wehub-resource-sync
409 lines
18 KiB
Python
409 lines
18 KiB
Python
from typing import Optional, Union, Tuple, List, Callable
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import os
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import torch
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from torch import nn
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from transformers.models.mistral.modeling_mistral import (
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MistralForCausalLM as HfMistralForCausalLM,
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CausalLMOutputWithPast,
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MistralConfig,
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MistralModel,
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MistralPreTrainedModel
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)
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from general_util.logger import get_child_logger
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from models.dpo_utils import (
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llama_dpo_batch_forward,
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llama_last_token_cls_batch_forward,
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llama_token_batch_forward,
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llama_last_token_forward_value,
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llama_batch_forward,
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sft_loss_on_logits,
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tdpo_get_batch_logps,
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)
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from models.mixin import PreTrainedModelPeftMixin, return_reference_model
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from models.utils import DPOModelOutput, RewardModelOutput
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logger = get_child_logger(__name__)
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class MistralForCausalLMDPO(PreTrainedModelPeftMixin, HfMistralForCausalLM):
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def __init__(self, config, beta: float = 0.1, label_smoothing: float = 0.0, use_ipo: bool = False, loss_type: str = "sigmoid",
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sft_loss: bool = False, sft_loss_weight: float = 1.0):
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super().__init__(config)
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self.beta = beta
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self.label_smoothing = label_smoothing
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self.use_ipo = use_ipo
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self.loss_type = loss_type
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self.sft_loss = sft_loss
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self.sft_loss_weight = sft_loss_weight
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logger.warning(f"Using loss type: {self.loss_type}")
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# Initialize weights and apply final processing
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self.post_init()
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@torch.cuda.amp.autocast(enabled=True, dtype=torch.float32)
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def dpo_loss(
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self,
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policy_chosen_logps: torch.FloatTensor,
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policy_rejected_logps: torch.FloatTensor,
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reference_chosen_logps: torch.FloatTensor,
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reference_rejected_logps: torch.FloatTensor,
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reference_free: bool = False,
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) -> Tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:
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"""Compute the DPO loss for a batch of policy and reference model log probabilities.
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Args:
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policy_chosen_logps: Log probabilities of the policy model for the chosen responses. Shape: (batch_size,)
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policy_rejected_logps: Log probabilities of the policy model for the rejected responses. Shape: (batch_size,)
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reference_chosen_logps: Log probabilities of the reference model for the chosen responses. Shape: (batch_size,)
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reference_rejected_logps: Log probabilities of the reference model for the rejected responses. Shape: (batch_size,)
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beta: Temperature parameter for the DPO loss, typically something in the range of 0.1 to 0.5. We ignore the reference model as beta -> 0.
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reference_free: If True, we ignore the _provided_ reference model and implicitly use a reference model that assigns equal probability to all responses.
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Returns:
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A tuple of three tensors: (losses, chosen_rewards, rejected_rewards).
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The losses tensor contains the DPO loss for each example in the batch.
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The chosen_rewards and rejected_rewards tensors contain the rewards for the chosen and rejected responses, respectively.
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"""
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pi_logratios = policy_chosen_logps - policy_rejected_logps
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ref_logratios = reference_chosen_logps - reference_rejected_logps
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if reference_free:
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ref_logratios = 0
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logits = pi_logratios - ref_logratios
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if self.use_ipo:
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losses = (logits - 1 / (2 * self.beta)) ** 2
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elif self.loss_type == "hinge":
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losses = torch.relu(1 - self.beta * logits)
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elif self.loss_type == "sigmoid":
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log_sigmoid = nn.LogSigmoid()
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losses = -log_sigmoid(self.beta * logits) * (1 - self.label_smoothing) - log_sigmoid(-self.beta * logits) * self.label_smoothing
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else:
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raise ValueError(f"Unsupported loss type: {self.loss_type}")
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chosen_rewards = self.beta * (policy_chosen_logps - reference_chosen_logps).detach()
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rejected_rewards = self.beta * (policy_rejected_logps - reference_rejected_logps).detach()
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return losses.mean(), chosen_rewards, rejected_rewards
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def forward(
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self,
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input_ids: torch.LongTensor = None,
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attention_mask: Optional[torch.Tensor] = None,
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labels: Optional[torch.LongTensor] = None,
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use_cache: Optional[bool] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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**kwargs,
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) -> Union[Tuple, DPOModelOutput]:
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half = input_ids.size(0) // 2
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policy_logits, policy_logprobs, policy_loss_mask = llama_dpo_batch_forward(self, input_ids, attention_mask, labels)
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with torch.no_grad():
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ref_logits, ref_logprobs, ref_loss_mask = llama_dpo_batch_forward(return_reference_model(), input_ids, attention_mask, labels,
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pad_token_id=self.config.pad_token_id)
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policy_chosen_logits, policy_reject_logits = policy_logits[:half], policy_logits[half:]
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policy_chosen_logprobs, policy_reject_logprobs = policy_logprobs[:half], policy_logprobs[half:]
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ref_chosen_logprobs, ref_reject_logprobs = ref_logprobs[:half], ref_logprobs[half:]
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loss, chosen_rewards, rejected_rewards = self.dpo_loss(
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policy_chosen_logps=policy_chosen_logprobs,
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policy_rejected_logps=policy_reject_logprobs,
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reference_chosen_logps=ref_chosen_logprobs,
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reference_rejected_logps=ref_reject_logprobs,
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reference_free=False,
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)
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if self.sft_loss:
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sft_loss = sft_loss_on_logits(policy_chosen_logits, labels[:half], self.config.pad_token_id)
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loss += self.sft_loss_weight * sft_loss
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else:
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sft_loss = None
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return DPOModelOutput(
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loss=loss,
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chosen_reward=chosen_rewards.mean(),
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rejected_reward=rejected_rewards.mean(),
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policy_chosen_logits=policy_chosen_logits,
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policy_rejected_logits=policy_reject_logits,
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sft_loss=sft_loss,
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)
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def save_pretrained(
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self,
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save_directory: Union[str, os.PathLike],
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is_main_process: bool = True,
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state_dict: Optional[dict] = None,
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save_function: Callable = torch.save,
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push_to_hub: bool = False,
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max_shard_size: Union[int, str] = "5GB",
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safe_serialization: bool = True,
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variant: Optional[str] = None,
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token: Optional[Union[str, bool]] = None,
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save_peft_format: bool = True,
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**kwargs,
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):
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super().save_pretrained(save_directory, is_main_process, state_dict, save_function, push_to_hub, max_shard_size, safe_serialization, variant, token,
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**kwargs)
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if is_main_process:
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config = self.config
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config.architectures = ["MistralForCausalLM"]
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config.save_pretrained(save_directory)
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logger.warning("Config architecture is override to MistralForCausalLM")
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class MistralForCausalLMTDPO(PreTrainedModelPeftMixin, HfMistralForCausalLM):
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def __init__(self, config, beta: float, alpha: float = 0.5, sft_loss: bool = False, sft_loss_weight: float = 1.0, if_tdpo2: bool = True, ):
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super().__init__(config)
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self.beta = beta
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self.alpha = alpha
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self.sft_loss = sft_loss
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self.sft_loss_weight = sft_loss_weight
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self.if_tdpo2 = if_tdpo2
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# Initialize weights and apply final processing
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self.post_init()
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@torch.cuda.amp.autocast(enabled=True, dtype=torch.float32)
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def tdpo_loss(self, chosen_logps_margin: torch.FloatTensor,
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rejected_logps_margin: torch.FloatTensor,
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chosen_position_kl: torch.FloatTensor,
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rejected_position_kl: torch.FloatTensor,
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) -> Tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:
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"""Compute the TDPO loss for a batch of policy and reference model log probabilities.
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Args:
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chosen_logps_margin: The difference of log probabilities between the policy model and the reference model for the chosen responses. Shape: (batch_size,)
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rejected_logps_margin: The difference of log probabilities between the policy model and the reference model for the rejected responses. Shape: (batch_size,)
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chosen_position_kl: The difference of sequential kl divergence between the policy model and the reference model for the chosen responses. Shape: (batch_size,)
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rejected_position_kl: The difference of sequential kl divergence between the policy model and the reference model for the rejected responses. Shape: (batch_size,)
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beta: Temperature parameter for the TDPO loss, typically something in the range of 0.1 to 0.5. We ignore the reference model as beta -> 0.
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alpha: Temperature parameter for the TDPO loss, used to adjust the impact of sequential kl divergence.
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if_tdpo2: Determine whether to use method TDPO2, default is True; if False, then use method TDPO1.
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Returns:
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A tuple of two tensors: (losses, rewards).
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The losses tensor contains the TDPO loss for each example in the batch.
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The rewards tensors contain the rewards for response pair.
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"""
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chosen_values = chosen_logps_margin + chosen_position_kl
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rejected_values = rejected_logps_margin + rejected_position_kl
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chosen_rejected_logps_margin = chosen_logps_margin - rejected_logps_margin
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if not self.if_tdpo2:
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logits = chosen_rejected_logps_margin - (rejected_position_kl - chosen_position_kl) # tdpo1
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else:
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logits = chosen_rejected_logps_margin - self.alpha * (rejected_position_kl - chosen_position_kl.detach()) # tdpo2
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log_sigmoid = torch.nn.LogSigmoid()
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losses = -log_sigmoid(self.beta * logits)
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chosen_rewards = self.beta * chosen_values.detach()
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rejected_rewards = self.beta * rejected_values.detach()
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return losses.mean(), chosen_rewards, rejected_rewards
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def forward(
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self,
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input_ids: torch.LongTensor = None,
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attention_mask: Optional[torch.Tensor] = None,
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labels: Optional[torch.LongTensor] = None,
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use_cache: Optional[bool] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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**kwargs,
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) -> Union[Tuple, DPOModelOutput]:
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half = input_ids.size(0) // 2
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policy_logits = llama_batch_forward(self, input_ids, attention_mask).to(torch.float32)
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with torch.no_grad():
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ref_logits = llama_batch_forward(return_reference_model(), input_ids, attention_mask).to(torch.float32)
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logps_margin, position_kl, logps = tdpo_get_batch_logps(policy_logits, ref_logits, labels, self.config.pad_token_id,
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average_log_prob=False)
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chosen_logps_margin, rejected_logps_margin = logps_margin[:half], logps_margin[half:]
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chosen_position_kl, rejected_position_kl = position_kl[:half], position_kl[half:]
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chosen_logps, rejected_logps = logps[:half].detach(), logps[half:].detach()
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loss, chosen_rewards, rejected_rewards = self.tdpo_loss(
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chosen_logps_margin=chosen_logps_margin,
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rejected_logps_margin=rejected_logps_margin,
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chosen_position_kl=chosen_position_kl,
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rejected_position_kl=rejected_position_kl,
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)
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if self.sft_loss:
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sft_loss = sft_loss_on_logits(policy_logits[:half], labels[:half], self.config.pad_token_id)
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loss += self.sft_loss_weight * sft_loss
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else:
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sft_loss = None
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return DPOModelOutput(
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loss=loss,
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chosen_reward=chosen_rewards.mean(),
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rejected_reward=rejected_rewards.mean(),
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policy_chosen_logits=policy_logits[:half],
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policy_rejected_logits=policy_logits[half:],
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sft_loss=sft_loss,
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)
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def save_pretrained(
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self,
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save_directory: Union[str, os.PathLike],
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is_main_process: bool = True,
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state_dict: Optional[dict] = None,
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save_function: Callable = torch.save,
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push_to_hub: bool = False,
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max_shard_size: Union[int, str] = "5GB",
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safe_serialization: bool = True,
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variant: Optional[str] = None,
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token: Optional[Union[str, bool]] = None,
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save_peft_format: bool = True,
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**kwargs,
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):
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super().save_pretrained(save_directory, is_main_process, state_dict, save_function, push_to_hub, max_shard_size, safe_serialization, variant, token,
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**kwargs)
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if is_main_process:
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config = self.config
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config.architectures = ["MistralForCausalLM"]
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config.save_pretrained(save_directory)
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logger.warning("Config architecture is override to MistralForCausalLM")
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class MistralForCausalLM(PreTrainedModelPeftMixin, HfMistralForCausalLM):
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def forward(
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self,
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input_ids: torch.LongTensor = None,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_values: Optional[List[torch.FloatTensor]] = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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labels: Optional[torch.LongTensor] = None,
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use_cache: Optional[bool] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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**kwargs,
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) -> Union[Tuple, CausalLMOutputWithPast]:
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output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
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output_hidden_states = (
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output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
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)
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
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outputs = self.model(
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input_ids=input_ids,
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attention_mask=attention_mask,
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position_ids=position_ids,
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past_key_values=past_key_values,
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inputs_embeds=inputs_embeds,
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use_cache=use_cache,
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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return_dict=return_dict,
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)
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hidden_states = outputs[0]
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logits = self.lm_head(hidden_states)
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logits = logits.float()
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loss = None
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if labels is not None:
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# Shift so that tokens < n predict n
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shift_logits = logits[..., :-1, :].contiguous()
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shift_labels = labels[..., 1:].contiguous()
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# Flatten the tokens
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loss_fct = nn.CrossEntropyLoss()
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shift_logits = shift_logits.view(-1, self.config.vocab_size)
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shift_labels = shift_labels.view(-1)
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# Enable model parallelism
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shift_labels = shift_labels.to(shift_logits.device)
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shift_labels[shift_labels.eq(self.config.pad_token_id)] = -100
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loss = loss_fct(shift_logits, shift_labels)
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if not return_dict:
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output = (logits,) + outputs[1:]
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return (loss,) + output if loss is not None else output
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return CausalLMOutputWithPast(
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loss=loss,
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logits=logits,
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past_key_values=outputs.past_key_values,
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hidden_states=outputs.hidden_states,
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attentions=outputs.attentions,
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)
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class MistralForSequenceClassification(PreTrainedModelPeftMixin, MistralPreTrainedModel):
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def __init__(self, config: MistralConfig):
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super().__init__(config)
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self.model = MistralModel(config)
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self.score = nn.Linear(config.hidden_size, config.num_labels, bias=False)
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# Initialize weights and apply final processing
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self.post_init()
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def forward(
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self,
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input_ids: torch.LongTensor = None,
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attention_mask: Optional[torch.Tensor] = None,
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values: Optional[torch.LongTensor] = None,
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labels: Optional[torch.LongTensor] = None,
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use_cache: Optional[bool] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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**kwargs,
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) -> Union[Tuple, DPOModelOutput]:
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rewards, sequence_lengths = llama_last_token_cls_batch_forward(self.model, self.score, input_ids, attention_mask, self.config.pad_token_id)
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loss_fct = nn.CrossEntropyLoss()
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loss = loss_fct(rewards, values)
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return DPOModelOutput(
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loss=loss,
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logits=rewards,
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)
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class MistralForSequenceClassificationForEval(MistralForSequenceClassification):
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def __init__(self, config: MistralConfig, return_full_logits: bool = True):
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super().__init__(config)
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self.model = MistralModel(config)
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self.score = nn.Linear(config.hidden_size, config.num_labels, bias=False)
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# Initialize weights and apply final processing
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self.post_init()
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self.return_full_logits = return_full_logits
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def forward(
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self,
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input_ids: torch.LongTensor = None,
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attention_mask: Optional[torch.Tensor] = None,
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**kwargs,
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) -> Union[Tuple, DPOModelOutput]:
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rewards, sequence_lengths = llama_last_token_cls_batch_forward(self.model, self.score, input_ids, attention_mask, self.config.pad_token_id,
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return_full_logits=self.return_full_logits)
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if self.return_full_logits:
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return DPOModelOutput(
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logits=rewards,
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)
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return DPOModelOutput(
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batch_chosen_reward=rewards,
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)
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