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# --------------------------------------------------------
# InternVL
# Copyright (c) 2024 OpenGVLab
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------
from typing import Dict, List, Literal, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import ConcatDataset
from trl import DPOTrainer
from trl.trainer.utils import RunningMoments, pad_to_length
def _map(self, *args, **kwargs):
return self
ConcatDataset.map = _map
class MultimodalDPOTrainer(DPOTrainer):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
if self.loss_type != 'bco_pair' and 'bco_pair' in self.loss_type:
self.running = RunningMoments(self.accelerator)
@staticmethod
def concatenated_inputs(
batch: Dict[str, Union[List, torch.LongTensor]],
is_encoder_decoder: bool = False,
is_vision_model: bool = False,
label_pad_token_id: int = -100,
padding_value: int = 0,
device: Optional[torch.device] = None,
) -> Dict[str, torch.LongTensor]:
"""Concatenate the chosen and rejected inputs into a single tensor.
Args:
batch: A batch of data. Must contain the keys 'chosen_input_ids' and 'rejected_input_ids', which are tensors of shape (batch_size, sequence_length).
is_encoder_decoder: Whether the model is an encoder-decoder model.
label_pad_token_id: The label pad token id.
padding_value: The padding value to use for the concatenated inputs_ids.
device: The device for the concatenated inputs.
Returns:
A dictionary containing the concatenated inputs under the key 'concatenated_input_ids'.
"""
concatenated_batch = {}
if is_encoder_decoder:
max_length = max(batch['chosen_labels'].shape[1], batch['rejected_labels'].shape[1])
else:
max_length = max(batch['chosen_input_ids'].shape[1], batch['rejected_input_ids'].shape[1])
for k in batch:
if k.startswith('chosen') and isinstance(batch[k], torch.Tensor):
if 'labels' in k or is_encoder_decoder:
pad_value = label_pad_token_id
elif k.endswith('_input_ids'):
pad_value = padding_value
elif k.endswith('_attention_mask'):
pad_value = 0
concatenated_key = k.replace('chosen', 'concatenated')
concatenated_batch[concatenated_key] = pad_to_length(batch[k], max_length, pad_value=pad_value)
for k in batch:
if k.startswith('rejected') and isinstance(batch[k], torch.Tensor):
if 'labels' in k or is_encoder_decoder:
pad_value = label_pad_token_id
elif k.endswith('_input_ids'):
pad_value = padding_value
elif k.endswith('_attention_mask'):
pad_value = 0
concatenated_key = k.replace('rejected', 'concatenated')
concatenated_batch[concatenated_key] = torch.cat(
(
concatenated_batch[concatenated_key],
pad_to_length(batch[k], max_length, pad_value=pad_value),
),
dim=0,
).to(device=device)
if is_encoder_decoder:
concatenated_batch['concatenated_input_ids'] = batch['prompt_input_ids'].repeat(2, 1).to(device=device)
concatenated_batch['concatenated_attention_mask'] = (
batch['prompt_attention_mask'].repeat(2, 1).to(device=device)
)
if 'pixel_values' in batch:
concatenated_batch['pixel_values'] = batch['pixel_values'].repeat(2, 1, 1, 1)
concatenated_batch['image_flags'] = batch['image_flags'].repeat(2)
return concatenated_batch
def concatenated_forward(
self, model: nn.Module, batch: Dict[str, Union[List, torch.LongTensor]]
) -> Tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:
"""Run the given model on the given batch of inputs, concatenating the chosen and rejected inputs together.
We do this to avoid doing two forward passes, because it's faster for FSDP.
"""
concatenated_batch = self.concatenated_inputs(
batch,
is_encoder_decoder=self.is_encoder_decoder,
is_vision_model=self.is_vision_model,
label_pad_token_id=self.label_pad_token_id,
padding_value=self.padding_value,
device=self.accelerator.device,
)
len_chosen = batch['chosen_labels'].shape[0]
model_kwargs = {}
if self.is_encoder_decoder:
model_kwargs['labels'] = concatenated_batch['concatenated_labels']
model_kwargs['decoder_input_ids'] = concatenated_batch.pop('concatenated_decoder_input_ids', None)
if self.is_vision_model:
model_kwargs['pixel_values'] = concatenated_batch['pixel_values']
model_kwargs['pixel_attention_mask'] = concatenated_batch['pixel_attention_mask']
if self.aux_loss_enabled:
model_kwargs['output_router_logits'] = True
outputs = model(
input_ids=concatenated_batch['concatenated_input_ids'],
attention_mask=concatenated_batch['concatenated_attention_mask'],
pixel_values=concatenated_batch['pixel_values'],
image_flags=concatenated_batch['image_flags'],
use_cache=False,
**model_kwargs,
)
all_logits = outputs.logits
all_logps, size_completion = self.get_batch_logps(
all_logits,
concatenated_batch['concatenated_labels'],
# average_log_prob=self.loss_type == "ipo",
is_encoder_decoder=self.is_encoder_decoder,
label_pad_token_id=self.label_pad_token_id,
)
def cross_entropy_loss(logits, labels):
if not self.is_encoder_decoder:
# Shift so that tokens < n predict n
logits = logits[..., :-1, :].contiguous()
labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = nn.CrossEntropyLoss()
logits = logits.view(-1, logits.shape[-1])
labels = labels.view(-1)
# Enable model parallelism
labels = labels.to(logits.device)
loss = loss_fct(logits, labels)
return loss
labels = concatenated_batch['concatenated_labels'].clone()
nll_loss = cross_entropy_loss(all_logits[:len_chosen], labels[:len_chosen])
if self.loss_type == 'ipo':
all_logps = all_logps / size_completion
chosen_logps = all_logps[:len_chosen]
rejected_logps = all_logps[len_chosen:]
chosen_logits = all_logits[:len_chosen]
rejected_logits = all_logits[len_chosen:]
if self.aux_loss_enabled:
return (chosen_logps, rejected_logps, chosen_logits, rejected_logits, nll_loss, outputs.aux_loss)
return (chosen_logps, rejected_logps, chosen_logits, rejected_logits, nll_loss)
def _prepare_deepspeed(self, model):
deepspeed_plugin = self.accelerator.state.deepspeed_plugin
config_kwargs = deepspeed_plugin.deepspeed_config
if config_kwargs['zero_optimization']['stage'] == 3:
print('Enable DPOTrainer._prepare_deepspeed')
return super()._prepare_deepspeed(model)
print('Disable DPOTrainer._prepare_deepspeed')
for param in model.parameters():
param.requires_grad = False
model.eval()
model = model.to(self.accelerator.device)
return model
def get_batch_loss_metrics(
self,
model,
batch: Dict[str, Union[List, torch.LongTensor]],
train_eval: Literal['train', 'eval'] = 'train',
):
"""Compute the DPO loss and other metrics for the given batch of inputs for train or test."""
metrics = {}
forward_output = self.concatenated_forward(model, batch)
(
policy_chosen_logps,
policy_rejected_logps,
policy_chosen_logits,
policy_rejected_logits,
policy_nll_loss,
) = forward_output[:5]
if self.aux_loss_enabled:
aux_loss = forward_output[5]
# if reference_chosen_logps and reference_rejected_logps in batch use them, otherwise use the reference model
if (
'reference_chosen_logps' in batch
and 'reference_rejected_logps' in batch
and self.args.rpo_alpha is not None
):
reference_chosen_logps = batch['reference_chosen_logps']
reference_rejected_logps = batch['reference_rejected_logps']
else:
with torch.no_grad():
if self.ref_model is None:
with self.null_ref_context():
(
reference_chosen_logps,
reference_rejected_logps,
_,
_,
_,
) = self.concatenated_forward(self.model, batch)
else:
(
reference_chosen_logps,
reference_rejected_logps,
_,
_,
_,
) = self.concatenated_forward(self.ref_model, batch)
if ',' in self.loss_type:
loss_type = self.loss_type
loss_type_list = loss_type.split(',')
losses, chosen_rewards, rejected_rewards = 0, 0, 0
for curr_type in loss_type_list:
self.loss_type = curr_type
curr_losses, curr_chosen_rewards, curr_rejected_rewards = self.dpo_loss(
policy_chosen_logps,
policy_rejected_logps,
reference_chosen_logps,
reference_rejected_logps,
)
curr_weight = getattr(self.args, f'{curr_type}_loss_weight')
losses = losses + curr_losses * curr_weight
chosen_rewards = chosen_rewards + curr_chosen_rewards * curr_weight
rejected_rewards = rejected_rewards + curr_rejected_rewards * curr_weight
self.loss_type = loss_type
else:
losses, chosen_rewards, rejected_rewards = self.dpo_loss(
policy_chosen_logps,
policy_rejected_logps,
reference_chosen_logps,
reference_rejected_logps,
)
reward_accuracies = (chosen_rewards > rejected_rewards).float()
if self.args.rpo_alpha is not None:
# losses = losses * self.args.rpo_alpha + policy_nll_loss
losses = losses + policy_nll_loss * self.args.rpo_alpha
prefix = 'eval_' if train_eval == 'eval' else ''
metrics[f'{prefix}rewards/chosen'] = chosen_rewards.mean().cpu()
metrics[f'{prefix}rewards/rejected'] = rejected_rewards.mean().cpu()
metrics[f'{prefix}rewards/accuracies'] = reward_accuracies.mean().cpu()
metrics[f'{prefix}rewards/margins'] = (chosen_rewards - rejected_rewards).mean().cpu()
metrics[f'{prefix}logps/rejected'] = policy_rejected_logps.detach().mean().cpu()
metrics[f'{prefix}logps/chosen'] = policy_chosen_logps.detach().mean().cpu()
metrics[f'{prefix}logits/rejected'] = policy_rejected_logits.detach().mean().cpu()
metrics[f'{prefix}logits/chosen'] = policy_chosen_logits.detach().mean().cpu()
if self.args.rpo_alpha is not None:
metrics[f'{prefix}nll_loss'] = policy_nll_loss.detach().mean().cpu()
if self.aux_loss_enabled:
return losses.mean() + getattr(model.config, 'router_aux_loss_coef', 0.0) * aux_loss, metrics
return losses.mean(), metrics
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