src/model/patcher.py

# Copyright 2024 the LlamaFactory team, the Llamole 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 os
from types import MethodType
from typing import TYPE_CHECKING, Any, Dict

import torch
from peft import PeftModel
from transformers import PreTrainedModel, PreTrainedTokenizerBase, is_torch_npu_available
from transformers.integrations import is_deepspeed_zero3_enabled
from transformers.modeling_utils import is_fsdp_enabled

from ..extras.logging import get_logger
from ..extras.misc import infer_optim_dtype
from .model_utils.attention import configure_attn_implementation, print_attn_implementation
from .model_utils.checkpointing import prepare_model_for_training
from .model_utils.embedding import resize_embedding_layer
from .model_utils.longlora import configure_longlora
from .model_utils.moe import add_z3_leaf_module, configure_moe
from .model_utils.quantization import configure_quantization
from .model_utils.rope import configure_rope
from .model_utils.valuehead import prepare_valuehead_model

if TYPE_CHECKING:
    from transformers import PretrainedConfig, PreTrainedTokenizer
    from trl import AutoModelForCausalLMWithValueHead

    from ..hparams import ModelArguments


logger = get_logger(__name__)


def patch_tokenizer(tokenizer: "PreTrainedTokenizer") -> None:
    if "PreTrainedTokenizerBase" not in str(tokenizer._pad.__func__):
        tokenizer._pad = MethodType(PreTrainedTokenizerBase._pad, tokenizer)


def patch_config(
    config: "PretrainedConfig",
    tokenizer: "PreTrainedTokenizer",
    model_args: "ModelArguments",
    init_kwargs: Dict[str, Any],
    is_trainable: bool,
) -> None:
    if model_args.compute_dtype is None:  # priority: bf16 > fp16 > fp32
        if model_args.infer_dtype != "auto" and not is_trainable:
            model_args.compute_dtype = getattr(torch, model_args.infer_dtype)
        else:
            model_args.compute_dtype = infer_optim_dtype(model_dtype=getattr(config, "torch_dtype", None))

    if is_torch_npu_available():
        use_jit_compile = os.environ.get("JIT_COMPILE", "0").lower() in ["true", "1"]
        torch.npu.set_compile_mode(jit_compile=use_jit_compile)

    configure_attn_implementation(config, model_args, is_trainable)
    configure_rope(config, model_args, is_trainable)
    configure_longlora(config, model_args, is_trainable)
    configure_quantization(config, tokenizer, model_args, init_kwargs)
    configure_moe(config, model_args, is_trainable)

    # configure_visual_model(config)

    if model_args.use_cache and not is_trainable:
        setattr(config, "use_cache", True)
        logger.info("Using KV cache for faster generation.")

    if getattr(config, "model_type", None) == "qwen":
        setattr(config, "use_flash_attn", model_args.flash_attn == "fa2")
        for dtype_name, dtype in [("fp16", torch.float16), ("bf16", torch.bfloat16), ("fp32", torch.float32)]:
            setattr(config, dtype_name, model_args.compute_dtype == dtype)

    if getattr(config, "model_type", None) == "qwen2" and is_trainable and model_args.flash_attn == "fa2":
        setattr(config, "use_cache", False)  # qwen2 does not support use_cache when using flash attn

    # deepspeed zero3 is not compatible with low_cpu_mem_usage
    init_kwargs["low_cpu_mem_usage"] = model_args.low_cpu_mem_usage and (not is_deepspeed_zero3_enabled())

    # cast data type of the model if:
    # 1. not deepspeed zero3 and not fsdp (keep zero3 or fsdp in float32)
    # 2. quantization_bit is not None (qlora)
    if (not is_deepspeed_zero3_enabled() and not is_fsdp_enabled()) or model_args.quantization_bit is not None:
        init_kwargs["torch_dtype"] = model_args.compute_dtype

        if init_kwargs["low_cpu_mem_usage"]:  # device map requires low_cpu_mem_usage=True
            if "device_map" not in init_kwargs and model_args.device_map:
                init_kwargs["device_map"] = model_args.device_map

            if init_kwargs.get("device_map", None) == "auto":
                init_kwargs["offload_folder"] = model_args.offload_folder


def patch_model(
    model: "PreTrainedModel",
    tokenizer: "PreTrainedTokenizer",
    model_args: "ModelArguments",
    is_trainable: bool,
    add_valuehead: bool,
) -> None:
    gen_config = model.generation_config  # check and fix generation config
    if not gen_config.do_sample and (
        (gen_config.temperature is not None and gen_config.temperature != 1.0)
        or (gen_config.top_p is not None and gen_config.top_p != 1.0)
        or (gen_config.typical_p is not None and gen_config.typical_p != 1.0)
    ):
        gen_config.do_sample = True

    if "GenerationMixin" not in str(model.generate.__func__):
        model.generate = MethodType(PreTrainedModel.generate, model)

    if add_valuehead:
        prepare_valuehead_model(model)

    if model_args.resize_vocab:
        resize_embedding_layer(model, tokenizer)

    if model_args.visual_inputs:
        autocast_projector_dtype(model, model_args)

    if is_trainable:
        prepare_model_for_training(model, model_args)
        add_z3_leaf_module(model)

    if not model_args.use_unsloth:
        print_attn_implementation(model.config)

def patch_valuehead_model(model: "AutoModelForCausalLMWithValueHead") -> None:
    def tie_weights(self: "AutoModelForCausalLMWithValueHead") -> None:
        if isinstance(self.pretrained_model, PreTrainedModel):
            self.pretrained_model.tie_weights()

    def get_input_embeddings(self: "AutoModelForCausalLMWithValueHead") -> torch.nn.Module:
        if isinstance(self.pretrained_model, PreTrainedModel):
            return self.pretrained_model.get_input_embeddings()

    def get_output_embeddings(self: "AutoModelForCausalLMWithValueHead") -> torch.nn.Module:
        if isinstance(self.pretrained_model, PreTrainedModel):
            return self.pretrained_model.get_output_embeddings()

    def create_or_update_model_card(self: "AutoModelForCausalLMWithValueHead", output_dir: str) -> None:
        if isinstance(self.pretrained_model, PeftModel):
            self.pretrained_model.create_or_update_model_card(output_dir)

    ignore_modules = [name for name, _ in model.named_parameters() if "pretrained_model" in name]
    setattr(model, "_keys_to_ignore_on_save", ignore_modules)
    setattr(model, "tie_weights", MethodType(tie_weights, model))
    setattr(model, "get_input_embeddings", MethodType(get_input_embeddings, model))
    setattr(model, "get_output_embeddings", MethodType(get_output_embeddings, model))
    setattr(model, "create_or_update_model_card", MethodType(create_or_update_model_card, model))