You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
1496 lines
62 KiB
1496 lines
62 KiB
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
|
|
# This file was automatically generated from src/transformers/models/evolla/modular_evolla.py.
|
|
# Do NOT edit this file manually as any edits will be overwritten by the generation of
|
|
# the file from the modular. If any change should be done, please apply the change to the
|
|
# modular_evolla.py file directly. One of our CI enforces this.
|
|
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
|
|
# Copyright 2025 Westlake Representational Learning Lab (Fajie Yuan Lab) team and the HuggingFace Inc. team. All rights reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
import math
|
|
from collections.abc import Callable
|
|
from dataclasses import dataclass
|
|
from typing import Optional
|
|
|
|
import torch
|
|
from torch import nn
|
|
|
|
from ... import initialization as init
|
|
from ...activations import ACT2FN
|
|
from ...cache_utils import Cache, DynamicCache
|
|
from ...generation import GenerationMixin
|
|
from ...integrations import use_kernel_forward_from_hub, use_kernel_func_from_hub, use_kernelized_func
|
|
from ...masking_utils import create_bidirectional_mask, create_causal_mask
|
|
from ...modeling_layers import GradientCheckpointingLayer
|
|
from ...modeling_outputs import (
|
|
BaseModelOutputWithCrossAttentions,
|
|
BaseModelOutputWithPast,
|
|
BaseModelOutputWithPoolingAndCrossAttentions,
|
|
CausalLMOutputWithPast,
|
|
ModelOutput,
|
|
)
|
|
from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
|
|
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
|
|
from ...processing_utils import Unpack
|
|
from ...utils import TransformersKwargs, auto_docstring, can_return_tuple
|
|
from ...utils.generic import OutputRecorder, check_model_inputs, maybe_autocast
|
|
from .configuration_evolla import EvollaConfig, SaProtConfig
|
|
|
|
|
|
def create_position_ids_from_input_ids(input_ids, padding_idx):
|
|
"""
|
|
Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
|
|
are ignored. This is modified from fairseq's `utils.make_positions`.
|
|
|
|
Args:
|
|
x: torch.Tensor x:
|
|
|
|
Returns: torch.Tensor
|
|
"""
|
|
# The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
|
|
mask = input_ids.ne(padding_idx).int()
|
|
incremental_indices = torch.cumsum(mask, dim=1).type_as(mask) * mask
|
|
return incremental_indices.long() + padding_idx
|
|
|
|
|
|
class EvollaSaProtEmbeddings(nn.Module):
|
|
"""
|
|
Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
|
|
"""
|
|
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
|
|
|
|
if config.emb_layer_norm_before:
|
|
self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
else:
|
|
self.layer_norm = None
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
# position_ids (1, len position emb) is contiguous in memory and exported when serialized
|
|
self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
|
|
self.register_buffer(
|
|
"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
|
|
)
|
|
|
|
self.padding_idx = config.pad_token_id
|
|
if self.position_embedding_type == "absolute":
|
|
self.position_embeddings = nn.Embedding(
|
|
config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx
|
|
)
|
|
self.token_dropout = config.token_dropout
|
|
self.mask_token_id = config.mask_token_id
|
|
# remove the position_ids in EsmEmbeddings
|
|
self.position_ids = None
|
|
|
|
def forward(
|
|
self,
|
|
input_ids=None,
|
|
attention_mask=None,
|
|
position_ids=None,
|
|
inputs_embeds=None,
|
|
):
|
|
if position_ids is None:
|
|
if input_ids is not None:
|
|
# Create the position ids from the input token ids. Any padded tokens remain padded.
|
|
position_ids = create_position_ids_from_input_ids(input_ids, self.padding_idx)
|
|
else:
|
|
position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.word_embeddings(input_ids)
|
|
|
|
# Note that if we want to support EVOLLA_SA_PROT-1 (not 1b!) in future then we need to support an
|
|
# embedding_scale factor here.
|
|
embeddings = inputs_embeds
|
|
|
|
# Matt: EVOLLA_SA_PROT has the option to handle masking in MLM in a slightly unusual way. If the token_dropout
|
|
# flag is False then it is handled in the same was as BERT/RoBERTa. If it is set to True, however,
|
|
# masked tokens are treated as if they were selected for input dropout and zeroed out.
|
|
# This "mask-dropout" is compensated for when masked tokens are not present, by scaling embeddings by
|
|
# a factor of (fraction of unmasked tokens during training) / (fraction of unmasked tokens in sample).
|
|
# This is analogous to the way that dropout layers scale down outputs during evaluation when not
|
|
# actually dropping out values (or, equivalently, scale up their un-dropped outputs in training).
|
|
if self.token_dropout and input_ids is not None:
|
|
embeddings = embeddings.masked_fill((input_ids == self.mask_token_id).unsqueeze(-1), 0.0)
|
|
mask_ratio_train = 0.15 * 0.8 # Hardcoded as the ratio used in all EVOLLA_SA_PROT model training runs
|
|
src_lengths = attention_mask.sum(-1) if attention_mask is not None else input_ids.shape[1]
|
|
mask_ratio_observed = (input_ids == self.mask_token_id).sum(-1).float() / src_lengths
|
|
embeddings = (embeddings * (1 - mask_ratio_train) / (1 - mask_ratio_observed)[:, None, None]).to(
|
|
embeddings.dtype
|
|
)
|
|
|
|
if self.position_embedding_type == "absolute":
|
|
position_embeddings = self.position_embeddings(position_ids)
|
|
embeddings = embeddings + position_embeddings
|
|
|
|
if self.layer_norm is not None:
|
|
embeddings = self.layer_norm(embeddings)
|
|
if attention_mask is not None:
|
|
embeddings = (embeddings * attention_mask.unsqueeze(-1)).to(embeddings.dtype)
|
|
# Matt: I think this line was copied incorrectly from BERT, disabling it for now.
|
|
# embeddings = self.dropout(embeddings)
|
|
return embeddings
|
|
|
|
def create_position_ids_from_inputs_embeds(self, inputs_embeds):
|
|
"""
|
|
We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.
|
|
|
|
Args:
|
|
inputs_embeds: torch.Tensor
|
|
|
|
Returns: torch.Tensor
|
|
"""
|
|
input_shape = inputs_embeds.size()[:-1]
|
|
sequence_length = input_shape[1]
|
|
|
|
position_ids = torch.arange(
|
|
self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device
|
|
)
|
|
return position_ids.unsqueeze(0).expand(input_shape)
|
|
|
|
|
|
def rotate_half_esm(x):
|
|
x1, x2 = x.chunk(2, dim=-1)
|
|
return torch.cat((-x2, x1), dim=-1)
|
|
|
|
|
|
def apply_rotary_pos_emb_esm(x, cos, sin):
|
|
cos = cos[:, :, : x.shape[-2], :]
|
|
sin = sin[:, :, : x.shape[-2], :]
|
|
|
|
return (x * cos) + (rotate_half_esm(x) * sin)
|
|
|
|
|
|
class EvollaSaProtRotaryEmbedding(nn.Module):
|
|
"""
|
|
Rotary position embeddings based on those in
|
|
[RoFormer](https://huggingface.co/docs/transformers/model_doc/roformer). Query and keys are transformed by rotation
|
|
matrices which depend on their relative positions.
|
|
"""
|
|
|
|
inv_freq: torch.Tensor # fix linting for `register_buffer`
|
|
|
|
def __init__(self, dim: int):
|
|
super().__init__()
|
|
self.dim = dim
|
|
# Generate and save the inverse frequency buffer (non trainable)
|
|
inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2, dtype=torch.int64).float() / dim))
|
|
self.register_buffer("inv_freq", inv_freq)
|
|
|
|
self._seq_len_cached = None
|
|
self._cos_cached = None
|
|
self._sin_cached = None
|
|
|
|
def _update_cos_sin_tables(self, x, seq_dimension=2):
|
|
seq_len = x.shape[seq_dimension]
|
|
|
|
# Reset the tables if the sequence length has changed,
|
|
# or if we're on a new device (possibly due to tracing for instance)
|
|
if seq_len != self._seq_len_cached or self._cos_cached.device != x.device:
|
|
self._seq_len_cached = seq_len
|
|
t = torch.arange(x.shape[seq_dimension], device=x.device).type_as(self.inv_freq)
|
|
freqs = torch.outer(t, self.inv_freq)
|
|
emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
|
|
|
|
self._cos_cached = emb.cos()[None, None, :, :]
|
|
self._sin_cached = emb.sin()[None, None, :, :]
|
|
|
|
return self._cos_cached, self._sin_cached
|
|
|
|
def forward(self, q: torch.Tensor, k: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
|
|
self._cos_cached, self._sin_cached = self._update_cos_sin_tables(k, seq_dimension=-2)
|
|
|
|
return (
|
|
apply_rotary_pos_emb_esm(q, self._cos_cached, self._sin_cached).to(dtype=q.dtype),
|
|
apply_rotary_pos_emb_esm(k, self._cos_cached, self._sin_cached).to(dtype=k.dtype),
|
|
)
|
|
|
|
|
|
def eager_attention_forward(
|
|
module: nn.Module,
|
|
query: torch.Tensor,
|
|
key: torch.Tensor,
|
|
value: torch.Tensor,
|
|
attention_mask: torch.Tensor | None,
|
|
scaling: float | None = None,
|
|
dropout: float = 0.0,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
):
|
|
if scaling is None:
|
|
scaling = query.size(-1) ** -0.5
|
|
|
|
# Take the dot product between "query" and "key" to get the raw attention scores.
|
|
attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling
|
|
|
|
if attention_mask is not None:
|
|
attention_mask = attention_mask[:, :, :, : key.shape[-2]]
|
|
attn_weights = attn_weights + attention_mask
|
|
|
|
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
|
|
attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
|
|
|
|
attn_output = torch.matmul(attn_weights, value)
|
|
attn_output = attn_output.transpose(1, 2).contiguous()
|
|
|
|
return attn_output, attn_weights
|
|
|
|
|
|
class EvollaSaProtSelfAttention(nn.Module):
|
|
def __init__(self, config, position_embedding_type=None, layer_idx=None, is_cross_attention=False):
|
|
super().__init__()
|
|
self.config = config
|
|
|
|
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
|
|
raise ValueError(
|
|
f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
|
|
f"heads ({config.num_attention_heads})"
|
|
)
|
|
|
|
self.num_attention_heads = config.num_attention_heads
|
|
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
|
|
self.all_head_size = self.num_attention_heads * self.attention_head_size
|
|
|
|
self.query = nn.Linear(config.hidden_size, self.all_head_size)
|
|
self.key = nn.Linear(config.hidden_size, self.all_head_size)
|
|
self.value = nn.Linear(config.hidden_size, self.all_head_size)
|
|
|
|
self.dropout = config.attention_probs_dropout_prob
|
|
|
|
self.rotary_embeddings = None
|
|
self.position_embedding_type = position_embedding_type or getattr(
|
|
config, "position_embedding_type", "absolute"
|
|
)
|
|
if self.position_embedding_type == "rotary":
|
|
self.rotary_embeddings = EvollaSaProtRotaryEmbedding(dim=self.attention_head_size)
|
|
|
|
self.is_decoder = config.is_decoder
|
|
self.layer_idx = layer_idx
|
|
self.scaling = 1.0
|
|
self.is_causal = self.is_decoder and not is_cross_attention
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: torch.FloatTensor | None = None,
|
|
encoder_hidden_states: torch.FloatTensor | None = None,
|
|
encoder_attention_mask: torch.FloatTensor | None = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> tuple[torch.Tensor]:
|
|
batch_size, seq_length = hidden_states.shape[:-1]
|
|
hidden_shape = (batch_size, seq_length, -1, self.attention_head_size)
|
|
|
|
query_layer = self.query(hidden_states).view(hidden_shape).transpose(1, 2)
|
|
|
|
is_cross_attention = encoder_hidden_states is not None
|
|
current_states = encoder_hidden_states if is_cross_attention else hidden_states
|
|
attention_mask = encoder_attention_mask if is_cross_attention else attention_mask
|
|
key_layer = self.key(current_states).view(hidden_shape).transpose(1, 2)
|
|
value_layer = self.value(current_states).view(hidden_shape).transpose(1, 2)
|
|
|
|
# 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)
|
|
|
|
attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
|
|
self.config._attn_implementation, eager_attention_forward
|
|
)
|
|
|
|
attn_output, attn_weights = attention_interface(
|
|
self,
|
|
query_layer,
|
|
key_layer,
|
|
value_layer,
|
|
attention_mask,
|
|
dropout=0.0 if not self.training else self.dropout,
|
|
scaling=self.scaling,
|
|
**kwargs,
|
|
)
|
|
|
|
attn_output = attn_output.reshape(batch_size, seq_length, -1).contiguous()
|
|
return attn_output, attn_weights
|
|
|
|
|
|
class EvollaSaProtSelfOutput(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.hidden_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 EvollaSaProtAttention(nn.Module):
|
|
def __init__(self, config, layer_idx=None, is_cross_attention=False):
|
|
super().__init__()
|
|
self.self = EvollaSaProtSelfAttention(config, layer_idx=layer_idx, is_cross_attention=is_cross_attention)
|
|
self.output = EvollaSaProtSelfOutput(config)
|
|
|
|
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states,
|
|
attention_mask=None,
|
|
encoder_hidden_states=None,
|
|
encoder_attention_mask=None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
):
|
|
hidden_states_ln = self.LayerNorm(hidden_states)
|
|
attn_output, _ = self.self(
|
|
hidden_states_ln,
|
|
attention_mask=attention_mask,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
encoder_attention_mask=encoder_attention_mask,
|
|
**kwargs,
|
|
)
|
|
attn_output = self.output(attn_output, hidden_states)
|
|
return attn_output
|
|
|
|
|
|
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, is_cross_attention=True)
|
|
self.intermediate = EvollaSaProtIntermediate(config)
|
|
self.output = EvollaSaProtOutput(config)
|
|
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states,
|
|
attention_mask=None,
|
|
encoder_hidden_states=None,
|
|
encoder_attention_mask=None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
):
|
|
attention_output = self.attention(
|
|
hidden_states,
|
|
attention_mask=attention_mask,
|
|
**kwargs,
|
|
)
|
|
|
|
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`"
|
|
)
|
|
|
|
attention_output = self.crossattention(
|
|
attention_output,
|
|
attention_mask=attention_mask,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
encoder_attention_mask=encoder_attention_mask,
|
|
**kwargs,
|
|
)
|
|
|
|
layer_output = self.feed_forward_chunk(attention_output)
|
|
return layer_output
|
|
|
|
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
|
|
|
|
@can_return_tuple
|
|
def forward(
|
|
self,
|
|
hidden_states,
|
|
attention_mask=None,
|
|
encoder_hidden_states=None,
|
|
encoder_attention_mask=None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
):
|
|
for i, layer_module in enumerate(self.layer):
|
|
hidden_states = layer_module(
|
|
hidden_states,
|
|
attention_mask=attention_mask,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
encoder_attention_mask=encoder_attention_mask,
|
|
**kwargs,
|
|
)
|
|
|
|
if self.emb_layer_norm_after:
|
|
hidden_states = self.emb_layer_norm_after(hidden_states)
|
|
|
|
return BaseModelOutputWithCrossAttentions(last_hidden_state=hidden_states)
|
|
|
|
|
|
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
|
|
_supports_sdpa = True
|
|
_supports_flex_attn = True
|
|
_supports_attention_backend = True
|
|
|
|
_can_record_outputs = {
|
|
"hidden_states": EvollaSaProtLayer,
|
|
"attentions": [OutputRecorder(EvollaSaProtSelfAttention, index=1, layer_name="attention")],
|
|
"cross_attentions": [
|
|
OutputRecorder(EvollaSaProtSelfAttention, index=1, layer_name="crossattention"),
|
|
],
|
|
}
|
|
|
|
def _init_weights(self, module):
|
|
super()._init_weights(module)
|
|
if isinstance(module, EvollaSaProtRotaryEmbedding):
|
|
inv_freq = 1.0 / (10000 ** (torch.arange(0, module.dim, 2, dtype=torch.int64).float() / module.dim))
|
|
init.copy_(module.inv_freq, inv_freq)
|
|
|
|
|
|
class EvollaSaProtProteinEncoder(EvollaSaProtPreTrainedModel):
|
|
def __init__(self, config: SaProtConfig):
|
|
super().__init__(config)
|
|
self.embeddings = EvollaSaProtEmbeddings(config)
|
|
self.encoder = EvollaSaProtEncoder(config)
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.embeddings.word_embeddings
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.embeddings.word_embeddings = value
|
|
|
|
@check_model_inputs
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor | None,
|
|
attention_mask: torch.Tensor | None = None,
|
|
**kwargs,
|
|
) -> 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)
|
|
|
|
attention_mask = create_bidirectional_mask(
|
|
config=self.config,
|
|
input_embeds=inputs_embeds,
|
|
attention_mask=attention_mask,
|
|
)
|
|
|
|
encoder_outputs = self.encoder(inputs_embeds, attention_mask=attention_mask, **kwargs)
|
|
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,
|
|
)
|
|
|
|
|
|
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 = None
|
|
last_hidden_state: torch.FloatTensor | None = None
|
|
hidden_states: tuple[torch.FloatTensor, ...] | None = None
|
|
attentions: tuple[torch.FloatTensor, ...] | None = 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: int | None = None,
|
|
structure_encoder_dim: int | None = None,
|
|
msa_encoder_dim: int | None = 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 stabilize 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_values=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):
|
|
inv_freq: torch.Tensor # fix linting for `register_buffer`
|
|
|
|
def __init__(self, config: EvollaConfig, device=None):
|
|
super().__init__()
|
|
self.max_seq_len_cached = config.max_position_embeddings
|
|
self.original_max_seq_len = config.max_position_embeddings
|
|
|
|
self.config = config
|
|
|
|
self.rope_type = self.config.rope_parameters["rope_type"]
|
|
rope_init_fn: Callable = self.compute_default_rope_parameters
|
|
if self.rope_type != "default":
|
|
rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
|
|
inv_freq, self.attention_scaling = rope_init_fn(self.config, device)
|
|
|
|
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
|
self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False)
|
|
|
|
@staticmethod
|
|
def compute_default_rope_parameters(
|
|
config: EvollaConfig | None = None,
|
|
device: Optional["torch.device"] = None,
|
|
seq_len: int | None = None,
|
|
) -> tuple["torch.Tensor", float]:
|
|
"""
|
|
Computes the inverse frequencies according to the original RoPE implementation
|
|
Args:
|
|
config ([`~transformers.PreTrainedConfig`]):
|
|
The model configuration.
|
|
device (`torch.device`):
|
|
The device to use for initialization of the inverse frequencies.
|
|
seq_len (`int`, *optional*):
|
|
The current sequence length. Unused for this type of RoPE.
|
|
Returns:
|
|
Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
|
|
post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
|
|
"""
|
|
base = config.rope_parameters["rope_theta"]
|
|
dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads
|
|
|
|
attention_factor = 1.0 # Unused in this type of RoPE
|
|
|
|
# Compute the inverse frequencies
|
|
inv_freq = 1.0 / (
|
|
base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim)
|
|
)
|
|
return inv_freq, attention_factor
|
|
|
|
@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 maybe_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)
|
|
|
|
|
|
@use_kernel_func_from_hub("rotary_pos_emb")
|
|
def apply_rotary_pos_emb(q, k, cos, sin, 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.
|
|
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)
|
|
|
|
|
|
@use_kernelized_func(apply_rotary_pos_emb)
|
|
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] | None = None,
|
|
attention_mask: torch.Tensor | None = None,
|
|
past_key_values: Cache | None = None,
|
|
cache_position: torch.LongTensor | None = 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_values 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_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
|
|
|
attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
|
|
self.config._attn_implementation, eager_attention_forward
|
|
)
|
|
|
|
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] | None = None,
|
|
attention_mask: torch.Tensor | None = None,
|
|
position_ids: torch.LongTensor | None = None,
|
|
past_key_values: Cache | None = None,
|
|
use_cache: bool | None = False,
|
|
cache_position: torch.LongTensor | None = None,
|
|
protein_kv_states: torch.Tensor | None = None,
|
|
structure_kv_states: torch.Tensor | None = None,
|
|
msa_kv_states: torch.Tensor | None = None,
|
|
protein_batch_mask: torch.Tensor | None = None,
|
|
structure_batch_mask: torch.Tensor | None = None,
|
|
msa_batch_mask: torch.Tensor | None = None,
|
|
query_attn_mask: torch.Tensor | None = None,
|
|
**kwargs,
|
|
) -> 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_values=past_key_values,
|
|
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",
|
|
"EvollaSequenceCompressorResampler",
|
|
"EvollaSequenceAlignerCrossAttention",
|
|
]
|
|
_skip_keys_device_placement = ["past_key_values"]
|
|
_supports_flash_attn = False # see dependency on `EvollaSequenceCompressorResampler`
|
|
_supports_sdpa = True
|
|
_supports_flex_attn = False # see dependency on `EvollaSequenceCompressorResampler`
|
|
|
|
_can_compile_fullgraph = True
|
|
_supports_attention_backend = False
|
|
_can_record_outputs = {
|
|
"hidden_states": EvollaDecoderLayer,
|
|
"attentions": EvollaAttention,
|
|
}
|
|
|
|
@torch.no_grad()
|
|
def _init_weights(self, module):
|
|
std = self.config.initializer_range
|
|
super()._init_weights(module)
|
|
if isinstance(module, EvollaSequenceAlignerCrossAttention):
|
|
init.zeros_(module.gate_attention)
|
|
init.zeros_(module.gate_ffw)
|
|
init.ones_(module.attention_norm.weight)
|
|
elif isinstance(module, EvollaSequenceCompressorResampler):
|
|
init.normal_(module.latents, 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.gradient_checkpointing = getattr(config, "gradient_checkpointing", False)
|
|
self.rotary_emb = EvollaRotaryEmbedding(config=config)
|
|
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 = None,
|
|
attention_mask: torch.Tensor | None = None,
|
|
position_ids: torch.LongTensor | None = None,
|
|
past_key_values: Cache | None = None,
|
|
inputs_embeds: torch.FloatTensor | None = None,
|
|
use_cache: bool | None = None,
|
|
cache_position: torch.LongTensor | None = None,
|
|
protein_input_ids: torch.LongTensor | None = None,
|
|
protein_attention_mask: torch.Tensor | None = None,
|
|
structure_feats: torch.FloatTensor | None = None,
|
|
msa_feats: torch.FloatTensor | None = None,
|
|
structure_batch_mask: torch.Tensor | None = None,
|
|
msa_batch_mask: torch.Tensor | None = None,
|
|
**kwargs,
|
|
) -> 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 (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(config=self.config)
|
|
|
|
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)
|
|
|
|
protein_feats = None
|
|
protein_batch_mask = None
|
|
# If provided, actually compute them
|
|
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.ones(
|
|
protein_input_ids.shape[0],
|
|
device=protein_input_ids.device,
|
|
dtype=torch.bool,
|
|
)
|
|
|
|
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
|
|
position_embeddings = self.rotary_emb(hidden_states, position_ids=position_ids)
|
|
|
|
for decoder_layer in self.layers:
|
|
hidden_states = decoder_layer(
|
|
hidden_states,
|
|
attention_mask=causal_mask,
|
|
position_ids=position_ids,
|
|
past_key_values=past_key_values,
|
|
use_cache=use_cache,
|
|
cache_position=cache_position,
|
|
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,
|
|
position_embeddings=position_embeddings,
|
|
**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 = None, # text input ids
|
|
attention_mask: torch.Tensor | None = None, # text attention mask
|
|
inputs_embeds: torch.FloatTensor | None = None, # text input embeddings
|
|
labels: torch.LongTensor | None = None,
|
|
protein_input_ids: torch.LongTensor | None = None,
|
|
protein_attention_mask: torch.Tensor | None = None,
|
|
use_cache: bool | None = None,
|
|
logits_to_keep: int | torch.Tensor = 0,
|
|
**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`.
|
|
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`.
|
|
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import EvollaProcessor, EvollaForProteinText2Text
|
|
>>> model = EvollaForProteinText2Text.from_pretrained("westlake/Evolla-10B-hf")
|
|
>>> processor = EvollaProcessor.from_pretrained("westlake/Evolla-10B-hf")
|
|
|
|
>>> protein_information = {
|
|
"aa_seq": "your amino acid sequence",
|
|
"foldseek": "your foldseek sequence",
|
|
}
|
|
>>> question = "What is the function of this protein?"
|
|
>>> message = [
|
|
{"role": "system", "content": "You are an AI expert that can answer any questions about protein."},
|
|
{"role": "user", "content": question},
|
|
]
|
|
|
|
>>> inputs = processor(proteins=[protein_information], messages_list=[message], return_tensors="pt", padding="longest")
|
|
>>> outputs = model.generate(**inputs)
|
|
|
|
>>> print(processor.batch_decode(outputs, skip_special_tokens=True))
|
|
```"""
|
|
outputs: BaseModelOutputWithPast = self.model(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
protein_input_ids=protein_input_ids,
|
|
protein_attention_mask=protein_attention_mask,
|
|
use_cache=use_cache,
|
|
**kwargs,
|
|
)
|
|
|
|
hidden_states = outputs.last_hidden_state
|
|
# Only compute necessary logits, and do not upcast them to float if we are not computing the loss
|
|
slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
|
|
logits = self.lm_head(hidden_states[:, slice_indices, :])
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.vocab_size, **kwargs)
|
|
|
|
lm_outputs = CausalLMOutputWithPast(
|
|
loss=loss,
|
|
logits=logits,
|
|
past_key_values=outputs.past_key_values,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
return lm_outputs
|
|
|
|
|
|
__all__ = ["EvollaForProteinText2Text", "EvollaModel", "EvollaPreTrainedModel"]
|