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Running
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Zero
File size: 8,139 Bytes
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import functools
import logging
import math
from typing import List, Dict, Tuple, Optional
import torch.nn.functional as F
import torch
from torch import nn
from transformers import AutoTokenizer, AutoConfig
from .modeling_llada import LLaDAModelLM
from .configuration_llada import LLaDAConfig
from transformers.modeling_outputs import CausalLMOutputWithPast
__all__ = ["LLaDAForMultiModalGeneration"]
def create_attention_mask(original_lengths, max_tokens, device):
batch_size = len(original_lengths)
attention_mask = torch.zeros(batch_size, max_tokens, dtype=torch.bool, device=device)
for i, length in enumerate(original_lengths):
attention_mask[i, :length] = 1
return attention_mask
class LLaDAForMultiModalGeneration(LLaDAModelLM):
config_class = LLaDAConfig
base_model_prefix = "model"
IMAGE_START_TOKEN = 126349
IMAGE_END_TOKEN = 126350
ANSWER_START_TOKEN = 126354
ANSWER_END_TOKEN = 126355
BREAKLINE_TOKEN = 126084
MASK_TOKEN = 126336
PAD_TOKEN = 126339
def __init__(self, config: LLaDAConfig, *args, **kwargs):
print(f"Initializing LLaDAForMultiModalGeneration with config: {config}")
super().__init__(config, *args, **kwargs)
self._debug_step = 0
def forward(
self,
input_ids=None,
labels=None,
infer=False,
use_cache=False,
return_dict=False,
compute_separate_losses=True,
t=None,
text_coeff=1.0,
image_coeff=1.0,
):
if infer:
input_ids = input_ids.tolist()
max_tokens = max([len(_) for _ in input_ids])
original_lengths = [len(example) for example in input_ids]
input_ids = [example + [0] * (max_tokens - len(example)) for example in input_ids]
input_ids = torch.tensor(input_ids, dtype=torch.int64, device=self.device)
attention_mask = create_attention_mask(original_lengths, max_tokens, self.device)
attention_bias = (attention_mask[:, :, None] & attention_mask[:, None, :]).bool().unsqueeze(1)
output = LLaDAModelLM.forward(
self,
input_ids=input_ids,
attention_bias=attention_bias,
use_cache=use_cache
)
if infer:
return output
if labels is None:
if return_dict:
return {'logits': output.logits}
else:
return output.logits
labels = [label + [-100] * (max_tokens - len(label)) for label in labels]
labels = torch.tensor(labels, dtype=torch.int64, device=self.device)
logits = output.logits
batch_size = logits.shape[0]
unscaled_loss = F.cross_entropy(
logits.contiguous().view(-1, logits.shape[-1]),
labels.contiguous().view(-1),
ignore_index=-100,
reduction='none'
).view(batch_size, -1)
valid_mask = (labels != -100)
if valid_mask.sum() > 0:
interleave_loss = unscaled_loss[valid_mask].mean()
else:
interleave_loss = torch.tensor(0.0, device=self.device)
if compute_separate_losses:
self._debug_step += 1
debug_this_step = (self._debug_step <= 3)
if debug_this_step:
print(f"\n{'='*80}")
print(f"DEBUG Step {self._debug_step}")
print(f"{'='*80}")
text_loss_list = []
image_loss_list = []
for b in range(batch_size):
answer_start_positions = (input_ids[b] == self.ANSWER_START_TOKEN).nonzero(as_tuple=True)[0]
if len(answer_start_positions) == 0:
continue
answer_start = answer_start_positions[0].item()
answer_end_in_search = (input_ids[b, answer_start:] == self.ANSWER_END_TOKEN).nonzero(as_tuple=True)[0]
if len(answer_end_in_search) > 0:
answer_end = answer_start + answer_end_in_search[0].item()
else:
answer_end = original_lengths[b]
answer_region_input = input_ids[b, answer_start:answer_end]
image_start_in_answer = (answer_region_input == self.IMAGE_START_TOKEN).nonzero(as_tuple=True)[0]
if len(image_start_in_answer) > 0:
image_start_pos = answer_start + image_start_in_answer[0].item()
image_end_search = input_ids[b, image_start_pos:]
image_end_in_search = (image_end_search == self.IMAGE_END_TOKEN).nonzero(as_tuple=True)[0]
if len(image_end_in_search) > 0 :
image_end_pos = image_start_pos + image_end_in_search[0].item()
for pos in range(image_start_pos + 1, image_end_pos):
if input_ids[b, pos] != self.BREAKLINE_TOKEN:
image_loss_list.append(unscaled_loss[b, pos])
for pos in range(image_end_pos + 1, answer_end):
if labels[b, pos] != -100:
text_loss_list.append(unscaled_loss[b, pos])
else:
for pos in range(answer_start + 1, answer_end):
if labels[b, pos] != -100:
text_loss_list.append(unscaled_loss[b, pos])
if debug_this_step:
print(f"Total text_loss_list length: {len(text_loss_list)}")
print(f"Total image_loss_list length: {len(image_loss_list)}")
if len(text_loss_list) > 0:
non_zero_text = [l.item() for l in text_loss_list if l.item() > 0]
print(f"Non-zero text losses count: {len(non_zero_text)}/{len(text_loss_list)}")
print(f"Sample non-zero text losses: {non_zero_text[:5]}")
if len(image_loss_list) > 0:
non_zero_image = [l.item() for l in image_loss_list if l.item() > 0]
print(f"Non-zero image losses count: {len(non_zero_image)}/{len(image_loss_list)}")
print(f"Sample non-zero image losses: {non_zero_image[:5]}")
print(f"{'='*80}\n")
if len(text_loss_list) > 0:
text_loss = torch.stack(text_loss_list).mean()
else:
text_loss = torch.tensor(0.0, device=self.device)
if len(image_loss_list) > 0:
image_loss = torch.stack(image_loss_list).mean()
else:
image_loss = torch.tensor(0.0, device=self.device)
if t is not None and len(text_loss_list) > 0:
text_loss = text_loss / t.mean().clamp(min=0.01)
if return_dict:
return {
'logits': logits,
'loss': interleave_loss,
'interleave_loss': interleave_loss,
'text_loss': text_loss,
'image_loss': image_loss,
'labels': labels,
}
else:
return interleave_loss, {
'text_loss': text_loss,
'image_loss': image_loss,
'interleave_loss': interleave_loss,
}
else:
if return_dict:
return {'logits': logits, 'loss': interleave_loss, 'labels': labels}
else:
return interleave_loss
def get_fsdp_wrap_module_list(self) -> List:
modules = [*list(self.model.transformer.blocks), self.model.transformer.ff_out]
return modules
def get_checkpointing_wrap_module_list(self) -> List:
return list(self.model.transformer.blocks) |