370 lines
12 KiB
Python
370 lines
12 KiB
Python
"""Training strategy implementations with factory pattern."""
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from abc import ABC, abstractmethod
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from typing import Callable, Dict, Union
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch import Tensor
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from astrai.factory import BaseFactory
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def create_ref_model(
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model_fn: Callable[[], nn.Module], state_dict: Dict[str, Tensor]
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) -> nn.Module:
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"""Create a frozen reference model from model_fn + full state dict."""
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ref_model = model_fn()
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ref_model.load_state_dict(state_dict)
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ref_model.requires_grad_(False)
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ref_model.eval()
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return ref_model
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def move_to_device(batch: Dict[str, Tensor], device: str) -> Dict[str, Tensor]:
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"""Move batch tensors to specified device with non-blocking transfer."""
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return {key: value.to(device, non_blocking=True) for key, value in batch.items()}
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def get_logprobs(
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model: Union[nn.Module, Callable[..., Dict[str, Tensor]]],
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input_ids: Tensor,
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mask: Tensor,
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reduction: str,
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) -> Tensor:
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"""Compute token-wise log probabilities from model outputs.
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Args:
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model: The language model
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input_ids: Input token IDs of shape [batch_size, seq_len]
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mask: Attention mask of shape [batch_size, seq_len]
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reduction: How to reduce over sequence dimension ("mean", "sum", "none")
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Returns:
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Log probabilities with reduction applied over sequence dimension
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"""
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allowed_reductions = ["mean", "sum", "none"]
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if reduction not in allowed_reductions:
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raise ValueError(
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f"reduction must be one of {allowed_reductions}, got '{reduction}'"
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)
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shifted_input_ids = input_ids[:, 1:]
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shifted_mask = mask[:, 1:]
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logits = model(input_ids[:, :-1], mask[:, :-1])["logits"]
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log_probs = torch.log_softmax(logits.float(), dim=-1)
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token_logprobs = torch.gather(
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log_probs, dim=-1, index=shifted_input_ids.unsqueeze(-1)
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).squeeze(-1)
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if reduction == "mean":
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return (token_logprobs * shifted_mask).sum(dim=-1) / shifted_mask.sum(
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dim=-1
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).clamp(min=1.0)
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elif reduction == "sum":
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return (token_logprobs * shifted_mask).sum(dim=-1)
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else:
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return token_logprobs * shifted_mask
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def make_doc_boundary_mask(position_ids: Tensor) -> Tensor:
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S = position_ids.size(1)
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device = position_ids.device
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boundaries = position_ids[:, 1:] <= position_ids[:, :-1]
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doc_ids = torch.cat(
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[
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torch.zeros(position_ids.size(0), 1, dtype=torch.long, device=device),
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boundaries.long().cumsum(dim=1),
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],
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dim=1,
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)
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same_doc = doc_ids.unsqueeze(-1) == doc_ids.unsqueeze(-2)
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causal = torch.tril(torch.ones(S, S, dtype=torch.bool, device=device))
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return (same_doc & causal).unsqueeze(1)
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class BaseStrategy(ABC):
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"""Abstract base class for training strategies."""
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def __init__(
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self,
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model: Union[nn.Module, Callable[..., Dict[str, Tensor]]],
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device: str,
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**kwargs,
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):
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self.model = model
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self.device = device
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self.executor = kwargs.pop("executor", None)
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self.model_fn = kwargs.pop("model_fn", None)
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self.extra_kwargs = kwargs
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@abstractmethod
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def compute_loss(self, batch: Dict[str, Tensor]) -> Tensor:
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"""Compute loss for the given batch.
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Args:
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batch: Dictionary containing batch tensors
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Returns:
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Computed loss tensor
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"""
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raise NotImplementedError
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def __call__(self, batch: Dict[str, Tensor]) -> Tensor:
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"""Allow calling strategy directly as a callable."""
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return self.compute_loss(batch)
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class StrategyFactory(BaseFactory["BaseStrategy"]):
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"""Factory class for creating training strategy instances.
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Supports decorator-based registration for extensible strategy types.
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All default strategies (seq, sft, dpo, grpo) are automatically registered.
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Example usage:
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@StrategyFactory.register("custom")
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class CustomStrategy(BaseStrategy):
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...
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strategy = StrategyFactory.create("custom", model, device)
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"""
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# ============== Strategy Classes ==============
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# All strategies are registered at class definition time using the decorator
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@StrategyFactory.register("seq")
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class SEQStrategy(BaseStrategy):
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"""Standard next-token prediction training strategy.
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Computes cross-entropy loss for next token prediction.
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"""
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def __init__(
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self,
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model: Union[nn.Module, Callable[..., Dict[str, Tensor]]],
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device: str,
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label_smoothing: float = 0.0,
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**kwargs,
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):
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super().__init__(model, device, **kwargs)
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self.label_smoothing = label_smoothing
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def compute_loss(self, batch: Dict[str, Tensor]) -> Tensor:
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batch = move_to_device(batch, self.device)
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input_ids, target_ids = batch["input_ids"], batch["target_ids"]
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logits = self.model(input_ids=input_ids)["logits"]
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loss = F.cross_entropy(
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input=logits.flatten(0, 1).float(),
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target=target_ids.flatten(),
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label_smoothing=self.label_smoothing,
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)
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return loss
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@StrategyFactory.register("sft")
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class SFTStrategy(BaseStrategy):
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"""Supervised Fine-tuning strategy with loss masking.
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Applies cross-entropy loss only to tokens where loss_mask is True.
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"""
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def __init__(
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self,
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model: Union[nn.Module, Callable[..., Dict[str, Tensor]]],
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device: str,
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label_smoothing: float = 0.0,
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**kwargs,
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):
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super().__init__(model, device, **kwargs)
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self.label_smoothing = label_smoothing
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def compute_loss(self, batch: Dict[str, Tensor]) -> Tensor:
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batch = move_to_device(batch, self.device)
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input_ids, target_ids, position_ids, loss_mask = (
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batch["input_ids"],
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batch["target_ids"],
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batch["position_ids"],
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batch["loss_mask"],
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)
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ignore_index = -100
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input_mask = make_doc_boundary_mask(position_ids)
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target_ids = target_ids.masked_fill(~loss_mask, ignore_index)
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logits = self.model(
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input_ids=input_ids, position_ids=position_ids, input_mask=input_mask
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)["logits"]
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loss = F.cross_entropy(
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input=logits.flatten(0, 1).float(),
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target=target_ids.flatten(),
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ignore_index=ignore_index,
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label_smoothing=self.label_smoothing,
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)
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return loss
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@StrategyFactory.register("dpo")
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class DPOStrategy(BaseStrategy):
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"""Direct Preference Optimization strategy.
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Implements the DPO loss from the paper "Direct Preference Optimization".
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Uses a reference model to compute KL divergence penalty.
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"""
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def __init__(
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self,
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model: nn.Module,
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device: str,
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beta: float = 0.1,
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reduction: str = "mean",
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**kwargs,
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):
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super().__init__(model, device, **kwargs)
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self.ref_model = create_ref_model(
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self.model_fn, self.executor.unwrap_model(model)
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).to(device=self.device)
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self.beta = beta
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self.reduction = reduction
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def compute_loss(self, batch: Dict[str, Tensor]) -> Tensor:
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batch = move_to_device(batch, self.device)
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chosen_ids, rejected_ids = batch["chosen"], batch["rejected"]
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chosen_mask, rejected_mask = batch["chosen_mask"], batch["rejected_mask"]
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concat_ids = torch.cat([chosen_ids, rejected_ids], dim=0)
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concat_mask = torch.cat([chosen_mask, rejected_mask], dim=0)
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log_pi = get_logprobs(self.model, concat_ids, concat_mask, self.reduction)
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with torch.no_grad():
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log_ref = get_logprobs(
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self.ref_model, concat_ids, concat_mask, self.reduction
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)
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log_pi_chosen = log_pi[: chosen_ids.shape[0]]
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log_pi_rejected = log_pi[chosen_ids.shape[0] :]
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log_ref_chosen = log_ref[: chosen_ids.shape[0]]
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log_ref_rejected = log_ref[chosen_ids.shape[0] :]
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pi_log_ratio = log_pi_chosen - log_pi_rejected
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ref_log_ratio = log_ref_chosen - log_ref_rejected
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ratio_diff = pi_log_ratio - ref_log_ratio
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dpo_loss = -F.logsigmoid(self.beta * ratio_diff).mean()
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return dpo_loss
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@StrategyFactory.register("grpo")
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class GRPOStrategy(BaseStrategy):
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"""Group Relative Policy Optimization strategy.
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Implements GRPO following DeepSeek-R1 with token-level PPO clipping.
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Advantages are group-normalized from scalar per-response rewards and
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broadcast across all response tokens. The loss is computed **only on
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response tokens** — prompt tokens are masked out.
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The strategy expects offline-collected batches (``responses`` / ``rewards``
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pre-generated by the current or a recent policy). Call ``sync_ref_model()``
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after each data-generation round so ``ref_model`` tracks the sampling policy.
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"""
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def __init__(
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self,
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model: nn.Module,
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device: str,
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clip_eps: float = 0.2,
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kl_coef: float = 0.01,
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group_size: int = 4,
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sync_interval: int = 200,
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**kwargs,
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):
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super().__init__(model, device, **kwargs)
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self.ref_model = create_ref_model(
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self.model_fn, self.executor.unwrap_model(model)
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).to(device=self.device)
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self.clip_eps = clip_eps
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self.kl_coef = kl_coef
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self.group_size = group_size
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self.sync_interval = sync_interval
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self._step = 0
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def sync_ref_model(self):
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"""Copy current model weights to ref model."""
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self.ref_model.load_state_dict(self.executor.unwrap_model(self.model))
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def compute_loss(self, batch: Dict[str, Tensor]) -> Tensor:
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self._step += 1
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if self._step % self.sync_interval == 0:
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self.sync_ref_model()
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batch = move_to_device(batch, self.device)
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prompts = batch["prompts"]
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responses = batch["responses"]
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masks = batch["masks"]
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rewards = batch["rewards"]
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batch_size, group_size, response_len = responses.shape
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responses_flat = responses.view(-1, response_len)
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masks_flat = masks.view(-1, response_len)
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prompt_expanded = prompts.unsqueeze(1).repeat(1, group_size, 1).flatten(0, 1)
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prompt_len = prompt_expanded.size(1)
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full_sequences = torch.cat([prompt_expanded, responses_flat], dim=-1)
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# Prompt tokens are masked out (0) so logprobs are computed only for
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# response tokens. get_logprobs shifts the mask by one position, so
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# the first response token's logprob (predicted from the last prompt
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# token) is correctly included.
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full_masks = torch.cat([torch.zeros_like(prompt_expanded), masks_flat], dim=-1)
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# get_logprobs returns [B*G, S-1] (S = prompt_len + response_len).
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# Response token logprobs occupy the last ``response_len`` positions
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# (the first response token is predicted from the last prompt token).
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token_log_probs_policy = get_logprobs(
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self.model, full_sequences, full_masks, "none"
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)[:, prompt_len - 1 :]
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with torch.no_grad():
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token_log_probs_ref = get_logprobs(
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self.ref_model, full_sequences, full_masks, "none"
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)[:, prompt_len - 1 :]
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# Reshape to [B, G, response_len]
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token_log_probs_policy = token_log_probs_policy.view(batch_size, group_size, -1)
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token_log_probs_ref = token_log_probs_ref.view(batch_size, group_size, -1)
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token_masks = masks_flat.view(batch_size, group_size, -1).float()
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# Group-normalized advantages from scalar per-response rewards.
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eps = 1e-8
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mean = rewards.mean(dim=-1, keepdim=True)
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std = rewards.std(dim=-1, keepdim=True)
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advantages = (rewards - mean) / (std + eps)
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# Broadcast scalar advantage to every response token: [B, G, 1]
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advantages = advantages.unsqueeze(-1)
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# Token-level ratio and PPO clipping.
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log_ratio = token_log_probs_policy - token_log_probs_ref
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ratio = torch.exp(log_ratio)
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surr1 = ratio * advantages
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surr2 = torch.clamp(ratio, 1 - self.clip_eps, 1 + self.clip_eps) * advantages
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per_token_policy_loss = -torch.min(surr1, surr2)
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token_count = token_masks.sum().clamp(min=1.0)
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policy_loss = (per_token_policy_loss * token_masks).sum() / token_count
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# KL penalty with k1 estimator (non-negative): r - log(r) - 1, r=π_ref/π_θ.
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r = torch.exp(-log_ratio)
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kl_per_token = r - torch.log(r + eps) - 1.0
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kl_penalty = self.kl_coef * (kl_per_token * token_masks).sum() / token_count
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total_loss = policy_loss + kl_penalty
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return total_loss
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