1761 lines
76 KiB
Python
1761 lines
76 KiB
Python
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# This file was automatically generated from src/transformers/models/evolla/modular_evolla.py.
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# Do NOT edit this file manually as any edits will be overwritten by the generation of
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# the file from the modular. If any change should be done, please apply the change to the
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# modular_evolla.py file directly. One of our CI enforces this.
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# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# coding=utf-8
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# Copyright 2025 Westlake Representational Learning Lab (Fajie Yuan Lab) team and the HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import math
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import warnings
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from dataclasses import dataclass
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from typing import Callable, Optional, Union
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import torch
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from torch import Tensor, nn
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from ...activations import ACT2FN
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from ...cache_utils import Cache, DynamicCache
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from ...generation import GenerationMixin
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from ...integrations import use_kernel_forward_from_hub
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from ...masking_utils import create_causal_mask
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from ...modeling_flash_attention_utils import flash_attn_supports_top_left_mask, is_flash_attn_available
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from ...modeling_layers import GradientCheckpointingLayer
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from ...modeling_outputs import (
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BaseModelOutputWithCrossAttentions,
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BaseModelOutputWithPast,
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BaseModelOutputWithPoolingAndCrossAttentions,
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CausalLMOutputWithPast,
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ModelOutput,
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)
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from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
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from ...modeling_utils import (
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ALL_ATTENTION_FUNCTIONS,
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ModuleUtilsMixin,
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PreTrainedModel,
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find_pruneable_heads_and_indices,
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get_parameter_dtype,
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prune_linear_layer,
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)
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from ...processing_utils import Unpack
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from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, logging
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from ...utils.deprecation import deprecate_kwarg
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from ...utils.generic import check_model_inputs
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from .configuration_evolla import EvollaConfig, SaProtConfig
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if is_flash_attn_available():
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from ...modeling_flash_attention_utils import _flash_attention_forward
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logger = logging.get_logger(__name__)
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def create_position_ids_from_input_ids(input_ids, padding_idx):
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"""
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Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
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are ignored. This is modified from fairseq's `utils.make_positions`.
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Args:
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x: torch.Tensor x:
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Returns: torch.Tensor
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"""
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# The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
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mask = input_ids.ne(padding_idx).int()
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incremental_indices = torch.cumsum(mask, dim=1).type_as(mask) * mask
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return incremental_indices.long() + padding_idx
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class EvollaSaProtEmbeddings(nn.Module):
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"""
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Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
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"""
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def __init__(self, config):
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super().__init__()
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self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
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if config.emb_layer_norm_before:
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self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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else:
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self.layer_norm = None
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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# position_ids (1, len position emb) is contiguous in memory and exported when serialized
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self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
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self.register_buffer(
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"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
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)
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self.padding_idx = config.pad_token_id
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if self.position_embedding_type == "absolute":
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self.position_embeddings = nn.Embedding(
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config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx
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)
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self.token_dropout = config.token_dropout
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self.mask_token_id = config.mask_token_id
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# remove the position_ids in EsmEmbeddings
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self.position_ids = None
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def forward(
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self,
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input_ids=None,
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attention_mask=None,
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position_ids=None,
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inputs_embeds=None,
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):
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if position_ids is None:
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if input_ids is not None:
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# Create the position ids from the input token ids. Any padded tokens remain padded.
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position_ids = create_position_ids_from_input_ids(input_ids, self.padding_idx)
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else:
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position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)
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if inputs_embeds is None:
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inputs_embeds = self.word_embeddings(input_ids)
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# Note that if we want to support EVOLLA_SA_PROT-1 (not 1b!) in future then we need to support an
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# embedding_scale factor here.
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embeddings = inputs_embeds
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# Matt: EVOLLA_SA_PROT has the option to handle masking in MLM in a slightly unusual way. If the token_dropout
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# flag is False then it is handled in the same was as BERT/RoBERTa. If it is set to True, however,
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# masked tokens are treated as if they were selected for input dropout and zeroed out.
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# This "mask-dropout" is compensated for when masked tokens are not present, by scaling embeddings by
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# a factor of (fraction of unmasked tokens during training) / (fraction of unmasked tokens in sample).
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# This is analogous to the way that dropout layers scale down outputs during evaluation when not
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# actually dropping out values (or, equivalently, scale up their un-dropped outputs in training).
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if self.token_dropout:
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embeddings = embeddings.masked_fill((input_ids == self.mask_token_id).unsqueeze(-1), 0.0)
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mask_ratio_train = 0.15 * 0.8 # Hardcoded as the ratio used in all EVOLLA_SA_PROT model training runs
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src_lengths = attention_mask.sum(-1)
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mask_ratio_observed = (input_ids == self.mask_token_id).sum(-1).float() / src_lengths
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embeddings = (embeddings * (1 - mask_ratio_train) / (1 - mask_ratio_observed)[:, None, None]).to(
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embeddings.dtype
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)
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if self.position_embedding_type == "absolute":
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position_embeddings = self.position_embeddings(position_ids)
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embeddings = embeddings + position_embeddings
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if self.layer_norm is not None:
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embeddings = self.layer_norm(embeddings)
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if attention_mask is not None:
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embeddings = (embeddings * attention_mask.unsqueeze(-1)).to(embeddings.dtype)
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# Matt: I think this line was copied incorrectly from BERT, disabling it for now.
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# embeddings = self.dropout(embeddings)
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return embeddings
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def create_position_ids_from_inputs_embeds(self, inputs_embeds):
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"""
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We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.
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Args:
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inputs_embeds: torch.Tensor
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Returns: torch.Tensor
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"""
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input_shape = inputs_embeds.size()[:-1]
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sequence_length = input_shape[1]
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position_ids = torch.arange(
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self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device
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)
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return position_ids.unsqueeze(0).expand(input_shape)
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def rotate_half_esm(x):
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x1, x2 = x.chunk(2, dim=-1)
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return torch.cat((-x2, x1), dim=-1)
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def apply_rotary_pos_emb_esm(x, cos, sin):
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cos = cos[:, :, : x.shape[-2], :]
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sin = sin[:, :, : x.shape[-2], :]
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return (x * cos) + (rotate_half_esm(x) * sin)
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class EvollaSaProtRotaryEmbedding(nn.Module):
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"""
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Rotary position embeddings based on those in
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[RoFormer](https://huggingface.co/docs/transformers/model_doc/roformer). Query and keys are transformed by rotation
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matrices which depend on their relative positions.
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"""
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def __init__(self, dim: int):
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super().__init__()
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# Generate and save the inverse frequency buffer (non trainable)
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inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2, dtype=torch.int64).float() / dim))
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inv_freq = inv_freq
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self.register_buffer("inv_freq", inv_freq)
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self._seq_len_cached = None
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self._cos_cached = None
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self._sin_cached = None
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def _update_cos_sin_tables(self, x, seq_dimension=2):
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seq_len = x.shape[seq_dimension]
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# Reset the tables if the sequence length has changed,
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# or if we're on a new device (possibly due to tracing for instance)
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if seq_len != self._seq_len_cached or self._cos_cached.device != x.device:
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self._seq_len_cached = seq_len
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t = torch.arange(x.shape[seq_dimension], device=x.device).type_as(self.inv_freq)
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freqs = torch.outer(t, self.inv_freq)
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emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
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self._cos_cached = emb.cos()[None, None, :, :]
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self._sin_cached = emb.sin()[None, None, :, :]
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return self._cos_cached, self._sin_cached
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def forward(self, q: torch.Tensor, k: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
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self._cos_cached, self._sin_cached = self._update_cos_sin_tables(k, seq_dimension=-2)
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return (
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apply_rotary_pos_emb_esm(q, self._cos_cached, self._sin_cached),
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apply_rotary_pos_emb_esm(k, self._cos_cached, self._sin_cached),
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)
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class EvollaSaProtSelfAttention(nn.Module):
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def __init__(self, config, position_embedding_type=None, layer_idx=None):
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super().__init__()
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self.config = config
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if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
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raise ValueError(
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f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
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f"heads ({config.num_attention_heads})"
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)
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self.num_attention_heads = config.num_attention_heads
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self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
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self.all_head_size = self.num_attention_heads * self.attention_head_size
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self.query = nn.Linear(config.hidden_size, self.all_head_size)
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self.key = nn.Linear(config.hidden_size, self.all_head_size)
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self.value = nn.Linear(config.hidden_size, self.all_head_size)
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self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
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self.position_embedding_type = position_embedding_type or getattr(
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config, "position_embedding_type", "absolute"
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)
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self.rotary_embeddings = None
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if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
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self.max_position_embeddings = config.max_position_embeddings
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self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
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elif self.position_embedding_type == "rotary":
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self.rotary_embeddings = EvollaSaProtRotaryEmbedding(dim=self.attention_head_size)
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self.is_decoder = config.is_decoder
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self.layer_idx = layer_idx
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@deprecate_kwarg("past_key_value", version="4.54.0")
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.FloatTensor] = None,
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head_mask: Optional[torch.FloatTensor] = None,
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encoder_hidden_states: Optional[torch.FloatTensor] = None,
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encoder_attention_mask: Optional[torch.FloatTensor] = None,
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past_key_value: Optional[tuple[tuple[torch.FloatTensor]]] = None,
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output_attentions: Optional[bool] = False,
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) -> tuple[torch.Tensor]:
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hidden_shape = (hidden_states.shape[0], -1, self.num_attention_heads, self.attention_head_size)
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query_layer = self.query(hidden_states).view(hidden_shape).transpose(1, 2)
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# If this is instantiated as a cross-attention module, the keys
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# and values come from an encoder; the attention mask needs to be
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# such that the encoder's padding tokens are not attended to.
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is_cross_attention = encoder_hidden_states is not None
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if is_cross_attention:
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key_layer = self.key(encoder_hidden_states).view(hidden_shape).transpose(1, 2)
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value_layer = self.value(encoder_hidden_states).view(hidden_shape).transpose(1, 2)
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attention_mask = encoder_attention_mask
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else:
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key_layer = self.key(hidden_states).view(hidden_shape).transpose(1, 2)
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value_layer = self.value(hidden_states).view(hidden_shape).transpose(1, 2)
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# Matt: Our BERT model (which this code was derived from) scales attention logits down by sqrt(head_dim).
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# EVOLLA_SA_PROT scales the query down by the same factor instead. Modulo numerical stability these are equivalent,
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# but not when rotary embeddings get involved. Therefore, we scale the query here to match the original
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# EVOLLA_SA_PROT code and fix rotary embeddings.
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query_layer = query_layer * self.attention_head_size**-0.5
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if self.position_embedding_type == "rotary":
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query_layer, key_layer = self.rotary_embeddings(query_layer, key_layer)
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# Take the dot product between "query" and "key" to get the raw attention scores.
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attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
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if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
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seq_length = hidden_states.size()[1]
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position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
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position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
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distance = position_ids_l - position_ids_r
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positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
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positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility
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if self.position_embedding_type == "relative_key":
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relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
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attention_scores = attention_scores + relative_position_scores
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elif self.position_embedding_type == "relative_key_query":
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relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
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relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
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attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
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if attention_mask is not None:
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# Apply the attention mask is (precomputed for all layers in EvollaSaProtModel forward() function)
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attention_scores = attention_scores + attention_mask
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# Normalize the attention scores to probabilities.
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attention_probs = nn.functional.softmax(attention_scores, dim=-1)
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# This is actually dropping out entire tokens to attend to, which might
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# seem a bit unusual, but is taken from the original Transformer paper.
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attention_probs = self.dropout(attention_probs)
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# Mask heads if we want to
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if head_mask is not None:
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attention_probs = attention_probs * head_mask
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context_layer = torch.matmul(attention_probs.to(value_layer.dtype), value_layer)
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context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
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new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
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context_layer = context_layer.view(new_context_layer_shape)
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outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
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if self.is_decoder:
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outputs = outputs + (None,)
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return outputs
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class EvollaSaProtSelfOutput(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.dense = nn.Linear(config.hidden_size, config.hidden_size)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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def forward(self, hidden_states, input_tensor):
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hidden_states = self.dense(hidden_states)
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hidden_states = self.dropout(hidden_states)
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hidden_states = hidden_states + input_tensor
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return hidden_states
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class EvollaSaProtFlashAttention2(EvollaSaProtSelfAttention):
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"""
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EVOLLA_SA_PROT flash attention module. This module inherits from `EvollaSaProtSelfAttention` as the weights of the module stays
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untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
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flash attention and deal with padding tokens in case the input contains any of them.
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"""
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def __init__(self, config, position_embedding_type=None, layer_idx=None):
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super().__init__(config, position_embedding_type=position_embedding_type, layer_idx=layer_idx)
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# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
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# flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
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# Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
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self._flash_attn_uses_top_left_mask = flash_attn_supports_top_left_mask()
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self.dropout_prob = config.attention_probs_dropout_prob
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@deprecate_kwarg("past_key_value", version="4.54.0")
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.FloatTensor] = None,
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head_mask: Optional[torch.FloatTensor] = None,
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encoder_hidden_states: Optional[torch.FloatTensor] = None,
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encoder_attention_mask: Optional[torch.FloatTensor] = None,
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past_key_value: Optional[tuple[tuple[torch.FloatTensor]]] = None,
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output_attentions: Optional[bool] = False,
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) -> tuple[torch.Tensor]:
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# Flash attention doesn't support output_attentions or cross attention
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if output_attentions or head_mask is not None or encoder_hidden_states is not None:
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logger.warning_once(
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"EvollaSaProtFlashAttention2 does not support output_attentions, head_mask, or cross_attention. "
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"Falling back to the manual attention implementation. This warning can be removed using "
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'the argument `attn_implementation="eager"` when loading the model.'
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)
|
|
return super().forward(
|
|
hidden_states,
|
|
attention_mask,
|
|
head_mask,
|
|
encoder_hidden_states,
|
|
encoder_attention_mask,
|
|
output_attentions,
|
|
)
|
|
|
|
bsz, q_len, _ = hidden_states.size()
|
|
|
|
query_layer = self.transpose_for_scores(self.query(hidden_states))
|
|
key_layer = self.transpose_for_scores(self.key(hidden_states))
|
|
value_layer = self.transpose_for_scores(self.value(hidden_states))
|
|
|
|
# In PEFT, usually we cast the layer norms in float32 for training stability reasons
|
|
# therefore the input hidden states gets silently casted in float32. Hence, we need
|
|
# cast them back in the correct dtype just to be sure everything works as expected.
|
|
# This might slowdown training & inference so it is recommended to not cast the LayerNorms
|
|
# in fp32.
|
|
input_dtype = query_layer.dtype
|
|
device_type = query_layer.device.type if query_layer.device.type != "mps" else "cpu"
|
|
if input_dtype == torch.float32:
|
|
if torch.is_autocast_enabled():
|
|
target_dtype = (
|
|
torch.get_autocast_dtype(device_type)
|
|
if hasattr(torch, "get_autocast_dtype")
|
|
else torch.get_autocast_gpu_dtype()
|
|
)
|
|
# Handle the case where the model is quantized
|
|
elif hasattr(self.config, "_pre_quantization_dtype"):
|
|
target_dtype = self.config._pre_quantization_dtype
|
|
else:
|
|
target_dtype = self.query.weight.dtype
|
|
|
|
logger.warning_once(
|
|
f"The input hidden states seems to be silently casted in float32, this might be related to"
|
|
f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
|
|
f" {target_dtype}."
|
|
)
|
|
|
|
query_layer = query_layer.to(target_dtype)
|
|
key_layer = key_layer.to(target_dtype)
|
|
value_layer = value_layer.to(target_dtype)
|
|
|
|
# Matt: Our BERT model (which this code was derived from) scales attention logits down by sqrt(head_dim).
|
|
# EVOLLA_SA_PROT scales the query down by the same factor instead. Modulo numerical stability these are equivalent,
|
|
# but not when rotary embeddings get involved. Therefore, we scale the query here to match the original
|
|
# EVOLLA_SA_PROT code and fix rotary embeddings.
|
|
query_layer = query_layer * self.attention_head_size**-0.5
|
|
|
|
if self.position_embedding_type == "rotary":
|
|
query_layer, key_layer = self.rotary_embeddings(query_layer, key_layer)
|
|
elif self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
|
|
raise ValueError(f"ESM flash attention does not support {self.position_embedding_type} embeddings")
|
|
|
|
# It would likely be faster to change self.transpose_for_scores to output the correct
|
|
# dimensions for flash_attention_2, but that would also mean changing the rotary embedding
|
|
# functions. Here we just permute the dimensions to match the expected input.
|
|
attn_output = _flash_attention_forward(
|
|
query_layer.permute(0, 2, 1, 3),
|
|
key_layer.permute(0, 2, 1, 3),
|
|
value_layer.permute(0, 2, 1, 3),
|
|
attention_mask,
|
|
query_length=q_len,
|
|
is_causal=self.is_decoder,
|
|
softmax_scale=1.0,
|
|
dropout=self.dropout_prob if self.training else 0.0,
|
|
use_top_left_mask=self._flash_attn_uses_top_left_mask,
|
|
)
|
|
|
|
attn_output = attn_output.reshape(bsz, q_len, -1)
|
|
|
|
outputs = (attn_output, None)
|
|
if self.is_decoder:
|
|
outputs = outputs + (None,)
|
|
|
|
return outputs
|
|
|
|
|
|
EVOLLA_SA_PROT_ATTENTION_CLASSES = {
|
|
"eager": EvollaSaProtSelfAttention,
|
|
"flash_attention_2": EvollaSaProtFlashAttention2,
|
|
}
|
|
|
|
|
|
class EvollaSaProtAttention(nn.Module):
|
|
def __init__(self, config, layer_idx=None):
|
|
super().__init__()
|
|
self.self = EVOLLA_SA_PROT_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx=layer_idx)
|
|
self.output = EvollaSaProtSelfOutput(config)
|
|
self.pruned_heads = set()
|
|
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
|
|
def prune_heads(self, heads):
|
|
if len(heads) == 0:
|
|
return
|
|
heads, index = find_pruneable_heads_and_indices(
|
|
heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
|
|
)
|
|
|
|
# Prune linear layers
|
|
self.self.query = prune_linear_layer(self.self.query, index)
|
|
self.self.key = prune_linear_layer(self.self.key, index)
|
|
self.self.value = prune_linear_layer(self.self.value, index)
|
|
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
|
|
|
|
# Update hyper params and store pruned heads
|
|
self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
|
|
self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
|
|
self.pruned_heads = self.pruned_heads.union(heads)
|
|
|
|
@deprecate_kwarg("past_key_value", version="4.54.0")
|
|
def forward(
|
|
self,
|
|
hidden_states,
|
|
attention_mask=None,
|
|
head_mask=None,
|
|
encoder_hidden_states=None,
|
|
encoder_attention_mask=None,
|
|
past_key_value=None,
|
|
output_attentions=False,
|
|
cache_position=None,
|
|
):
|
|
hidden_states_ln = self.LayerNorm(hidden_states)
|
|
self_outputs = self.self(
|
|
hidden_states_ln,
|
|
attention_mask=attention_mask,
|
|
head_mask=head_mask,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
encoder_attention_mask=encoder_attention_mask,
|
|
output_attentions=output_attentions,
|
|
)
|
|
attention_output = self.output(self_outputs[0], hidden_states)
|
|
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
|
|
return outputs
|
|
|
|
|
|
def gelu(x):
|
|
"""
|
|
This is the gelu implementation from the original EVOLLA_SA_PROT repo. Using F.gelu yields subtly wrong results.
|
|
"""
|
|
return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
|
|
|
|
|
|
class EvollaSaProtIntermediate(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = gelu(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
class EvollaSaProtOutput(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
|
|
def forward(self, hidden_states, input_tensor):
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.dropout(hidden_states)
|
|
hidden_states = hidden_states + input_tensor
|
|
return hidden_states
|
|
|
|
|
|
class EvollaSaProtLayer(GradientCheckpointingLayer):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.chunk_size_feed_forward = config.chunk_size_feed_forward
|
|
self.seq_len_dim = 1
|
|
self.attention = EvollaSaProtAttention(config)
|
|
self.is_decoder = config.is_decoder
|
|
self.add_cross_attention = config.add_cross_attention
|
|
if self.add_cross_attention:
|
|
if not self.is_decoder:
|
|
raise RuntimeError(f"{self} should be used as a decoder model if cross attention is added")
|
|
self.crossattention = EvollaSaProtAttention(config)
|
|
self.intermediate = EvollaSaProtIntermediate(config)
|
|
self.output = EvollaSaProtOutput(config)
|
|
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
|
|
@deprecate_kwarg("past_key_value", version="4.54.0")
|
|
def forward(
|
|
self,
|
|
hidden_states,
|
|
attention_mask=None,
|
|
head_mask=None,
|
|
encoder_hidden_states=None,
|
|
encoder_attention_mask=None,
|
|
past_key_value=None,
|
|
output_attentions=False,
|
|
cache_position=None,
|
|
):
|
|
self_attention_outputs = self.attention(
|
|
hidden_states,
|
|
attention_mask=attention_mask,
|
|
head_mask=head_mask,
|
|
output_attentions=output_attentions,
|
|
)
|
|
attention_output = self_attention_outputs[0]
|
|
|
|
# if decoder, the last output is tuple of self-attn cache
|
|
if self.is_decoder:
|
|
outputs = self_attention_outputs[1:-1]
|
|
else:
|
|
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
|
|
|
|
if self.is_decoder and encoder_hidden_states is not None:
|
|
if not hasattr(self, "crossattention"):
|
|
raise AttributeError(
|
|
f"If `encoder_hidden_states` are passed, {self} has to be instantiated"
|
|
" with cross-attention layers by setting `config.add_cross_attention=True`"
|
|
)
|
|
|
|
cross_attention_outputs = self.crossattention(
|
|
attention_output,
|
|
attention_mask=attention_mask,
|
|
head_mask=head_mask,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
encoder_attention_mask=encoder_attention_mask,
|
|
output_attentions=output_attentions,
|
|
)
|
|
attention_output = cross_attention_outputs[0]
|
|
outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights
|
|
|
|
layer_output = self.feed_forward_chunk(attention_output)
|
|
|
|
outputs = (layer_output,) + outputs
|
|
|
|
# if decoder, return the attn key/values as the last output
|
|
if self.is_decoder:
|
|
outputs = outputs + (None,)
|
|
return outputs
|
|
|
|
def feed_forward_chunk(self, attention_output):
|
|
attention_output_ln = self.LayerNorm(attention_output)
|
|
intermediate_output = self.intermediate(attention_output_ln)
|
|
layer_output = self.output(intermediate_output, attention_output)
|
|
return layer_output
|
|
|
|
|
|
class EvollaSaProtEncoder(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.config = config
|
|
self.layer = nn.ModuleList([EvollaSaProtLayer(config) for _ in range(config.num_hidden_layers)])
|
|
self.emb_layer_norm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
self.gradient_checkpointing = False
|
|
|
|
@deprecate_kwarg("past_key_value", version="4.54.0")
|
|
@deprecate_kwarg("use_cache", version="4.54.0")
|
|
@can_return_tuple
|
|
def forward(
|
|
self,
|
|
hidden_states,
|
|
attention_mask=None,
|
|
head_mask=None,
|
|
encoder_hidden_states=None,
|
|
encoder_attention_mask=None,
|
|
past_key_values=None,
|
|
use_cache=None,
|
|
output_attentions=False,
|
|
output_hidden_states=False,
|
|
return_dict=True,
|
|
cache_position=None,
|
|
):
|
|
all_hidden_states = () if output_hidden_states else None
|
|
all_self_attentions = () if output_attentions else None
|
|
all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
|
|
|
|
for i, layer_module in enumerate(self.layer):
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
layer_head_mask = head_mask[i] if head_mask is not None else None
|
|
|
|
layer_outputs = layer_module(
|
|
hidden_states=hidden_states,
|
|
attention_mask=attention_mask,
|
|
head_mask=layer_head_mask,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
encoder_attention_mask=encoder_attention_mask,
|
|
output_attentions=output_attentions,
|
|
)
|
|
|
|
hidden_states = layer_outputs[0]
|
|
if output_attentions:
|
|
all_self_attentions = all_self_attentions + (layer_outputs[1],)
|
|
if self.config.add_cross_attention:
|
|
all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
|
|
|
|
if self.emb_layer_norm_after:
|
|
hidden_states = self.emb_layer_norm_after(hidden_states)
|
|
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
return BaseModelOutputWithCrossAttentions(
|
|
last_hidden_state=hidden_states,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_self_attentions,
|
|
cross_attentions=all_cross_attentions,
|
|
)
|
|
|
|
|
|
class EvollaSaProtPooler(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
|
self.activation = nn.Tanh()
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
# We "pool" the model by simply taking the hidden state corresponding
|
|
# to the first token.
|
|
first_token_tensor = hidden_states[:, 0]
|
|
pooled_output = self.dense(first_token_tensor)
|
|
pooled_output = self.activation(pooled_output)
|
|
return pooled_output
|
|
|
|
|
|
@auto_docstring
|
|
class EvollaSaProtPreTrainedModel(PreTrainedModel):
|
|
config: SaProtConfig
|
|
_no_split_modules = ["EvollaSaProtLayer"]
|
|
_supports_flash_attn = True
|
|
|
|
def _init_weights(self, module):
|
|
"""Initialize the weights"""
|
|
std = self.config.initializer_range
|
|
if isinstance(module, nn.Linear):
|
|
module.weight.data.normal_(mean=0.0, std=std)
|
|
if module.bias is not None:
|
|
module.bias.data.zero_()
|
|
elif isinstance(module, nn.Embedding):
|
|
module.weight.data.normal_(mean=0.0, std=std)
|
|
if module.padding_idx is not None:
|
|
module.weight.data[module.padding_idx].zero_()
|
|
elif isinstance(module, nn.LayerNorm):
|
|
module.bias.data.zero_()
|
|
module.weight.data.fill_(1.0)
|
|
|
|
|
|
class EvollaSaProtProteinEncoder(EvollaSaProtPreTrainedModel):
|
|
def __init__(self, config: SaProtConfig):
|
|
super().__init__(config)
|
|
self.embeddings = EvollaSaProtEmbeddings(config)
|
|
self.encoder = EvollaSaProtEncoder(config)
|
|
|
|
def get_input_embeddings(self):
|
|
return self.embeddings.word_embeddings
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.embeddings.word_embeddings = value
|
|
|
|
def _prune_heads(self, heads_to_prune):
|
|
"""
|
|
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
|
|
class PreTrainedModel
|
|
"""
|
|
for layer, heads in heads_to_prune.items():
|
|
self.encoder.layer[layer].attention.prune_heads(heads)
|
|
|
|
@can_return_tuple
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor],
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
) -> Union[tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
|
|
input_shape = input_ids.size()
|
|
batch_size, seq_length = input_shape
|
|
|
|
device = input_ids.device
|
|
if attention_mask is None:
|
|
attention_mask = torch.ones(((batch_size, seq_length)), device=device)
|
|
|
|
inputs_embeds = self.embeddings(input_ids=input_ids, attention_mask=attention_mask)
|
|
extended_attention_mask = self.get_extended_attention_mask(attention_mask, input_shape)
|
|
encoder_outputs = self.encoder(inputs_embeds, attention_mask=extended_attention_mask)
|
|
sequence_output = encoder_outputs[0]
|
|
|
|
return BaseModelOutputWithPoolingAndCrossAttentions(
|
|
last_hidden_state=sequence_output,
|
|
hidden_states=encoder_outputs.hidden_states,
|
|
attentions=encoder_outputs.attentions,
|
|
cross_attentions=encoder_outputs.cross_attentions,
|
|
)
|
|
|
|
def get_extended_attention_mask(
|
|
self, attention_mask: Tensor, input_shape: tuple[int], device: torch.device = None, dtype: torch.float = None
|
|
) -> Tensor:
|
|
"""
|
|
Makes broadcastable attention and causal masks so that future and masked tokens are ignored.
|
|
|
|
Arguments:
|
|
attention_mask (`torch.Tensor`):
|
|
Mask with ones indicating tokens to attend to, zeros for tokens to ignore.
|
|
input_shape (`Tuple[int]`):
|
|
The shape of the input to the model.
|
|
|
|
Returns:
|
|
`torch.Tensor` The extended attention mask, with a the same dtype as `attention_mask.dtype`.
|
|
"""
|
|
if dtype is None:
|
|
dtype = get_parameter_dtype(self)
|
|
|
|
if not (attention_mask.dim() == 2 and self.config.is_decoder):
|
|
# show warning only if it won't be shown in `create_extended_attention_mask_for_decoder`
|
|
if device is not None:
|
|
warnings.warn(
|
|
"The `device` argument is deprecated and will be removed in v5 of Transformers.", FutureWarning
|
|
)
|
|
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
|
|
# ourselves in which case we just need to make it broadcastable to all heads.
|
|
if attention_mask.dim() == 3:
|
|
extended_attention_mask = attention_mask[:, None, :, :]
|
|
elif attention_mask.dim() == 2:
|
|
# Provided a padding mask of dimensions [batch_size, seq_length]
|
|
# - if the model is a decoder, apply a causal mask in addition to the padding mask
|
|
# - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]
|
|
if self.config.is_decoder:
|
|
extended_attention_mask = ModuleUtilsMixin.create_extended_attention_mask_for_decoder(
|
|
input_shape, attention_mask, device
|
|
)
|
|
else:
|
|
extended_attention_mask = attention_mask[:, None, None, :]
|
|
else:
|
|
raise ValueError(
|
|
f"Wrong shape for input_ids (shape {input_shape}) or attention_mask (shape {attention_mask.shape})"
|
|
)
|
|
|
|
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
|
|
# masked positions, this operation will create a tensor which is 0.0 for
|
|
# positions we want to attend and the dtype's smallest value for masked positions.
|
|
# Since we are adding it to the raw scores before the softmax, this is
|
|
# effectively the same as removing these entirely.
|
|
extended_attention_mask = extended_attention_mask.to(dtype=dtype) # fp16 compatibility
|
|
extended_attention_mask = (1.0 - extended_attention_mask) * torch.finfo(dtype).min
|
|
return extended_attention_mask
|
|
|
|
|
|
class EvollaSequenceCompressorAttention(nn.Module):
|
|
def __init__(self, dim, dim_head=64, heads=8):
|
|
super().__init__()
|
|
self.scale = dim_head**-0.5
|
|
self.heads = heads
|
|
inner_dim = dim_head * heads
|
|
|
|
self.norm_media = nn.LayerNorm(dim)
|
|
self.norm_latents = nn.LayerNorm(dim)
|
|
|
|
self.to_q = nn.Linear(dim, inner_dim, bias=False)
|
|
self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False)
|
|
self.to_out = nn.Linear(inner_dim, dim, bias=False)
|
|
|
|
def forward(self, x, latents, mask):
|
|
"""
|
|
Args:
|
|
x (torch.Tensor): image features
|
|
shape (b, n1, D)
|
|
latent (torch.Tensor): latent features
|
|
shape (b, n2, D); n2: num of latent tokens
|
|
"""
|
|
x = self.norm_media(x)
|
|
latents = self.norm_latents(latents)
|
|
|
|
h = self.heads
|
|
|
|
q = self.to_q(latents)
|
|
kv_input = torch.cat((x, latents), dim=-2)
|
|
k, v = self.to_kv(kv_input).chunk(
|
|
2, dim=-1
|
|
) # each: batch_size, max_protein_length+num_latents, dim_head*num_heads
|
|
|
|
q = q.view(q.size(0), q.size(1), h, -1).permute(0, 2, 1, 3)
|
|
k = k.view(k.size(0), k.size(1), h, -1).permute(0, 2, 1, 3)
|
|
v = v.view(v.size(0), v.size(1), h, -1).permute(0, 2, 1, 3)
|
|
q = q * self.scale # batch_size, num_heads, num_latents, dim_head
|
|
|
|
# attention
|
|
sim = torch.matmul(q, k.transpose(-1, -2))
|
|
sim = sim - sim.amax(dim=-1, keepdim=True).detach()
|
|
bs, nh, skd, okd = sim.shape
|
|
ones = torch.ones(nh, skd).to(mask.device) # Create a tensor of ones with shape (nh, skd)
|
|
mask_exp = mask[:, None, None, :]
|
|
ones_exp = ones[None, :, :, None]
|
|
mask = mask_exp * ones_exp
|
|
|
|
sim = sim.masked_fill((1 - mask).bool(), -1e4)
|
|
attn = sim.softmax(dim=-1)
|
|
out = torch.matmul(attn, v)
|
|
out = out.permute(0, 2, 1, 3)
|
|
|
|
# [batch, seq, head, features] -> [batch, seq, head*features]
|
|
out = out.reshape(out.size(0), out.size(1), -1)
|
|
|
|
return self.to_out(out)
|
|
|
|
|
|
class EvollaFeedForward(nn.Module):
|
|
def __init__(self, dim, mult=4):
|
|
super().__init__()
|
|
inner_dim = int(dim * mult)
|
|
|
|
self.norm = nn.LayerNorm(dim)
|
|
self.fc1 = nn.Linear(dim, inner_dim, bias=False)
|
|
self.activation = nn.GELU()
|
|
self.fc2 = nn.Linear(inner_dim, dim, bias=False)
|
|
|
|
def forward(self, x):
|
|
return self.fc2(self.activation(self.fc1(self.norm(x))))
|
|
|
|
|
|
class EvollaSequenceCompressorResampler(nn.Module):
|
|
def __init__(self, config: EvollaConfig):
|
|
super().__init__()
|
|
protein_repr_dim = config.protein_encoder_config.hidden_size
|
|
self.num_latents = config.resampler_num_latents
|
|
self.latents = nn.Parameter(torch.randn(self.num_latents, protein_repr_dim), requires_grad=True)
|
|
self.layers = nn.ModuleList([])
|
|
for _ in range(config.resampler_depth):
|
|
self.layers.append(
|
|
nn.ModuleList(
|
|
[
|
|
EvollaSequenceCompressorAttention(
|
|
dim=protein_repr_dim, dim_head=config.resampler_dim_head, heads=config.resampler_heads
|
|
),
|
|
EvollaFeedForward(dim=protein_repr_dim, mult=config.resampler_ff_mult),
|
|
]
|
|
)
|
|
)
|
|
|
|
self.norm = nn.LayerNorm(config.hidden_size)
|
|
self.protein_projector = nn.Linear(protein_repr_dim, config.hidden_size)
|
|
|
|
def forward(self, embeds, mask):
|
|
b = embeds.shape[0]
|
|
|
|
bs, _ = mask.shape # bs, max_protein_length
|
|
latent_mask = torch.ones(bs, self.num_latents).to(mask.device)
|
|
mask = torch.cat((mask, latent_mask), dim=1) # bs, max_protein_length + num_latents
|
|
|
|
# blocks
|
|
ones = torch.ones(b).to(self.latents.device)
|
|
latents = self.latents[None] * ones.view(-1, 1, 1) # [b,n,d]
|
|
latents = latents.to(embeds.dtype)
|
|
for attn, ff in self.layers:
|
|
latents = attn(embeds, latents, mask) + latents
|
|
latents = ff(latents) + latents
|
|
|
|
transformed_feature = self.protein_projector(latents)
|
|
|
|
return self.norm(transformed_feature)
|
|
|
|
|
|
@dataclass
|
|
@auto_docstring
|
|
class EvollaProteinEncoderModelOutput(ModelOutput):
|
|
sequence_compressor_output: torch.FloatTensor = None
|
|
last_hidden_state: Optional[torch.FloatTensor] = None
|
|
hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
|
|
attentions: Optional[tuple[torch.FloatTensor, ...]] = None
|
|
|
|
|
|
class EvollaProteinEncoder(nn.Module):
|
|
def __init__(self, config: EvollaConfig):
|
|
super().__init__()
|
|
self.model = EvollaSaProtProteinEncoder(config=config.protein_encoder_config)
|
|
self.sequence_compressor_resampler = EvollaSequenceCompressorResampler(config=config)
|
|
|
|
@can_return_tuple
|
|
def forward(self, input_ids: torch.LongTensor, attention_mask: torch.FloatTensor, **kwargs):
|
|
protein_output = self.model(input_ids=input_ids, attention_mask=attention_mask)
|
|
protein_embeds = protein_output.last_hidden_state
|
|
sequence_repr = self.sequence_compressor_resampler(protein_embeds, attention_mask)
|
|
|
|
return EvollaProteinEncoderModelOutput(
|
|
sequence_compressor_output=sequence_repr,
|
|
last_hidden_state=protein_output.last_hidden_state,
|
|
)
|
|
|
|
|
|
class EvollaSequenceAlignerCrossAttention(nn.Module):
|
|
def __init__(
|
|
self,
|
|
config,
|
|
protein_encoder_dim: Optional[int] = None,
|
|
structure_encoder_dim: Optional[int] = None,
|
|
msa_encoder_dim: Optional[int] = None,
|
|
):
|
|
super().__init__()
|
|
|
|
self.hidden_size = config.hidden_size
|
|
self.num_attention_heads = config.num_attention_heads
|
|
self.scale = self.num_attention_heads**-0.5
|
|
self.attention_head_size = int(self.hidden_size / self.num_attention_heads)
|
|
self.all_head_size = self.num_attention_heads * self.attention_head_size
|
|
|
|
attention_probs_dropout_prob = config.aligner_attention_probs_dropout_prob
|
|
enable_bias = config.aligner_enable_bias
|
|
ffn_mult = config.aligner_ffn_mult
|
|
|
|
self.query = nn.Linear(self.hidden_size, self.all_head_size)
|
|
if protein_encoder_dim is not None:
|
|
self.key_protein = nn.Linear(protein_encoder_dim, self.all_head_size)
|
|
self.value_protein = nn.Linear(protein_encoder_dim, self.all_head_size)
|
|
else:
|
|
self.key_protein = None
|
|
self.value_protein = None
|
|
|
|
if structure_encoder_dim is not None:
|
|
self.key_structure = nn.Linear(structure_encoder_dim, self.all_head_size)
|
|
self.value_structure = nn.Linear(structure_encoder_dim, self.all_head_size)
|
|
else:
|
|
self.key_structure = None
|
|
self.value_structure = None
|
|
|
|
if msa_encoder_dim is not None:
|
|
self.key_msa = nn.Linear(msa_encoder_dim, self.all_head_size)
|
|
self.value_msa = nn.Linear(msa_encoder_dim, self.all_head_size)
|
|
else:
|
|
self.key_msa = None
|
|
self.value_msa = None
|
|
|
|
self.attention_norm = EvollaRMSNorm(self.hidden_size)
|
|
|
|
self.dropout = nn.Dropout(attention_probs_dropout_prob)
|
|
|
|
self.out_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=enable_bias)
|
|
|
|
self.ff = EvollaFeedForward(self.hidden_size, ffn_mult)
|
|
self.gate_attention = nn.Parameter(torch.tensor([0.0]))
|
|
self.gate_ffw = nn.Parameter(torch.tensor([0.0]))
|
|
|
|
def cross_attention(
|
|
self,
|
|
query_states,
|
|
protein_key_value_states,
|
|
structure_key_value_states,
|
|
msa_key_value_states,
|
|
query_attn_mask,
|
|
protein_kv_attn_mask,
|
|
structure_kv_attn_mask,
|
|
msa_kv_attn_mask,
|
|
):
|
|
"""
|
|
query_states: text
|
|
key_value_states: protein
|
|
query_states: [bs, query_seq_len, dim]
|
|
key_value_states: [bs, kv_seq_len, dim]
|
|
query_attn_mask: [bs, query_seq_len]
|
|
kv_attn_mask: [bs, kv_seq_len]
|
|
"""
|
|
|
|
# Concatenate protein and structure
|
|
kv_attn_mask = [protein_kv_attn_mask, structure_kv_attn_mask, msa_kv_attn_mask]
|
|
kv_attn_mask = [_ for _ in kv_attn_mask if _ is not None]
|
|
if not kv_attn_mask:
|
|
raise ValueError("At least one modality should be provided for cross attention.")
|
|
kv_attn_mask = torch.cat(kv_attn_mask, dim=1)
|
|
|
|
query_layer = self.attention_norm(query_states)
|
|
|
|
# Warning: This place might cause issues, refers to
|
|
# https://discuss.pytorch.org/t/cuda-error-cublas-status-not-supported-when-calling-cublasltmatmul-from-torch-nn-functional-linear/170214/13
|
|
# Solution: add `DISABLE_ADDMM_CUDA_LT=1` as environment variable
|
|
# Apply linear transformation to input_query, input_key, and input_value
|
|
query_layer = self.query(query_layer) # [bs, querylength, dim]
|
|
|
|
if self.key_protein is not None and self.value_protein is not None:
|
|
protein_key_value_states = protein_key_value_states.to(query_states)
|
|
key_layer_protein = self.key_protein(protein_key_value_states) # [bs, keylength, dim]
|
|
value_layer_protein = self.value_protein(protein_key_value_states) # [bs, keylength, dim]
|
|
else:
|
|
key_layer_protein = None
|
|
value_layer_protein = None
|
|
|
|
if self.key_structure is not None and self.value_structure is not None:
|
|
structure_key_value_states = structure_key_value_states.to(query_states)
|
|
key_layer_structure = self.key_structure(structure_key_value_states) # [bs, keylength, dim]
|
|
value_layer_structure = self.value_structure(structure_key_value_states) # [bs, keylength, dim]
|
|
else:
|
|
key_layer_structure = None
|
|
value_layer_structure = None
|
|
|
|
if self.key_msa is not None and self.value_msa is not None:
|
|
msa_key_value_states = msa_key_value_states.to(query_states)
|
|
key_layer_msa = self.key_msa(msa_key_value_states) # [bs, keylength, dim]
|
|
value_layer_msa = self.value_msa(msa_key_value_states) # [bs, keylength, dim]
|
|
else:
|
|
key_layer_msa = None
|
|
value_layer_msa = None
|
|
|
|
key_layer = [key_layer_protein, key_layer_structure, key_layer_msa]
|
|
key_layer = [_ for _ in key_layer if _ is not None]
|
|
key_layer = torch.cat(key_layer, dim=1)
|
|
|
|
value_layer = [value_layer_protein, value_layer_structure, value_layer_msa]
|
|
value_layer = [_ for _ in value_layer if _ is not None]
|
|
value_layer = torch.cat(value_layer, dim=1)
|
|
|
|
new_query_layer_shape = query_layer.size()[:-1] + (
|
|
self.num_attention_heads,
|
|
self.attention_head_size,
|
|
)
|
|
query_layer = query_layer.view(*new_query_layer_shape).permute(0, 2, 1, 3)
|
|
|
|
new_key_layer_shape = key_layer.size()[:-1] + (
|
|
self.num_attention_heads,
|
|
self.attention_head_size,
|
|
)
|
|
key_layer = key_layer.view(*new_key_layer_shape).permute(0, 2, 1, 3)
|
|
|
|
new_value_layer_shape = value_layer.size()[:-1] + (
|
|
self.num_attention_heads,
|
|
self.attention_head_size,
|
|
)
|
|
value_layer = value_layer.view(*new_value_layer_shape).permute(0, 2, 1, 3)
|
|
|
|
query_layer = query_layer * self.scale
|
|
|
|
# attention_mask: [bs, 1, querylength, keylength]
|
|
if query_attn_mask is None:
|
|
query_attn_mask = torch.ones(query_states.size(0), query_states.size(1)).to(query_states.device)
|
|
attention_mask = query_attn_mask[:, None, :, None] * kv_attn_mask[:, None, None, :]
|
|
# Compute the scaled dot-product attention scores
|
|
attn_weights = torch.matmul(query_layer, key_layer.transpose(-1, -2)) # [bs, numheads, querylength, keylength]
|
|
attn_weights = attn_weights - attn_weights.amax(dim=-1, keepdim=True).detach() # To stablize score
|
|
attention_scores = attn_weights.masked_fill(
|
|
(1 - attention_mask).bool(), torch.finfo(attn_weights.dtype).min
|
|
) # [bs, numheads, querylength, keylength]
|
|
|
|
attention_probs = nn.Softmax(dim=-1)(attention_scores)
|
|
|
|
# attention_probs_dropped = self.dropout(attention_probs)
|
|
|
|
context_layer = torch.matmul(attention_probs, value_layer) # [bs, numheads, querylength, dim/numheads]
|
|
|
|
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
|
|
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
|
|
context_layer = context_layer.view(*new_context_layer_shape)
|
|
|
|
context_layer = self.out_proj(context_layer)
|
|
|
|
return context_layer
|
|
|
|
def forward(
|
|
self,
|
|
query_states,
|
|
protein_kv_states,
|
|
structure_kv_states,
|
|
msa_kv_states,
|
|
query_attn_mask,
|
|
protein_kv_attn_mask=None,
|
|
structure_kv_attn_mask=None,
|
|
msa_kv_attn_mask=None,
|
|
protein_batch_mask=None,
|
|
structure_batch_mask=None,
|
|
msa_batch_mask=None,
|
|
past_key_value=None,
|
|
):
|
|
if protein_kv_states is not None:
|
|
bs, protein_kv_seq_len, dim = protein_kv_states.shape
|
|
if protein_kv_attn_mask is None:
|
|
protein_kv_attn_mask = (
|
|
torch.ones(bs, protein_kv_seq_len).to(protein_batch_mask.device)
|
|
* protein_batch_mask.expand(size=(protein_kv_seq_len, bs)).T
|
|
).to(protein_kv_states.device)
|
|
else:
|
|
protein_kv_attn_mask = None
|
|
|
|
if structure_kv_states is not None:
|
|
bs, structure_kv_seq_len, dim = structure_kv_states.shape
|
|
if structure_kv_attn_mask is None:
|
|
structure_kv_attn_mask = (
|
|
torch.ones(bs, structure_kv_seq_len).to(protein_batch_mask.device)
|
|
* structure_batch_mask.expand(size=(structure_kv_seq_len, bs)).T
|
|
).to(structure_kv_states.device)
|
|
else:
|
|
structure_kv_attn_mask = None
|
|
|
|
if msa_kv_states is not None:
|
|
bs, msa_kv_seq_len, dim = msa_kv_states.shape
|
|
if msa_kv_attn_mask is None:
|
|
msa_kv_attn_mask = (
|
|
torch.ones(bs, msa_kv_seq_len).to(protein_batch_mask.device)
|
|
* msa_batch_mask.expand(size=(msa_kv_seq_len, bs)).T
|
|
).to(msa_kv_states.device)
|
|
else:
|
|
msa_kv_attn_mask = None
|
|
hidden_states = query_states
|
|
# only when there's at least one valid modality, crossattention will be performed
|
|
if (
|
|
(protein_kv_states is not None and protein_kv_attn_mask.any())
|
|
or (structure_kv_states is not None and structure_kv_attn_mask.any())
|
|
or (msa_kv_states is not None and msa_kv_attn_mask.any())
|
|
):
|
|
residual = hidden_states
|
|
hidden_states = self.cross_attention(
|
|
query_states=hidden_states,
|
|
protein_key_value_states=protein_kv_states,
|
|
structure_key_value_states=structure_kv_states,
|
|
msa_key_value_states=msa_kv_states,
|
|
query_attn_mask=query_attn_mask,
|
|
protein_kv_attn_mask=protein_kv_attn_mask,
|
|
structure_kv_attn_mask=structure_kv_attn_mask,
|
|
msa_kv_attn_mask=msa_kv_attn_mask,
|
|
) # [bs, query_seq_len, dim]
|
|
# tanh gate
|
|
hidden_states = torch.tanh(self.gate_attention) * hidden_states
|
|
|
|
hidden_states = residual + hidden_states # input_query
|
|
|
|
residual = hidden_states
|
|
hidden_states = self.ff(hidden_states) * torch.tanh(self.gate_ffw)
|
|
hidden_states = residual + hidden_states
|
|
|
|
return hidden_states
|
|
|
|
|
|
@use_kernel_forward_from_hub("RMSNorm")
|
|
class EvollaRMSNorm(nn.Module):
|
|
def __init__(self, hidden_size, eps=1e-6):
|
|
"""
|
|
EvollaRMSNorm is equivalent to T5LayerNorm
|
|
"""
|
|
super().__init__()
|
|
self.weight = nn.Parameter(torch.ones(hidden_size))
|
|
self.variance_epsilon = eps
|
|
|
|
def forward(self, hidden_states):
|
|
input_dtype = hidden_states.dtype
|
|
hidden_states = hidden_states.to(torch.float32)
|
|
variance = hidden_states.pow(2).mean(-1, keepdim=True)
|
|
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
|
|
return self.weight * hidden_states.to(input_dtype)
|
|
|
|
def extra_repr(self):
|
|
return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
|
|
|
|
|
|
class EvollaRotaryEmbedding(nn.Module):
|
|
def __init__(self, config: EvollaConfig, device=None):
|
|
super().__init__()
|
|
# BC: "rope_type" was originally "type"
|
|
if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
|
|
self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
|
|
else:
|
|
self.rope_type = "default"
|
|
self.max_seq_len_cached = config.max_position_embeddings
|
|
self.original_max_seq_len = config.max_position_embeddings
|
|
|
|
self.config = config
|
|
self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
|
|
|
|
inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
|
|
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
|
self.original_inv_freq = self.inv_freq
|
|
|
|
@torch.no_grad()
|
|
@dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
|
|
def forward(self, x, position_ids):
|
|
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
|
|
position_ids_expanded = position_ids[:, None, :].float()
|
|
|
|
device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
|
|
with torch.autocast(device_type=device_type, enabled=False): # Force float32
|
|
freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
|
|
emb = torch.cat((freqs, freqs), dim=-1)
|
|
cos = emb.cos() * self.attention_scaling
|
|
sin = emb.sin() * self.attention_scaling
|
|
|
|
return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
|
|
|
|
|
|
class EvollaMLP(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.config = config
|
|
self.hidden_size = config.hidden_size
|
|
self.intermediate_size = config.intermediate_size
|
|
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
|
|
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
|
|
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias)
|
|
self.act_fn = ACT2FN[config.hidden_act]
|
|
|
|
def forward(self, x):
|
|
down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
|
|
return down_proj
|
|
|
|
|
|
def rotate_half(x):
|
|
"""Rotates half the hidden dims of the input."""
|
|
x1 = x[..., : x.shape[-1] // 2]
|
|
x2 = x[..., x.shape[-1] // 2 :]
|
|
return torch.cat((-x2, x1), dim=-1)
|
|
|
|
|
|
def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
|
|
"""Applies Rotary Position Embedding to the query and key tensors.
|
|
|
|
Args:
|
|
q (`torch.Tensor`): The query tensor.
|
|
k (`torch.Tensor`): The key tensor.
|
|
cos (`torch.Tensor`): The cosine part of the rotary embedding.
|
|
sin (`torch.Tensor`): The sine part of the rotary embedding.
|
|
position_ids (`torch.Tensor`, *optional*):
|
|
Deprecated and unused.
|
|
unsqueeze_dim (`int`, *optional*, defaults to 1):
|
|
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
|
|
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
|
|
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
|
|
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
|
|
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
|
|
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
|
|
Returns:
|
|
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
|
|
"""
|
|
cos = cos.unsqueeze(unsqueeze_dim)
|
|
sin = sin.unsqueeze(unsqueeze_dim)
|
|
q_embed = (q * cos) + (rotate_half(q) * sin)
|
|
k_embed = (k * cos) + (rotate_half(k) * sin)
|
|
return q_embed, k_embed
|
|
|
|
|
|
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
|
|
"""
|
|
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
|
|
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
|
|
"""
|
|
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
|
|
if n_rep == 1:
|
|
return hidden_states
|
|
hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
|
|
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
|
|
|
|
|
|
def eager_attention_forward(
|
|
module: nn.Module,
|
|
query: torch.Tensor,
|
|
key: torch.Tensor,
|
|
value: torch.Tensor,
|
|
attention_mask: Optional[torch.Tensor],
|
|
scaling: float,
|
|
dropout: float = 0.0,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
):
|
|
key_states = repeat_kv(key, module.num_key_value_groups)
|
|
value_states = repeat_kv(value, module.num_key_value_groups)
|
|
|
|
attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
|
|
if attention_mask is not None:
|
|
causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
|
|
attn_weights = attn_weights + causal_mask
|
|
|
|
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
|
|
attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
|
|
attn_output = torch.matmul(attn_weights, value_states)
|
|
attn_output = attn_output.transpose(1, 2).contiguous()
|
|
|
|
return attn_output, attn_weights
|
|
|
|
|
|
class EvollaAttention(nn.Module):
|
|
"""Multi-headed attention from 'Attention Is All You Need' paper"""
|
|
|
|
def __init__(self, config: EvollaConfig, layer_idx: int):
|
|
super().__init__()
|
|
self.config = config
|
|
self.layer_idx = layer_idx
|
|
self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
|
|
self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
|
|
self.scaling = self.head_dim**-0.5
|
|
self.attention_dropout = config.attention_dropout
|
|
self.is_causal = True
|
|
|
|
self.q_proj = nn.Linear(
|
|
config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
|
|
)
|
|
self.k_proj = nn.Linear(
|
|
config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
|
|
)
|
|
self.v_proj = nn.Linear(
|
|
config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
|
|
)
|
|
self.o_proj = nn.Linear(
|
|
config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
position_embeddings: tuple[torch.Tensor, torch.Tensor],
|
|
attention_mask: Optional[torch.Tensor],
|
|
past_key_value: Optional[Cache] = None,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> tuple[torch.Tensor, torch.Tensor]:
|
|
input_shape = hidden_states.shape[:-1]
|
|
hidden_shape = (*input_shape, -1, self.head_dim)
|
|
|
|
query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
|
|
key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
|
|
value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
|
|
|
|
cos, sin = position_embeddings
|
|
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
|
|
|
|
if past_key_value is not None:
|
|
# sin and cos are specific to RoPE models; cache_position needed for the static cache
|
|
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
|
|
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
|
|
|
attention_interface: Callable = eager_attention_forward
|
|
if self.config._attn_implementation != "eager":
|
|
attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
|
|
|
|
attn_output, attn_weights = attention_interface(
|
|
self,
|
|
query_states,
|
|
key_states,
|
|
value_states,
|
|
attention_mask,
|
|
dropout=0.0 if not self.training else self.attention_dropout,
|
|
scaling=self.scaling,
|
|
**kwargs,
|
|
)
|
|
|
|
attn_output = attn_output.reshape(*input_shape, -1).contiguous()
|
|
attn_output = self.o_proj(attn_output)
|
|
return attn_output, attn_weights
|
|
|
|
|
|
class EvollaDecoderLayer(GradientCheckpointingLayer):
|
|
def __init__(self, config: EvollaConfig, layer_idx: int):
|
|
super().__init__()
|
|
self.hidden_size = config.hidden_size
|
|
|
|
self.self_attn = EvollaAttention(config=config, layer_idx=layer_idx)
|
|
|
|
self.mlp = EvollaMLP(config)
|
|
self.input_layernorm = EvollaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
self.post_attention_layernorm = EvollaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
if (layer_idx + 1) % max(config.num_hidden_layers // config.aligner_num_add_layers, 1) == 0:
|
|
self.adapter = EvollaSequenceAlignerCrossAttention(
|
|
config,
|
|
protein_encoder_dim=config.hidden_size,
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
position_embeddings: tuple[torch.Tensor, torch.Tensor],
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_value: Optional[Cache] = None,
|
|
output_attentions: Optional[bool] = False,
|
|
use_cache: Optional[bool] = False,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
protein_kv_states: Optional[torch.Tensor] = None,
|
|
structure_kv_states: Optional[torch.Tensor] = None,
|
|
msa_kv_states: Optional[torch.Tensor] = None,
|
|
protein_batch_mask: Optional[torch.Tensor] = None,
|
|
structure_batch_mask: Optional[torch.Tensor] = None,
|
|
msa_batch_mask: Optional[torch.Tensor] = None,
|
|
query_attn_mask: Optional[torch.Tensor] = None,
|
|
**kwargs,
|
|
) -> tuple[torch.Tensor]:
|
|
residual = hidden_states
|
|
|
|
hidden_states = self.input_layernorm(hidden_states)
|
|
|
|
# Self Attention
|
|
hidden_states, _ = self.self_attn(
|
|
hidden_states=hidden_states,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_value=past_key_value,
|
|
use_cache=use_cache,
|
|
cache_position=cache_position,
|
|
position_embeddings=position_embeddings,
|
|
**kwargs,
|
|
)
|
|
hidden_states = residual + hidden_states
|
|
|
|
# Fully Connected
|
|
residual = hidden_states
|
|
hidden_states = self.post_attention_layernorm(hidden_states)
|
|
hidden_states = self.mlp(hidden_states)
|
|
hidden_states = residual + hidden_states
|
|
|
|
if hasattr(self, "adapter"):
|
|
hidden_states = self.adapter(
|
|
query_states=hidden_states,
|
|
protein_kv_states=protein_kv_states,
|
|
structure_kv_states=structure_kv_states,
|
|
msa_kv_states=msa_kv_states,
|
|
query_attn_mask=query_attn_mask,
|
|
protein_batch_mask=protein_batch_mask,
|
|
structure_batch_mask=structure_batch_mask,
|
|
msa_batch_mask=msa_batch_mask,
|
|
)
|
|
|
|
return hidden_states
|
|
|
|
|
|
@auto_docstring
|
|
class EvollaPreTrainedModel(PreTrainedModel):
|
|
config: EvollaConfig
|
|
base_model_prefix = "model"
|
|
supports_gradient_checkpointing = True
|
|
_no_split_modules = ["EvollaDecoderLayer"]
|
|
_skip_keys_device_placement = ["past_key_values"]
|
|
_supports_flash_attn = True
|
|
_supports_sdpa = True
|
|
_supports_flex_attn = True
|
|
|
|
_can_compile_fullgraph = True
|
|
_supports_attention_backend = False
|
|
_can_record_outputs = {
|
|
"hidden_states": EvollaDecoderLayer,
|
|
"attentions": EvollaAttention,
|
|
}
|
|
|
|
def _init_weights(self, module):
|
|
std = self.config.initializer_range
|
|
if isinstance(module, nn.Linear):
|
|
module.weight.data.normal_(mean=0.0, std=std)
|
|
if module.bias is not None:
|
|
module.bias.data.zero_()
|
|
elif isinstance(module, nn.Embedding):
|
|
module.weight.data.normal_(mean=0.0, std=std)
|
|
if module.padding_idx is not None:
|
|
module.weight.data[module.padding_idx].zero_()
|
|
elif isinstance(module, nn.LayerNorm):
|
|
module.bias.data.zero_()
|
|
module.weight.data.fill_(1.0)
|
|
elif isinstance(module, EvollaRMSNorm):
|
|
module.weight.data.fill_(1.0)
|
|
elif isinstance(module, EvollaSequenceAlignerCrossAttention):
|
|
module.gate_attention.zero_()
|
|
module.gate_ffw.zero_()
|
|
module.attention_norm.weight.data.fill_(1.0)
|
|
elif isinstance(module, EvollaSequenceCompressorResampler):
|
|
module.latents.data.normal_(mean=0.0, std=std)
|
|
|
|
|
|
class EvollaModel(EvollaPreTrainedModel):
|
|
def __init__(self, config: EvollaConfig):
|
|
super().__init__(config)
|
|
self.padding_idx = config.pad_token_id
|
|
self.vocab_size = config.vocab_size
|
|
self.embed_tokens = nn.Embedding(self.vocab_size, config.hidden_size, self.padding_idx)
|
|
self.protein_encoder = EvollaProteinEncoder(config=config)
|
|
self.layers = nn.ModuleList(
|
|
[
|
|
EvollaDecoderLayer(
|
|
config=config,
|
|
layer_idx=layer_idx,
|
|
)
|
|
for layer_idx in range(config.num_hidden_layers)
|
|
]
|
|
)
|
|
|
|
self.norm = EvollaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
self.rotary_emb = EvollaRotaryEmbedding(config=config)
|
|
self.gradient_checkpointing = getattr(config, "gradient_checkpointing", False)
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.embed_tokens
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.embed_tokens = value
|
|
|
|
@auto_docstring
|
|
@check_model_inputs
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[Cache] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
protein_input_ids: Optional[torch.LongTensor] = None,
|
|
protein_attention_mask: Optional[torch.Tensor] = None,
|
|
structure_feats: Optional[torch.FloatTensor] = None,
|
|
msa_feats: Optional[torch.FloatTensor] = None,
|
|
structure_batch_mask: Optional[torch.Tensor] = None,
|
|
msa_batch_mask: Optional[torch.Tensor] = None,
|
|
**kwargs,
|
|
) -> Union[tuple, BaseModelOutputWithPast]:
|
|
r"""
|
|
protein_input_ids (torch.LongTensor):
|
|
The input IDs for the protein sequence in structure-aware tokens. Should be of shape `(batch_size, protein_seq_length)` and type `torch.LongTensor`.
|
|
protein_attention_mask (torch.Tensor):
|
|
The attention mask for the protein sequence. Should be of shape `(batch_size, protein_seq_length)` and type `torch.Tensor`.
|
|
structure_feats (torch.FloatTensor):
|
|
The input IDs for purely structure-based features. Should be of shape `(batch_size, structure_seq_length, structure_feat_dim)` and type `torch.FloatTensor`. Dummy input for now.
|
|
msa_feats (torch.FloatTensor):
|
|
The input IDs for purely MSA-based features. Should be of shape `(batch_size, msa_seq_length, msa_feat_dim)` and type `torch.FloatTensor`. Dummy input for now.
|
|
structure_batch_mask (torch.Tensor):
|
|
The batch mask to decide which protein sequences are purely structure-based. Should be of shape `(batch_size)` and type `torch.Tensor`. Should be paired with `structure_feats`. Dummpy input for now.
|
|
msa_batch_mask (torch.Tensor):
|
|
The batch mask to decide which protein sequences are purely MSA-based. Should be of shape `(batch_size)` and type `torch.Tensor`. Should be paired with `msa_feats`. Dummpy input for now.
|
|
"""
|
|
# If not provided `protein_feats`, use the `protein_encoder` to get the protein features
|
|
if protein_input_ids is not None and protein_attention_mask is not None:
|
|
protein_outputs = self.protein_encoder(
|
|
input_ids=protein_input_ids,
|
|
attention_mask=protein_attention_mask,
|
|
)
|
|
protein_feats = protein_outputs.sequence_compressor_output
|
|
protein_batch_mask = torch.tensor([True] * protein_input_ids.shape[0], device=protein_input_ids.device)
|
|
|
|
if (input_ids is None) ^ (inputs_embeds is not None):
|
|
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.embed_tokens(input_ids)
|
|
|
|
if use_cache and past_key_values is None:
|
|
past_key_values = DynamicCache()
|
|
|
|
if cache_position is None:
|
|
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
|
|
cache_position = torch.arange(
|
|
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
|
|
)
|
|
|
|
if position_ids is None:
|
|
position_ids = cache_position.unsqueeze(0)
|
|
|
|
causal_mask = create_causal_mask(
|
|
config=self.config,
|
|
input_embeds=inputs_embeds,
|
|
attention_mask=attention_mask,
|
|
cache_position=cache_position,
|
|
past_key_values=past_key_values,
|
|
)
|
|
|
|
hidden_states = inputs_embeds
|
|
|
|
# create position embeddings to be shared across the decoder layers
|
|
position_embeddings = self.rotary_emb(hidden_states, position_ids)
|
|
|
|
for decoder_layer in self.layers:
|
|
hidden_states = decoder_layer(
|
|
hidden_states,
|
|
attention_mask=causal_mask,
|
|
position_ids=position_ids,
|
|
past_key_value=past_key_values,
|
|
use_cache=use_cache,
|
|
cache_position=cache_position,
|
|
position_embeddings=position_embeddings,
|
|
protein_kv_states=protein_feats,
|
|
structure_kv_states=structure_feats,
|
|
msa_kv_states=msa_feats,
|
|
protein_batch_mask=protein_batch_mask,
|
|
structure_batch_mask=structure_batch_mask,
|
|
msa_batch_mask=msa_batch_mask,
|
|
query_attn_mask=attention_mask,
|
|
**kwargs,
|
|
)
|
|
|
|
hidden_states = self.norm(hidden_states)
|
|
|
|
output = BaseModelOutputWithPast(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=past_key_values,
|
|
)
|
|
return output
|
|
|
|
|
|
class EvollaForProteinText2Text(EvollaPreTrainedModel, GenerationMixin):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.model = EvollaModel(config)
|
|
self.vocab_size = config.vocab_size
|
|
self.lm_head = nn.Linear(config.hidden_size, self.vocab_size, bias=False)
|
|
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.model.get_input_embeddings()
|
|
|
|
def set_input_embeddings(self, value):
|
|
return self.model.set_input_embeddings(value)
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor = None, # text input ids
|
|
attention_mask: Optional[torch.Tensor] = None, # text attention mask
|
|
inputs_embeds: Optional[torch.FloatTensor] = None, # text input embeddings
|
|
labels: Optional[torch.LongTensor] = None,
|
|
protein_input_ids: torch.LongTensor = None,
|
|
protein_attention_mask: Optional[torch.Tensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
**kwargs,
|
|
):
|
|
r"""
|
|
protein_input_ids (torch.LongTensor):
|
|
The input IDs for the protein sequence. Should be of shape `(batch_size, protein_seq_length)` and type `torch.LongTensor`.
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protein_attention_mask (torch.Tensor):
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The attention mask for the protein sequence. Should be of shape `(batch_size, protein_seq_length)` and type `torch.Tensor`.
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Example:
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```python
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>>> from transformers import EvollaProcessor, EvollaForProteinText2Text
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>>> model = EvollaForProteinText2Text.from_pretrained("westlake/Evolla-10B-hf")
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>>> processor = EvollaProcessor.from_pretrained("westlake/Evolla-10B-hf")
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>>> protein_information = {
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"aa_seq": "your amino acid sequence",
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"foldseek": "your foldseek sequence",
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}
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>>> question = "What is the function of this protein?"
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>>> message = [
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{"role": "system", "content": "You are an AI expert that can answer any questions about protein."},
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{"role": "user", "content": question},
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]
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>>> inputs = processor(proteins=[protein_information], messages_list=[message], return_tensors="pt", padding="longest")
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>>> outputs = model.generate(**inputs)
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>>> print(processor.batch_decode(outputs, skip_special_tokens=True))
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```"""
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outputs = self.model(
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input_ids=input_ids,
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attention_mask=attention_mask,
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inputs_embeds=inputs_embeds,
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protein_input_ids=protein_input_ids,
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protein_attention_mask=protein_attention_mask,
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use_cache=use_cache,
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**kwargs,
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)
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hidden_states = outputs[0]
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logits = self.lm_head(hidden_states)
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loss = None
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if labels is not None:
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loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.vocab_size, **kwargs)
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lm_outputs = CausalLMOutputWithPast(
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loss=loss,
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logits=logits,
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past_key_values=outputs.past_key_values,
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hidden_states=outputs.hidden_states,
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attentions=outputs.attentions,
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)
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return lm_outputs
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__all__ = ["EvollaForProteinText2Text", "EvollaModel", "EvollaPreTrainedModel"]
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