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Zero
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from typing import List, Optional, Tuple, Union
import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss
from transformers import (
AutoConfig,
AutoModelForCausalLM,
MistralConfig,
MistralModel,
MistralForCausalLM,
)
from transformers.modeling_outputs import CausalLMOutputWithPast
from multi_token.language_models.base_model import (
LMMMetaModel,
LMMMetaForCausalLM,
)
class MistralLMMConfig(MistralConfig):
model_type = "mistral-lmm"
class MistralLMMModel(LMMMetaModel, MistralModel):
config_class = MistralLMMConfig
def __init__(self, config: MistralLMMConfig):
super(MistralLMMModel, self).__init__(config)
class MistralLMMForCausalLM(MistralForCausalLM, LMMMetaForCausalLM):
config_class = MistralLMMConfig
def __init__(self, config):
super(MistralForCausalLM, self).__init__(config)
self.model = MistralLMMModel(config)
self.vocab_size = config.vocab_size
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.modalities = None
# Initialize weights and apply final processing
self.post_init()
def get_model(self) -> "MistralLMMForCausalLM":
return self.model
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[List] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
**kwargs
) -> Union[Tuple, CausalLMOutputWithPast]:
#print("Past keys ",past_key_values)
output_attentions = (
output_attentions
if output_attentions is not None
else self.config.output_attentions
)
output_hidden_states = (
output_hidden_states
if output_hidden_states is not None
else self.config.output_hidden_states
)
return_dict = (
return_dict if return_dict is not None else self.config.use_return_dict
)
if labels != None:
labels_inp = labels[0]
else:
labels_inp = labels
(
input_ids,
attention_mask,
past_key_values,
inputs_embeds,
lmm_labels,
task_values
) = self.prepare_inputs_labels_for_multimodal(
input_ids, attention_mask, past_key_values, labels_inp, **kwargs
)
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = outputs[0]
logits = self.lm_head(hidden_states)
logits = logits.float()
# print("Labels 1 size ", len(labels[1]))
# print("labels 1 element size ", len(labels[1][0]))
# print("labels 1 element 1 task size ", labels[1][0][0].shape)
# print("labels 1 element 2 task size ", labels[1][0][1].shape)
# print("labels 1 element 3 task size ", labels[1][0][2].shape)
# print("task vals size ", len(task_values))
# for task in task_values.keys():
# print(" task ", task, len(task_values[task]))
# print(" task element", task, task_values[task][0].shape)
if labels != None:
task_pairs = {}
task_list = list(task_values.keys())
for task_id in range(len(task_list)):
_task_labels = []
_task_outputs = []
_task = task_list[task_id]
for inst in range(len(task_values[_task])):
# print("task output shape ", _task, task_values[_task][inst].shape)
_task_outputs.append(task_values[_task][inst].unsqueeze(0))
_task_labels.append(torch.stack([labels[1][inst][task_id]]))
task_pairs[_task] = [_task_labels, _task_outputs]
# print("TASK ", _task)
# print(" LABELS LEN ", len(task_pairs[_task][0]))
# print(" LABELS ELEM shape ", task_pairs[_task][0][0].shape)
# print(" VALUES LEN ", len(task_pairs[_task][1]))
# print(" VALUES ELEM shape ", task_pairs[_task][1][0].shape)
loss = None
if lmm_labels is not None:
# Shift so that tokens < n predict n
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = lmm_labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = CrossEntropyLoss()
shift_logits = shift_logits.view(-1, self.config.vocab_size)
shift_labels = shift_labels.view(-1)
# Enable model parallelism
shift_labels = shift_labels.to(shift_logits.device)
loss = loss_fct(shift_logits, shift_labels)
# print("loss ", loss)
if labels != None:
task_loss = {}
for task in task_list:
preds = torch.cat(task_pairs[task][1], dim=0)
labs = torch.cat(task_pairs[task][0], dim=0)
preds_flat = preds.view(-1, preds.size(-1)) # Reshape to (batch_size * sequence_length, num_classes)
labs_flat = labs.view(-1) # Reshape to (batch_size * sequence_length)
#print("task ", task)
#print("preds shape ", preds.shape)
#print("labs shape ", labs.shape)
if task == "lmm_projector":
task_loss[task] = CrossEntropyLoss()(preds,labs)
else:
task_loss[task] = nn.BCEWithLogitsLoss()(preds, labs)
# print("task losses ", task_loss)
total_loss = None
if labels != None:
total_task_loss = None
for task in task_list:
if self.modalities[0].tasks["task_heads"][task]["weight"] != 0.0:
if total_task_loss != None:
total_task_loss += self.modalities[0].tasks["task_heads"][task]["weight"]*task_loss[task]
else:
total_task_loss = self.modalities[0].tasks["task_heads"][task]["weight"]*task_loss[task]
if total_task_loss != None:
total_loss = self.modalities[0].tasks["task_heads"]["lmm_projector"]["weight"]*loss + total_task_loss
else:
total_loss = loss
if not return_dict:
output = (logits,) + outputs[1:]
return (total_loss,) + output if total_loss is not None else output
return CausalLMOutputWithPast(
loss=total_loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def prepare_inputs_for_generation(
self,
input_ids,
past_key_values=None,
attention_mask=None,
inputs_embeds=None,
modality_inputs=None,
**kwargs
):
#print("hoooo", past_key_values)
#past_key_values = None
if past_key_values:
input_ids = input_ids[:, -1:]
if inputs_embeds is not None:
raise ValueError("inputs_embeds not supported")
model_inputs = {
"input_ids": input_ids,
"position_ids": None,
"past_key_values": past_key_values,
"use_cache": kwargs.get("use_cache"),
"attention_mask": attention_mask,
**(modality_inputs or {}),
}
return model_inputs
AutoConfig.register("mistral-lmm", MistralLMMConfig)
AutoModelForCausalLM.register(MistralLMMConfig, MistralLMMForCausalLM)
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