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from typing import Dict, Optional, Tuple, Union
import torch
from transformers import AutoConfig, AutoModelForCausalLM
from transformers import EsmConfig, LlamaConfig, PretrainedConfig
from transformers import EsmModel, LlamaForCausalLM, PreTrainedModel
from transformers.modeling_outputs import CausalLMOutputWithPast
from transformers.generation.utils import Cache, GenerateOutput
class ModalityAdapterConfig(PretrainedConfig):
model_type = "modality_adapter"
def __init__(
self,
input_dim: int,
intermediate_dim: int,
output_dim: int,
dropout_rate: float = 0.3,
**kwargs
):
super().__init__(**kwargs)
self.input_dim = input_dim
self.intermediate_dim = intermediate_dim
self.output_dim = output_dim
self.dropout_rate = dropout_rate
class Esm2LlamaInstructConfig(PretrainedConfig):
model_type = "esm2llama_instruct"
def __init__(
self,
# model components
esm_config: Optional[Union[EsmConfig, Dict]] = None,
adapter_config: Optional[Union[ModalityAdapterConfig, Dict]] = None,
llama_config: Optional[Union[LlamaConfig, Dict]] = None,
# standalone attributes
placeholder_id: int = 128003,
**kwargs
):
super().__init__(**kwargs)
if isinstance(esm_config, dict):
self.esm_config = EsmConfig(**esm_config)
else:
self.esm_config = esm_config
if isinstance(llama_config, dict):
self.llama_config = LlamaConfig(**llama_config)
else:
self.llama_config = llama_config
if isinstance(adapter_config, dict):
self.adapter_config = ModalityAdapterConfig(**adapter_config)
else:
self.adapter_config = adapter_config
self.placeholder_id = placeholder_id
class ModalityAdapter(PreTrainedModel):
config_class = ModalityAdapterConfig
def __init__(self, config: ModalityAdapterConfig):
super().__init__(config)
self.config = config
self.fc1 = torch.nn.Linear(config.input_dim, config.intermediate_dim)
self.fc2 = torch.nn.Linear(config.intermediate_dim, config.output_dim)
self.activation = torch.nn.GELU()
self.ln1 = torch.nn.LayerNorm(normalized_shape=config.intermediate_dim) # DEPRECATED
self.ln2 = torch.nn.LayerNorm(normalized_shape=config.output_dim) # DEPRECATED
self.dropout = torch.nn.Dropout(p=config.dropout_rate)
self.post_init() # initialize weights and apply final processing
def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
# input: (bsz, seq_len, input_dim)
hidden_states = self.activation(self.fc1(hidden_states))
hidden_states = self.dropout(hidden_states)
# interm: (bsz, seq_len, interm_dim)
hidden_states = self.activation(self.fc2(hidden_states))
hidden_states = self.dropout(hidden_states)
hidden_states = torch.nn.functional.normalize(hidden_states, p=2, dim=-1)
return hidden_states # (bsz, seq_len, output_dim)
class Esm2LlamaInstructForCausalLM(PreTrainedModel):
"""
Esm2LlamaInstructForCausalLM model for protein function prediction.
Similar to `EncoderDecoderModel` but with more complicated architecture.
Initialize with either a configuration OR all three components.
`kwargs` can override standalone attributes in `Esm2LlamaInstructConfig`.
"""
config_class = Esm2LlamaInstructConfig
def __init__(
self,
config: Optional[Esm2LlamaInstructConfig] = None,
esm_encoder: Optional[EsmModel] = None,
adapter: Optional[ModalityAdapter] = None,
llama_decoder: Optional[LlamaForCausalLM] = None,
**kwargs
):
if config is not None: # components ignored if config is provided
super().__init__(config)
self.esm_encoder = EsmModel(
config.esm_config,
add_pooling_layer=False
)
self.adapter = ModalityAdapter(config.adapter_config)
self.llama_decoder = LlamaForCausalLM(config.llama_config)
else:
config = Esm2LlamaInstructConfig(
esm_config=esm_encoder.config,
adapter_config=adapter.config,
llama_config=llama_decoder.config,
**kwargs # override standalone attributes
)
super().__init__(config)
self.esm_encoder = esm_encoder
self.adapter = adapter
self.llama_decoder = llama_decoder
def prepare_decoder_inputs(
self,
input_ids: torch.LongTensor,
encoder_hidden_states: torch.FloatTensor,
attention_mask: Optional[torch.LongTensor] = None,
encoder_attention_mask: Optional[torch.LongTensor] = None,
):
"""
Embed and replace placeholder in `input_ids` by encoder hidden states.
`input_ids` must be passed to locate placeholder for replacement.
"""
# preparation
batch_size, seq_len = input_ids.size()
_, encoder_seq_len, _ = encoder_hidden_states.size()
if attention_mask is None:
attention_mask = torch.ones(
(batch_size, seq_len),
dtype=torch.long,
device=input_ids.device
)
if encoder_attention_mask is None:
encoder_attention_mask = torch.ones(
(batch_size, encoder_seq_len),
dtype=torch.long,
device=encoder_hidden_states.device
)
inputs_embeds = self.llama_decoder.get_input_embeddings()(input_ids)
# replacement
placeholder_mask = input_ids == self.config.placeholder_id
encoder_mask = encoder_attention_mask.bool()
inputs_embeds[placeholder_mask] = encoder_hidden_states[encoder_mask]
return inputs_embeds, attention_mask
def forward(
self,
# chat template text inputs
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Cache] = None,
labels: Optional[torch.LongTensor] = None,
# protein amino-acid sequence inputs
protein_input_ids: Optional[torch.LongTensor] = None,
protein_attention_mask: Optional[torch.LongTensor] = None,
protein_position_ids: Optional[torch.LongTensor] = None,
protein_head_mask: Optional[torch.LongTensor] = None,
protein_inputs_embeds: Optional[torch.FloatTensor] = None,
# behavior control arguments
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
return_encoder_outputs: bool = False,
return_adapter_outputs: bool = False,
return_decoder_inputs: bool = False,
cache_position: Optional[torch.LongTensor] = None
) -> Union[Tuple, CausalLMOutputWithPast]:
"""
Compute encoder and adapter outputs, then pass to decoder.
`input_ids` is expected to be [prompt + description] in teacher-forcing
scenario and [prompt] only in first iteration of inference (with
return_decoder_inputs=True).
Attention: possible concatenation of the mask and labels should be
handled before calling this method.
`inputs_embeds` not allowed due to placeholder replacement scheme.
"""
# esm_encoder forward
encoder_output = self.esm_encoder(
input_ids=protein_input_ids,
attention_mask=protein_attention_mask,
position_ids=protein_position_ids,
head_mask=protein_head_mask,
inputs_embeds=protein_inputs_embeds,
use_cache=False, # because config.esm_config.is_decoder=False
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict
)
encoder_hidden_states = encoder_output[0]
encoder_attention_mask = protein_attention_mask
if return_encoder_outputs:
return encoder_output
# adapter forward
adapter_output = self.adapter(encoder_hidden_states)
if return_adapter_outputs:
return adapter_output, encoder_attention_mask
# decoder input preparation
inputs_embeds, attention_mask = self.prepare_decoder_inputs(
input_ids=input_ids,
encoder_hidden_states=adapter_output,
attention_mask=attention_mask,
encoder_attention_mask=encoder_attention_mask,
)
if return_decoder_inputs:
return inputs_embeds, attention_mask
# llama_decoder forward
return self.llama_decoder.forward(
input_ids=None,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
labels=labels,
use_cache=use_cache,
output_attentions=output_attentions,
return_dict=return_dict,
cache_position=cache_position
)
def generate(
self,
inputs: torch.LongTensor, # alias of `input_ids`
attention_mask: Optional[torch.LongTensor] = None,
protein_input_ids: Optional[torch.LongTensor] = None,
protein_attention_mask: Optional[torch.LongTensor] = None,
protein_inputs_embeds: Optional[torch.FloatTensor] = None,
**kwargs
) -> Union[GenerateOutput, torch.LongTensor]:
"""
Do inference based on given input prompt.
`inputs` is expected to be [prompt] only.
Output will not keep the input prompt due to input in form of embeds.
Generation behavior can be controlled by `args` and `kwargs`, read
`GenerationMixin.generate` for more info.
"""
# get decoder inputs
prompt_inputs_embeds, prompt_attention_mask = self(
input_ids=inputs,
attention_mask=attention_mask,
protein_input_ids=protein_input_ids,
protein_attention_mask=protein_attention_mask,
protein_inputs_embeds=protein_inputs_embeds,
use_cache=False,
output_attentions=False,
output_hidden_states=False,
return_dict=False,
return_decoder_inputs=True
)
# do generate on llama_decoder
return self.llama_decoder.generate(
inputs_embeds=prompt_inputs_embeds,
attention_mask=prompt_attention_mask,
**kwargs
)
def gradient_checkpointing_enable(self):
"""
Enable gradient checkpointing for all submodules that support it.
Attention! Model need to be in train mode before calling this method.
"""
if hasattr(self.esm_encoder, "gradient_checkpointing_enable"):
self.esm_encoder.gradient_checkpointing_enable()
if hasattr(self.llama_decoder, "gradient_checkpointing_enable"):
self.llama_decoder.gradient_checkpointing_enable()
# simple adapter no need to implement gradient checkpointing
def gradient_checkpointing_disable(self):
if hasattr(self.esm_encoder, "gradient_checkpointing_disable"):
self.esm_encoder.gradient_checkpointing_disable()
if hasattr(self.llama_decoder, "gradient_checkpointing_disable"):
self.llama_decoder.gradient_checkpointing_disable()
AutoConfig.register("esm2llama_instruct", Esm2LlamaInstructConfig)
AutoModelForCausalLM.register(Esm2LlamaInstructConfig, Esm2LlamaInstructForCausalLM) |