1008 lines
40 KiB
Python
1008 lines
40 KiB
Python
# 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 warnings
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from dataclasses import dataclass
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from typing import Optional, Union
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import torch
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import torch.utils.checkpoint
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from torch import Tensor, nn
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from ...cache_utils import Cache, DynamicCache
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from ...generation import GenerationMixin
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from ...masking_utils import create_causal_mask
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from ...modeling_outputs import (
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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_utils import ModuleUtilsMixin, PreTrainedModel, get_parameter_dtype
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from ...utils import (
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auto_docstring,
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can_return_tuple,
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logging,
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)
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from ...utils.generic import check_model_inputs
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from ..esm.modeling_esm import (
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EsmAttention,
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EsmEmbeddings,
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EsmEncoder,
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EsmIntermediate,
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EsmLayer,
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EsmOutput,
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EsmPooler,
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EsmSelfAttention,
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EsmSelfOutput,
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)
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from ..llama.modeling_llama import (
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LlamaAttention,
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LlamaDecoderLayer,
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LlamaMLP,
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LlamaPreTrainedModel,
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LlamaRMSNorm,
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LlamaRotaryEmbedding,
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)
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from .configuration_evolla import EvollaConfig, SaProtConfig
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logger = logging.get_logger(__name__)
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class EvollaSaProtEmbeddings(EsmEmbeddings):
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def __init__(self, config):
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super().__init__()
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# remove the position_ids in EsmEmbeddings
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self.position_ids = None
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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(EsmSelfAttention, nn.Module):
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def __init__(self, config, position_embedding_type=None, layer_idx=None):
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nn.Module.__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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class EvollaSaProtSelfOutput(EsmSelfOutput):
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pass
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class EvollaSaProtAttention(EsmAttention):
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pass
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class EvollaSaProtIntermediate(EsmIntermediate):
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pass
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class EvollaSaProtOutput(EsmOutput):
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pass
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class EvollaSaProtLayer(EsmLayer):
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pass
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class EvollaSaProtEncoder(EsmEncoder):
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pass
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class EvollaSaProtPooler(EsmPooler):
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pass
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@auto_docstring
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class EvollaSaProtPreTrainedModel(PreTrainedModel):
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config: SaProtConfig
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_no_split_modules = ["EvollaSaProtLayer"]
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_supports_flash_attn = True
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def _init_weights(self, module):
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"""Initialize the weights"""
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std = self.config.initializer_range
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if isinstance(module, nn.Linear):
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module.weight.data.normal_(mean=0.0, std=std)
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if module.bias is not None:
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module.bias.data.zero_()
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elif isinstance(module, nn.Embedding):
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module.weight.data.normal_(mean=0.0, std=std)
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if module.padding_idx is not None:
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module.weight.data[module.padding_idx].zero_()
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elif isinstance(module, nn.LayerNorm):
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module.bias.data.zero_()
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module.weight.data.fill_(1.0)
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class EvollaSaProtProteinEncoder(EvollaSaProtPreTrainedModel):
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def __init__(self, config: SaProtConfig):
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super().__init__(config)
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self.embeddings = EvollaSaProtEmbeddings(config)
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self.encoder = EvollaSaProtEncoder(config)
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def get_input_embeddings(self):
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return self.embeddings.word_embeddings
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def set_input_embeddings(self, value):
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self.embeddings.word_embeddings = value
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def _prune_heads(self, heads_to_prune):
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"""
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Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
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class PreTrainedModel
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"""
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for layer, heads in heads_to_prune.items():
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self.encoder.layer[layer].attention.prune_heads(heads)
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@can_return_tuple
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def forward(
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self,
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input_ids: Optional[torch.Tensor],
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attention_mask: Optional[torch.Tensor] = None,
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) -> Union[tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
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input_shape = input_ids.size()
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batch_size, seq_length = input_shape
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device = input_ids.device
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if attention_mask is None:
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attention_mask = torch.ones(((batch_size, seq_length)), device=device)
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inputs_embeds = self.embeddings(input_ids=input_ids, attention_mask=attention_mask)
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extended_attention_mask = self.get_extended_attention_mask(attention_mask, input_shape)
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encoder_outputs = self.encoder(inputs_embeds, attention_mask=extended_attention_mask)
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sequence_output = encoder_outputs[0]
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return BaseModelOutputWithPoolingAndCrossAttentions(
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last_hidden_state=sequence_output,
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hidden_states=encoder_outputs.hidden_states,
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attentions=encoder_outputs.attentions,
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cross_attentions=encoder_outputs.cross_attentions,
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)
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def get_extended_attention_mask(
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self, attention_mask: Tensor, input_shape: tuple[int], device: torch.device = None, dtype: torch.float = None
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) -> Tensor:
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"""
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Makes broadcastable attention and causal masks so that future and masked tokens are ignored.
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Arguments:
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attention_mask (`torch.Tensor`):
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Mask with ones indicating tokens to attend to, zeros for tokens to ignore.
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input_shape (`Tuple[int]`):
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The shape of the input to the model.
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Returns:
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`torch.Tensor` The extended attention mask, with a the same dtype as `attention_mask.dtype`.
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"""
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if dtype is None:
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dtype = get_parameter_dtype(self)
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if not (attention_mask.dim() == 2 and self.config.is_decoder):
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# show warning only if it won't be shown in `create_extended_attention_mask_for_decoder`
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if device is not None:
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warnings.warn(
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"The `device` argument is deprecated and will be removed in v5 of Transformers.", FutureWarning
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)
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# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
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# ourselves in which case we just need to make it broadcastable to all heads.
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if attention_mask.dim() == 3:
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extended_attention_mask = attention_mask[:, None, :, :]
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elif attention_mask.dim() == 2:
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# Provided a padding mask of dimensions [batch_size, seq_length]
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# - if the model is a decoder, apply a causal mask in addition to the padding mask
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# - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]
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if self.config.is_decoder:
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extended_attention_mask = ModuleUtilsMixin.create_extended_attention_mask_for_decoder(
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input_shape, attention_mask, device
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)
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else:
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extended_attention_mask = attention_mask[:, None, None, :]
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else:
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raise ValueError(
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f"Wrong shape for input_ids (shape {input_shape}) or attention_mask (shape {attention_mask.shape})"
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)
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# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
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# masked positions, this operation will create a tensor which is 0.0 for
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# positions we want to attend and the dtype's smallest value for masked positions.
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# Since we are adding it to the raw scores before the softmax, this is
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# effectively the same as removing these entirely.
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extended_attention_mask = extended_attention_mask.to(dtype=dtype) # fp16 compatibility
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extended_attention_mask = (1.0 - extended_attention_mask) * torch.finfo(dtype).min
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return extended_attention_mask
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class EvollaSequenceCompressorAttention(nn.Module):
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def __init__(self, dim, dim_head=64, heads=8):
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super().__init__()
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self.scale = dim_head**-0.5
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self.heads = heads
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inner_dim = dim_head * heads
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self.norm_media = nn.LayerNorm(dim)
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self.norm_latents = nn.LayerNorm(dim)
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self.to_q = nn.Linear(dim, inner_dim, bias=False)
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self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False)
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self.to_out = nn.Linear(inner_dim, dim, bias=False)
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def forward(self, x, latents, mask):
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"""
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Args:
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x (torch.Tensor): image features
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shape (b, n1, D)
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latent (torch.Tensor): latent features
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shape (b, n2, D); n2: num of latent tokens
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"""
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x = self.norm_media(x)
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latents = self.norm_latents(latents)
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h = self.heads
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q = self.to_q(latents)
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kv_input = torch.cat((x, latents), dim=-2)
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k, v = self.to_kv(kv_input).chunk(
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2, dim=-1
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) # each: batch_size, max_protein_length+num_latents, dim_head*num_heads
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q = q.view(q.size(0), q.size(1), h, -1).permute(0, 2, 1, 3)
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k = k.view(k.size(0), k.size(1), h, -1).permute(0, 2, 1, 3)
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v = v.view(v.size(0), v.size(1), h, -1).permute(0, 2, 1, 3)
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q = q * self.scale # batch_size, num_heads, num_latents, dim_head
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# attention
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sim = torch.matmul(q, k.transpose(-1, -2))
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sim = sim - sim.amax(dim=-1, keepdim=True).detach()
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bs, nh, skd, okd = sim.shape
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ones = torch.ones(nh, skd).to(mask.device) # Create a tensor of ones with shape (nh, skd)
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mask_exp = mask[:, None, None, :]
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ones_exp = ones[None, :, :, None]
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mask = mask_exp * ones_exp
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sim = sim.masked_fill((1 - mask).bool(), -1e4)
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attn = sim.softmax(dim=-1)
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out = torch.matmul(attn, v)
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out = out.permute(0, 2, 1, 3)
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# [batch, seq, head, features] -> [batch, seq, head*features]
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out = out.reshape(out.size(0), out.size(1), -1)
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return self.to_out(out)
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class EvollaFeedForward(nn.Module):
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def __init__(self, dim, mult=4):
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super().__init__()
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inner_dim = int(dim * mult)
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self.norm = nn.LayerNorm(dim)
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self.fc1 = nn.Linear(dim, inner_dim, bias=False)
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self.activation = nn.GELU()
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self.fc2 = nn.Linear(inner_dim, dim, bias=False)
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def forward(self, x):
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return self.fc2(self.activation(self.fc1(self.norm(x))))
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class EvollaSequenceCompressorResampler(nn.Module):
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def __init__(self, config: EvollaConfig):
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super().__init__()
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protein_repr_dim = config.protein_encoder_config.hidden_size
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self.num_latents = config.resampler_num_latents
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self.latents = nn.Parameter(torch.randn(self.num_latents, protein_repr_dim), requires_grad=True)
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self.layers = nn.ModuleList([])
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for _ in range(config.resampler_depth):
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self.layers.append(
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nn.ModuleList(
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[
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EvollaSequenceCompressorAttention(
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dim=protein_repr_dim, dim_head=config.resampler_dim_head, heads=config.resampler_heads
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),
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EvollaFeedForward(dim=protein_repr_dim, mult=config.resampler_ff_mult),
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]
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)
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)
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self.norm = nn.LayerNorm(config.hidden_size)
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self.protein_projector = nn.Linear(protein_repr_dim, config.hidden_size)
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def forward(self, embeds, mask):
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b = embeds.shape[0]
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bs, _ = mask.shape # bs, max_protein_length
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latent_mask = torch.ones(bs, self.num_latents).to(mask.device)
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mask = torch.cat((mask, latent_mask), dim=1) # bs, max_protein_length + num_latents
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# blocks
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ones = torch.ones(b).to(self.latents.device)
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latents = self.latents[None] * ones.view(-1, 1, 1) # [b,n,d]
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latents = latents.to(embeds.dtype)
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for attn, ff in self.layers:
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latents = attn(embeds, latents, mask) + latents
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latents = ff(latents) + latents
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transformed_feature = self.protein_projector(latents)
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return self.norm(transformed_feature)
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@dataclass
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@auto_docstring
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class EvollaProteinEncoderModelOutput(ModelOutput):
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sequence_compressor_output: torch.FloatTensor = None
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last_hidden_state: Optional[torch.FloatTensor] = None
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hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
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attentions: Optional[tuple[torch.FloatTensor, ...]] = None
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class EvollaProteinEncoder(nn.Module):
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def __init__(self, config: EvollaConfig):
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super().__init__()
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self.model = EvollaSaProtProteinEncoder(config=config.protein_encoder_config)
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self.sequence_compressor_resampler = EvollaSequenceCompressorResampler(config=config)
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@can_return_tuple
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def forward(self, input_ids: torch.LongTensor, attention_mask: torch.FloatTensor, **kwargs):
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protein_output = self.model(input_ids=input_ids, attention_mask=attention_mask)
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protein_embeds = protein_output.last_hidden_state
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sequence_repr = self.sequence_compressor_resampler(protein_embeds, attention_mask)
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return EvollaProteinEncoderModelOutput(
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sequence_compressor_output=sequence_repr,
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last_hidden_state=protein_output.last_hidden_state,
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)
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class EvollaSequenceAlignerCrossAttention(nn.Module):
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def __init__(
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self,
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config,
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protein_encoder_dim: Optional[int] = None,
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structure_encoder_dim: Optional[int] = None,
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msa_encoder_dim: Optional[int] = None,
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):
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super().__init__()
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self.hidden_size = config.hidden_size
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self.num_attention_heads = config.num_attention_heads
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self.scale = self.num_attention_heads**-0.5
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self.attention_head_size = int(self.hidden_size / self.num_attention_heads)
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self.all_head_size = self.num_attention_heads * self.attention_head_size
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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
|
|
|
|
|
|
class EvollaRMSNorm(LlamaRMSNorm):
|
|
pass
|
|
|
|
|
|
class EvollaRotaryEmbedding(LlamaRotaryEmbedding):
|
|
pass
|
|
|
|
|
|
class EvollaMLP(LlamaMLP):
|
|
pass
|
|
|
|
|
|
class EvollaAttention(LlamaAttention):
|
|
pass
|
|
|
|
|
|
class EvollaDecoderLayer(LlamaDecoderLayer):
|
|
def __init__(self, config: EvollaConfig, layer_idx: int):
|
|
super().__init__(config, layer_idx)
|
|
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,
|
|
):
|
|
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
|
|
|
|
|
|
class EvollaPreTrainedModel(LlamaPreTrainedModel):
|
|
_supports_attention_backend = False
|
|
|
|
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`.
|
|
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 = 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[0]
|
|
logits = self.lm_head(hidden_states)
|
|
|
|
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"]
|