240 lines
8.3 KiB
Python
240 lines
8.3 KiB
Python
# Copyright 2023-present Daniel Han-Chen & the Unsloth team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import warnings
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from dataclasses import dataclass, field
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from typing import Optional
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from functools import wraps
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import trl
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import inspect
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from trl import SFTTrainer
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from . import is_bfloat16_supported
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from unsloth_zoo.training_utils import (
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unsloth_train as _unsloth_train,
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)
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from unsloth_zoo.vision_utils import (
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UnslothVisionDataCollator,
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)
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from packaging.version import Version
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import dataclasses
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__all__ = [
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"UnslothTrainingArguments",
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"UnslothTrainer",
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"unsloth_train",
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"_patch_trl_trainer",
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"UnslothVisionDataCollator",
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]
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# Unsloth gradient accumulation fix:
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from transformers import __version__ as transformers_version
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if Version(transformers_version) > Version("4.45.2"):
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def unsloth_train(trainer, *args, **kwargs):
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return trainer.train(*args, **kwargs)
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else:
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def unsloth_train(trainer, *args, **kwargs):
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if len(args) != 0 or len(kwargs) != 0:
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raise RuntimeError(
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"Unsloth: Our custom gradient accumulation fixed trainer does not support other arguments.\n"
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"If you want to use our fix inside of HF, please update `transformers` to the latest version via:\n"
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"`pip uninstall transformers -y && pip install --upgrade --no-cache-dir transformers`"
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)
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print(
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"Unsloth: Using our custom gradient accumulation fixed trainer, which is not feature complete.\n"
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"If you want to use our fix inside of HF, please update `transformers` to the latest version via:\n"
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"`pip uninstall transformers -y && pip install --upgrade --no-cache-dir transformers`"
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)
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return _unsloth_train(trainer)
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try:
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from trl import SFTConfig as TrainingArguments
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except:
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from transformers import TrainingArguments
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class UnslothTrainingArguments(TrainingArguments):
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def __init__(self, embedding_learning_rate: float = None, *args, **kwargs):
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embedding_learning_rate = embedding_learning_rate
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super().__init__(*args, **kwargs)
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def _create_unsloth_optimizer(
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model,
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optimizer_cls,
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optimizer_kwargs,
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embedding_lr = 5e-5,
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):
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lr = optimizer_kwargs["lr"]
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weight_decay = optimizer_kwargs.get("weight_decay", 0.0)
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param_groups = {
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"non_embeddings": {},
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"embeddings": {},
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}
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for name, param in model.named_parameters():
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if not param.requires_grad:
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continue
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if name.endswith("modules_to_save.default.weight"):
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partial_name = name[: -len(".modules_to_save.default.weight")]
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partial_name = partial_name[partial_name.rfind(".") + 1 :]
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print(
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f"Unsloth: Setting lr = {embedding_lr:.2e} instead of {lr:.2e} for {partial_name}."
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)
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param_groups["embeddings"][name] = param
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else:
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param_groups["non_embeddings"][name] = param
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optimizer_grouped_parameters = [
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{
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"params": list(param_groups["non_embeddings"].values()),
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"weight_decay": weight_decay,
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"lr": lr,
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},
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{
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"params": list(param_groups["embeddings"].values()),
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"weight_decay": weight_decay,
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"lr": embedding_lr,
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},
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]
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optimizer = optimizer_cls(optimizer_grouped_parameters, **optimizer_kwargs)
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return optimizer
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class UnslothTrainer(SFTTrainer):
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def create_optimizer(self):
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embedding_learning_rate = getattr(self.args, "embedding_learning_rate", None)
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if embedding_learning_rate is None:
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return super().create_optimizer()
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if self.optimizer is None:
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optimizer_cls, optimizer_kwargs = SFTTrainer.get_optimizer_cls_and_kwargs(
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self.args
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)
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self.optimizer = _create_unsloth_optimizer(
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self.model,
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optimizer_cls,
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optimizer_kwargs,
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embedding_learning_rate,
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)
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return self.optimizer
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# From `trl>=0.13.0`, they changed how to pass several params to the trainer
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# We need to patch to make the transition smooth
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def _backwards_compatible_trainer(trainer_class, config_class):
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original_init = trainer_class.__init__
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@wraps(original_init)
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def new_init(self, *args, **kwargs):
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# All Trainer tokenizer are now called processing_class
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trainer_params = set(inspect.signature(original_init).parameters.keys())
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if "processing_class" in trainer_params and "tokenizer" in kwargs:
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kwargs["processing_class"] = kwargs.pop("tokenizer")
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if ("args" in kwargs) and (Version(trl.__version__) >= Version("0.13.0.dev0")):
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training_args = kwargs.pop("args", None)
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# Get parameters that Trainer.__init__ actually expects
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trainer_params.remove("self")
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trainer_params.remove("args")
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# Get fields that should be passed to Config init
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config_fields = {
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field.name: field
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for field in dataclasses.fields(config_class)
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if field.init
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}
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# Create config dict with valid fields from training_args
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config_dict = {
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name: getattr(training_args, name)
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for name in config_fields
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if hasattr(training_args, name)
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}
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# Get parameters that exist in Config but not in TrainingArguments
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from transformers import TrainingArguments
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moved_params = set(inspect.signature(config_class).parameters.keys()) - set(
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inspect.signature(TrainingArguments).parameters.keys()
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)
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# Separate kwargs into trainer kwargs and config kwargs
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trainer_kwargs = {}
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additional_config_kwargs = {}
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for key, value in kwargs.items():
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if key in trainer_params:
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trainer_kwargs[key] = value
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elif key in moved_params or key in config_fields:
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additional_config_kwargs[key] = value
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else:
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additional_config_kwargs[key] = value
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# Update config_dict with additional kwargs
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config_dict.update(additional_config_kwargs)
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# Create Config with all the collected parameters
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# Reinitialising config class with parameters (that were none initially but populated on first init)
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# causes the 2nd init to fail as there are mutual exclusive checks on pairs of parameters.
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# Refer: https://github.com/huggingface/trl/blob/main/trl/trainer/grpo_config.py#L499-L502 for example
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# So we only create config class if the previous init was not TrainingArguments
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if not isinstance(training_args, TrainingArguments):
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config = config_class(**config_dict)
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else:
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config = training_args
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# Reconstruct kwargs for Trainer
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kwargs = trainer_kwargs
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kwargs["args"] = config
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original_init(self, *args, **kwargs)
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return new_init
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def _patch_trl_trainer():
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import trl
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if hasattr(trl, "__UNSLOTH_BACKWARDS_COMPATIBLE__"):
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return
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if Version(trl.__version__) <= Version("0.11.0"):
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return
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import trl.trainer
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trl_classes = dir(trl.trainer)
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trl_trainers = set(
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x[: -len("Trainer")] for x in trl_classes if x.endswith("Trainer")
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)
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trl_configs = set(x[: -len("Config")] for x in trl_classes if x.endswith("Config"))
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trl_classes = list(trl_trainers & trl_configs)
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for x in trl_classes:
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try:
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exec(
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f"trl.{x}Trainer.__init__ = _backwards_compatible_trainer(trl.{x}Trainer, trl.{x}Config)",
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globals(),
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)
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except:
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continue
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trl.__UNSLOTH_BACKWARDS_COMPATIBLE__ = True
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