ppo_save_utils.py

# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import json
import logging
import os
import signal
import sys
import time
from concurrent.futures import ThreadPoolExecutor
from datetime import timedelta
from typing import TYPE_CHECKING, Any, Dict, Optional

import torch
import transformers
from peft import PeftModel
from transformers import PreTrainedModel, ProcessorMixin, TrainerCallback
from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR, has_length
from transformers.utils import (
    SAFE_WEIGHTS_NAME,
    WEIGHTS_NAME,
    is_safetensors_available,
)

from llamafactory.extras.constants import TRAINER_LOG, V_HEAD_SAFE_WEIGHTS_NAME, V_HEAD_WEIGHTS_NAME
from llamafactory.extras.logging import LoggerHandler, get_logger


if is_safetensors_available():
    from safetensors import safe_open
    from safetensors.torch import save_file

if TYPE_CHECKING:
    from transformers import TrainerControl, TrainerState, TrainingArguments
    from trl import AutoModelForCausalLMWithValueHead


logger = get_logger(__name__)


def fix_valuehead_checkpoint(
    model: "AutoModelForCausalLMWithValueHead", output_dir: str, safe_serialization: bool
) -> None:
    r"""
    The model is already unwrapped.

    There are three cases:
    1. full tuning without ds_zero3: state_dict = {"model.layers.*": ..., "v_head.summary.*": ...}
    2. lora tuning without ds_zero3: state_dict = {"v_head.summary.*": ...}
    3. under deepspeed zero3: state_dict = {"pretrained_model.model.layers.*": ..., "v_head.summary.*": ...}

    We assume `stage3_gather_16bit_weights_on_model_save=true`.
    """
    if not isinstance(model.pretrained_model, (PreTrainedModel, PeftModel)):
        return

    if safe_serialization:
        path_to_checkpoint = os.path.join(output_dir, SAFE_WEIGHTS_NAME)
        with safe_open(path_to_checkpoint, framework="pt", device="cpu") as f:
            state_dict: Dict[str, torch.Tensor] = {key: f.get_tensor(key) for key in f.keys()}
    else:
        path_to_checkpoint = os.path.join(output_dir, WEIGHTS_NAME)
        state_dict: Dict[str, torch.Tensor] = torch.load(path_to_checkpoint, map_location="cpu")

    decoder_state_dict = {}
    v_head_state_dict = {}
    for name, param in state_dict.items():
        if name.startswith("v_head."):
            v_head_state_dict[name] = param
        else:
            decoder_state_dict[name.replace("pretrained_model.", "")] = param

    os.remove(path_to_checkpoint)
    model.pretrained_model.save_pretrained(
        output_dir, state_dict=decoder_state_dict or None, safe_serialization=safe_serialization
    )

    if safe_serialization:
        save_file(v_head_state_dict, os.path.join(output_dir, V_HEAD_SAFE_WEIGHTS_NAME), metadata={"format": "pt"})
    else:
        torch.save(v_head_state_dict, os.path.join(output_dir, V_HEAD_WEIGHTS_NAME))

    logger.info("Value head model saved at: {}".format(output_dir))


class FixValueHeadModelCallback(TrainerCallback):
    def on_save(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs):
        r"""
        Event called after a checkpoint save.
        """
        if args.should_save:
            if args.local_process_index == 0 if args.save_on_each_node else args.process_index == 0:
                fix_valuehead_checkpoint(
                    model=kwargs.pop("model"),
                    output_dir=os.path.join(args.output_dir, "{}-{}".format(PREFIX_CHECKPOINT_DIR, state.global_step)),
                    safe_serialization=args.save_safetensors,
                )