1399 lines
53 KiB
Python
1399 lines
53 KiB
Python
"""Module for models and model loading"""
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# pylint: disable=too-many-lines
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import gc
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import importlib
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import logging
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import math
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import os
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import types
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from functools import cached_property
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from typing import Any, Dict, Optional, Tuple, Union # noqa: F401
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import addict
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import bitsandbytes as bnb
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import torch
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import transformers
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import transformers.modeling_utils
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from accelerate import init_empty_weights
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from bitsandbytes.nn import Params4bit
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from peft import (
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LoftQConfig,
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PeftConfig,
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PeftModel,
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PeftModelForCausalLM,
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prepare_model_for_kbit_training,
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)
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from peft.tuners.lora import QuantLinear
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from torch import nn
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from transformers import ( # noqa: F401
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AddedToken,
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AutoConfig,
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AutoModelForCausalLM,
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AutoModelForVision2Seq,
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AutoProcessor,
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AutoTokenizer,
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AwqConfig,
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BitsAndBytesConfig,
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GPTQConfig,
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LlavaForConditionalGeneration,
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MllamaForConditionalGeneration,
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PreTrainedModel,
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PreTrainedTokenizerBase,
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ProcessorMixin,
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)
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from transformers.integrations.deepspeed import (
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HfTrainerDeepSpeedConfig,
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is_deepspeed_zero3_enabled,
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)
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from axolotl.common.architectures import MOE_ARCH_BLOCK
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from axolotl.models.mamba import fix_mamba_attn_for_loss
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from axolotl.monkeypatch.multipack import (
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SUPPORTED_MULTIPACK_MODEL_TYPES,
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patch_for_multipack,
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)
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from axolotl.prompt_tokenizers import LLAMA_DEFAULT_EOS_TOKEN
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from axolotl.utils.bench import log_gpu_memory_usage
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from axolotl.utils.chat_templates import get_chat_template_from_config
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from axolotl.utils.dict import DictDefault
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from axolotl.utils.distributed import get_device_count, get_device_type, zero_only
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from axolotl.utils.gradient_checkpointing import hf_grad_checkpoint_unsloth_wrapper
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from axolotl.utils.lora_embeddings import get_linear_embedding_layers
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from axolotl.utils.model_shard_quant import load_sharded_model, load_sharded_model_quant
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LOG = logging.getLogger("axolotl")
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# copied from accelerator.FullyShardedDataParallelPlugin
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def get_module_class_from_name(module, name):
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"""
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Gets a class from a module by its name.
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Args:
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module (`torch.nn.Module`): The module to get the class from.
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name (`str`): The name of the class.
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"""
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modules_children = list(module.children())
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if module.__class__.__name__ == name:
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return module.__class__
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if len(modules_children) == 0:
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return None
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for child_module in modules_children:
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module_class = get_module_class_from_name(child_module, name)
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if module_class is not None:
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return module_class
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return None
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def check_model_config(cfg: DictDefault, model_config: Union[AutoConfig, DictDefault]):
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if cfg.is_multimodal:
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model_config = model_config.text_config
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quant_config_exists = (
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hasattr(model_config, "quantization_config")
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and model_config.quantization_config
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)
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quant_config_method_is_gptq = (
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quant_config_exists
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and "quant_method" in model_config.quantization_config
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and model_config.quantization_config["quant_method"] == "gptq"
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)
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if cfg.gptq and not quant_config_method_is_gptq:
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raise ValueError(
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"model_config.quantization_config is not set or quant_method is not set to gptq. "
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"Please make sure to point to a GPTQ model."
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)
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if not cfg.gptq and quant_config_exists and not cfg.load_in_4bit:
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raise ValueError(
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"model_config.quantization_config is set but `gptq` flag is not. "
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"Please use the `gptq` flag to train quantized model or point to a non-quantized model."
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)
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lora_modules_to_save = get_linear_embedding_layers(model_config.model_type)
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if (
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cfg.adapter
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and cfg.tokens
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and (
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not cfg.lora_modules_to_save
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or not all(x in cfg.lora_modules_to_save for x in lora_modules_to_save)
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)
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):
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lora_modules_to_save = ", ".join(map(lambda x: f"`{x}`", lora_modules_to_save))
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raise ValueError(
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f"`lora_modules_to_save` not properly set when adding new tokens. Please include [{lora_modules_to_save}] in `lora_modules_to_save`."
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)
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def load_model_config(cfg):
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model_config_name = cfg.base_model_config or cfg.base_model
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if not model_config_name and cfg.tokenizer_config:
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model_config_name = cfg.tokenizer_config
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trust_remote_code = cfg.trust_remote_code is True
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config_kwargs = {}
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if cfg.revision_of_model:
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config_kwargs["revision"] = cfg.revision_of_model
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if cfg.num_labels:
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# num_labels is used to initialize classifier models
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config_kwargs["num_labels"] = cfg.num_labels
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try:
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model_config = AutoConfig.from_pretrained(
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model_config_name,
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trust_remote_code=trust_remote_code,
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**config_kwargs,
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)
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except ValueError as err:
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if "mamba" in model_config_name:
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return addict.Dict(
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{
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"model_type": "mamba",
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}
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)
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raise err
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if cfg.overrides_of_model_config:
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for key, val in cfg.overrides_of_model_config.items():
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setattr(model_config, key, val)
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check_model_config(cfg, model_config)
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return model_config
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def load_tokenizer(cfg):
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model_config = load_model_config(cfg)
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tokenizer_kwargs = {}
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use_fast = True # this is the default
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if cfg.tokenizer_use_fast is not None:
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use_fast = cfg.tokenizer_use_fast
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if cfg.tokenizer_legacy is not None:
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# True is the default w/ https://github.com/huggingface/transformers/pull/25224
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tokenizer_kwargs["legacy"] = cfg.tokenizer_legacy
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tokenizer_cls = AutoTokenizer
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if cfg.tokenizer_type:
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tokenizer_cls = getattr(transformers, cfg.tokenizer_type)
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tokenizer = tokenizer_cls.from_pretrained(
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cfg.tokenizer_config,
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trust_remote_code=cfg.trust_remote_code or False,
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use_fast=use_fast,
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**tokenizer_kwargs,
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)
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if (
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tokenizer.__class__.__name__
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in [
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"LlamaTokenizer",
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"LlamaTokenizerFast",
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"CodeLlamaTokenizer",
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"CodeLlamaTokenizerFast",
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]
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and hasattr(tokenizer, "pad_token")
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and not tokenizer.pad_token
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):
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# set a pad_token, but use eos_token so we don't add a new token
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tokenizer.pad_token = LLAMA_DEFAULT_EOS_TOKEN
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if tokenizer.__class__.__name__ == "GPTNeoXTokenizerFast":
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tokenizer.add_special_tokens({"pad_token": "[PAD]"})
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os.environ["TOKENIZERS_PARALLELISM"] = "false"
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# Mistral's official FA implementation requires left padding
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if cfg.is_mistral_derived_model and cfg.flash_attention and not cfg.sample_packing:
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tokenizer.padding_side = "left"
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# Qwen base only has single token, so we need to set the special tokens
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if cfg.is_qwen_derived_model:
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token_ids = ["bos_token_id", "eos_token_id", "pad_token_id", "unk_token_id"]
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for attr_name in token_ids:
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if getattr(tokenizer, attr_name) is None:
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setattr(tokenizer, attr_name, tokenizer.eod_id)
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token_names = ["bos_token", "eos_token", "pad_token", "unk_token"]
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for attr_name in token_names:
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if getattr(tokenizer, attr_name) is None:
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setattr(tokenizer, attr_name, "<|endoftext|>")
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additional_special_tokens = None
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if cfg.special_tokens:
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special_tokens = cfg.special_tokens.to_dict()
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additional_special_tokens = special_tokens.pop(
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"additional_special_tokens", None
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)
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lora_modules_to_save = get_linear_embedding_layers(model_config.model_type)
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for k, val in special_tokens.items():
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# check if new special token is not already in tokenizer and
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# is adapter training to make sure lora_modules_to_save is set
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# pylint: disable=too-many-boolean-expressions
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if (
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(getattr(tokenizer, k) is None or getattr(tokenizer, k) != val)
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and (len(tokenizer.encode(val, add_special_tokens=False)) > 2)
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and cfg.adapter
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and (
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not cfg.lora_modules_to_save
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or not all(
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x in cfg.lora_modules_to_save for x in lora_modules_to_save
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)
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)
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and k != "pad_token"
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):
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lora_modules_to_save = ", ".join(
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[f"`{x}`" for x in lora_modules_to_save]
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)
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raise ValueError(
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f"Please set lora_modules_to_save to [{lora_modules_to_save}] when using an adapter and changing the special tokens."
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)
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tokenizer.add_special_tokens(
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{k: AddedToken(val, rstrip=False, lstrip=False, normalized=False)}
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)
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# If we add bos_token and eos_token, we need to update the post processor to
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# handle them correctly.
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# https://github.com/huggingface/transformers/pull/24132
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bos_or_eos_in_special_tokens = (
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"bos_token" in cfg.special_tokens and "eos_token" in cfg.special_tokens
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)
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if (
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tokenizer.__class__.__name__
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in (
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"LlamaTokenizerFast",
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"CodeLlamaTokenizerFast",
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)
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and bos_or_eos_in_special_tokens
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):
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tokenizer.update_post_processor()
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if cfg.tokens:
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tokenizer.add_tokens(
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[
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AddedToken(token, rstrip=False, lstrip=False, normalized=False)
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for token in cfg.tokens
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]
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)
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# Additional special tokens are a List, and need to be treated differently than regular special
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# tokens. We add them after we have called `add_tokens` in case these additional special tokens
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# are new tokens.
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#
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# Usage:
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#
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# ```py
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# special_tokens:
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# additional_special_tokens: ["<|im_start|>", "<|im_end|>"]
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# ```
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if additional_special_tokens is not None:
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tokenizer.add_special_tokens(
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{"additional_special_tokens": additional_special_tokens}
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)
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with zero_only():
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LOG.debug(f"EOS: {tokenizer.eos_token_id} / {tokenizer.eos_token}")
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LOG.debug(f"BOS: {tokenizer.bos_token_id} / {tokenizer.bos_token}")
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LOG.debug(f"PAD: {tokenizer.pad_token_id} / {tokenizer.pad_token}")
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LOG.debug(f"UNK: {tokenizer.unk_token_id} / {tokenizer.unk_token}")
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if cfg.chat_template:
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chat_template_string = get_chat_template_from_config(
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cfg=cfg,
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tokenizer=tokenizer,
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)
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if cfg.default_system_message and cfg.chat_template == "chatml":
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chat_template_string = chat_template_string.replace(
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"You are a helpful assistant.", cfg.default_system_message
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)
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tokenizer.chat_template = chat_template_string
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else:
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LOG.info(
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"No Chat template selected. Consider adding a chat template for easier inference."
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)
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return tokenizer
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def load_processor(cfg: DictDefault, tokenizer: PreTrainedTokenizerBase):
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processor_kwargs: Dict[str, Any] = {} # do we actually need this?
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processor_cls = AutoProcessor
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if cfg.processor_type:
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processor_cls = getattr(transformers, cfg.processor_type)
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processor = processor_cls.from_pretrained(
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cfg.processor_config,
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trust_remote_code=cfg.trust_remote_code or False,
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tokenizer=tokenizer,
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**processor_kwargs,
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)
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return processor
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class ModelLoader:
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"""
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ModelLoader: managing all the config and monkey patches while loading model
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"""
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def __init__(
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self,
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cfg: DictDefault,
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tokenizer: PreTrainedTokenizerBase,
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*,
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processor: ProcessorMixin = None, # pylint: disable=unused-argument
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inference: bool = False,
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reference_model: bool = False,
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**kwargs, # pylint: disable=unused-argument
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) -> None:
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self.cfg = cfg
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self.tokenizer = tokenizer
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self.inference: bool = inference
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self.reference_model: bool = reference_model
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# init model kwargs
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self.model_kwargs: Dict[str, Any] = {}
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if cfg.model_kwargs:
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for key, val in cfg.model_kwargs.items():
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self.model_kwargs[key] = val
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# init model
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self.model: PreTrainedModel
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self.base_model = cfg.base_model
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self.model_type = cfg.type_of_model
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# init model config
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self.model_config = load_model_config(cfg)
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if cfg.is_multimodal:
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self.text_model_config = self.model_config.text_config
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else:
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self.text_model_config = self.model_config
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self.AutoModelLoader = AutoModelForCausalLM # pylint: disable=invalid-name
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def apply_patches(self) -> None:
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# load any patches from plugins
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from axolotl.integrations.base import PluginManager
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plugin_manager = PluginManager.get_instance()
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plugin_manager.pre_model_load(self.cfg)
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if self.cfg.adapter:
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from axolotl.monkeypatch.transformers_fa_utils import (
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patch_fa_peft_integration,
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)
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patch_fa_peft_integration()
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if self.cfg.gradient_checkpointing == "unsloth":
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transformers.modeling_utils.checkpoint = hf_grad_checkpoint_unsloth_wrapper
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if self.cfg.flash_attention:
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self.patch_attention()
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if self.cfg.sample_packing and self.cfg.s2_attention:
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raise ValueError(
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"Received `sample_packing=true` and `s2_attention=true`; however, \
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shifted-sparse attention does not currently support sample packing."
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)
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if (
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self.cfg.model_config_type in SUPPORTED_MULTIPACK_MODEL_TYPES
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and self.cfg.flash_attention
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and self.cfg.sample_packing
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):
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if "auto_map" in self.model_config:
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try:
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auto_map_config = self.model_config["auto_map"]
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except TypeError:
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auto_map_config = self.model_config.auto_map
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has_remote_code = "AutoModelForCausalLM" in auto_map_config
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else:
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has_remote_code = False
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if has_remote_code and self.cfg.trust_remote_code is False:
|
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# if explicitly set in the YAML, we should prefer that, for example if explicitly disabled
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has_remote_code = self.cfg.trust_remote_code
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patch_for_multipack(
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self.cfg.model_config_type,
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model_name=self.cfg.base_model,
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has_remote_code=has_remote_code,
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)
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if self.cfg.is_llama_derived_model:
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self.patch_loss_llama()
|
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if self.cfg.unsloth_lora_qkv or self.cfg.unsloth_lora_o:
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from axolotl.monkeypatch.unsloth_ import patch_self_attn_lora
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|
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patch_self_attn_lora()
|
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elif self.cfg.is_llama_derived_model:
|
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self.patch_llama_derived_model()
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|
|
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if (
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self.cfg.model_config_type == "mistral"
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and self.cfg.flash_attn_cross_entropy_loss
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):
|
|
from axolotl.monkeypatch.mistral_attn_hijack_flash import (
|
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patch_mistral_cross_entropy,
|
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)
|
|
|
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patch_mistral_cross_entropy()
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|
|
|
def patch_attention(self) -> None:
|
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if hasattr(self.model_config, "model_type"):
|
|
if self.model_config.model_type == "mllama" and self.cfg.flash_attention:
|
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from axolotl.monkeypatch.attention.mllama import patch_mllama
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|
|
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patch_mllama()
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|
|
|
if self.model_config.model_type == "btlm":
|
|
from axolotl.monkeypatch.btlm_attn_hijack_flash import (
|
|
replace_btlm_attn_with_flash_attn,
|
|
)
|
|
|
|
replace_btlm_attn_with_flash_attn(self.cfg.base_model)
|
|
|
|
if (
|
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self.model_config.model_type == "stablelm_epoch"
|
|
and self.cfg.sample_packing
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|
):
|
|
from axolotl.monkeypatch.stablelm_attn_hijack_flash import (
|
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replace_stablelm_attn_with_flash_attn,
|
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)
|
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|
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replace_stablelm_attn_with_flash_attn(self.cfg.base_model)
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|
|
@cached_property
|
|
def has_flash_attn(self) -> bool:
|
|
"""Check if flash attention is installed"""
|
|
return importlib.util.find_spec("flash_attn") is not None
|
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|
|
def patch_loss_llama(self) -> None:
|
|
"""
|
|
Patch loss functions
|
|
"""
|
|
if self.has_flash_attn:
|
|
from axolotl.monkeypatch.llama_attn_hijack_flash import (
|
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patch_fa_llama_cross_entropy,
|
|
patch_llama_rms_norm,
|
|
)
|
|
|
|
if self.cfg.flash_attn_cross_entropy and self.has_flash_attn:
|
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patch_fa_llama_cross_entropy()
|
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elif self.cfg.unsloth_cross_entropy_loss:
|
|
from axolotl.monkeypatch.unsloth_ import integrate_cross_entropy_loss_patch
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|
|
|
integrate_cross_entropy_loss_patch(model_type="llama")
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|
|
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if self.cfg.flash_attn_rms_norm and self.has_flash_attn:
|
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patch_llama_rms_norm()
|
|
elif self.cfg.unsloth_rms_norm:
|
|
from axolotl.monkeypatch.unsloth_ import patch_unsloth_layernorm
|
|
|
|
patch_unsloth_layernorm()
|
|
if self.cfg.unsloth_lora_qkv or self.cfg.unsloth_lora_o:
|
|
from axolotl.monkeypatch.unsloth_ import patch_self_attn_lora
|
|
|
|
patch_self_attn_lora()
|
|
|
|
def patch_llama_derived_model(self) -> None:
|
|
"""
|
|
Modify all llama derived models in one block
|
|
"""
|
|
self.patch_loss_llama()
|
|
|
|
if self.cfg.flash_attention:
|
|
from axolotl.monkeypatch.llama_attn_hijack_flash import (
|
|
replace_llama_attn_with_flash_attn,
|
|
)
|
|
|
|
if self.cfg.sample_packing:
|
|
if self.cfg.device not in ["mps", "cpu"] and not self.inference:
|
|
LOG.info("patching with flash attention for sample packing")
|
|
replace_llama_attn_with_flash_attn(
|
|
packed=True,
|
|
cross_entropy=self.cfg.flash_attn_cross_entropy,
|
|
rms_norm=self.cfg.flash_attn_rms_norm,
|
|
)
|
|
elif self.cfg.s2_attention:
|
|
LOG.info("patching w/ flash-enabled, shifted-sparse attention")
|
|
replace_llama_attn_with_flash_attn(
|
|
packed=False,
|
|
cross_entropy=self.cfg.flash_attn_cross_entropy,
|
|
rms_norm=self.cfg.flash_attn_rms_norm,
|
|
use_shifted_sparse_attn=True,
|
|
)
|
|
elif self.cfg.flash_attn_cross_entropy or self.cfg.flash_attn_rms_norm:
|
|
replace_llama_attn_with_flash_attn(
|
|
packed=False,
|
|
cross_entropy=self.cfg.flash_attn_cross_entropy,
|
|
rms_norm=self.cfg.flash_attn_rms_norm,
|
|
)
|
|
elif self.cfg.xformers_attention:
|
|
from axolotl.monkeypatch.llama_attn_hijack_xformers import (
|
|
hijack_llama_attention,
|
|
)
|
|
|
|
LOG.info("patching with xformers attention")
|
|
hijack_llama_attention()
|
|
elif self.cfg.sample_packing:
|
|
from axolotl.monkeypatch.llama_patch_multipack import (
|
|
hijack_llama_prepare_4d_mask,
|
|
)
|
|
|
|
LOG.info("patching llama _prepare_4d_causal_attention_mask*")
|
|
hijack_llama_prepare_4d_mask()
|
|
elif self.cfg.s2_attention:
|
|
raise NotImplementedError(
|
|
"Shifted-sparse attention not currently implemented without flash attention."
|
|
)
|
|
|
|
def set_auto_model_loader(self) -> None:
|
|
"""set self.AutoModelLoader
|
|
- default value: AutoModelForCausalLM (set at __init__)
|
|
- when using a multi modality model, self.AutoModelLoader should
|
|
be set according to model type of the model
|
|
"""
|
|
if self.cfg.is_multimodal:
|
|
if self.model_config.model_type == "llava":
|
|
self.AutoModelLoader = ( # pylint: disable=invalid-name
|
|
LlavaForConditionalGeneration
|
|
)
|
|
elif self.model_config.model_type == "mllama":
|
|
self.AutoModelLoader = ( # pylint: disable=invalid-name
|
|
MllamaForConditionalGeneration
|
|
)
|
|
else:
|
|
self.AutoModelLoader = (
|
|
AutoModelForVision2Seq # pylint: disable=invalid-name
|
|
)
|
|
|
|
def set_device_map_config(self) -> None:
|
|
device_map = self.cfg.device_map
|
|
max_memory = self.cfg.max_memory
|
|
|
|
if self.cfg.gpu_memory_limit:
|
|
gpu_memory_limit = (
|
|
str(self.cfg.gpu_memory_limit) + "GiB"
|
|
if isinstance(self.cfg.gpu_memory_limit, int)
|
|
else self.cfg.gpu_memory_limit
|
|
)
|
|
|
|
max_memory = {}
|
|
num_device = get_device_count()
|
|
for i in range(num_device):
|
|
max_memory[i] = gpu_memory_limit
|
|
max_memory["cpu"] = "256GiB" # something sufficiently large to fit anything
|
|
|
|
if max_memory is not None:
|
|
# Based on https://github.com/togethercomputer/OpenChatKit/blob/main/inference/bot.py
|
|
from accelerate import infer_auto_device_map
|
|
|
|
with init_empty_weights():
|
|
model_canvas = self.AutoModelLoader.from_config(
|
|
self.model_config,
|
|
trust_remote_code=self.cfg.trust_remote_code or False,
|
|
)
|
|
model_canvas.tie_weights()
|
|
device_map = infer_auto_device_map(
|
|
model_canvas,
|
|
max_memory=max_memory,
|
|
dtype=self.cfg.torch_dtype,
|
|
)
|
|
# We can discard max_memory now as we have a device map set up for us
|
|
max_memory = None
|
|
|
|
self.model_kwargs["device_map"] = device_map
|
|
self.model_kwargs["torch_dtype"] = self.cfg.torch_dtype
|
|
|
|
cur_device = get_device_type()
|
|
if "mps" in str(cur_device):
|
|
self.model_kwargs["device_map"] = "mps:0"
|
|
elif "npu" in str(cur_device):
|
|
self.model_kwargs["device_map"] = "npu:0"
|
|
|
|
# TODO can we put the reference model on it's own gpu? I think we have to move logits around to calculate loss
|
|
# if cfg.rl:
|
|
# if torch.cuda.device_count() > 1:
|
|
# if reference_model:
|
|
# model_kwargs["device_map"] = "cuda:" + str(
|
|
# torch.cuda.current_device() + 1
|
|
# )
|
|
# else:
|
|
# model_kwargs["device_map"] = "cuda:" + str(torch.cuda.current_device())
|
|
|
|
if is_deepspeed_zero3_enabled():
|
|
del self.model_kwargs["device_map"]
|
|
|
|
def set_quantization_config(self) -> None:
|
|
self.model_kwargs["load_in_8bit"] = self.cfg.load_in_8bit
|
|
self.model_kwargs["load_in_4bit"] = self.cfg.load_in_4bit
|
|
|
|
if self.cfg.gptq:
|
|
if not hasattr(self.model_config, "quantization_config"):
|
|
LOG.warning(
|
|
"model config does not contain quantization_config information"
|
|
)
|
|
else:
|
|
if self.cfg.gptq_disable_exllama is not None:
|
|
self.model_config.quantization_config[
|
|
"disable_exllama"
|
|
] = self.cfg.gptq_disable_exllama
|
|
self.model_kwargs["quantization_config"] = GPTQConfig(
|
|
**self.model_config.quantization_config
|
|
)
|
|
if (
|
|
self.cfg.adapter in ["qlora", "lora"]
|
|
and hasattr(self.model_config, "quantization_config")
|
|
and self.model_config.quantization_config["quant_method"]
|
|
in ["gptq", "awq", "bitsandbytes"]
|
|
):
|
|
if self.model_config.quantization_config["quant_method"] == "gptq":
|
|
self.model_kwargs["quantization_config"] = GPTQConfig(
|
|
**self.model_config.quantization_config
|
|
)
|
|
elif self.model_config.quantization_config["quant_method"] == "awq":
|
|
self.model_kwargs["quantization_config"] = AwqConfig(
|
|
**self.model_config.quantization_config
|
|
)
|
|
elif (
|
|
self.model_config.quantization_config["quant_method"] == "bitsandbytes"
|
|
):
|
|
self.model_kwargs["quantization_config"] = BitsAndBytesConfig(
|
|
**self.model_config.quantization_config
|
|
)
|
|
elif self.cfg.adapter == "qlora" and self.model_kwargs["load_in_4bit"]:
|
|
bnb_config = {
|
|
"load_in_4bit": True,
|
|
"llm_int8_threshold": 6.0,
|
|
"llm_int8_has_fp16_weight": False,
|
|
"bnb_4bit_compute_dtype": self.cfg.torch_dtype,
|
|
"bnb_4bit_use_double_quant": True,
|
|
"bnb_4bit_quant_type": "nf4",
|
|
"bnb_4bit_quant_storage": torch.bfloat16,
|
|
}
|
|
if self.cfg.model_config_type in ["jamba", "qwen2_moe"] and not (
|
|
self.cfg.deepspeed or self.cfg.fsdp
|
|
):
|
|
# for some reason, this causes the loss to be off by an order of magnitude
|
|
# but deepspeed needs this still in bfloat16
|
|
bnb_config["bnb_4bit_quant_storage"] = torch.float32
|
|
|
|
if self.cfg.bnb_config_kwargs:
|
|
bnb_config.update(self.cfg.bnb_config_kwargs)
|
|
|
|
self.model_kwargs["quantization_config"] = BitsAndBytesConfig(
|
|
**bnb_config,
|
|
)
|
|
elif self.cfg.adapter == "lora" and self.model_kwargs["load_in_8bit"]:
|
|
bnb_config = {
|
|
"load_in_8bit": True,
|
|
}
|
|
# Exclude mamba blocks from int8 quantization for jamba
|
|
if self.cfg.model_config_type == "jamba":
|
|
bnb_config["llm_int8_skip_modules"] = ["mamba"]
|
|
self.model_kwargs["quantization_config"] = BitsAndBytesConfig(
|
|
**bnb_config,
|
|
)
|
|
|
|
# no longer needed per https://github.com/huggingface/transformers/pull/26610
|
|
if "quantization_config" in self.model_kwargs or self.cfg.gptq:
|
|
self.model_kwargs.pop("load_in_8bit", None)
|
|
self.model_kwargs.pop("load_in_4bit", None)
|
|
|
|
def set_attention_config(self) -> None:
|
|
"""
|
|
sample packing uses custom FA2 patch
|
|
"""
|
|
if self.cfg.flash_attention:
|
|
if not self.cfg.sample_packing and self.cfg.s2_attention:
|
|
pass
|
|
self.model_kwargs["attn_implementation"] = "flash_attention_2"
|
|
self.model_config._attn_implementation = ( # pylint: disable=protected-access
|
|
"flash_attention_2"
|
|
)
|
|
elif self.cfg.sdp_attention:
|
|
self.model_kwargs["attn_implementation"] = "sdpa"
|
|
self.model_config._attn_implementation = ( # pylint: disable=protected-access
|
|
"sdpa"
|
|
)
|
|
elif self.cfg.eager_attention:
|
|
self.model_kwargs["attn_implementation"] = "eager"
|
|
self.model_config._attn_implementation = ( # pylint: disable=protected-access
|
|
"eager"
|
|
)
|
|
|
|
if self.cfg.low_cpu_mem_usage:
|
|
self.model_kwargs["low_cpu_mem_usage"] = True
|
|
|
|
def build_model(self, qlora_fsdp) -> bool:
|
|
def _configure_zero3_memory_efficient_loading():
|
|
"""
|
|
Set the deepspeed config to load the model into RAM first before moving to VRAM.
|
|
|
|
We need to return hf_ds_cfg as it needs to exist before model loading.
|
|
"""
|
|
hf_ds_cfg = None
|
|
|
|
if os.getenv("ACCELERATE_DEEPSPEED_ZERO_STAGE") == "3":
|
|
hf_ds_cfg = HfTrainerDeepSpeedConfig(self.cfg.deepspeed)
|
|
hf_ds_cfg.fill_match(
|
|
"train_micro_batch_size_per_gpu", self.cfg.micro_batch_size
|
|
)
|
|
hf_ds_cfg.fill_match(
|
|
"gradient_accumulation_steps", self.cfg.gradient_accumulation_steps
|
|
)
|
|
hf_ds_cfg.fill_match(
|
|
"train_batch_size",
|
|
int(os.getenv("WORLD_SIZE", "1"))
|
|
* self.cfg.micro_batch_size
|
|
* self.cfg.gradient_accumulation_steps,
|
|
)
|
|
if "device_map" in self.model_kwargs:
|
|
del self.model_kwargs["device_map"]
|
|
|
|
transformers.modeling_utils.is_deepspeed_zero3_enabled = lambda: True
|
|
transformers.integrations.deepspeed.is_deepspeed_zero3_enabled = (
|
|
lambda: True
|
|
)
|
|
|
|
return hf_ds_cfg
|
|
|
|
skip_move_to_device = False
|
|
if ( # pylint: disable=condition-evals-to-constant)
|
|
(self.cfg.fsdp and self.cfg.fsdp_config.fsdp_cpu_ram_efficient_loading)
|
|
and not qlora_fsdp
|
|
and False
|
|
):
|
|
self.model = load_sharded_model(
|
|
self.base_model,
|
|
self.model_config,
|
|
self.cfg,
|
|
torch_dtype=self.cfg.torch_dtype,
|
|
)
|
|
skip_move_to_device = True
|
|
elif (
|
|
qlora_fsdp
|
|
and self.cfg.fsdp_config.fsdp_cpu_ram_efficient_loading
|
|
and (
|
|
self.cfg.model_config_type == "dbrx"
|
|
or self.cfg.qlora_sharded_model_loading
|
|
)
|
|
):
|
|
quant_storage = self.cfg.torch_dtype
|
|
quantization_config = hasattr(
|
|
self.model_config, "quantization_config"
|
|
) and getattr(self.model_config, "quantization_config")
|
|
quantization_config = (
|
|
quantization_config or self.model_kwargs["quantization_config"]
|
|
)
|
|
if self.cfg.is_multimodal:
|
|
self.model_config.text_config = self.text_model_config
|
|
self.model = load_sharded_model_quant(
|
|
self.base_model,
|
|
self.model_config,
|
|
self.cfg,
|
|
quant_storage=quant_storage,
|
|
quantization_config=quantization_config,
|
|
)
|
|
skip_move_to_device = True
|
|
elif (
|
|
self.model_config.model_type == "llama"
|
|
and not self.cfg.trust_remote_code
|
|
and not self.cfg.gptq
|
|
):
|
|
if self.cfg.fsdp and self.cfg.fsdp_config.fsdp_cpu_ram_efficient_loading:
|
|
skip_move_to_device = True
|
|
if "device_map" in self.model_kwargs:
|
|
del self.model_kwargs["device_map"]
|
|
|
|
_ = _configure_zero3_memory_efficient_loading()
|
|
|
|
if self.cfg.is_multimodal:
|
|
self.model_config.text_config = self.text_model_config
|
|
self.model = self.AutoModelLoader.from_pretrained(
|
|
self.base_model,
|
|
config=self.model_config,
|
|
**self.model_kwargs,
|
|
)
|
|
|
|
# TODO (MengqingCao) split these patches seperately
|
|
if self.cfg.flash_attention and not self.inference:
|
|
from axolotl.monkeypatch.llama_attn_hijack_flash import (
|
|
is_xformers_swiglu_available,
|
|
replace_llama_mlp_with_swiglu,
|
|
replace_llama_qkv_with_fused,
|
|
)
|
|
|
|
if self.cfg.flash_attn_fuse_mlp and is_xformers_swiglu_available():
|
|
LOG.info("patching with SwiGLU")
|
|
replace_llama_mlp_with_swiglu(self.model)
|
|
|
|
if self.cfg.flash_attn_fuse_qkv:
|
|
LOG.info("patching with fused QKV")
|
|
replace_llama_qkv_with_fused(self.model)
|
|
elif self.model_type == "MambaLMHeadModel":
|
|
# FIXME this is janky at best and hacked together to make it work
|
|
MambaLMHeadModel = fix_mamba_attn_for_loss() # pylint: disable=invalid-name
|
|
|
|
self.model_kwargs["dtype"] = self.model_kwargs["torch_dtype"]
|
|
self.model_kwargs["device"] = torch.cuda.current_device()
|
|
del self.model_kwargs["torch_dtype"]
|
|
del self.model_kwargs["device_map"]
|
|
|
|
self.model = MambaLMHeadModel.from_pretrained(
|
|
self.base_model,
|
|
**self.model_kwargs,
|
|
)
|
|
elif (
|
|
self.model_type
|
|
and self.model_type != "AutoModelForCausalLM"
|
|
and not self.cfg.trust_remote_code
|
|
):
|
|
if self.cfg.is_multimodal:
|
|
self.model_config.text_config = self.text_model_config
|
|
if self.cfg.gptq:
|
|
self.model = self.AutoModelLoader.from_pretrained(
|
|
self.base_model,
|
|
config=self.model_config,
|
|
trust_remote_code=self.cfg.trust_remote_code or False,
|
|
**self.model_kwargs,
|
|
)
|
|
else:
|
|
self.model = getattr(transformers, self.model_type).from_pretrained(
|
|
self.base_model,
|
|
config=self.model_config,
|
|
trust_remote_code=self.cfg.trust_remote_code or False,
|
|
**self.model_kwargs,
|
|
)
|
|
else:
|
|
# Shouldn't be a problem most of the time. will obviously error if the model doesn't support this
|
|
# when training starts
|
|
if (
|
|
hasattr(self.text_model_config, "max_seq_len")
|
|
and self.text_model_config.max_seq_len
|
|
and self.cfg.sequence_len > self.text_model_config.max_seq_len
|
|
):
|
|
self.text_model_config.max_seq_len = self.cfg.sequence_len
|
|
LOG.warning(f"increasing context length to {self.cfg.sequence_len}")
|
|
elif (
|
|
hasattr(self.text_model_config, "max_sequence_length")
|
|
and self.text_model_config.max_sequence_length
|
|
and self.cfg.sequence_len > self.text_model_config.max_sequence_length
|
|
):
|
|
self.text_model_config.max_sequence_length = self.cfg.sequence_len
|
|
LOG.warning(f"increasing context length to {self.cfg.sequence_len}")
|
|
if self.cfg.gptq:
|
|
if self.cfg.is_multimodal:
|
|
self.model_config.text_config = self.text_model_config
|
|
self.model = self.AutoModelLoader.from_pretrained(
|
|
self.base_model,
|
|
config=self.model_config,
|
|
trust_remote_code=self.cfg.trust_remote_code or False,
|
|
**self.model_kwargs,
|
|
)
|
|
else:
|
|
if (
|
|
self.cfg.fsdp
|
|
and self.cfg.fsdp_config.fsdp_cpu_ram_efficient_loading
|
|
):
|
|
# disabling either of these two still leads to VRAM spike before setting back down
|
|
skip_move_to_device = True
|
|
if "device_map" in self.model_kwargs:
|
|
del self.model_kwargs["device_map"]
|
|
|
|
_ = _configure_zero3_memory_efficient_loading()
|
|
|
|
if self.cfg.is_multimodal:
|
|
self.model_config.text_config = self.text_model_config
|
|
self.model = self.AutoModelLoader.from_pretrained(
|
|
self.base_model,
|
|
config=self.model_config,
|
|
trust_remote_code=self.cfg.trust_remote_code or False,
|
|
**self.model_kwargs,
|
|
)
|
|
if is_deepspeed_zero3_enabled():
|
|
skip_move_to_device = True
|
|
|
|
return skip_move_to_device
|
|
|
|
def ajust_model_config(self) -> None:
|
|
if (
|
|
hasattr(self.model, "config")
|
|
and hasattr(self.model.config, "max_position_embeddings")
|
|
and self.model.config.max_position_embeddings
|
|
and self.cfg.sequence_len > self.model.config.max_position_embeddings
|
|
):
|
|
LOG.warning(
|
|
f"increasing model.config.max_position_embeddings from {self.model.config.max_position_embeddings} to {self.cfg.sequence_len}"
|
|
)
|
|
self.model.config.max_position_embeddings = self.cfg.sequence_len
|
|
|
|
if (
|
|
hasattr(self.model, "config")
|
|
and hasattr(self.model.config, "bos_token_id")
|
|
and self.model.config.bos_token_id
|
|
and self.model.config.bos_token_id != self.tokenizer.bos_token_id
|
|
):
|
|
self.model.config.bos_token_id = self.tokenizer.bos_token_id
|
|
|
|
if (
|
|
hasattr(self.model, "config")
|
|
and hasattr(self.model.config, "eos_token_id")
|
|
and self.model.config.eos_token_id
|
|
and self.model.config.eos_token_id != self.tokenizer.eos_token_id
|
|
):
|
|
self.model.config.eos_token_id = self.tokenizer.eos_token_id
|
|
|
|
def set_z3_leaf_modules(self) -> None:
|
|
from deepspeed.utils import ( # pylint: disable=no-name-in-module
|
|
set_z3_leaf_modules,
|
|
)
|
|
|
|
if self.cfg.model_config_type in MOE_ARCH_BLOCK:
|
|
moe_blocks = MOE_ARCH_BLOCK[self.cfg.model_config_type]
|
|
moe_blocks = [moe_blocks] if isinstance(moe_blocks, str) else moe_blocks
|
|
set_z3_leaf_modules(
|
|
self.model,
|
|
[
|
|
get_module_class_from_name(self.model, module_name)
|
|
for module_name in moe_blocks
|
|
],
|
|
)
|
|
|
|
def prepare_model(self, qlora_fsdp) -> None:
|
|
skip_prepare_model_for_kbit_training = False
|
|
if self.cfg.model_config_type == "qwen" and self.cfg.adapter == "lora":
|
|
# Qwen doesn't play nicely with LoRA if this is enabled
|
|
skip_prepare_model_for_kbit_training = True
|
|
|
|
loftq_bits = (
|
|
self.cfg.peft
|
|
and self.cfg.peft.loftq_config
|
|
and self.cfg.peft.loftq_config.loftq_bits
|
|
)
|
|
if self.cfg.adapter == "lora" and loftq_bits:
|
|
skip_prepare_model_for_kbit_training = True
|
|
|
|
if qlora_fsdp or (
|
|
self.cfg.fsdp and self.cfg.fsdp_config.fsdp_cpu_ram_efficient_loading
|
|
):
|
|
# make sure everything is in the same dtype
|
|
skip_prepare_model_for_kbit_training = True
|
|
|
|
if is_deepspeed_zero3_enabled():
|
|
skip_prepare_model_for_kbit_training = True
|
|
|
|
if (
|
|
not skip_prepare_model_for_kbit_training
|
|
and self.cfg.adapter in ["lora", "qlora"]
|
|
and (self.cfg.load_in_8bit or self.cfg.load_in_4bit)
|
|
):
|
|
LOG.info("converting PEFT model w/ prepare_model_for_kbit_training")
|
|
self.model = prepare_model_for_kbit_training(
|
|
self.model, use_gradient_checkpointing=self.cfg.gradient_checkpointing
|
|
)
|
|
|
|
def convert_embedding_modules_dtype(
|
|
self, embedding_modules, dist_dtype, before_kbit_train_or_finetune
|
|
) -> None:
|
|
for name, module in self.model.named_modules():
|
|
if "norm" in name:
|
|
module.to(dist_dtype)
|
|
if before_kbit_train_or_finetune:
|
|
if name.endswith(".gate"):
|
|
module.to(dist_dtype)
|
|
if self.model_config.model_type == "btlm":
|
|
# don't upcast lm_head for btlm
|
|
continue
|
|
if any(m in name for m in embedding_modules):
|
|
if hasattr(module, "weight"):
|
|
module.to(dist_dtype)
|
|
|
|
def apply_lora_patch(self) -> None:
|
|
if self.cfg.unsloth_lora_mlp:
|
|
from axolotl.monkeypatch.unsloth_ import integrate_lora_mlp_patch
|
|
|
|
integrate_lora_mlp_patch(self.model)
|
|
if self.cfg.unsloth_lora_qkv or self.cfg.unsloth_lora_o:
|
|
from axolotl.monkeypatch.unsloth_ import integrate_lora_patch
|
|
|
|
integrate_lora_patch(self.model, self.cfg)
|
|
if self.cfg.unsloth_rope:
|
|
from axolotl.monkeypatch.unsloth_ import integrate_rope_embeddings
|
|
|
|
integrate_rope_embeddings()
|
|
|
|
def load_model(self) -> Tuple[PreTrainedModel, Optional[PeftConfig]]:
|
|
self.apply_patches()
|
|
self.set_auto_model_loader()
|
|
self.set_device_map_config()
|
|
if self.cfg.revision_of_model:
|
|
self.model_kwargs["revision"] = self.cfg.revision_of_model
|
|
self.set_quantization_config()
|
|
self.set_attention_config()
|
|
|
|
qlora_fsdp = self.cfg.fsdp and self.cfg.adapter == "qlora"
|
|
skip_move_to_device = False
|
|
|
|
try:
|
|
skip_move_to_device = self.build_model(qlora_fsdp)
|
|
except Exception as err: # pylint: disable=broad-exception-caught
|
|
LOG.exception(err)
|
|
raise err
|
|
|
|
if isinstance(self.model, (PeftModel, PeftModelForCausalLM)) and not qlora_fsdp:
|
|
self.model = self.model.merge_and_unload()
|
|
|
|
embeddings_len = (
|
|
math.ceil(len(self.tokenizer) / 32) * 32
|
|
if self.cfg.resize_token_embeddings_to_32x
|
|
else len(self.tokenizer)
|
|
)
|
|
if hasattr(self.model, "get_input_embeddings") and (
|
|
self.model.get_input_embeddings().num_embeddings < embeddings_len
|
|
or (
|
|
self.model.get_input_embeddings().num_embeddings > embeddings_len
|
|
and self.cfg.shrink_embeddings
|
|
)
|
|
):
|
|
resize_kwargs = {}
|
|
if self.cfg.mean_resizing_embeddings is not None:
|
|
resize_kwargs["mean_resizing"] = self.cfg.mean_resizing_embeddings
|
|
self.model.resize_token_embeddings(embeddings_len, **resize_kwargs)
|
|
else:
|
|
self.model.tie_weights()
|
|
|
|
self.ajust_model_config()
|
|
|
|
# log device memory usage
|
|
if hasattr(self.model, "device") and self.model.device.type in (
|
|
"cuda",
|
|
"mps",
|
|
"npu",
|
|
):
|
|
log_gpu_memory_usage(LOG, "after model load", self.model.device)
|
|
|
|
# make sure these are fp32 per Ramesh et al. (2021)
|
|
embedding_modules = get_linear_embedding_layers(self.cfg.model_config_type)
|
|
if not self.cfg.fsdp:
|
|
# FSDP doesn't like mixed Float and BFloat16
|
|
self.convert_embedding_modules_dtype(
|
|
embedding_modules,
|
|
dist_dtype=torch.float32,
|
|
before_kbit_train_or_finetune=True,
|
|
)
|
|
|
|
if is_deepspeed_zero3_enabled():
|
|
self.set_z3_leaf_modules()
|
|
|
|
needs_fa2_dtype = self.cfg.adapter or self.cfg.fsdp
|
|
if self.cfg.adapter in ["lora", "qlora"]:
|
|
needs_fa2_dtype = True
|
|
if self.cfg.gradient_checkpointing:
|
|
self.model.gradient_checkpointing_enable(
|
|
gradient_checkpointing_kwargs=self.cfg.gradient_checkpointing_kwargs
|
|
)
|
|
|
|
self.prepare_model(qlora_fsdp)
|
|
|
|
should_convert = (
|
|
# LlamaRMSNorm layers are in fp32 after kbit_training or full finetune, so we need to
|
|
# convert them back to fp16/bf16 for flash-attn compatibility.
|
|
((needs_fa2_dtype or self.cfg.flash_attention) and not qlora_fsdp)
|
|
or self.cfg.cut_cross_entropy # Cut cross entropy requires embedding layers to be in fp16/bf16 for backward pass
|
|
)
|
|
|
|
if should_convert:
|
|
LOG.info("Converting modules to %s", self.cfg.torch_dtype)
|
|
self.convert_embedding_modules_dtype(
|
|
embedding_modules=embedding_modules,
|
|
dist_dtype=self.cfg.torch_dtype,
|
|
before_kbit_train_or_finetune=False,
|
|
)
|
|
|
|
# ---------------------------------------------------------
|
|
# load lora or adapter
|
|
# ---------------------------------------------------------
|
|
lora_config = None
|
|
if not self.reference_model or self.cfg.lora_model_dir:
|
|
# if we're not loading the reference model, then we're loading the model for training
|
|
# then the dpo trainer doesn't want the peft model loaded over it, it just wants the lora/peft config
|
|
if (
|
|
self.cfg.adapter
|
|
and self.cfg.rl in ["dpo", "ipo", "kto"]
|
|
and not self.cfg.merge_lora
|
|
):
|
|
_, lora_config = load_lora(
|
|
self.model, self.cfg, inference=False, config_only=True
|
|
)
|
|
else:
|
|
self.model, lora_config = load_adapter(
|
|
self.model, self.cfg, self.cfg.adapter
|
|
)
|
|
|
|
# ---------------------------------------------------------
|
|
# put model to accelerator
|
|
# ---------------------------------------------------------
|
|
if (
|
|
self.cfg.ddp
|
|
and not self.cfg.load_in_8bit
|
|
and not (self.cfg.rl and self.cfg.load_in_4bit)
|
|
and not skip_move_to_device
|
|
):
|
|
# TODO revaldate this conditional
|
|
self.model.to(f"{str(get_device_type())}:{self.cfg.local_rank}")
|
|
|
|
if get_device_count() > 1 and int(os.getenv("WORLD_SIZE", "1")) == 1:
|
|
setattr(self.model, "is_parallelizable", True)
|
|
setattr(self.model, "model_parallel", True)
|
|
|
|
# ---------------------------------------------------------
|
|
# parameters that require gradient updates
|
|
# ---------------------------------------------------------
|
|
requires_grad = []
|
|
for name, param in self.model.named_parameters(recurse=True):
|
|
if param.requires_grad:
|
|
requires_grad.append(f"{name}: {param.requires_grad}")
|
|
if len(requires_grad) == 0:
|
|
LOG.warning("there are no parameters that require gradient updates")
|
|
if hasattr(self.model, "config"):
|
|
self.model.config.use_cache = False
|
|
|
|
if self.cfg.flash_optimum:
|
|
from optimum.bettertransformer import BetterTransformer
|
|
|
|
self.model = BetterTransformer.transform(self.model)
|
|
|
|
if self.cfg.adapter is not None:
|
|
log_gpu_memory_usage(LOG, "after adapters", self.model.device)
|
|
|
|
self.apply_lora_patch()
|
|
|
|
for _ in range(3):
|
|
gc.collect()
|
|
torch.cuda.empty_cache()
|
|
|
|
# TODO resume_from_checkpoint handling
|
|
return self.model, lora_config
|
|
|
|
|
|
def load_model(
|
|
cfg: DictDefault,
|
|
tokenizer: PreTrainedTokenizerBase,
|
|
*,
|
|
processor: ProcessorMixin = None, # pylint: disable=unused-argument
|
|
inference: bool = False,
|
|
reference_model: bool = False,
|
|
**kwargs, # pylint: disable=unused-argument
|
|
) -> Tuple[PreTrainedModel, Optional[PeftConfig]]:
|
|
"""
|
|
Load a model for a given configuration and tokenizer.
|
|
"""
|
|
loader = ModelLoader(
|
|
cfg,
|
|
tokenizer,
|
|
processor=processor,
|
|
inference=inference,
|
|
reference_model=reference_model,
|
|
**kwargs,
|
|
)
|
|
return loader.load_model()
|
|
|
|
|
|
def load_adapter(model, cfg, adapter, inference=False):
|
|
# type: (PreTrainedModel, DictDefault, Optional[str], bool) -> Tuple[PreTrainedModel, Optional[PeftConfig]]
|
|
|
|
if adapter is None:
|
|
return model, None
|
|
if hasattr(model, "enable_input_require_grads"):
|
|
model.enable_input_require_grads()
|
|
if adapter in ["lora", "qlora"]:
|
|
return load_lora(model, cfg, inference=inference)
|
|
if adapter == "llama-adapter":
|
|
return load_llama_adapter(model, cfg)
|
|
|
|
raise NotImplementedError(f"{adapter} peft adapter not available")
|
|
|
|
|
|
def load_llama_adapter(model, cfg):
|
|
# type: (PreTrainedModel, DictDefault) -> Tuple[PreTrainedModel, Optional[PeftConfig]]
|
|
from peft import AdaptionPromptConfig, get_peft_model
|
|
|
|
peft_config = AdaptionPromptConfig(
|
|
adapter_layers=cfg.peft_adapter.layers, # layers (L)
|
|
adapter_len=cfg.peft_adapter.len, # prompt length (K)
|
|
task_type="CAUSAL_LM",
|
|
)
|
|
|
|
if cfg.lora_model_dir:
|
|
LOG.debug("Loading pretrained PEFT - llama_adapter")
|
|
model = PeftModel.from_pretrained(
|
|
model,
|
|
cfg.lora_model_dir,
|
|
torch_dtype=torch.float16,
|
|
)
|
|
else:
|
|
model = get_peft_model(model, peft_config)
|
|
|
|
model.print_trainable_parameters()
|
|
|
|
return model, peft_config
|
|
|
|
|
|
def find_all_linear_names(model):
|
|
cls = (bnb.nn.Linear4bit, bnb.nn.Linear8bitLt, torch.nn.Linear, QuantLinear)
|
|
lora_module_names = set()
|
|
for name, module in model.named_modules():
|
|
if (
|
|
isinstance(module, cls)
|
|
or "Linear" in module.__class__.__name__
|
|
and module.__class__.__name__ not in ("LlamaLinearScalingRotaryEmbedding",)
|
|
):
|
|
names = name.split(".")
|
|
lora_module_names.add(names[0] if len(names) == 1 else names[-1])
|
|
|
|
embedding_modules = get_linear_embedding_layers(model.config.model_type)
|
|
output_embedding = embedding_modules[1]
|
|
if output_embedding in lora_module_names: # needed for 16-bit
|
|
lora_module_names.remove(output_embedding)
|
|
|
|
return list(lora_module_names)
|
|
|
|
|
|
def setup_quantized_meta_for_peft(model: nn.Module):
|
|
"""Replaces `quant_state.to` with a dummy function to prevent PEFT from moving `quant_state` to meta device"""
|
|
|
|
def temp_to_method(self, *args, **kwargs): # pylint: disable=unused-argument
|
|
return self
|
|
|
|
for param in model.parameters():
|
|
if isinstance(param, Params4bit):
|
|
param.quant_state._orig_to = ( # pylint: disable=protected-access
|
|
param.quant_state.to
|
|
)
|
|
param.quant_state.to = types.MethodType(temp_to_method, param.quant_state)
|
|
|
|
|
|
def setup_quantized_peft_meta_for_training(model: nn.Module):
|
|
"""Replaces dummy `quant_state.to` method with the original function to allow training to continue"""
|
|
for param in model.parameters():
|
|
if isinstance(param, Params4bit) and hasattr(param.quant_state, "_orig_to"):
|
|
param.quant_state.to = (
|
|
param.quant_state._orig_to # pylint: disable=protected-access
|
|
)
|
|
param.quant_state._orig_to = None # pylint: disable=protected-access
|
|
|
|
|
|
def load_lora(model, cfg, inference=False, config_only=False):
|
|
# type: (PreTrainedModel, DictDefault, bool, bool) -> Tuple[Optional[PreTrainedModel], Optional[PeftConfig]]
|
|
|
|
from peft import LoraConfig, get_peft_model
|
|
|
|
lora_target_modules = cfg.lora_target_modules or []
|
|
|
|
if cfg.lora_target_linear:
|
|
linear_names = find_all_linear_names(model)
|
|
LOG.info(f"found linear modules: {repr(sorted(linear_names))}")
|
|
lora_target_modules_as_list = (
|
|
lora_target_modules
|
|
if isinstance(lora_target_modules, list)
|
|
else [lora_target_modules]
|
|
)
|
|
lora_target_modules = list(set(lora_target_modules_as_list + linear_names))
|
|
|
|
lora_config_kwargs = {}
|
|
loftq_bits = cfg.peft and cfg.peft.loftq_config and cfg.peft.loftq_config.loftq_bits
|
|
if loftq_bits:
|
|
lora_config_kwargs["loftq_config"] = LoftQConfig(loftq_bits=loftq_bits)
|
|
lora_config_kwargs["init_lora_weights"] = "loftq"
|
|
if cfg.peft_use_dora:
|
|
lora_config_kwargs["use_dora"] = cfg.peft_use_dora
|
|
if cfg.peft_use_rslora:
|
|
lora_config_kwargs["use_rslora"] = cfg.peft_use_rslora
|
|
if cfg.peft_layer_replication:
|
|
lora_config_kwargs["layer_replication"] = cfg.peft_layer_replication
|
|
|
|
lora_config = LoraConfig(
|
|
r=cfg.lora_r,
|
|
lora_alpha=cfg.lora_alpha,
|
|
target_modules=lora_target_modules,
|
|
layers_to_transform=cfg.peft_layers_to_transform,
|
|
layers_pattern=cfg.peft_layers_pattern,
|
|
lora_dropout=cfg.lora_dropout,
|
|
fan_in_fan_out=cfg.lora_fan_in_fan_out,
|
|
modules_to_save=cfg.lora_modules_to_save if cfg.lora_modules_to_save else None,
|
|
bias="none",
|
|
task_type="CAUSAL_LM",
|
|
**lora_config_kwargs,
|
|
)
|
|
|
|
if config_only:
|
|
return None, lora_config
|
|
|
|
rank = int(os.environ.get("LOCAL_RANK", 0))
|
|
|
|
if (
|
|
cfg.fsdp
|
|
and cfg.adapter
|
|
and cfg.fsdp_config.fsdp_cpu_ram_efficient_loading
|
|
and rank != 0
|
|
):
|
|
setup_quantized_meta_for_peft(model)
|
|
|
|
if cfg.lora_model_dir:
|
|
LOG.debug("Loading pretrained PEFT - LoRA")
|
|
model_kwargs: Any = {}
|
|
if cfg.lora_on_cpu:
|
|
model_kwargs["max_memory"] = {"cpu": "256GiB"}
|
|
model_kwargs["device_map"] = {"": "cpu"}
|
|
model = PeftModel.from_pretrained(
|
|
model,
|
|
cfg.lora_model_dir,
|
|
is_trainable=(not inference),
|
|
**model_kwargs,
|
|
)
|
|
else:
|
|
model = get_peft_model(model, lora_config)
|
|
|
|
if rank == 0:
|
|
try:
|
|
model.print_trainable_parameters()
|
|
except AttributeError as exc:
|
|
LOG.warning(
|
|
"Exception caught during model.print_trainable_parameters(): %s", exc
|
|
)
|
|
elif (
|
|
cfg.fsdp
|
|
and cfg.adapter
|
|
and cfg.fsdp_config.fsdp_cpu_ram_efficient_loading
|
|
and rank != 0
|
|
):
|
|
setup_quantized_peft_meta_for_training(model)
|
|
|
|
return model, lora_config
|
|
|
|
|
|
def ensure_dtype(model, dtype=torch.bfloat16):
|
|
for name, module in model.named_modules():
|
|
weight_mismatch = False
|
|
bias_mismatch = False
|
|
try:
|
|
weight_mismatch = module.weight.dtype != dtype
|
|
except AttributeError:
|
|
pass
|
|
try:
|
|
bias_mismatch = module.bias.dtype != dtype
|
|
except AttributeError:
|
|
pass
|
|
|
|
if weight_mismatch:
|
|
print(f"Converting module {name}.weight: {module.weight.dtype} -> {dtype}")
|
|
if bias_mismatch:
|
|
print(f"Converting module {name}.bias: {module.bias.dtype} -> {dtype}")
|
|
if weight_mismatch or bias_mismatch:
|
|
module.to(dtype)
|