | 3 +++ deepmd/pt/utils/utils.py | 4 ++++ deepmd/pt_expt/utils/network.py | 3 +++ deepmd/tf/common.py | 26 ++++++++++++++++++++++++++ 5 files changed, 37 insertions(+) diff --git a/deepmd/common.py b/deepmd/common.py index 98cf2461bd..fea637328a 100644 --- a/deepmd/common.py +++ b/deepmd/common.py @@ -53,6 +53,7 @@ "tanh", "gelu", "gelu_tf", + "gelu_erf", "silu", "silut", "none", diff --git a/deepmd/pd/utils/utils.py b/deepmd/pd/utils/utils.py index 158b76f0df..1849a4b2d4 100644 --- a/deepmd/pd/utils/utils.py +++ b/deepmd/pd/utils/utils.py @@ -224,6 +224,9 @@ def forward(self, x: paddle.Tensor) -> paddle.Tensor: return F.relu(x) elif self.activation.lower() == "gelu" or self.activation.lower() == "gelu_tf": return F.gelu(x, approximate=True) + elif self.activation.lower() == "gelu_erf": + # Exact GELU; the two names above are the tanh approximation. + return F.gelu(x, approximate=False) elif self.activation.lower() == "tanh": return paddle.tanh(x) elif self.activation.lower() == "relu6": diff --git a/deepmd/pt/utils/utils.py b/deepmd/pt/utils/utils.py index 9f95c59adc..878cf0d713 100644 --- a/deepmd/pt/utils/utils.py +++ b/deepmd/pt/utils/utils.py @@ -198,6 +198,10 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: return F.relu(x) elif self.activation.lower() == "gelu" or self.activation.lower() == "gelu_tf": return F.gelu(x, approximate="tanh") + elif self.activation.lower() == "gelu_erf": + # Exact GELU. "gelu"/"gelu_tf" above are the tanh approximation, + # which differs from this by up to 4.7e-4 per element. + return F.gelu(x, approximate="none") elif self.activation.lower() == "tanh": return torch.tanh(x) elif self.activation.lower() == "relu6": diff --git a/deepmd/pt_expt/utils/network.py b/deepmd/pt_expt/utils/network.py index adc7d4f326..651c8bb4f8 100644 --- a/deepmd/pt_expt/utils/network.py +++ b/deepmd/pt_expt/utils/network.py @@ -181,6 +181,9 @@ def _torch_activation(x: torch.Tensor, name: str) -> torch.Tensor: return torch.relu(x) elif name in ("gelu", "gelu_tf"): return torch.nn.functional.gelu(x, approximate="tanh") + elif name == "gelu_erf": + # Exact GELU; the two names above are the tanh approximation. + return torch.nn.functional.gelu(x, approximate="none") elif name == "relu6": return torch.clamp(x, min=0.0, max=6.0) elif name == "softplus": diff --git a/deepmd/tf/common.py b/deepmd/tf/common.py index 1a37769dfe..66444f9a60 100644 --- a/deepmd/tf/common.py +++ b/deepmd/tf/common.py @@ -186,6 +186,31 @@ def silut(x: tf.Tensor) -> tf.Tensor: return silut +def gelu_erf(x: tf.Tensor) -> tf.Tensor: + """Exact Gaussian Error Linear Unit. + + Unlike :func:`gelu` and :func:`gelu_tf`, which are the tanh approximation, + this evaluates ``x * Phi(x)`` through the error function. The two forms + differ by up to 4.7e-4 per element. + + Parameters + ---------- + x : tf.Tensor + float Tensor to perform activation + + Returns + ------- + tf.Tensor + `x` with the exact GELU activation applied + + References + ---------- + Original paper + https://arxiv.org/abs/1606.08415 + """ + return 0.5 * x * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0), x.dtype))) + + ACTIVATION_FN_DICT = { "relu": tf.nn.relu, "relu6": tf.nn.relu6, @@ -194,6 +219,7 @@ def silut(x: tf.Tensor) -> tf.Tensor: "tanh": tf.nn.tanh, "gelu": gelu, "gelu_tf": gelu_tf, + "gelu_erf": gelu_erf, "silu": silu, "silut": get_silut("silut"), "linear": lambda x: x, From 0fecbd63d7447c30788a99bb4d92b11df7b0bbda Mon Sep 17 00:00:00 2001 From: Duo <50307526+iProzd@users.noreply.github.com> Date: Sat, 12 Sep 2026 05:41:53 -0400 Subject: [PATCH 04/34] feat(dpmodel): add the Uni-Mol v1 pretraining heads and loss Ports the three pretraining heads (element prediction, coordinate denoising through the pair channel, pairwise distance prediction) and the five-term objective from Uni-Mol 90f52c4 (MIT), with upstream's README weights of 1, 5, 10, 0.01 and 0.01 and its hard-coded distance normalisation. The coordinate update takes the post-#211 form: the normaliser counts every non-padding token, BOS and EOS included, and pairs touching padding are zeroed before the sum. The distance term covers the corrupted rows against every non-padding column, diagonal included. Verified against tensors dumped from upstream, on both a small random model and the released mol_pre_all_h_220816 weights. Heads agree to fp64 rounding: 5e-16 relative on the logits, 2.5e-16 on the distances, 1.8e-20 on the coordinates. All five loss terms agree to 1e-7 relative or better; that floor is upstream's own, since it evaluates log_softmax and both norm regularisers in fp32 regardless of model precision, and those casts are reproduced. --- .../dpmodel/descriptor/unimol_nn/__init__.py | 8 + deepmd/dpmodel/descriptor/unimol_nn/heads.py | 195 ++++++++++++++++ deepmd/dpmodel/loss/__init__.py | 4 + deepmd/dpmodel/loss/unimol.py | 219 ++++++++++++++++++ 4 files changed, 426 insertions(+) create mode 100644 deepmd/dpmodel/descriptor/unimol_nn/heads.py create mode 100644 deepmd/dpmodel/loss/unimol.py diff --git a/deepmd/dpmodel/descriptor/unimol_nn/__init__.py b/deepmd/dpmodel/descriptor/unimol_nn/__init__.py index 179bbe0b51..bf0c9535a5 100644 --- a/deepmd/dpmodel/descriptor/unimol_nn/__init__.py +++ b/deepmd/dpmodel/descriptor/unimol_nn/__init__.py @@ -1,6 +1,11 @@ # SPDX-License-Identifier: LGPL-3.0-or-later """Neural-network building blocks of the Uni-Mol v1 backbone.""" +from .heads import ( + DistanceHead, + MaskLMHead, + coord_update, +) from .encoder import ( GaussianLayer, NonLinearHead, @@ -10,9 +15,12 @@ ) __all__ = [ + "DistanceHead", "GaussianLayer", + "MaskLMHead", "NonLinearHead", "SelfMultiheadAttention", "TransformerEncoderLayer", "TransformerEncoderWithPair", + "coord_update", ] diff --git a/deepmd/dpmodel/descriptor/unimol_nn/heads.py b/deepmd/dpmodel/descriptor/unimol_nn/heads.py new file mode 100644 index 0000000000..c4af38fd1f --- /dev/null +++ b/deepmd/dpmodel/descriptor/unimol_nn/heads.py @@ -0,0 +1,195 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +"""The three Uni-Mol v1 pretraining heads. + +Ported from Uni-Mol (https://github.com/deepmodeling/Uni-Mol) at commit 90f52c4, +MIT licensed: + + Copyright (c) DP Technology + This source code is licensed under the MIT license found in the LICENSE + file in the root directory of that source tree. + +``MaskLMHead`` predicts the element of every corrupted atom, ``coord_update`` +denoises the coordinates through the pair channel, and ``DistanceHead`` +predicts the clean pairwise distances. Together with the two norm regularisers +of the encoder these make up the five pretraining objectives. +""" + +import array_api_compat + +from deepmd.dpmodel.common import ( + NativeOP, +) +from deepmd.dpmodel.utils.network import ( + LayerNorm, + NativeLayer, +) + +__all__ = ["DistanceHead", "MaskLMHead", "coord_update"] + + +class MaskLMHead(NativeOP): + """Element prediction for the corrupted atoms. + + Mirrors unimol/models/unimol.py:292. The output projection is a free + parameter, not tied to the token embedding: upstream takes the weight of a + throw-away ``nn.Linear`` and keeps it (``:301-303``). + """ + + def __init__( + self, + embed_dim: int, + output_dim: int, + activation_function: str = "gelu_erf", + precision: str = "float64", + seed: int | list[int] | None = None, + ) -> None: + self.embed_dim = embed_dim + self.output_dim = output_dim + self.activation_function = activation_function + self.precision = precision + self.dense = NativeLayer( + embed_dim, + embed_dim, + activation_function=activation_function, + precision=precision, + seed=seed, + ) + self.layer_norm = LayerNorm(embed_dim, precision=precision, seed=seed) + self.out_proj = NativeLayer( + embed_dim, output_dim, bias=True, precision=precision, seed=seed + ) + + def call(self, features, masked_tokens=None): # noqa: ANN001, ANN201 + """Project the selected positions onto the vocabulary. + + Parameters + ---------- + features + nf x nt x embed_dim node representation. + masked_tokens + nf x nt boolean mask; only these positions are projected, which is + what upstream does to save memory. + + Returns + ------- + Array + n_masked x output_dim logits, or nf x nt x output_dim if no mask. + """ + xp = array_api_compat.array_namespace(features) + if masked_tokens is not None: + features = features[xp.astype(masked_tokens, xp.bool), :] + return self.out_proj(self.layer_norm(self.dense(features))) + + def serialize(self) -> dict: + return { + "embed_dim": self.embed_dim, + "output_dim": self.output_dim, + "activation_function": self.activation_function, + "precision": self.precision, + "dense": self.dense.serialize(), + "layer_norm": self.layer_norm.serialize(), + "out_proj": self.out_proj.serialize(), + } + + @classmethod + def deserialize(cls, data: dict) -> "MaskLMHead": + data = data.copy() + parts = {k: data.pop(k) for k in ("dense", "layer_norm", "out_proj")} + obj = cls(**data) + obj.dense = NativeLayer.deserialize(parts["dense"]) + obj.layer_norm = LayerNorm.deserialize(parts["layer_norm"]) + obj.out_proj = NativeLayer.deserialize(parts["out_proj"]) + return obj + + +class DistanceHead(NativeOP): + """Pairwise distance prediction from the pair representation. + + Mirrors unimol/models/unimol.py:370. It reads the final attention logits, + not the pair delta, and symmetrises its output. + """ + + def __init__( + self, + heads: int, + activation_function: str = "gelu_erf", + precision: str = "float64", + seed: int | list[int] | None = None, + ) -> None: + self.heads = heads + self.activation_function = activation_function + self.precision = precision + self.dense = NativeLayer( + heads, + heads, + activation_function=activation_function, + precision=precision, + seed=seed, + ) + self.layer_norm = LayerNorm(heads, precision=precision, seed=seed) + self.out_proj = NativeLayer(heads, 1, bias=True, precision=precision, seed=seed) + + def call(self, pair_rep): # noqa: ANN001, ANN201 + """Map nf x nt x nt x heads onto a symmetric nf x nt x nt matrix.""" + xp = array_api_compat.array_namespace(pair_rep) + x = self.out_proj(self.layer_norm(self.dense(pair_rep))) + x = xp.reshape(x, x.shape[:3]) + return (x + xp.matrix_transpose(x)) * 0.5 + + def serialize(self) -> dict: + return { + "heads": self.heads, + "activation_function": self.activation_function, + "precision": self.precision, + "dense": self.dense.serialize(), + "layer_norm": self.layer_norm.serialize(), + "out_proj": self.out_proj.serialize(), + } + + @classmethod + def deserialize(cls, data: dict) -> "DistanceHead": + data = data.copy() + parts = {k: data.pop(k) for k in ("dense", "layer_norm", "out_proj")} + obj = cls(**data) + obj.dense = NativeLayer.deserialize(parts["dense"]) + obj.layer_norm = LayerNorm.deserialize(parts["layer_norm"]) + obj.out_proj = NativeLayer.deserialize(parts["out_proj"]) + return obj + + +def coord_update(coord, delta_pair_rep, padding_mask, pair2coord_proj): # noqa: ANN001, ANN201 + r"""Denoise coordinates through the pair channel. + + Mirrors unimol/models/unimol.py:229-245, the form introduced by upstream + #211: the normaliser counts every non-padding token, BOS and EOS included, + and pairs touching padding are zeroed before the sum. + + .. math:: + + \hat{x}_i = x_i + \sum_j \frac{x_j - x_i}{N} c_{ij} + + Parameters + ---------- + coord + nf x nt x 3 input (noisy) coordinates. + delta_pair_rep + nf x nt x nt x heads pair delta from the encoder. + padding_mask + nf x nt, 1 on padded positions. + pair2coord_proj + Head mapping heads -> 1. + + Returns + ------- + Array + nf x nt x 3 updated coordinates. + """ + xp = array_api_compat.array_namespace(coord) + pad = xp.astype(padding_mask, coord.dtype) + atom_num = xp.reshape(xp.sum(1 - pad, axis=1), (-1, 1, 1, 1)) + delta_pos = coord[:, None, :, :] - coord[:, :, None, :] + attn_probs = pair2coord_proj(delta_pair_rep) + update = delta_pos / atom_num * attn_probs + pair_coords_mask = (1 - pad)[..., None] * (1 - pad)[:, None, :] + update = update * pair_coords_mask[..., None] + return coord + xp.sum(update, axis=2) diff --git a/deepmd/dpmodel/loss/__init__.py b/deepmd/dpmodel/loss/__init__.py index 115f1f6b03..605949aaa9 100644 --- a/deepmd/dpmodel/loss/__init__.py +++ b/deepmd/dpmodel/loss/__init__.py @@ -14,6 +14,9 @@ from deepmd.dpmodel.loss.tensor import ( TensorLoss, ) +from deepmd.dpmodel.loss.unimol import ( + UniMolLoss, +) __all__ = [ "DOSLoss", @@ -21,4 +24,5 @@ "EnergySpinLoss", "PropertyLoss", "TensorLoss", + "UniMolLoss", ] diff --git a/deepmd/dpmodel/loss/unimol.py b/deepmd/dpmodel/loss/unimol.py new file mode 100644 index 0000000000..bdb3f31409 --- /dev/null +++ b/deepmd/dpmodel/loss/unimol.py @@ -0,0 +1,219 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +"""The Uni-Mol v1 molecular-pretraining objective. + +Ported from Uni-Mol (https://github.com/deepmodeling/Uni-Mol) at commit 90f52c4, +MIT licensed: + + Copyright (c) DP Technology + This source code is licensed under the MIT license found in the LICENSE + file in the root directory of that source tree. + +Five terms, with upstream's default weights from its README pretraining recipe: +element prediction (1), coordinate denoising (5), distance prediction (10), and +the two norm regularisers (0.01 each). The regularisers are produced by the +backbone, so the loss only weights them. +""" + +import array_api_compat + +from deepmd.dpmodel.array_api import ( + Array, +) +from deepmd.dpmodel.loss.loss import ( + Loss, +) +from deepmd.utils.data import ( + DataRequirementItem, +) +from deepmd.utils.version import ( + check_version_compatibility, +) + +# Upstream normalises the distance target with these two constants +# (unimol/losses/unimol.py:17-18). They are hard-coded there, not fitted. +DIST_MEAN = 6.312581655060595 +DIST_STD = 3.3899264663911888 + + +def _smooth_l1(pred: Array, label: Array, beta: float = 1.0) -> Array: + r"""Mean smooth L1, matching ``F.smooth_l1_loss(reduction="mean")``. + + .. math:: + + \ell_\beta(e)=\begin{cases} + e^2/(2\beta),&|e|<\beta,\\ + |e|-\beta/2,&|e|\ge\beta. + \end{cases} + """ + xp = array_api_compat.array_namespace(pred) + diff = xp.abs(pred - label) + elementwise = xp.where(diff < beta, 0.5 * diff**2 / beta, diff - 0.5 * beta) + return xp.mean(elementwise) + + +def _masked_nll(logits: Array, target: Array, pad_idx: int) -> Array: + """Negative log likelihood over the selected positions. + + Upstream evaluates ``log_softmax`` in fp32 (``losses/unimol.py:36``) because + it pretrains an fp16 model; the cast is kept so the value matches. + """ + xp = array_api_compat.array_namespace(logits) + logits = xp.astype(logits, xp.float32) + x_max = xp.max(logits, axis=-1, keepdims=True) + shifted = logits - x_max + log_probs = shifted - xp.log(xp.sum(xp.exp(shifted), axis=-1, keepdims=True)) + target = xp.reshape(target, (-1,)) + keep = target != pad_idx + picked = xp.take_along_axis(log_probs, xp.reshape(target, (-1, 1)), axis=1) + picked = xp.reshape(picked, (-1,)) + picked = xp.where(keep, picked, xp.zeros_like(picked)) + return -xp.sum(picked) / xp.astype( + xp.sum(xp.astype(keep, logits.dtype)), logits.dtype + ) + + +@Loss.register("unimol") +class UniMolLoss(Loss): + r"""Uni-Mol v1 self-supervised pretraining loss. + + .. math:: + + L = w_t L_\text{token} + w_c L_\text{coord} + w_d L_\text{dist} + + w_x L_{\|x\|} + w_p L_{\|\Delta p\|} + + Every term is a flat mean over the positions it covers, so molecules with + more corrupted atoms weigh more, exactly as upstream. + + Parameters + ---------- + masked_token_loss : float + Weight of the element-prediction term. + masked_coord_loss : float + Weight of the coordinate-denoising term. + masked_dist_loss : float + Weight of the distance-prediction term. + x_norm_loss : float + Weight of the node-norm regulariser. + delta_pair_repr_norm_loss : float + Weight of the pair-delta-norm regulariser. + beta : float + The transition point of the smooth L1 used by the coordinate and + distance terms. + pad_idx : int + Token id that marks padding, excluded from every term. + """ + + def __init__( + self, + masked_token_loss: float = 1.0, + masked_coord_loss: float = 5.0, + masked_dist_loss: float = 10.0, + x_norm_loss: float = 0.01, + delta_pair_repr_norm_loss: float = 0.01, + beta: float = 1.0, + pad_idx: int = 0, + **kwargs: float, + ) -> None: + self.masked_token_loss = masked_token_loss + self.masked_coord_loss = masked_coord_loss + self.masked_dist_loss = masked_dist_loss + self.x_norm_loss = x_norm_loss + self.delta_pair_repr_norm_loss = delta_pair_repr_norm_loss + self.beta = beta + self.pad_idx = pad_idx + + def call( + self, + learning_rate: float, + natoms: int, + model_dict: dict[str, Array], + label_dict: dict[str, Array], + mae: bool = False, + ) -> tuple[Array, dict[str, Array]]: + """Evaluate the five terms and their weighted sum.""" + del learning_rate, natoms, mae + token_target = label_dict["unimol_token_target"] + xp = array_api_compat.array_namespace(token_target) + masked = token_target != self.pad_idx + more_loss = {} + loss = None + + def add(term: Array, weight: float, name: str) -> None: + nonlocal loss + more_loss[name] = term + loss = weight * term if loss is None else loss + weight * term + + if self.masked_token_loss > 0: + add( + _masked_nll( + model_dict["unimol_logits"], token_target[masked], self.pad_idx + ), + self.masked_token_loss, + "token_loss", + ) + if self.masked_coord_loss > 0: + coord_pred = model_dict["unimol_coord"][masked] + coord_label = label_dict["unimol_coord_target"][masked] + add( + _smooth_l1( + xp.reshape(coord_pred, (-1, 3)), + xp.reshape(coord_label, (-1, 3)), + self.beta, + ), + self.masked_coord_loss, + "coord_loss", + ) + if self.masked_dist_loss > 0: + # Rows are the corrupted atoms; columns are every non-padding token, + # BOS, EOS and the diagonal included (losses/unimol.py:159-180). + token_mask = label_dict["unimol_token_mask"] + pair_mask = masked[..., None] & xp.astype(token_mask, xp.bool)[:, None, :] + dist_label = ( + label_dict["unimol_dist_target"][pair_mask] - DIST_MEAN + ) / DIST_STD + add( + _smooth_l1(model_dict["unimol_dist"][pair_mask], dist_label, self.beta), + self.masked_dist_loss, + "dist_loss", + ) + if self.x_norm_loss > 0: + add(model_dict["unimol_x_norm"], self.x_norm_loss, "x_norm_loss") + if self.delta_pair_repr_norm_loss > 0: + add( + model_dict["unimol_delta_pair_norm"], + self.delta_pair_repr_norm_loss, + "delta_pair_norm_loss", + ) + return loss, more_loss + + @property + def label_requirement(self) -> list[DataRequirementItem]: + """Labels produced by the Uni-Mol data transform, not by a simulation.""" + return [ + DataRequirementItem("unimol_token_target", ndof=1, atomic=True, must=True), + DataRequirementItem("unimol_token_mask", ndof=1, atomic=True, must=True), + DataRequirementItem("unimol_coord_target", ndof=3, atomic=True, must=True), + DataRequirementItem("unimol_dist_target", ndof=1, atomic=True, must=True), + ] + + def serialize(self) -> dict: + """Serialize the loss module.""" + return { + "@class": "UniMolLoss", + "@version": 1, + "masked_token_loss": self.masked_token_loss, + "masked_coord_loss": self.masked_coord_loss, + "masked_dist_loss": self.masked_dist_loss, + "x_norm_loss": self.x_norm_loss, + "delta_pair_repr_norm_loss": self.delta_pair_repr_norm_loss, + "beta": self.beta, + "pad_idx": self.pad_idx, + } + + @classmethod + def deserialize(cls, data: dict) -> "UniMolLoss": + """Deserialize the loss module.""" + data = data.copy() + check_version_compatibility(data.pop("@version"), 1, 1) + data.pop("@class") + return cls(**data) From c6b85ef7164355917a3b870e8dd1b77ec6c85d08 Mon Sep 17 00:00:00 2001 From: Duo <50307526+iProzd@users.noreply.github.com> Date: Sat, 12 Sep 2026 05:46:46 -0400 Subject: [PATCH 05/34] feat(dpmodel): add the unimol descriptor Wraps the ported Uni-Mol v1 backbone in the descriptor interface: it turns a padded deepmd frame into Uni-Mol's token sequence, runs the encoder, and returns the per-atom representation with the two virtual tokens dropped. A second entry point returns everything at token resolution, because the five-tuple cannot carry the virtual tokens or the norm regularisers that the pretraining heads need. Real atoms are identified from the neighbour list rather than from atype: by the time a descriptor is called, virtual atoms have been clamped to type 0 and cannot be told apart from a real first element, while the neighbour list still shows them as empty rows. Frames with fewer than two real atoms are rejected, since that inference is ambiguous for them. Uni-Mol's own 31-token vocabulary is kept because the released weights are indexed by it, and a deepmd type_map is mapped onto it, with unknown elements becoming [UNK]. The descriptor declares itself non-periodic, non-extensive, stat-free and unavailable for edge-parallel or communication paths, and it rejects frames that carry periodic images. The virtual tokens sit at the centroid of the real atoms by default, which keeps the sequence translation invariant; "origin" reproduces upstream exactly for data that its own pipeline has already centred. Checked end to end against the upstream dump, driven through deepmd-shaped inputs: the token sequence is identical, the node representation agrees to 8.2e-9 relative and the pair-delta norm to 6.2e-8, both inherited from the fp32 Gaussian basis. Padding length does not affect the result, as intended. --- deepmd/dpmodel/descriptor/__init__.py | 4 + deepmd/dpmodel/descriptor/unimol.py | 489 ++++++++++++++++++++++++++ 2 files changed, 493 insertions(+) create mode 100644 deepmd/dpmodel/descriptor/unimol.py diff --git a/deepmd/dpmodel/descriptor/__init__.py b/deepmd/dpmodel/descriptor/__init__.py index ae9cc66e39..cbc87e5552 100644 --- a/deepmd/dpmodel/descriptor/__init__.py +++ b/deepmd/dpmodel/descriptor/__init__.py @@ -26,6 +26,9 @@ from .se_e2_a import ( DescrptSeA, ) +from .unimol import ( + DescrptUniMol, +) from .se_r import ( DescrptSeR, ) @@ -48,5 +51,6 @@ "DescrptSeR", "DescrptSeT", "DescrptSeTTebd", + "DescrptUniMol", "make_base_descriptor", ] diff --git a/deepmd/dpmodel/descriptor/unimol.py b/deepmd/dpmodel/descriptor/unimol.py new file mode 100644 index 0000000000..bebb2f04d8 --- /dev/null +++ b/deepmd/dpmodel/descriptor/unimol.py @@ -0,0 +1,489 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +"""The Uni-Mol v1 backbone as a deepmd descriptor. + +Wraps the ported Uni-Mol encoder (:mod:`deepmd.dpmodel.descriptor.unimol_nn`) in +the descriptor interface. Unlike every other descriptor here, Uni-Mol is global +rather than local: it attends over all atom pairs with no cut-off and no smooth +envelope, so it is neither extensive nor periodic and its forces are not +conserved. It is meant for molecular property and pretraining work, and the +model layer refuses to pair it with an energy fitting. + +Uni-Mol's own vocabulary is kept, because the released weights are indexed by +it: four special tokens, then 26 elements, then ``[MASK]``. A deepmd +``type_map`` is mapped onto those ids, and an element outside the vocabulary +becomes ``[UNK]``. +""" + +import array_api_compat + +from deepmd.dpmodel.array_api import ( + Array, +) +from deepmd.dpmodel.common import ( + NativeOP, + to_numpy_array, +) +from deepmd.dpmodel.descriptor.base_descriptor import ( + BaseDescriptor, +) +from deepmd.dpmodel.descriptor.unimol_nn import ( + GaussianLayer, + NonLinearHead, + TransformerEncoderWithPair, +) +from deepmd.dpmodel.utils.network import ( + NativeLayer, +) +from deepmd.utils.version import ( + check_version_compatibility, +) + +# Uni-Mol's molecular dictionary, in file order. The order is load-bearing: it +# fixes the embedding rows, the edge-type ids (t_i * V + t_j) and the output +# width of the element head in the released checkpoints. +UNIMOL_SPECIAL_TOKENS = ["[PAD]", "[CLS]", "[SEP]", "[UNK]"] +UNIMOL_ELEMENTS = [ + "C", "N", "O", "S", "H", "Cl", "F", "Br", "I", "Si", "P", "B", "Na", "K", + "Al", "Ca", "Sn", "As", "Hg", "Fe", "Zn", "Cr", "Se", "Gd", "Au", "Li", +] # fmt: skip +UNIMOL_MASK_TOKEN = "[MASK]" + + +def unimol_vocabulary() -> list[str]: + """Return the 31 Uni-Mol tokens in checkpoint order.""" + return [*UNIMOL_SPECIAL_TOKENS, *UNIMOL_ELEMENTS, UNIMOL_MASK_TOKEN] + + +@BaseDescriptor.register("unimol") +class DescrptUniMol(NativeOP, BaseDescriptor): + r"""Uni-Mol v1 transformer backbone. + + Every atom attends to every other atom; geometry enters only through + pairwise distances, expanded in a Gaussian basis whose affine parameters are + specific to the ordered element pair, and injected as a per-head attention + bias. Two virtual tokens wrap each molecule, and the running sum of + per-layer attention logits is the pair representation that the pretraining + heads read. + + Parameters + ---------- + type_map : list[str] + Element names, mapped onto the Uni-Mol vocabulary. + encoder_layers : int + Number of transformer blocks. + encoder_embed_dim : int + Width of the node representation. + encoder_ffn_embed_dim : int + Width of the feed-forward hidden layer. + encoder_attention_heads : int + Number of attention heads, which is also the width of the pair channel. + max_atoms : int + Largest molecule accepted, which fixes ``sel``. + max_seq_len : int + Upstream's sequence guard, kept for configuration compatibility. + activation_function : str + Activation of the blocks and heads. Uni-Mol uses the exact GELU. + dropout, emb_dropout, attention_dropout, activation_dropout : float + Dropout rates. They are inert in this backend-agnostic implementation + and are applied by the PyTorch-Exportable wrapper during training. + no_final_head_layer_norm : bool + Skip the layer norm on the pair delta. Upstream builds that norm unless + its weight is negative. + single_precision_basis : bool + Evaluate the Gaussian basis in fp32, which is what upstream does. + virtual_token_position : str + Where the two virtual tokens sit. ``"centroid"`` places them at the + centroid of the real atoms, which keeps the sequence translation + invariant. ``"origin"`` places them at the origin, reproducing upstream + exactly for data that its own pipeline has already centred. + precision : str + Floating-point precision of the parameters. + seed : int, optional + Random seed for initialization. + """ + + def __init__( + self, + type_map: list[str], + encoder_layers: int = 15, + encoder_embed_dim: int = 512, + encoder_ffn_embed_dim: int = 2048, + encoder_attention_heads: int = 64, + max_atoms: int = 256, + max_seq_len: int = 512, + activation_function: str = "gelu_erf", + dropout: float = 0.1, + emb_dropout: float = 0.1, + attention_dropout: float = 0.1, + activation_dropout: float = 0.0, + no_final_head_layer_norm: bool = False, + single_precision_basis: bool = True, + virtual_token_position: str = "centroid", + gaussian_kernels: int = 128, + precision: str = "float64", + seed: int | list[int] | None = None, + type_map_tokens: list[str] | None = None, + **kwargs: float | str | bool, + ) -> None: + del kwargs + self.type_map = list(type_map) + self.encoder_layers = encoder_layers + self.encoder_embed_dim = encoder_embed_dim + self.encoder_ffn_embed_dim = encoder_ffn_embed_dim + self.encoder_attention_heads = encoder_attention_heads + self.max_atoms = max_atoms + self.max_seq_len = max_seq_len + self.activation_function = activation_function + self.dropout = dropout + self.emb_dropout = emb_dropout + self.attention_dropout = attention_dropout + self.activation_dropout = activation_dropout + self.no_final_head_layer_norm = no_final_head_layer_norm + self.single_precision_basis = single_precision_basis + if virtual_token_position not in ("centroid", "origin"): + raise ValueError( + f"virtual_token_position must be centroid or origin, got {virtual_token_position}" + ) + self.virtual_token_position = virtual_token_position + self.gaussian_kernels = gaussian_kernels + self.precision = precision + self.vocabulary = type_map_tokens or unimol_vocabulary() + self.ntokens = len(self.vocabulary) + + import numpy as np + + token_of = {sym: i for i, sym in enumerate(self.vocabulary)} + self.pad_idx = token_of["[PAD]"] + self.bos_idx = token_of["[CLS]"] + self.eos_idx = token_of["[SEP]"] + self.unk_idx = token_of["[UNK]"] + # type id -> Uni-Mol token id; unknown elements fall back to [UNK]. + self.type_to_token = np.array( + [token_of.get(sym, self.unk_idx) for sym in self.type_map], dtype=np.int64 + ) + + rng = np.random.default_rng(seed if isinstance(seed, int) else None) + self.embed_tokens = rng.normal( + 0.0, 0.02, size=(self.ntokens, encoder_embed_dim) + ) + self.embed_tokens[self.pad_idx, :] = 0.0 + self.gbf = GaussianLayer( + gaussian_kernels, + self.ntokens**2, + single_precision_basis=single_precision_basis, + precision=precision, + seed=seed, + ) + self.gbf_proj = NonLinearHead( + gaussian_kernels, + encoder_attention_heads, + activation_function, + precision=precision, + seed=seed, + ) + self.encoder = TransformerEncoderWithPair( + encoder_layers=encoder_layers, + embed_dim=encoder_embed_dim, + ffn_embed_dim=encoder_ffn_embed_dim, + attention_heads=encoder_attention_heads, + emb_dropout=emb_dropout, + dropout=dropout, + attention_dropout=attention_dropout, + activation_dropout=activation_dropout, + max_seq_len=max_seq_len, + activation_function=activation_function, + no_final_head_layer_norm=no_final_head_layer_norm, + precision=precision, + seed=seed, + ) + + # ------------------------------------------------------------------ + # capability queries + # ------------------------------------------------------------------ + def get_rcut(self) -> float: + """All pairs are neighbours, so the radius is effectively unbounded.""" + return float(self.max_seq_len) * 1e3 + + def get_rcut_smth(self) -> float: + """No smoothing exists; smoothing starts where the cut-off is.""" + return self.get_rcut() + + def get_sel(self) -> list[int]: + """One entry, because the neighbour list is type-blind.""" + return [self.max_atoms - 1] + + def get_ntypes(self) -> int: + """Number of element types.""" + return len(self.type_map) + + def get_type_map(self) -> list[str]: + """Element names.""" + return self.type_map + + def get_dim_out(self) -> int: + """Width of the node representation.""" + return self.encoder_embed_dim + + def get_dim_emb(self) -> int: + """Width of the pair channel, which is the head count.""" + return self.encoder_attention_heads + + def mixed_types(self) -> bool: + """The neighbour list is not split by type.""" + return True + + def has_message_passing(self) -> bool: + """No ghost-atom exchange.""" + return False + + def need_sorted_nlist_for_lower(self) -> bool: + """All pairs take part, so their order does not matter.""" + return False + + def get_env_protection(self) -> float: + """No environment-matrix protection is used.""" + return 0.0 + + def supports_edge_parallel(self) -> bool: + """Global attention cannot be split across ranks.""" + return False + + def dense_lower_supports_comm(self) -> bool: + """There is no ghost-communication path.""" + return False + + def compression_needs_min_nbor_dist(self) -> bool: + """Compression is not supported, so no neighbour statistics are needed.""" + return False + + def compute_input_stats(self, merged, path=None) -> None: # noqa: ANN001 + """No environment statistics: the basis is learned, not normalized.""" + + def set_stat_mean_and_stddev(self, mean, stddev) -> None: # noqa: ANN001 + """Stat-free descriptor; nothing to assign.""" + + def get_stat_mean_and_stddev(self) -> tuple[list, list]: + """Stat-free descriptor; no statistics to report.""" + return [], [] + + def share_params(self, base_class, shared_level, resume=False) -> None: # noqa: ANN001 + """Parameter sharing is implemented by the PyTorch-Exportable wrapper.""" + raise NotImplementedError + + def change_type_map( + self, type_map: list[str], model_with_new_type_stat=None + ) -> None: # noqa: ANN001 + """Remap the element names onto Uni-Mol tokens. + + The token embedding itself is indexed by Uni-Mol token, not by deepmd + type, so only the lookup table changes. + """ + import numpy as np + + token_of = {sym: i for i, sym in enumerate(self.vocabulary)} + self.type_map = list(type_map) + self.type_to_token = np.array( + [token_of.get(sym, self.unk_idx) for sym in self.type_map], dtype=np.int64 + ) + + @classmethod + def update_sel(cls, train_data, type_map, local_jdata: dict) -> tuple[dict, None]: # noqa: ANN001 + """``sel`` follows from ``max_atoms``, so neighbour statistics are moot.""" + return local_jdata.copy(), None + + # ------------------------------------------------------------------ + # forward + # ------------------------------------------------------------------ + def build_tokens(self, coord_ext: Array, atype_ext: Array, nlist: Array) -> dict: + """Turn a padded deepmd frame into Uni-Mol's token sequence. + + Real atoms are identified from the neighbour list rather than from + ``atype``: by the time a descriptor is called, virtual atoms have been + clamped to type 0 and are indistinguishable from a real first element, + whereas the neighbour list still shows them as empty rows. + + Returns + ------- + dict + ``tokens``, ``coord``, ``padding_mask``, ``real_mask`` and + ``n_real``; the sequence is ``[CLS] atoms [SEP] pad...``. + """ + xp = array_api_compat.array_namespace(coord_ext) + nf, nloc = nlist.shape[0], nlist.shape[1] + coord = xp.reshape(coord_ext, (nf, -1, 3)) + nall = coord.shape[1] + if nall != nloc: + raise ValueError( + "the unimol descriptor is molecular and does not support periodic " + f"images: got {nall} extended atoms for {nloc} local atoms" + ) + + real_mask = xp.any(nlist >= 0, axis=-1) + n_real = xp.sum(xp.astype(real_mask, xp.int64), axis=-1) + if bool(xp.any(n_real < 2)): + raise ValueError( + "the unimol descriptor needs at least two real atoms per frame; " + "single-atom frames cannot be told apart from padding" + ) + + real = xp.astype(real_mask, coord.dtype) + # BOS and EOS sit at the centroid of the real atoms, which keeps the + # sequence translation invariant. Upstream centres the coordinates in + # its data pipeline and then places both at the origin, so the two agree + # whenever the data went through that transform. + if self.virtual_token_position == "centroid": + centroid = ( + xp.sum(coord * real[..., None], axis=1) + / xp.astype(n_real, coord.dtype)[:, None] + ) + else: + centroid = xp.zeros((nf, 3), dtype=coord.dtype) + + token_table = xp.asarray(self.type_to_token) + atom_tokens = xp.take(token_table, xp.reshape(atype_ext, (-1,)), axis=0) + atom_tokens = xp.reshape(atom_tokens, (nf, nloc)) + atom_tokens = xp.where( + real_mask, atom_tokens, xp.full_like(atom_tokens, self.pad_idx) + ) + + nt = nloc + 2 + positions = xp.arange(nt)[None, :] + eos_at = (n_real + 1)[:, None] + bos_row = xp.full((nf, 1), self.bos_idx, dtype=atom_tokens.dtype) + pad_row = xp.full((nf, 1), self.pad_idx, dtype=atom_tokens.dtype) + tokens = xp.concat([bos_row, atom_tokens, pad_row], axis=1) + tokens = xp.where( + positions == eos_at, xp.full_like(tokens, self.eos_idx), tokens + ) + + virtual = centroid[:, None, :] + coord_full = xp.concat([virtual, coord, virtual], axis=1) + at_virtual = (positions == 0) | (positions == eos_at) + coord_full = xp.where( + at_virtual[..., None], + xp.broadcast_to(virtual, coord_full.shape), + coord_full, + ) + + padding_mask = xp.astype(tokens == self.pad_idx, coord.dtype) + return { + "tokens": tokens, + "coord": coord_full, + "padding_mask": padding_mask, + "real_mask": real_mask, + "n_real": n_real, + } + + def forward_tokens(self, coord_ext: Array, atype_ext: Array, nlist: Array) -> dict: + """Run the backbone and return everything at token resolution. + + The five-tuple of :meth:`call` cannot carry the two virtual tokens or + the norm regularisers, which the pretraining heads need, so the + pretraining path goes through here instead. + """ + xp = array_api_compat.array_namespace(coord_ext) + seq = self.build_tokens(coord_ext, atype_ext, nlist) + tokens, coord, padding_mask = seq["tokens"], seq["coord"], seq["padding_mask"] + nf, nt = tokens.shape + + emb = xp.take(xp.asarray(self.embed_tokens), xp.reshape(tokens, (-1,)), axis=0) + emb = xp.reshape(emb, (nf, nt, self.encoder_embed_dim)) + emb = xp.astype(emb, coord.dtype) + + diff = coord[:, :, None, :] - coord[:, None, :, :] + dist = xp.sqrt(xp.sum(diff**2, axis=-1)) + edge_type = tokens[:, :, None] * self.ntokens + tokens[:, None, :] + + bias = self.gbf_proj(self.gbf(dist, edge_type)) + bias = xp.reshape(xp.permute_dims(bias, (0, 3, 1, 2)), (-1, nt, nt)) + + x, pair_rep, delta_pair_rep, x_norm, delta_pair_norm = self.encoder( + emb, bias, padding_mask + ) + # Upstream clears the -inf that padding leaves on the pair channel + # before any head reads it (unimol/models/unimol.py:221). + pair_rep = xp.where(xp.isinf(pair_rep), xp.zeros_like(pair_rep), pair_rep) + return { + **seq, + "node_ebd": x, + "pair_rep": pair_rep, + "delta_pair_rep": delta_pair_rep, + "x_norm": x_norm, + "delta_pair_norm": delta_pair_norm, + } + + def call( + self, + coord_ext: Array, + atype_ext: Array, + nlist: Array, + mapping: Array | None = None, + fparam: Array | None = None, + comm_dict: dict | None = None, + charge_spin: Array | None = None, + ) -> tuple[Array, None, None, None, None]: + """Return the per-atom representation. + + The two virtual tokens are dropped so that the output lines up with the + local atoms. Slots the backbone does not produce are ``None``, which the + atomic model accepts; the pretraining path uses :meth:`forward_tokens`. + """ + del mapping, fparam, comm_dict, charge_spin + out = self.forward_tokens(coord_ext, atype_ext, nlist) + nloc = nlist.shape[1] + return out["node_ebd"][:, 1 : nloc + 1, :], None, None, None, None + + # ------------------------------------------------------------------ + # serialization + # ------------------------------------------------------------------ + def serialize(self) -> dict: + """Serialize the descriptor.""" + return { + "@class": "Descriptor", + "type": "unimol", + "@version": 1, + "type_map": self.type_map, + "encoder_layers": self.encoder_layers, + "encoder_embed_dim": self.encoder_embed_dim, + "encoder_ffn_embed_dim": self.encoder_ffn_embed_dim, + "encoder_attention_heads": self.encoder_attention_heads, + "max_atoms": self.max_atoms, + "max_seq_len": self.max_seq_len, + "activation_function": self.activation_function, + "dropout": self.dropout, + "emb_dropout": self.emb_dropout, + "attention_dropout": self.attention_dropout, + "activation_dropout": self.activation_dropout, + "no_final_head_layer_norm": self.no_final_head_layer_norm, + "single_precision_basis": self.single_precision_basis, + "virtual_token_position": self.virtual_token_position, + "gaussian_kernels": self.gaussian_kernels, + "precision": self.precision, + "type_map_tokens": self.vocabulary, + "@variables": {"embed_tokens": to_numpy_array(self.embed_tokens)}, + "gbf": self.gbf.serialize(), + "gbf_proj": self.gbf_proj.serialize(), + "encoder": self.encoder.serialize(), + } + + @classmethod + def deserialize(cls, data: dict) -> "DescrptUniMol": + """Deserialize the descriptor.""" + data = data.copy() + check_version_compatibility(data.pop("@version"), 1, 1) + data.pop("@class", None) + data.pop("type", None) + variables = data.pop("@variables") + gbf = data.pop("gbf") + gbf_proj = data.pop("gbf_proj") + encoder = data.pop("encoder") + obj = cls(**data) + obj.embed_tokens = variables["embed_tokens"] + obj.gbf = GaussianLayer.deserialize(gbf) + obj.gbf_proj = NonLinearHead.deserialize(gbf_proj) + obj.encoder = TransformerEncoderWithPair.deserialize(encoder) + return obj + + +# Keep a reference so linters see the import is used by type hints above. +_ = NativeLayer From df72506b9c4d1ae408b88897b4369bb1c25c4cb4 Mon Sep 17 00:00:00 2001 From: Duo <50307526+iProzd@users.noreply.github.com> Date: Sat, 12 Sep 2026 05:51:33 -0400 Subject: [PATCH 06/34] feat: import released Uni-Mol v1 checkpoints Adds the converter for the released mol_pre_all_h_220816 and mol_pre_no_h_220816 files (MIT). Parameter names line up one to one with the ported modules, but the arrays do not: deepmd stores a linear weight as (num_in, num_out) and applies it as x @ w, the transpose of torch.nn.Linear.weight, and names layer-norm parameters w/b. Every weight is renamed and transposed rather than loaded directly, so there is no "just add a prefix" path on this backend. The released files carry only their weights and no training state, so they read with weights_only=True. Torch is imported lazily and only to read the file, which keeps the converter off every other code path. Also adds an option to round the pairwise distances to fp32 before the Gaussian basis. Upstream precomputes its distance matrix in fp32 in the data pipeline, while the descriptor computes distances inside the model, which is more accurate and is what gradients flow through. The Gaussian basis is narrow enough that the difference matters: on the released 15-layer weights the node representation lands 5.1e-6 from upstream with fp64 distances and 3.7e-7 with upstream's own fp32 rounding. The default stays on the accurate path. Measured on the released weights driven through deepmd-shaped inputs: the encoder fed upstream's own attention bias agrees to 1.2e-15 relative at full depth, so the remaining gap is entirely the two precision choices upstream makes in front of it. --- deepmd/dpmodel/descriptor/unimol.py | 16 ++- deepmd/utils/unimol_checkpoint.py | 182 ++++++++++++++++++++++++++++ 2 files changed, 196 insertions(+), 2 deletions(-) create mode 100644 deepmd/utils/unimol_checkpoint.py diff --git a/deepmd/dpmodel/descriptor/unimol.py b/deepmd/dpmodel/descriptor/unimol.py index bebb2f04d8..fe82748c9a 100644 --- a/deepmd/dpmodel/descriptor/unimol.py +++ b/deepmd/dpmodel/descriptor/unimol.py @@ -91,6 +91,11 @@ class DescrptUniMol(NativeOP, BaseDescriptor): its weight is negative. single_precision_basis : bool Evaluate the Gaussian basis in fp32, which is what upstream does. + single_precision_distance : bool + Round the pairwise distances to fp32 before the Gaussian basis. Upstream + precomputes its distance matrix in fp32 in the data pipeline, so this + reproduces its numbers; the default computes them in the working + precision, which is more accurate and is what gradients flow through. virtual_token_position : str Where the two virtual tokens sit. ``"centroid"`` places them at the centroid of the real atoms, which keeps the sequence translation @@ -118,6 +123,7 @@ def __init__( activation_dropout: float = 0.0, no_final_head_layer_norm: bool = False, single_precision_basis: bool = True, + single_precision_distance: bool = False, virtual_token_position: str = "centroid", gaussian_kernels: int = 128, precision: str = "float64", @@ -140,6 +146,7 @@ def __init__( self.activation_dropout = activation_dropout self.no_final_head_layer_norm = no_final_head_layer_norm self.single_precision_basis = single_precision_basis + self.single_precision_distance = single_precision_distance if virtual_token_position not in ("centroid", "origin"): raise ValueError( f"virtual_token_position must be centroid or origin, got {virtual_token_position}" @@ -271,8 +278,8 @@ def share_params(self, base_class, shared_level, resume=False) -> None: # noqa: raise NotImplementedError def change_type_map( - self, type_map: list[str], model_with_new_type_stat=None - ) -> None: # noqa: ANN001 + self, type_map: list[str], model_with_new_type_stat: object = None + ) -> None: """Remap the element names onto Uni-Mol tokens. The token embedding itself is indexed by Uni-Mol token, not by deepmd @@ -392,6 +399,10 @@ def forward_tokens(self, coord_ext: Array, atype_ext: Array, nlist: Array) -> di diff = coord[:, :, None, :] - coord[:, None, :, :] dist = xp.sqrt(xp.sum(diff**2, axis=-1)) + if self.single_precision_distance: + # Upstream's data pipeline stores the distance matrix in fp32, and + # the Gaussian basis is narrow enough for that rounding to matter. + dist = xp.astype(xp.astype(dist, xp.float32), dist.dtype) edge_type = tokens[:, :, None] * self.ntokens + tokens[:, None, :] bias = self.gbf_proj(self.gbf(dist, edge_type)) @@ -456,6 +467,7 @@ def serialize(self) -> dict: "activation_dropout": self.activation_dropout, "no_final_head_layer_norm": self.no_final_head_layer_norm, "single_precision_basis": self.single_precision_basis, + "single_precision_distance": self.single_precision_distance, "virtual_token_position": self.virtual_token_position, "gaussian_kernels": self.gaussian_kernels, "precision": self.precision, diff --git a/deepmd/utils/unimol_checkpoint.py b/deepmd/utils/unimol_checkpoint.py new file mode 100644 index 0000000000..ea9f240579 --- /dev/null +++ b/deepmd/utils/unimol_checkpoint.py @@ -0,0 +1,182 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +"""Import released Uni-Mol v1 checkpoints into deepmd. + +The released files (``mol_pre_all_h_220816.pt`` and ``mol_pre_no_h_220816.pt``, +MIT licensed, from the Uni-Mol repository and its Hugging Face mirror) hold a +single ``model`` entry with 211 float32 tensors and no training state, so they +load with ``weights_only=True``. + +The parameter names line up one to one with the ported modules, but the arrays +do not: deepmd stores a linear weight as ``(num_in, num_out)`` and applies it as +``x @ w``, which is the transpose of ``torch.nn.Linear.weight``, and it calls +layer-norm parameters ``w``/``b`` rather than ``weight``/``bias``. Every weight +is therefore renamed and transposed here rather than loaded directly. +""" + +from typing import ( + TYPE_CHECKING, + Any, +) + +import numpy as np + +if TYPE_CHECKING: + from deepmd.dpmodel.descriptor.unimol import ( + DescrptUniMol, + ) + +__all__ = [ + "UNIMOL_V1_BASE_ARCHITECTURE", + "descriptor_from_unimol_checkpoint", + "load_unimol_state_dict", + "split_unimol_state_dict", +] + +# Uni-Mol's ``unimol_base`` architecture (unimol/models/unimol.py:423-442). +UNIMOL_V1_BASE_ARCHITECTURE = { + "encoder_layers": 15, + "encoder_embed_dim": 512, + "encoder_ffn_embed_dim": 2048, + "encoder_attention_heads": 64, + "activation_function": "gelu_erf", + "dropout": 0.1, + "emb_dropout": 0.1, + "attention_dropout": 0.1, + "activation_dropout": 0.0, + "max_seq_len": 512, +} + +_BACKBONE_PREFIXES = ("embed_tokens", "gbf.", "gbf_proj.", "encoder.") +_HEAD_PREFIXES = ("lm_head.", "pair2coord_proj.", "dist_head.") + + +def load_unimol_state_dict(path: str) -> dict[str, np.ndarray]: + """Read a released checkpoint into NumPy arrays. + + Parameters + ---------- + path : str + Local path to the ``.pt`` file. Nothing is downloaded here; see + :func:`descriptor_from_unimol_checkpoint` for the optional download. + + Returns + ------- + dict[str, np.ndarray] + The ``model`` entry of the checkpoint. + """ + try: + import torch + except ImportError as e: + raise ImportError( + "reading a Uni-Mol checkpoint needs PyTorch; install it with " + "`pip install torch`" + ) from e + obj = torch.load(path, map_location="cpu", weights_only=True) + state = obj["model"] if "model" in obj else obj + return {k: v.numpy() for k, v in state.items()} + + +def split_unimol_state_dict( + state_dict: dict[str, np.ndarray], +) -> tuple[dict[str, np.ndarray], dict[str, np.ndarray]]: + """Split a checkpoint into backbone and pretraining-head parameters. + + Returns + ------- + tuple + ``(backbone, heads)``. The heads are the element, coordinate and + distance heads, which belong to the fitting rather than the descriptor. + """ + backbone = {k: v for k, v in state_dict.items() if k.startswith(_BACKBONE_PREFIXES)} + heads = {k: v for k, v in state_dict.items() if k.startswith(_HEAD_PREFIXES)} + unknown = set(state_dict) - set(backbone) - set(heads) + if unknown: + raise ValueError(f"unexpected parameters in the checkpoint: {sorted(unknown)}") + return backbone, heads + + +def _set_linear(layer: Any, state: dict[str, np.ndarray], prefix: str) -> None: + """Assign a torch Linear onto a deepmd layer, transposing the weight.""" + layer.w = np.ascontiguousarray(state[prefix + ".weight"].T).astype(layer.w.dtype) + bias = state.get(prefix + ".bias") + if bias is not None and layer.b is not None: + layer.b = bias.astype(layer.b.dtype) + + +def _set_layer_norm(layer: Any, state: dict[str, np.ndarray], prefix: str) -> None: + """Assign a torch LayerNorm onto a deepmd layer norm.""" + layer.w = state[prefix + ".weight"].astype(layer.w.dtype) + layer.b = state[prefix + ".bias"].astype(layer.b.dtype) + + +def apply_unimol_backbone( + descriptor: "DescrptUniMol", state_dict: dict[str, np.ndarray] +) -> None: + """Load backbone parameters into a descriptor, in place.""" + dtype = descriptor.embed_tokens.dtype + descriptor.embed_tokens = state_dict["embed_tokens.weight"].astype(dtype) + descriptor.gbf.means = state_dict["gbf.means.weight"].astype(dtype) + descriptor.gbf.stds = state_dict["gbf.stds.weight"].astype(dtype) + descriptor.gbf.mul = state_dict["gbf.mul.weight"].astype(dtype) + descriptor.gbf.bias = state_dict["gbf.bias.weight"].astype(dtype) + _set_linear(descriptor.gbf_proj.linear1, state_dict, "gbf_proj.linear1") + _set_linear(descriptor.gbf_proj.linear2, state_dict, "gbf_proj.linear2") + + encoder = descriptor.encoder + _set_layer_norm(encoder.emb_layer_norm, state_dict, "encoder.emb_layer_norm") + _set_layer_norm(encoder.final_layer_norm, state_dict, "encoder.final_layer_norm") + if encoder.final_head_layer_norm is not None: + _set_layer_norm( + encoder.final_head_layer_norm, state_dict, "encoder.final_head_layer_norm" + ) + for i, layer in enumerate(encoder.layers): + prefix = f"encoder.layers.{i}." + _set_linear(layer.self_attn.in_proj, state_dict, prefix + "self_attn.in_proj") + _set_linear(layer.self_attn.out_proj, state_dict, prefix + "self_attn.out_proj") + _set_layer_norm( + layer.self_attn_layer_norm, state_dict, prefix + "self_attn_layer_norm" + ) + _set_linear(layer.fc1, state_dict, prefix + "fc1") + _set_linear(layer.fc2, state_dict, prefix + "fc2") + _set_layer_norm(layer.final_layer_norm, state_dict, prefix + "final_layer_norm") + + +def descriptor_from_unimol_checkpoint( + path: str, + type_map: list[str] | None = None, + precision: str = "float64", + **overrides: Any, +) -> "DescrptUniMol": + """Build a descriptor carrying the released Uni-Mol v1 weights. + + Parameters + ---------- + path : str + Local path to the checkpoint. + type_map : list[str], optional + Element names for the model. Defaults to Uni-Mol's own 26 elements. + precision : str + Precision to hold the parameters in. + **overrides + Architecture overrides on top of ``UNIMOL_V1_BASE_ARCHITECTURE``. + + Returns + ------- + DescrptUniMol + A descriptor whose backbone reproduces upstream. + """ + from deepmd.dpmodel.descriptor.unimol import ( + UNIMOL_ELEMENTS, + DescrptUniMol, + ) + + state_dict = load_unimol_state_dict(path) + backbone, _ = split_unimol_state_dict(state_dict) + arch = {**UNIMOL_V1_BASE_ARCHITECTURE, **overrides} + descriptor = DescrptUniMol( + type_map=type_map if type_map is not None else list(UNIMOL_ELEMENTS), + precision=precision, + **arch, + ) + apply_unimol_backbone(descriptor, backbone) + return descriptor From bc535056b5b4ca171da31138a97f1f3e33e36969 Mon Sep 17 00:00:00 2001 From: Duo <50307526+iProzd@users.noreply.github.com> Date: Sat, 12 Sep 2026 05:56:06 -0400 Subject: [PATCH 07/34] feat(dpmodel): add the unimol_pretrain fitting Wraps the three pretraining heads as a fitting: the element head reads the node representation, the coordinate head reads the pair delta, the distance head reads the pair representation. None is reducible to a frame total and none is differentiated with respect to coordinates, because the task denoises structures rather than modelling a potential energy surface. Upstream's distance objective counts the two virtual tokens among the columns, so the distance output keeps them and is padded to max_atoms + 2 columns, which the loss masks back down. That keeps the output shape static, as the output definition requires, without dropping columns the objective needs. The heads read token-resolution backbone output, which the descriptor's five-tuple cannot carry, so they are driven through call_tokens; the standard call raises with that explanation rather than silently returning something else. The loss now gathers the corrupted positions itself, since the model emits one row per local atom. End-to-end on the released mol_pre_all_h_220816 weights, driven through deepmd-shaped inputs: all five terms of the objective agree with upstream, the worst at 5.6e-7 relative and the total at 3.4e-7. --- deepmd/dpmodel/fitting/unimol_pretrain.py | 282 ++++++++++++++++++++++ deepmd/dpmodel/loss/unimol.py | 9 +- 2 files changed, 288 insertions(+), 3 deletions(-) create mode 100644 deepmd/dpmodel/fitting/unimol_pretrain.py diff --git a/deepmd/dpmodel/fitting/unimol_pretrain.py b/deepmd/dpmodel/fitting/unimol_pretrain.py new file mode 100644 index 0000000000..e2f66e9bdb --- /dev/null +++ b/deepmd/dpmodel/fitting/unimol_pretrain.py @@ -0,0 +1,282 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +"""The Uni-Mol v1 pretraining head set, as a deepmd fitting. + +Ported from Uni-Mol (https://github.com/deepmodeling/Uni-Mol) at commit 90f52c4, +MIT licensed: + + Copyright (c) DP Technology + This source code is licensed under the MIT license found in the LICENSE + file in the root directory of that source tree. + +Three heads sit on the Uni-Mol backbone: the element head reads the node +representation, the coordinate head reads the pair delta, and the distance head +reads the pair representation. None of the three is reducible to a frame total +and none is differentiated with respect to the coordinates, because this task +denoises structures rather than predicting a potential energy surface. + +The distance head predicts a full row per atom, and upstream's objective counts +the two virtual tokens among the columns, so the output is padded to +``max_atoms + 2`` columns and the loss masks it back down. +""" + +import array_api_compat + +from deepmd.dpmodel.array_api import ( + Array, +) +from deepmd.dpmodel.common import ( + NativeOP, +) +from deepmd.dpmodel.descriptor.unimol_nn import ( + DistanceHead, + MaskLMHead, + NonLinearHead, + coord_update, +) +from deepmd.dpmodel.fitting.base_fitting import ( + BaseFitting, +) +from deepmd.dpmodel.output_def import ( + FittingOutputDef, + OutputVariableDef, +) +from deepmd.utils.version import ( + check_version_compatibility, +) + + +@BaseFitting.register("unimol_pretrain") +class UniMolPretrainFitting(NativeOP, BaseFitting): + r"""The three self-supervised heads of Uni-Mol v1. + + Parameters + ---------- + ntypes : int + Number of element types of the model. + dim_descrpt : int + Width of the node representation produced by the backbone. + n_token : int + Size of the Uni-Mol vocabulary, which is the width of the element head. + attention_heads : int + Width of the pair channel, which both pair-reading heads consume. + max_atoms : int + Largest molecule accepted, which fixes the column count of the distance + head at ``max_atoms + 2``. + activation_function : str + Activation of the heads; Uni-Mol uses the exact GELU. + mask_token_head : bool + Build the element head. + coord_head : bool + Build the coordinate head. + dist_head : bool + Build the distance head. + precision : str + Floating-point precision of the parameters. + seed : int, optional + Random seed for initialization. + """ + + def __init__( + self, + ntypes: int, + dim_descrpt: int, + n_token: int = 31, + attention_heads: int = 64, + max_atoms: int = 256, + activation_function: str = "gelu_erf", + mask_token_head: bool = True, + coord_head: bool = True, + dist_head: bool = True, + precision: str = "float64", + seed: int | list[int] | None = None, + type_map: list[str] | None = None, + **kwargs: float | str | bool, + ) -> None: + del kwargs + self.type_map = list(type_map) if type_map is not None else None + self.ntypes = ntypes + self.dim_descrpt = dim_descrpt + self.n_token = n_token + self.attention_heads = attention_heads + self.max_atoms = max_atoms + self.activation_function = activation_function + self.precision = precision + self.lm_head = ( + MaskLMHead(dim_descrpt, n_token, activation_function, precision, seed) + if mask_token_head + else None + ) + self.pair2coord_proj = ( + NonLinearHead( + attention_heads, + 1, + activation_function, + hidden=attention_heads, + precision=precision, + seed=seed, + ) + if coord_head + else None + ) + self.dist_head = ( + DistanceHead(attention_heads, activation_function, precision, seed) + if dist_head + else None + ) + + def get_type_map(self) -> list[str]: + """Element names, as configured on the model.""" + return self.type_map if self.type_map is not None else [] + + def change_type_map( + self, type_map: list[str], model_with_new_type_stat: object = None + ) -> None: + """Adopt a new element list. + + The heads are indexed by Uni-Mol token, not by deepmd type, so nothing + but the recorded names changes. + """ + del model_with_new_type_stat + self.type_map = list(type_map) + self.ntypes = len(type_map) + + def output_def(self) -> FittingOutputDef: + """Declare the three head outputs. + + All three are per-atom, none reduces to a frame total, and none is + differentiated with respect to coordinates or cell. + """ + variables = [] + if self.lm_head is not None: + variables.append( + OutputVariableDef( + "token_logits", + [self.n_token], + reducible=False, + r_differentiable=False, + c_differentiable=False, + ) + ) + if self.pair2coord_proj is not None: + variables.append( + OutputVariableDef( + "coord_update", + [3], + reducible=False, + r_differentiable=False, + c_differentiable=False, + ) + ) + if self.dist_head is not None: + variables.append( + OutputVariableDef( + "pair_dist", + [self.max_atoms + 2], + reducible=False, + r_differentiable=False, + c_differentiable=False, + ) + ) + return FittingOutputDef(variables) + + def call_tokens(self, backbone: dict[str, Array]) -> dict[str, Array]: + """Run the heads on the token-resolution backbone output. + + Parameters + ---------- + backbone + The dictionary returned by + :meth:`deepmd.dpmodel.descriptor.unimol.DescrptUniMol.forward_tokens`. + + Returns + ------- + dict + ``token_logits`` and ``coord_update`` carry one row per local atom, + with the two virtual tokens dropped; ``pair_dist`` keeps the virtual + columns, padded out to ``max_atoms + 2``; the two norm regularisers + are passed through for the loss. + """ + xp = array_api_compat.array_namespace(backbone["node_ebd"]) + node = backbone["node_ebd"] + nf, nt = node.shape[0], node.shape[1] + nloc = nt - 2 + out = { + "x_norm": backbone["x_norm"], + "delta_pair_norm": backbone["delta_pair_norm"], + } + if self.lm_head is not None: + logits = self.lm_head(node) + out["token_logits"] = logits[:, 1 : nloc + 1, :] + if self.pair2coord_proj is not None: + updated = coord_update( + backbone["coord"], + backbone["delta_pair_rep"], + backbone["padding_mask"], + self.pair2coord_proj, + ) + out["coord_update"] = updated[:, 1 : nloc + 1, :] + if self.dist_head is not None: + dist = self.dist_head(backbone["pair_rep"])[:, 1 : nloc + 1, :] + width = self.max_atoms + 2 + if dist.shape[-1] < width: + pad = xp.zeros((nf, nloc, width - dist.shape[-1]), dtype=dist.dtype) + dist = xp.concat([dist, pad], axis=-1) + out["pair_dist"] = dist + return out + + def call(self, descriptor: Array, atype: Array, **kwargs) -> dict[str, Array]: # noqa: ANN003 + """Not reachable: the heads need token-resolution inputs. + + The pretraining path goes through :meth:`call_tokens`, driven by the + Uni-Mol atomic model, because the two virtual tokens and the pair + channel do not fit the descriptor's five-tuple. + """ + raise NotImplementedError( + "unimol_pretrain reads token-resolution backbone output; it is driven " + "through call_tokens by the unimol atomic model" + ) + + def serialize(self) -> dict: + """Serialize the fitting.""" + return { + "@class": "Fitting", + "type": "unimol_pretrain", + "@version": 1, + "ntypes": self.ntypes, + "type_map": self.type_map, + "dim_descrpt": self.dim_descrpt, + "n_token": self.n_token, + "attention_heads": self.attention_heads, + "max_atoms": self.max_atoms, + "activation_function": self.activation_function, + "precision": self.precision, + "mask_token_head": self.lm_head is not None, + "coord_head": self.pair2coord_proj is not None, + "dist_head": self.dist_head is not None, + "lm_head": None if self.lm_head is None else self.lm_head.serialize(), + "pair2coord_proj": None + if self.pair2coord_proj is None + else self.pair2coord_proj.serialize(), + "dist_head_net": None + if self.dist_head is None + else self.dist_head.serialize(), + } + + @classmethod + def deserialize(cls, data: dict) -> "UniMolPretrainFitting": + """Deserialize the fitting.""" + data = data.copy() + check_version_compatibility(data.pop("@version"), 1, 1) + data.pop("@class", None) + data.pop("type", None) + parts = { + k: data.pop(k) for k in ("lm_head", "pair2coord_proj", "dist_head_net") + } + obj = cls(**data) + if parts["lm_head"] is not None: + obj.lm_head = MaskLMHead.deserialize(parts["lm_head"]) + if parts["pair2coord_proj"] is not None: + obj.pair2coord_proj = NonLinearHead.deserialize(parts["pair2coord_proj"]) + if parts["dist_head_net"] is not None: + obj.dist_head = DistanceHead.deserialize(parts["dist_head_net"]) + return obj diff --git a/deepmd/dpmodel/loss/unimol.py b/deepmd/dpmodel/loss/unimol.py index bdb3f31409..7192b3b48e 100644 --- a/deepmd/dpmodel/loss/unimol.py +++ b/deepmd/dpmodel/loss/unimol.py @@ -144,10 +144,13 @@ def add(term: Array, weight: float, name: str) -> None: loss = weight * term if loss is None else loss + weight * term if self.masked_token_loss > 0: + # The model emits one row per local atom; the objective covers only + # the corrupted ones, so they are gathered here. + logits = model_dict["unimol_logits"] + if logits.ndim == 3: + logits = logits[masked] add( - _masked_nll( - model_dict["unimol_logits"], token_target[masked], self.pad_idx - ), + _masked_nll(logits, token_target[masked], self.pad_idx), self.masked_token_loss, "token_loss", ) From d488e558ff66eee4ea4756ee17264611388c8427 Mon Sep 17 00:00:00 2001 From: Duo <50307526+iProzd@users.noreply.github.com> Date: Sat, 12 Sep 2026 06:01:07 -0400 Subject: [PATCH 08/34] feat: register the unimol descriptor, fitting and loss in argcheck Three entries, all labelled PyTorch-Exportable: the unimol descriptor, the unimol_pretrain fitting and the unimol loss, with upstream's defaults, which are 15 layers of width 512 with 64 heads for the backbone and weights of 1, 5, 10, 0.01 and 0.01 for the objective. The two precision switches are exposed as arguments, since they decide whether a run reproduces upstream's published numbers or takes the more accurate path, and the docs say which is which. A complete Uni-Mol configuration now normalizes, so the components are reachable from a training input file. --- deepmd/utils/argcheck.py | 234 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 234 insertions(+) diff --git a/deepmd/utils/argcheck.py b/deepmd/utils/argcheck.py index 65ab8a85a2..98e752933f 100644 --- a/deepmd/utils/argcheck.py +++ b/deepmd/utils/argcheck.py @@ -2753,6 +2753,144 @@ def descrpt_se_a_mask_args() -> list[Argument]: ] +doc_descrpt_unimol = ( + "The Uni-Mol v1 transformer backbone. Every atom attends to every other atom, so the " + "descriptor is neither local nor extensive and does not support periodic boundaries; it is " + "intended for molecular property and self-supervised pretraining work." +) +doc_fitting_unimol_pretrain = ( + "The three self-supervised heads of Uni-Mol v1 pretraining: element prediction, coordinate " + "denoising and pairwise distance prediction." +) + + +@descrpt_args_plugin.register( + "unimol", doc=supported_backends("pt_expt") + doc_descrpt_unimol +) +def descrpt_unimol_args() -> list[Argument]: + doc_seed = "Random seed for parameter initialization" + doc_precision = f"The precision of the parameters, supported options are {list_to_doc(PRECISION_DICT.keys())} Default follows the interface precision." + doc_encoder_layers = "Number of transformer blocks." + doc_encoder_embed_dim = "Width of the node representation." + doc_encoder_ffn_embed_dim = "Width of the feed-forward hidden layer." + doc_encoder_attention_heads = ( + "Number of attention heads, which is also the width of the pair channel." + ) + doc_max_atoms = "Largest molecule accepted, which fixes the neighbor count." + doc_max_seq_len = ( + "Upstream's sequence-length guard, including the two virtual tokens." + ) + doc_activation_function = ( + f"The activation function. Uni-Mol uses the exact GELU, `gelu_erf`. " + f"Supported: {list_to_doc(ACTIVATION_FN_DICT.keys())}" + ) + doc_dropout = "Dropout on the residual branches, applied during training." + doc_emb_dropout = "Dropout on the token embedding, applied during training." + doc_attention_dropout = ( + "Dropout on the attention probabilities, applied during training." + ) + doc_activation_dropout = "Dropout after the feed-forward activation." + doc_no_final_head_layer_norm = "Skip the layer norm on the pair delta. Upstream builds it unless its loss weight is negative." + doc_single_precision_basis = "Evaluate the Gaussian basis in fp32, as upstream does. Required to reproduce the released weights." + doc_single_precision_distance = ( + "Round pairwise distances to fp32 before the basis, matching upstream's precomputed distance " + "matrix. The default computes them in the working precision, which is more accurate." + ) + doc_virtual_token_position = ( + "Where the two virtual tokens sit: `centroid` of the real atoms, which keeps the sequence " + "translation invariant, or `origin`, which reproduces upstream for pre-centred data." + ) + doc_gaussian_kernels = "Number of Gaussian radial basis functions." + return [ + Argument( + "encoder_layers", int, optional=True, default=15, doc=doc_encoder_layers + ), + Argument( + "encoder_embed_dim", + int, + optional=True, + default=512, + doc=doc_encoder_embed_dim, + ), + Argument( + "encoder_ffn_embed_dim", + int, + optional=True, + default=2048, + doc=doc_encoder_ffn_embed_dim, + ), + Argument( + "encoder_attention_heads", + int, + optional=True, + default=64, + doc=doc_encoder_attention_heads, + ), + Argument("max_atoms", int, optional=True, default=256, doc=doc_max_atoms), + Argument("max_seq_len", int, optional=True, default=512, doc=doc_max_seq_len), + Argument( + "activation_function", + str, + optional=True, + default="gelu_erf", + doc=doc_activation_function, + ), + Argument("dropout", float, optional=True, default=0.1, doc=doc_dropout), + Argument("emb_dropout", float, optional=True, default=0.1, doc=doc_emb_dropout), + Argument( + "attention_dropout", + float, + optional=True, + default=0.1, + doc=doc_attention_dropout, + ), + Argument( + "activation_dropout", + float, + optional=True, + default=0.0, + doc=doc_activation_dropout, + ), + Argument( + "no_final_head_layer_norm", + bool, + optional=True, + default=False, + doc=doc_no_final_head_layer_norm, + ), + Argument( + "single_precision_basis", + bool, + optional=True, + default=True, + doc=doc_single_precision_basis, + ), + Argument( + "single_precision_distance", + bool, + optional=True, + default=False, + doc=doc_single_precision_distance, + ), + Argument( + "virtual_token_position", + str, + optional=True, + default="centroid", + doc=doc_virtual_token_position, + ), + Argument( + "gaussian_kernels", + int, + optional=True, + default=128, + doc=doc_gaussian_kernels, + ), + Argument("precision", str, optional=True, default="default", doc=doc_precision), + Argument("seed", [int, list, None], optional=True, doc=doc_seed), + ] + + def descrpt_variant_type_args(exclude_hybrid: bool = False) -> Variant: doc_descrpt_type = "The type of the descriptor." @@ -3313,6 +3451,52 @@ def fitting_dipole() -> list[Argument]: # YWolfeee: Delete global polar mode, merge it into polar mode and use loss setting to support. +@fitting_args_plugin.register( + "unimol_pretrain", doc=supported_backends("pt_expt") + doc_fitting_unimol_pretrain +) +def fitting_unimol_pretrain() -> list[Argument]: + doc_seed = "Random seed for parameter initialization" + doc_precision = f"The precision of the parameters, supported options are {list_to_doc(PRECISION_DICT.keys())} Default follows the interface precision." + doc_n_token = ( + "Size of the Uni-Mol vocabulary, which is the width of the element head." + ) + doc_attention_heads = ( + "Width of the pair channel, which the two pair-reading heads consume." + ) + doc_max_atoms = ( + "Largest molecule accepted, which fixes the distance head's column count." + ) + doc_activation_function = f"The activation function of the heads. Supported: {list_to_doc(ACTIVATION_FN_DICT.keys())}" + doc_mask_token_head = "Build the element-prediction head." + doc_coord_head = "Build the coordinate-denoising head." + doc_dist_head = "Build the distance-prediction head." + return [ + Argument("n_token", int, optional=True, default=31, doc=doc_n_token), + Argument( + "attention_heads", int, optional=True, default=64, doc=doc_attention_heads + ), + Argument("max_atoms", int, optional=True, default=256, doc=doc_max_atoms), + Argument( + "activation_function", + str, + optional=True, + default="gelu_erf", + doc=doc_activation_function, + ), + Argument( + "mask_token_head", + bool, + optional=True, + default=True, + doc=doc_mask_token_head, + ), + Argument("coord_head", bool, optional=True, default=True, doc=doc_coord_head), + Argument("dist_head", bool, optional=True, default=True, doc=doc_dist_head), + Argument("precision", str, optional=True, default="default", doc=doc_precision), + Argument("seed", [int, list, None], optional=True, doc=doc_seed), + ] + + def fitting_variant_type_args() -> Variant: doc_descrpt_type = "The type of the fitting." @@ -5332,6 +5516,56 @@ def loss_tensor() -> list[Argument]: ] +@loss_args_plugin.register("unimol", doc=supported_backends("pt_expt")) +def loss_unimol() -> list[Argument]: + doc_masked_token_loss = "Weight of the element-prediction term." + doc_masked_coord_loss = "Weight of the coordinate-denoising term." + doc_masked_dist_loss = "Weight of the distance-prediction term." + doc_x_norm_loss = "Weight of the node-norm regularizer." + doc_delta_pair_repr_norm_loss = "Weight of the pair-delta-norm regularizer." + doc_beta = ( + "Transition point of the smooth L1 used by the coordinate and distance terms." + ) + return [ + Argument( + "masked_token_loss", + [float, int], + optional=True, + default=1.0, + doc=doc_masked_token_loss, + ), + Argument( + "masked_coord_loss", + [float, int], + optional=True, + default=5.0, + doc=doc_masked_coord_loss, + ), + Argument( + "masked_dist_loss", + [float, int], + optional=True, + default=10.0, + doc=doc_masked_dist_loss, + ), + Argument( + "x_norm_loss", + [float, int], + optional=True, + default=0.01, + doc=doc_x_norm_loss, + ), + Argument( + "delta_pair_repr_norm_loss", + [float, int], + optional=True, + default=0.01, + doc=doc_delta_pair_repr_norm_loss, + ), + Argument("beta", [float, int], optional=True, default=1.0, doc=doc_beta), + ] + + def loss_variant_type_args() -> Variant: doc_loss = "The type of the loss. When the fitting type is `ener`, the loss type should be set to `ener`, its legacy alias `ener_hess`, `dens` (Only DPA4/SeZM supported), or left unset. Hessian supervision is configured through `start_pref_h` and `limit_pref_h` on the `ener` loss. When the fitting type is `property`, the loss type should be set to `property`. When the fitting type is `dipole` or `polar`, the loss type should be set to `tensor`." From dbd8ab3ff5a4152c1119f111c7d709ed380985ff Mon Sep 17 00:00:00 2001 From: Duo <50307526+iProzd@users.noreply.github.com> Date: Sat, 12 Sep 2026 06:05:41 -0400 Subject: [PATCH 09/34] feat(dpmodel): register the unimol_pretrain model Adds the atomic model and the model class. The atomic model overrides one method to route the backbone's token-resolution output into the heads, because the standard descriptor five-tuple cannot carry the virtual tokens, the pair channel or the norm regularisers. It also returns the head outputs untouched by out-stat: self-supervised targets have no per-element bias to add back. The two norm regularisers are frame scalars, but only per-atom variables survive the atomic-output machinery, so each is broadcast over the local atoms and the loss averages it back with the real-atom mask, which returns the original value exactly. A configuration now goes all the way through: argcheck normalizes it, the model factory picks UniMolPretrainModel by fitting type, and the model returns the three head outputs plus the two regularisers. Driven that way on the released weights, the five-term objective still matches upstream, total at 3.4e-7 relative. --- deepmd/dpmodel/atomic_model/__init__.py | 4 + .../atomic_model/unimol_atomic_model.py | 88 +++++++++++++++++ deepmd/dpmodel/fitting/unimol_pretrain.py | 21 +++- deepmd/dpmodel/loss/unimol.py | 28 +++++- deepmd/dpmodel/model/__init__.py | 4 + deepmd/dpmodel/model/unimol_pretrain_model.py | 97 +++++++++++++++++++ 6 files changed, 233 insertions(+), 9 deletions(-) create mode 100644 deepmd/dpmodel/atomic_model/unimol_atomic_model.py create mode 100644 deepmd/dpmodel/model/unimol_pretrain_model.py diff --git a/deepmd/dpmodel/atomic_model/__init__.py b/deepmd/dpmodel/atomic_model/__init__.py index 4d882d5e4b..c0eec82462 100644 --- a/deepmd/dpmodel/atomic_model/__init__.py +++ b/deepmd/dpmodel/atomic_model/__init__.py @@ -23,6 +23,9 @@ from .dos_atomic_model import ( DPDOSAtomicModel, ) +from .unimol_atomic_model import ( + DPUniMolAtomicModel, +) from .dp_atomic_model import ( DPAtomicModel, ) @@ -54,6 +57,7 @@ "DPEnergyAtomicModel", "DPPolarAtomicModel", "DPPropertyAtomicModel", + "DPUniMolAtomicModel", "DPZBLLinearEnergyAtomicModel", "LinearEnergyAtomicModel", "PairTabAtomicModel", diff --git a/deepmd/dpmodel/atomic_model/unimol_atomic_model.py b/deepmd/dpmodel/atomic_model/unimol_atomic_model.py new file mode 100644 index 0000000000..8cc4c4dce9 --- /dev/null +++ b/deepmd/dpmodel/atomic_model/unimol_atomic_model.py @@ -0,0 +1,88 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +"""Atomic model for Uni-Mol v1 self-supervised pretraining.""" + +from typing import ( + Any, +) + +from deepmd.dpmodel.array_api import ( + Array, +) +from deepmd.dpmodel.descriptor.unimol import ( + DescrptUniMol, +) +from deepmd.dpmodel.fitting.unimol_pretrain import ( + UniMolPretrainFitting, +) + +from .dp_atomic_model import ( + DPAtomicModel, +) + + +class DPUniMolAtomicModel(DPAtomicModel): + r"""Uni-Mol pretraining, wired at token resolution. + + The standard path hands a descriptor's five-tuple to a fitting, which + cannot carry the two virtual tokens, the pair channel or the norm + regularisers that these heads read. This model therefore overrides one + method to route the backbone's token-resolution output straight into the + heads. Nothing else about the atomic model changes. + """ + + def __init__( + self, descriptor: Any, fitting: Any, type_map: list[str], **kwargs: Any + ) -> None: + if not isinstance(descriptor, DescrptUniMol): + raise TypeError( + "DPUniMolAtomicModel needs the unimol descriptor, which is the only " + "one producing a Uni-Mol token sequence" + ) + if not isinstance(fitting, UniMolPretrainFitting): + raise TypeError("DPUniMolAtomicModel needs the unimol_pretrain fitting") + super().__init__(descriptor, fitting, type_map, **kwargs) + + def forward_atomic( + self, + extended_coord: Array, + extended_atype: Array, + nlist: Array, + mapping: Array | None = None, + fparam: Array | None = None, + aparam: Array | None = None, + comm_dict: dict | None = None, + charge_spin: Array | None = None, + ) -> dict[str, Array]: + """Run the backbone and its heads at token resolution. + + Parameters + ---------- + extended_coord + nf x (nall x 3) coordinates; the descriptor rejects any frame that + carries periodic images. + extended_atype + nf x nall element types, already clamped to be non-negative. + nlist + nf x nloc x nnei neighbour list, which is how real atoms are told + apart from padding. + mapping, fparam, aparam, comm_dict, charge_spin + Unused by this model. + + Returns + ------- + dict + The three head outputs plus the two norm regularisers. + """ + del mapping, fparam, aparam, comm_dict, charge_spin + backbone = self.descriptor.forward_tokens(extended_coord, extended_atype, nlist) + return self.fitting_net.call_tokens(backbone) + + def apply_out_stat(self, ret: dict[str, Array], atype: Array) -> dict[str, Array]: + """Return the head outputs untouched. + + Self-supervised targets carry no per-element bias to add back: the + element head predicts a distribution, and the coordinate and distance + heads predict geometry the data already fixes. + """ + del atype + return ret diff --git a/deepmd/dpmodel/fitting/unimol_pretrain.py b/deepmd/dpmodel/fitting/unimol_pretrain.py index e2f66e9bdb..f1b171f2fc 100644 --- a/deepmd/dpmodel/fitting/unimol_pretrain.py +++ b/deepmd/dpmodel/fitting/unimol_pretrain.py @@ -177,6 +177,19 @@ def output_def(self) -> FittingOutputDef: c_differentiable=False, ) ) + # The two regularisers are frame scalars, but only per-atom variables + # survive the atomic-output machinery, so each is broadcast over the + # local atoms and the loss averages it back with the real-atom mask. + for name in ("x_norm", "delta_pair_norm"): + variables.append( + OutputVariableDef( + name, + [1], + reducible=False, + r_differentiable=False, + c_differentiable=False, + ) + ) return FittingOutputDef(variables) def call_tokens(self, backbone: dict[str, Array]) -> dict[str, Array]: @@ -200,10 +213,10 @@ def call_tokens(self, backbone: dict[str, Array]) -> dict[str, Array]: node = backbone["node_ebd"] nf, nt = node.shape[0], node.shape[1] nloc = nt - 2 - out = { - "x_norm": backbone["x_norm"], - "delta_pair_norm": backbone["delta_pair_norm"], - } + out = {} + for name in ("x_norm", "delta_pair_norm"): + value = xp.astype(backbone[name], node.dtype) + out[name] = xp.full((nf, nloc, 1), value, dtype=node.dtype) if self.lm_head is not None: logits = self.lm_head(node) out["token_logits"] = logits[:, 1 : nloc + 1, :] diff --git a/deepmd/dpmodel/loss/unimol.py b/deepmd/dpmodel/loss/unimol.py index 7192b3b48e..87bdab7f30 100644 --- a/deepmd/dpmodel/loss/unimol.py +++ b/deepmd/dpmodel/loss/unimol.py @@ -51,6 +51,23 @@ def _smooth_l1(pred: Array, label: Array, beta: float = 1.0) -> Array: return xp.mean(elementwise) +def _frame_scalar(value: Array, mask: Array | None) -> Array: + """Recover a frame scalar that was broadcast over the local atoms. + + The two norm regularisers are frame quantities, but the model can only emit + per-atom variables, so each real atom carries the same value and padded ones + are zeroed. Averaging over the real atoms returns the original scalar. + """ + xp = array_api_compat.array_namespace(value) + if value.ndim <= 1: + return xp.reshape(value, ()) + per_atom = xp.reshape(value, value.shape[:2]) + if mask is None: + return xp.mean(per_atom) + weights = xp.astype(mask, per_atom.dtype) + return xp.sum(per_atom * weights) / xp.sum(weights) + + def _masked_nll(logits: Array, target: Array, pad_idx: int) -> Array: """Negative log likelihood over the selected positions. @@ -132,6 +149,7 @@ def call( ) -> tuple[Array, dict[str, Array]]: """Evaluate the five terms and their weighted sum.""" del learning_rate, natoms, mae + mask = model_dict.get("mask") token_target = label_dict["unimol_token_target"] xp = array_api_compat.array_namespace(token_target) masked = token_target != self.pad_idx @@ -146,7 +164,7 @@ def add(term: Array, weight: float, name: str) -> None: if self.masked_token_loss > 0: # The model emits one row per local atom; the objective covers only # the corrupted ones, so they are gathered here. - logits = model_dict["unimol_logits"] + logits = model_dict["token_logits"] if logits.ndim == 3: logits = logits[masked] add( @@ -155,7 +173,7 @@ def add(term: Array, weight: float, name: str) -> None: "token_loss", ) if self.masked_coord_loss > 0: - coord_pred = model_dict["unimol_coord"][masked] + coord_pred = model_dict["coord_update"][masked] coord_label = label_dict["unimol_coord_target"][masked] add( _smooth_l1( @@ -175,15 +193,15 @@ def add(term: Array, weight: float, name: str) -> None: label_dict["unimol_dist_target"][pair_mask] - DIST_MEAN ) / DIST_STD add( - _smooth_l1(model_dict["unimol_dist"][pair_mask], dist_label, self.beta), + _smooth_l1(model_dict["pair_dist"][pair_mask], dist_label, self.beta), self.masked_dist_loss, "dist_loss", ) if self.x_norm_loss > 0: - add(model_dict["unimol_x_norm"], self.x_norm_loss, "x_norm_loss") + add(_frame_scalar(model_dict["x_norm"], mask), self.x_norm_loss, "x_norm_loss") if self.delta_pair_repr_norm_loss > 0: add( - model_dict["unimol_delta_pair_norm"], + _frame_scalar(model_dict["delta_pair_norm"], mask), self.delta_pair_repr_norm_loss, "delta_pair_norm_loss", ) diff --git a/deepmd/dpmodel/model/__init__.py b/deepmd/dpmodel/model/__init__.py index 462ee802d2..3e8b6a3c92 100644 --- a/deepmd/dpmodel/model/__init__.py +++ b/deepmd/dpmodel/model/__init__.py @@ -42,6 +42,9 @@ from .polar_model import ( PolarModel, ) +from .unimol_pretrain_model import ( + UniMolPretrainModel, +) from .property_model import ( PropertyModel, ) @@ -61,5 +64,6 @@ "PolarModel", "PropertyModel", "SpinModel", + "UniMolPretrainModel", "make_model", ] diff --git a/deepmd/dpmodel/model/unimol_pretrain_model.py b/deepmd/dpmodel/model/unimol_pretrain_model.py new file mode 100644 index 0000000000..fa4ea373b4 --- /dev/null +++ b/deepmd/dpmodel/model/unimol_pretrain_model.py @@ -0,0 +1,97 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +"""Model wrapper for Uni-Mol v1 self-supervised pretraining.""" + +from typing import ( + Any, +) + +from deepmd.dpmodel.array_api import ( + Array, +) +from deepmd.dpmodel.atomic_model import ( + DPUniMolAtomicModel, +) +from deepmd.dpmodel.common import ( + NativeOP, +) +from deepmd.dpmodel.model.base_model import ( + BaseModel, +) +from deepmd.dpmodel.output_def import ( + OutputVariableDef, +) + +from .dp_model import ( + DPModelCommon, +) +from .make_model import ( + make_model, +) + +DPUniMolPretrainModel_ = make_model(DPUniMolAtomicModel, T_Bases=(NativeOP, BaseModel)) + + +@BaseModel.register("unimol_pretrain") +class UniMolPretrainModel(DPModelCommon, DPUniMolPretrainModel_): + r"""Uni-Mol v1 molecular pretraining. + + Predicts the element of every corrupted atom, denoises the coordinates and + predicts the clean pairwise distances, and reports the two norm + regularisers of the backbone. Nothing here reduces to a frame total and + nothing is differentiated with respect to the coordinates: this is + representation learning, not a potential energy surface. + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + DPModelCommon.__init__(self) + DPUniMolPretrainModel_.__init__(self, *args, **kwargs) + + def call( + self, + coord: Array, + atype: Array, + box: Array | None = None, + fparam: Array | None = None, + aparam: Array | None = None, + do_atomic_virial: bool = False, + charge_spin: Array | None = None, + ) -> dict[str, Array]: + """Evaluate the pretraining heads on a frame.""" + model_ret = self.call_common( + coord, + atype, + box, + fparam=fparam, + aparam=aparam, + do_atomic_virial=do_atomic_virial, + charge_spin=charge_spin, + ) + return {k: v for k, v in model_ret.items() if v is not None} + + def call_lower( + self, + extended_coord: Array, + extended_atype: Array, + nlist: Array, + mapping: Array | None = None, + fparam: Array | None = None, + aparam: Array | None = None, + do_atomic_virial: bool = False, + charge_spin: Array | None = None, + ) -> dict[str, Array]: + """Evaluate the pretraining heads on an extended frame.""" + model_ret = self.call_common_lower( + extended_coord, + extended_atype, + nlist, + mapping, + fparam=fparam, + aparam=aparam, + do_atomic_virial=do_atomic_virial, + charge_spin=charge_spin, + ) + return {k: v for k, v in model_ret.items() if v is not None} + + def translated_output_def(self) -> dict[str, OutputVariableDef]: + """The head outputs, passed through under their own names.""" + return dict(self.model_output_def().get_data()) From 24f46d4a57ccf50dd7003b3276a32ad3d2a6a072 Mon Sep 17 00:00:00 2001 From: Duo <50307526+iProzd@users.noreply.github.com> Date: Sat, 12 Sep 2026 06:10:24 -0400 Subject: [PATCH 10/34] feat(pt-expt): wire the unimol model into the PyTorch-Exportable backend Registers the descriptor, the fitting, the loss and the model. The wrappers are thin, as elsewhere in this backend: the descriptor adds parameter sharing for multi-task training, where level 0 shares the whole backbone and level 1 only the token embedding, and the loss is a straight re-export because the dpmodel one is a pure function of predictions and labels. Two bugs that only the real backend could show, both fixed here: - The element-to-token lookup table and the token embedding were read as plain arrays, so on a CUDA model they stayed on the host and indexing failed. They are now placed on the device of the incoming data, as are the four Gaussian basis tables. - The two regularisers were broadcast with a fill value that torch refuses when it is a tensor rather than a number; they are broadcast by addition now. Checked on GPU through the registered path: a configuration normalizes, the factory builds the model, and the five-term objective on the released weights matches upstream with the total at 8.1e-8 relative. --- deepmd/dpmodel/descriptor/unimol.py | 9 +- .../dpmodel/descriptor/unimol_nn/encoder.py | 18 +++- deepmd/dpmodel/fitting/unimol_pretrain.py | 4 +- deepmd/pt_expt/descriptor/__init__.py | 5 ++ deepmd/pt_expt/descriptor/unimol.py | 43 ++++++++++ deepmd/pt_expt/fitting/__init__.py | 5 ++ deepmd/pt_expt/fitting/unimol_pretrain.py | 18 ++++ deepmd/pt_expt/loss/__init__.py | 5 ++ deepmd/pt_expt/loss/unimol.py | 6 ++ deepmd/pt_expt/model/__init__.py | 5 ++ deepmd/pt_expt/model/unimol_pretrain_model.py | 86 +++++++++++++++++++ 11 files changed, 196 insertions(+), 8 deletions(-) create mode 100644 deepmd/pt_expt/descriptor/unimol.py create mode 100644 deepmd/pt_expt/fitting/unimol_pretrain.py create mode 100644 deepmd/pt_expt/loss/unimol.py create mode 100644 deepmd/pt_expt/model/unimol_pretrain_model.py diff --git a/deepmd/dpmodel/descriptor/unimol.py b/deepmd/dpmodel/descriptor/unimol.py index fe82748c9a..f9b9c0ff06 100644 --- a/deepmd/dpmodel/descriptor/unimol.py +++ b/deepmd/dpmodel/descriptor/unimol.py @@ -346,7 +346,11 @@ def build_tokens(self, coord_ext: Array, atype_ext: Array, nlist: Array) -> dict else: centroid = xp.zeros((nf, 3), dtype=coord.dtype) - token_table = xp.asarray(self.type_to_token) + # The lookup table is plain integer data rather than a parameter, so it + # does not travel with the module and has to be placed explicitly. + token_table = xp.asarray( + self.type_to_token, device=array_api_compat.device(nlist) + ) atom_tokens = xp.take(token_table, xp.reshape(atype_ext, (-1,)), axis=0) atom_tokens = xp.reshape(atom_tokens, (nf, nloc)) atom_tokens = xp.where( @@ -393,7 +397,8 @@ def forward_tokens(self, coord_ext: Array, atype_ext: Array, nlist: Array) -> di tokens, coord, padding_mask = seq["tokens"], seq["coord"], seq["padding_mask"] nf, nt = tokens.shape - emb = xp.take(xp.asarray(self.embed_tokens), xp.reshape(tokens, (-1,)), axis=0) + embed = xp.asarray(self.embed_tokens, device=array_api_compat.device(coord_ext)) + emb = xp.take(embed, xp.reshape(tokens, (-1,)), axis=0) emb = xp.reshape(emb, (nf, nt, self.encoder_embed_dim)) emb = xp.astype(emb, coord.dtype) diff --git a/deepmd/dpmodel/descriptor/unimol_nn/encoder.py b/deepmd/dpmodel/descriptor/unimol_nn/encoder.py index 7054ea215a..76d3ffa6d5 100644 --- a/deepmd/dpmodel/descriptor/unimol_nn/encoder.py +++ b/deepmd/dpmodel/descriptor/unimol_nn/encoder.py @@ -178,12 +178,17 @@ def __init__( def call(self, dist, edge_type): # noqa: ANN001, ANN201 """Expand ``dist`` (nf x nt x nt) into ``k`` Gaussians per atom pair.""" xp = array_api_compat.array_namespace(dist) + dev = array_api_compat.device(dist) mul = xp.reshape( - xp.take(xp.asarray(self.mul), xp.reshape(edge_type, (-1,)), axis=0), + xp.take( + xp.asarray(self.mul, device=dev), xp.reshape(edge_type, (-1,)), axis=0 + ), (*edge_type.shape, 1), ) bias = xp.reshape( - xp.take(xp.asarray(self.bias), xp.reshape(edge_type, (-1,)), axis=0), + xp.take( + xp.asarray(self.bias, device=dev), xp.reshape(edge_type, (-1,)), axis=0 + ), (*edge_type.shape, 1), ) mul = xp.astype(mul, dist.dtype) @@ -192,8 +197,13 @@ def call(self, dist, edge_type): # noqa: ANN001, ANN201 x = xp.repeat(x, self.k, axis=-1) work = xp.float32 if self.single_precision_basis else x.dtype x = xp.astype(x, work) - mean = xp.astype(xp.reshape(xp.asarray(self.means), (-1,)), work) - std = xp.abs(xp.astype(xp.reshape(xp.asarray(self.stds), (-1,)), work)) + 1e-5 + mean = xp.astype(xp.reshape(xp.asarray(self.means, device=dev), (-1,)), work) + std = ( + xp.abs( + xp.astype(xp.reshape(xp.asarray(self.stds, device=dev), (-1,)), work) + ) + + 1e-5 + ) a = (2 * UNIMOL_PI) ** 0.5 out = xp.exp(-0.5 * (((x - mean) / std) ** 2)) / (a * std) return xp.astype(out, dist.dtype) diff --git a/deepmd/dpmodel/fitting/unimol_pretrain.py b/deepmd/dpmodel/fitting/unimol_pretrain.py index f1b171f2fc..fa9d28d8d9 100644 --- a/deepmd/dpmodel/fitting/unimol_pretrain.py +++ b/deepmd/dpmodel/fitting/unimol_pretrain.py @@ -215,8 +215,8 @@ def call_tokens(self, backbone: dict[str, Array]) -> dict[str, Array]: nloc = nt - 2 out = {} for name in ("x_norm", "delta_pair_norm"): - value = xp.astype(backbone[name], node.dtype) - out[name] = xp.full((nf, nloc, 1), value, dtype=node.dtype) + value = xp.astype(xp.reshape(backbone[name], (1, 1, 1)), node.dtype) + out[name] = xp.zeros((nf, nloc, 1), dtype=node.dtype) + value if self.lm_head is not None: logits = self.lm_head(node) out["token_logits"] = logits[:, 1 : nloc + 1, :] diff --git a/deepmd/pt_expt/descriptor/__init__.py b/deepmd/pt_expt/descriptor/__init__.py index f2718a6df0..51f435370e 100644 --- a/deepmd/pt_expt/descriptor/__init__.py +++ b/deepmd/pt_expt/descriptor/__init__.py @@ -47,6 +47,10 @@ DescrptSeTTebd, ) +from .unimol import ( + DescrptUniMol, +) + __all__ = [ "BaseDescriptor", "DescrptDPA1", @@ -60,4 +64,5 @@ "DescrptSeR", "DescrptSeT", "DescrptSeTTebd", + "DescrptUniMol", ] diff --git a/deepmd/pt_expt/descriptor/unimol.py b/deepmd/pt_expt/descriptor/unimol.py new file mode 100644 index 0000000000..c65bb6fd4f --- /dev/null +++ b/deepmd/pt_expt/descriptor/unimol.py @@ -0,0 +1,43 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later + +from deepmd.dpmodel.descriptor.unimol import ( + DescrptUniMol as DescrptUniMolDP, +) +from deepmd.pt_expt.common import ( + torch_module, +) +from deepmd.pt_expt.descriptor.base_descriptor import ( + BaseDescriptor, +) + + +@BaseDescriptor.register("unimol") +@torch_module +class DescrptUniMol(DescrptUniMolDP): + """Uni-Mol v1 backbone on the PyTorch-Exportable backend.""" + + def share_params( + self, + base_class: "DescrptUniMol", + shared_level: int, + model_prob: float = 1.0, + resume: bool = False, + ) -> None: + """Share parameters with ``base_class`` for multi-task training. + + Level 0 shares the whole backbone, level 1 only the token embedding. + There are no environment statistics to merge, so ``model_prob`` and + ``resume`` play no part. + """ + del model_prob, resume + assert self.__class__ == base_class.__class__, ( + "Only descriptors of the same type can share params!" + ) + if shared_level == 0: + for key in ("gbf", "gbf_proj", "encoder"): + self._modules[key] = base_class._modules[key] + self.embed_tokens = base_class.embed_tokens + elif shared_level == 1: + self.embed_tokens = base_class.embed_tokens + else: + raise NotImplementedError diff --git a/deepmd/pt_expt/fitting/__init__.py b/deepmd/pt_expt/fitting/__init__.py index 8217f64bd2..c2838272fe 100644 --- a/deepmd/pt_expt/fitting/__init__.py +++ b/deepmd/pt_expt/fitting/__init__.py @@ -24,6 +24,10 @@ PropertyFittingNet, ) +from .unimol_pretrain import ( + UniMolPretrainFitting, +) + __all__ = [ "BaseFitting", "DOSFittingNet", @@ -33,4 +37,5 @@ "PolarFitting", "PropertyFittingNet", "SeZMEnergyFittingNet", + "UniMolPretrainFitting", ] diff --git a/deepmd/pt_expt/fitting/unimol_pretrain.py b/deepmd/pt_expt/fitting/unimol_pretrain.py new file mode 100644 index 0000000000..0ad5c121a8 --- /dev/null +++ b/deepmd/pt_expt/fitting/unimol_pretrain.py @@ -0,0 +1,18 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later + +from deepmd.dpmodel.fitting.unimol_pretrain import ( + UniMolPretrainFitting as UniMolPretrainFittingDP, +) +from deepmd.pt_expt.common import ( + torch_module, +) + +from .base_fitting import ( + BaseFitting, +) + + +@BaseFitting.register("unimol_pretrain") +@torch_module +class UniMolPretrainFitting(UniMolPretrainFittingDP): + """The Uni-Mol pretraining heads on the PyTorch-Exportable backend.""" diff --git a/deepmd/pt_expt/loss/__init__.py b/deepmd/pt_expt/loss/__init__.py index 77350a8cb0..fdff99e331 100644 --- a/deepmd/pt_expt/loss/__init__.py +++ b/deepmd/pt_expt/loss/__init__.py @@ -15,10 +15,15 @@ TensorLoss, ) +from deepmd.pt_expt.loss.unimol import ( + UniMolLoss, +) + __all__ = [ "DOSLoss", "EnergyLoss", "EnergySpinLoss", "PropertyLoss", "TensorLoss", + "UniMolLoss", ] diff --git a/deepmd/pt_expt/loss/unimol.py b/deepmd/pt_expt/loss/unimol.py new file mode 100644 index 0000000000..e456cb877f --- /dev/null +++ b/deepmd/pt_expt/loss/unimol.py @@ -0,0 +1,6 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from deepmd.dpmodel.loss.unimol import ( + UniMolLoss, +) + +__all__ = ["UniMolLoss"] diff --git a/deepmd/pt_expt/model/__init__.py b/deepmd/pt_expt/model/__init__.py index b0d7ca1402..0fd3cbde09 100644 --- a/deepmd/pt_expt/model/__init__.py +++ b/deepmd/pt_expt/model/__init__.py @@ -43,6 +43,10 @@ SpinEnergyModel, ) +from .unimol_pretrain_model import ( + UniMolPretrainModel, +) + __all__ = [ "BaseModel", "DOSModel", @@ -56,6 +60,7 @@ "PolarModel", "PropertyModel", "SpinEnergyModel", + "UniMolPretrainModel", "get_model", "make_hessian_model", ] diff --git a/deepmd/pt_expt/model/unimol_pretrain_model.py b/deepmd/pt_expt/model/unimol_pretrain_model.py new file mode 100644 index 0000000000..eaa6b1586d --- /dev/null +++ b/deepmd/pt_expt/model/unimol_pretrain_model.py @@ -0,0 +1,86 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from typing import ( + Any, +) + +import torch + +from deepmd.dpmodel.atomic_model import ( + DPUniMolAtomicModel, +) +from deepmd.dpmodel.model.dp_model import ( + DPModelCommon, +) +from deepmd.dpmodel.output_def import ( + OutputVariableDef, +) + +from .make_model import ( + make_model, +) +from .model import ( + BaseModel, +) + +DPUniMolPretrainModel_ = make_model(DPUniMolAtomicModel, T_Bases=(BaseModel,)) + + +@BaseModel.register("unimol_pretrain") +class UniMolPretrainModel(DPModelCommon, DPUniMolPretrainModel_): + """Uni-Mol v1 self-supervised pretraining on the PyTorch-Exportable backend.""" + + def __init__(self, *args: Any, **kwargs: Any) -> None: + DPModelCommon.__init__(self) + DPUniMolPretrainModel_.__init__(self, *args, **kwargs) + + def forward( + self, + coord: torch.Tensor, + atype: torch.Tensor, + box: torch.Tensor | None = None, + fparam: torch.Tensor | None = None, + aparam: torch.Tensor | None = None, + do_atomic_virial: bool = False, + charge_spin: torch.Tensor | None = None, + ) -> dict[str, torch.Tensor]: + """Evaluate the pretraining heads on a frame.""" + model_ret = self.forward_common( + coord, + atype, + box, + fparam=fparam, + aparam=aparam, + do_atomic_virial=do_atomic_virial, + charge_spin=charge_spin, + ) + return {k: v for k, v in model_ret.items() if v is not None} + + def forward_lower( + self, + extended_coord: torch.Tensor, + extended_atype: torch.Tensor, + nlist: torch.Tensor, + mapping: torch.Tensor | None = None, + fparam: torch.Tensor | None = None, + aparam: torch.Tensor | None = None, + do_atomic_virial: bool = False, + comm_dict: dict[str, torch.Tensor] | None = None, + charge_spin: torch.Tensor | None = None, + ) -> dict[str, torch.Tensor]: + """Evaluate the pretraining heads on an extended frame.""" + model_ret = self.forward_common_lower( + extended_coord, + extended_atype, + nlist, + mapping, + fparam=fparam, + aparam=aparam, + do_atomic_virial=do_atomic_virial, + comm_dict=comm_dict, + charge_spin=charge_spin, + ) + return {k: v for k, v in model_ret.items() if v is not None} + + def translated_output_def(self) -> dict[str, OutputVariableDef]: + """The head outputs, passed through under their own names.""" + return dict(self.model_output_def().get_data()) From e6fb4d781b49a353f83e723adee2a471b42812ae Mon Sep 17 00:00:00 2001 From: Duo <50307526+iProzd@users.noreply.github.com> Date: Sat, 12 Sep 2026 06:17:22 -0400 Subject: [PATCH 11/34] test: parity tests for the Uni-Mol v1 port Adds the golden archive and two test files. Every expected value was produced by running upstream Uni-Mol 90f52c4 unmodified on CPU over four molecules of its own example data at a fixed seed and epoch; the header of the dpmodel test says how to regenerate it. The dpmodel tests cover the data-side transforms, the encoder, the Gaussian basis, the three heads, the descriptor and the five-term objective, plus serialization round trips and the two guards the descriptor raises. The transform test asserts bitwise equality on tokens, targets, edge types and both coordinate arrays, which is what shows the random stream itself is reproduced rather than merely its statistics. The PyTorch-Exportable tests cover the registered path end to end, agreement with the array-API implementation on identical weights, the objective against upstream, and that gradients reach the parameters. Tolerances have stated causes rather than being tuned until they pass. Where upstream's fp32 Gaussian basis is in play, agreement is one fp32 unit in the last place; a dedicated test measures that gap so the looser bound elsewhere is justified, and with the basis in full precision the two backends agree to fp64 rounding. --- source/tests/common/dpmodel/test_unimol.py | 466 ++++++++++++++++++ .../tests/common/dpmodel/unimol_v1_golden.npz | Bin 0 -> 1413467 bytes source/tests/pt_expt/model/test_unimol.py | 352 +++++++++++++ 3 files changed, 818 insertions(+) create mode 100644 source/tests/common/dpmodel/test_unimol.py create mode 100644 source/tests/common/dpmodel/unimol_v1_golden.npz create mode 100644 source/tests/pt_expt/model/test_unimol.py diff --git a/source/tests/common/dpmodel/test_unimol.py b/source/tests/common/dpmodel/test_unimol.py new file mode 100644 index 0000000000..5905ce838c --- /dev/null +++ b/source/tests/common/dpmodel/test_unimol.py @@ -0,0 +1,466 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +"""Parity tests for the Uni-Mol v1 port. + +Every expected value in ``unimol_v1_golden.npz`` was produced by running +upstream Uni-Mol (commit 90f52c4) unmodified on CPU, over molecules 0 to 3 of +the example data shipped with that repository, at seed 1 and epoch 1. The +generator is ``golden/dump_golden.py`` in the development task directory; it +needs the upstream sources on ``PYTHONPATH`` and is not run by the test suite. + +Tolerances are not arbitrary. Upstream makes three fp32 choices that deepmd +does not have to make, and they set the floor: + +* the Gaussian basis is evaluated in fp32, +* the distance matrix is precomputed in fp32 by its data pipeline, +* ``log_softmax`` and both norm regularisers are evaluated in fp32. + +With those matched, agreement is at the fp32 rounding level, around 1e-7 +relative. The encoder itself, fed upstream's own attention bias, agrees to fp64 +rounding, which is what the strictest test below asserts. +""" + +import os +import unittest + +import numpy as np + +from deepmd.dpmodel.descriptor.unimol import ( + UNIMOL_ELEMENTS, + DescrptUniMol, + unimol_vocabulary, +) +from deepmd.dpmodel.descriptor.unimol_nn import ( + DistanceHead, + GaussianLayer, + MaskLMHead, + NonLinearHead, + TransformerEncoderWithPair, + coord_update, +) +from deepmd.dpmodel.fitting.unimol_pretrain import ( + UniMolPretrainFitting, +) +from deepmd.dpmodel.loss.unimol import ( + UniMolLoss, +) +from deepmd.dpmodel.utils.unimol_transform import ( + unimol_frame_transform, +) + +GOLDEN = os.path.join(os.path.dirname(__file__), "unimol_v1_golden.npz") +SEED, EPOCH, INDICES = 1, 1, [0, 1, 2, 3] +SMALL = {"layers": 2, "dim": 32, "ffn": 64, "heads": 4, "k": 128, "vocab": 31} + + +def _set_linear(layer, weights, prefix, weight_key=None, bias_key=None): + layer.w = np.ascontiguousarray(weights[weight_key or prefix + ".weight"].T) + key = bias_key or prefix + ".bias" + if key in weights and layer.b is not None: + layer.b = weights[key].copy() + + +def _set_layer_norm(layer, weights, prefix): + layer.w = weights[prefix + ".weight"].copy() + layer.b = weights[prefix + ".bias"].copy() + + +class UniMolGoldenMixin: + @classmethod + def setUpClass(cls) -> None: + cls.golden = np.load(GOLDEN) + cls.weights = { + k[len("small_weights/") :]: v + for k, v in cls.golden.items() + if k.startswith("small_weights/") + } + cls.n_real = (cls.golden["input/src_tokens"] != 0).sum(axis=1) - 2 + + def build_encoder(self): + enc = TransformerEncoderWithPair( + encoder_layers=SMALL["layers"], + embed_dim=SMALL["dim"], + ffn_embed_dim=SMALL["ffn"], + attention_heads=SMALL["heads"], + activation_function="gelu_erf", + ) + w = self.weights + _set_layer_norm(enc.emb_layer_norm, w, "encoder.emb_layer_norm") + _set_layer_norm(enc.final_layer_norm, w, "encoder.final_layer_norm") + _set_layer_norm(enc.final_head_layer_norm, w, "encoder.final_head_layer_norm") + for i, layer in enumerate(enc.layers): + p = f"encoder.layers.{i}." + _set_linear(layer.self_attn.in_proj, w, p + "self_attn.in_proj") + _set_linear(layer.self_attn.out_proj, w, p + "self_attn.out_proj") + _set_layer_norm(layer.self_attn_layer_norm, w, p + "self_attn_layer_norm") + _set_linear(layer.fc1, w, p + "fc1") + _set_linear(layer.fc2, w, p + "fc2") + _set_layer_norm(layer.final_layer_norm, w, p + "final_layer_norm") + return enc + + def build_descriptor(self, **overrides): + kwargs = { + "type_map": [*UNIMOL_ELEMENTS, "[MASK]"], + "encoder_layers": SMALL["layers"], + "encoder_embed_dim": SMALL["dim"], + "encoder_ffn_embed_dim": SMALL["ffn"], + "encoder_attention_heads": SMALL["heads"], + "max_atoms": int(self.n_real.max()) + 1, + "activation_function": "gelu_erf", + "virtual_token_position": "origin", + } + kwargs.update(overrides) + desc = DescrptUniMol(**kwargs) + w = self.weights + desc.embed_tokens = w["embed_tokens.weight"].copy() + for key in ("means", "stds", "mul", "bias"): + setattr(desc.gbf, key, w[f"gbf.{key}.weight"].copy()) + _set_linear(desc.gbf_proj.linear1, w, "gbf_proj.linear1") + _set_linear(desc.gbf_proj.linear2, w, "gbf_proj.linear2") + enc = self.build_encoder() + desc.encoder = enc + return desc + + def deepmd_inputs(self): + """Rebuild the deepmd-side inputs for the molecules in the golden batch.""" + vocab = unimol_vocabulary() + type_map = [*UNIMOL_ELEMENTS, "[MASK]"] + type_of = {sym: i for i, sym in enumerate(type_map)} + src_tokens = self.golden["input/src_tokens"] + src_coord = self.golden["input/src_coord"].astype(np.float64) + nf, nloc = len(self.n_real), int(self.n_real.max()) + coord = np.zeros((nf, nloc, 3)) + atype = np.zeros((nf, nloc), dtype=np.int64) + nlist = np.full((nf, nloc, nloc - 1), -1, dtype=np.int64) + for f in range(nf): + n = int(self.n_real[f]) + coord[f, :n] = src_coord[f, 1 : n + 1] + atype[f, :n] = [type_of[vocab[t]] for t in src_tokens[f, 1 : n + 1]] + for i in range(n): + others = [j for j in range(n) if j != i] + nlist[f, i, : len(others)] = others + return coord, atype, nlist + + +class TestUniMolTransform(UniMolGoldenMixin, unittest.TestCase): + """The data-side corruption must reproduce upstream's random stream.""" + + def test_frame_transform_matches_upstream(self) -> None: + vocab = {sym: i for i, sym in enumerate(unimol_vocabulary())} + specials = [vocab[s] for s in ("[PAD]", "[CLS]", "[SEP]", "[UNK]", "[MASK]")] + for row, index in enumerate(INDICES): + atoms = self.golden[f"raw/{index}/atoms"] + conformers = self.golden[f"raw/{index}/conformers"] + got = unimol_frame_transform( + atoms, + conformers, + vocab=vocab, + num_types=len(vocab), + special_indices=specials, + pad_idx=vocab["[PAD]"], + bos_idx=vocab["[CLS]"], + eos_idx=vocab["[SEP]"], + mask_idx=vocab["[MASK]"], + unk_idx=vocab["[UNK]"], + seed=SEED, + epoch=EPOCH, + index=index, + max_atoms=256, + mask_prob=0.15, + leave_unmasked_prob=0.05, + random_token_prob=0.05, + noise_type="uniform", + noise=1.0, + ) + n = int(self.n_real[row]) + 2 + with self.subTest(molecule=index): + # Tokens, targets and both coordinate arrays are bitwise equal, + # which is what proves the random stream itself is reproduced. + np.testing.assert_array_equal( + got["src_tokens"], self.golden["input/src_tokens"][row, :n] + ) + np.testing.assert_array_equal( + got["tokens_target"], self.golden["target/tokens_target"][row, :n] + ) + np.testing.assert_array_equal( + got["src_edge_type"], + self.golden["input/src_edge_type"][row, :n, :n], + ) + np.testing.assert_array_equal( + got["src_coord"], self.golden["input/src_coord"][row, :n] + ) + np.testing.assert_array_equal( + got["coord_target"], self.golden["target/coord_target"][row, :n] + ) + # scipy's distance_matrix and a sqrt of summed squares differ in + # the last fp32 place. + np.testing.assert_allclose( + got["src_distance"], + self.golden["input/src_distance"][row, :n, :n], + atol=1e-5, + ) + + def test_masking_statistics(self) -> None: + """About 15% of atoms are selected, and only replaced ones are moved.""" + for row, index in enumerate(INDICES): + n = int(self.n_real[row]) + targets = self.golden["target/tokens_target"][row, 1 : n + 1] + selected = int((targets != 0).sum()) + self.assertGreater(selected, 0) + self.assertLessEqual(selected, max(1, int(0.5 * n))) + noisy = self.golden["input/src_coord"][row, 1 : n + 1] + clean = self.golden["target/coord_target"][row, 1 : n + 1] + moved = np.abs(noisy - clean).max(axis=-1) > 0 + # Every moved atom is a selected atom; the unchanged 5% are not moved. + self.assertTrue(bool(np.all(~moved | (targets != 0)))) + self.assertLessEqual(int(moved.sum()), selected) + + +class TestUniMolEncoder(UniMolGoldenMixin, unittest.TestCase): + """The backbone, isolated from upstream's fp32 choices.""" + + def test_encoder_matches_upstream_in_fp64(self) -> None: + enc = self.build_encoder() + emb = self.golden["small_fp64/enc_in/emb"].astype(np.float64) + bias = self.golden["small_fp64/enc_in/attn_bias"].astype(np.float64) + pad = self.golden["small_fp64/enc_in/padding_mask"].astype(np.float64) + x, pair, delta, x_norm, delta_norm = enc(emb, bias.copy(), pad) + # Fed upstream's own bias, everything is fp64 end to end. + np.testing.assert_allclose( + x, self.golden["small_fp64/enc_out/x"], rtol=1e-11, atol=1e-11 + ) + finite = np.isfinite(self.golden["small_fp64/enc_out/pair_rep"]) + np.testing.assert_allclose( + np.asarray(pair)[finite], + self.golden["small_fp64/enc_out/pair_rep"][finite], + rtol=1e-11, + atol=1e-11, + ) + np.testing.assert_allclose( + delta, + self.golden["small_fp64/enc_out/delta_pair_rep"], + rtol=1e-10, + atol=1e-10, + ) + np.testing.assert_allclose( + float(x_norm), + float(self.golden["small_fp64/enc_out/x_norm"]), + rtol=1e-6, + atol=1e-8, + ) + np.testing.assert_allclose( + float(delta_norm), + float(self.golden["small_fp64/enc_out/delta_pair_norm"]), + rtol=1e-6, + atol=1e-8, + ) + + def test_gaussian_basis_and_projection(self) -> None: + gbf = GaussianLayer(SMALL["k"], SMALL["vocab"] ** 2) + for key in ("means", "stds", "mul", "bias"): + setattr(gbf, key, self.weights[f"gbf.{key}.weight"].copy()) + proj = NonLinearHead(SMALL["k"], SMALL["heads"], "gelu_erf", hidden=SMALL["k"]) + _set_linear(proj.linear1, self.weights, "gbf_proj.linear1") + _set_linear(proj.linear2, self.weights, "gbf_proj.linear2") + dist = self.golden["input/src_distance"].astype(np.float64) + bias = proj(gbf(dist, self.golden["input/src_edge_type"])) + nt = dist.shape[1] + bias = np.ascontiguousarray(np.transpose(bias, (0, 3, 1, 2))).reshape( + -1, nt, nt + ) + # The basis is evaluated in fp32 upstream, so this is one fp32 ulp. + np.testing.assert_allclose( + bias, self.golden["small_fp64/enc_in/attn_bias"], rtol=1e-6, atol=1e-6 + ) + + +class TestUniMolHeads(UniMolGoldenMixin, unittest.TestCase): + """The three pretraining heads.""" + + def test_heads_match_upstream(self) -> None: + w = self.golden + lm = MaskLMHead(SMALL["dim"], SMALL["vocab"], "gelu_erf") + _set_linear(lm.dense, self.weights, "lm_head.dense") + _set_layer_norm(lm.layer_norm, self.weights, "lm_head.layer_norm") + _set_linear( + lm.out_proj, + self.weights, + None, + weight_key="lm_head.weight", + bias_key="lm_head.bias", + ) + dist_head = DistanceHead(SMALL["heads"], "gelu_erf") + _set_linear(dist_head.dense, self.weights, "dist_head.dense") + _set_layer_norm(dist_head.layer_norm, self.weights, "dist_head.layer_norm") + _set_linear(dist_head.out_proj, self.weights, "dist_head.out_proj") + p2c = NonLinearHead(SMALL["heads"], 1, "gelu_erf", hidden=SMALL["heads"]) + _set_linear(p2c.linear1, self.weights, "pair2coord_proj.linear1") + _set_linear(p2c.linear2, self.weights, "pair2coord_proj.linear2") + + x = w["small_fp64/enc_out/x"].astype(np.float64) + pair = w["small_fp64/enc_out/pair_rep"].astype(np.float64) + pair = np.where(np.isneginf(pair), 0.0, pair) + np.testing.assert_allclose( + lm(x, w["input/masked_tokens"]), + w["small_fp64/model/logits"], + rtol=1e-11, + atol=1e-11, + ) + np.testing.assert_allclose( + dist_head(pair), + w["small_fp64/model/encoder_distance"], + rtol=1e-11, + atol=1e-11, + ) + np.testing.assert_allclose( + coord_update( + w["input/src_coord"].astype(np.float64), + w["small_fp64/enc_out/delta_pair_rep"].astype(np.float64), + w["small_fp64/enc_in/padding_mask"].astype(np.float64), + p2c, + ), + w["small_fp64/model/encoder_coord"], + rtol=1e-11, + atol=1e-11, + ) + + +class TestUniMolDescriptor(UniMolGoldenMixin, unittest.TestCase): + """The descriptor, driven through deepmd-shaped inputs.""" + + def test_forward_matches_upstream(self) -> None: + desc = self.build_descriptor() + coord, atype, nlist = self.deepmd_inputs() + out = desc.forward_tokens(coord, atype, nlist) + ref = self.golden["small_fp64/enc_out/x"].astype(np.float64) + for f in range(len(self.n_real)): + n = int(self.n_real[f]) + with self.subTest(molecule=f): + np.testing.assert_allclose( + out["node_ebd"][f, 1 : n + 1], + ref[f, 1 : n + 1], + rtol=1e-6, + atol=1e-6, + ) + np.testing.assert_array_equal( + out["tokens"][f, : n + 2], + self.golden["input/src_tokens"][f, : n + 2], + ) + + def test_call_drops_the_virtual_tokens(self) -> None: + desc = self.build_descriptor() + coord, atype, nlist = self.deepmd_inputs() + node, rot, g2, h2, sw = desc.call(coord, atype, nlist) + self.assertEqual(node.shape, (coord.shape[0], coord.shape[1], SMALL["dim"])) + self.assertIsNone(rot) + self.assertIsNone(g2) + self.assertIsNone(h2) + self.assertIsNone(sw) + + def test_rejects_periodic_and_tiny_frames(self) -> None: + desc = self.build_descriptor() + coord, atype, nlist = self.deepmd_inputs() + with self.assertRaisesRegex(ValueError, "periodic"): + desc.forward_tokens(np.concatenate([coord, coord], axis=1), atype, nlist) + lonely = np.full_like(nlist[:, :1, :], -1) + with self.assertRaisesRegex(ValueError, "two real atoms"): + desc.forward_tokens(coord[:, :1], atype[:, :1], lonely) + + def test_serialize_round_trip(self) -> None: + desc = self.build_descriptor() + clone = DescrptUniMol.deserialize(desc.serialize()) + coord, atype, nlist = self.deepmd_inputs() + np.testing.assert_allclose( + clone.forward_tokens(coord, atype, nlist)["node_ebd"], + desc.forward_tokens(coord, atype, nlist)["node_ebd"], + rtol=1e-14, + atol=1e-14, + ) + + +class TestUniMolLoss(UniMolGoldenMixin, unittest.TestCase): + """The five-term objective.""" + + def _labels(self, nloc, ncol): + nf = len(self.n_real) + labels = { + "unimol_token_target": np.zeros((nf, nloc), dtype=np.int64), + "unimol_coord_target": np.zeros((nf, nloc, 3)), + "unimol_dist_target": np.zeros((nf, nloc, ncol)), + "unimol_token_mask": np.zeros((nf, ncol), dtype=np.int64), + } + for f in range(nf): + n = int(self.n_real[f]) + labels["unimol_token_target"][f, :n] = self.golden["target/tokens_target"][ + f, 1 : n + 1 + ] + labels["unimol_coord_target"][f, :n] = self.golden["target/coord_target"][ + f, 1 : n + 1 + ] + labels["unimol_dist_target"][f, :n, : n + 2] = self.golden[ + "target/distance_target" + ][f, 1 : n + 1, : n + 2] + labels["unimol_token_mask"][f, : n + 2] = 1 + return labels + + def test_terms_match_upstream(self) -> None: + desc = self.build_descriptor() + fitting = UniMolPretrainFitting( + ntypes=len(desc.get_type_map()), + dim_descrpt=SMALL["dim"], + n_token=SMALL["vocab"], + attention_heads=SMALL["heads"], + max_atoms=desc.max_atoms, + activation_function="gelu_erf", + ) + w = self.weights + _set_linear(fitting.lm_head.dense, w, "lm_head.dense") + _set_layer_norm(fitting.lm_head.layer_norm, w, "lm_head.layer_norm") + _set_linear( + fitting.lm_head.out_proj, + w, + None, + weight_key="lm_head.weight", + bias_key="lm_head.bias", + ) + _set_linear(fitting.pair2coord_proj.linear1, w, "pair2coord_proj.linear1") + _set_linear(fitting.pair2coord_proj.linear2, w, "pair2coord_proj.linear2") + _set_linear(fitting.dist_head.dense, w, "dist_head.dense") + _set_layer_norm(fitting.dist_head.layer_norm, w, "dist_head.layer_norm") + _set_linear(fitting.dist_head.out_proj, w, "dist_head.out_proj") + + coord, atype, nlist = self.deepmd_inputs() + pred = fitting.call_tokens(desc.forward_tokens(coord, atype, nlist)) + labels = self._labels(coord.shape[1], fitting.max_atoms + 2) + total, more = UniMolLoss().call(1.0, 0, pred, labels) + + expected = { + "token_loss": "loss_token", + "coord_loss": "loss_coord", + "dist_loss": "loss_dist", + "x_norm_loss": "loss_x_norm", + "delta_pair_norm_loss": "loss_delta_pair_norm", + } + for mine, ref in expected.items(): + with self.subTest(term=mine): + np.testing.assert_allclose( + float(more[mine]), + float(self.golden[f"small_fp64/loss/{ref}"]), + rtol=1e-5, + atol=1e-8, + ) + np.testing.assert_allclose( + float(total), + float(self.golden["small_fp64/loss/loss_total"]), + rtol=1e-5, + atol=1e-8, + ) + + def test_serialize_round_trip(self) -> None: + loss = UniMolLoss(masked_coord_loss=3.0, beta=0.5) + clone = UniMolLoss.deserialize(loss.serialize()) + self.assertEqual(clone.masked_coord_loss, 3.0) + self.assertEqual(clone.beta, 0.5) + + +if __name__ == "__main__": + unittest.main() diff --git a/source/tests/common/dpmodel/unimol_v1_golden.npz b/source/tests/common/dpmodel/unimol_v1_golden.npz new file mode 100644 index 0000000000000000000000000000000000000000..02466c35489eb2bb1f6423e49cc792af5b62167e GIT binary patch literal 1413467 zcmcG#by(Ex);6pnAT8aXQUcQ5A|l<=CEYo6ib!`i1Jccq(juLMbR*p$9mCALpxb-j z_r3Ri_w#+<^UZO<9Q*;Cv#zzyb*^(=KSddYhfMeG-FtNV_4FPMSExArA1|VNc=t^0 zxj9*^?ErwE-}IgAtW0c~ZSCFf-+O%T)&2eWT=~=4e}lrijh2P2y|WVwz|rVe%>jM`dVg1^x3BeIZ~v+2EdFbb z)AFO5(*CTxwFk^^kRHAB8?`p~dd0~YpV;v z$r!Wq=$j6&xz6_c>ElO2LO9A2mi*#itnvQCkE6u31IVXNXkosm_U)?WE1|xI9YDpK zPeCk2M-pY1*)HzF8Utv5J3r;8McThZh5MIK{e=zQqyGv~%I}CJdh)7o6ZA-7XRifC zd)}DG_tGRl%YoENqe2C;Z`TTwqh(E=CFvk{NPa4$jvo~lKX3A(ij$Eykrq?JO<IwaK1T~xwr 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b/source/tests/pt_expt/model/test_unimol.py @@ -0,0 +1,352 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +"""The Uni-Mol model on the PyTorch-Exportable backend. + +Checks that the registered path builds, that it agrees with the array-API +implementation on the same weights, and that the objective still reproduces the +values dumped from upstream. The golden archive and how it was produced are +described in ``source/tests/common/dpmodel/test_unimol.py``. +""" + +import os +import unittest + +import numpy as np +import torch + +from deepmd.dpmodel.descriptor.unimol import ( + UNIMOL_ELEMENTS, + DescrptUniMol as DescrptUniMolDP, + unimol_vocabulary, +) +from deepmd.dpmodel.fitting.unimol_pretrain import ( + UniMolPretrainFitting as UniMolPretrainFittingDP, +) +from deepmd.pt_expt.loss.unimol import ( + UniMolLoss, +) +from deepmd.pt_expt.model import ( + get_model, +) +from deepmd.utils.argcheck import ( + normalize, +) + +GOLDEN = os.path.join( + os.path.dirname(__file__), "..", "..", "common", "dpmodel", "unimol_v1_golden.npz" +) +SMALL = {"layers": 2, "dim": 32, "ffn": 64, "heads": 4, "vocab": 31} + + +def _assign(module, name, array): + """Put a NumPy array into a torch-backed slot, keeping its kind.""" + current = getattr(module, name) + tensor = torch.as_tensor(np.ascontiguousarray(array), dtype=current.dtype) + if isinstance(current, torch.nn.Parameter): + tensor = torch.nn.Parameter(tensor, requires_grad=current.requires_grad) + setattr(module, name, tensor) + + +class TestUniMolPtExpt(unittest.TestCase): + @classmethod + def setUpClass(cls) -> None: + cls.golden = np.load(GOLDEN) + cls.weights = { + k[len("small_weights/") :]: v + for k, v in cls.golden.items() + if k.startswith("small_weights/") + } + cls.n_real = (cls.golden["input/src_tokens"] != 0).sum(axis=1) - 2 + cls.type_map = [*UNIMOL_ELEMENTS, "[MASK]"] + + def inputs(self): + vocab = unimol_vocabulary() + type_of = {sym: i for i, sym in enumerate(self.type_map)} + src_tokens = self.golden["input/src_tokens"] + src_coord = self.golden["input/src_coord"].astype(np.float64) + nf, nloc = len(self.n_real), int(self.n_real.max()) + coord = np.zeros((nf, nloc, 3)) + atype = np.zeros((nf, nloc), dtype=np.int64) + nlist = np.full((nf, nloc, nloc - 1), -1, dtype=np.int64) + for f in range(nf): + n = int(self.n_real[f]) + coord[f, :n] = src_coord[f, 1 : n + 1] + atype[f, :n] = [type_of[vocab[t]] for t in src_tokens[f, 1 : n + 1]] + for i in range(n): + others = [j for j in range(n) if j != i] + nlist[f, i, : len(others)] = others + return coord, atype, nlist + + def config(self, nloc, single_precision_basis: bool = True): + return { + "model": { + "type_map": self.type_map, + "descriptor": { + "type": "unimol", + "encoder_layers": SMALL["layers"], + "encoder_embed_dim": SMALL["dim"], + "encoder_ffn_embed_dim": SMALL["ffn"], + "encoder_attention_heads": SMALL["heads"], + "max_atoms": nloc, + "virtual_token_position": "origin", + "single_precision_basis": single_precision_basis, + "precision": "float64", + }, + "fitting_net": { + "type": "unimol_pretrain", + "n_token": SMALL["vocab"], + "attention_heads": SMALL["heads"], + "max_atoms": nloc, + "precision": "float64", + }, + }, + "learning_rate": {"type": "exp", "start_lr": 1e-4, "stop_lr": 1e-6}, + "loss": {"type": "unimol"}, + "training": { + "training_data": {"systems": ["x"]}, + "numb_steps": 1, + "seed": 1, + }, + } + + def load_weights(self, descriptor, fitting, torch_backend: bool) -> None: + w = self.weights + put = ( + _assign + if torch_backend + else (lambda m, n, a: setattr(m, n, np.ascontiguousarray(a))) + ) + + def lin(layer, wkey, bkey): + put(layer, "w", w[wkey].T) + if bkey in w and layer.b is not None: + put(layer, "b", w[bkey]) + + def ln(layer, prefix): + put(layer, "w", w[prefix + ".weight"]) + put(layer, "b", w[prefix + ".bias"]) + + put(descriptor, "embed_tokens", w["embed_tokens.weight"]) + for key in ("means", "stds", "mul", "bias"): + put(descriptor.gbf, key, w[f"gbf.{key}.weight"]) + lin( + descriptor.gbf_proj.linear1, + "gbf_proj.linear1.weight", + "gbf_proj.linear1.bias", + ) + lin( + descriptor.gbf_proj.linear2, + "gbf_proj.linear2.weight", + "gbf_proj.linear2.bias", + ) + ln(descriptor.encoder.emb_layer_norm, "encoder.emb_layer_norm") + ln(descriptor.encoder.final_layer_norm, "encoder.final_layer_norm") + ln(descriptor.encoder.final_head_layer_norm, "encoder.final_head_layer_norm") + for i, layer in enumerate(descriptor.encoder.layers): + p = f"encoder.layers.{i}." + lin( + layer.self_attn.in_proj, + p + "self_attn.in_proj.weight", + p + "self_attn.in_proj.bias", + ) + lin( + layer.self_attn.out_proj, + p + "self_attn.out_proj.weight", + p + "self_attn.out_proj.bias", + ) + ln(layer.self_attn_layer_norm, p + "self_attn_layer_norm") + lin(layer.fc1, p + "fc1.weight", p + "fc1.bias") + lin(layer.fc2, p + "fc2.weight", p + "fc2.bias") + ln(layer.final_layer_norm, p + "final_layer_norm") + lin(fitting.lm_head.dense, "lm_head.dense.weight", "lm_head.dense.bias") + ln(fitting.lm_head.layer_norm, "lm_head.layer_norm") + lin(fitting.lm_head.out_proj, "lm_head.weight", "lm_head.bias") + lin( + fitting.pair2coord_proj.linear1, + "pair2coord_proj.linear1.weight", + "pair2coord_proj.linear1.bias", + ) + lin( + fitting.pair2coord_proj.linear2, + "pair2coord_proj.linear2.weight", + "pair2coord_proj.linear2.bias", + ) + lin(fitting.dist_head.dense, "dist_head.dense.weight", "dist_head.dense.bias") + ln(fitting.dist_head.layer_norm, "dist_head.layer_norm") + lin( + fitting.dist_head.out_proj, + "dist_head.out_proj.weight", + "dist_head.out_proj.bias", + ) + + def build_torch_model(self, nloc, single_precision_basis: bool = True): + model = get_model(normalize(self.config(nloc, single_precision_basis))["model"]) + self.load_weights( + model.atomic_model.descriptor, + model.atomic_model.fitting_net, + torch_backend=True, + ) + model.eval() + return model + + def test_registered_path_builds(self) -> None: + coord, _, _ = self.inputs() + model = self.build_torch_model(coord.shape[1]) + self.assertIsInstance(model, torch.nn.Module) + self.assertEqual(type(model).__name__, "UniMolPretrainModel") + self.assertEqual( + sorted(model.translated_output_def().keys()), + [ + "coord_update", + "delta_pair_norm", + "mask", + "pair_dist", + "token_logits", + "x_norm", + ], + ) + + def test_matches_the_array_api_implementation(self) -> None: + """Same weights, same numbers, once the fp32 basis is out of the way. + + Upstream evaluates the Gaussian basis in fp32, and NumPy and Torch round + that last place differently, which is worth about 2e-8 relative on the + node representation. Turning that off isolates the two implementations + from each other, and then they agree to fp64 rounding. + """ + coord, atype, nlist = self.inputs() + nloc = coord.shape[1] + model = self.build_torch_model(nloc, single_precision_basis=False) + ret = model.forward_lower( + torch.as_tensor(coord.reshape(len(self.n_real), -1)), + torch.as_tensor(atype), + torch.as_tensor(nlist), + None, + ) + + cfg = normalize(self.config(nloc, single_precision_basis=False))["model"] + descriptor = DescrptUniMolDP( + type_map=self.type_map, + **{k: v for k, v in cfg["descriptor"].items() if k != "type"}, + ) + fitting = UniMolPretrainFittingDP( + ntypes=len(self.type_map), + dim_descrpt=SMALL["dim"], + **{k: v for k, v in cfg["fitting_net"].items() if k != "type"}, + ) + self.load_weights(descriptor, fitting, torch_backend=False) + reference = fitting.call_tokens(descriptor.forward_tokens(coord, atype, nlist)) + + for key in ( + "token_logits", + "coord_update", + "pair_dist", + "x_norm", + "delta_pair_norm", + ): + with self.subTest(output=key): + np.testing.assert_allclose( + ret[key].detach().cpu().numpy(), + np.asarray(reference[key]), + rtol=1e-12, + atol=1e-12, + ) + + def test_single_precision_basis_costs_one_fp32_place(self) -> None: + """With upstream's fp32 basis the two backends part company measurably. + + The gap is one fp32 unit in the last place in the basis, amplified by + the stack. It is recorded here so that the looser tolerance elsewhere + has a stated cause rather than being tuned until tests pass. + """ + coord, atype, nlist = self.inputs() + nloc = coord.shape[1] + exact = self.build_torch_model(nloc, single_precision_basis=False) + upstream_like = self.build_torch_model(nloc, single_precision_basis=True) + args = ( + torch.as_tensor(coord.reshape(len(self.n_real), -1)), + torch.as_tensor(atype), + torch.as_tensor(nlist), + None, + ) + a = exact.forward_lower(*args)["token_logits"].detach().cpu().numpy() + b = upstream_like.forward_lower(*args)["token_logits"].detach().cpu().numpy() + gap = np.abs(a - b).max() / np.abs(a).max() + self.assertLess(gap, 1e-5) + self.assertGreater(gap, 1e-12) + + def test_objective_matches_upstream(self) -> None: + coord, atype, nlist = self.inputs() + nloc = coord.shape[1] + model = self.build_torch_model(nloc) + ret = model.forward_lower( + torch.as_tensor(coord.reshape(len(self.n_real), -1)), + torch.as_tensor(atype), + torch.as_tensor(nlist), + None, + ) + ncol = model.atomic_model.fitting_net.max_atoms + 2 + nf = len(self.n_real) + labels = { + "unimol_token_target": torch.zeros((nf, nloc), dtype=torch.int64), + "unimol_coord_target": torch.zeros((nf, nloc, 3), dtype=torch.float64), + "unimol_dist_target": torch.zeros((nf, nloc, ncol), dtype=torch.float64), + "unimol_token_mask": torch.zeros((nf, ncol), dtype=torch.int64), + } + for f in range(nf): + n = int(self.n_real[f]) + labels["unimol_token_target"][f, :n] = torch.as_tensor( + self.golden["target/tokens_target"][f, 1 : n + 1].astype(np.int64) + ) + labels["unimol_coord_target"][f, :n] = torch.as_tensor( + self.golden["target/coord_target"][f, 1 : n + 1].astype(np.float64) + ) + labels["unimol_dist_target"][f, :n, : n + 2] = torch.as_tensor( + self.golden["target/distance_target"][f, 1 : n + 1, : n + 2].astype( + np.float64 + ) + ) + labels["unimol_token_mask"][f, : n + 2] = 1 + labels = {k: v.to(ret["token_logits"].device) for k, v in labels.items()} + + total, more = UniMolLoss().call(1.0, 0, ret, labels) + expected = { + "token_loss": "loss_token", + "coord_loss": "loss_coord", + "dist_loss": "loss_dist", + "x_norm_loss": "loss_x_norm", + "delta_pair_norm_loss": "loss_delta_pair_norm", + } + for mine, ref in expected.items(): + with self.subTest(term=mine): + np.testing.assert_allclose( + float(more[mine].detach()), + float(self.golden[f"small_fp64/loss/{ref}"]), + rtol=1e-5, + atol=1e-8, + ) + np.testing.assert_allclose( + float(total.detach()), + float(self.golden["small_fp64/loss/loss_total"]), + rtol=1e-5, + atol=1e-8, + ) + + def test_gradients_flow(self) -> None: + coord, atype, nlist = self.inputs() + model = self.build_torch_model(coord.shape[1]) + ret = model.forward_lower( + torch.as_tensor(coord.reshape(len(self.n_real), -1)), + torch.as_tensor(atype), + torch.as_tensor(nlist), + None, + ) + ret["token_logits"].sum().backward() + grads = [ + p.grad for p in model.parameters() if p.requires_grad and p.grad is not None + ] + self.assertGreater(len(grads), 0) + self.assertTrue(any(bool(torch.any(g != 0)) for g in grads)) + + +if __name__ == "__main__": + unittest.main() From b77b533f28a157ba4f7ca642811ffa36b59d66c0 Mon Sep 17 00:00:00 2001 From: Duo <50307526+iProzd@users.noreply.github.com> Date: Sat, 12 Sep 2026 06:18:52 -0400 Subject: [PATCH 12/34] feat(dpmodel): apply Uni-Mol's dropout during training Uni-Mol regularises with dropout at three sites, 0.1 each on the embedding, on the attention probabilities and on both residual branches, while deepmd has no dropout anywhere. The rates were already carried in the configuration; this makes them act. The array API has no random numbers, so the helper dispatches to torch when a training step needs it and is the identity during inference, which is what the array-API backends are for. Training on a non-torch backend raises rather than quietly dropping the regularisation, which would be a silent parity bug. The flag travels down the call chain rather than relying on nested module state, since the encoder's sub-objects are plain data on the array-API path. A test pins the behaviour: eval-mode forwards are bit-identical to each other, train-mode forwards under different seeds are not. --- deepmd/dpmodel/descriptor/unimol.py | 4 +- .../dpmodel/descriptor/unimol_nn/encoder.py | 41 +++++++++++++++---- source/tests/pt_expt/model/test_unimol.py | 23 +++++++++++ 3 files changed, 59 insertions(+), 9 deletions(-) diff --git a/deepmd/dpmodel/descriptor/unimol.py b/deepmd/dpmodel/descriptor/unimol.py index f9b9c0ff06..9b9f7f4673 100644 --- a/deepmd/dpmodel/descriptor/unimol.py +++ b/deepmd/dpmodel/descriptor/unimol.py @@ -413,8 +413,10 @@ def forward_tokens(self, coord_ext: Array, atype_ext: Array, nlist: Array) -> di bias = self.gbf_proj(self.gbf(dist, edge_type)) bias = xp.reshape(xp.permute_dims(bias, (0, 3, 1, 2)), (-1, nt, nt)) + # ``training`` exists only once the PyTorch-Exportable wrapper has made + # this a torch module; on the array-API path it is always inference. x, pair_rep, delta_pair_rep, x_norm, delta_pair_norm = self.encoder( - emb, bias, padding_mask + emb, bias, padding_mask, training=bool(getattr(self, "training", False)) ) # Upstream clears the -inf that padding leaves on the pair channel # before any head reads it (unimol/models/unimol.py:221). diff --git a/deepmd/dpmodel/descriptor/unimol_nn/encoder.py b/deepmd/dpmodel/descriptor/unimol_nn/encoder.py index 76d3ffa6d5..b6dbe11e17 100644 --- a/deepmd/dpmodel/descriptor/unimol_nn/encoder.py +++ b/deepmd/dpmodel/descriptor/unimol_nn/encoder.py @@ -57,6 +57,27 @@ def softmax(x, axis: int = -1): # noqa: ANN001, ANN201 return e / xp.sum(e, axis=axis, keepdims=True) +def dropout(x, p: float, training: bool): # noqa: ANN001, ANN201 + """Apply dropout, but only while training. + + deepmd has no dropout anywhere else, and the array API has no random + numbers, so this dispatches to torch when a training step actually needs + it. Inference, which is what the array-API backends are for, is the + identity. Training on a non-torch backend is refused rather than silently + dropping the regularisation, which would be a quiet parity bug. + """ + if not training or p <= 0.0: + return x + if array_api_compat.is_torch_array(x): + import torch + + return torch.nn.functional.dropout(x, p=p, training=True) + raise NotImplementedError( + "dropout during training is only implemented for the PyTorch backends; " + "the array-API path is for inference" + ) + + def norm_loss(x, eps: float = 1e-10, tolerance: float = 1.0): # noqa: ANN001, ANN201 """Hinge on the deviation of the row norm from ``sqrt(dim)``. @@ -268,7 +289,7 @@ def __init__( embed_dim, embed_dim, bias=bias, precision=precision, seed=seed ) - def call(self, query, attn_bias): # noqa: ANN001, ANN201 + def call(self, query, attn_bias, training: bool = False): # noqa: ANN001, ANN201 """Return the attended values and the biased pre-softmax logits. Parameters @@ -304,7 +325,7 @@ def split_heads(t): # noqa: ANN001, ANN202 # Upstream adds the bias in place and returns the biased logits, which # become the next layer's bias (multihead_attention.py:100-103). attn_weights = attn_weights + attn_bias - attn = softmax(attn_weights, axis=-1) + attn = dropout(softmax(attn_weights, axis=-1), self.dropout, training) o = attn @ v o = xp.reshape(o, (nf, self.num_heads, nt, self.head_dim)) o = xp.permute_dims(o, (0, 2, 1, 3)) @@ -383,15 +404,19 @@ def __init__( ) self.final_layer_norm = LayerNorm(embed_dim, precision=precision, seed=seed) - def call(self, x, attn_bias): # noqa: ANN001, ANN201 + def call(self, x, attn_bias, training: bool = False): # noqa: ANN001, ANN201 residual = x x = self.self_attn_layer_norm(x) - x, attn_weights = self.self_attn(x, attn_bias=attn_bias) + x, attn_weights = self.self_attn(x, attn_bias=attn_bias, training=training) + x = dropout(x, self.dropout, training) x = residual + x residual = x x = self.final_layer_norm(x) - x = self.fc2(self.fc1(x)) + # fc1 carries the activation, so the activation dropout sits between + # the two linears, as upstream has it. + x = dropout(self.fc1(x), self.activation_dropout, training) + x = dropout(self.fc2(x), self.dropout, training) x = residual + x return x, attn_weights @@ -493,7 +518,7 @@ def __init__( for _ in range(encoder_layers) ] - def call(self, emb, attn_mask, padding_mask): # noqa: ANN001, ANN201 + def call(self, emb, attn_mask, padding_mask, training: bool = False): # noqa: ANN001, ANN201 """Run the stack. Parameters @@ -512,7 +537,7 @@ def call(self, emb, attn_mask, padding_mask): # noqa: ANN001, ANN201 """ xp = array_api_compat.array_namespace(emb) nf, nt = emb.shape[0], emb.shape[1] - x = self.emb_layer_norm(emb) + x = dropout(self.emb_layer_norm(emb), self.emb_dropout, training) pad = xp.astype(padding_mask, x.dtype) x = x * (1 - pad[..., None]) @@ -529,7 +554,7 @@ def call(self, emb, attn_mask, padding_mask): # noqa: ANN001, ANN201 attn_mask = xp.reshape(attn_mask, (-1, nt, nt)) for layer in self.layers: - x, attn_mask = layer(x, attn_bias=attn_mask) + x, attn_mask = layer(x, attn_bias=attn_mask, training=training) token_mask = 1.0 - pad x_norm = masked_mean(token_mask, norm_loss(x)) diff --git a/source/tests/pt_expt/model/test_unimol.py b/source/tests/pt_expt/model/test_unimol.py index aa46ccbe58..11dc9cbabe 100644 --- a/source/tests/pt_expt/model/test_unimol.py +++ b/source/tests/pt_expt/model/test_unimol.py @@ -331,6 +331,29 @@ def test_objective_matches_upstream(self) -> None: atol=1e-8, ) + def test_dropout_is_active_only_in_training(self) -> None: + """Uni-Mol regularises with dropout at three sites; deepmd had none.""" + coord, atype, nlist = self.inputs() + model = self.build_torch_model(coord.shape[1]) + args = ( + torch.as_tensor(coord.reshape(len(self.n_real), -1)), + torch.as_tensor(atype), + torch.as_tensor(nlist), + None, + ) + model.eval() + first = model.forward_lower(*args)["token_logits"].detach().clone() + second = model.forward_lower(*args)["token_logits"].detach() + torch.testing.assert_close(first, second, rtol=0, atol=0) + + model.train() + torch.manual_seed(0) + a = model.forward_lower(*args)["token_logits"].detach() + torch.manual_seed(1) + b = model.forward_lower(*args)["token_logits"].detach() + self.assertFalse(bool(torch.allclose(a, b))) + model.eval() + def test_gradients_flow(self) -> None: coord, atype, nlist = self.inputs() model = self.build_torch_model(coord.shape[1]) From d98f85aeb0f93bca7ceb58ba3038a5860807b087 Mon Sep 17 00:00:00 2001 From: Duo <50307526+iProzd@users.noreply.github.com> Date: Sat, 12 Sep 2026 06:22:26 -0400 Subject: [PATCH 13/34] feat: convert Uni-Mol pretraining data into a deepmd dataset Uni-Mol ships its pretraining set as one LMDB file of pickled dicts with about ten conformers per molecule; deepmd reads a different layout. The conversion runs once, offline, and streams, so the 115 GB set does not have to fit in memory. One conformer becomes one frame, so ordinary frame sampling stands in for upstream's per-epoch conformer draw, and frames of the same molecule share a system id. The two-dimensional RDKit conformer that upstream appends while loading is added here instead, behind a flag, so the training data path never needs RDKit. Records that cannot be used are skipped rather than written misleadingly: a single-atom molecule, which the descriptor cannot tell from padding, and any molecule with an element outside the Uni-Mol vocabulary, which would silently become [UNK]. Tested against deepmd's own reader: coordinates, elements and the zero cell come back matching the source. --- deepmd/utils/unimol_data.py | 236 ++++++++++++++++++ .../tests/common/dpmodel/test_unimol_data.py | 109 ++++++++ 2 files changed, 345 insertions(+) create mode 100644 deepmd/utils/unimol_data.py create mode 100644 source/tests/common/dpmodel/test_unimol_data.py diff --git a/deepmd/utils/unimol_data.py b/deepmd/utils/unimol_data.py new file mode 100644 index 0000000000..694e816b7e --- /dev/null +++ b/deepmd/utils/unimol_data.py @@ -0,0 +1,236 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +"""Convert Uni-Mol pretraining data into a deepmd LMDB dataset. + +Uni-Mol ships its molecular pretraining set as a single LMDB file whose values +are pickled dicts, ``{"atoms": [element symbols], "coordinates": [n x 3 arrays], +"smi": str}``, with roughly ten RDKit conformers per molecule. deepmd reads a +different LMDB layout, so the data is converted once, offline, rather than +taught to the training loop. + +One conformer becomes one frame, which is what makes the conversion streaming +and lets ordinary frame sampling stand in for Uni-Mol's per-epoch conformer +draw. Frames of the same molecule share a system id, so they can be kept +together if needed. + +Upstream also appends a two-dimensional RDKit conformer to the pool while +loading. That belongs here rather than in the training loop, so the data path +never needs RDKit; pass ``add_2d_conformer=True`` to reproduce it. +""" + +import argparse +import logging +import os +import pickle +import shutil +from collections.abc import ( + Iterator, +) +from typing import ( + Any, +) + +import numpy as np + +__all__ = ["convert_unimol_lmdb", "read_unimol_lmdb"] + +log = logging.getLogger(__name__) + + +def _encode_array(arr: np.ndarray) -> dict[str, Any]: + """Encode an array the way a deepmd LMDB frame stores it.""" + return { + "nd": None, + "type": str(arr.dtype), + "kind": "", + "shape": list(arr.shape), + "data": arr.tobytes(), + } + + +def read_unimol_lmdb(path: str) -> Iterator[dict[str, Any]]: + """Stream molecules out of a Uni-Mol LMDB file. + + Parameters + ---------- + path : str + Path to the ``.lmdb`` file, which upstream writes as a single file + rather than a directory. + + Yields + ------ + dict + ``atoms``, ``coordinates`` and ``smi`` as stored upstream. + """ + import lmdb + + env = lmdb.open( + path, subdir=False, readonly=True, lock=False, readahead=False, meminit=False + ) + try: + with env.begin() as txn: + cursor = txn.cursor() + for _, value in cursor: + # The upstream records are pickles written by the dataset + # authors; only convert files you trust. + yield pickle.loads(value) + finally: + env.close() + + +def _conformers(record: dict[str, Any], add_2d_conformer: bool) -> list[np.ndarray]: + conformers = [np.asarray(c, dtype=np.float32) for c in record["coordinates"]] + if add_2d_conformer: + from rdkit import ( + Chem, + ) + from rdkit.Chem import ( + AllChem, + ) + + mol = Chem.AddHs(Chem.MolFromSmiles(record["smi"])) + AllChem.Compute2DCoords(mol) + coords = mol.GetConformer().GetPositions().astype(np.float32) + conformers.append(coords[: len(record["atoms"])]) + return conformers + + +def convert_unimol_lmdb( + src: str, + dst: str, + type_map: list[str] | None = None, + add_2d_conformer: bool = False, + max_molecules: int | None = None, + max_conformers: int | None = None, + map_size: int = 1024**4, +) -> dict[str, int]: + """Write a deepmd LMDB dataset from a Uni-Mol one. + + Parameters + ---------- + src : str + The Uni-Mol ``.lmdb`` file. + dst : str + Directory for the deepmd dataset, replaced if it exists. + type_map : list[str], optional + Element names for the output. Defaults to Uni-Mol's own 26 elements. + add_2d_conformer : bool + Append the RDKit two-dimensional conformer to each pool, as upstream + does while loading. Needs RDKit. + max_molecules : int, optional + Stop after this many molecules, which is useful for a trial run. + max_conformers : int, optional + Keep at most this many conformers per molecule. + map_size : int + Maximum size of the output database. + + Returns + ------- + dict + Counts of molecules, frames and skipped records. + """ + import lmdb + import msgpack + + from deepmd.dpmodel.descriptor.unimol import ( + UNIMOL_ELEMENTS, + ) + + names = list(type_map) if type_map is not None else list(UNIMOL_ELEMENTS) + index_of = {sym: i for i, sym in enumerate(names)} + + if os.path.exists(dst): + shutil.rmtree(dst) + env = lmdb.open(dst, map_size=map_size) + fmt = "012d" + frame_idx = 0 + molecules = 0 + skipped = 0 + frame_system_ids: list[int] = [] + frame_nlocs: list[int] = [] + + try: + with env.begin(write=True) as txn: + for record in read_unimol_lmdb(src): + if max_molecules is not None and molecules >= max_molecules: + break + atoms = [str(a) for a in record["atoms"]] + if len(atoms) < 2 or any(a not in index_of for a in atoms): + # The descriptor cannot tell a single real atom from + # padding, and an unmapped element would silently become + # [UNK]; skip both rather than write something misleading. + skipped += 1 + continue + atom_types = np.array([index_of[a] for a in atoms], dtype=np.int64) + atom_numbs = [int((atom_types == i).sum()) for i in range(len(names))] + pool = _conformers(record, add_2d_conformer) + if max_conformers is not None: + pool = pool[:max_conformers] + for coords in pool: + if coords.shape[0] != len(atoms): + skipped += 1 + continue + frame = { + "atom_numbs": atom_numbs, + "atom_names": names, + "atom_types": _encode_array(atom_types), + "orig": _encode_array(np.zeros(3, dtype=np.float64)), + # A zero cell marks a molecule: the descriptor refuses + # periodic images anyway. + "cells": _encode_array(np.zeros((3, 3), dtype=np.float64)), + "coords": _encode_array(coords.astype(np.float64)), + } + txn.put( + format(frame_idx, fmt).encode(), + msgpack.packb(frame, use_bin_type=True), + ) + frame_system_ids.append(molecules) + frame_nlocs.append(len(atoms)) + frame_idx += 1 + molecules += 1 + metadata = { + "nframes": frame_idx, + "frame_idx_fmt": fmt, + "type_map": names, + "frame_system_ids": frame_system_ids, + "frame_nlocs": frame_nlocs, + "system_info": {"nframes": frame_idx}, + } + txn.put(b"__metadata__", msgpack.packb(metadata, use_bin_type=True)) + finally: + env.close() + return {"molecules": molecules, "frames": frame_idx, "skipped": skipped} + + +def main(args: list[str] | None = None) -> None: + """Command line entry point.""" + parser = argparse.ArgumentParser( + description="Convert a Uni-Mol pretraining LMDB into a deepmd LMDB dataset." + ) + parser.add_argument("src", help="the Uni-Mol .lmdb file") + parser.add_argument("dst", help="output directory for the deepmd dataset") + parser.add_argument( + "--add-2d-conformer", + action="store_true", + help="append the RDKit 2D conformer to every pool, as upstream does", + ) + parser.add_argument("--max-molecules", type=int, default=None) + parser.add_argument("--max-conformers", type=int, default=None) + parsed = parser.parse_args(args) + counts = convert_unimol_lmdb( + parsed.src, + parsed.dst, + add_2d_conformer=parsed.add_2d_conformer, + max_molecules=parsed.max_molecules, + max_conformers=parsed.max_conformers, + ) + logging.basicConfig(level=logging.INFO) + log.info( + "converted %d molecules into %d frames (%d records skipped)", + counts["molecules"], + counts["frames"], + counts["skipped"], + ) + + +if __name__ == "__main__": + main() diff --git a/source/tests/common/dpmodel/test_unimol_data.py b/source/tests/common/dpmodel/test_unimol_data.py new file mode 100644 index 0000000000..3bb6ffdf75 --- /dev/null +++ b/source/tests/common/dpmodel/test_unimol_data.py @@ -0,0 +1,109 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +"""The Uni-Mol to deepmd data conversion.""" + +import os +import pickle +import shutil +import tempfile +import unittest + +import numpy as np + +from deepmd.dpmodel.descriptor.unimol import ( + UNIMOL_ELEMENTS, +) +from deepmd.dpmodel.utils.lmdb_data import ( + LmdbDataReader, +) +from deepmd.utils.unimol_data import ( + convert_unimol_lmdb, + read_unimol_lmdb, +) + + +def _write_unimol_lmdb(path: str, molecules: list[dict]) -> None: + """Write a file in the upstream layout: one pickle per molecule.""" + import lmdb + + env = lmdb.open(path, subdir=False, map_size=1 << 24) + with env.begin(write=True) as txn: + for i, mol in enumerate(molecules): + txn.put(f"{i}".encode(), pickle.dumps(mol)) + env.close() + + +class TestUniMolDataConversion(unittest.TestCase): + def setUp(self) -> None: + self.tmp = tempfile.mkdtemp() + rng = np.random.default_rng(0) + self.molecules = [ + { + "atoms": ["C", "N", "O", "H"], + "coordinates": [ + rng.normal(size=(4, 3)).astype(np.float32) for _ in range(3) + ], + "smi": "CNO", + }, + { + "atoms": ["C", "C", "H"], + "coordinates": [ + rng.normal(size=(3, 3)).astype(np.float32) for _ in range(2) + ], + "smi": "CC", + }, + # A single atom cannot be told apart from padding downstream, and an + # unmapped element would silently become [UNK]; both are skipped. + { + "atoms": ["C"], + "coordinates": [rng.normal(size=(1, 3)).astype(np.float32)], + "smi": "C", + }, + { + "atoms": ["C", "Xx"], + "coordinates": [rng.normal(size=(2, 3)).astype(np.float32)], + "smi": "C", + }, + ] + self.src = os.path.join(self.tmp, "mol.lmdb") + _write_unimol_lmdb(self.src, self.molecules) + + def tearDown(self) -> None: + shutil.rmtree(self.tmp, ignore_errors=True) + + def test_reader_streams_the_upstream_layout(self) -> None: + records = list(read_unimol_lmdb(self.src)) + self.assertEqual(len(records), len(self.molecules)) + self.assertEqual(records[0]["atoms"], ["C", "N", "O", "H"]) + self.assertEqual(len(records[0]["coordinates"]), 3) + + def test_conversion_round_trips_through_the_deepmd_reader(self) -> None: + dst = os.path.join(self.tmp, "converted") + counts = convert_unimol_lmdb(self.src, dst, map_size=1 << 24) + # One frame per conformer, and the two unusable records are skipped. + self.assertEqual(counts["frames"], 5) + self.assertEqual(counts["molecules"], 2) + self.assertEqual(counts["skipped"], 2) + + reader = LmdbDataReader(dst, list(UNIMOL_ELEMENTS)) + self.assertEqual(len(reader), 5) + frame = reader[0] + coord = np.asarray(frame["coord"]).reshape(-1, 3) + np.testing.assert_allclose( + coord, self.molecules[0]["coordinates"][0].astype(np.float64), atol=1e-6 + ) + symbols = [UNIMOL_ELEMENTS[i] for i in np.asarray(frame["atype"]).reshape(-1)] + self.assertEqual(symbols, self.molecules[0]["atoms"]) + # Molecules are not periodic; the cell is zero. + np.testing.assert_array_equal(np.asarray(frame["box"]).reshape(-1), np.zeros(9)) + + def test_limits_are_respected(self) -> None: + dst = os.path.join(self.tmp, "limited") + counts = convert_unimol_lmdb( + self.src, dst, max_molecules=1, max_conformers=2, map_size=1 << 24 + ) + self.assertEqual(counts["molecules"], 1) + self.assertEqual(counts["frames"], 2) + + +if __name__ == "__main__": + unittest.main() From 8042bece45c1d8f789b20afe09afc9ce5ad37342 Mon Sep 17 00:00:00 2001 From: Duo <50307526+iProzd@users.noreply.github.com> Date: Sat, 12 Sep 2026 06:32:11 -0400 Subject: [PATCH 14/34] feat: make Uni-Mol pretraining reachable from a training run Adds the last pieces between the model and a configuration file. The LMDB reader gains a per-frame transform hook, carried on the decoder configuration so it reaches every decoding path, worker processes included, and defaulting to none so decoding is unchanged without it. Self-supervised objectives have to corrupt their inputs and derive their labels there, because the PyTorch-Exportable backend runs the model before the loss sees a frame. The transform builder turns a converted frame into a corrupted one plus its labels. Masked atoms are carried as a [MASK] pseudo-element, which the model's type_map must declare, and a randomly drawn replacement maps back onto a type the model knows. The loss now derives the distance target and the token column mask when they are not supplied. Storing the distance target would cost O(natoms^2) per frame, which is impractical at 209 million conformers; deriving it from the clean coordinates and the real-atom mask gives the same number, to the fp32 rounding of the stored alternative. Also adds the documentation page, its toctree entry and a pretraining example whose configuration is checked against argcheck in the test suite. --- deepmd/dpmodel/loss/unimol.py | 50 ++++++-- deepmd/dpmodel/utils/lmdb_data.py | 20 +++ deepmd/dpmodel/utils/unimol_transform.py | 105 +++++++++++++++- doc/model/index.rst | 1 + doc/model/unimol.md | 114 ++++++++++++++++++ examples/unimol/pretrain/input.json | 93 ++++++++++++++ source/tests/common/dpmodel/test_unimol.py | 36 ++++++ .../tests/common/dpmodel/test_unimol_data.py | 44 +++++++ 8 files changed, 455 insertions(+), 8 deletions(-) create mode 100644 doc/model/unimol.md create mode 100644 examples/unimol/pretrain/input.json diff --git a/deepmd/dpmodel/loss/unimol.py b/deepmd/dpmodel/loss/unimol.py index 87bdab7f30..ff7aff5f7f 100644 --- a/deepmd/dpmodel/loss/unimol.py +++ b/deepmd/dpmodel/loss/unimol.py @@ -68,6 +68,34 @@ def _frame_scalar(value: Array, mask: Array | None) -> Array: return xp.sum(per_atom * weights) / xp.sum(weights) +def _token_mask_from_atoms(mask: Array, ncol: int) -> Array: + """Mark the non-padding token columns: BOS, the real atoms, then EOS.""" + xp = array_api_compat.array_namespace(mask) + n_real = xp.sum(xp.astype(mask, xp.int64), axis=-1) + positions = xp.arange(ncol)[None, :] + return xp.astype(positions < (n_real + 2)[:, None], xp.int64) + + +def _clean_distances(coord_target: Array, mask: Array, ncol: int) -> Array: + """Pairwise distances from the clean coordinates, virtual tokens included. + + Storing this as a label would cost O(natoms^2) per frame, so it is derived + here instead. The two virtual tokens sit at the origin, which is the + centroid of the clean coordinates because the transform centres them. + """ + xp = array_api_compat.array_namespace(coord_target) + nf = coord_target.shape[0] + real = xp.astype(mask, coord_target.dtype)[..., None] + atoms = coord_target * real + zero = xp.zeros((nf, 1, 3), dtype=coord_target.dtype) + tokens = xp.concat([zero, atoms, zero], axis=1) + if tokens.shape[1] < ncol: + pad = xp.zeros((nf, ncol - tokens.shape[1], 3), dtype=coord_target.dtype) + tokens = xp.concat([tokens, pad], axis=1) + diff = atoms[:, :, None, :] - tokens[:, None, :, :] + return xp.sqrt(xp.sum(diff**2, axis=-1)) + + def _masked_nll(logits: Array, target: Array, pad_idx: int) -> Array: """Negative log likelihood over the selected positions. @@ -187,18 +215,28 @@ def add(term: Array, weight: float, name: str) -> None: if self.masked_dist_loss > 0: # Rows are the corrupted atoms; columns are every non-padding token, # BOS, EOS and the diagonal included (losses/unimol.py:159-180). - token_mask = label_dict["unimol_token_mask"] + ncol = model_dict["pair_dist"].shape[-1] + token_mask = label_dict.get("unimol_token_mask") + if token_mask is None: + token_mask = _token_mask_from_atoms(mask, ncol) + dist_target = label_dict.get("unimol_dist_target") + if dist_target is None: + dist_target = _clean_distances( + label_dict["unimol_coord_target"], mask, ncol + ) pair_mask = masked[..., None] & xp.astype(token_mask, xp.bool)[:, None, :] - dist_label = ( - label_dict["unimol_dist_target"][pair_mask] - DIST_MEAN - ) / DIST_STD + dist_label = (dist_target[pair_mask] - DIST_MEAN) / DIST_STD add( _smooth_l1(model_dict["pair_dist"][pair_mask], dist_label, self.beta), self.masked_dist_loss, "dist_loss", ) if self.x_norm_loss > 0: - add(_frame_scalar(model_dict["x_norm"], mask), self.x_norm_loss, "x_norm_loss") + add( + _frame_scalar(model_dict["x_norm"], mask), + self.x_norm_loss, + "x_norm_loss", + ) if self.delta_pair_repr_norm_loss > 0: add( _frame_scalar(model_dict["delta_pair_norm"], mask), @@ -212,9 +250,7 @@ def label_requirement(self) -> list[DataRequirementItem]: """Labels produced by the Uni-Mol data transform, not by a simulation.""" return [ DataRequirementItem("unimol_token_target", ndof=1, atomic=True, must=True), - DataRequirementItem("unimol_token_mask", ndof=1, atomic=True, must=True), DataRequirementItem("unimol_coord_target", ndof=3, atomic=True, must=True), - DataRequirementItem("unimol_dist_target", ndof=1, atomic=True, must=True), ] def serialize(self) -> dict: diff --git a/deepmd/dpmodel/utils/lmdb_data.py b/deepmd/dpmodel/utils/lmdb_data.py index 0396640a4a..715be7c63b 100644 --- a/deepmd/dpmodel/utils/lmdb_data.py +++ b/deepmd/dpmodel/utils/lmdb_data.py @@ -690,6 +690,12 @@ class LmdbDecodeConfig: Registered data requirements keyed by field name. dataset Dataset identifier used in frame-level diagnostics. + frame_transform + Optional callable applied to every decoded frame, as + ``transform(frame, frame_index)``. Self-supervised objectives corrupt + their inputs and derive their labels here, before the model runs, which + is the only place that works for a backend whose loss never sees the + model. ``None``, the default, leaves decoding unchanged. """ ntypes: int @@ -697,6 +703,7 @@ class LmdbDecodeConfig: type_remap: np.ndarray | None data_requirements: dict[str, Any] dataset: str = "" + frame_transform: Callable[[dict[str, Any], int], dict[str, Any]] | None = None def _requirement_dtype(requirement: Any) -> np.dtype: @@ -1018,6 +1025,8 @@ def decode_lmdb_frame( ) frame["fid"] = original_key + if config.frame_transform is not None: + frame = config.frame_transform(frame, original_key) return frame @@ -2598,6 +2607,17 @@ def print_summary(self, name: str, prob: Any) -> None: def set_noise(self, noise_settings: dict[str, Any]) -> None: """No-op for now.""" + def set_frame_transform( + self, transform: Callable[[dict[str, Any], int], dict[str, Any]] | None + ) -> None: + """Install a per-frame transform, or remove it with ``None``. + + The transform runs on every decoded frame, in whichever process decodes + it, and receives ``(frame, frame_index)``. Self-supervised training uses + it to corrupt inputs and derive labels before the model runs. + """ + self._decode_config.frame_transform = transform + # --- Properties --- @property diff --git a/deepmd/dpmodel/utils/unimol_transform.py b/deepmd/dpmodel/utils/unimol_transform.py index 87c314b911..fcc8152d5f 100644 --- a/deepmd/dpmodel/utils/unimol_transform.py +++ b/deepmd/dpmodel/utils/unimol_transform.py @@ -26,6 +26,7 @@ import contextlib from collections.abc import ( + Callable, Iterator, Sequence, ) @@ -37,6 +38,7 @@ "center_coordinates", "crop_atoms", "edge_type", + "make_unimol_data_transform", "mask_points", "numpy_seed", "pair_distance", @@ -183,7 +185,9 @@ def mask_points( def noise_f(n): # noqa: ANN001, ANN202 return np.clip( - np.random.randn(n, 3) * noise, a_min=-noise * 2.0, a_max=noise * 2.0 # noqa: NPY002 + np.random.randn(n, 3) * noise, # noqa: NPY002 + a_min=-noise * 2.0, + a_max=noise * 2.0, ) elif noise_type == "normal": @@ -285,6 +289,105 @@ def edge_type(tokens: np.ndarray, num_types: int) -> np.ndarray: return (tokens[:, None] * num_types + tokens[None, :]).astype(np.int64) +def make_unimol_data_transform( + type_map: Sequence[str], + *, + seed: int = 1, + epoch: int = 1, + max_atoms: int = 256, + mask_token: str = "[MASK]", + **mask_kwargs: float | str, +) -> Callable[[dict, int], dict]: + """Build the per-frame transform a deepmd data reader installs. + + The reader hands over one already-converted frame, so the conformer draw and + the hydrogen policy are behind us; what remains is cropping, centring, and + the corruption itself. The frame comes back with corrupted coordinates and + element types, plus the two labels the objective needs. The distance target + is not stored, because it would cost O(natoms^2) per frame; the loss derives + it from the clean coordinates. + + Parameters + ---------- + type_map : Sequence[str] + Element names of the model. It must contain ``mask_token``, since a + masked atom has to be expressible as a type. + seed, epoch : int + Together with the frame index these seed the corruption, the way + upstream seeds it per sample. + max_atoms : int + Crop larger molecules down to this many atoms. + mask_token : str + Name of the pseudo-element standing for ``[MASK]``. + **mask_kwargs + Passed to :func:`mask_points`. + + Returns + ------- + callable + ``transform(frame, index) -> frame``, matching the reader's hook. + """ + from deepmd.dpmodel.descriptor.unimol import ( + unimol_vocabulary, + ) + + vocabulary = unimol_vocabulary() + token_of = {sym: i for i, sym in enumerate(vocabulary)} + type_map = list(type_map) + if mask_token not in type_map: + raise ValueError( + f"the model type_map must contain {mask_token!r} for Uni-Mol " + "pretraining, because masked atoms are carried as a pseudo-element" + ) + type_index = {sym: i for i, sym in enumerate(type_map)} + unk = token_of["[UNK]"] + pad = token_of["[PAD]"] + type_to_token = np.array( + [token_of.get(sym, unk) for sym in type_map], dtype=np.int64 + ) + # Uni-Mol draws replacements over its own vocabulary, so a drawn token must + # map back onto a type the model knows. + token_to_type = np.array( + [type_index.get(sym, type_index[mask_token]) for sym in vocabulary], + dtype=np.int64, + ) + specials = [token_of[s] for s in ("[PAD]", "[CLS]", "[SEP]", "[UNK]", mask_token)] + + def transform(frame: dict, index: int) -> dict: + coord = np.asarray(frame["coord"], dtype=np.float64).reshape(-1, 3) + atype = np.asarray(frame["atype"], dtype=np.int64).reshape(-1) + atoms = np.arange(len(atype)) + atoms, coord = crop_atoms(atoms, coord, seed, epoch, index, max_atoms) + atype = atype[np.asarray(atoms, dtype=np.int64)] + coord = center_coordinates(coord) + tokens = type_to_token[atype] + + corrupted = mask_points( + tokens, + coord, + num_types=len(vocabulary), + special_indices=specials, + pad_idx=pad, + mask_idx=token_of[mask_token], + seed=seed, + epoch=epoch, + index=index, + **mask_kwargs, + ) + frame = dict(frame) + frame["coord"] = corrupted["coordinates"].astype(np.float64) + frame["atype"] = token_to_type[corrupted["tokens"]] + # Targets stay in Uni-Mol token space, which is what the element head + # predicts over; unselected atoms carry the padding id. + frame["unimol_token_target"] = corrupted["targets"].astype(np.int64) + frame["unimol_coord_target"] = coord.astype(np.float64) + frame["find_unimol_token_target"] = np.float32(1.0) + frame["find_unimol_coord_target"] = np.float32(1.0) + return frame + + return transform + + def unimol_frame_transform( atoms: Sequence[str], conformers: Sequence[np.ndarray], diff --git a/doc/model/index.rst b/doc/model/index.rst index d08b932059..c6db98d169 100644 --- a/doc/model/index.rst +++ b/doc/model/index.rst @@ -13,6 +13,7 @@ Model dpa3 dpa4 dpa4c + unimol train-hybrid sel train-energy diff --git a/doc/model/unimol.md b/doc/model/unimol.md new file mode 100644 index 0000000000..e0454cf9a3 --- /dev/null +++ b/doc/model/unimol.md @@ -0,0 +1,114 @@ +# Descriptor Uni-Mol {{ pytorch_icon }} {{ dpmodel_icon }} + +> [!NOTE] +> **Supported backends**: PyTorch-Exportable {{ pytorch_icon }}, DP {{ dpmodel_icon }} + +Uni-Mol is a molecular representation model: a transformer over all atom pairs, +in which geometry enters only through pairwise distances. It was pretrained on +about 209 million RDKit conformers with three self-supervised objectives and no +energies or forces at all. + +This is a port of Uni-Mol v1, faithful enough to load the released weights and +to reproduce the published objective. It exists for two reasons: to make +Uni-Mol's data and objectives available to multi-task training alongside +DFT-labelled data, and to give molecular property work a pretrained backbone. + +> [!IMPORTANT] +> Uni-Mol is **not** a potential energy surface model. It attends over every +> atom pair with no cut-off and no smooth envelope, so it is not extensive, it +> does not support periodic boundaries, and its forces are neither smooth nor +> conserved. The descriptor refuses frames that carry periodic images, and it +> is not available for molecular dynamics or frozen deployment. + +## Architecture + +The backbone is a 15-layer pre-layer-norm transformer of width 512 with 64 +attention heads. Distances are expanded in 128 Gaussians whose affine +parameters depend on the ordered element pair, projected to one bias per head, +and added to the attention logits. Each layer's pre-softmax logits become the +next layer's bias, so the running sum of logits is a pair representation that +the pretraining heads read. Two virtual tokens wrap each molecule. + +Uni-Mol's own 31-token vocabulary is kept, because the released weights are +indexed by it: four special tokens, 26 elements, then `[MASK]`. A `type_map` is +mapped onto those ids, and an element outside the vocabulary becomes `[UNK]`. + +## Pretraining objective + +Fifteen percent of the atoms are selected. Of those, 90% become `[MASK]`, 5% +become a random element and 5% are left alone; all three are predicted. The +masked and randomly replaced atoms also have uniform noise of ±1 Å added to +each coordinate component. Five terms are minimized: + +| Term | Weight | What it predicts | +| --- | --- | --- | +| element | 1 | the true element of every selected atom | +| coordinate | 5 | the clean coordinates, through the pair channel | +| distance | 10 | the clean pairwise distances | +| node norm | 0.01 | keeps node norms near $\sqrt{512}$ | +| pair-delta norm | 0.01 | keeps pair-delta norms near $\sqrt{64}$ | + +Corruption happens in the data pipeline rather than inside the loss, which is +both what upstream does and what the PyTorch-Exportable backend requires, since +it runs the model before the loss sees a frame. + +## Using the released weights + +The released checkpoints, `mol_pre_all_h_220816.pt` and +`mol_pre_no_h_220816.pt`, hold weights only and load with `weights_only=True`: + +```py +from deepmd.utils.unimol_checkpoint import descriptor_from_unimol_checkpoint + +descriptor = descriptor_from_unimol_checkpoint("mol_pre_all_h_220816.pt") +``` + +Parameter names line up one to one with the ported modules, but the arrays do +not: deepmd stores a linear weight as `(num_in, num_out)` and applies it as +`x @ w`, the transpose of `torch.nn.Linear.weight`. The converter renames and +transposes every weight, so there is no "just add a prefix" path. + +## Converting the pretraining data + +```sh +python -m deepmd.utils.unimol_data ligands.lmdb ./unimol_train --add-2d-conformer +``` + +One conformer becomes one frame, so ordinary frame sampling stands in for +upstream's per-epoch conformer draw. The two-dimensional RDKit conformer that +upstream appends while loading is added at conversion time, behind that flag, +so the training data path never needs RDKit. + +## How closely this matches upstream + +Every component is checked against tensors dumped from upstream running +unmodified on the same inputs. The data-side transforms agree bitwise, which +means the random stream itself is reproduced, down to which atoms are masked +and what noise each one receives. The encoder, fed upstream's own attention +bias, agrees to fp64 rounding. + +What remains is upstream's own use of fp32 in three places, which sets the +floor at about 1e-7 relative on the whole objective: + +- the Gaussian basis is evaluated in fp32, reproduced by default and + switchable with `single_precision_basis`; +- the distance matrix is precomputed in fp32 by upstream's data pipeline, + while the descriptor computes distances in the working precision, which is + more accurate and is what gradients flow through; `single_precision_distance` + reproduces upstream's numbers instead; +- `log_softmax` and both norm regularisers are evaluated in fp32, which is + reproduced. + +Training trajectories cannot be reproduced exactly in any case: upstream +pretrained a pure fp16 model with fused kernels and its own Adam variant. + +Uni-Mol uses the exact error-function GELU, available here as `gelu_erf`. +deepmd's `gelu` and `gelu_tf` are the tanh approximation, which differs by up +to 4.7e-4 per element. + +## Attribution + +The ported code follows Uni-Mol (commit `90f52c4`) and the Uni-Core modules it +builds on (commit `ace6fae`), both MIT licensed, Copyright (c) DP Technology. +Parts of Uni-Core derive in turn from fairseq, Copyright (c) Facebook, Inc. and +its affiliates, also MIT licensed. Each ported file records its provenance. diff --git a/examples/unimol/pretrain/input.json b/examples/unimol/pretrain/input.json new file mode 100644 index 0000000000..1ecbec1ea8 --- /dev/null +++ b/examples/unimol/pretrain/input.json @@ -0,0 +1,93 @@ +{ + "_comment": "Uni-Mol v1 self-supervised molecular pretraining. Convert the data first with: python -m deepmd.utils.unimol_data ligands.lmdb ./unimol_train --add-2d-conformer", + "model": { + "type_map": [ + "C", + "N", + "O", + "S", + "H", + "Cl", + "F", + "Br", + "I", + "Si", + "P", + "B", + "Na", + "K", + "Al", + "Ca", + "Sn", + "As", + "Hg", + "Fe", + "Zn", + "Cr", + "Se", + "Gd", + "Au", + "Li" + ], + "descriptor": { + "type": "unimol", + "encoder_layers": 15, + "encoder_embed_dim": 512, + "encoder_ffn_embed_dim": 2048, + "encoder_attention_heads": 64, + "max_atoms": 256, + "activation_function": "gelu_erf", + "_comment": "dropout acts during training only; the two precision switches decide whether a run reproduces upstream's published numbers or takes the more accurate path", + "dropout": 0.1, + "emb_dropout": 0.1, + "attention_dropout": 0.1, + "single_precision_basis": true, + "single_precision_distance": false, + "seed": 1 + }, + "fitting_net": { + "type": "unimol_pretrain", + "n_token": 31, + "attention_heads": 64, + "max_atoms": 256, + "activation_function": "gelu_erf", + "seed": 1 + } + }, + "learning_rate": { + "type": "wsd", + "start_lr": 0.0001, + "stop_lr": 1e-12, + "warmup_steps": 10000, + "decay_type": "linear", + "decay_phase_ratio": 1.0, + "_comment": "upstream warms up over 10k steps then decays linearly to zero over 1M; WSD requires a positive stop_lr, so it goes to 1e-12" + }, + "loss": { + "type": "unimol", + "masked_token_loss": 1.0, + "masked_coord_loss": 5.0, + "masked_dist_loss": 10.0, + "x_norm_loss": 0.01, + "delta_pair_repr_norm_loss": 0.01 + }, + "training": { + "training_data": { + "systems": [ + "./unimol_train" + ], + "batch_size": 16 + }, + "validation_data": { + "systems": [ + "./unimol_valid" + ], + "batch_size": 16 + }, + "numb_steps": 1000000, + "seed": 1, + "disp_file": "lcurve.out", + "disp_freq": 100, + "save_freq": 10000 + } +} diff --git a/source/tests/common/dpmodel/test_unimol.py b/source/tests/common/dpmodel/test_unimol.py index 5905ce838c..86a2f66e92 100644 --- a/source/tests/common/dpmodel/test_unimol.py +++ b/source/tests/common/dpmodel/test_unimol.py @@ -455,6 +455,42 @@ def test_terms_match_upstream(self) -> None: atol=1e-8, ) + def test_derived_labels_match_explicit_ones(self) -> None: + """Storing the distance target would cost O(natoms^2) per frame. + + The loss derives it, and the token column mask, from the clean + coordinates and the real-atom mask instead. Both routes must agree. + """ + nf, nloc = len(self.n_real), int(self.n_real.max()) + ncol = nloc + 2 + rng = np.random.default_rng(0) + labels = self._labels(nloc, ncol) + mask = np.zeros((nf, nloc), dtype=np.int64) + for f in range(nf): + mask[f, : int(self.n_real[f])] = 1 + pred = { + "token_logits": rng.normal(size=(nf, nloc, SMALL["vocab"])), + "coord_update": rng.normal(size=(nf, nloc, 3)), + "pair_dist": rng.normal(size=(nf, nloc, ncol)), + "x_norm": np.zeros((nf, nloc, 1)), + "delta_pair_norm": np.zeros((nf, nloc, 1)), + "mask": mask, + } + explicit, _ = UniMolLoss().call(1.0, 0, pred, labels) + derived, _ = UniMolLoss().call( + 1.0, + 0, + pred, + { + k: v + for k, v in labels.items() + if k not in ("unimol_dist_target", "unimol_token_mask") + }, + ) + # The explicit target is upstream's, stored in fp32; the derived one is + # computed in the working precision, so they part company there. + np.testing.assert_allclose(float(derived), float(explicit), rtol=1e-7) + def test_serialize_round_trip(self) -> None: loss = UniMolLoss(masked_coord_loss=3.0, beta=0.5) clone = UniMolLoss.deserialize(loss.serialize()) diff --git a/source/tests/common/dpmodel/test_unimol_data.py b/source/tests/common/dpmodel/test_unimol_data.py index 3bb6ffdf75..62d340b72f 100644 --- a/source/tests/common/dpmodel/test_unimol_data.py +++ b/source/tests/common/dpmodel/test_unimol_data.py @@ -15,6 +15,9 @@ from deepmd.dpmodel.utils.lmdb_data import ( LmdbDataReader, ) +from deepmd.dpmodel.utils.unimol_transform import ( + make_unimol_data_transform, +) from deepmd.utils.unimol_data import ( convert_unimol_lmdb, read_unimol_lmdb, @@ -96,6 +99,47 @@ def test_conversion_round_trips_through_the_deepmd_reader(self) -> None: # Molecules are not periodic; the cell is zero. np.testing.assert_array_equal(np.asarray(frame["box"]).reshape(-1), np.zeros(9)) + def test_transform_corrupts_frames_in_the_data_path(self) -> None: + """The reader hook is what makes self-supervised training possible.""" + type_map = [*UNIMOL_ELEMENTS, "[MASK]"] + dst = os.path.join(self.tmp, "for_training") + convert_unimol_lmdb(self.src, dst, type_map=type_map, map_size=1 << 24) + reader = LmdbDataReader(dst, type_map) + plain = reader[0] + reader.set_frame_transform( + make_unimol_data_transform(type_map, seed=1, epoch=1) + ) + corrupted = reader[0] + + self.assertEqual( + sorted(set(corrupted) - set(plain)), + [ + "find_unimol_coord_target", + "find_unimol_token_target", + "unimol_coord_target", + "unimol_token_target", + ], + ) + target = np.asarray(corrupted["unimol_token_target"]) + clean = np.asarray(corrupted["unimol_coord_target"]).reshape(-1, 3) + noisy = np.asarray(corrupted["coord"]).reshape(-1, 3) + # Only selected atoms may move, and the clean target is centred. + moved = np.abs(noisy - clean).max(axis=-1) > 0 + self.assertTrue(bool(np.all(~moved | (target != 0)))) + # Centring is exact only to fp32, because the transform keeps upstream's + # fp32 coordinates. + np.testing.assert_allclose(clean.mean(axis=0), np.zeros(3), atol=1e-6) + # Masked atoms are carried as the pseudo-element. Of the selected + # atoms, 90% are masked and 5% take a random element; both are moved, + # so the masked ones are a subset of the moved ones. + is_mask = np.asarray(corrupted["atype"]) == type_map.index("[MASK]") + self.assertLessEqual(int(is_mask.sum()), int(moved.sum())) + self.assertTrue(bool(np.all(~is_mask | moved))) + + def test_transform_requires_the_mask_pseudo_element(self) -> None: + with self.assertRaisesRegex(ValueError, r"\[MASK\]"): + make_unimol_data_transform(list(UNIMOL_ELEMENTS)) + def test_limits_are_respected(self) -> None: dst = os.path.join(self.tmp, "limited") counts = convert_unimol_lmdb( From e69c6af40a3e35cc128db9b196b678d4d2d15506 Mon Sep 17 00:00:00 2001 From: Duo <50307526+iProzd@users.noreply.github.com> Date: Sat, 12 Sep 2026 06:48:28 -0400 Subject: [PATCH 15/34] fix(dpmodel): place every constructed array on the input's device A short training run on GPU turned up the last of these: the two virtual tokens, the position index and the zero centroid were built without a device, so they landed on the host while the rest of the batch was on the accelerator, and concatenating them failed. The same omission was present in the loss, when it derives the token mask and the clean distances, and in the fitting, when it broadcasts the regularisers and pads the distance output. Array-API code has to say where an array lives; only operations derived from an existing array inherit it. Every construction now takes the device of the data it will be combined with. With this, training runs: converting the bundled example molecules, installing the transform on the reader and stepping Adam for 60 steps takes the objective from 8.48 to 3.02, with all five terms falling. --- deepmd/dpmodel/descriptor/unimol.py | 13 ++++++------- deepmd/dpmodel/fitting/unimol_pretrain.py | 15 +++++++++++++-- deepmd/dpmodel/loss/unimol.py | 9 ++++++--- 3 files changed, 25 insertions(+), 12 deletions(-) diff --git a/deepmd/dpmodel/descriptor/unimol.py b/deepmd/dpmodel/descriptor/unimol.py index 9b9f7f4673..e9463a980d 100644 --- a/deepmd/dpmodel/descriptor/unimol.py +++ b/deepmd/dpmodel/descriptor/unimol.py @@ -316,6 +316,7 @@ def build_tokens(self, coord_ext: Array, atype_ext: Array, nlist: Array) -> dict ``n_real``; the sequence is ``[CLS] atoms [SEP] pad...``. """ xp = array_api_compat.array_namespace(coord_ext) + dev = array_api_compat.device(coord_ext) nf, nloc = nlist.shape[0], nlist.shape[1] coord = xp.reshape(coord_ext, (nf, -1, 3)) nall = coord.shape[1] @@ -344,13 +345,11 @@ def build_tokens(self, coord_ext: Array, atype_ext: Array, nlist: Array) -> dict / xp.astype(n_real, coord.dtype)[:, None] ) else: - centroid = xp.zeros((nf, 3), dtype=coord.dtype) + centroid = xp.zeros((nf, 3), dtype=coord.dtype, device=dev) # The lookup table is plain integer data rather than a parameter, so it # does not travel with the module and has to be placed explicitly. - token_table = xp.asarray( - self.type_to_token, device=array_api_compat.device(nlist) - ) + token_table = xp.asarray(self.type_to_token, device=dev) atom_tokens = xp.take(token_table, xp.reshape(atype_ext, (-1,)), axis=0) atom_tokens = xp.reshape(atom_tokens, (nf, nloc)) atom_tokens = xp.where( @@ -358,10 +357,10 @@ def build_tokens(self, coord_ext: Array, atype_ext: Array, nlist: Array) -> dict ) nt = nloc + 2 - positions = xp.arange(nt)[None, :] + positions = xp.arange(nt, device=dev)[None, :] eos_at = (n_real + 1)[:, None] - bos_row = xp.full((nf, 1), self.bos_idx, dtype=atom_tokens.dtype) - pad_row = xp.full((nf, 1), self.pad_idx, dtype=atom_tokens.dtype) + bos_row = xp.full((nf, 1), self.bos_idx, dtype=atom_tokens.dtype, device=dev) + pad_row = xp.full((nf, 1), self.pad_idx, dtype=atom_tokens.dtype, device=dev) tokens = xp.concat([bos_row, atom_tokens, pad_row], axis=1) tokens = xp.where( positions == eos_at, xp.full_like(tokens, self.eos_idx), tokens diff --git a/deepmd/dpmodel/fitting/unimol_pretrain.py b/deepmd/dpmodel/fitting/unimol_pretrain.py index fa9d28d8d9..e5a87a5d50 100644 --- a/deepmd/dpmodel/fitting/unimol_pretrain.py +++ b/deepmd/dpmodel/fitting/unimol_pretrain.py @@ -216,7 +216,14 @@ def call_tokens(self, backbone: dict[str, Array]) -> dict[str, Array]: out = {} for name in ("x_norm", "delta_pair_norm"): value = xp.astype(xp.reshape(backbone[name], (1, 1, 1)), node.dtype) - out[name] = xp.zeros((nf, nloc, 1), dtype=node.dtype) + value + out[name] = ( + xp.zeros( + (nf, nloc, 1), + dtype=node.dtype, + device=array_api_compat.device(node), + ) + + value + ) if self.lm_head is not None: logits = self.lm_head(node) out["token_logits"] = logits[:, 1 : nloc + 1, :] @@ -232,7 +239,11 @@ def call_tokens(self, backbone: dict[str, Array]) -> dict[str, Array]: dist = self.dist_head(backbone["pair_rep"])[:, 1 : nloc + 1, :] width = self.max_atoms + 2 if dist.shape[-1] < width: - pad = xp.zeros((nf, nloc, width - dist.shape[-1]), dtype=dist.dtype) + pad = xp.zeros( + (nf, nloc, width - dist.shape[-1]), + dtype=dist.dtype, + device=array_api_compat.device(dist), + ) dist = xp.concat([dist, pad], axis=-1) out["pair_dist"] = dist return out diff --git a/deepmd/dpmodel/loss/unimol.py b/deepmd/dpmodel/loss/unimol.py index ff7aff5f7f..dd062dc325 100644 --- a/deepmd/dpmodel/loss/unimol.py +++ b/deepmd/dpmodel/loss/unimol.py @@ -72,7 +72,7 @@ def _token_mask_from_atoms(mask: Array, ncol: int) -> Array: """Mark the non-padding token columns: BOS, the real atoms, then EOS.""" xp = array_api_compat.array_namespace(mask) n_real = xp.sum(xp.astype(mask, xp.int64), axis=-1) - positions = xp.arange(ncol)[None, :] + positions = xp.arange(ncol, device=array_api_compat.device(mask))[None, :] return xp.astype(positions < (n_real + 2)[:, None], xp.int64) @@ -87,10 +87,13 @@ def _clean_distances(coord_target: Array, mask: Array, ncol: int) -> Array: nf = coord_target.shape[0] real = xp.astype(mask, coord_target.dtype)[..., None] atoms = coord_target * real - zero = xp.zeros((nf, 1, 3), dtype=coord_target.dtype) + dev = array_api_compat.device(coord_target) + zero = xp.zeros((nf, 1, 3), dtype=coord_target.dtype, device=dev) tokens = xp.concat([zero, atoms, zero], axis=1) if tokens.shape[1] < ncol: - pad = xp.zeros((nf, ncol - tokens.shape[1], 3), dtype=coord_target.dtype) + pad = xp.zeros( + (nf, ncol - tokens.shape[1], 3), dtype=coord_target.dtype, device=dev + ) tokens = xp.concat([tokens, pad], axis=1) diff = atoms[:, :, None, :] - tokens[:, None, :, :] return xp.sqrt(xp.sum(diff**2, axis=-1)) From 04d917d170578d59b8189e6e50cbd57a13afc050 Mon Sep 17 00:00:00 2001 From: Duo <50307526+iProzd@users.noreply.github.com> Date: Sat, 12 Sep 2026 06:52:20 -0400 Subject: [PATCH 16/34] fix: refuse a periodic cell at the model boundary Calling the model with a cell used to die on an allocation of several million gigabytes rather than on a readable error: the descriptor has no cut-off, so the neighbour-list builder went looking for an astronomical number of periodic images, and the descriptor's own check on extended atoms never got the chance to fire. Both model classes now reject a non-zero cell up front, with an explanation. The upper entry point, which builds its own neighbour list from coordinates and types, is covered by a test as well; it was previously exercised only through the lower one. --- deepmd/dpmodel/model/unimol_pretrain_model.py | 26 ++++++++++++++- deepmd/pt_expt/model/unimol_pretrain_model.py | 13 +++++++- source/tests/pt_expt/model/test_unimol.py | 32 +++++++++++++++++++ 3 files changed, 69 insertions(+), 2 deletions(-) diff --git a/deepmd/dpmodel/model/unimol_pretrain_model.py b/deepmd/dpmodel/model/unimol_pretrain_model.py index fa4ea373b4..53cf8a723e 100644 --- a/deepmd/dpmodel/model/unimol_pretrain_model.py +++ b/deepmd/dpmodel/model/unimol_pretrain_model.py @@ -5,6 +5,8 @@ Any, ) +import array_api_compat + from deepmd.dpmodel.array_api import ( Array, ) @@ -28,6 +30,19 @@ make_model, ) + +def _reject_periodic(box) -> None: # noqa: ANN001 + """Refuse a periodic cell, with an explanation rather than an allocation.""" + if box is None: + return + xp = array_api_compat.array_namespace(box) + if bool(xp.any(box != 0)): + raise ValueError( + "the unimol descriptor is molecular and does not support periodic " + "boundaries; pass box=None" + ) + + DPUniMolPretrainModel_ = make_model(DPUniMolAtomicModel, T_Bases=(NativeOP, BaseModel)) @@ -56,7 +71,16 @@ def call( do_atomic_virial: bool = False, charge_spin: Array | None = None, ) -> dict[str, Array]: - """Evaluate the pretraining heads on a frame.""" + """Evaluate the pretraining heads on a frame. + + Raises + ------ + ValueError + If a periodic cell is supplied. Uni-Mol is molecular: it has no + cut-off, so a cell would ask the neighbour-list builder for an + astronomical number of images before any other check could fire. + """ + _reject_periodic(box) model_ret = self.call_common( coord, atype, diff --git a/deepmd/pt_expt/model/unimol_pretrain_model.py b/deepmd/pt_expt/model/unimol_pretrain_model.py index eaa6b1586d..f454ef179c 100644 --- a/deepmd/pt_expt/model/unimol_pretrain_model.py +++ b/deepmd/pt_expt/model/unimol_pretrain_model.py @@ -43,7 +43,18 @@ def forward( do_atomic_virial: bool = False, charge_spin: torch.Tensor | None = None, ) -> dict[str, torch.Tensor]: - """Evaluate the pretraining heads on a frame.""" + """Evaluate the pretraining heads on a frame. + + Raises + ------ + ValueError + If a periodic cell is supplied; Uni-Mol is molecular. + """ + if box is not None and bool(torch.any(box != 0)): + raise ValueError( + "the unimol descriptor is molecular and does not support periodic " + "boundaries; pass box=None" + ) model_ret = self.forward_common( coord, atype, diff --git a/source/tests/pt_expt/model/test_unimol.py b/source/tests/pt_expt/model/test_unimol.py index 11dc9cbabe..2839386d26 100644 --- a/source/tests/pt_expt/model/test_unimol.py +++ b/source/tests/pt_expt/model/test_unimol.py @@ -354,6 +354,38 @@ def test_dropout_is_active_only_in_training(self) -> None: self.assertFalse(bool(torch.allclose(a, b))) model.eval() + def test_upper_path_builds_its_own_neighbour_list(self) -> None: + """What a user actually calls: coordinates and types, no neighbour list.""" + coord, atype, _ = self.inputs() + model = self.build_torch_model(coord.shape[1]) + small = 12 + ret = model.forward( + torch.as_tensor(coord[:1, :small].reshape(1, -1)), + torch.as_tensor(atype[:1, :small]), + None, + ) + self.assertEqual(tuple(ret["token_logits"].shape), (1, small, SMALL["vocab"])) + self.assertEqual(tuple(ret["coord_update"].shape), (1, small, 3)) + self.assertEqual(tuple(ret["mask"].shape), (1, small)) + + def test_periodic_cell_is_refused(self) -> None: + """A cell has to be caught here, before the neighbour list is built. + + The descriptor has no cut-off, so a cell would send the neighbour-list + builder looking for an astronomical number of periodic images and the + run would die on allocation rather than on a readable error. + """ + coord, atype, _ = self.inputs() + model = self.build_torch_model(coord.shape[1]) + small = 8 + box = torch.eye(3, dtype=torch.float64).reshape(1, 9) * 20.0 + with self.assertRaisesRegex(ValueError, "periodic"): + model.forward( + torch.as_tensor(coord[:1, :small].reshape(1, -1)), + torch.as_tensor(atype[:1, :small]), + box.to(next(model.parameters()).device), + ) + def test_gradients_flow(self) -> None: coord, atype, nlist = self.inputs() model = self.build_torch_model(coord.shape[1]) From 63251e2815ede3c2ae96ea80064cbce4908a7d64 Mon Sep 17 00:00:00 2001 From: Duo <50307526+iProzd@users.noreply.github.com> Date: Sat, 12 Sep 2026 06:55:35 -0400 Subject: [PATCH 17/34] feat: let an objective declare the data transform it needs Until now the Uni-Mol corruption had to be installed by hand, so a training run started from a configuration file would have found no labels. The loss base class gains an optional frame_transform, defaulting to none, and the PyTorch-Exportable trainer installs whatever the task's objective returns on that task's datasets, right where it already registers the label requirements. Supervised losses return nothing and their data path is untouched. The corruption settings move onto the loss, which is where they belong: the labels are whatever the corruption produced. They are exposed through argcheck, so the masking rate, the 90/5/5 split, the noise and the seed are all configurable, with upstream's values as defaults. A dataset type that cannot take a transform now fails with an explanation rather than with missing labels much later. --- deepmd/dpmodel/loss/loss.py | 10 +++++ deepmd/dpmodel/loss/unimol.py | 40 +++++++++++++++++++ deepmd/pt_expt/train/training.py | 20 ++++++++++ deepmd/pt_expt/utils/lmdb_dataset.py | 4 ++ deepmd/utils/argcheck.py | 36 +++++++++++++++++ .../tests/common/dpmodel/test_unimol_data.py | 35 ++++++++++++++++ 6 files changed, 145 insertions(+) diff --git a/deepmd/dpmodel/loss/loss.py b/deepmd/dpmodel/loss/loss.py index 926c6e28c5..a91bd0104f 100644 --- a/deepmd/dpmodel/loss/loss.py +++ b/deepmd/dpmodel/loss/loss.py @@ -51,6 +51,16 @@ def call( def label_requirement(self) -> list[DataRequirementItem]: """Return data label requirements needed for this loss calculation.""" + def frame_transform(self, type_map: list[str]): # noqa: ANN201 + """Return a per-frame data transform this objective needs, or None. + + Self-supervised objectives build their own labels by corrupting the + input, which has to happen while the data is read rather than inside + the loss. A trainer installs whatever this returns on the datasets of + the corresponding task. Supervised losses need nothing and return None. + """ + del type_map + @property def supports_ragged_batches(self) -> bool: """Whether this objective accepts a flat per-node batch axis.""" diff --git a/deepmd/dpmodel/loss/unimol.py b/deepmd/dpmodel/loss/unimol.py index dd062dc325..ab394c5a5e 100644 --- a/deepmd/dpmodel/loss/unimol.py +++ b/deepmd/dpmodel/loss/unimol.py @@ -160,6 +160,13 @@ def __init__( delta_pair_repr_norm_loss: float = 0.01, beta: float = 1.0, pad_idx: int = 0, + mask_prob: float = 0.15, + leave_unmasked_prob: float = 0.05, + random_token_prob: float = 0.05, + noise_type: str = "uniform", + noise: float = 1.0, + max_atoms: int = 256, + data_seed: int = 1, **kwargs: float, ) -> None: self.masked_token_loss = masked_token_loss @@ -169,6 +176,15 @@ def __init__( self.delta_pair_repr_norm_loss = delta_pair_repr_norm_loss self.beta = beta self.pad_idx = pad_idx + # The corruption settings live here because the objective owns them: + # the labels are whatever the corruption produced. + self.mask_prob = mask_prob + self.leave_unmasked_prob = leave_unmasked_prob + self.random_token_prob = random_token_prob + self.noise_type = noise_type + self.noise = noise + self.max_atoms = max_atoms + self.data_seed = data_seed def call( self, @@ -248,6 +264,23 @@ def add(term: Array, weight: float, name: str) -> None: ) return loss, more_loss + def frame_transform(self, type_map: list[str]): # noqa: ANN201 + """Build Uni-Mol's corruption, which also produces the labels.""" + from deepmd.dpmodel.utils.unimol_transform import ( + make_unimol_data_transform, + ) + + return make_unimol_data_transform( + type_map, + seed=self.data_seed, + max_atoms=self.max_atoms, + mask_prob=self.mask_prob, + leave_unmasked_prob=self.leave_unmasked_prob, + random_token_prob=self.random_token_prob, + noise_type=self.noise_type, + noise=self.noise, + ) + @property def label_requirement(self) -> list[DataRequirementItem]: """Labels produced by the Uni-Mol data transform, not by a simulation.""" @@ -268,6 +301,13 @@ def serialize(self) -> dict: "delta_pair_repr_norm_loss": self.delta_pair_repr_norm_loss, "beta": self.beta, "pad_idx": self.pad_idx, + "mask_prob": self.mask_prob, + "leave_unmasked_prob": self.leave_unmasked_prob, + "random_token_prob": self.random_token_prob, + "noise_type": self.noise_type, + "noise": self.noise, + "max_atoms": self.max_atoms, + "data_seed": self.data_seed, } @classmethod diff --git a/deepmd/pt_expt/train/training.py b/deepmd/pt_expt/train/training.py index aecfc56b6a..53a450884d 100644 --- a/deepmd/pt_expt/train/training.py +++ b/deepmd/pt_expt/train/training.py @@ -1872,6 +1872,26 @@ def __init__( self.validation_data_by_task[model_key].add_data_requirements( data_requirement ) + # A self-supervised objective builds its labels by corrupting the + # input, which has to happen as the data is read. + frame_transform = self.losses[model_key].frame_transform( + self.model_params_by_task[model_key]["type_map"] + ) + if frame_transform is not None: + for dataset in ( + self.training_data_by_task[model_key], + self.validation_data_by_task[model_key], + ): + if dataset is None: + continue + if not hasattr(dataset, "set_frame_transform"): + raise ValueError( + f"the {self.losses[model_key].__class__.__name__} " + "objective corrupts its input as the data is read, " + "which this dataset type does not support; convert " + "the data to LMDB first" + ) + dataset.set_frame_transform(frame_transform) if self.multi_task: valid_params = ( training_params["data_dict"][model_key].get("validation_data", {}) diff --git a/deepmd/pt_expt/utils/lmdb_dataset.py b/deepmd/pt_expt/utils/lmdb_dataset.py index 9af91be82c..f16de39a01 100644 --- a/deepmd/pt_expt/utils/lmdb_dataset.py +++ b/deepmd/pt_expt/utils/lmdb_dataset.py @@ -211,6 +211,10 @@ def add_data_requirements( self._reader.add_data_requirement(data_requirement) self._refresh_stat_groups() + def set_frame_transform(self, transform) -> None: # noqa: ANN001 + """Install a per-frame transform on the underlying reader.""" + self._reader.set_frame_transform(transform) + def close(self) -> None: """Cancel prefetched work and release decoder processes.""" iterator = getattr(self, "_batch_iterator", None) diff --git a/deepmd/utils/argcheck.py b/deepmd/utils/argcheck.py index 98e752933f..3647601ea8 100644 --- a/deepmd/utils/argcheck.py +++ b/deepmd/utils/argcheck.py @@ -5526,6 +5526,19 @@ def loss_unimol() -> list[Argument]: doc_beta = ( "Transition point of the smooth L1 used by the coordinate and distance terms." ) + doc_mask_prob = "Expected fraction of atoms selected for corruption." + doc_leave_unmasked_prob = ( + "Fraction of the selected atoms left with their true element, still predicted." + ) + doc_random_token_prob = "Fraction of the selected atoms given a random element." + doc_noise_type = ( + "Coordinate noise distribution: 'uniform', 'normal', 'trunc_normal' or 'none'." + ) + doc_noise = "Scale of the coordinate noise, in the units of the coordinates." + doc_loss_max_atoms = "Crop molecules larger than this before corrupting them." + doc_data_seed = ( + "Seed of the corruption, combined with the epoch and the frame index." + ) return [ Argument( "masked_token_loss", @@ -5563,6 +5576,29 @@ def loss_unimol() -> list[Argument]: doc=doc_delta_pair_repr_norm_loss, ), Argument("beta", [float, int], optional=True, default=1.0, doc=doc_beta), + Argument( + "mask_prob", [float, int], optional=True, default=0.15, doc=doc_mask_prob + ), + Argument( + "leave_unmasked_prob", + [float, int], + optional=True, + default=0.05, + doc=doc_leave_unmasked_prob, + ), + Argument( + "random_token_prob", + [float, int], + optional=True, + default=0.05, + doc=doc_random_token_prob, + ), + Argument( + "noise_type", str, optional=True, default="uniform", doc=doc_noise_type + ), + Argument("noise", [float, int], optional=True, default=1.0, doc=doc_noise), + Argument("max_atoms", int, optional=True, default=256, doc=doc_loss_max_atoms), + Argument("data_seed", int, optional=True, default=1, doc=doc_data_seed), ] diff --git a/source/tests/common/dpmodel/test_unimol_data.py b/source/tests/common/dpmodel/test_unimol_data.py index 62d340b72f..7b5ececd29 100644 --- a/source/tests/common/dpmodel/test_unimol_data.py +++ b/source/tests/common/dpmodel/test_unimol_data.py @@ -15,6 +15,9 @@ from deepmd.dpmodel.utils.lmdb_data import ( LmdbDataReader, ) +from deepmd.dpmodel.loss.unimol import ( + UniMolLoss, +) from deepmd.dpmodel.utils.unimol_transform import ( make_unimol_data_transform, ) @@ -136,6 +139,38 @@ def test_transform_corrupts_frames_in_the_data_path(self) -> None: self.assertLessEqual(int(is_mask.sum()), int(moved.sum())) self.assertTrue(bool(np.all(~is_mask | moved))) + def test_the_objective_supplies_its_own_transform(self) -> None: + """A trainer installs whatever the loss declares, and nothing else. + + Supervised losses return None here, so the data path is untouched for + them; this objective returns the corruption that produces its labels. + """ + from deepmd.dpmodel.loss.property import ( + PropertyLoss, + ) + + type_map = [*UNIMOL_ELEMENTS, "[MASK]"] + self.assertIsNone( + PropertyLoss(task_dim=1, var_name="property").frame_transform(type_map) + ) + + loss = UniMolLoss(mask_prob=0.2) + self.assertEqual( + [r.key for r in loss.label_requirement], + ["unimol_token_target", "unimol_coord_target"], + ) + dst = os.path.join(self.tmp, "through_the_loss") + convert_unimol_lmdb(self.src, dst, type_map=type_map, map_size=1 << 24) + reader = LmdbDataReader(dst, type_map) + before = set(reader[0]) + reader.set_frame_transform(loss.frame_transform(type_map)) + after = reader[0] + for key in ("unimol_token_target", "unimol_coord_target"): + self.assertIn(key, set(after) - before) + self.assertEqual( + len(np.asarray(after["unimol_token_target"])), len(after["atype"]) + ) + def test_transform_requires_the_mask_pseudo_element(self) -> None: with self.assertRaisesRegex(ValueError, r"\[MASK\]"): make_unimol_data_transform(list(UNIMOL_ELEMENTS)) From 18649c03619d35524c32ac3459fe9343c3de135b Mon Sep 17 00:00:00 2001 From: Duo <50307526+iProzd@users.noreply.github.com> Date: Sat, 12 Sep 2026 07:00:10 -0400 Subject: [PATCH 18/34] docs: describe how a Uni-Mol run is configured, and validate the example The documentation now says how training is launched, that the dataset has to be an LMDB one because the corruption happens as frames are read, that the objective carries the corruption settings, and that the type_map needs the [MASK] pseudo-element. The example configuration gains that pseudo-element and the corruption settings with upstream's values, and a test validates it against argcheck. It is checked there rather than in the shared example test, because that one also requires the referenced dataset to exist in the repository, while this example points at data the user converts from upstream. --- doc/model/unimol.md | 17 ++++++++++- examples/unimol/pretrain/input.json | 15 ++++++++-- source/tests/common/dpmodel/test_unimol.py | 34 ++++++++++++++++++++++ 3 files changed, 62 insertions(+), 4 deletions(-) diff --git a/doc/model/unimol.md b/doc/model/unimol.md index e0454cf9a3..6ddac4e80c 100644 --- a/doc/model/unimol.md +++ b/doc/model/unimol.md @@ -50,7 +50,22 @@ each coordinate component. Five terms are minimized: Corruption happens in the data pipeline rather than inside the loss, which is both what upstream does and what the PyTorch-Exportable backend requires, since -it runs the model before the loss sees a frame. +it runs the model before the loss sees a frame. The objective owns the settings +and hands the trainer the transform it needs, so a training run installs it +automatically; the masking rate, the 90/5/5 split, the noise and the seed are +all configurable under `loss`. + +A masked atom is carried as a `[MASK]` pseudo-element, so the model's +`type_map` has to declare it alongside the elements. + +## Training + +```sh +dp --pt-expt train examples/unimol/pretrain/input.json +``` + +The dataset has to be an LMDB one, because the corruption happens as frames are +read; `deepmd.utils.unimol_data` below produces it. ## Using the released weights diff --git a/examples/unimol/pretrain/input.json b/examples/unimol/pretrain/input.json index 1ecbec1ea8..fd668c6399 100644 --- a/examples/unimol/pretrain/input.json +++ b/examples/unimol/pretrain/input.json @@ -27,7 +27,8 @@ "Se", "Gd", "Au", - "Li" + "Li", + "[MASK]" ], "descriptor": { "type": "unimol", @@ -52,7 +53,8 @@ "max_atoms": 256, "activation_function": "gelu_erf", "seed": 1 - } + }, + "_comment_type_map": "[MASK] is the pseudo-element a corrupted atom is carried as; the objective needs it" }, "learning_rate": { "type": "wsd", @@ -69,7 +71,14 @@ "masked_coord_loss": 5.0, "masked_dist_loss": 10.0, "x_norm_loss": 0.01, - "delta_pair_repr_norm_loss": 0.01 + "delta_pair_repr_norm_loss": 0.01, + "mask_prob": 0.15, + "leave_unmasked_prob": 0.05, + "random_token_prob": 0.05, + "noise_type": "uniform", + "noise": 1.0, + "max_atoms": 256, + "data_seed": 1 }, "training": { "training_data": { diff --git a/source/tests/common/dpmodel/test_unimol.py b/source/tests/common/dpmodel/test_unimol.py index 86a2f66e92..b7b3fb9ceb 100644 --- a/source/tests/common/dpmodel/test_unimol.py +++ b/source/tests/common/dpmodel/test_unimol.py @@ -498,5 +498,39 @@ def test_serialize_round_trip(self) -> None: self.assertEqual(clone.beta, 0.5) +class TestUniMolExample(unittest.TestCase): + """The shipped example must stay valid as the arguments evolve. + + It is checked here rather than in the shared example test, because that one + also requires the referenced dataset to exist in the repository, and this + example points at data the user converts from upstream. + """ + + def test_example_configuration_is_valid(self) -> None: + import json + from pathlib import ( + Path, + ) + + from deepmd.utils.argcheck import ( + normalize, + ) + + path = ( + Path(__file__).parents[4] + / "examples" + / "unimol" + / "pretrain" + / "input.json" + ) + self.assertTrue(path.is_file(), f"missing example: {path}") + config = normalize(json.loads(path.read_text())) + self.assertEqual(config["model"]["descriptor"]["type"], "unimol") + self.assertEqual(config["model"]["fitting_net"]["type"], "unimol_pretrain") + self.assertEqual(config["loss"]["type"], "unimol") + # A corrupted atom is carried as this pseudo-element, so the map needs it. + self.assertIn("[MASK]", config["model"]["type_map"]) + + if __name__ == "__main__": unittest.main() From 5d4043d948c24de1e26ace0f1e268a1f786a4f08 Mon Sep 17 00:00:00 2001 From: Duo <50307526+iProzd@users.noreply.github.com> Date: Sat, 12 Sep 2026 07:07:30 -0400 Subject: [PATCH 19/34] fix: make `dp --pt-expt train` work for the Uni-Mol objective Running the command line end to end turned up five gaps that no unit test would have shown, because each sits in the path between a configuration file and the first training step. - The trainer's loss factory did not know the objective, so a configuration naming it was rejected outright. - The fitting was missing the accessors the atomic model calls on any fitting: frame and atomic parameter dimensions, the default frame parameter, selected types, exclusion re-initialisation, case embeddings and input statistics. The ones that do not apply now say so instead of raising AttributeError. - The per-frame transform ran after the reader checked that the mandatory fields were present, so a self-supervised run failed on the very labels the transform was about to produce. It now runs before that check. - The converter wrote a zero cell to mark a molecule, and the neighbour-list builder took it for a real cell and tried to invert it. Molecular frames now carry no cell at all. - The example pointed at its dataset with a list, while LMDB datasets are addressed with a plain string. The example and the documentation say so now. With these, a run from the shipped example trains: both the training and validation curves report all five terms and a checkpoint is written. --- deepmd/dpmodel/fitting/unimol_pretrain.py | 39 +++++++++++++++++++ deepmd/dpmodel/utils/lmdb_data.py | 8 +++- deepmd/pt_expt/train/training.py | 5 +++ deepmd/utils/unimol_data.py | 5 +-- doc/model/unimol.md | 3 +- examples/unimol/pretrain/input.json | 11 ++---- .../tests/common/dpmodel/test_unimol_data.py | 6 ++- 7 files changed, 62 insertions(+), 15 deletions(-) diff --git a/deepmd/dpmodel/fitting/unimol_pretrain.py b/deepmd/dpmodel/fitting/unimol_pretrain.py index e5a87a5d50..6887ee3215 100644 --- a/deepmd/dpmodel/fitting/unimol_pretrain.py +++ b/deepmd/dpmodel/fitting/unimol_pretrain.py @@ -140,6 +140,45 @@ def change_type_map( self.type_map = list(type_map) self.ntypes = len(type_map) + def compute_input_stats(self, merged, stat_file_path=None, **kwargs) -> None: # noqa: ANN001, ANN003 + """No input statistics: the heads read a learned representation. + + Nothing here is normalized against the training set, so there is + nothing to accumulate. + """ + + def get_dim_fparam(self) -> int: + """No frame parameters: the objective reads structure only.""" + return 0 + + def get_dim_aparam(self) -> int: + """No atomic parameters.""" + return 0 + + def has_default_fparam(self) -> bool: + """There are no frame parameters, so there is no default either.""" + return False + + def get_default_fparam(self): # noqa: ANN201 + """There are no frame parameters.""" + return None + + def get_sel_type(self) -> list[int]: + """Every element takes part in the objective.""" + return [] + + def reinit_exclude(self, exclude_types: list[int] | None = None) -> None: + """Type exclusion is meaningless here: every atom is predicted.""" + if exclude_types: + raise NotImplementedError( + "unimol_pretrain predicts every atom and does not support " + "excluded types" + ) + + def set_case_embd(self, case_idx: int) -> None: + """Case embeddings are a multi-task feature this fitting does not use.""" + raise NotImplementedError("unimol_pretrain does not support case embeddings") + def output_def(self) -> FittingOutputDef: """Declare the three head outputs. diff --git a/deepmd/dpmodel/utils/lmdb_data.py b/deepmd/dpmodel/utils/lmdb_data.py index 715be7c63b..07ea27370d 100644 --- a/deepmd/dpmodel/utils/lmdb_data.py +++ b/deepmd/dpmodel/utils/lmdb_data.py @@ -974,6 +974,12 @@ def decode_lmdb_frame( "fid", } ) + if config.frame_transform is not None: + # Ahead of the requirement checks below: a self-supervised transform is + # what produces the fields those checks look for, by corrupting the + # input it was handed. + frame = config.frame_transform(frame, original_key) + for key in list(frame): if key.startswith("find_") or key in structural_keys or key in requirements: continue @@ -1025,8 +1031,6 @@ def decode_lmdb_frame( ) frame["fid"] = original_key - if config.frame_transform is not None: - frame = config.frame_transform(frame, original_key) return frame diff --git a/deepmd/pt_expt/train/training.py b/deepmd/pt_expt/train/training.py index 53a450884d..4fcb763c15 100644 --- a/deepmd/pt_expt/train/training.py +++ b/deepmd/pt_expt/train/training.py @@ -91,6 +91,7 @@ ) from deepmd.pt.utils.compile_compat import trace_pad_dim as _trace_pad_dim from deepmd.pt_expt.loss import ( + UniMolLoss, DOSLoss, EnergyLoss, EnergySpinLoss, @@ -343,6 +344,10 @@ def get_loss( loss_params["var_name"] = var_name loss_params["intensive"] = intensive return PropertyLoss(**loss_params) + elif loss_type == "unimol": + # Self-supervised: it takes no learning rate and no model geometry, + # because its targets come from the corruption it defines itself. + return UniMolLoss(**loss_params) else: raise ValueError(f"Unsupported loss type for pt_expt: {loss_type}") diff --git a/deepmd/utils/unimol_data.py b/deepmd/utils/unimol_data.py index 694e816b7e..8db5039ed7 100644 --- a/deepmd/utils/unimol_data.py +++ b/deepmd/utils/unimol_data.py @@ -174,9 +174,8 @@ def convert_unimol_lmdb( "atom_names": names, "atom_types": _encode_array(atom_types), "orig": _encode_array(np.zeros(3, dtype=np.float64)), - # A zero cell marks a molecule: the descriptor refuses - # periodic images anyway. - "cells": _encode_array(np.zeros((3, 3), dtype=np.float64)), + # No cell at all: molecules are not periodic, and a zero + # cell would be taken for a real one and inverted. "coords": _encode_array(coords.astype(np.float64)), } txn.put( diff --git a/doc/model/unimol.md b/doc/model/unimol.md index 6ddac4e80c..c738a033d8 100644 --- a/doc/model/unimol.md +++ b/doc/model/unimol.md @@ -65,7 +65,8 @@ dp --pt-expt train examples/unimol/pretrain/input.json ``` The dataset has to be an LMDB one, because the corruption happens as frames are -read; `deepmd.utils.unimol_data` below produces it. +read; `deepmd.utils.unimol_data` below produces it. Give its path as a string +under `systems`, not as a list, which is how LMDB datasets are addressed. ## Using the released weights diff --git a/examples/unimol/pretrain/input.json b/examples/unimol/pretrain/input.json index fd668c6399..6e0a72c809 100644 --- a/examples/unimol/pretrain/input.json +++ b/examples/unimol/pretrain/input.json @@ -82,15 +82,12 @@ }, "training": { "training_data": { - "systems": [ - "./unimol_train" - ], - "batch_size": 16 + "systems": "./unimol_train", + "batch_size": 16, + "_comment": "an LMDB dataset path, given as a string; build it with deepmd.utils.unimol_data" }, "validation_data": { - "systems": [ - "./unimol_valid" - ], + "systems": "./unimol_valid", "batch_size": 16 }, "numb_steps": 1000000, diff --git a/source/tests/common/dpmodel/test_unimol_data.py b/source/tests/common/dpmodel/test_unimol_data.py index 7b5ececd29..e4af8b75be 100644 --- a/source/tests/common/dpmodel/test_unimol_data.py +++ b/source/tests/common/dpmodel/test_unimol_data.py @@ -99,8 +99,10 @@ def test_conversion_round_trips_through_the_deepmd_reader(self) -> None: ) symbols = [UNIMOL_ELEMENTS[i] for i in np.asarray(frame["atype"]).reshape(-1)] self.assertEqual(symbols, self.molecules[0]["atoms"]) - # Molecules are not periodic; the cell is zero. - np.testing.assert_array_equal(np.asarray(frame["box"]).reshape(-1), np.zeros(9)) + # Molecules are not periodic, so no cell is written at all. A zero cell + # would not do: the neighbour-list builder takes any cell at face value + # and inverts it. + self.assertNotIn("box", frame) def test_transform_corrupts_frames_in_the_data_path(self) -> None: """The reader hook is what makes self-supervised training possible.""" From 9aa9930e4f9a21a7ec40153ca5624ecffb0e05b6 Mon Sep 17 00:00:00 2001 From: Duo <50307526+iProzd@users.noreply.github.com> Date: Sat, 12 Sep 2026 07:10:39 -0400 Subject: [PATCH 20/34] test: train from a configuration, the way the feature is used Covers everything between a configuration file and the first training step: the loss factory, the accessors the atomic model calls on any fitting, the reader hook that produces the labels, and the absence of a cell on molecular frames. Each of those was broken at some point, and none of the component tests would have shown it. --- source/tests/pt_expt/model/test_unimol.py | 95 +++++++++++++++++++++++ 1 file changed, 95 insertions(+) diff --git a/source/tests/pt_expt/model/test_unimol.py b/source/tests/pt_expt/model/test_unimol.py index 2839386d26..0f0a2df913 100644 --- a/source/tests/pt_expt/model/test_unimol.py +++ b/source/tests/pt_expt/model/test_unimol.py @@ -8,6 +8,8 @@ """ import os +import shutil +import tempfile import unittest import numpy as np @@ -403,5 +405,98 @@ def test_gradients_flow(self) -> None: self.assertTrue(any(bool(torch.any(g != 0)) for g in grads)) +class TestUniMolTraining(unittest.TestCase): + """A run from a configuration file, which is how the feature is used. + + Everything between the configuration and the first step is exercised here: + the loss factory, the accessors the atomic model calls on any fitting, the + reader hook that produces the labels, and the absence of a cell on + molecular frames. Each of those was broken at some point and no unit test + would have shown it. + """ + + @classmethod + def setUpClass(cls) -> None: + import pickle + + import lmdb + + from deepmd.utils.unimol_data import ( + convert_unimol_lmdb, + ) + + cls.tmp = tempfile.mkdtemp() + cls.type_map = [*UNIMOL_ELEMENTS, "[MASK]"] + rng = np.random.default_rng(0) + molecules = [ + { + "atoms": ["C", "N", "O", "H", "C", "H"], + "coordinates": [rng.normal(size=(6, 3)).astype(np.float32) * 2.0], + "smi": "CNOC", + } + for _ in range(8) + ] + src = os.path.join(cls.tmp, "mol.lmdb") + env = lmdb.open(src, subdir=False, map_size=1 << 24) + with env.begin(write=True) as txn: + for i, mol in enumerate(molecules): + txn.put(f"{i}".encode(), pickle.dumps(mol)) + env.close() + cls.data = os.path.join(cls.tmp, "converted") + convert_unimol_lmdb(src, cls.data, type_map=cls.type_map, map_size=1 << 24) + + @classmethod + def tearDownClass(cls) -> None: + shutil.rmtree(cls.tmp, ignore_errors=True) + + def test_a_configuration_trains(self) -> None: + from deepmd.pt_expt.entrypoints.main import ( + get_trainer, + ) + + config = { + "model": { + "type_map": self.type_map, + "descriptor": { + "type": "unimol", + "encoder_layers": 1, + "encoder_embed_dim": 16, + "encoder_ffn_embed_dim": 32, + "encoder_attention_heads": 2, + "max_atoms": 16, + "seed": 1, + }, + "fitting_net": { + "type": "unimol_pretrain", + "attention_heads": 2, + "max_atoms": 16, + "seed": 1, + }, + }, + "learning_rate": {"type": "exp", "start_lr": 1e-3, "stop_lr": 1e-4}, + "loss": {"type": "unimol"}, + "training": { + # LMDB datasets are addressed with a string, not a list. + "training_data": {"systems": self.data, "batch_size": 2}, + "numb_steps": 4, + "seed": 1, + "disp_freq": 10, + "save_freq": 100, + "disp_file": os.path.join(self.tmp, "lcurve.out"), + "save_ckpt": os.path.join(self.tmp, "model.ckpt"), + }, + } + from deepmd.utils.compat import ( + update_deepmd_input, + ) + + trainer = get_trainer(normalize(update_deepmd_input(config, warning=False))) + self.assertEqual(type(trainer.model).__name__, "UniMolPretrainModel") + # The objective installed its own corruption on the dataset. + frame = trainer.training_data._reader[0] + self.assertIn("unimol_token_target", frame) + trainer.run() + + if __name__ == "__main__": unittest.main() From f3333986878f49a8d7be895c7cf45d4b87c87221 Mon Sep 17 00:00:00 2001 From: Duo <50307526+iProzd@users.noreply.github.com> Date: Sat, 12 Sep 2026 07:12:35 -0400 Subject: [PATCH 21/34] fix: say what the locality guard actually rules out Freezing a Uni-Mol model failed with "does not support periodic images", which is not what a user doing that was attempting: the export machinery feeds the ghost-atom layout with symbolic dimensions, not a periodic cell. The guard now names both cases, since the underlying requirement is the same one, that every atom be local, and the documentation says so too. --- deepmd/dpmodel/descriptor/unimol.py | 6 ++++-- doc/model/unimol.md | 6 ++++-- source/tests/common/dpmodel/test_unimol.py | 2 +- 3 files changed, 9 insertions(+), 5 deletions(-) diff --git a/deepmd/dpmodel/descriptor/unimol.py b/deepmd/dpmodel/descriptor/unimol.py index e9463a980d..7a715028fe 100644 --- a/deepmd/dpmodel/descriptor/unimol.py +++ b/deepmd/dpmodel/descriptor/unimol.py @@ -322,8 +322,10 @@ def build_tokens(self, coord_ext: Array, atype_ext: Array, nlist: Array) -> dict nall = coord.shape[1] if nall != nloc: raise ValueError( - "the unimol descriptor is molecular and does not support periodic " - f"images: got {nall} extended atoms for {nloc} local atoms" + "the unimol descriptor needs every atom to be local: it attends " + "over all pairs, so it supports neither periodic images nor the " + "ghost-atom layout that freezing and parallel evaluation assume " + f"(got {nall} extended atoms for {nloc} local atoms)" ) real_mask = xp.any(nlist >= 0, axis=-1) diff --git a/doc/model/unimol.md b/doc/model/unimol.md index c738a033d8..7451bfd7db 100644 --- a/doc/model/unimol.md +++ b/doc/model/unimol.md @@ -17,8 +17,10 @@ DFT-labelled data, and to give molecular property work a pretrained backbone. > Uni-Mol is **not** a potential energy surface model. It attends over every > atom pair with no cut-off and no smooth envelope, so it is not extensive, it > does not support periodic boundaries, and its forces are neither smooth nor -> conserved. The descriptor refuses frames that carry periodic images, and it -> is not available for molecular dynamics or frozen deployment. +> conserved. The descriptor refuses any frame whose atoms are not all local, +> which rules out periodic images and the ghost-atom layout that freezing and +> parallel evaluation assume, so it is not available for molecular dynamics or +> frozen deployment. ## Architecture diff --git a/source/tests/common/dpmodel/test_unimol.py b/source/tests/common/dpmodel/test_unimol.py index b7b3fb9ceb..3e24ea5fde 100644 --- a/source/tests/common/dpmodel/test_unimol.py +++ b/source/tests/common/dpmodel/test_unimol.py @@ -359,7 +359,7 @@ def test_call_drops_the_virtual_tokens(self) -> None: def test_rejects_periodic_and_tiny_frames(self) -> None: desc = self.build_descriptor() coord, atype, nlist = self.deepmd_inputs() - with self.assertRaisesRegex(ValueError, "periodic"): + with self.assertRaisesRegex(ValueError, "every atom to be local"): desc.forward_tokens(np.concatenate([coord, coord], axis=1), atype, nlist) lonely = np.full_like(nlist[:, :1, :], -1) with self.assertRaisesRegex(ValueError, "two real atoms"): From d0c192db1609026a06b7a78e38ef27469882f629 Mon Sep 17 00:00:00 2001 From: Duo <50307526+iProzd@users.noreply.github.com> Date: Sat, 12 Sep 2026 07:20:34 -0400 Subject: [PATCH 22/34] feat: expose the Adam epsilon Recipes carried over from other frameworks often assume a different epsilon than PyTorch's, and Uni-Mol is one of them: it pretrains with 1e-6 where the default here is 1e-8. The option defaults to the current value, so existing configurations are unaffected, and the example now carries upstream's optimizer values. This matches the value, not the placement: upstream's own Adam puts epsilon outside the bias correction, so the update differs slightly early in training whatever epsilon is configured. The documentation says so. --- deepmd/pt_expt/train/training.py | 2 ++ deepmd/utils/argcheck.py | 11 +++++++++++ doc/model/unimol.md | 5 ++++- examples/unimol/pretrain/input.json | 8 ++++++++ 4 files changed, 25 insertions(+), 1 deletion(-) diff --git a/deepmd/pt_expt/train/training.py b/deepmd/pt_expt/train/training.py index 4fcb763c15..381f75076a 100644 --- a/deepmd/pt_expt/train/training.py +++ b/deepmd/pt_expt/train/training.py @@ -2271,12 +2271,14 @@ def update_finetune_bias( float(optimizer_params["adam_beta2"]), ) weight_decay = float(optimizer_params["weight_decay"]) + adam_eps = float(optimizer_params.get("adam_eps", 1e-8)) if opt_type in ("Adam", "AdamW"): self.optimizer = self._create_optimizer( torch.optim.Adam if opt_type == "Adam" else torch.optim.AdamW, lr=initial_lr, betas=adam_betas, + eps=adam_eps, weight_decay=weight_decay, ) else: diff --git a/deepmd/utils/argcheck.py b/deepmd/utils/argcheck.py index 3647601ea8..a6a1938366 100644 --- a/deepmd/utils/argcheck.py +++ b/deepmd/utils/argcheck.py @@ -4491,6 +4491,10 @@ def _check_lr_args(data: dict[str, Any]) -> bool: def optimizer_adam() -> list[Argument]: doc_adam_beta1 = "Adam beta1 coefficient for first moment decay." doc_adam_beta2 = "Adam beta2 coefficient for second moment decay." + doc_adam_eps = ( + "Adam epsilon, added for numerical stability. The default is PyTorch's own; " + "recipes carried over from other frameworks sometimes assume a different one." + ) doc_weight_decay = ( "Weight decay coefficient for Adam, applied as an L2 penalty to gradients." ) @@ -4509,6 +4513,13 @@ def optimizer_adam() -> list[Argument]: default=0.999, doc=supported_backends("tf", "pt", "pd", "tf2") + doc_adam_beta2, ), + Argument( + "adam_eps", + float, + optional=True, + default=1e-8, + doc=supported_backends("pt_expt") + doc_adam_eps, + ), Argument( "weight_decay", float, diff --git a/doc/model/unimol.md b/doc/model/unimol.md index 7451bfd7db..9f794f5e77 100644 --- a/doc/model/unimol.md +++ b/doc/model/unimol.md @@ -118,7 +118,10 @@ floor at about 1e-7 relative on the whole objective: reproduced. Training trajectories cannot be reproduced exactly in any case: upstream -pretrained a pure fp16 model with fused kernels and its own Adam variant. +pretrained a pure fp16 model with fused kernels and its own Adam variant, which +places epsilon differently from PyTorch's. The example carries upstream's +optimizer values, including `adam_eps`, so the recipe matches even though the +trajectory cannot. Uni-Mol uses the exact error-function GELU, available here as `gelu_erf`. deepmd's `gelu` and `gelu_tf` are the tanh approximation, which differs by up diff --git a/examples/unimol/pretrain/input.json b/examples/unimol/pretrain/input.json index 6e0a72c809..c7c3bd326c 100644 --- a/examples/unimol/pretrain/input.json +++ b/examples/unimol/pretrain/input.json @@ -95,5 +95,13 @@ "disp_file": "lcurve.out", "disp_freq": 100, "save_freq": 10000 + }, + "optimizer": { + "type": "Adam", + "adam_beta1": 0.9, + "adam_beta2": 0.99, + "adam_eps": 1e-06, + "weight_decay": 0.0001, + "_comment": "upstream's recipe; its own Adam places eps differently, so this matches the value but not the placement" } } From 6ae1a1e28225e74b7a9f34897cd4ba0694afffea Mon Sep 17 00:00:00 2001 From: Duo <50307526+iProzd@users.noreply.github.com> Date: Sat, 12 Sep 2026 07:34:28 -0400 Subject: [PATCH 23/34] fix(dpmodel): train the token embedding and the distance basis An adversarial review of this branch found that the token embedding and all four Gaussian basis tables never received a gradient. They were assigned as bare numpy arrays, and the PyTorch-Exportable wrapper turns a bare array into a buffer, not a parameter: 2,701 values in a small model, and the whole element-pair affine table in a real one, sat frozen at their initial values while the rest of the network trained. Inference and checkpoint parity were unaffected, which is why the parity tests did not catch it. They are layers now, which is how deepmd expresses a trained array. The same review found that every module was handed the same seed. Since each layer seeds its own generator, two layers of the same shape drew identical numbers: all fifteen encoder blocks started bitwise identical, and so did several head pairs. Seeds are split with child_seed, as everywhere else in deepmd. With a seed set, the layers now differ and a from-scratch run no longer starts from a degenerate state. Parity with the released weights is unchanged: the backbone still lands at 3.7e-7 relative and the five-term objective at 3.4e-7. --- deepmd/dpmodel/atomic_model/__init__.py | 6 +- deepmd/dpmodel/descriptor/__init__.py | 6 +- deepmd/dpmodel/descriptor/unimol.py | 37 +++--- .../dpmodel/descriptor/unimol_nn/__init__.py | 10 +- .../dpmodel/descriptor/unimol_nn/encoder.py | 117 +++++++++++------- deepmd/dpmodel/descriptor/unimol_nn/heads.py | 25 +++- deepmd/dpmodel/fitting/unimol_pretrain.py | 17 ++- deepmd/dpmodel/model/__init__.py | 6 +- deepmd/dpmodel/utils/network.py | 8 +- deepmd/pt_expt/descriptor/__init__.py | 1 - deepmd/pt_expt/descriptor/unimol.py | 4 +- deepmd/pt_expt/fitting/__init__.py | 1 - deepmd/pt_expt/loss/__init__.py | 1 - deepmd/pt_expt/model/__init__.py | 1 - deepmd/pt_expt/train/training.py | 2 +- deepmd/utils/unimol_checkpoint.py | 17 ++- doc/model/unimol.md | 14 +-- source/tests/common/dpmodel/test_unimol.py | 6 +- .../tests/common/dpmodel/test_unimol_data.py | 6 +- source/tests/pt_expt/model/test_unimol.py | 8 +- 20 files changed, 173 insertions(+), 120 deletions(-) diff --git a/deepmd/dpmodel/atomic_model/__init__.py b/deepmd/dpmodel/atomic_model/__init__.py index c0eec82462..c55eead5f3 100644 --- a/deepmd/dpmodel/atomic_model/__init__.py +++ b/deepmd/dpmodel/atomic_model/__init__.py @@ -23,9 +23,6 @@ from .dos_atomic_model import ( DPDOSAtomicModel, ) -from .unimol_atomic_model import ( - DPUniMolAtomicModel, -) from .dp_atomic_model import ( DPAtomicModel, ) @@ -48,6 +45,9 @@ from .property_atomic_model import ( DPPropertyAtomicModel, ) +from .unimol_atomic_model import ( + DPUniMolAtomicModel, +) __all__ = [ "BaseAtomicModel", diff --git a/deepmd/dpmodel/descriptor/__init__.py b/deepmd/dpmodel/descriptor/__init__.py index cbc87e5552..9ba61078b0 100644 --- a/deepmd/dpmodel/descriptor/__init__.py +++ b/deepmd/dpmodel/descriptor/__init__.py @@ -26,9 +26,6 @@ from .se_e2_a import ( DescrptSeA, ) -from .unimol import ( - DescrptUniMol, -) from .se_r import ( DescrptSeR, ) @@ -38,6 +35,9 @@ from .se_t_tebd import ( DescrptSeTTebd, ) +from .unimol import ( + DescrptUniMol, +) __all__ = [ "DescrptDPA1", diff --git a/deepmd/dpmodel/descriptor/unimol.py b/deepmd/dpmodel/descriptor/unimol.py index 7a715028fe..67c74bcb8b 100644 --- a/deepmd/dpmodel/descriptor/unimol.py +++ b/deepmd/dpmodel/descriptor/unimol.py @@ -21,7 +21,6 @@ ) from deepmd.dpmodel.common import ( NativeOP, - to_numpy_array, ) from deepmd.dpmodel.descriptor.base_descriptor import ( BaseDescriptor, @@ -34,6 +33,9 @@ from deepmd.dpmodel.utils.network import ( NativeLayer, ) +from deepmd.dpmodel.utils.seed import ( + child_seed, +) from deepmd.utils.version import ( check_version_compatibility, ) @@ -169,24 +171,29 @@ def __init__( [token_of.get(sym, self.unk_idx) for sym in self.type_map], dtype=np.int64 ) - rng = np.random.default_rng(seed if isinstance(seed, int) else None) - self.embed_tokens = rng.normal( - 0.0, 0.02, size=(self.ntokens, encoder_embed_dim) + # The token embedding is trained, so it is a layer rather than a bare + # array: a bare array becomes a buffer on the torch backends, and a + # buffer never receives a gradient. + self.embed_tokens = NativeLayer( + self.ntokens, + encoder_embed_dim, + bias=False, + precision=precision, + seed=child_seed(seed, 0), ) - self.embed_tokens[self.pad_idx, :] = 0.0 self.gbf = GaussianLayer( gaussian_kernels, self.ntokens**2, single_precision_basis=single_precision_basis, precision=precision, - seed=seed, + seed=child_seed(seed, 1), ) self.gbf_proj = NonLinearHead( gaussian_kernels, encoder_attention_heads, activation_function, precision=precision, - seed=seed, + seed=child_seed(seed, 2), ) self.encoder = TransformerEncoderWithPair( encoder_layers=encoder_layers, @@ -201,7 +208,7 @@ def __init__( activation_function=activation_function, no_final_head_layer_norm=no_final_head_layer_norm, precision=precision, - seed=seed, + seed=child_seed(seed, 3), ) # ------------------------------------------------------------------ @@ -398,7 +405,9 @@ def forward_tokens(self, coord_ext: Array, atype_ext: Array, nlist: Array) -> di tokens, coord, padding_mask = seq["tokens"], seq["coord"], seq["padding_mask"] nf, nt = tokens.shape - embed = xp.asarray(self.embed_tokens, device=array_api_compat.device(coord_ext)) + embed = xp.asarray( + self.embed_tokens.w, device=array_api_compat.device(coord_ext) + ) emb = xp.take(embed, xp.reshape(tokens, (-1,)), axis=0) emb = xp.reshape(emb, (nf, nt, self.encoder_embed_dim)) emb = xp.astype(emb, coord.dtype) @@ -480,7 +489,7 @@ def serialize(self) -> dict: "gaussian_kernels": self.gaussian_kernels, "precision": self.precision, "type_map_tokens": self.vocabulary, - "@variables": {"embed_tokens": to_numpy_array(self.embed_tokens)}, + "embed_tokens": self.embed_tokens.serialize(), "gbf": self.gbf.serialize(), "gbf_proj": self.gbf_proj.serialize(), "encoder": self.encoder.serialize(), @@ -493,17 +502,13 @@ def deserialize(cls, data: dict) -> "DescrptUniMol": check_version_compatibility(data.pop("@version"), 1, 1) data.pop("@class", None) data.pop("type", None) - variables = data.pop("@variables") + embed_tokens = data.pop("embed_tokens") gbf = data.pop("gbf") gbf_proj = data.pop("gbf_proj") encoder = data.pop("encoder") obj = cls(**data) - obj.embed_tokens = variables["embed_tokens"] + obj.embed_tokens = NativeLayer.deserialize(embed_tokens) obj.gbf = GaussianLayer.deserialize(gbf) obj.gbf_proj = NonLinearHead.deserialize(gbf_proj) obj.encoder = TransformerEncoderWithPair.deserialize(encoder) return obj - - -# Keep a reference so linters see the import is used by type hints above. -_ = NativeLayer diff --git a/deepmd/dpmodel/descriptor/unimol_nn/__init__.py b/deepmd/dpmodel/descriptor/unimol_nn/__init__.py index bf0c9535a5..06a0a8de85 100644 --- a/deepmd/dpmodel/descriptor/unimol_nn/__init__.py +++ b/deepmd/dpmodel/descriptor/unimol_nn/__init__.py @@ -1,11 +1,6 @@ # SPDX-License-Identifier: LGPL-3.0-or-later """Neural-network building blocks of the Uni-Mol v1 backbone.""" -from .heads import ( - DistanceHead, - MaskLMHead, - coord_update, -) from .encoder import ( GaussianLayer, NonLinearHead, @@ -13,6 +8,11 @@ TransformerEncoderLayer, TransformerEncoderWithPair, ) +from .heads import ( + DistanceHead, + MaskLMHead, + coord_update, +) __all__ = [ "DistanceHead", diff --git a/deepmd/dpmodel/descriptor/unimol_nn/encoder.py b/deepmd/dpmodel/descriptor/unimol_nn/encoder.py index b6dbe11e17..7e7fddf881 100644 --- a/deepmd/dpmodel/descriptor/unimol_nn/encoder.py +++ b/deepmd/dpmodel/descriptor/unimol_nn/encoder.py @@ -39,6 +39,9 @@ LayerNorm, NativeLayer, ) +from deepmd.dpmodel.utils.seed import ( + child_seed, +) # Uni-Mol's Gaussian basis uses a truncated pi (unimol/models/unimol.py:393-397). # Keeping it is required for bitwise agreement with the released weights. @@ -132,10 +135,14 @@ def __init__( hidden, activation_function=activation_function, precision=precision, - seed=seed, + seed=child_seed(seed, 0), ) self.linear2 = NativeLayer( - hidden, out_dim, activation_function=None, precision=precision, seed=seed + hidden, + out_dim, + activation_function=None, + precision=precision, + seed=child_seed(seed, 1), ) def call(self, x): # noqa: ANN001, ANN201 @@ -178,8 +185,6 @@ def __init__( precision: str = "float64", seed: int | list[int] | None = None, ) -> None: - import numpy as np - self.k = k self.edge_types = edge_types # Upstream evaluates the basis in fp32 because it pretrains an fp16 @@ -187,29 +192,35 @@ def __init__( # the released weights reproduce; set False to keep the working dtype. self.single_precision_basis = single_precision_basis self.precision = precision - rng = np.random.default_rng(seed if isinstance(seed, int) else None) - # Upstream initialises means/stds as U(0, 3) and mul/bias as 1/0, but - # init_bert_params then overwrites all four with N(0, 0.02); see - # unicore/modules/transformer_encoder.py:16 and the note in the spec. - self.means = rng.uniform(0.0, 3.0, size=(1, k)) - self.stds = rng.uniform(0.0, 3.0, size=(1, k)) - self.mul = np.ones((edge_types, 1)) - self.bias = np.zeros((edge_types, 1)) + # All four tables are trained. They are layers rather than bare arrays + # because a bare array becomes a buffer on the torch backends, and a + # buffer never receives a gradient: the whole geometry pathway would + # sit frozen at its initial values. Each gets its own seed, or they + # would draw identical numbers wherever their shapes agree. + self.means = NativeLayer( + 1, k, bias=False, precision=precision, seed=child_seed(seed, 0) + ) + self.stds = NativeLayer( + 1, k, bias=False, precision=precision, seed=child_seed(seed, 1) + ) + self.mul = NativeLayer( + edge_types, 1, bias=False, precision=precision, seed=child_seed(seed, 2) + ) + self.bias = NativeLayer( + edge_types, 1, bias=False, precision=precision, seed=child_seed(seed, 3) + ) def call(self, dist, edge_type): # noqa: ANN001, ANN201 """Expand ``dist`` (nf x nt x nt) into ``k`` Gaussians per atom pair.""" xp = array_api_compat.array_namespace(dist) dev = array_api_compat.device(dist) + flat = xp.reshape(edge_type, (-1,)) mul = xp.reshape( - xp.take( - xp.asarray(self.mul, device=dev), xp.reshape(edge_type, (-1,)), axis=0 - ), + xp.take(xp.asarray(self.mul.w, device=dev), flat, axis=0), (*edge_type.shape, 1), ) bias = xp.reshape( - xp.take( - xp.asarray(self.bias, device=dev), xp.reshape(edge_type, (-1,)), axis=0 - ), + xp.take(xp.asarray(self.bias.w, device=dev), flat, axis=0), (*edge_type.shape, 1), ) mul = xp.astype(mul, dist.dtype) @@ -218,10 +229,10 @@ def call(self, dist, edge_type): # noqa: ANN001, ANN201 x = xp.repeat(x, self.k, axis=-1) work = xp.float32 if self.single_precision_basis else x.dtype x = xp.astype(x, work) - mean = xp.astype(xp.reshape(xp.asarray(self.means, device=dev), (-1,)), work) + mean = xp.astype(xp.reshape(xp.asarray(self.means.w, device=dev), (-1,)), work) std = ( xp.abs( - xp.astype(xp.reshape(xp.asarray(self.stds, device=dev), (-1,)), work) + xp.astype(xp.reshape(xp.asarray(self.stds.w, device=dev), (-1,)), work) ) + 1e-5 ) @@ -230,30 +241,26 @@ def call(self, dist, edge_type): # noqa: ANN001, ANN201 return xp.astype(out, dist.dtype) def serialize(self) -> dict: - from deepmd.dpmodel.common import ( - to_numpy_array, - ) - + """Serialize the basis.""" return { "k": self.k, "edge_types": self.edge_types, "single_precision_basis": self.single_precision_basis, "precision": self.precision, - "@variables": { - "means": to_numpy_array(self.means), - "stds": to_numpy_array(self.stds), - "mul": to_numpy_array(self.mul), - "bias": to_numpy_array(self.bias), - }, + "means": self.means.serialize(), + "stds": self.stds.serialize(), + "mul": self.mul.serialize(), + "bias": self.bias.serialize(), } @classmethod def deserialize(cls, data: dict) -> "GaussianLayer": + """Deserialize the basis.""" data = data.copy() - variables = data.pop("@variables") + tables = {key: data.pop(key) for key in ("means", "stds", "mul", "bias")} obj = cls(**data) - for key, value in variables.items(): - setattr(obj, key, value) + for key, value in tables.items(): + setattr(obj, key, NativeLayer.deserialize(value)) return obj @@ -283,10 +290,18 @@ def __init__( self.scaling = (self.head_dim * scaling_factor) ** -0.5 self.precision = precision self.in_proj = NativeLayer( - embed_dim, embed_dim * 3, bias=bias, precision=precision, seed=seed + embed_dim, + embed_dim * 3, + bias=bias, + precision=precision, + seed=child_seed(seed, 0), ) self.out_proj = NativeLayer( - embed_dim, embed_dim, bias=bias, precision=precision, seed=seed + embed_dim, + embed_dim, + bias=bias, + precision=precision, + seed=child_seed(seed, 1), ) def call(self, query, attn_bias, training: bool = False): # noqa: ANN001, ANN201 @@ -385,24 +400,28 @@ def __init__( attention_heads, dropout=attention_dropout, precision=precision, - seed=seed, + seed=child_seed(seed, 0), + ) + self.self_attn_layer_norm = LayerNorm( + embed_dim, precision=precision, seed=child_seed(seed, 1) ) - self.self_attn_layer_norm = LayerNorm(embed_dim, precision=precision, seed=seed) self.fc1 = NativeLayer( embed_dim, ffn_embed_dim, activation_function=activation_function, precision=precision, - seed=seed, + seed=child_seed(seed, 2), ) self.fc2 = NativeLayer( ffn_embed_dim, embed_dim, activation_function=None, precision=precision, - seed=seed, + seed=child_seed(seed, 3), + ) + self.final_layer_norm = LayerNorm( + embed_dim, precision=precision, seed=child_seed(seed, 4) ) - self.final_layer_norm = LayerNorm(embed_dim, precision=precision, seed=seed) def call(self, x, attn_bias, training: bool = False): # noqa: ANN001, ANN201 residual = x @@ -496,12 +515,18 @@ def __init__( self.activation_function = activation_function self.no_final_head_layer_norm = no_final_head_layer_norm self.precision = precision - self.emb_layer_norm = LayerNorm(embed_dim, precision=precision, seed=seed) - self.final_layer_norm = LayerNorm(embed_dim, precision=precision, seed=seed) + self.emb_layer_norm = LayerNorm( + embed_dim, precision=precision, seed=child_seed(seed, 0) + ) + self.final_layer_norm = LayerNorm( + embed_dim, precision=precision, seed=child_seed(seed, 1) + ) self.final_head_layer_norm = ( None if no_final_head_layer_norm - else LayerNorm(attention_heads, precision=precision, seed=seed) + else LayerNorm( + attention_heads, precision=precision, seed=child_seed(seed, 2) + ) ) self.layers = [ TransformerEncoderLayer( @@ -513,9 +538,11 @@ def __init__( activation_dropout=activation_dropout, activation_function=activation_function, precision=precision, - seed=seed, + # Each block draws its own numbers; sharing one seed would make + # every layer start bitwise identical. + seed=child_seed(seed, 3 + index), ) - for _ in range(encoder_layers) + for index in range(encoder_layers) ] def call(self, emb, attn_mask, padding_mask, training: bool = False): # noqa: ANN001, ANN201 diff --git a/deepmd/dpmodel/descriptor/unimol_nn/heads.py b/deepmd/dpmodel/descriptor/unimol_nn/heads.py index c4af38fd1f..6c1d5b6763 100644 --- a/deepmd/dpmodel/descriptor/unimol_nn/heads.py +++ b/deepmd/dpmodel/descriptor/unimol_nn/heads.py @@ -23,6 +23,9 @@ LayerNorm, NativeLayer, ) +from deepmd.dpmodel.utils.seed import ( + child_seed, +) __all__ = ["DistanceHead", "MaskLMHead", "coord_update"] @@ -52,11 +55,17 @@ def __init__( embed_dim, activation_function=activation_function, precision=precision, - seed=seed, + seed=child_seed(seed, 0), + ) + self.layer_norm = LayerNorm( + embed_dim, precision=precision, seed=child_seed(seed, 1) ) - self.layer_norm = LayerNorm(embed_dim, precision=precision, seed=seed) self.out_proj = NativeLayer( - embed_dim, output_dim, bias=True, precision=precision, seed=seed + embed_dim, + output_dim, + bias=True, + precision=precision, + seed=child_seed(seed, 2), ) def call(self, features, masked_tokens=None): # noqa: ANN001, ANN201 @@ -124,10 +133,14 @@ def __init__( heads, activation_function=activation_function, precision=precision, - seed=seed, + seed=child_seed(seed, 0), + ) + self.layer_norm = LayerNorm( + heads, precision=precision, seed=child_seed(seed, 1) + ) + self.out_proj = NativeLayer( + heads, 1, bias=True, precision=precision, seed=child_seed(seed, 2) ) - self.layer_norm = LayerNorm(heads, precision=precision, seed=seed) - self.out_proj = NativeLayer(heads, 1, bias=True, precision=precision, seed=seed) def call(self, pair_rep): # noqa: ANN001, ANN201 """Map nf x nt x nt x heads onto a symmetric nf x nt x nt matrix.""" diff --git a/deepmd/dpmodel/fitting/unimol_pretrain.py b/deepmd/dpmodel/fitting/unimol_pretrain.py index 6887ee3215..82c654f1ae 100644 --- a/deepmd/dpmodel/fitting/unimol_pretrain.py +++ b/deepmd/dpmodel/fitting/unimol_pretrain.py @@ -40,6 +40,9 @@ FittingOutputDef, OutputVariableDef, ) +from deepmd.dpmodel.utils.seed import ( + child_seed, +) from deepmd.utils.version import ( check_version_compatibility, ) @@ -102,7 +105,13 @@ def __init__( self.activation_function = activation_function self.precision = precision self.lm_head = ( - MaskLMHead(dim_descrpt, n_token, activation_function, precision, seed) + MaskLMHead( + dim_descrpt, + n_token, + activation_function, + precision, + child_seed(seed, 0), + ) if mask_token_head else None ) @@ -113,13 +122,15 @@ def __init__( activation_function, hidden=attention_heads, precision=precision, - seed=seed, + seed=child_seed(seed, 1), ) if coord_head else None ) self.dist_head = ( - DistanceHead(attention_heads, activation_function, precision, seed) + DistanceHead( + attention_heads, activation_function, precision, child_seed(seed, 2) + ) if dist_head else None ) diff --git a/deepmd/dpmodel/model/__init__.py b/deepmd/dpmodel/model/__init__.py index 3e8b6a3c92..ada9aa58e0 100644 --- a/deepmd/dpmodel/model/__init__.py +++ b/deepmd/dpmodel/model/__init__.py @@ -42,15 +42,15 @@ from .polar_model import ( PolarModel, ) -from .unimol_pretrain_model import ( - UniMolPretrainModel, -) from .property_model import ( PropertyModel, ) from .spin_model import ( SpinModel, ) +from .unimol_pretrain_model import ( + UniMolPretrainModel, +) __all__ = [ "DOSModel", diff --git a/deepmd/dpmodel/utils/network.py b/deepmd/dpmodel/utils/network.py index 0fbda20a57..80a7e591ac 100644 --- a/deepmd/dpmodel/utils/network.py +++ b/deepmd/dpmodel/utils/network.py @@ -25,9 +25,9 @@ Array, xp_add_at, xp_bincount, + xp_erf, xp_setitem_at, xp_sigmoid, - xp_erf, ) from deepmd.dpmodel.common import ( to_numpy_array, @@ -358,11 +358,7 @@ def fn(x): # noqa: ANN001, ANN202 xp = array_api_compat.array_namespace(x) # Exact GELU, x * Phi(x). deepmd's "gelu"/"gelu_tf" are the tanh # approximation, which differs from this by up to 4.7e-4 per element. - return ( - 0.5 - * x - * (1 + xp_erf(x / xp.sqrt(xp.asarray(2.0, dtype=x.dtype)))) - ) + return 0.5 * x * (1 + xp_erf(x / xp.sqrt(xp.asarray(2.0, dtype=x.dtype)))) return fn elif activation_function == "relu6": diff --git a/deepmd/pt_expt/descriptor/__init__.py b/deepmd/pt_expt/descriptor/__init__.py index 51f435370e..672c65c77c 100644 --- a/deepmd/pt_expt/descriptor/__init__.py +++ b/deepmd/pt_expt/descriptor/__init__.py @@ -46,7 +46,6 @@ from .se_t_tebd import ( DescrptSeTTebd, ) - from .unimol import ( DescrptUniMol, ) diff --git a/deepmd/pt_expt/descriptor/unimol.py b/deepmd/pt_expt/descriptor/unimol.py index c65bb6fd4f..abad236ba2 100644 --- a/deepmd/pt_expt/descriptor/unimol.py +++ b/deepmd/pt_expt/descriptor/unimol.py @@ -1,8 +1,6 @@ # SPDX-License-Identifier: LGPL-3.0-or-later -from deepmd.dpmodel.descriptor.unimol import ( - DescrptUniMol as DescrptUniMolDP, -) +from deepmd.dpmodel.descriptor.unimol import DescrptUniMol as DescrptUniMolDP from deepmd.pt_expt.common import ( torch_module, ) diff --git a/deepmd/pt_expt/fitting/__init__.py b/deepmd/pt_expt/fitting/__init__.py index c2838272fe..63bcc6c2ce 100644 --- a/deepmd/pt_expt/fitting/__init__.py +++ b/deepmd/pt_expt/fitting/__init__.py @@ -23,7 +23,6 @@ from .property_fitting import ( PropertyFittingNet, ) - from .unimol_pretrain import ( UniMolPretrainFitting, ) diff --git a/deepmd/pt_expt/loss/__init__.py b/deepmd/pt_expt/loss/__init__.py index fdff99e331..57e7e4eee9 100644 --- a/deepmd/pt_expt/loss/__init__.py +++ b/deepmd/pt_expt/loss/__init__.py @@ -14,7 +14,6 @@ from deepmd.pt_expt.loss.tensor import ( TensorLoss, ) - from deepmd.pt_expt.loss.unimol import ( UniMolLoss, ) diff --git a/deepmd/pt_expt/model/__init__.py b/deepmd/pt_expt/model/__init__.py index 0fd3cbde09..22a1c2e7db 100644 --- a/deepmd/pt_expt/model/__init__.py +++ b/deepmd/pt_expt/model/__init__.py @@ -42,7 +42,6 @@ from .spin_ener_model import ( SpinEnergyModel, ) - from .unimol_pretrain_model import ( UniMolPretrainModel, ) diff --git a/deepmd/pt_expt/train/training.py b/deepmd/pt_expt/train/training.py index 381f75076a..06fb1d398f 100644 --- a/deepmd/pt_expt/train/training.py +++ b/deepmd/pt_expt/train/training.py @@ -91,12 +91,12 @@ ) from deepmd.pt.utils.compile_compat import trace_pad_dim as _trace_pad_dim from deepmd.pt_expt.loss import ( - UniMolLoss, DOSLoss, EnergyLoss, EnergySpinLoss, PropertyLoss, TensorLoss, + UniMolLoss, ) from deepmd.pt_expt.model import ( get_model, diff --git a/deepmd/utils/unimol_checkpoint.py b/deepmd/utils/unimol_checkpoint.py index ea9f240579..71db08203f 100644 --- a/deepmd/utils/unimol_checkpoint.py +++ b/deepmd/utils/unimol_checkpoint.py @@ -95,6 +95,11 @@ def split_unimol_state_dict( return backbone, heads +def _set_table(layer: Any, value: np.ndarray) -> None: + """Assign a lookup table, which is stored as a layer weight.""" + layer.w = np.ascontiguousarray(value).astype(layer.w.dtype) + + def _set_linear(layer: Any, state: dict[str, np.ndarray], prefix: str) -> None: """Assign a torch Linear onto a deepmd layer, transposing the weight.""" layer.w = np.ascontiguousarray(state[prefix + ".weight"].T).astype(layer.w.dtype) @@ -113,12 +118,12 @@ def apply_unimol_backbone( descriptor: "DescrptUniMol", state_dict: dict[str, np.ndarray] ) -> None: """Load backbone parameters into a descriptor, in place.""" - dtype = descriptor.embed_tokens.dtype - descriptor.embed_tokens = state_dict["embed_tokens.weight"].astype(dtype) - descriptor.gbf.means = state_dict["gbf.means.weight"].astype(dtype) - descriptor.gbf.stds = state_dict["gbf.stds.weight"].astype(dtype) - descriptor.gbf.mul = state_dict["gbf.mul.weight"].astype(dtype) - descriptor.gbf.bias = state_dict["gbf.bias.weight"].astype(dtype) + # The embedding and the four basis tables are layers, so their values live + # in ``w``; they carry no transpose because they are lookup tables rather + # than projections. + _set_table(descriptor.embed_tokens, state_dict["embed_tokens.weight"]) + for name in ("means", "stds", "mul", "bias"): + _set_table(getattr(descriptor.gbf, name), state_dict[f"gbf.{name}.weight"]) _set_linear(descriptor.gbf_proj.linear1, state_dict, "gbf_proj.linear1") _set_linear(descriptor.gbf_proj.linear2, state_dict, "gbf_proj.linear2") diff --git a/doc/model/unimol.md b/doc/model/unimol.md index 9f794f5e77..a6bdfb9483 100644 --- a/doc/model/unimol.md +++ b/doc/model/unimol.md @@ -42,13 +42,13 @@ become a random element and 5% are left alone; all three are predicted. The masked and randomly replaced atoms also have uniform noise of ±1 Å added to each coordinate component. Five terms are minimized: -| Term | Weight | What it predicts | -| --- | --- | --- | -| element | 1 | the true element of every selected atom | -| coordinate | 5 | the clean coordinates, through the pair channel | -| distance | 10 | the clean pairwise distances | -| node norm | 0.01 | keeps node norms near $\sqrt{512}$ | -| pair-delta norm | 0.01 | keeps pair-delta norms near $\sqrt{64}$ | +| Term | Weight | What it predicts | +| --------------- | ------ | ----------------------------------------------- | +| element | 1 | the true element of every selected atom | +| coordinate | 5 | the clean coordinates, through the pair channel | +| distance | 10 | the clean pairwise distances | +| node norm | 0.01 | keeps node norms near $\sqrt{512}$ | +| pair-delta norm | 0.01 | keeps pair-delta norms near $\sqrt{64}$ | Corruption happens in the data pipeline rather than inside the loss, which is both what upstream does and what the PyTorch-Exportable backend requires, since diff --git a/source/tests/common/dpmodel/test_unimol.py b/source/tests/common/dpmodel/test_unimol.py index 3e24ea5fde..6fcade3b7c 100644 --- a/source/tests/common/dpmodel/test_unimol.py +++ b/source/tests/common/dpmodel/test_unimol.py @@ -111,9 +111,9 @@ def build_descriptor(self, **overrides): kwargs.update(overrides) desc = DescrptUniMol(**kwargs) w = self.weights - desc.embed_tokens = w["embed_tokens.weight"].copy() + desc.embed_tokens.w = w["embed_tokens.weight"].copy() for key in ("means", "stds", "mul", "bias"): - setattr(desc.gbf, key, w[f"gbf.{key}.weight"].copy()) + getattr(desc.gbf, key).w = w[f"gbf.{key}.weight"].copy() _set_linear(desc.gbf_proj.linear1, w, "gbf_proj.linear1") _set_linear(desc.gbf_proj.linear2, w, "gbf_proj.linear2") enc = self.build_encoder() @@ -257,7 +257,7 @@ def test_encoder_matches_upstream_in_fp64(self) -> None: def test_gaussian_basis_and_projection(self) -> None: gbf = GaussianLayer(SMALL["k"], SMALL["vocab"] ** 2) for key in ("means", "stds", "mul", "bias"): - setattr(gbf, key, self.weights[f"gbf.{key}.weight"].copy()) + getattr(gbf, key).w = self.weights[f"gbf.{key}.weight"].copy() proj = NonLinearHead(SMALL["k"], SMALL["heads"], "gelu_erf", hidden=SMALL["k"]) _set_linear(proj.linear1, self.weights, "gbf_proj.linear1") _set_linear(proj.linear2, self.weights, "gbf_proj.linear2") diff --git a/source/tests/common/dpmodel/test_unimol_data.py b/source/tests/common/dpmodel/test_unimol_data.py index e4af8b75be..38733c3c1e 100644 --- a/source/tests/common/dpmodel/test_unimol_data.py +++ b/source/tests/common/dpmodel/test_unimol_data.py @@ -12,12 +12,12 @@ from deepmd.dpmodel.descriptor.unimol import ( UNIMOL_ELEMENTS, ) -from deepmd.dpmodel.utils.lmdb_data import ( - LmdbDataReader, -) from deepmd.dpmodel.loss.unimol import ( UniMolLoss, ) +from deepmd.dpmodel.utils.lmdb_data import ( + LmdbDataReader, +) from deepmd.dpmodel.utils.unimol_transform import ( make_unimol_data_transform, ) diff --git a/source/tests/pt_expt/model/test_unimol.py b/source/tests/pt_expt/model/test_unimol.py index 0f0a2df913..5f371a194f 100644 --- a/source/tests/pt_expt/model/test_unimol.py +++ b/source/tests/pt_expt/model/test_unimol.py @@ -17,7 +17,9 @@ from deepmd.dpmodel.descriptor.unimol import ( UNIMOL_ELEMENTS, - DescrptUniMol as DescrptUniMolDP, +) +from deepmd.dpmodel.descriptor.unimol import DescrptUniMol as DescrptUniMolDP +from deepmd.dpmodel.descriptor.unimol import ( unimol_vocabulary, ) from deepmd.dpmodel.fitting.unimol_pretrain import ( @@ -127,9 +129,9 @@ def ln(layer, prefix): put(layer, "w", w[prefix + ".weight"]) put(layer, "b", w[prefix + ".bias"]) - put(descriptor, "embed_tokens", w["embed_tokens.weight"]) + put(descriptor.embed_tokens, "w", w["embed_tokens.weight"]) for key in ("means", "stds", "mul", "bias"): - put(descriptor.gbf, key, w[f"gbf.{key}.weight"]) + put(getattr(descriptor.gbf, key), "w", w[f"gbf.{key}.weight"]) lin( descriptor.gbf_proj.linear1, "gbf_proj.linear1.weight", From 7fbcd2e3fec14e54032805751b5372d4cee3d98a Mon Sep 17 00:00:00 2001 From: Duo <50307526+iProzd@users.noreply.github.com> Date: Sat, 12 Sep 2026 07:45:57 -0400 Subject: [PATCH 24/34] fix: corruption varies per epoch, and cropping moves to the converter Three defects the review found in the data path. The corruption was frozen: the objective built its transform once with the default epoch, so every frame was masked identically on every pass. Upstream draws afresh each epoch. The transform now counts how often it has seen each frame and uses that count where upstream uses the epoch, so a molecule is corrupted differently each time it comes round. Passing an epoch explicitly is refused, since it is no longer a build-time constant. Cropping moved out of the transform. A frame's atom count and the batch layout are settled before any per-frame transform runs, so shortening a frame there would leave it inconsistent with the batch it belongs to. The converter applies the size cap instead, which is also where upstream's other preprocessing lives. An element the model's type_map cannot express is no longer drawn as a random replacement. It used to be mapped onto [MASK], which quietly turned a random-element atom into a masked one and skewed the 90/5/5 split. With the full element set nothing is excluded and the distribution is upstream's. Also: the descriptor now honours its configured precision instead of silently working in the input dtype; the distance head refuses a frame wider than the width it declares rather than returning a wider array than its output definition; TensorFlow's exact GELU computes its square root in the tensor dtype rather than rounding it through fp32; and every array construction states its dtype, which the repository's pylint gate requires. The golden archive is regenerated with two molecules instead of four, which brings it under the repository's file-size limit while keeping frames of different lengths. pre-commit now passes on every changed file. --- deepmd/dpmodel/descriptor/unimol.py | 4 +- deepmd/dpmodel/fitting/unimol_pretrain.py | 7 ++ deepmd/dpmodel/loss/unimol.py | 5 +- deepmd/dpmodel/utils/unimol_transform.py | 68 ++++++++++++------ deepmd/tf/common.py | 2 +- deepmd/utils/unimol_data.py | 17 +++++ source/tests/common/dpmodel/test_unimol.py | 2 +- .../tests/common/dpmodel/test_unimol_data.py | 4 +- .../tests/common/dpmodel/unimol_v1_golden.npz | Bin 1413467 -> 766324 bytes source/tests/pt_expt/model/test_unimol.py | 19 +++-- 10 files changed, 91 insertions(+), 37 deletions(-) diff --git a/deepmd/dpmodel/descriptor/unimol.py b/deepmd/dpmodel/descriptor/unimol.py index 67c74bcb8b..b5a35c88f2 100644 --- a/deepmd/dpmodel/descriptor/unimol.py +++ b/deepmd/dpmodel/descriptor/unimol.py @@ -21,6 +21,7 @@ ) from deepmd.dpmodel.common import ( NativeOP, + cast_precision, ) from deepmd.dpmodel.descriptor.base_descriptor import ( BaseDescriptor, @@ -366,7 +367,7 @@ def build_tokens(self, coord_ext: Array, atype_ext: Array, nlist: Array) -> dict ) nt = nloc + 2 - positions = xp.arange(nt, device=dev)[None, :] + positions = xp.arange(nt, dtype=xp.int64, device=dev)[None, :] eos_at = (n_real + 1)[:, None] bos_row = xp.full((nf, 1), self.bos_idx, dtype=atom_tokens.dtype, device=dev) pad_row = xp.full((nf, 1), self.pad_idx, dtype=atom_tokens.dtype, device=dev) @@ -393,6 +394,7 @@ def build_tokens(self, coord_ext: Array, atype_ext: Array, nlist: Array) -> dict "n_real": n_real, } + @cast_precision def forward_tokens(self, coord_ext: Array, atype_ext: Array, nlist: Array) -> dict: """Run the backbone and return everything at token resolution. diff --git a/deepmd/dpmodel/fitting/unimol_pretrain.py b/deepmd/dpmodel/fitting/unimol_pretrain.py index 82c654f1ae..923aeb3a33 100644 --- a/deepmd/dpmodel/fitting/unimol_pretrain.py +++ b/deepmd/dpmodel/fitting/unimol_pretrain.py @@ -288,6 +288,13 @@ def call_tokens(self, backbone: dict[str, Array]) -> dict[str, Array]: if self.dist_head is not None: dist = self.dist_head(backbone["pair_rep"])[:, 1 : nloc + 1, :] width = self.max_atoms + 2 + if dist.shape[-1] > width: + raise ValueError( + f"a frame of {nloc} atoms exceeds max_atoms={self.max_atoms}; " + "the distance head declares a fixed width, so larger frames " + "cannot be expressed. Convert the data with a matching " + "max_atoms, or raise it here" + ) if dist.shape[-1] < width: pad = xp.zeros( (nf, nloc, width - dist.shape[-1]), diff --git a/deepmd/dpmodel/loss/unimol.py b/deepmd/dpmodel/loss/unimol.py index ab394c5a5e..d70560aea0 100644 --- a/deepmd/dpmodel/loss/unimol.py +++ b/deepmd/dpmodel/loss/unimol.py @@ -72,7 +72,9 @@ def _token_mask_from_atoms(mask: Array, ncol: int) -> Array: """Mark the non-padding token columns: BOS, the real atoms, then EOS.""" xp = array_api_compat.array_namespace(mask) n_real = xp.sum(xp.astype(mask, xp.int64), axis=-1) - positions = xp.arange(ncol, device=array_api_compat.device(mask))[None, :] + positions = xp.arange(ncol, dtype=xp.int64, device=array_api_compat.device(mask))[ + None, : + ] return xp.astype(positions < (n_real + 2)[:, None], xp.int64) @@ -273,7 +275,6 @@ def frame_transform(self, type_map: list[str]): # noqa: ANN201 return make_unimol_data_transform( type_map, seed=self.data_seed, - max_atoms=self.max_atoms, mask_prob=self.mask_prob, leave_unmasked_prob=self.leave_unmasked_prob, random_token_prob=self.random_token_prob, diff --git a/deepmd/dpmodel/utils/unimol_transform.py b/deepmd/dpmodel/utils/unimol_transform.py index fcc8152d5f..a96c7dbd5d 100644 --- a/deepmd/dpmodel/utils/unimol_transform.py +++ b/deepmd/dpmodel/utils/unimol_transform.py @@ -177,7 +177,7 @@ def mask_points( weights = None if random_token_prob > 0.0: - weights = np.ones(num_types) + weights = np.ones(num_types, dtype=np.float64) weights[list(special_indices)] = 0 weights = weights / weights.sum() @@ -209,10 +209,10 @@ def noise_f(n): # noqa: ANN001, ANN202 # molecule can end up with no masked atom at all. num_mask = int(mask_prob * sz + np.random.rand()) # noqa: NPY002 mask_idc = np.random.choice(sz, num_mask, replace=False) # noqa: NPY002 - mask = np.full(sz, False) + mask = np.full(sz, False, dtype=bool) mask[mask_idc] = True - targets = np.full(len(mask), pad_idx) + targets = np.full(len(mask), pad_idx, dtype=np.int64) targets[mask] = np.asarray(tokens)[mask] rand_or_unmask_prob = random_token_prob + leave_unmasked_prob @@ -293,30 +293,38 @@ def make_unimol_data_transform( type_map: Sequence[str], *, seed: int = 1, - epoch: int = 1, - max_atoms: int = 256, mask_token: str = "[MASK]", **mask_kwargs: float | str, ) -> Callable[[dict, int], dict]: """Build the per-frame transform a deepmd data reader installs. - The reader hands over one already-converted frame, so the conformer draw and - the hydrogen policy are behind us; what remains is cropping, centring, and - the corruption itself. The frame comes back with corrupted coordinates and - element types, plus the two labels the objective needs. The distance target - is not stored, because it would cost O(natoms^2) per frame; the loss derives - it from the clean coordinates. + The reader hands over one already-converted frame, so the conformer draw, + the hydrogen policy and the size cap are behind us; what remains is centring + and the corruption itself. The frame comes back with corrupted coordinates + and element types, plus the two labels the objective needs. The distance + target is not stored, because it would cost O(natoms^2) per frame; the loss + derives it from the clean coordinates. + + The atom count is left alone. Cropping here would not work: a frame's atom + count and the batch layout are settled before the transform runs, so a + shorter frame would not match the batch it belongs to. The converter applies + the size cap instead. + + Upstream draws a fresh corruption for the same molecule in every epoch. The + transform reproduces that by counting how often it has been handed each + frame and using that count where upstream uses the epoch number, so a + molecule is corrupted differently each time it comes round. With more than + one loader process each keeps its own count, which changes the stream but + not its statistics. Parameters ---------- type_map : Sequence[str] Element names of the model. It must contain ``mask_token``, since a masked atom has to be expressible as a type. - seed, epoch : int - Together with the frame index these seed the corruption, the way - upstream seeds it per sample. - max_atoms : int - Crop larger molecules down to this many atoms. + seed : int + Together with the frame index and the visit count, this seeds the + corruption, the way upstream seeds it per sample and epoch. mask_token : str Name of the pseudo-element standing for ``[MASK]``. **mask_kwargs @@ -345,20 +353,36 @@ def make_unimol_data_transform( type_to_token = np.array( [token_of.get(sym, unk) for sym in type_map], dtype=np.int64 ) - # Uni-Mol draws replacements over its own vocabulary, so a drawn token must - # map back onto a type the model knows. token_to_type = np.array( [type_index.get(sym, type_index[mask_token]) for sym in vocabulary], dtype=np.int64, ) + # Upstream draws a replacement over all 26 of its elements. A model whose + # type_map covers fewer of them could not express the others, and mapping + # them onto [MASK] would quietly turn a random-element atom into a masked + # one, so they are excluded from the draw instead. With the full element + # set, which is what the example configures, nothing is excluded and the + # distribution is upstream's. + if "epoch" in mask_kwargs: + raise TypeError( + "the epoch is not fixed at build time: the transform advances it " + "every time it sees a frame, so that a molecule is corrupted " + "differently each time it comes round" + ) specials = [token_of[s] for s in ("[PAD]", "[CLS]", "[SEP]", "[UNK]", mask_token)] + inexpressible = [ + i + for i, sym in enumerate(vocabulary) + if i not in specials and sym not in type_index + ] + excluded = [*specials, *inexpressible] + visits: dict[int, int] = {} def transform(frame: dict, index: int) -> dict: + epoch = visits.get(index, 0) + 1 + visits[index] = epoch coord = np.asarray(frame["coord"], dtype=np.float64).reshape(-1, 3) atype = np.asarray(frame["atype"], dtype=np.int64).reshape(-1) - atoms = np.arange(len(atype)) - atoms, coord = crop_atoms(atoms, coord, seed, epoch, index, max_atoms) - atype = atype[np.asarray(atoms, dtype=np.int64)] coord = center_coordinates(coord) tokens = type_to_token[atype] @@ -366,7 +390,7 @@ def transform(frame: dict, index: int) -> dict: tokens, coord, num_types=len(vocabulary), - special_indices=specials, + special_indices=excluded, pad_idx=pad, mask_idx=token_of[mask_token], seed=seed, diff --git a/deepmd/tf/common.py b/deepmd/tf/common.py index 66444f9a60..aa9abbc09f 100644 --- a/deepmd/tf/common.py +++ b/deepmd/tf/common.py @@ -208,7 +208,7 @@ def gelu_erf(x: tf.Tensor) -> tf.Tensor: Original paper https://arxiv.org/abs/1606.08415 """ - return 0.5 * x * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0), x.dtype))) + return 0.5 * x * (1.0 + tf.math.erf(x / tf.sqrt(tf.cast(2.0, x.dtype)))) ACTIVATION_FN_DICT = { diff --git a/deepmd/utils/unimol_data.py b/deepmd/utils/unimol_data.py index 8db5039ed7..0a87e4aa9d 100644 --- a/deepmd/utils/unimol_data.py +++ b/deepmd/utils/unimol_data.py @@ -101,6 +101,7 @@ def convert_unimol_lmdb( add_2d_conformer: bool = False, max_molecules: int | None = None, max_conformers: int | None = None, + max_atoms: int | None = 256, map_size: int = 1024**4, ) -> dict[str, int]: """Write a deepmd LMDB dataset from a Uni-Mol one. @@ -120,6 +121,11 @@ def convert_unimol_lmdb( Stop after this many molecules, which is useful for a trial run. max_conformers : int, optional Keep at most this many conformers per molecule. + max_atoms : int, optional + Crop molecules larger than this to a random subset of that many atoms, + as upstream does. It happens here rather than while training, because a + frame's atom count and the batch layout are settled before any per-frame + transform runs. map_size : int Maximum size of the output database. @@ -160,12 +166,23 @@ def convert_unimol_lmdb( # [UNK]; skip both rather than write something misleading. skipped += 1 continue + if max_atoms is not None and len(atoms) > max_atoms: + keep = np.sort( + np.random.default_rng(molecules).choice( + len(atoms), max_atoms, replace=False + ) + ) + atoms = [atoms[i] for i in keep] + else: + keep = None atom_types = np.array([index_of[a] for a in atoms], dtype=np.int64) atom_numbs = [int((atom_types == i).sum()) for i in range(len(names))] pool = _conformers(record, add_2d_conformer) if max_conformers is not None: pool = pool[:max_conformers] for coords in pool: + if keep is not None and coords.shape[0] >= int(keep[-1]) + 1: + coords = coords[keep] if coords.shape[0] != len(atoms): skipped += 1 continue diff --git a/source/tests/common/dpmodel/test_unimol.py b/source/tests/common/dpmodel/test_unimol.py index 6fcade3b7c..adaa0c9991 100644 --- a/source/tests/common/dpmodel/test_unimol.py +++ b/source/tests/common/dpmodel/test_unimol.py @@ -48,7 +48,7 @@ ) GOLDEN = os.path.join(os.path.dirname(__file__), "unimol_v1_golden.npz") -SEED, EPOCH, INDICES = 1, 1, [0, 1, 2, 3] +SEED, EPOCH, INDICES = 1, 1, [0, 1] SMALL = {"layers": 2, "dim": 32, "ffn": 64, "heads": 4, "k": 128, "vocab": 31} diff --git a/source/tests/common/dpmodel/test_unimol_data.py b/source/tests/common/dpmodel/test_unimol_data.py index 38733c3c1e..5d4a7427dc 100644 --- a/source/tests/common/dpmodel/test_unimol_data.py +++ b/source/tests/common/dpmodel/test_unimol_data.py @@ -111,9 +111,7 @@ def test_transform_corrupts_frames_in_the_data_path(self) -> None: convert_unimol_lmdb(self.src, dst, type_map=type_map, map_size=1 << 24) reader = LmdbDataReader(dst, type_map) plain = reader[0] - reader.set_frame_transform( - make_unimol_data_transform(type_map, seed=1, epoch=1) - ) + reader.set_frame_transform(make_unimol_data_transform(type_map, seed=1)) corrupted = reader[0] self.assertEqual( diff --git a/source/tests/common/dpmodel/unimol_v1_golden.npz b/source/tests/common/dpmodel/unimol_v1_golden.npz index 02466c35489eb2bb1f6423e49cc792af5b62167e..6dd3801eabf478870c7fe901876a27a16a3ebf75 100644 GIT binary patch delta 444403 zcmagF2T;>Z7cZ=$@F)T*3er^sL@Cmxi-@2!1?eRsO?t0^{6!Q*ni7iChzKaX_Yja? zL+=ojUP2&&gb+x+@RYaBckj&%lVMoSp1ym2=j`-X!`^7bhg*3Y)(E+_tb;j5x2NuRRrCdH3mQb`&RJoN|1&*Z7c0r~8k zC;xk4Xa9^S^xjF)|5zD3xJ>lA`V##=D=&Jfy@mFk44^GizYRv8Oe(&0iXCcuD*E(0 zzFsRR{**tLNUD8(VS&AKHQoPkQ)+r{7#c~#_s40f6x2^f(kG!)+|VyEH%>l+5^1cN z|7ZPLRTB|fb^0bi``7vdn!mo6;((`6AI=V$TP$BA#FuxxRN+To*v9u|Uw|8R!{x6o zKe{}pbM9g|XWYcK#hb;dbO|aCHeP;#W?pzxTIG0I8y^H~`Q0)9T}$`Yp#I3YHy3n< zLKcwT5nFXrS3qO&Y#h$|^vjHE2xFtX zo>`cF!F1N%>W7)Hm(aZMZ`TN~@q^V*Z!EX9y$=|?A%?k&iEzBK0NR^e#J$pc)yf@n 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zG)1*=S2E1*)LUw0o(&oHz9gTp(PY)yGrG}x9YV{E< zJ#>!ZH;H8wTfR%TA*GSOT-NF+-q(I#=h~A5sueDi29DU#dp5_x89PlOtN7u%9s5jT zIZ!)L8Nd&@4pjOxIrs(@HZO})94R^;gMUhN&>(o|KnM5sa^N|J_?Pcf L8aM4>4y*eYeFxZA diff --git a/source/tests/pt_expt/model/test_unimol.py b/source/tests/pt_expt/model/test_unimol.py index 5f371a194f..900d18b288 100644 --- a/source/tests/pt_expt/model/test_unimol.py +++ b/source/tests/pt_expt/model/test_unimol.py @@ -240,13 +240,7 @@ def test_matches_the_array_api_implementation(self) -> None: self.load_weights(descriptor, fitting, torch_backend=False) reference = fitting.call_tokens(descriptor.forward_tokens(coord, atype, nlist)) - for key in ( - "token_logits", - "coord_update", - "pair_dist", - "x_norm", - "delta_pair_norm", - ): + for key in ("token_logits", "coord_update", "pair_dist"): with self.subTest(output=key): np.testing.assert_allclose( ret[key].detach().cpu().numpy(), @@ -254,6 +248,17 @@ def test_matches_the_array_api_implementation(self) -> None: rtol=1e-12, atol=1e-12, ) + # The two regularisers are evaluated in fp32, because upstream + # evaluates them there, so the two backends round the last place + # differently. + for key in ("x_norm", "delta_pair_norm"): + with self.subTest(output=key): + np.testing.assert_allclose( + ret[key].detach().cpu().numpy(), + np.asarray(reference[key]), + rtol=1e-6, + atol=1e-8, + ) def test_single_precision_basis_costs_one_fp32_place(self) -> None: """With upstream's fp32 basis the two backends part company measurably. From fe88321263d791ba6f57b5065db480896f80a02d Mon Sep 17 00:00:00 2001 From: Duo <50307526+iProzd@users.noreply.github.com> Date: Sat, 12 Sep 2026 07:52:52 -0400 Subject: [PATCH 25/34] fix: keep the embedding and the distance basis in the gradient Making them parameters was not enough: reading them through the array API's asarray, which the device fix had introduced, copied them out of the autograd graph, so they still received no gradient. They are indexed directly now. Parameters already live on the model's device, so the wrapper was never needed for them; it stays only for the plain lookup table, which is not a parameter. The tests that should have caught both of these are the ones the review found could not fail, so they are strengthened here: - the gradient test names the backbone parameters it expects to reach, rather than accepting any parameter with a gradient, which the three heads alone satisfied; - the dropout test also builds a model with every rate at zero and asserts that training mode is then deterministic, which a single hard-coded dropout call would not survive; - the descriptor's five-tuple entry point is compared against the token-resolution one by value, not only by shape; - the masking statistics are measured by running the ported corruption over two dozen molecules rather than by reading the fixture back; - the norm regularisers get a direct test of the hinge and of the masked mean, including an all-padding row, since the golden values for them are zero and constrain nothing; - the released-checkpoint importer gets a test, driven with the golden's upstream-named weights, covering both the transposed projections and the untransposed lookup tables; - the data fixture is large enough that the 15% selection selects something, and a new test pins that revisiting a frame corrupts it differently. --- deepmd/dpmodel/descriptor/unimol.py | 16 ++- .../dpmodel/descriptor/unimol_nn/encoder.py | 22 +--- doc/model/unimol.md | 12 +- source/tests/common/dpmodel/test_unimol.py | 84 +++++++++++++- .../tests/common/dpmodel/test_unimol_data.py | 28 ++++- source/tests/pt_expt/model/test_unimol.py | 105 +++++++++++++++++- 6 files changed, 235 insertions(+), 32 deletions(-) diff --git a/deepmd/dpmodel/descriptor/unimol.py b/deepmd/dpmodel/descriptor/unimol.py index b5a35c88f2..c91f2b7dd9 100644 --- a/deepmd/dpmodel/descriptor/unimol.py +++ b/deepmd/dpmodel/descriptor/unimol.py @@ -5,8 +5,13 @@ the descriptor interface. Unlike every other descriptor here, Uni-Mol is global rather than local: it attends over all atom pairs with no cut-off and no smooth envelope, so it is neither extensive nor periodic and its forces are not -conserved. It is meant for molecular property and pretraining work, and the -model layer refuses to pair it with an energy fitting. +conserved. It is meant for molecular property and pretraining work. + +Nothing stops a configuration from pairing it with an energy fitting, and +nothing could usefully: the descriptor does not see the fitting. Such a model +would train and evaluate, but its forces would be neither smooth at any cutoff +nor conserved, and its energy would not be extensive, so it should not be used +as a potential energy surface. Uni-Mol's own vocabulary is kept, because the released weights are indexed by it: four special tokens, then 26 elements, then ``[MASK]``. A deepmd @@ -407,10 +412,9 @@ def forward_tokens(self, coord_ext: Array, atype_ext: Array, nlist: Array) -> di tokens, coord, padding_mask = seq["tokens"], seq["coord"], seq["padding_mask"] nf, nt = tokens.shape - embed = xp.asarray( - self.embed_tokens.w, device=array_api_compat.device(coord_ext) - ) - emb = xp.take(embed, xp.reshape(tokens, (-1,)), axis=0) + # The embedding is a parameter, so it is indexed directly: wrapping it + # with asarray would detach it from the gradient. + emb = xp.take(self.embed_tokens.w, xp.reshape(tokens, (-1,)), axis=0) emb = xp.reshape(emb, (nf, nt, self.encoder_embed_dim)) emb = xp.astype(emb, coord.dtype) diff --git a/deepmd/dpmodel/descriptor/unimol_nn/encoder.py b/deepmd/dpmodel/descriptor/unimol_nn/encoder.py index 7e7fddf881..5d6e909269 100644 --- a/deepmd/dpmodel/descriptor/unimol_nn/encoder.py +++ b/deepmd/dpmodel/descriptor/unimol_nn/encoder.py @@ -213,29 +213,19 @@ def __init__( def call(self, dist, edge_type): # noqa: ANN001, ANN201 """Expand ``dist`` (nf x nt x nt) into ``k`` Gaussians per atom pair.""" xp = array_api_compat.array_namespace(dist) - dev = array_api_compat.device(dist) flat = xp.reshape(edge_type, (-1,)) - mul = xp.reshape( - xp.take(xp.asarray(self.mul.w, device=dev), flat, axis=0), - (*edge_type.shape, 1), - ) - bias = xp.reshape( - xp.take(xp.asarray(self.bias.w, device=dev), flat, axis=0), - (*edge_type.shape, 1), - ) + # The tables are parameters: they already live on the right device, and + # re-wrapping them with asarray would cut them out of the gradient. + mul = xp.reshape(xp.take(self.mul.w, flat, axis=0), (*edge_type.shape, 1)) + bias = xp.reshape(xp.take(self.bias.w, flat, axis=0), (*edge_type.shape, 1)) mul = xp.astype(mul, dist.dtype) bias = xp.astype(bias, dist.dtype) x = mul * dist[..., None] + bias x = xp.repeat(x, self.k, axis=-1) work = xp.float32 if self.single_precision_basis else x.dtype x = xp.astype(x, work) - mean = xp.astype(xp.reshape(xp.asarray(self.means.w, device=dev), (-1,)), work) - std = ( - xp.abs( - xp.astype(xp.reshape(xp.asarray(self.stds.w, device=dev), (-1,)), work) - ) - + 1e-5 - ) + mean = xp.astype(xp.reshape(self.means.w, (-1,)), work) + std = xp.abs(xp.astype(xp.reshape(self.stds.w, (-1,)), work)) + 1e-5 a = (2 * UNIMOL_PI) ** 0.5 out = xp.exp(-0.5 * (((x - mean) / std) ** 2)) / (a * std) return xp.astype(out, dist.dtype) diff --git a/doc/model/unimol.md b/doc/model/unimol.md index a6bdfb9483..495363f884 100644 --- a/doc/model/unimol.md +++ b/doc/model/unimol.md @@ -20,7 +20,17 @@ DFT-labelled data, and to give molecular property work a pretrained backbone. > conserved. The descriptor refuses any frame whose atoms are not all local, > which rules out periodic images and the ghost-atom layout that freezing and > parallel evaluation assume, so it is not available for molecular dynamics or -> frozen deployment. +> frozen deployment. Nothing prevents a configuration from pairing it with an +> energy fitting; such a model would run, but it would not be a usable +> potential energy surface. + +Two further limits are worth knowing before configuring a run. The two virtual +tokens sit at the centroid of the real atoms by default, which keeps the +sequence translation invariant; set `virtual_token_position` to `origin` to +reproduce upstream exactly on data its own pipeline has centred. And the +descriptor decides on the data it is given -- rejecting periodic frames and +frames of fewer than two atoms -- so it cannot be traced or compiled, and +`enable_compile` is not available for it. ## Architecture diff --git a/source/tests/common/dpmodel/test_unimol.py b/source/tests/common/dpmodel/test_unimol.py index adaa0c9991..7787cab323 100644 --- a/source/tests/common/dpmodel/test_unimol.py +++ b/source/tests/common/dpmodel/test_unimol.py @@ -44,6 +44,7 @@ UniMolLoss, ) from deepmd.dpmodel.utils.unimol_transform import ( + mask_points, unimol_frame_transform, ) @@ -200,7 +201,43 @@ def test_frame_transform_matches_upstream(self) -> None: ) def test_masking_statistics(self) -> None: - """About 15% of atoms are selected, and only replaced ones are moved.""" + """About 15% of atoms are selected, and only replaced ones are moved. + + The ported corruption is run here rather than read off the fixture, so + a change in the port can fail this test. + """ + vocab = {sym: i for i, sym in enumerate(unimol_vocabulary())} + specials = [vocab[s] for s in ("[PAD]", "[CLS]", "[SEP]", "[UNK]", "[MASK]")] + rng = np.random.default_rng(0) + selected_counts = [] + for index in range(24): + size = 40 + tokens = rng.integers(4, 30, size=size) + coords = rng.normal(size=(size, 3)).astype(np.float32) + out = mask_points( + tokens, + coords, + num_types=len(vocab), + special_indices=specials, + pad_idx=0, + mask_idx=vocab["[MASK]"], + seed=1, + epoch=1, + index=index, + ) + picked = out["targets"] != 0 + selected_counts.append(int(picked.sum())) + moved = np.abs(out["coordinates"] - coords).max(axis=-1) > 0 + # Only selected atoms move, and an atom left with its own element + # is still predicted. + self.assertTrue(bool(np.all(~moved | picked))) + np.testing.assert_array_equal( + out["targets"][picked], np.asarray(tokens)[picked] + ) + mean_fraction = float(np.mean(selected_counts)) / 40 + self.assertGreater(mean_fraction, 0.10) + self.assertLess(mean_fraction, 0.20) + for row, index in enumerate(INDICES): n = int(self.n_real[row]) targets = self.golden["target/tokens_target"][row, 1 : n + 1] @@ -273,6 +310,46 @@ def test_gaussian_basis_and_projection(self) -> None: ) +class TestUniMolNormRegularisers(unittest.TestCase): + """The hinge itself, which the golden values cannot constrain. + + On the small random model both regularisers sit at exactly zero, because + the node norms happen to fall inside the tolerance. Comparing against zero + says nothing about the formula, so it is checked directly here. + """ + + def test_hinge_is_zero_inside_the_tolerance_and_grows_outside(self) -> None: + from deepmd.dpmodel.descriptor.unimol_nn.encoder import ( + norm_loss, + ) + + dim = 16 + root = dim**0.5 + unit = np.ones((1, 1, dim)) / root # norm 1 + # Upstream's tolerance is 1: a norm within 1 of sqrt(dim) costs nothing. + inside = unit * root + np.testing.assert_allclose(norm_loss(inside), 0.0, atol=1e-6) + np.testing.assert_allclose(norm_loss(unit * (root + 0.5)), 0.0, atol=1e-6) + # Beyond it the cost is the excess, either side. + np.testing.assert_allclose(norm_loss(unit * (root + 3.0)), 2.0, atol=1e-5) + np.testing.assert_allclose(norm_loss(unit * (root - 3.0)), 2.0, atol=1e-5) + + def test_masked_mean_ignores_padding_and_survives_an_empty_row(self) -> None: + from deepmd.dpmodel.descriptor.unimol_nn.encoder import ( + masked_mean, + ) + + value = np.array([[1.0, 2.0, 99.0], [4.0, 99.0, 99.0]]) + mask = np.array([[1.0, 1.0, 0.0], [1.0, 0.0, 0.0]]) + # Per row: 1.5 and 4.0, then the mean over rows. + np.testing.assert_allclose(float(masked_mean(mask, value)), 2.75, atol=1e-12) + # An all-padding row returns zero rather than dividing by zero, which + # is what upstream's epsilon in the denominator is for. + empty = masked_mean(np.zeros((1, 3)), np.ones((1, 3))) + self.assertTrue(np.isfinite(float(empty))) + np.testing.assert_allclose(float(empty), 0.0, atol=1e-6) + + class TestUniMolHeads(UniMolGoldenMixin, unittest.TestCase): """The three pretraining heads.""" @@ -351,6 +428,11 @@ def test_call_drops_the_virtual_tokens(self) -> None: coord, atype, nlist = self.deepmd_inputs() node, rot, g2, h2, sw = desc.call(coord, atype, nlist) self.assertEqual(node.shape, (coord.shape[0], coord.shape[1], SMALL["dim"])) + # The values must be the token-level ones with BOS and EOS removed, + # not merely an array of the right shape. + tokens = desc.forward_tokens(coord, atype, nlist)["node_ebd"] + np.testing.assert_allclose(node, tokens[:, 1 : coord.shape[1] + 1, :], atol=0) + self.assertGreater(float(np.abs(np.asarray(node)).max()), 0.0) self.assertIsNone(rot) self.assertIsNone(g2) self.assertIsNone(h2) diff --git a/source/tests/common/dpmodel/test_unimol_data.py b/source/tests/common/dpmodel/test_unimol_data.py index 5d4a7427dc..f3600c2ddb 100644 --- a/source/tests/common/dpmodel/test_unimol_data.py +++ b/source/tests/common/dpmodel/test_unimol_data.py @@ -42,11 +42,16 @@ class TestUniMolDataConversion(unittest.TestCase): def setUp(self) -> None: self.tmp = tempfile.mkdtemp() rng = np.random.default_rng(0) + # Large enough that the 15% selection actually selects something: with + # four atoms it usually selects none, and every assertion about the + # corruption would hold vacuously. + big = ["C", "N", "O", "H", "C", "C", "N", "O", "H", "H", "C", "F", "S", "C"] self.molecules = [ { - "atoms": ["C", "N", "O", "H"], + "atoms": big, "coordinates": [ - rng.normal(size=(4, 3)).astype(np.float32) for _ in range(3) + rng.normal(size=(len(big), 3)).astype(np.float32) * 2.0 + for _ in range(3) ], "smi": "CNO", }, @@ -79,7 +84,7 @@ def tearDown(self) -> None: def test_reader_streams_the_upstream_layout(self) -> None: records = list(read_unimol_lmdb(self.src)) self.assertEqual(len(records), len(self.molecules)) - self.assertEqual(records[0]["atoms"], ["C", "N", "O", "H"]) + self.assertEqual(records[0]["atoms"], self.molecules[0]["atoms"]) self.assertEqual(len(records[0]["coordinates"]), 3) def test_conversion_round_trips_through_the_deepmd_reader(self) -> None: @@ -98,7 +103,7 @@ def test_conversion_round_trips_through_the_deepmd_reader(self) -> None: coord, self.molecules[0]["coordinates"][0].astype(np.float64), atol=1e-6 ) symbols = [UNIMOL_ELEMENTS[i] for i in np.asarray(frame["atype"]).reshape(-1)] - self.assertEqual(symbols, self.molecules[0]["atoms"]) + self.assertEqual(symbols, list(self.molecules[0]["atoms"])) # Molecules are not periodic, so no cell is written at all. A zero cell # would not do: the neighbour-list builder takes any cell at face value # and inverts it. @@ -138,6 +143,21 @@ def test_transform_corrupts_frames_in_the_data_path(self) -> None: is_mask = np.asarray(corrupted["atype"]) == type_map.index("[MASK]") self.assertLessEqual(int(is_mask.sum()), int(moved.sum())) self.assertTrue(bool(np.all(~is_mask | moved))) + # The fixture is large enough that something is actually corrupted, so + # the assertions above are not vacuous. + self.assertGreater(int((target != 0).sum()), 0) + self.assertGreater(int(moved.sum()), 0) + + def test_corruption_changes_between_visits(self) -> None: + """Upstream redraws every epoch; a frozen mask would be memorised.""" + type_map = [*UNIMOL_ELEMENTS, "[MASK]"] + dst = os.path.join(self.tmp, "revisited") + convert_unimol_lmdb(self.src, dst, type_map=type_map, map_size=1 << 24) + reader = LmdbDataReader(dst, type_map) + reader.set_frame_transform(make_unimol_data_transform(type_map, seed=1)) + first = np.asarray(reader[0]["unimol_token_target"]).copy() + second = np.asarray(reader[0]["unimol_token_target"]).copy() + self.assertFalse(np.array_equal(first, second)) def test_the_objective_supplies_its_own_transform(self) -> None: """A trainer installs whatever the loss declares, and nothing else. diff --git a/source/tests/pt_expt/model/test_unimol.py b/source/tests/pt_expt/model/test_unimol.py index 900d18b288..c75acd98ea 100644 --- a/source/tests/pt_expt/model/test_unimol.py +++ b/source/tests/pt_expt/model/test_unimol.py @@ -363,6 +363,35 @@ def test_dropout_is_active_only_in_training(self) -> None: self.assertFalse(bool(torch.allclose(a, b))) model.eval() + # The rates are what drive it: with every rate at zero, training mode + # is deterministic again. Without this, a single hard-coded dropout + # call would satisfy the test above. + quiet = self.build_torch_model(coord.shape[1]) + for module in ( + quiet.atomic_model.descriptor, + quiet.atomic_model.descriptor.encoder, + ): + for attr in ( + "dropout", + "emb_dropout", + "attention_dropout", + "activation_dropout", + ): + if hasattr(module, attr): + setattr(module, attr, 0.0) + for layer in quiet.atomic_model.descriptor.encoder.layers: + layer.dropout = 0.0 + layer.attention_dropout = 0.0 + layer.activation_dropout = 0.0 + layer.self_attn.dropout = 0.0 + quiet.train() + torch.manual_seed(0) + c = quiet.forward_lower(*args)["token_logits"].detach() + torch.manual_seed(1) + d = quiet.forward_lower(*args)["token_logits"].detach() + torch.testing.assert_close(c, d, rtol=0, atol=0) + quiet.eval() + def test_upper_path_builds_its_own_neighbour_list(self) -> None: """What a user actually calls: coordinates and types, no neighbour list.""" coord, atype, _ = self.inputs() @@ -405,11 +434,79 @@ def test_gradients_flow(self) -> None: None, ) ret["token_logits"].sum().backward() - grads = [ - p.grad for p in model.parameters() if p.requires_grad and p.grad is not None + named = dict(model.named_parameters()) + # The heads alone would satisfy "some parameter has a gradient", so the + # backbone is named explicitly: the embedding, the distance basis and + # the first block all have to be reached. + backbone = [ + name + for name in named + if "embed_tokens" in name or ".gbf." in name or "layers.0." in name ] - self.assertGreater(len(grads), 0) - self.assertTrue(any(bool(torch.any(g != 0)) for g in grads)) + self.assertGreater(len(backbone), 5) + for name in backbone: + with self.subTest(parameter=name): + grad = named[name].grad + self.assertIsNotNone(grad, f"{name} received no gradient") + self.assertTrue( + bool(torch.any(grad != 0)), f"{name} got a zero gradient" + ) + + +class TestUniMolCheckpointImport(unittest.TestCase): + """The documented way to use the released weights. + + The golden archive carries the small model's weights under upstream's own + names, so the importer can be driven with exactly the layout a released + checkpoint has, without shipping a 190 MB file. + """ + + def test_imports_upstream_named_weights(self) -> None: + from deepmd.dpmodel.descriptor.unimol import DescrptUniMol as DescrptUniMolDP + from deepmd.utils.unimol_checkpoint import ( + apply_unimol_backbone, + split_unimol_state_dict, + ) + + golden = np.load(GOLDEN) + state = { + k[len("small_weights/") :]: v + for k, v in golden.items() + if k.startswith("small_weights/") + } + backbone, heads = split_unimol_state_dict(state) + self.assertTrue(all(not k.startswith("lm_head") for k in backbone)) + self.assertTrue(any(k.startswith("lm_head") for k in heads)) + with self.assertRaisesRegex(ValueError, "unexpected"): + split_unimol_state_dict({**state, "something.unexpected": np.zeros(1)}) + + descriptor = DescrptUniMolDP( + type_map=[*UNIMOL_ELEMENTS, "[MASK]"], + encoder_layers=SMALL["layers"], + encoder_embed_dim=SMALL["dim"], + encoder_ffn_embed_dim=SMALL["ffn"], + encoder_attention_heads=SMALL["heads"], + max_atoms=16, + ) + apply_unimol_backbone(descriptor, backbone) + # Lookup tables arrive as they are; projections arrive transposed, + # because deepmd applies a linear weight the other way round. + np.testing.assert_allclose( + descriptor.embed_tokens.w, state["embed_tokens.weight"], atol=0 + ) + np.testing.assert_allclose( + descriptor.gbf.mul.w, state["gbf.mul.weight"], atol=0 + ) + np.testing.assert_allclose( + descriptor.encoder.layers[0].fc1.w, + state["encoder.layers.0.fc1.weight"].T, + atol=0, + ) + np.testing.assert_allclose( + descriptor.encoder.layers[0].fc1.b, + state["encoder.layers.0.fc1.bias"], + atol=0, + ) class TestUniMolTraining(unittest.TestCase): From 8cc8e69d2edafb3dd92cf863436118f2760af1a0 Mon Sep 17 00:00:00 2001 From: Duo <50307526+iProzd@users.noreply.github.com> Date: Sat, 12 Sep 2026 13:42:46 -0400 Subject: [PATCH 26/34] fix: act on the review of this pull request Six findings from the automated review, all local to this feature. An element the model's type_map cannot express was written back as [MASK], which quietly turned an ordinary atom into a corrupted one. That happens when the type_map reaches beyond Uni-Mol's 26 elements, since the extras tokenize to [UNK] and [UNK] has no type to return to. Such a frame is refused now, with the offending elements named. The per-frame visit counter grew one dictionary entry per frame, on a data format whose own design point is a hundred million frames. One counter for the whole transform does the same job in constant space. The training and validation datasets shared a transform, so validation passes advanced the corruption that training was drawing from. Each dataset gets its own. The objective carried a max_atoms it could not apply, since the transform runs after batching and the converter is what caps the size. It is gone from the loss, the schema and the example. Loading a released checkpoint into a descriptor of the wrong shape used to proceed and ignore the extra layers. The importer checks the layer count and the key shapes first. The loss base class deleted a parameter it simply does not use, which a static analyser flagged; it documents it instead. Tests cover the two new refusals. --- deepmd/dpmodel/loss/loss.py | 8 +++- deepmd/dpmodel/loss/unimol.py | 9 +++-- deepmd/dpmodel/utils/unimol_transform.py | 29 +++++++++++--- deepmd/pt_expt/train/training.py | 40 ++++++++++--------- deepmd/utils/argcheck.py | 2 - deepmd/utils/unimol_checkpoint.py | 35 ++++++++++++++++ examples/unimol/pretrain/input.json | 4 +- .../tests/common/dpmodel/test_unimol_data.py | 28 +++++++++++++ source/tests/pt_expt/model/test_unimol.py | 37 +++++++++++++++++ 9 files changed, 159 insertions(+), 33 deletions(-) diff --git a/deepmd/dpmodel/loss/loss.py b/deepmd/dpmodel/loss/loss.py index a91bd0104f..eca3b92ada 100644 --- a/deepmd/dpmodel/loss/loss.py +++ b/deepmd/dpmodel/loss/loss.py @@ -58,8 +58,14 @@ def frame_transform(self, type_map: list[str]): # noqa: ANN201 input, which has to happen while the data is read rather than inside the loss. A trainer installs whatever this returns on the datasets of the corresponding task. Supervised losses need nothing and return None. + + Parameters + ---------- + type_map : list[str] + Element names of the model, which a transform needs in order to map + elements onto types. """ - del type_map + return None @property def supports_ragged_batches(self) -> bool: diff --git a/deepmd/dpmodel/loss/unimol.py b/deepmd/dpmodel/loss/unimol.py index d70560aea0..a85763b653 100644 --- a/deepmd/dpmodel/loss/unimol.py +++ b/deepmd/dpmodel/loss/unimol.py @@ -167,7 +167,6 @@ def __init__( random_token_prob: float = 0.05, noise_type: str = "uniform", noise: float = 1.0, - max_atoms: int = 256, data_seed: int = 1, **kwargs: float, ) -> None: @@ -185,7 +184,6 @@ def __init__( self.random_token_prob = random_token_prob self.noise_type = noise_type self.noise = noise - self.max_atoms = max_atoms self.data_seed = data_seed def call( @@ -267,7 +265,11 @@ def add(term: Array, weight: float, name: str) -> None: return loss, more_loss def frame_transform(self, type_map: list[str]): # noqa: ANN201 - """Build Uni-Mol's corruption, which also produces the labels.""" + """Build Uni-Mol's corruption, which also produces the labels. + + A fresh object each time: it carries the counter that stands in for the + epoch, and the training and validation sets must not share one. + """ from deepmd.dpmodel.utils.unimol_transform import ( make_unimol_data_transform, ) @@ -307,7 +309,6 @@ def serialize(self) -> dict: "random_token_prob": self.random_token_prob, "noise_type": self.noise_type, "noise": self.noise, - "max_atoms": self.max_atoms, "data_seed": self.data_seed, } diff --git a/deepmd/dpmodel/utils/unimol_transform.py b/deepmd/dpmodel/utils/unimol_transform.py index a96c7dbd5d..04a9a055cb 100644 --- a/deepmd/dpmodel/utils/unimol_transform.py +++ b/deepmd/dpmodel/utils/unimol_transform.py @@ -353,9 +353,11 @@ def make_unimol_data_transform( type_to_token = np.array( [token_of.get(sym, unk) for sym in type_map], dtype=np.int64 ) + # A token the model cannot express must not be written back as [MASK], + # which would turn an ordinary atom into a corrupted one. Such tokens are + # marked here and refused if they ever appear. token_to_type = np.array( - [type_index.get(sym, type_index[mask_token]) for sym in vocabulary], - dtype=np.int64, + [type_index.get(sym, -1) for sym in vocabulary], dtype=np.int64 ) # Upstream draws a replacement over all 26 of its elements. A model whose # type_map covers fewer of them could not express the others, and mapping @@ -376,11 +378,16 @@ def make_unimol_data_transform( if i not in specials and sym not in type_index ] excluded = [*specials, *inexpressible] - visits: dict[int, int] = {} + # One counter for the whole transform rather than one entry per frame: a + # dataset of a hundred million frames would otherwise grow a dictionary + # entry for each. Advancing it on every call still gives a frame a fresh + # corruption every time it comes round, which is what the epoch number + # does upstream. + calls = [0] def transform(frame: dict, index: int) -> dict: - epoch = visits.get(index, 0) + 1 - visits[index] = epoch + calls[0] += 1 + epoch = calls[0] coord = np.asarray(frame["coord"], dtype=np.float64).reshape(-1, 3) atype = np.asarray(frame["atype"], dtype=np.int64).reshape(-1) coord = center_coordinates(coord) @@ -400,7 +407,17 @@ def transform(frame: dict, index: int) -> dict: ) frame = dict(frame) frame["coord"] = corrupted["coordinates"].astype(np.float64) - frame["atype"] = token_to_type[corrupted["tokens"]] + new_types = token_to_type[corrupted["tokens"]] + if np.any(new_types < 0): + unknown = sorted( + {vocabulary[tok] for tok in corrupted["tokens"][new_types < 0]} + ) + raise ValueError( + f"frame {index} holds element(s) {unknown} that the model's " + "type_map cannot express; convert the data with a type_map that " + "covers them, or leave those molecules out" + ) + frame["atype"] = new_types # Targets stay in Uni-Mol token space, which is what the element head # predicts over; unselected atoms carry the padding id. frame["unimol_token_target"] = corrupted["targets"].astype(np.int64) diff --git a/deepmd/pt_expt/train/training.py b/deepmd/pt_expt/train/training.py index 06fb1d398f..20117dbdbe 100644 --- a/deepmd/pt_expt/train/training.py +++ b/deepmd/pt_expt/train/training.py @@ -1879,24 +1879,28 @@ def __init__( ) # A self-supervised objective builds its labels by corrupting the # input, which has to happen as the data is read. - frame_transform = self.losses[model_key].frame_transform( - self.model_params_by_task[model_key]["type_map"] - ) - if frame_transform is not None: - for dataset in ( - self.training_data_by_task[model_key], - self.validation_data_by_task[model_key], - ): - if dataset is None: - continue - if not hasattr(dataset, "set_frame_transform"): - raise ValueError( - f"the {self.losses[model_key].__class__.__name__} " - "objective corrupts its input as the data is read, " - "which this dataset type does not support; convert " - "the data to LMDB first" - ) - dataset.set_frame_transform(frame_transform) + for dataset in ( + self.training_data_by_task[model_key], + self.validation_data_by_task[model_key], + ): + if dataset is None: + continue + # A fresh transform per dataset: it carries the counter that + # stands in for the epoch, so sharing one would let validation + # passes advance the training corruption. + frame_transform = self.losses[model_key].frame_transform( + self.model_params_by_task[model_key]["type_map"] + ) + if frame_transform is None: + break + if not hasattr(dataset, "set_frame_transform"): + raise ValueError( + f"the {self.losses[model_key].__class__.__name__} " + "objective corrupts its input as the data is read, " + "which this dataset type does not support; convert " + "the data to LMDB first" + ) + dataset.set_frame_transform(frame_transform) if self.multi_task: valid_params = ( training_params["data_dict"][model_key].get("validation_data", {}) diff --git a/deepmd/utils/argcheck.py b/deepmd/utils/argcheck.py index a6a1938366..a4a0f6f197 100644 --- a/deepmd/utils/argcheck.py +++ b/deepmd/utils/argcheck.py @@ -5546,7 +5546,6 @@ def loss_unimol() -> list[Argument]: "Coordinate noise distribution: 'uniform', 'normal', 'trunc_normal' or 'none'." ) doc_noise = "Scale of the coordinate noise, in the units of the coordinates." - doc_loss_max_atoms = "Crop molecules larger than this before corrupting them." doc_data_seed = ( "Seed of the corruption, combined with the epoch and the frame index." ) @@ -5608,7 +5607,6 @@ def loss_unimol() -> list[Argument]: "noise_type", str, optional=True, default="uniform", doc=doc_noise_type ), Argument("noise", [float, int], optional=True, default=1.0, doc=doc_noise), - Argument("max_atoms", int, optional=True, default=256, doc=doc_loss_max_atoms), Argument("data_seed", int, optional=True, default=1, doc=doc_data_seed), ] diff --git a/deepmd/utils/unimol_checkpoint.py b/deepmd/utils/unimol_checkpoint.py index 71db08203f..e1b6b8b692 100644 --- a/deepmd/utils/unimol_checkpoint.py +++ b/deepmd/utils/unimol_checkpoint.py @@ -114,10 +114,45 @@ def _set_layer_norm(layer: Any, state: dict[str, np.ndarray], prefix: str) -> No layer.b = state[prefix + ".bias"].astype(layer.b.dtype) +def _check_architecture( + descriptor: "DescrptUniMol", state_dict: dict[str, np.ndarray] +) -> None: + """Refuse a descriptor whose shape does not match the checkpoint. + + Architecture overrides are easy to get wrong, and a mismatch would + otherwise pass silently: too few layers would ignore the rest of the + checkpoint, and a different width would be caught only as a dtype or shape + error somewhere deep in the first forward pass. + """ + layers = len( + {k.split(".")[2] for k in state_dict if k.startswith("encoder.layers.")} + ) + if layers != len(descriptor.encoder.layers): + raise ValueError( + f"the checkpoint holds {layers} encoder layers but the descriptor " + f"was built with {len(descriptor.encoder.layers)}" + ) + expected = { + "embed_tokens.weight": descriptor.embed_tokens.w.shape, + "encoder.layers.0.fc1.weight": descriptor.encoder.layers[0].fc1.w.shape[::-1], + "gbf.mul.weight": descriptor.gbf.mul.w.shape, + "gbf.means.weight": descriptor.gbf.means.w.shape, + } + for key, shape in expected.items(): + found = tuple(state_dict[key].shape) + if found != tuple(shape): + raise ValueError( + f"the checkpoint's {key} has shape {found}, but the descriptor " + f"expects {tuple(shape)}; check the architecture overrides and " + "the type_map against the checkpoint" + ) + + def apply_unimol_backbone( descriptor: "DescrptUniMol", state_dict: dict[str, np.ndarray] ) -> None: """Load backbone parameters into a descriptor, in place.""" + _check_architecture(descriptor, state_dict) # The embedding and the four basis tables are layers, so their values live # in ``w``; they carry no transpose because they are lookup tables rather # than projections. diff --git a/examples/unimol/pretrain/input.json b/examples/unimol/pretrain/input.json index c7c3bd326c..97d630fa7b 100644 --- a/examples/unimol/pretrain/input.json +++ b/examples/unimol/pretrain/input.json @@ -44,7 +44,8 @@ "attention_dropout": 0.1, "single_precision_basis": true, "single_precision_distance": false, - "seed": 1 + "seed": 1, + "_comment_max_atoms": "the size cap is applied by the converter, before batching; the descriptor and the distance head only declare the width they expect" }, "fitting_net": { "type": "unimol_pretrain", @@ -77,7 +78,6 @@ "random_token_prob": 0.05, "noise_type": "uniform", "noise": 1.0, - "max_atoms": 256, "data_seed": 1 }, "training": { diff --git a/source/tests/common/dpmodel/test_unimol_data.py b/source/tests/common/dpmodel/test_unimol_data.py index f3600c2ddb..eef4b15e76 100644 --- a/source/tests/common/dpmodel/test_unimol_data.py +++ b/source/tests/common/dpmodel/test_unimol_data.py @@ -191,6 +191,34 @@ def test_the_objective_supplies_its_own_transform(self) -> None: len(np.asarray(after["unimol_token_target"])), len(after["atype"]) ) + def test_an_element_outside_unimol_vocabulary_is_refused(self) -> None: + """Rewriting it as [MASK] would quietly corrupt an ordinary atom. + + Uni-Mol knows 26 elements. A model whose type_map goes beyond them + tokenizes the extras as [UNK], and [UNK] has no type to come back to. + """ + wider = ["C", "N", "O", "H", "Mg", "[MASK]"] + transform = make_unimol_data_transform(wider, seed=1) + frame = { + "coord": np.array( + [[0.0, 0.0, 0.0], [1.5, 0.0, 0.0], [0.0, 1.5, 0.0], [0.0, 0.0, 1.5]] + ), + # the third atom is magnesium, which Uni-Mol has no token for + "atype": np.array([0, 1, 4, 3], dtype=np.int64), + } + with self.assertRaisesRegex(ValueError, "cannot express"): + transform(frame, 0) + + # Without it, the same frame goes through. + ordinary = make_unimol_data_transform(["C", "N", "O", "H", "[MASK]"], seed=1) + ordinary( + { + "coord": frame["coord"], + "atype": np.array([0, 1, 2, 3], dtype=np.int64), + }, + 0, + ) + def test_transform_requires_the_mask_pseudo_element(self) -> None: with self.assertRaisesRegex(ValueError, r"\[MASK\]"): make_unimol_data_transform(list(UNIMOL_ELEMENTS)) diff --git a/source/tests/pt_expt/model/test_unimol.py b/source/tests/pt_expt/model/test_unimol.py index c75acd98ea..6ed10811dd 100644 --- a/source/tests/pt_expt/model/test_unimol.py +++ b/source/tests/pt_expt/model/test_unimol.py @@ -508,6 +508,43 @@ def test_imports_upstream_named_weights(self) -> None: atol=0, ) + def test_rejects_a_descriptor_the_checkpoint_does_not_fit(self) -> None: + """A wrong architecture override must not load silently.""" + from deepmd.dpmodel.descriptor.unimol import DescrptUniMol as DescrptUniMolDP + from deepmd.utils.unimol_checkpoint import ( + apply_unimol_backbone, + split_unimol_state_dict, + ) + + golden = np.load(GOLDEN) + state = { + k[len("small_weights/") :]: v + for k, v in golden.items() + if k.startswith("small_weights/") + } + backbone, _ = split_unimol_state_dict(state) + wrong_depth = DescrptUniMolDP( + type_map=[*UNIMOL_ELEMENTS, "[MASK]"], + encoder_layers=SMALL["layers"] + 1, + encoder_embed_dim=SMALL["dim"], + encoder_ffn_embed_dim=SMALL["ffn"], + encoder_attention_heads=SMALL["heads"], + max_atoms=16, + ) + with self.assertRaisesRegex(ValueError, "encoder layers"): + apply_unimol_backbone(wrong_depth, backbone) + + wrong_width = DescrptUniMolDP( + type_map=[*UNIMOL_ELEMENTS, "[MASK]"], + encoder_layers=SMALL["layers"], + encoder_embed_dim=SMALL["dim"] * 2, + encoder_ffn_embed_dim=SMALL["ffn"], + encoder_attention_heads=SMALL["heads"], + max_atoms=16, + ) + with self.assertRaisesRegex(ValueError, "expects"): + apply_unimol_backbone(wrong_width, backbone) + class TestUniMolTraining(unittest.TestCase): """A run from a configuration file, which is how the feature is used. From 7f486c1d6cfcab9a418b3897c9cd400fbdac1885 Mon Sep 17 00:00:00 2001 From: Duo <50307526+iProzd@users.noreply.github.com> Date: Sat, 12 Sep 2026 13:54:32 -0400 Subject: [PATCH 27/34] fix: keep the objective finite when nothing is corrupted Two more from the review. The number of corrupted atoms is rounded stochastically, so a frame can draw none at all, and a batch of one such frame leaves every term a mean over an empty set. Upstream returns NaN there and it would reach backward, so the one division is guarded and the smooth-L1 mean returns zero on an empty input. Batches that select something are bit-for-bit unchanged; the objective still agrees with upstream to 7.7e-07 on the released weights. The masked-token branch of the element head indexed with a boolean mask alongside a slice, which the array API allows only as a sole index. --- deepmd/dpmodel/descriptor/unimol_nn/heads.py | 3 ++- deepmd/dpmodel/loss/unimol.py | 15 ++++++++--- source/tests/common/dpmodel/test_unimol.py | 28 ++++++++++++++++++++ 3 files changed, 42 insertions(+), 4 deletions(-) diff --git a/deepmd/dpmodel/descriptor/unimol_nn/heads.py b/deepmd/dpmodel/descriptor/unimol_nn/heads.py index 6c1d5b6763..e96db45952 100644 --- a/deepmd/dpmodel/descriptor/unimol_nn/heads.py +++ b/deepmd/dpmodel/descriptor/unimol_nn/heads.py @@ -86,7 +86,8 @@ def call(self, features, masked_tokens=None): # noqa: ANN001, ANN201 """ xp = array_api_compat.array_namespace(features) if masked_tokens is not None: - features = features[xp.astype(masked_tokens, xp.bool), :] + # sole index: the array API allows a boolean mask only on its own + features = features[xp.astype(masked_tokens, xp.bool)] return self.out_proj(self.layer_norm(self.dense(features))) def serialize(self) -> dict: diff --git a/deepmd/dpmodel/loss/unimol.py b/deepmd/dpmodel/loss/unimol.py index a85763b653..5f19444cb6 100644 --- a/deepmd/dpmodel/loss/unimol.py +++ b/deepmd/dpmodel/loss/unimol.py @@ -48,6 +48,15 @@ def _smooth_l1(pred: Array, label: Array, beta: float = 1.0) -> Array: xp = array_api_compat.array_namespace(pred) diff = xp.abs(pred - label) elementwise = xp.where(diff < beta, 0.5 * diff**2 / beta, diff - 0.5 * beta) + if 0 in elementwise.shape: + # The number of corrupted atoms is rounded stochastically, so a frame + # can draw none at all, and a batch of one such frame leaves nothing to + # average. Upstream returns NaN there, which would go on to poison every + # weight; zero is the only finite answer. Non-empty batches, which is + # every batch upstream ever trained on, are untouched. + return xp.zeros( + (), dtype=elementwise.dtype, device=array_api_compat.device(elementwise) + ) return xp.mean(elementwise) @@ -117,9 +126,9 @@ def _masked_nll(logits: Array, target: Array, pad_idx: int) -> Array: picked = xp.take_along_axis(log_probs, xp.reshape(target, (-1, 1)), axis=1) picked = xp.reshape(picked, (-1,)) picked = xp.where(keep, picked, xp.zeros_like(picked)) - return -xp.sum(picked) / xp.astype( - xp.sum(xp.astype(keep, logits.dtype)), logits.dtype - ) + count = xp.astype(xp.sum(xp.astype(keep, logits.dtype)), logits.dtype) + # An empty selection would divide zero by zero; see :func:`_smooth_l1`. + return -xp.sum(picked) / xp.where(count > 0, count, xp.ones_like(count)) @Loss.register("unimol") diff --git a/source/tests/common/dpmodel/test_unimol.py b/source/tests/common/dpmodel/test_unimol.py index 7787cab323..65830dc747 100644 --- a/source/tests/common/dpmodel/test_unimol.py +++ b/source/tests/common/dpmodel/test_unimol.py @@ -537,6 +537,34 @@ def test_terms_match_upstream(self) -> None: atol=1e-8, ) + def test_an_empty_selection_stays_finite(self) -> None: + """A frame can draw no corrupted atom, and then there is nothing to average. + + The count is rounded stochastically, so a small molecule in a batch of + one reaches this. Every term would be a mean over an empty set; NaN + there would spread to every weight at the next backward pass. + """ + nf, nloc, ncol = 1, 4, 8 + rng = np.random.default_rng(0) + pred = { + "token_logits": rng.normal(size=(nf, nloc, SMALL["vocab"])), + "coord_update": rng.normal(size=(nf, nloc, 3)), + "pair_dist": rng.normal(size=(nf, nloc, ncol)), + "x_norm": np.zeros((nf, nloc, 1)), + "delta_pair_norm": np.zeros((nf, nloc, 1)), + "mask": np.ones((nf, nloc), dtype=np.int64), + } + labels = { + # every position is padding, which is what "nothing selected" means + "unimol_token_target": np.zeros((nf, nloc), dtype=np.int64), + "unimol_coord_target": rng.normal(size=(nf, nloc, 3)), + } + total, more = UniMolLoss().call(1.0, 0, pred, labels) + self.assertTrue(np.isfinite(float(total))) + for name, term in more.items(): + with self.subTest(term=name): + self.assertTrue(np.isfinite(float(term))) + def test_derived_labels_match_explicit_ones(self) -> None: """Storing the distance target would cost O(natoms^2) per frame. From ee98665874ec8541547e1f05ec960c7056ba6af3 Mon Sep 17 00:00:00 2001 From: Duo <50307526+iProzd@users.noreply.github.com> Date: Sat, 12 Sep 2026 14:10:12 -0400 Subject: [PATCH 28/34] fix: let the corruption survive a decoder worker The reader decodes batches in spawned worker processes, which pickle whatever the decoder configuration carries. The corruption was a closure, so a run with the default worker count and a batch no smaller than that count died with "Can't pickle local object" the moment it drew its first batch. It is a class now, and a test pickles it. Being picklable is not enough on its own. Each batch sends the worker a fresh copy, so a counter standing in for the epoch resets over and over and every visit corrupts a molecule the same way -- which is what the counter existed to prevent. The number that stands in for the epoch is therefore drawn from a generator that lives in the process, keyed by the transform, and the option documents what that costs: a run is reproducible from data_seed only when one process decodes it. The element check moved to the input side. Refusing a frame only when an unexpressible token survived the corruption meant the refusal depended on the draw, so the same molecule was refused or silently masked depending on the day. An element Uni-Mol has no token for is now refused as soon as it is seen. A misspelt noise_type fell through to adding no noise at all, which trains on clean coordinates and looks like a converged run. It is rejected. The converter builds beside its destination and moves it into place, so a malformed record hours in no longer destroys the dataset it was replacing, and a conversion that yields no usable frame says so rather than writing a dataset whose first read raises. The pickle trust boundary is documented where a reader will meet it. adam_eps was registered for Adam only, while the trainer passed it to AdamW too, where a user-supplied value was rejected as unknown. AdamW declares it now. --- deepmd/dpmodel/utils/unimol_transform.py | 252 +++++++++++------- deepmd/pt_expt/train/training.py | 3 +- deepmd/utils/argcheck.py | 23 +- deepmd/utils/unimol_data.py | 36 ++- doc/model/unimol.md | 10 +- examples/unimol/pretrain/input.json | 3 +- .../tests/common/dpmodel/test_unimol_data.py | 30 ++- 7 files changed, 251 insertions(+), 106 deletions(-) diff --git a/deepmd/dpmodel/utils/unimol_transform.py b/deepmd/dpmodel/utils/unimol_transform.py index 04a9a055cb..3316efdc0c 100644 --- a/deepmd/dpmodel/utils/unimol_transform.py +++ b/deepmd/dpmodel/utils/unimol_transform.py @@ -25,6 +25,7 @@ """ import contextlib +import os from collections.abc import ( Callable, Iterator, @@ -34,6 +35,7 @@ import numpy as np __all__ = [ + "UniMolFrameTransform", "add_bos_eos", "center_coordinates", "crop_atoms", @@ -197,10 +199,17 @@ def noise_f(n): # noqa: ANN001, ANN202 def noise_f(n): # noqa: ANN001, ANN202 return np.random.uniform(low=-noise, high=noise, size=(n, 3)) # noqa: NPY002 - else: + elif noise_type == "none": def noise_f(n): # noqa: ANN001, ANN202 return 0.0 + else: + # Upstream's fall-through silently adds no noise at all, which turns a + # misspelt setting into a run that trains on clean coordinates. + raise ValueError( + f"unknown noise_type {noise_type!r}; it must be one of " + "'uniform', 'normal', 'trunc_normal' or 'none'" + ) with numpy_seed(seed, epoch, index): sz = len(tokens) @@ -289,14 +298,34 @@ def edge_type(tokens: np.ndarray, num_types: int) -> np.ndarray: return (tokens[:, None] * num_types + tokens[None, :]).astype(np.int64) -def make_unimol_data_transform( - type_map: Sequence[str], - *, - seed: int = 1, - mask_token: str = "[MASK]", - **mask_kwargs: float | str, -) -> Callable[[dict, int], dict]: - """Build the per-frame transform a deepmd data reader installs. +_EPOCH_STREAMS: dict[tuple[int, int], np.random.Generator] = {} + + +def _next_epoch(stream: int, seed: int | None) -> int: + """Draw the number that stands in for upstream's epoch. + + Upstream seeds each sample with ``(seed, epoch, index)``, so a molecule is + corrupted differently in every epoch. There is no epoch to read here, and no + counter would do: decoding runs in worker processes that receive a fresh + copy of the transform for every batch, so anything the transform carries is + reset over and over and the corruption freezes. The generator therefore + lives in the process, keyed by the transform's stream, and each call draws + the next number from it. + + A run is reproducible only when one process decodes it; with workers, the + draws depend on how frames were distributed. That is upstream's situation + too, whose stream also depends on its loader count. + """ + key = (stream, os.getpid()) + rng = _EPOCH_STREAMS.get(key) + if rng is None: + entropy = [stream, os.getpid()] if seed is None else [seed, stream, os.getpid()] + _EPOCH_STREAMS[key] = rng = np.random.default_rng(entropy) + return int(rng.integers(1 << 62)) + + +class UniMolFrameTransform: + """The per-frame corruption a deepmd data reader installs. The reader hands over one already-converted frame, so the conformer draw, the hydrogen policy and the size cap are behind us; what remains is centring @@ -310,12 +339,8 @@ def make_unimol_data_transform( shorter frame would not match the batch it belongs to. The converter applies the size cap instead. - Upstream draws a fresh corruption for the same molecule in every epoch. The - transform reproduces that by counting how often it has been handed each - frame and using that count where upstream uses the epoch number, so a - molecule is corrupted differently each time it comes round. With more than - one loader process each keeps its own count, which changes the stream but - not its statistics. + This is a class rather than a closure because the reader may decode in + worker processes, which pickle whatever the decoder configuration carries. Parameters ---------- @@ -323,101 +348,116 @@ def make_unimol_data_transform( Element names of the model. It must contain ``mask_token``, since a masked atom has to be expressible as a type. seed : int - Together with the frame index and the visit count, this seeds the + Together with the frame index and the epoch draw, this seeds the corruption, the way upstream seeds it per sample and epoch. mask_token : str Name of the pseudo-element standing for ``[MASK]``. **mask_kwargs Passed to :func:`mask_points`. - - Returns - ------- - callable - ``transform(frame, index) -> frame``, matching the reader's hook. """ - from deepmd.dpmodel.descriptor.unimol import ( - unimol_vocabulary, - ) - vocabulary = unimol_vocabulary() - token_of = {sym: i for i, sym in enumerate(vocabulary)} - type_map = list(type_map) - if mask_token not in type_map: - raise ValueError( - f"the model type_map must contain {mask_token!r} for Uni-Mol " - "pretraining, because masked atoms are carried as a pseudo-element" + def __init__( + self, + type_map: Sequence[str], + *, + seed: int = 1, + mask_token: str = "[MASK]", + **mask_kwargs: float | str, + ) -> None: + from deepmd.dpmodel.descriptor.unimol import ( + unimol_vocabulary, ) - type_index = {sym: i for i, sym in enumerate(type_map)} - unk = token_of["[UNK]"] - pad = token_of["[PAD]"] - type_to_token = np.array( - [token_of.get(sym, unk) for sym in type_map], dtype=np.int64 - ) - # A token the model cannot express must not be written back as [MASK], - # which would turn an ordinary atom into a corrupted one. Such tokens are - # marked here and refused if they ever appear. - token_to_type = np.array( - [type_index.get(sym, -1) for sym in vocabulary], dtype=np.int64 - ) - # Upstream draws a replacement over all 26 of its elements. A model whose - # type_map covers fewer of them could not express the others, and mapping - # them onto [MASK] would quietly turn a random-element atom into a masked - # one, so they are excluded from the draw instead. With the full element - # set, which is what the example configures, nothing is excluded and the - # distribution is upstream's. - if "epoch" in mask_kwargs: - raise TypeError( - "the epoch is not fixed at build time: the transform advances it " - "every time it sees a frame, so that a molecule is corrupted " - "differently each time it comes round" + + vocabulary = unimol_vocabulary() + token_of = {sym: i for i, sym in enumerate(vocabulary)} + type_map = list(type_map) + if mask_token not in type_map: + raise ValueError( + f"the model type_map must contain {mask_token!r} for Uni-Mol " + "pretraining, because masked atoms are carried as a pseudo-element" + ) + if "epoch" in mask_kwargs: + raise TypeError( + "the epoch is not fixed at build time: the transform draws a new " + "one every time it sees a frame, so that a molecule is corrupted " + "differently each time it comes round" + ) + type_index = {sym: i for i, sym in enumerate(type_map)} + self.vocabulary = vocabulary + self.seed = seed + self.mask_kwargs = mask_kwargs + self.pad = token_of["[PAD]"] + self.mask_idx = token_of[mask_token] + self.type_to_token = np.array( + [token_of.get(sym, token_of["[UNK]"]) for sym in type_map], dtype=np.int64 + ) + self.token_to_type = np.array( + [type_index.get(sym, 0) for sym in vocabulary], dtype=np.int64 + ) + # An element Uni-Mol has no token for tokenizes to [UNK], and [UNK] has + # no element to come back to: the atom would return as [MASK] if it were + # corrupted, and as whatever [UNK] mapped to if it were not. Refuse the + # frame instead of quietly changing an ordinary atom. + self.type_map = type_map + self.untokenizable = np.array( + [i for i, sym in enumerate(type_map) if sym not in token_of], + dtype=np.int64, ) - specials = [token_of[s] for s in ("[PAD]", "[CLS]", "[SEP]", "[UNK]", mask_token)] - inexpressible = [ - i - for i, sym in enumerate(vocabulary) - if i not in specials and sym not in type_index - ] - excluded = [*specials, *inexpressible] - # One counter for the whole transform rather than one entry per frame: a - # dataset of a hundred million frames would otherwise grow a dictionary - # entry for each. Advancing it on every call still gives a frame a fresh - # corruption every time it comes round, which is what the epoch number - # does upstream. - calls = [0] - - def transform(frame: dict, index: int) -> dict: - calls[0] += 1 - epoch = calls[0] + # Upstream draws a replacement over all 26 of its elements. A model + # whose type_map covers fewer of them could not express the others, and + # mapping them onto [MASK] would quietly turn a random-element atom into + # a masked one, so they are excluded from the draw instead. With the + # full element set, which is what the example configures, nothing is + # excluded and the distribution is upstream's. + specials = [ + token_of[s] for s in ("[PAD]", "[CLS]", "[SEP]", "[UNK]", mask_token) + ] + self.excluded = [ + *specials, + *( + i + for i, sym in enumerate(vocabulary) + if i not in specials and sym not in type_index + ), + ] + # Identifies this transform's draw sequence within a process, so that + # the training and the validation set do not share one. + self.stream = int(np.random.SeedSequence().generate_state(1)[0]) + + def __call__(self, frame: dict, index: int) -> dict: + """Corrupt one frame and attach the labels the objective reads.""" coord = np.asarray(frame["coord"], dtype=np.float64).reshape(-1, 3) atype = np.asarray(frame["atype"], dtype=np.int64).reshape(-1) + if self.untokenizable.size and bool(np.any(np.isin(atype, self.untokenizable))): + unknown = sorted( + {self.type_map[t] for t in atype[np.isin(atype, self.untokenizable)]} + ) + raise ValueError( + f"frame {index} holds element(s) {unknown} that Uni-Mol's " + "vocabulary cannot express; leave those molecules out, or train " + "on a type_map that Uni-Mol covers" + ) coord = center_coordinates(coord) - tokens = type_to_token[atype] + tokens = self.type_to_token[atype] corrupted = mask_points( tokens, coord, - num_types=len(vocabulary), - special_indices=excluded, - pad_idx=pad, - mask_idx=token_of[mask_token], - seed=seed, - epoch=epoch, + num_types=len(self.vocabulary), + special_indices=self.excluded, + pad_idx=self.pad, + mask_idx=self.mask_idx, + seed=self.seed, + epoch=_next_epoch(self.stream, self.seed), index=index, - **mask_kwargs, + **self.mask_kwargs, ) frame = dict(frame) frame["coord"] = corrupted["coordinates"].astype(np.float64) - new_types = token_to_type[corrupted["tokens"]] - if np.any(new_types < 0): - unknown = sorted( - {vocabulary[tok] for tok in corrupted["tokens"][new_types < 0]} - ) - raise ValueError( - f"frame {index} holds element(s) {unknown} that the model's " - "type_map cannot express; convert the data with a type_map that " - "covers them, or leave those molecules out" - ) - frame["atype"] = new_types + # Every token that can come out of the corruption is one the type_map + # expresses: the input was checked above, and the random replacement + # draws from the expressible elements only. + frame["atype"] = self.token_to_type[corrupted["tokens"]] # Targets stay in Uni-Mol token space, which is what the element head # predicts over; unselected atoms carry the padding id. frame["unimol_token_target"] = corrupted["targets"].astype(np.int64) @@ -426,7 +466,37 @@ def transform(frame: dict, index: int) -> dict: frame["find_unimol_coord_target"] = np.float32(1.0) return frame - return transform + +def make_unimol_data_transform( + type_map: Sequence[str], + *, + seed: int = 1, + mask_token: str = "[MASK]", + **mask_kwargs: float | str, +) -> Callable[[dict, int], dict]: + """Build the per-frame transform a deepmd data reader installs. + + See :class:`UniMolFrameTransform`, which this returns. + + Parameters + ---------- + type_map : Sequence[str] + Element names of the model, including ``mask_token``. + seed : int + Seeds the corruption, together with the frame index and the epoch draw. + mask_token : str + Name of the pseudo-element standing for ``[MASK]``. + **mask_kwargs + Passed to :func:`mask_points`. + + Returns + ------- + callable + ``transform(frame, index) -> frame``, matching the reader's hook. + """ + return UniMolFrameTransform( + type_map, seed=seed, mask_token=mask_token, **mask_kwargs + ) def unimol_frame_transform( diff --git a/deepmd/pt_expt/train/training.py b/deepmd/pt_expt/train/training.py index 20117dbdbe..e21a50a85e 100644 --- a/deepmd/pt_expt/train/training.py +++ b/deepmd/pt_expt/train/training.py @@ -2275,14 +2275,13 @@ def update_finetune_bias( float(optimizer_params["adam_beta2"]), ) weight_decay = float(optimizer_params["weight_decay"]) - adam_eps = float(optimizer_params.get("adam_eps", 1e-8)) if opt_type in ("Adam", "AdamW"): self.optimizer = self._create_optimizer( torch.optim.Adam if opt_type == "Adam" else torch.optim.AdamW, lr=initial_lr, betas=adam_betas, - eps=adam_eps, + eps=float(optimizer_params["adam_eps"]), weight_decay=weight_decay, ) else: diff --git a/deepmd/utils/argcheck.py b/deepmd/utils/argcheck.py index a4a0f6f197..f5de3a02bd 100644 --- a/deepmd/utils/argcheck.py +++ b/deepmd/utils/argcheck.py @@ -4534,6 +4534,10 @@ def optimizer_adam() -> list[Argument]: def optimizer_adamw() -> list[Argument]: doc_adam_beta1 = "AdamW beta1 coefficient for first moment decay." doc_adam_beta2 = "AdamW beta2 coefficient for second moment decay." + doc_adam_eps = ( + "AdamW epsilon, added for numerical stability. The default is PyTorch's own; " + "recipes carried over from other frameworks sometimes assume a different one." + ) doc_weight_decay = "Decoupled weight decay coefficient for the AdamW optimizer." return [ Argument( @@ -4550,6 +4554,13 @@ def optimizer_adamw() -> list[Argument]: default=0.999, doc=supported_backends("pt", "pd", "tf2") + doc_adam_beta2, ), + Argument( + "adam_eps", + float, + optional=True, + default=1e-8, + doc=supported_backends("pt_expt") + doc_adam_eps, + ), Argument( "weight_decay", float, @@ -5547,7 +5558,11 @@ def loss_unimol() -> list[Argument]: ) doc_noise = "Scale of the coordinate noise, in the units of the coordinates." doc_data_seed = ( - "Seed of the corruption, combined with the epoch and the frame index." + "Seed of the corruption, combined with the frame index and a per-visit " + "draw so that a molecule is corrupted differently each time it comes " + "round. A run is reproducible from it only when one process decodes the " + "data (DP_LMDB_NUM_WORKERS=0); with decoder workers the draws follow how " + "frames were distributed." ) return [ Argument( @@ -5604,7 +5619,11 @@ def loss_unimol() -> list[Argument]: doc=doc_random_token_prob, ), Argument( - "noise_type", str, optional=True, default="uniform", doc=doc_noise_type + "noise_type", + str, + optional=True, + default="uniform", + doc=doc_noise_type, ), Argument("noise", [float, int], optional=True, default=1.0, doc=doc_noise), Argument("data_seed", int, optional=True, default=1, doc=doc_data_seed), diff --git a/deepmd/utils/unimol_data.py b/deepmd/utils/unimol_data.py index 0a87e4aa9d..edf6230dbc 100644 --- a/deepmd/utils/unimol_data.py +++ b/deepmd/utils/unimol_data.py @@ -50,6 +50,12 @@ def _encode_array(arr: np.ndarray) -> dict[str, Any]: def read_unimol_lmdb(path: str) -> Iterator[dict[str, Any]]: """Stream molecules out of a Uni-Mol LMDB file. + .. warning:: + Upstream stores each record as a Python pickle, and unpickling runs + whatever the file says. Convert only files you obtained from a source you + trust; the conversion this module performs is one-off, and the deepmd + dataset it writes is msgpack, which carries no such risk. + Parameters ---------- path : str @@ -70,8 +76,8 @@ def read_unimol_lmdb(path: str) -> Iterator[dict[str, Any]]: with env.begin() as txn: cursor = txn.cursor() for _, value in cursor: - # The upstream records are pickles written by the dataset - # authors; only convert files you trust. + # See the warning above: this executes whatever the file + # says, because that is the format upstream published. yield pickle.loads(value) finally: env.close() @@ -144,9 +150,14 @@ def convert_unimol_lmdb( names = list(type_map) if type_map is not None else list(UNIMOL_ELEMENTS) index_of = {sym: i for i, sym in enumerate(names)} - if os.path.exists(dst): - shutil.rmtree(dst) - env = lmdb.open(dst, map_size=map_size) + # Build beside the destination and move it into place at the end. A source + # error, a malformed record or an RDKit failure part way through would + # otherwise have destroyed a dataset that took hours to write and left + # nothing, or half of something, in its place. + staging = f"{dst}.partial" + if os.path.exists(staging): + shutil.rmtree(staging) + env = lmdb.open(staging, map_size=map_size) fmt = "012d" frame_idx = 0 molecules = 0 @@ -212,8 +223,21 @@ def convert_unimol_lmdb( "system_info": {"nframes": frame_idx}, } txn.put(b"__metadata__", msgpack.packb(metadata, use_bin_type=True)) - finally: + except BaseException: env.close() + shutil.rmtree(staging, ignore_errors=True) + raise + env.close() + if frame_idx == 0: + shutil.rmtree(staging, ignore_errors=True) + raise ValueError( + f"{src} produced no usable frame, so there is nothing to train on; " + f"{skipped} record(s) were skipped for holding one atom or an " + "element outside the type_map" + ) + if os.path.exists(dst): + shutil.rmtree(dst) + os.rename(staging, dst) return {"molecules": molecules, "frames": frame_idx, "skipped": skipped} diff --git a/doc/model/unimol.md b/doc/model/unimol.md index 495363f884..75625fd811 100644 --- a/doc/model/unimol.md +++ b/doc/model/unimol.md @@ -98,10 +98,18 @@ transposes every weight, so there is no "just add a prefix" path. ## Converting the pretraining data +The released archive holds `train.lmdb` and `valid.lmdb`; the example +configuration expects both, converted separately: + ```sh -python -m deepmd.utils.unimol_data ligands.lmdb ./unimol_train --add-2d-conformer +python -m deepmd.utils.unimol_data ligands/train.lmdb ./unimol_train --add-2d-conformer +python -m deepmd.utils.unimol_data ligands/valid.lmdb ./unimol_valid --add-2d-conformer ``` +Upstream stores each molecule as a Python pickle, so the converter unpickles +whatever the file contains; run it only on data from a source you trust. What it +writes is msgpack, which carries no such risk, and the conversion happens once. + One conformer becomes one frame, so ordinary frame sampling stands in for upstream's per-epoch conformer draw. The two-dimensional RDKit conformer that upstream appends while loading is added at conversion time, behind that flag, diff --git a/examples/unimol/pretrain/input.json b/examples/unimol/pretrain/input.json index 97d630fa7b..f236a4eb99 100644 --- a/examples/unimol/pretrain/input.json +++ b/examples/unimol/pretrain/input.json @@ -88,7 +88,8 @@ }, "validation_data": { "systems": "./unimol_valid", - "batch_size": 16 + "batch_size": 16, + "_comment": "convert the released valid.lmdb the same way as train.lmdb" }, "numb_steps": 1000000, "seed": 1, diff --git a/source/tests/common/dpmodel/test_unimol_data.py b/source/tests/common/dpmodel/test_unimol_data.py index eef4b15e76..45fba0657b 100644 --- a/source/tests/common/dpmodel/test_unimol_data.py +++ b/source/tests/common/dpmodel/test_unimol_data.py @@ -159,6 +159,27 @@ def test_corruption_changes_between_visits(self) -> None: second = np.asarray(reader[0]["unimol_token_target"]).copy() self.assertFalse(np.array_equal(first, second)) + def test_the_transform_survives_a_worker_process(self) -> None: + """LMDB decoding runs in spawned workers, which pickle the decoder config. + + Each batch sends a fresh copy, so anything the transform carries is + reset over and over. A counter would therefore freeze the corruption, + and a closure would not have made it across at all. + """ + type_map = [*UNIMOL_ELEMENTS, "[MASK]"] + transform = make_unimol_data_transform(type_map, seed=1) + frame = { + "coord": np.array( + [[0.0, 0.0, 0.0], [1.5, 0.0, 0.0], [0.0, 1.5, 0.0], [0.0, 0.0, 1.5]] * 4 + ), + "atype": np.array([0, 1, 2, 3] * 4, dtype=np.int64), + } + drawn = set() + for _ in range(12): + revived = pickle.loads(pickle.dumps(transform)) + drawn.add(revived(frame, 0)["unimol_token_target"].tobytes()) + self.assertGreater(len(drawn), 1) + def test_the_objective_supplies_its_own_transform(self) -> None: """A trainer installs whatever the loss declares, and nothing else. @@ -195,7 +216,9 @@ def test_an_element_outside_unimol_vocabulary_is_refused(self) -> None: """Rewriting it as [MASK] would quietly corrupt an ordinary atom. Uni-Mol knows 26 elements. A model whose type_map goes beyond them - tokenizes the extras as [UNK], and [UNK] has no type to come back to. + tokenizes the extras as [UNK], and [UNK] has no element to come back to, + so such an atom would return as [MASK] once it happened to be selected. + The frame is refused whatever the draw does. """ wider = ["C", "N", "O", "H", "Mg", "[MASK]"] transform = make_unimol_data_transform(wider, seed=1) @@ -206,8 +229,9 @@ def test_an_element_outside_unimol_vocabulary_is_refused(self) -> None: # the third atom is magnesium, which Uni-Mol has no token for "atype": np.array([0, 1, 4, 3], dtype=np.int64), } - with self.assertRaisesRegex(ValueError, "cannot express"): - transform(frame, 0) + for _ in range(8): + with self.assertRaisesRegex(ValueError, "cannot express"): + transform(frame, 0) # Without it, the same frame goes through. ordinary = make_unimol_data_transform(["C", "N", "O", "H", "[MASK]"], seed=1) From e82b575c8e0f50e190f5b53cde803422d1a26c97 Mon Sep 17 00:00:00 2001 From: Duo <50307526+iProzd@users.noreply.github.com> Date: Sat, 12 Sep 2026 15:30:43 -0400 Subject: [PATCH 29/34] fix: honour a precision the backbone does not share Setting `precision` on this model failed outright. The backbone hands its output back at the global precision, because its own forward is wrapped in cast_precision, so heads configured at anything else were handed the wrong dtype and torch refused to multiply. The decorator could not cover it: it casts arrays it is given directly, and what the heads are given is a dictionary. The fitting casts that dictionary itself now, and casts the results back. Every test here pinned float64, which is the global precision, so none of them could see it; the failure turned up on a real training run. The new test asks for float32 and is in the torch suite deliberately -- NumPy upcasts a float64 activation against a float32 weight without complaint, so the array-API backend cannot fail this way and a test there would pass either way. Worth knowing: the released example inherits the float64 default, and float32 is about five times faster on the same data and hardware. --- deepmd/dpmodel/fitting/unimol_pretrain.py | 14 ++++++++++- source/tests/pt_expt/model/test_unimol.py | 29 +++++++++++++++++++++++ 2 files changed, 42 insertions(+), 1 deletion(-) diff --git a/deepmd/dpmodel/fitting/unimol_pretrain.py b/deepmd/dpmodel/fitting/unimol_pretrain.py index 923aeb3a33..bb804d60ea 100644 --- a/deepmd/dpmodel/fitting/unimol_pretrain.py +++ b/deepmd/dpmodel/fitting/unimol_pretrain.py @@ -26,6 +26,7 @@ ) from deepmd.dpmodel.common import ( NativeOP, + safe_cast_array, ) from deepmd.dpmodel.descriptor.unimol_nn import ( DistanceHead, @@ -259,6 +260,15 @@ def call_tokens(self, backbone: dict[str, Array]) -> dict[str, Array]: columns, padded out to ``max_atoms + 2``; the two norm regularisers are passed through for the loss. """ + # The backbone hands its output back at the global precision, because + # its own forward is wrapped in ``cast_precision``. These heads may be + # configured at a different one, so the dictionary is cast here and the + # results cast back on the way out. ``cast_precision`` cannot do it: it + # casts arrays it is handed directly, and this argument is a dictionary. + backbone = { + kk: safe_cast_array(vv, "global", self.precision) + for kk, vv in backbone.items() + } xp = array_api_compat.array_namespace(backbone["node_ebd"]) node = backbone["node_ebd"] nf, nt = node.shape[0], node.shape[1] @@ -303,7 +313,9 @@ def call_tokens(self, backbone: dict[str, Array]) -> dict[str, Array]: ) dist = xp.concat([dist, pad], axis=-1) out["pair_dist"] = dist - return out + return { + kk: safe_cast_array(vv, self.precision, "global") for kk, vv in out.items() + } def call(self, descriptor: Array, atype: Array, **kwargs) -> dict[str, Array]: # noqa: ANN003 """Not reachable: the heads need token-resolution inputs. diff --git a/source/tests/pt_expt/model/test_unimol.py b/source/tests/pt_expt/model/test_unimol.py index 6ed10811dd..6f5b353306 100644 --- a/source/tests/pt_expt/model/test_unimol.py +++ b/source/tests/pt_expt/model/test_unimol.py @@ -209,6 +209,35 @@ def test_registered_path_builds(self) -> None: ], ) + def test_a_non_default_precision_runs(self) -> None: + """Torch refuses to multiply a float64 activation by a float32 weight. + + The backbone returns its output at the global precision, because its own + forward casts back, so heads configured at another precision are handed + the wrong dtype. Every other test here pins float64, which is the global + precision and hides it. NumPy hides it too, by upcasting silently. + """ + import copy + + coord, atype, nlist = self.inputs() + config = copy.deepcopy(self.config(coord.shape[1])) + for part in ("descriptor", "fitting_net"): + config["model"][part]["precision"] = "float32" + model = get_model(normalize(config)["model"]) + model.eval() + # The other tests here overwrite every parameter with a CPU array while + # loading weights, which quietly moves the model; this one keeps the + # model where it was built, so the inputs go to it. + device = next(model.parameters()).device + out = model.forward_lower( + torch.as_tensor(coord, device=device), + torch.as_tensor(atype, device=device), + torch.as_tensor(nlist, device=device), + ) + for name in ("token_logits", "coord_update", "pair_dist"): + with self.subTest(output=name): + self.assertTrue(torch.isfinite(out[name]).all()) + def test_matches_the_array_api_implementation(self) -> None: """Same weights, same numbers, once the fp32 basis is out of the way. From aacaa7e56fd5a3517f4fc0894e7d6c75e66258ba Mon Sep 17 00:00:00 2001 From: Duo <50307526+iProzd@users.noreply.github.com> Date: Sat, 12 Sep 2026 23:38:37 -0400 Subject: [PATCH 30/34] feat: match the published LMDB layout, and train in single precision Two things the reference dataset at OMat24 settles. The converter wrote coordinates as float64 and types as int64, and gave every frame an `atom_names` list and an `orig` vector. The datasets already published in this format store float32 and int32, carry neither of those fields, and encode an array with three keys rather than five. The reader discards `atom_names` and `orig` on the way in, so they were dead weight in all 188 million frames. float32 is also what the source holds: upstream generated these conformers in single precision, so widening them stored zeros. The converted validation split goes from 3.6 GB to 1.4 GB and reads about eight percent faster; the same reader still reads both, and the reference dataset. The example now trains in single precision, which is what DPA models train in. The backbone is a transformer, not a potential energy surface, and the difference is not small: measured over the same 120 steps at the same batch on the same data, float64 takes 1.0263 s/batch and float32 takes 0.0924, eleven times faster. At the old default one pass over the pretraining set would have taken about seventy GPU-days. --- deepmd/utils/unimol_data.py | 20 +++++++++++++------- doc/model/unimol.md | 11 +++++++++++ examples/unimol/pretrain/input.json | 7 +++++-- 3 files changed, 29 insertions(+), 9 deletions(-) diff --git a/deepmd/utils/unimol_data.py b/deepmd/utils/unimol_data.py index edf6230dbc..0afe801966 100644 --- a/deepmd/utils/unimol_data.py +++ b/deepmd/utils/unimol_data.py @@ -37,11 +37,13 @@ def _encode_array(arr: np.ndarray) -> dict[str, Any]: - """Encode an array the way a deepmd LMDB frame stores it.""" + """Encode an array the way a deepmd LMDB frame stores it. + + Three keys, matching the datasets already published in this format; the + reader takes the dtype and shape from here and casts on the way out. + """ return { - "nd": None, "type": str(arr.dtype), - "kind": "", "shape": list(arr.shape), "data": arr.tobytes(), } @@ -199,12 +201,16 @@ def convert_unimol_lmdb( continue frame = { "atom_numbs": atom_numbs, - "atom_names": names, - "atom_types": _encode_array(atom_types), - "orig": _encode_array(np.zeros(3, dtype=np.float64)), + # int32 types and float32 coordinates, which is what the + # datasets already published in this format use, and + # what the source holds: upstream generated these + # conformers in single precision, so widening them here + # would store zeros. The reader casts to the precision + # the model asks for. + "atom_types": _encode_array(atom_types.astype(np.int32)), # No cell at all: molecules are not periodic, and a zero # cell would be taken for a real one and inverted. - "coords": _encode_array(coords.astype(np.float64)), + "coords": _encode_array(coords.astype(np.float32)), } txn.put( format(frame_idx, fmt).encode(), diff --git a/doc/model/unimol.md b/doc/model/unimol.md index 75625fd811..13fbc5c3c3 100644 --- a/doc/model/unimol.md +++ b/doc/model/unimol.md @@ -110,6 +110,13 @@ Upstream stores each molecule as a Python pickle, so the converter unpickles whatever the file contains; run it only on data from a source you trust. What it writes is msgpack, which carries no such risk, and the conversion happens once. +The result is an LMDB dataset in the same layout as the other datasets +published in this format: zero-padded twelve-digit keys, one msgpack frame each, +and a `__metadata__` entry. Coordinates are stored as `float32` and types as +`int32`, which is both what that format uses and what the source holds, since +upstream generated these conformers in single precision; the reader casts to +whatever precision the model asks for. Molecules carry no cell at all. + One conformer becomes one frame, so ordinary frame sampling stands in for upstream's per-epoch conformer draw. The two-dimensional RDKit conformer that upstream appends while loading is added at conversion time, behind that flag, @@ -135,6 +142,10 @@ floor at about 1e-7 relative on the whole objective: - `log_softmax` and both norm regularisers are evaluated in fp32, which is reproduced. +The example trains in single precision, which is the default DPA models train +in. This backbone is a transformer rather than a potential energy surface, so +double precision buys nothing and costs several times the training time. + Training trajectories cannot be reproduced exactly in any case: upstream pretrained a pure fp16 model with fused kernels and its own Adam variant, which places epsilon differently from PyTorch's. The example carries upstream's diff --git a/examples/unimol/pretrain/input.json b/examples/unimol/pretrain/input.json index f236a4eb99..bbb510cdc0 100644 --- a/examples/unimol/pretrain/input.json +++ b/examples/unimol/pretrain/input.json @@ -45,7 +45,9 @@ "single_precision_basis": true, "single_precision_distance": false, "seed": 1, - "_comment_max_atoms": "the size cap is applied by the converter, before batching; the descriptor and the distance head only declare the width they expect" + "_comment_max_atoms": "the size cap is applied by the converter, before batching; the descriptor and the distance head only declare the width they expect", + "precision": "float32", + "_comment_precision": "single precision, which is what DPA models train in by default; the backbone is a transformer, not a potential energy surface, so float64 buys nothing here and costs several times the training time" }, "fitting_net": { "type": "unimol_pretrain", @@ -53,7 +55,8 @@ "attention_heads": 64, "max_atoms": 256, "activation_function": "gelu_erf", - "seed": 1 + "seed": 1, + "precision": "float32" }, "_comment_type_map": "[MASK] is the pseudo-element a corrupted atom is carried as; the objective needs it" }, From 3b48dd1edde77dbe11c4dc1ddbe5bc668b83bab2 Mon Sep 17 00:00:00 2001 From: Duo <50307526+iProzd@users.noreply.github.com> Date: Mon, 14 Sep 2026 07:17:14 -0400 Subject: [PATCH 31/34] fix: act on the review of this pull request Three blocking, three not. The default virtual_token_position placed the virtual tokens at the centroid of the coordinates the descriptor was handed, which during pretraining are the corrupted ones, while the distance target places them at the origin. Every corrupted row's two virtual columns therefore trained against a label for a different position, off by about the size of the noise: on a six-atom frame with one noised atom the descriptor put them 0.17 A from where the target assumed. Pretraining now requires 'origin', which is where upstream puts them and, since the corruption centres every frame, where the clean centroid is -- so nothing is given up. The only end-to-end training test in the suite had been running with the wrong labels and now sets it, and a new test covers the default being refused rather than silently mistrained. The number standing in for the epoch was drawn from OS entropy, so two runs of one configuration disagreed and the seed's documented guarantee was false. That was a regression from fixing the worker-pickling bug: replacing the counter with a random stream id fixed the freeze but broke reproducibility. The stream is derived from the seed and a caller's label now, and the process id keys only the cache, never the seed. The converter deleted the old dataset before renaming the new one into place, leaving a window with neither -- the loss the staging directory exists to prevent. It moves the old one aside, renames, then removes it, and puts it back if the rename fails. Non-blocking: max_seq_len is documented as feeding get_rcut rather than as inert; _frame_scalar guards its divisor like the two reductions beside it; and the docs say the validation set is re-corrupted every pass, so its loss reads as a trend rather than a comparable number. --- .../atomic_model/unimol_atomic_model.py | 18 +++++++ deepmd/dpmodel/descriptor/unimol.py | 10 +++- deepmd/dpmodel/loss/unimol.py | 5 +- deepmd/dpmodel/utils/unimol_transform.py | 41 ++++++++++++--- deepmd/utils/unimol_data.py | 21 ++++++-- doc/model/unimol.md | 5 ++ examples/unimol/pretrain/input.json | 4 +- .../tests/common/dpmodel/test_unimol_data.py | 51 +++++++++++++++++++ source/tests/pt_expt/model/test_unimol.py | 18 +++++++ 9 files changed, 159 insertions(+), 14 deletions(-) diff --git a/deepmd/dpmodel/atomic_model/unimol_atomic_model.py b/deepmd/dpmodel/atomic_model/unimol_atomic_model.py index 8cc4c4dce9..f97ff40754 100644 --- a/deepmd/dpmodel/atomic_model/unimol_atomic_model.py +++ b/deepmd/dpmodel/atomic_model/unimol_atomic_model.py @@ -40,6 +40,24 @@ def __init__( ) if not isinstance(fitting, UniMolPretrainFitting): raise TypeError("DPUniMolAtomicModel needs the unimol_pretrain fitting") + # The objective compares against distances whose virtual tokens sit at + # the origin, which is where upstream puts them and, once the transform + # has centred a frame, where the clean centroid is. Under "centroid" the + # descriptor instead places them at the centroid of the coordinates it + # is handed -- the corrupted ones -- so the two virtual columns of every + # corrupted row would be regressed against a label for a different + # position, by about the size of the noise. Centring costs nothing here + # because the transform always centres, so the only effect would be that + # silent mismatch. + if getattr(descriptor, "virtual_token_position", "origin") != "origin": + raise ValueError( + "unimol pretraining needs virtual_token_position='origin': the " + "distance target places the virtual tokens at the origin, and " + f"this descriptor places them at the " + f"{descriptor.virtual_token_position}, so the two virtual " + "columns would train against the wrong label. The corruption " + "centres every frame, so 'origin' is the centroid anyway" + ) super().__init__(descriptor, fitting, type_map, **kwargs) def forward_atomic( diff --git a/deepmd/dpmodel/descriptor/unimol.py b/deepmd/dpmodel/descriptor/unimol.py index c91f2b7dd9..5c8e6d4ea6 100644 --- a/deepmd/dpmodel/descriptor/unimol.py +++ b/deepmd/dpmodel/descriptor/unimol.py @@ -88,7 +88,8 @@ class DescrptUniMol(NativeOP, BaseDescriptor): max_atoms : int Largest molecule accepted, which fixes ``sel``. max_seq_len : int - Upstream's sequence guard, kept for configuration compatibility. + Upstream's sequence guard. Nothing in the forward pass consults it, but + :meth:`get_rcut` reports a radius derived from it, so it is not inert. activation_function : str Activation of the blocks and heads. Uni-Mol uses the exact GELU. dropout, emb_dropout, attention_dropout, activation_dropout : float @@ -221,7 +222,12 @@ def __init__( # capability queries # ------------------------------------------------------------------ def get_rcut(self) -> float: - """All pairs are neighbours, so the radius is effectively unbounded.""" + """All pairs are neighbours, so the radius is effectively unbounded. + + The number is derived from ``max_seq_len`` only to scale with the + largest sequence configured; nothing compares against it as a real + cut-off, because this descriptor has none. + """ return float(self.max_seq_len) * 1e3 def get_rcut_smth(self) -> float: diff --git a/deepmd/dpmodel/loss/unimol.py b/deepmd/dpmodel/loss/unimol.py index 5f19444cb6..9644527aff 100644 --- a/deepmd/dpmodel/loss/unimol.py +++ b/deepmd/dpmodel/loss/unimol.py @@ -74,7 +74,10 @@ def _frame_scalar(value: Array, mask: Array | None) -> Array: if mask is None: return xp.mean(per_atom) weights = xp.astype(mask, per_atom.dtype) - return xp.sum(per_atom * weights) / xp.sum(weights) + total = xp.sum(weights) + # A frame of nothing but padding would divide zero by zero; the other two + # reductions in this file already refuse to, so this one does too. + return xp.sum(per_atom * weights) / xp.where(total > 0, total, xp.ones_like(total)) def _token_mask_from_atoms(mask: Array, ncol: int) -> Array: diff --git a/deepmd/dpmodel/utils/unimol_transform.py b/deepmd/dpmodel/utils/unimol_transform.py index 3316efdc0c..a25afee202 100644 --- a/deepmd/dpmodel/utils/unimol_transform.py +++ b/deepmd/dpmodel/utils/unimol_transform.py @@ -25,6 +25,7 @@ """ import contextlib +import hashlib import os from collections.abc import ( Callable, @@ -301,6 +302,20 @@ def edge_type(tokens: np.ndarray, num_types: int) -> np.ndarray: _EPOCH_STREAMS: dict[tuple[int, int], np.random.Generator] = {} +def _stream_entropy(value: int | str | None) -> int: + """A stable non-negative integer for a seed or a stream label. + + ``hash`` will not do: it is randomized per process for strings, which is + the very thing that would make a run irreproducible. + """ + if value is None: + return 0 + if isinstance(value, int): + return abs(int(value)) + digest = hashlib.blake2b(str(value).encode(), digest_size=8).digest() + return int.from_bytes(digest, "big") + + def _next_epoch(stream: int, seed: int | None) -> int: """Draw the number that stands in for upstream's epoch. @@ -312,14 +327,18 @@ def _next_epoch(stream: int, seed: int | None) -> int: lives in the process, keyed by the transform's stream, and each call draws the next number from it. - A run is reproducible only when one process decodes it; with workers, the - draws depend on how frames were distributed. That is upstream's situation - too, whose stream also depends on its loader count. + The generator is seeded from the configured seed and the stream alone, so + one process decoding a run twice draws the same sequence both times. With + workers the draws still depend on how frames were distributed between them. """ + # The process id keys the cache so a forked child builds its own generator + # rather than inheriting a half-consumed one. It deliberately does not enter + # the seed: it changes from run to run, and mixing it in would break the + # reproducibility this seed is supposed to provide. key = (stream, os.getpid()) rng = _EPOCH_STREAMS.get(key) if rng is None: - entropy = [stream, os.getpid()] if seed is None else [seed, stream, os.getpid()] + entropy = [stream] if seed is None else [seed, stream] _EPOCH_STREAMS[key] = rng = np.random.default_rng(entropy) return int(rng.integers(1 << 62)) @@ -362,6 +381,7 @@ def __init__( *, seed: int = 1, mask_token: str = "[MASK]", + stream: str = "default", **mask_kwargs: float | str, ) -> None: from deepmd.dpmodel.descriptor.unimol import ( @@ -421,8 +441,14 @@ def __init__( ), ] # Identifies this transform's draw sequence within a process, so that - # the training and the validation set do not share one. - self.stream = int(np.random.SeedSequence().generate_state(1)[0]) + # the training and the validation set do not share one. Derived from the + # configured seed and the caller's label rather than drawn from entropy: + # the same configuration has to corrupt the same way twice. + self.stream = int( + np.random.SeedSequence( + [_stream_entropy(seed), _stream_entropy(stream)] + ).generate_state(1)[0] + ) def __call__(self, frame: dict, index: int) -> dict: """Corrupt one frame and attach the labels the objective reads.""" @@ -472,6 +498,7 @@ def make_unimol_data_transform( *, seed: int = 1, mask_token: str = "[MASK]", + stream: str = "default", **mask_kwargs: float | str, ) -> Callable[[dict, int], dict]: """Build the per-frame transform a deepmd data reader installs. @@ -495,7 +522,7 @@ def make_unimol_data_transform( ``transform(frame, index) -> frame``, matching the reader's hook. """ return UniMolFrameTransform( - type_map, seed=seed, mask_token=mask_token, **mask_kwargs + type_map, seed=seed, mask_token=mask_token, stream=stream, **mask_kwargs ) diff --git a/deepmd/utils/unimol_data.py b/deepmd/utils/unimol_data.py index 0afe801966..e8f0a4caec 100644 --- a/deepmd/utils/unimol_data.py +++ b/deepmd/utils/unimol_data.py @@ -241,9 +241,24 @@ def convert_unimol_lmdb( f"{skipped} record(s) were skipped for holding one atom or an " "element outside the type_map" ) - if os.path.exists(dst): - shutil.rmtree(dst) - os.rename(staging, dst) + # Move the old dataset aside rather than deleting it first: between a + # delete and a rename there is a moment with nothing at `dst`, which is the + # loss this staging directory exists to prevent. A rename is atomic, so + # every instant has either the old dataset or the new one in place. + previous = f"{dst}.replaced" + if os.path.exists(previous): + shutil.rmtree(previous) + had_previous = os.path.exists(dst) + if had_previous: + os.rename(dst, previous) + try: + os.rename(staging, dst) + except BaseException: + if had_previous: + os.rename(previous, dst) + raise + if had_previous: + shutil.rmtree(previous, ignore_errors=True) return {"molecules": molecules, "frames": frame_idx, "skipped": skipped} diff --git a/doc/model/unimol.md b/doc/model/unimol.md index 13fbc5c3c3..ae2e88c54f 100644 --- a/doc/model/unimol.md +++ b/doc/model/unimol.md @@ -70,6 +70,11 @@ all configurable under `loss`. A masked atom is carried as a `[MASK]` pseudo-element, so the model's `type_map` has to declare it alongside the elements. +Each dataset gets its own corruption, and the validation set is corrupted afresh +on every pass, as upstream does. The validation loss therefore measures the model +against a different set of masked atoms each time and is not comparable +step-to-step the way a fixed validation set would be; read it as a trend. + ## Training ```sh diff --git a/examples/unimol/pretrain/input.json b/examples/unimol/pretrain/input.json index bbb510cdc0..b1f4b66745 100644 --- a/examples/unimol/pretrain/input.json +++ b/examples/unimol/pretrain/input.json @@ -47,7 +47,9 @@ "seed": 1, "_comment_max_atoms": "the size cap is applied by the converter, before batching; the descriptor and the distance head only declare the width they expect", "precision": "float32", - "_comment_precision": "single precision, which is what DPA models train in by default; the backbone is a transformer, not a potential energy surface, so float64 buys nothing here and costs several times the training time" + "_comment_precision": "single precision, which is what DPA models train in by default; the backbone is a transformer, not a potential energy surface, so float64 buys nothing here and costs several times the training time", + "virtual_token_position": "origin", + "_comment_virtual_token_position": "the objective's distance target puts the virtual tokens at the origin, which is where the centred frame's centroid is; the pretraining path requires it" }, "fitting_net": { "type": "unimol_pretrain", diff --git a/source/tests/common/dpmodel/test_unimol_data.py b/source/tests/common/dpmodel/test_unimol_data.py index 45fba0657b..a939fd4508 100644 --- a/source/tests/common/dpmodel/test_unimol_data.py +++ b/source/tests/common/dpmodel/test_unimol_data.py @@ -243,6 +243,57 @@ def test_an_element_outside_unimol_vocabulary_is_refused(self) -> None: 0, ) + def test_the_same_seed_corrupts_the_same_way_twice(self) -> None: + """Two runs of one configuration have to agree. + + The number standing in for the epoch used to come from OS entropy, so + the documented guarantee -- reproducible when a single process decodes + -- did not actually hold. It is derived from the seed now. + + The generator lives in a module-level table so that it survives the + transform being re-created for every batch in a worker. Clearing that + table is what a fresh process looks like. + """ + from deepmd.dpmodel.utils import unimol_transform as transform_module + + type_map = [*UNIMOL_ELEMENTS, "[MASK]"] + frame = { + "coord": np.arange(48, dtype=np.float64).reshape(16, 3), + "atype": np.zeros(16, dtype=np.int64), + } + + def one_run(): + transform_module._EPOCH_STREAMS.clear() + transform = make_unimol_data_transform(type_map, seed=1, stream="training") + return [ + transform(dict(frame), 0)["unimol_token_target"].tolist() + for _ in range(3) + ] + + first, second = one_run(), one_run() + self.assertEqual(first, second) + # and it is not reproducible by being frozen: the visits differ + self.assertGreater(len({tuple(v) for v in first}), 1) + # a different seed gives a different stream + transform_module._EPOCH_STREAMS.clear() + other = make_unimol_data_transform(type_map, seed=2, stream="training") + self.assertNotEqual( + first[0], other(dict(frame), 0)["unimol_token_target"].tolist() + ) + + def test_training_and_validation_do_not_share_a_stream(self) -> None: + type_map = [*UNIMOL_ELEMENTS, "[MASK]"] + frame = { + "coord": np.arange(48, dtype=np.float64).reshape(16, 3), + "atype": np.zeros(16, dtype=np.int64), + } + train = make_unimol_data_transform(type_map, seed=1, stream="training") + valid = make_unimol_data_transform(type_map, seed=1, stream="validation") + self.assertNotEqual( + train(dict(frame), 0)["unimol_token_target"].tolist(), + valid(dict(frame), 0)["unimol_token_target"].tolist(), + ) + def test_transform_requires_the_mask_pseudo_element(self) -> None: with self.assertRaisesRegex(ValueError, r"\[MASK\]"): make_unimol_data_transform(list(UNIMOL_ELEMENTS)) diff --git a/source/tests/pt_expt/model/test_unimol.py b/source/tests/pt_expt/model/test_unimol.py index 6f5b353306..2743406d67 100644 --- a/source/tests/pt_expt/model/test_unimol.py +++ b/source/tests/pt_expt/model/test_unimol.py @@ -238,6 +238,23 @@ def test_a_non_default_precision_runs(self) -> None: with self.subTest(output=name): self.assertTrue(torch.isfinite(out[name]).all()) + def test_the_default_virtual_token_position_is_refused(self) -> None: + """The default configuration, which no test used to cover. + + Under "centroid" the descriptor puts the virtual tokens at the centroid + of the coordinates it is handed, which during pretraining are the + corrupted ones, while the distance target puts them at the origin. The + two virtual columns of every corrupted row would then train against a + label for a different position, off by about the size of the noise. + """ + import copy + + config = copy.deepcopy(self.config(8)) + # what argcheck fills in when the key is absent + config["model"]["descriptor"]["virtual_token_position"] = "centroid" + with self.assertRaisesRegex(ValueError, "virtual_token_position='origin'"): + get_model(normalize(config)["model"]) + def test_matches_the_array_api_implementation(self) -> None: """Same weights, same numbers, once the fp32 basis is out of the way. @@ -634,6 +651,7 @@ def test_a_configuration_trains(self) -> None: "encoder_ffn_embed_dim": 32, "encoder_attention_heads": 2, "max_atoms": 16, + "virtual_token_position": "origin", "seed": 1, }, "fitting_net": { From 92ef3ef1a164035ac7b041041e3c6d9f982497f4 Mon Sep 17 00:00:00 2001 From: Duo <50307526+iProzd@users.noreply.github.com> Date: Wed, 16 Sep 2026 00:21:31 -0400 Subject: [PATCH 32/34] fix: act on the second review of this pull request Two problems, one of them introduced by the previous round's fix. Training and validation shared a corruption stream in production. Making the stream deterministic fixed reproducibility but removed what had been keeping the two datasets apart: the entropy that used to seed them separately. Both calls into `frame_transform` then took the same default label, hashed to the same stream, and drew from one generator, so a validation pass advanced the corruption training was about to see. The label now comes from the caller, and the trainer builds it from the task and the split, which also keeps two Uni-Mol tasks in one multi-task run apart. The test that was supposed to cover this passed two labels by hand, which no caller does, so it stayed green throughout. The replacement goes through the trainer and asks what the trainer actually did; it fails with the previous wiring restored. The converter could destroy the last copy of a dataset. A run interrupted between the two renames leaves the only complete copy beside the destination; the next run deleted it on the way past, and if that run then failed to publish, neither copy remained. It is restored instead, and the comment no longer claims the destination is readable at every instant, which two renames cannot promise -- what they promise is that a complete dataset is always somewhere. Also from the review: the epoch stream is reset when the trainer installs its transforms, so a second run in one interpreter repeats the first rather than continuing it; and the docstring now says what more than one decoder process does to the guarantee, which is that every worker draws the same sequence. --- deepmd/dpmodel/loss/loss.py | 8 ++- deepmd/dpmodel/loss/unimol.py | 11 ++- deepmd/dpmodel/utils/unimol_transform.py | 25 ++++++- deepmd/pt_expt/train/training.py | 26 +++++-- deepmd/utils/unimol_data.py | 19 +++-- .../tests/common/dpmodel/test_unimol_data.py | 71 +++++++++++++++++++ source/tests/pt_expt/model/test_unimol.py | 67 +++++++++++++++++ 7 files changed, 209 insertions(+), 18 deletions(-) diff --git a/deepmd/dpmodel/loss/loss.py b/deepmd/dpmodel/loss/loss.py index eca3b92ada..91a1c2137c 100644 --- a/deepmd/dpmodel/loss/loss.py +++ b/deepmd/dpmodel/loss/loss.py @@ -51,7 +51,7 @@ def call( def label_requirement(self) -> list[DataRequirementItem]: """Return data label requirements needed for this loss calculation.""" - def frame_transform(self, type_map: list[str]): # noqa: ANN201 + def frame_transform(self, type_map: list[str], stream: str = "default"): # noqa: ANN201 """Return a per-frame data transform this objective needs, or None. Self-supervised objectives build their own labels by corrupting the @@ -64,6 +64,12 @@ def frame_transform(self, type_map: list[str]): # noqa: ANN201 type_map : list[str] Element names of the model, which a transform needs in order to map elements onto types. + stream : str + Which dataset the transform is for. An objective whose randomness + must not be shared between datasets derives its draw sequence from + this, so the caller passes something stable and distinct per + dataset; the trainer uses ``/training`` and + ``/validation``. """ return None diff --git a/deepmd/dpmodel/loss/unimol.py b/deepmd/dpmodel/loss/unimol.py index 9644527aff..08c464ffc6 100644 --- a/deepmd/dpmodel/loss/unimol.py +++ b/deepmd/dpmodel/loss/unimol.py @@ -276,11 +276,15 @@ def add(term: Array, weight: float, name: str) -> None: ) return loss, more_loss - def frame_transform(self, type_map: list[str]): # noqa: ANN201 + def frame_transform(self, type_map: list[str], stream: str = "default"): # noqa: ANN201 """Build Uni-Mol's corruption, which also produces the labels. - A fresh object each time: it carries the counter that stands in for the - epoch, and the training and validation sets must not share one. + ``stream`` has to differ per dataset. The draw sequence standing in for + the epoch is derived from ``(data_seed, stream)`` and then kept per + process, so two datasets handed the same label share one generator and a + validation pass advances the corruption training is about to see. A + fresh object per dataset is not enough on its own -- the label is what + separates them. """ from deepmd.dpmodel.utils.unimol_transform import ( make_unimol_data_transform, @@ -289,6 +293,7 @@ def frame_transform(self, type_map: list[str]): # noqa: ANN201 return make_unimol_data_transform( type_map, seed=self.data_seed, + stream=stream, mask_prob=self.mask_prob, leave_unmasked_prob=self.leave_unmasked_prob, random_token_prob=self.random_token_prob, diff --git a/deepmd/dpmodel/utils/unimol_transform.py b/deepmd/dpmodel/utils/unimol_transform.py index a25afee202..c9d441289d 100644 --- a/deepmd/dpmodel/utils/unimol_transform.py +++ b/deepmd/dpmodel/utils/unimol_transform.py @@ -46,6 +46,7 @@ "numpy_seed", "pair_distance", "remove_hydrogen", + "reset_epoch_streams", "sample_conformer", "unimol_frame_transform", ] @@ -302,6 +303,18 @@ def edge_type(tokens: np.ndarray, num_types: int) -> np.ndarray: _EPOCH_STREAMS: dict[tuple[int, int], np.random.Generator] = {} +def reset_epoch_streams() -> None: + """Forget the per-process draw sequences. + + The generators live in the process so that they survive a transform being + re-created for every batch in a decoder worker. That also means a second + run inside one interpreter continues the first run's sequence rather than + repeating it, which would make the seed reproducible only for whichever run + happened to be first. A trainer calls this as it installs its transforms. + """ + _EPOCH_STREAMS.clear() + + def _stream_entropy(value: int | str | None) -> int: """A stable non-negative integer for a seed or a stream label. @@ -328,8 +341,16 @@ def _next_epoch(stream: int, seed: int | None) -> int: the next number from it. The generator is seeded from the configured seed and the stream alone, so - one process decoding a run twice draws the same sequence both times. With - workers the draws still depend on how frames were distributed between them. + one process decoding a run twice draws the same sequence both times, given + the reset the trainer performs between runs. + + With more than one decoder process (``DP_LMDB_NUM_WORKERS > 1``) every + worker seeds an identical generator and therefore draws the same sequence + of epoch numbers; frames still differ from one another because the index + enters the per-sample seed, but the draws are no longer decorrelated + between workers, and which frame meets which epoch depends on how the + batches were distributed. The reproducibility the seed offers is for a + single decoding process. """ # The process id keys the cache so a forked child builds its own generator # rather than inheriting a half-consumed one. It deliberately does not enter diff --git a/deepmd/pt_expt/train/training.py b/deepmd/pt_expt/train/training.py index e21a50a85e..84eef4b5cd 100644 --- a/deepmd/pt_expt/train/training.py +++ b/deepmd/pt_expt/train/training.py @@ -1866,6 +1866,14 @@ def __init__( self.loss = self.losses if self.multi_task else self.losses[DEFAULT_TASK_KEY] # Data requirements --------------------------------------------------- + # Draw sequences for input-corrupting objectives live in the process, so + # a second run in one interpreter would otherwise continue the first + # run's sequence instead of repeating it. + from deepmd.dpmodel.utils.unimol_transform import ( + reset_epoch_streams, + ) + + reset_epoch_streams() self.valid_numb_batch_by_task: dict[str, int] = {} for model_key in self.model_keys: data_requirement = list(self.losses[model_key].label_requirement) @@ -1879,17 +1887,21 @@ def __init__( ) # A self-supervised objective builds its labels by corrupting the # input, which has to happen as the data is read. - for dataset in ( - self.training_data_by_task[model_key], - self.validation_data_by_task[model_key], + for split, dataset in ( + ("training", self.training_data_by_task[model_key]), + ("validation", self.validation_data_by_task[model_key]), ): if dataset is None: continue - # A fresh transform per dataset: it carries the counter that - # stands in for the epoch, so sharing one would let validation - # passes advance the training corruption. + # A fresh transform per dataset is not enough: an objective that + # corrupts its input derives its draw sequence from this label, + # so two datasets given the same one share a generator and a + # validation pass advances the corruption training is about to + # see. The task name is in it too, so two Uni-Mol tasks in one + # multi-task run do not collide either. frame_transform = self.losses[model_key].frame_transform( - self.model_params_by_task[model_key]["type_map"] + self.model_params_by_task[model_key]["type_map"], + stream=f"{model_key}/{split}", ) if frame_transform is None: break diff --git a/deepmd/utils/unimol_data.py b/deepmd/utils/unimol_data.py index e8f0a4caec..712997e7d0 100644 --- a/deepmd/utils/unimol_data.py +++ b/deepmd/utils/unimol_data.py @@ -241,13 +241,22 @@ def convert_unimol_lmdb( f"{skipped} record(s) were skipped for holding one atom or an " "element outside the type_map" ) - # Move the old dataset aside rather than deleting it first: between a - # delete and a rename there is a moment with nothing at `dst`, which is the - # loss this staging directory exists to prevent. A rename is atomic, so - # every instant has either the old dataset or the new one in place. + # Move the old dataset aside rather than deleting it first. Two renames + # still leave one instant with nothing at `dst`, so this does not promise + # that `dst` is always readable -- it promises that a complete dataset is + # always somewhere on disk, at `dst` or beside it, and that an interrupted + # run can be recovered rather than having lost data. previous = f"{dst}.replaced" if os.path.exists(previous): - shutil.rmtree(previous) + if os.path.exists(dst): + # A leftover from a run that finished: `dst` is the current dataset + # and this is the superseded one. + shutil.rmtree(previous) + else: + # A run died between the two renames below, which makes this the + # only surviving copy. Deleting it here -- before the new + # conversion has proved itself -- is how both copies get lost. + os.rename(previous, dst) had_previous = os.path.exists(dst) if had_previous: os.rename(dst, previous) diff --git a/source/tests/common/dpmodel/test_unimol_data.py b/source/tests/common/dpmodel/test_unimol_data.py index a939fd4508..6a885e7cb1 100644 --- a/source/tests/common/dpmodel/test_unimol_data.py +++ b/source/tests/common/dpmodel/test_unimol_data.py @@ -6,6 +6,7 @@ import shutil import tempfile import unittest +import unittest.mock import numpy as np @@ -298,6 +299,76 @@ def test_transform_requires_the_mask_pseudo_element(self) -> None: with self.assertRaisesRegex(ValueError, r"\[MASK\]"): make_unimol_data_transform(list(UNIMOL_ELEMENTS)) + def _convert(self, dst, **kwargs): + return convert_unimol_lmdb(self.src, dst, map_size=1 << 24, **kwargs) + + def test_replacing_a_dataset_keeps_one_on_disk(self) -> None: + """The second conversion must not leave the destination empty.""" + dst = os.path.join(self.tmp, "replaced_twice") + self._convert(dst) + first = os.path.getsize(os.path.join(dst, "data.mdb")) + self._convert(dst) + self.assertTrue(os.path.isdir(dst)) + self.assertGreater(os.path.getsize(os.path.join(dst, "data.mdb")), 0) + # and the backup is cleaned up once the new one is in place + self.assertFalse(os.path.exists(dst + ".replaced")) + self.assertGreater(first, 0) + + def test_a_stranded_backup_survives_a_failed_publish(self) -> None: + """A run that died between the two renames leaves the only copy aside. + + Deleting that copy and then failing to put the new one in place loses + both. The publish is two renames, so the window is the second one + failing -- which is what this forces, because nothing else reaches it. + """ + dst = os.path.join(self.tmp, "stranded") + self._convert(dst) + # exactly the state an interrupted publish leaves behind + os.rename(dst, dst + ".replaced") + self.assertFalse(os.path.exists(dst)) + + real_rename = os.rename + + def fail_on_publish(a, b): + # let the restore through, refuse the staging -> dst move + if str(a).endswith(".partial"): + raise OSError("simulated failure publishing the new dataset") + return real_rename(a, b) + + with unittest.mock.patch("os.rename", side_effect=fail_on_publish): + with self.assertRaises(OSError): + self._convert(dst) + + survivor = dst if os.path.exists(dst) else dst + ".replaced" + self.assertTrue( + os.path.exists(survivor), "the only copy of the dataset was destroyed" + ) + self.assertGreater(os.path.getsize(os.path.join(survivor, "data.mdb")), 0) + + def test_a_failed_conversion_leaves_the_old_dataset_in_place(self) -> None: + """Rolling back has to put the previous dataset back at its name.""" + dst = os.path.join(self.tmp, "kept_on_failure") + self._convert(dst) + before = os.path.getsize(os.path.join(dst, "data.mdb")) + + # a source that cannot be read: the conversion raises before publishing + with self.assertRaises(Exception): + convert_unimol_lmdb( + os.path.join(self.tmp, "missing.lmdb"), dst, map_size=1 << 24 + ) + self.assertTrue(os.path.isdir(dst)) + self.assertEqual(os.path.getsize(os.path.join(dst, "data.mdb")), before) + + def test_an_empty_conversion_does_not_replace_a_good_dataset(self) -> None: + """Refusing an empty result must not cost the dataset already there.""" + dst = os.path.join(self.tmp, "kept_on_empty") + self._convert(dst) + before = os.path.getsize(os.path.join(dst, "data.mdb")) + with self.assertRaisesRegex(ValueError, "no usable frame"): + self._convert(dst, max_molecules=0) + self.assertTrue(os.path.isdir(dst)) + self.assertEqual(os.path.getsize(os.path.join(dst, "data.mdb")), before) + def test_limits_are_respected(self) -> None: dst = os.path.join(self.tmp, "limited") counts = convert_unimol_lmdb( diff --git a/source/tests/pt_expt/model/test_unimol.py b/source/tests/pt_expt/model/test_unimol.py index 2743406d67..b30bb85253 100644 --- a/source/tests/pt_expt/model/test_unimol.py +++ b/source/tests/pt_expt/model/test_unimol.py @@ -685,6 +685,73 @@ def test_a_configuration_trains(self) -> None: self.assertIn("unimol_token_target", frame) trainer.run() + def test_the_trainer_gives_each_dataset_its_own_corruption(self) -> None: + """Through the trainer, not by handing the labels over myself. + + The draw sequence is derived from a label the caller supplies. Passing + two distinct labels in a test proves nothing about production, because + the question is whether the *trainer* passes two -- and for a while it + did not: both datasets took the default, shared one generator, and a + validation pass advanced the corruption training was about to see. + """ + from deepmd.pt_expt.entrypoints.main import ( + get_trainer, + ) + from deepmd.utils.compat import ( + update_deepmd_input, + ) + + config = { + "model": { + "type_map": self.type_map, + "descriptor": { + "type": "unimol", + "encoder_layers": 1, + "encoder_embed_dim": 16, + "encoder_ffn_embed_dim": 32, + "encoder_attention_heads": 2, + "max_atoms": 16, + "virtual_token_position": "origin", + "seed": 1, + }, + "fitting_net": { + "type": "unimol_pretrain", + "attention_heads": 2, + "max_atoms": 16, + "seed": 1, + }, + }, + "learning_rate": {"type": "exp", "start_lr": 1e-3, "stop_lr": 1e-4}, + "loss": {"type": "unimol"}, + "training": { + "training_data": {"systems": self.data, "batch_size": 2}, + "validation_data": {"systems": self.data, "batch_size": 2}, + "numb_steps": 1, + "seed": 1, + "disp_freq": 10, + "save_freq": 100, + "disp_file": os.path.join(self.tmp, "lcurve_streams.out"), + "save_ckpt": os.path.join(self.tmp, "model_streams.ckpt"), + }, + } + trainer = get_trainer(normalize(update_deepmd_input(config, warning=False))) + + def installed(dataset): + return dataset._reader._decode_config.frame_transform + + train = installed(trainer.training_data) + valid = installed(trainer.validation_data) + self.assertIsNotNone(train) + self.assertIsNotNone(valid) + # different objects is the easy half; different draw sequences is the + # half that was broken + self.assertIsNot(train, valid) + self.assertNotEqual( + train.stream, + valid.stream, + "the trainer gave both datasets the same corruption stream", + ) + if __name__ == "__main__": unittest.main() From 4f43a33279745ca550991c2c8de08aee14f1548f Mon Sep 17 00:00:00 2001 From: Duo <50307526+iProzd@users.noreply.github.com> Date: Wed, 16 Sep 2026 09:56:52 -0400 Subject: [PATCH 33/34] test: cover the two review paths that had no test The guard on `virtual_token_position` was only ever reached by a test that wrote `"centroid"` by hand. That exercises the guard but not the path a user takes, which is to leave the key out and get whatever argcheck fills in -- so the test would have kept passing if the default moved out from under it. The key is now deleted, the filled-in default asserted, and the hand-written value kept as a second test. The norm regularisers are scalars broadcast over the atoms, and the atomic model zeroes its outputs at the padded rows, so averaging one over every row divides it by the fraction of the frame that is real. `mask` is what prevents that, and nothing checked it. A padded batch now asserts that the padded rows are zero, that the masked mean is the scalar, and that the unmasked mean is that scalar diluted by exactly the padding fraction. Two things that writing the second test turned up are worth recording, because both would have made it pass while proving nothing. The fixture pads with `atype = 0`, a real element, while a virtual atom is marked by a negative type -- so the first version found no padding at all. And `x_norm` is the wrong quantity to probe: upstream penalises only the part of `|norm - sqrt(d)|` past a tolerance of 1.0, so behind a LayerNorm it is exactly zero and compares equal either way. The test pads with `-1` and probes `delta_pair_norm`. --- source/tests/pt_expt/model/test_unimol.py | 85 ++++++++++++++++++++++- 1 file changed, 83 insertions(+), 2 deletions(-) diff --git a/source/tests/pt_expt/model/test_unimol.py b/source/tests/pt_expt/model/test_unimol.py index b30bb85253..8ca7052efb 100644 --- a/source/tests/pt_expt/model/test_unimol.py +++ b/source/tests/pt_expt/model/test_unimol.py @@ -239,22 +239,103 @@ def test_a_non_default_precision_runs(self) -> None: self.assertTrue(torch.isfinite(out[name]).all()) def test_the_default_virtual_token_position_is_refused(self) -> None: - """The default configuration, which no test used to cover. + """The default configuration, reached the way a user reaches it. Under "centroid" the descriptor puts the virtual tokens at the centroid of the coordinates it is handed, which during pretraining are the corrupted ones, while the distance target puts them at the origin. The two virtual columns of every corrupted row would then train against a label for a different position, off by about the size of the noise. + + The key is *omitted* here rather than set to "centroid" by hand. Those + are different paths: writing the value tests the guard, while leaving it + out tests what a user who never heard of the option actually gets, which + is whatever argcheck fills in. A test that only wrote the value would + keep passing if the default moved out from under it. """ import copy config = copy.deepcopy(self.config(8)) - # what argcheck fills in when the key is absent + del config["model"]["descriptor"]["virtual_token_position"] + normalized = normalize(config) + # argcheck fills the key, and what it fills is the position this + # objective cannot score + self.assertEqual( + normalized["model"]["descriptor"]["virtual_token_position"], "centroid" + ) + with self.assertRaisesRegex(ValueError, "virtual_token_position='origin'"): + get_model(normalized["model"]) + + def test_writing_the_wrong_position_by_hand_is_refused_too(self) -> None: + """The same guard, reached by stating the value instead of defaulting.""" + import copy + + config = copy.deepcopy(self.config(8)) config["model"]["descriptor"]["virtual_token_position"] = "centroid" with self.assertRaisesRegex(ValueError, "virtual_token_position='origin'"): get_model(normalize(config)["model"]) + def test_the_norm_regularisers_ignore_the_padding(self) -> None: + """The regulariser is recovered through the real-atom mask. + + A batch holds frames of different sizes, so the short ones are padded, + and the atomic model zeroes every output it hands back at the padded + rows. The norm regularisers are scalars broadcast over the atoms, so + averaging one over *all* the rows divides it by the fraction of the + frame that is real -- a number that depends on which other molecules + happen to share the batch. + + ``mask`` is what prevents that. This pins both halves: that the padded + rows really are zero, and that dropping the mask really would change + the answer. + + ``delta_pair_norm`` is the probe rather than ``x_norm`` because + ``x_norm`` is a hinge -- upstream penalises only the part of + ``|‖h‖ - sqrt(d)|`` past a tolerance of 1.0 -- so behind a LayerNorm it + sits at exactly zero and would compare equal either way, proving + nothing. + """ + coord, atype, nlist = self.inputs() + # A padded row is marked by a negative type, which is how a mixed-size + # batch reaches the model (deepmd/dpmodel/utils/lmdb_data.py). Padding + # it with type 0 instead would make the short frames look full of + # carbon, and this test would prove nothing. + atype = atype.copy() + for frame in range(atype.shape[0]): + atype[frame, int(self.n_real[frame]) :] = -1 + nloc = coord.shape[1] + model = self.build_torch_model(nloc) + out = model.forward_lower( + torch.as_tensor(coord), + torch.as_tensor(atype), + torch.as_tensor(nlist), + ) + self.assertIn("mask", out) + mask = out["mask"] + value = out["delta_pair_norm"].detach() + + short = [f for f in range(mask.shape[0]) if int(mask[f].sum()) < nloc] + # the batch has to actually contain a padded frame, or this proves nothing + self.assertTrue(short, "fixture carries no padded frame") + + for frame in short: + with self.subTest(frame=frame): + n_real = int(mask[frame].sum()) + rows = value[frame].reshape(nloc) + self.assertTrue(bool((rows[mask[frame] == 0] == 0).all())) + masked_mean = float(rows[mask[frame] == 1].mean()) + unmasked_mean = float(rows.mean()) + # every real row carries the same scalar, so the masked mean is + # that scalar + self.assertAlmostEqual( + float(rows[mask[frame] == 1][0]), masked_mean, places=10 + ) + # and the unmasked one is it diluted by exactly the padding + self.assertAlmostEqual( + unmasked_mean, masked_mean * n_real / nloc, places=6 + ) + self.assertLess(unmasked_mean, masked_mean) + def test_matches_the_array_api_implementation(self) -> None: """Same weights, same numbers, once the fp32 basis is out of the way. From a309c8841113380c068d2eeeabded88ae91f5ac3 Mon Sep 17 00:00:00 2001 From: Duo <50307526+iProzd@users.noreply.github.com> Date: Tue, 22 Sep 2026 03:44:36 -0400 Subject: [PATCH 34/34] fix: give `xp_erf` a TensorFlow branch `xp_erf` is introduced by this pull request, along with the exact-erf GELU it serves, so this is a defect this pull request introduced rather than a pre-existing one being tidied up. Every namespace other than JAX and torch fell through to a NumPy round-trip. For an `ndtensorflow` array that is wrong twice over: under `tf.function` the conversion is refused outright, because `__array__` raises on a graph tensor; and in eager mode it succeeds while detaching the `erf` factor from the tape, so the exact GELU differentiates as though it were `Phi(x)` alone. The second failure is silent and reaches every backend user of `gelu_erf`, not only Uni-Mol. Removing the branch again makes the point: the gradient test fails with the derivative collapsed onto `Phi(x)`, the graph-mode test fails, and the forward-value test still passes. A test that compares values could not have found this. The docstring for the dropout rates is corrected in the same commit: they are applied by the shared encoder for torch arrays in training mode, not inert and deferred to the PyTorch-Exportable wrapper. Two notes on the tests. The gradient probe uses an even number of points so the grid straddles zero without landing on it, because `relu` and `relu6` have no derivative there -- autodiff reports the subgradient 0 while a central difference reports 0.5, and neither is wrong. And the namespace anchor is deliberately not gated on `INSTALLED_TF2`: `find_spec` does not import the module, so it runs everywhere, including the ordinary runs where the tf2 cases skip. A guard that skips alongside the thing it guards would protect nothing. --- deepmd/dpmodel/array_api.py | 18 ++++- deepmd/dpmodel/descriptor/unimol.py | 7 +- source/tests/consistent/test_activation.py | 92 ++++++++++++++++++++++ 3 files changed, 114 insertions(+), 3 deletions(-) diff --git a/deepmd/dpmodel/array_api.py b/deepmd/dpmodel/array_api.py index 52f3bf1a6c..5c6822141d 100644 --- a/deepmd/dpmodel/array_api.py +++ b/deepmd/dpmodel/array_api.py @@ -533,6 +533,23 @@ def xp_erf(x: Array) -> Array: import torch return torch.special.erf(x) + + xp = array_api_compat.array_namespace(x) + if getattr(xp, "__name__", "") == "deepmd._vendors.ndtensorflow": + import tensorflow as tf + + # The NumPy round-trip below cannot serve TensorFlow. Under + # ``tf.function`` the conversion is refused outright, and in eager mode + # it detaches the erf factor from the tape, which leaves the exact GELU + # differentiating to Phi(x) alone -- silently, and for every backend + # user of ``gelu_erf`` rather than only Uni-Mol. + # + # Imported directly rather than through ``deepmd.tf2.env`` for symmetry + # with the JAX branch above: that module raises at import time unless + # eager execution is on, and this branch has to work inside + # ``tf.function``, where it is not. + return xp.asarray(tf.math.erf(x.unwrap())) + from scipy.special import ( erf, ) @@ -540,7 +557,6 @@ def xp_erf(x: Array) -> Array: if array_api_compat.is_numpy_array(x): return erf(x) # array-api-strict and friends: round-trip through NumPy. - xp = array_api_compat.array_namespace(x) return xp.asarray(erf(np.asarray(x)), dtype=x.dtype) diff --git a/deepmd/dpmodel/descriptor/unimol.py b/deepmd/dpmodel/descriptor/unimol.py index 5c8e6d4ea6..8314325871 100644 --- a/deepmd/dpmodel/descriptor/unimol.py +++ b/deepmd/dpmodel/descriptor/unimol.py @@ -93,8 +93,11 @@ class DescrptUniMol(NativeOP, BaseDescriptor): activation_function : str Activation of the blocks and heads. Uni-Mol uses the exact GELU. dropout, emb_dropout, attention_dropout, activation_dropout : float - Dropout rates. They are inert in this backend-agnostic implementation - and are applied by the PyTorch-Exportable wrapper during training. + Dropout rates. They are applied here, by the shared encoder, when the + arrays are torch tensors and the module is in training mode. Inference + is the identity, and training on any other array namespace raises + ``NotImplementedError`` rather than silently dropping the + regularisation (see ``unimol_nn.encoder.dropout``). no_final_head_layer_norm : bool Skip the layer norm on the pair delta. Upstream builds that norm unless its weight is negative. diff --git a/source/tests/consistent/test_activation.py b/source/tests/consistent/test_activation.py index 1c388b7305..9704ebe60f 100644 --- a/source/tests/consistent/test_activation.py +++ b/source/tests/consistent/test_activation.py @@ -1,6 +1,9 @@ # SPDX-License-Identifier: LGPL-3.0-or-later import sys import unittest +from importlib.util import ( + find_spec, +) import numpy as np @@ -111,6 +114,95 @@ def test_tf2_consistent_with_ref(self) -> None: test = get_activation_fn_dp(self.activation)(input) np.testing.assert_allclose(self.ref, to_numpy_array(test), atol=1e-7) + def test_the_tf2_namespace_name_still_resolves(self) -> None: + """Anchor the literal the tf2 dispatch compares against. + + ``xp_erf`` and its neighbours in ``deepmd/dpmodel/array_api.py`` select + the TensorFlow path by comparing ``xp.__name__`` against the import + path of the vendored namespace -- the idiom this tree uses in + ``utils/nlist.py`` and ``utils/default_neighbor_list.py`` too, nine + occurrences across three modules. + + A literal like that fails silently if the module is ever renamed or + moved: the comparison simply stops matching, the code falls back to the + NumPy round-trip, and the gradient is lost again with every + forward-value test still passing. This asserts the name resolves to a + real module, so a rename breaks a test instead of a derivative. + + Deliberately not gated on ``INSTALLED_TF2``: ``find_spec`` does not + import the module, so this runs everywhere, including the ordinary runs + where the tf2 cases skip. A guard that skips alongside the thing it + guards would protect nothing. + """ + self.assertIsNotNone( + find_spec("deepmd._vendors.ndtensorflow"), + "the namespace name that array_api.py dispatches on no longer " + "resolves; the TensorFlow branches there are now dead code", + ) + + @unittest.skipUnless(INSTALLED_TF2, "TensorFlow 2 is not installed") + def test_tf2_gradient_consistent_with_ref(self) -> None: + """The derivative has to survive the dispatch, not only the value. + + A backend that falls through to a NumPy conversion still returns the + right number, because the conversion happens after the value is + computed -- but it detaches the term from the tape, so the derivative + comes out wrong with nothing to show for it. Comparing forward values + alone cannot see that, which is why this compares the gradient. + + The input is deliberately a narrow range rather than the wide random + sample the value tests use: for the exact GELU the missing term is + ``x * phi(x)``, which vanishes for large ``|x|`` and would hide the + very defect this pins. + + The point count is even so that the grid straddles zero without + landing on it. ``relu`` and ``relu6`` have a kink there, where the + derivative does not exist: autodiff reports the subgradient 0 while a + central difference reports 0.5, and neither is wrong. Comparing them + at that point tests nothing about dispatch, which is what this is for. + """ + from deepmd._vendors import ndtensorflow as ndtf + from deepmd.tf2.env import ( + tf, + ) + + probe = np.linspace(-3.0, 3.0, 24) + raw = tf.constant(probe, dtype=tf.float64) + with tf.GradientTape() as tape: + tape.watch(raw) + out = get_activation_fn_dp(self.activation)(ndtf.asarray(raw)) + total = tf.reduce_sum(out.unwrap()) + grad = tape.gradient(total, raw) + self.assertIsNotNone( + grad, f"{self.activation} left no gradient path on the tf2 backend" + ) + # central differences on the reference implementation + eps = 1e-6 + ref = get_activation_fn_dp(self.activation) + expected = (ref(probe + eps) - ref(probe - eps)) / (2 * eps) + np.testing.assert_allclose(to_numpy_array(grad), expected, rtol=1e-5, atol=1e-6) + + @unittest.skipUnless(INSTALLED_TF2, "TensorFlow 2 is not installed") + def test_tf2_activation_is_traceable_in_graph_mode(self) -> None: + """Every activation must survive ``tf.function``. + + A NumPy conversion is refused outright on a graph tensor, so an + activation that reaches one cannot be trained or frozen on tf2 at all. + """ + from deepmd._vendors import ndtensorflow as ndtf + from deepmd.tf2.env import ( + tf, + ) + + activation = self.activation + + @tf.function + def traced(values): + return get_activation_fn_dp(activation)(ndtf.asarray(values)).unwrap() + + traced_out = traced(tf.constant(self.random_input, dtype=tf.float64)) + np.testing.assert_allclose(self.ref, traced_out.numpy(), atol=1e-7) + @unittest.skipUnless(INSTALLED_PD, "Paddle is not installed") def test_pd_consistent_with_ref(self): if INSTALLED_PD:

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