""" 2025.10.10 2025.10.9 4.56.2 0.23.0 __UNSLOTH_VERSIONING__ """ # Unsloth auto generated code # Copyright 2023-present Daniel Han-Chen, Michael Han-Chen & the Unsloth team. All rights reserved. # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with this program. If not, see . from torch import Tensor import torch import torch.nn as nn from torch.nn import functional as F from typing import Any, List, Optional, Tuple, Union, Dict, Set, Callable from trl.trainer.kto_trainer import (Any, AutoModelForCausalLM, BaseImageProcessor, Callable, DPODataCollatorWithPadding, DataCollator, DataLoader, Dataset, EvalLoopOutput, F, FeatureExtractionMixin, KTOConfig, KTOTrainer, Literal, Optional, PartialState, Path, PeftModel, PreTrainedModel, PreTrainedTokenizerBase, ProcessorMixin, SequentialSampler, Trainer, TrainerCallback, TrainingArguments, Union, _get_kl_dataset, _process_tokens, _tokenize, autocast, concatenate_datasets, contextmanager, create_reference_model, defaultdict, disable_dropout_in_model, generate_model_card, get_comet_experiment_url, has_length, inspect, is_comet_available, is_liger_kernel_available, is_peft_available, is_wandb_available, itemgetter, log_table_to_comet_experiment, logger, logging, maybe_apply_chat_template, maybe_extract_prompt, maybe_unpair_preference_dataset, nn, np, nullcontext, os, pad_to_length, pd, peft_module_casting_to_bf16, prepare_deepspeed, prepare_model_for_kbit_training, random, selective_log_softmax, textwrap, torch, tqdm, F, Optional, PeftModel, PreTrainedModel, Trainer, is_peft_available, logger, os, torch) import os from typing import * from dataclasses import dataclass, field from packaging.version import Version import torch import numpy as np from contextlib import nullcontext from torch.nn import functional as F from transformers import DataCollatorForSeq2Seq, DataCollatorForLanguageModeling as TransformersDataCollatorForLanguageModeling from transformers.training_args import ParallelMode # Wrap trainer with padding to right and enable training mode import functools from types import MethodType def prepare_for_training_mode(f): @functools.wraps(f) def wrapper(self, *args, **kwargs): # Enable training mode if hasattr(self, 'model') and hasattr(self.model, "for_training"): self.model.for_training() output = f(self, *args, **kwargs) # Return inference mode if hasattr(self, 'model') and hasattr(self.model, "for_inference"): self.model.for_inference() return output return wrapper pass torch_compile_options = { "epilogue_fusion" : True, "max_autotune" : False, "shape_padding" : True, "trace.enabled" : False, "triton.cudagraphs" : False, } @torch.compile(dynamic = True, fullgraph = True, options = torch_compile_options,) def chunked_selective_log_softmax(logits, index): # Split into 4 chunks only chunked_logits = torch.chunk(logits.reshape(-1, logits.shape[-1]), chunks = 4, dim = 0) chunked_index = torch.chunk(index.reshape(-1), chunks = 4, dim = 0) all_per_token_logps = [] # Below loop does the same as selective_log_softmax(chunk_logits, chunk_index) for chunk_logits, chunk_index in zip(chunked_logits, chunked_index): chunk_logits = chunk_logits.to(torch.float32) selected_logits = torch.gather(chunk_logits, dim = -1, index = chunk_index.unsqueeze(-1)).squeeze(-1) logsumexp_values = torch.logsumexp(chunk_logits, dim = -1) per_token_logps = selected_logits - logsumexp_values all_per_token_logps.append(per_token_logps) pass all_per_token_logps = torch.concat(all_per_token_logps) all_per_token_logps = all_per_token_logps.reshape((logits.shape[0], logits.shape[1])) return all_per_token_logps def calculate_pad_tokens_in_prompt( input_ids: torch.Tensor, logits_to_keep: int, pad_token_id: int ) -> torch.Tensor: """ Given prompt tensor, it returns all the left padded tokens in that sequence. so [pad, pad, pad, cat] = 3 tokens """ if logits_to_keep >= input_ids.shape[1]: raise ValueError("logits_to_keep must be smaller than the sequence length.") prompt_section = input_ids[:, :-logits_to_keep] padding_mask = (prompt_section == pad_token_id) pad_token_counts = padding_mask.sum(dim=1) return pad_token_counts def create_completion_attention_mask( completion_input_ids: torch.Tensor, left_pad_tokens_per_prompt: torch.Tensor, max_left_pad: int, pad_token_id: int ) -> torch.Tensor: """ Given that we have a sequence, [p,p,p,c,c,c,pad,pad,pad] Where p are extra prompt tokens we got from slicing the torch tensor, c is completion tokens and pad are pad tokens, this function would make a completion mask that would 0 out the pad and p tokens. so in this example [0,0,0,1,1,1,0,0,0] """ batch_size, completion_len = completion_input_ids.shape device = completion_input_ids.device num_tokens_to_mask = max_left_pad - left_pad_tokens_per_prompt indices = torch.arange(completion_len, device=device).unsqueeze(0) shift_mask = indices >= num_tokens_to_mask.unsqueeze(1) non_padding_mask = (completion_input_ids != pad_token_id) final_mask = shift_mask & non_padding_mask return final_mask def left_pack_padding(tensor: torch.Tensor, pad_id: int) -> torch.Tensor: """ Moves all padding tokens in each sequence of a batch to the right. """ mask = (tensor != pad_id) # Must do stable=True since binary mark is unordered sorted_indices = torch.argsort(mask, dim=1, descending=True, stable=True) packed_tensor = torch.gather(tensor, 1, sorted_indices) return packed_tensor @dataclass class UnslothKTOConfig(KTOConfig): """ Configuration class for the [`KTOTrainer`]. This class includes only the parameters that are specific to KTO training. For a full list of training arguments, please refer to the [`~transformers.TrainingArguments`] documentation. Note that default values in this class may differ from those in [`~transformers.TrainingArguments`]. Using [`~transformers.HfArgumentParser`] we can turn this class into [argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified on the command line. Parameters: max_length (`int` or `None`, *optional*, defaults to `1024`): Maximum length of the sequences (prompt + completion) in the batch. This argument is required if you want to use the default data collator. max_prompt_length (`int` or `None`, *optional*, defaults to `512`): Maximum length of the prompt. This argument is required if you want to use the default data collator. max_completion_length (`int` or `None`, *optional*, defaults to `None`): Maximum length of the completion. This argument is required if you want to use the default data collator and your model is an encoder-decoder. beta (`float`, *optional*, defaults to `0.1`): Parameter controlling the deviation from the reference model. Higher β means less deviation from the reference model. loss_type (`str`, *optional*, defaults to `"kto"`): Type of loss to use. Possible values are: - `"kto"`: KTO loss from the [KTO](https://huggingface.co/papers/2402.01306) paper. - `"apo_zero_unpaired"`: Unpaired variant of APO-zero loss from the [APO](https://huggingface.co/papers/2408.06266) paper. desirable_weight (`float`, *optional*, defaults to `1.0`): Desirable losses are weighed by this factor to counter unequal number of desirable and undesirable paris. undesirable_weight (`float`, *optional*, defaults to `1.0`): Undesirable losses are weighed by this factor to counter unequal number of desirable and undesirable pairs. label_pad_token_id (`int`, *optional*, defaults to `-100`): Label pad token id. This argument is required if you want to use the default data collator. padding_value (`int` or `None`, *optional*, defaults to `None`): Padding value to use. If `None`, the padding value of the tokenizer is used. truncation_mode (`str`, *optional*, defaults to `"keep_end"`): Truncation mode to use when the prompt is too long. Possible values are `"keep_end"` or `"keep_start"`. This argument is required if you want to use the default data collator. generate_during_eval (`bool`, *optional*, defaults to `False`): If `True`, generates and logs completions from both the model and the reference model to W&B or Comet during evaluation. is_encoder_decoder (`bool` or `None`, *optional*, defaults to `None`): When using the `model_init` argument (callable) to instantiate the model instead of the `model` argument, you need to specify if the model returned by the callable is an encoder-decoder model. precompute_ref_log_probs (`bool`, *optional*, defaults to `False`): Whether to precompute reference model log probabilities for training and evaluation datasets. This is useful when training without the reference model to reduce the total GPU memory needed. model_init_kwargs (`dict[str, Any]` or `None`, *optional*, defaults to `None`): Keyword arguments to pass to `AutoModelForCausalLM.from_pretrained` when instantiating the model from a string. ref_model_init_kwargs (`dict[str, Any]` or `None`, *optional*, defaults to `None`): Keyword arguments to pass to `AutoModelForCausalLM.from_pretrained` when instantiating the reference model from a string. dataset_num_proc: (`int` or `None`, *optional*, defaults to `None`): Number of processes to use for processing the dataset. disable_dropout (`bool`, *optional*, defaults to `True`): Whether to disable dropout in the model and reference model. use_liger_loss (`bool`, *optional*, defaults to `False`): Whether to use Liger loss. It requires liger-kernel to be installed. base_model_attribute_name (`str`, *optional*, defaults to `"model"`): Name of the attribute in the model that contains the base model. This is used to get the base model from the model when the model does not have a `get_decoder` method in the case when `use_liger_loss` is `True`. """ vllm_sampling_params: Optional[Any] = field( default = None, metadata = {'help': 'vLLM SamplingParams'}, ) unsloth_num_chunks : Optional[int] = field( default = -1, metadata = {'help': 'Chunk size to reduce memory usage. -1 is most efficient.'}, ) max_seq_length : Optional[int] = field( default = None, metadata = {'help': 'Maximum sequence length to truncate to.'}, ) def __init__( self, output_dir = None, overwrite_output_dir = None, do_train = False, do_eval = False, do_predict = False, eval_strategy = 'no', prediction_loss_only = False, per_device_train_batch_size = 4, per_device_eval_batch_size = 4, per_gpu_train_batch_size = None, per_gpu_eval_batch_size = None, gradient_accumulation_steps = 2, eval_accumulation_steps = 2, eval_delay = 0, torch_empty_cache_steps = 250, learning_rate = 5e-05, weight_decay = 0.01, adam_beta1 = 0.9, adam_beta2 = 0.999, adam_epsilon = 1e-08, max_grad_norm = 1.0, num_train_epochs = 3.0, max_steps = -1, lr_scheduler_type = 'linear', warmup_ratio = 0.1, warmup_steps = 0, log_level = 'passive', log_level_replica = 'warning', log_on_each_node = True, logging_dir = None, logging_strategy = 'steps', logging_first_step = False, logging_steps = 1, logging_nan_inf_filter = False, save_strategy = 'steps', save_steps = 500, save_total_limit = None, save_safetensors = True, save_on_each_node = False, save_only_model = False, restore_callback_states_from_checkpoint = False, no_cuda = False, use_cpu = False, use_mps_device = False, seed = 3407, data_seed = 3407, jit_mode_eval = False, use_ipex = False, bf16 = False, fp16 = False, fp16_opt_level = 'O1', half_precision_backend = 'auto', bf16_full_eval = False, fp16_full_eval = False, tf32 = None, local_rank = -1, ddp_backend = None, tpu_num_cores = None, tpu_metrics_debug = False, debug = '', dataloader_drop_last = False, eval_steps = None, dataloader_num_workers = 0, dataloader_prefetch_factor = None, past_index = -1, run_name = None, disable_tqdm = None, remove_unused_columns = True, label_names = None, load_best_model_at_end = False, metric_for_best_model = None, greater_is_better = None, ignore_data_skip = False, fsdp = '', fsdp_min_num_params = 0, fsdp_config = None, fsdp_transformer_layer_cls_to_wrap = None, accelerator_config = None, parallelism_config = None, deepspeed = None, label_smoothing_factor = 0.0, optim = 'adamw_8bit', optim_args = None, adafactor = False, group_by_length = False, length_column_name = 'length', report_to = None, ddp_find_unused_parameters = None, ddp_bucket_cap_mb = None, ddp_broadcast_buffers = None, dataloader_pin_memory = True, dataloader_persistent_workers = False, skip_memory_metrics = True, use_legacy_prediction_loop = False, push_to_hub = False, resume_from_checkpoint = None, hub_model_id = None, hub_strategy = 'every_save', hub_token = None, hub_private_repo = None, hub_always_push = False, hub_revision = None, gradient_checkpointing = True, gradient_checkpointing_kwargs = None, include_inputs_for_metrics = False, eval_do_concat_batches = True, fp16_backend = 'auto', push_to_hub_model_id = None, push_to_hub_organization = None, push_to_hub_token = None, mp_parameters = '', auto_find_batch_size = False, full_determinism = False, torchdynamo = None, ray_scope = 'last', ddp_timeout = 1800, torch_compile = False, torch_compile_backend = None, torch_compile_mode = None, include_tokens_per_second = False, include_num_input_tokens_seen = False, neftune_noise_alpha = None, optim_target_modules = None, batch_eval_metrics = False, eval_on_start = False, use_liger_kernel = False, liger_kernel_config = None, eval_use_gather_object = False, average_tokens_across_devices = True, max_length = 1024, max_prompt_length = 512, max_completion_length = None, beta = 0.1, loss_type = 'kto', desirable_weight = 1.0, undesirable_weight = 1.0, label_pad_token_id = -100, padding_value = None, truncation_mode = 'keep_end', generate_during_eval = False, is_encoder_decoder = None, disable_dropout = True, precompute_ref_log_probs = False, model_init_kwargs = None, ref_model_init_kwargs = None, dataset_num_proc = None, use_liger_loss = False, base_model_attribute_name = 'model', vllm_sampling_params = None, unsloth_num_chunks = -1, max_seq_length = None, **kwargs, ): if learning_rate < 1e-7: print(f'Unsloth: Your learning rate of `{learning_rate}` is too small and less than 1e-7! Consider increasing it, otherwise gradient updates will be close to 0!') if learning_rate > 1: print(f'Unsloth: Your learning rate of `{learning_rate}` is way too larger > 1! Consider decreasing it to 1e-1, otherwise gradient updates will explode!') if output_dir is None and save_strategy == 'steps' and save_steps == 500: output_dir = 'unsloth_training_checkpoints' save_strategy = 'no' if dataset_num_proc is None: from multiprocessing import cpu_count dataset_num_proc = min(max(cpu_count()+4, 2), 64) super().__init__( output_dir = output_dir, overwrite_output_dir = overwrite_output_dir, do_train = do_train, do_eval = do_eval, do_predict = do_predict, eval_strategy = eval_strategy, prediction_loss_only = prediction_loss_only, per_device_train_batch_size = per_device_train_batch_size, per_device_eval_batch_size = per_device_eval_batch_size, per_gpu_train_batch_size = per_gpu_train_batch_size, per_gpu_eval_batch_size = per_gpu_eval_batch_size, gradient_accumulation_steps = gradient_accumulation_steps, eval_accumulation_steps = eval_accumulation_steps, eval_delay = eval_delay, torch_empty_cache_steps = torch_empty_cache_steps, learning_rate = learning_rate, weight_decay = weight_decay, adam_beta1 = adam_beta1, adam_beta2 = adam_beta2, adam_epsilon = adam_epsilon, max_grad_norm = max_grad_norm, num_train_epochs = num_train_epochs, max_steps = max_steps, lr_scheduler_type = lr_scheduler_type, warmup_ratio = warmup_ratio, warmup_steps = warmup_steps, log_level = log_level, log_level_replica = log_level_replica, log_on_each_node = log_on_each_node, logging_dir = logging_dir, logging_strategy = logging_strategy, logging_first_step = logging_first_step, logging_steps = logging_steps, logging_nan_inf_filter = logging_nan_inf_filter, save_strategy = save_strategy, save_steps = save_steps, save_total_limit = save_total_limit, save_safetensors = save_safetensors, save_on_each_node = save_on_each_node, save_only_model = save_only_model, restore_callback_states_from_checkpoint = restore_callback_states_from_checkpoint, no_cuda = no_cuda, use_cpu = use_cpu, use_mps_device = use_mps_device, seed = seed, data_seed = data_seed, jit_mode_eval = jit_mode_eval, use_ipex = use_ipex, bf16 = bf16, fp16 = fp16, fp16_opt_level = fp16_opt_level, half_precision_backend = half_precision_backend, bf16_full_eval = bf16_full_eval, fp16_full_eval = fp16_full_eval, tf32 = tf32, local_rank = local_rank, ddp_backend = ddp_backend, tpu_num_cores = tpu_num_cores, tpu_metrics_debug = tpu_metrics_debug, debug = debug, dataloader_drop_last = dataloader_drop_last, eval_steps = eval_steps, dataloader_num_workers = dataloader_num_workers, dataloader_prefetch_factor = dataloader_prefetch_factor, past_index = past_index, run_name = run_name, disable_tqdm = disable_tqdm, remove_unused_columns = remove_unused_columns, label_names = label_names, load_best_model_at_end = load_best_model_at_end, metric_for_best_model = metric_for_best_model, greater_is_better = greater_is_better, ignore_data_skip = ignore_data_skip, fsdp = fsdp, fsdp_min_num_params = fsdp_min_num_params, fsdp_config = fsdp_config, fsdp_transformer_layer_cls_to_wrap = fsdp_transformer_layer_cls_to_wrap, accelerator_config = accelerator_config, parallelism_config = parallelism_config, deepspeed = deepspeed, label_smoothing_factor = label_smoothing_factor, optim = optim, optim_args = optim_args, adafactor = adafactor, group_by_length = group_by_length, length_column_name = length_column_name, report_to = report_to, ddp_find_unused_parameters = ddp_find_unused_parameters, ddp_bucket_cap_mb = ddp_bucket_cap_mb, ddp_broadcast_buffers = ddp_broadcast_buffers, dataloader_pin_memory = dataloader_pin_memory, dataloader_persistent_workers = dataloader_persistent_workers, skip_memory_metrics = skip_memory_metrics, use_legacy_prediction_loop = use_legacy_prediction_loop, push_to_hub = push_to_hub, resume_from_checkpoint = resume_from_checkpoint, hub_model_id = hub_model_id, hub_strategy = hub_strategy, hub_token = hub_token, hub_private_repo = hub_private_repo, hub_always_push = hub_always_push, hub_revision = hub_revision, gradient_checkpointing = gradient_checkpointing, gradient_checkpointing_kwargs = gradient_checkpointing_kwargs, include_inputs_for_metrics = include_inputs_for_metrics, eval_do_concat_batches = eval_do_concat_batches, fp16_backend = fp16_backend, push_to_hub_model_id = push_to_hub_model_id, push_to_hub_organization = push_to_hub_organization, push_to_hub_token = push_to_hub_token, mp_parameters = mp_parameters, auto_find_batch_size = auto_find_batch_size, full_determinism = full_determinism, torchdynamo = torchdynamo, ray_scope = ray_scope, ddp_timeout = ddp_timeout, torch_compile = torch_compile, torch_compile_backend = torch_compile_backend, torch_compile_mode = torch_compile_mode, include_tokens_per_second = include_tokens_per_second, include_num_input_tokens_seen = include_num_input_tokens_seen, neftune_noise_alpha = neftune_noise_alpha, optim_target_modules = optim_target_modules, batch_eval_metrics = batch_eval_metrics, eval_on_start = eval_on_start, use_liger_kernel = use_liger_kernel, liger_kernel_config = liger_kernel_config, eval_use_gather_object = eval_use_gather_object, average_tokens_across_devices = average_tokens_across_devices, max_length = max_length, max_prompt_length = max_prompt_length, max_completion_length = max_completion_length, beta = beta, loss_type = loss_type, desirable_weight = desirable_weight, undesirable_weight = undesirable_weight, label_pad_token_id = label_pad_token_id, padding_value = padding_value, truncation_mode = truncation_mode, generate_during_eval = generate_during_eval, is_encoder_decoder = is_encoder_decoder, disable_dropout = disable_dropout, precompute_ref_log_probs = precompute_ref_log_probs, model_init_kwargs = model_init_kwargs, ref_model_init_kwargs = ref_model_init_kwargs, dataset_num_proc = dataset_num_proc, use_liger_loss = use_liger_loss, base_model_attribute_name = base_model_attribute_name,**kwargs) self.vllm_sampling_params = vllm_sampling_params self.unsloth_num_chunks = unsloth_num_chunks self.max_seq_length = max_seq_length pass class _UnslothKTOTrainer(Trainer): r""" Initialize KTOTrainer. Args: model (`transformers.PreTrainedModel`): The model to train, preferably an `AutoModelForSequenceClassification`. ref_model (`PreTrainedModelWrapper`): Hugging Face transformer model with a casual language modelling head. Used for implicit reward computation and loss. If no reference model is provided, the trainer will create a reference model with the same architecture as the model to be optimized. args (`KTOConfig`): The arguments to use for training. train_dataset (`datasets.Dataset`): The dataset to use for training. eval_dataset (`datasets.Dataset`): The dataset to use for evaluation. processing_class ([`~transformers.PreTrainedTokenizerBase`], [`~transformers.BaseImageProcessor`], [`~transformers.FeatureExtractionMixin`] or [`~transformers.ProcessorMixin`], *optional*, defaults to `None`): Processing class used to process the data. If provided, will be used to automatically process the inputs for the model, and it will be saved along the model to make it easier to rerun an interrupted training or reuse the fine-tuned model. data_collator (`transformers.DataCollator`, *optional*, defaults to `None`): The data collator to use for training. If None is specified, the default data collator (`DPODataCollatorWithPadding`) will be used which will pad the sequences to the maximum length of the sequences in the batch, given a dataset of paired sequences. model_init (`Callable[[], transformers.PreTrainedModel]`): The model initializer to use for training. If None is specified, the default model initializer will be used. callbacks (`list[transformers.TrainerCallback]`): The callbacks to use for training. optimizers (`tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`): The optimizer and scheduler to use for training. preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`): The function to use to preprocess the logits before computing the metrics. peft_config (`dict`, defaults to `None`): The PEFT configuration to use for training. If you pass a PEFT configuration, the model will be wrapped in a PEFT model. compute_metrics (`Callable[[EvalPrediction], dict]`, *optional*): The function to use to compute the metrics. Must take a `EvalPrediction` and return a dictionary string to metric values. model_adapter_name (`str`, defaults to `None`): Name of the train target PEFT adapter, when using LoRA with multiple adapters. ref_adapter_name (`str`, defaults to `None`): Name of the reference PEFT adapter, when using LoRA with multiple adapters. """ _tag_names = ["trl", "kto"] def __init__( self, model: Union[PreTrainedModel, nn.Module, str] = None, ref_model: Optional[Union[PreTrainedModel, nn.Module, str]] = None, args: KTOConfig = None, train_dataset: Optional[Dataset] = None, eval_dataset: Optional[Union[Dataset, dict[str, Dataset]]] = None, processing_class: Optional[ Union[PreTrainedTokenizerBase, BaseImageProcessor, FeatureExtractionMixin, ProcessorMixin] ] = None, data_collator: Optional[DataCollator] = None, model_init: Optional[Callable[[], PreTrainedModel]] = None, callbacks: Optional[list[TrainerCallback]] = None, optimizers: tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None), preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None, peft_config: Optional[dict] = None, compute_metrics: Optional[Callable[[EvalLoopOutput], dict]] = None, model_adapter_name: Optional[str] = None, ref_adapter_name: Optional[str] = None, ): if type(args) is TrainingArguments: raise ValueError("Please use `KTOConfig` instead TrainingArguments.") if not isinstance(model, str) and ref_model is model: raise ValueError( "`model` and `ref_model` cannot be the same object. If you want `ref_model` to be the " "same as `model`, you must mass a copy of it, or `None` if you use peft." ) if args.model_init_kwargs is None: model_init_kwargs = {} elif not isinstance(model, str): raise ValueError("You passed model_kwargs to the KTOTrainer. But your model is already instantiated.") else: model_init_kwargs = args.model_init_kwargs dtype = model_init_kwargs.get("dtype") if dtype is not None: # Convert to `torch.dtype` if an str is passed if isinstance(dtype, str) and dtype != "auto": dtype = getattr(torch, dtype) if dtype != "auto" and not isinstance(dtype, torch.dtype): raise ValueError( f"Invalid `dtype` passed to the KTOConfig. Expected a string with either `torch.dtype` or 'auto', but got {dtype}." ) model_init_kwargs["dtype"] = dtype if args.ref_model_init_kwargs is None: ref_model_init_kwargs = {} elif not isinstance(ref_model, str): raise ValueError( "You passed ref_model_kwargs to the KTOTrainer. But your ref_model is already instantiated." ) else: ref_model_init_kwargs = args.ref_model_init_kwargs dtype = ref_model_init_kwargs.get("dtype") if dtype is not None: # Convert to `torch.dtype` if an str is passed if isinstance(dtype, str) and dtype != "auto": dtype = getattr(torch, dtype) if dtype != "auto" and not isinstance(dtype, torch.dtype): raise ValueError( f"Invalid `dtype` passed to the KTOConfig. Expected a string with either `torch.dtype` or 'auto', but got {dtype}." ) ref_model_init_kwargs["dtype"] = dtype if isinstance(model, str): model = AutoModelForCausalLM.from_pretrained(model, **model_init_kwargs) if isinstance(ref_model, str): ref_model = AutoModelForCausalLM.from_pretrained(ref_model, **ref_model_init_kwargs) # Initialize this variable to False. This helps tracking the case when `peft_module_casting_to_bf16` # has been called in order to properly call autocast if needed. self._peft_has_been_casted_to_bf16 = False if not is_peft_available() and peft_config is not None: raise ValueError( "PEFT is not installed and you passed a `peft_config` in the trainer's kwargs, please install it with `pip install peft` to use the PEFT models" ) elif is_peft_available() and peft_config is not None: # if model is a peft model and we have a peft_config, we merge and unload it first if isinstance(model, PeftModel): model = model.merge_and_unload() if getattr(model, "is_loaded_in_8bit", False) or getattr(model, "is_loaded_in_4bit", False): _support_gc_kwargs = hasattr( args, "gradient_checkpointing_kwargs" ) and "gradient_checkpointing_kwargs" in list( inspect.signature(prepare_model_for_kbit_training).parameters ) prepare_model_kwargs = {"use_gradient_checkpointing": args.gradient_checkpointing} if _support_gc_kwargs: prepare_model_kwargs["gradient_checkpointing_kwargs"] = args.gradient_checkpointing_kwargs model = prepare_model_for_kbit_training(model, **prepare_model_kwargs) elif args.gradient_checkpointing: # For backward compatibility with older versions of transformers if hasattr(model, "enable_input_require_grads"): model.enable_input_require_grads() else: def make_inputs_require_grad(module, input, output): output.requires_grad_(True) model.get_input_embeddings().register_forward_hook(make_inputs_require_grad) # get peft model with the given config model = model if args.bf16 and getattr(model, "is_loaded_in_4bit", False): peft_module_casting_to_bf16(model) # If args.bf16 we need to explicitly call `generate` with torch amp autocast context manager self._peft_has_been_casted_to_bf16 = True # For models that use gradient_checkpointing, we need to attach a hook that enables input # to explicitly have `requires_grad=True`, otherwise training will either silently # fail or completely fail. elif args.gradient_checkpointing: # For backward compatibility with older versions of transformers if hasattr(model, "enable_input_require_grads"): model.enable_input_require_grads() else: def make_inputs_require_grad(module, input, output): output.requires_grad_(True) model.get_input_embeddings().register_forward_hook(make_inputs_require_grad) if args.generate_during_eval and not (is_wandb_available() or is_comet_available()): raise ValueError( "`generate_during_eval=True` requires Weights and Biases or Comet to be installed." " Please install `wandb` or `comet-ml` to resolve." ) if model is not None: self.is_encoder_decoder = model.config.is_encoder_decoder elif args.is_encoder_decoder is None: raise ValueError("When no model is provided, you need to pass the parameter is_encoder_decoder.") else: self.is_encoder_decoder = args.is_encoder_decoder self.is_peft_model = is_peft_available() and isinstance(model, PeftModel) self.model_adapter_name = model_adapter_name self.ref_adapter_name = ref_adapter_name if ref_model: self.ref_model = ref_model elif self.is_peft_model or args.precompute_ref_log_probs: # The `model` with adapters turned off will be used as the reference model self.ref_model = None else: self.ref_model = create_reference_model(model) if processing_class is None: raise ValueError( "max_length or a processing_class must be specified when using the default DPODataCollatorWithPadding" ) if args.max_length is None: logger.warning( "When using DPODataCollatorWithPadding, you should set `max_length` in the KTOTrainer's init" " it will be set to `512` by default, but you should do it yourself in the future.", ) max_length = 512 if args.max_length is not None: max_length = args.max_length if args.max_prompt_length is None: logger.warning( "When using DPODataCollatorWithPadding, you should set `max_prompt_length` in the KTOTrainer's init" " it will be set to `128` by default, but you should do it yourself in the future.", ) max_prompt_length = 128 if args.max_prompt_length is not None: max_prompt_length = args.max_prompt_length max_completion_length = None if args.max_completion_length is None and self.is_encoder_decoder: logger.warning( "When using DPODataCollatorWithPadding with an encoder decoder architecture, you should set `max_completion_length` in the KTOTrainer's init" " it will be set to `128` by default, but you should do it yourself in the future.", ) max_completion_length = 128 if args.max_completion_length is not None and self.is_encoder_decoder: max_completion_length = args.max_completion_length if data_collator is None: data_collator = DPODataCollatorWithPadding( pad_token_id=processing_class.pad_token_id, label_pad_token_id=args.label_pad_token_id, is_encoder_decoder=self.is_encoder_decoder, ) if args.remove_unused_columns: args.remove_unused_columns = False # warn users logger.warning( "When using DPODataCollatorWithPadding, you should set `remove_unused_columns=False` in your KTOConfig" " we have set it for you, but you should do it yourself in the future.", ) self.use_dpo_data_collator = True else: self.use_dpo_data_collator = False # Disable dropout in the model and reference model if args.disable_dropout: disable_dropout_in_model(model) if self.ref_model is not None: disable_dropout_in_model(self.ref_model) self.loss_type = args.loss_type self.max_length = max_length self.generate_during_eval = args.generate_during_eval self.label_pad_token_id = args.label_pad_token_id self.padding_value = args.padding_value if args.padding_value is not None else processing_class.pad_token_id self.max_prompt_length = max_prompt_length self.truncation_mode = args.truncation_mode self.max_completion_length = max_completion_length self.processing_class = processing_class self.precompute_ref_log_probs = args.precompute_ref_log_probs # Not all losses require a KL calculation self.calculate_KL = True if self.loss_type in ["apo_zero_unpaired"]: self.calculate_KL = False # Since ref_logs are precomputed on the first call to get_train/eval_dataloader # keep track of first called to avoid computation of future calls self._precomputed_train_ref_log_probs = False self._precomputed_eval_ref_log_probs = False # metric self._stored_metrics = defaultdict(lambda: defaultdict(list)) # KTO parameter self.beta = args.beta self.desirable_weight = args.desirable_weight self.undesirable_weight = args.undesirable_weight self.aux_loss_enabled = getattr(model.config, "output_router_logits", False) self.aux_loss_coef = getattr(model.config, "router_aux_loss_coef", 0.0) if self.aux_loss_enabled and self.aux_loss_coef == 0.0: logger.warning( "You set `output_router_logits` to `True` in the model config, but `router_aux_loss_coef` is set to " "`0.0`, meaning the auxiliary loss will not be used. Either set `router_aux_loss_coef` to a value " "greater than `0.0`, or set `output_router_logits` to `False` if you don't want to use the auxiliary " "loss.", ) # The trainer estimates the number of FLOPs [floating-point operations] using the number of elements in the # input tensor associated with the key "input_ids". However, in KTO, the sampled data does not include the # "input_ids" key. Instead, the available keys are "prompt_input_ids" and "completion_input_ids". As a result, # the trainer issues the warning: "Could not estimate the number of tokens of the input, floating-point # operations will not be computed." To suppress this warning, we set the "estimate_tokens" key in the model's # "warnings_issued" dictionary to True. This acts as a flag to indicate that the warning has already been # issued. model.warnings_issued["estimate_tokens"] = True # Compute that only on the main process for faster data processing. # see: https://github.com/huggingface/trl/pull/1255 with PartialState().main_process_first(): # Extract the prompt if needed train_dataset = train_dataset.map( maybe_extract_prompt, num_proc=args.dataset_num_proc, desc="Extracting prompt from train dataset" ) # Unpair the dataset if needed train_dataset = maybe_unpair_preference_dataset( train_dataset, args.dataset_num_proc, desc="Unpairing train dataset" ) # Apply the chat template if needed train_dataset = train_dataset.map( maybe_apply_chat_template, fn_kwargs={"tokenizer": processing_class}, num_proc=args.dataset_num_proc, desc="Applying chat template to train dataset", ) if eval_dataset is not None: eval_dataset = eval_dataset.map( maybe_extract_prompt, num_proc=args.dataset_num_proc, desc="Extracting prompt from eval dataset" ) eval_dataset = maybe_unpair_preference_dataset( eval_dataset, args.dataset_num_proc, desc="Unpairing eval dataset" ) eval_dataset = eval_dataset.map( maybe_apply_chat_template, fn_kwargs={"tokenizer": processing_class}, num_proc=args.dataset_num_proc, desc="Applying chat template to eval dataset", ) # Tokenize and prepare the training datasets train_dataset = train_dataset.map( _tokenize, batched=True, fn_kwargs={"tokenizer": self.processing_class}, num_proc=args.dataset_num_proc, desc="Tokenizing train dataset", ) fn_kwargs = { "prefix": "", "is_encoder_decoder": self.is_encoder_decoder, "tokenizer": self.processing_class, "max_length": self.max_length, "truncation_mode": self.truncation_mode, "label_pad_token_id": self.label_pad_token_id, "max_prompt_length": self.max_prompt_length, "max_completion_length": self.max_completion_length, } train_dataset = train_dataset.map( _process_tokens, fn_kwargs=fn_kwargs, num_proc=args.dataset_num_proc, desc="Processing tokenized train dataset", ) # Tokenize and prepare the eval datasets if eval_dataset is not None: eval_dataset = eval_dataset.map( _tokenize, fn_kwargs={"tokenizer": self.processing_class}, batched=True, num_proc=args.dataset_num_proc, desc="Tokenizing eval dataset", ) eval_dataset = eval_dataset.map( _process_tokens, fn_kwargs=fn_kwargs, num_proc=args.dataset_num_proc, desc="Processing tokenized eval dataset", ) # Get KL datasets if needed if self.calculate_KL: if args.per_device_train_batch_size <= 1: raise ValueError( "Actual (not effective) batch size must be > 1. KTO will not work properly because the KL term will be equivalent to the implied reward." ) # create pairs for estimating the KL term by flipping the matched pairs in each batch of size total_batch_size # i.e., [x_1, y_1], ..., [x_n, y_n] --> [x_1, y_n], ..., [x_n, y_1] = [x'_1, y'_1], ..., [x'_n, y'_n] train_kl_dataset = train_dataset.map( _get_kl_dataset, batched=True, batch_size=args.per_device_train_batch_size, num_proc=args.dataset_num_proc, desc="Extracting KL train dataset", ) fn_kwargs["prefix"] = "KL_" train_kl_dataset = train_kl_dataset.map( _process_tokens, fn_kwargs=fn_kwargs, num_proc=args.dataset_num_proc, remove_columns=[c for c in train_kl_dataset.column_names if c in train_dataset.column_names], desc="Processing tokenized train KL dataset", ) # merge the datasets train_dataset = concatenate_datasets([train_dataset, train_kl_dataset], axis=1) if eval_dataset is not None: # Get KL dataset eval_kl_dataset = eval_dataset.map( _get_kl_dataset, batched=True, batch_size=args.per_device_train_batch_size, num_proc=args.dataset_num_proc, desc="Extracting eval KL dataset", ) eval_kl_dataset = eval_kl_dataset.map( _process_tokens, fn_kwargs=fn_kwargs, num_proc=args.dataset_num_proc, remove_columns=[c for c in eval_kl_dataset.column_names if c in eval_dataset.column_names], desc="Processing tokenized eval KL dataset", ) # merge the datasets eval_dataset = concatenate_datasets([eval_dataset, eval_kl_dataset], axis=1) # calculate dataset desirability balance num_desirable = max(sum(train_dataset["label"]), 1) num_undesirable = max(len(train_dataset["label"]) - num_desirable, 1) # "label" is binary if num_desirable != num_undesirable: # The lower and upper bounds come from Eq. [8] of https://huggingface.co/papers/2402.01306 des_weight_lower_bound = round((num_undesirable * self.undesirable_weight / num_desirable) * 1, 2) des_weight_upper_bound = round((num_undesirable * self.undesirable_weight / num_desirable) * 1.33, 2) und_weight_lower_bound = round((num_desirable * self.desirable_weight / num_undesirable) / 1.33, 2) und_weight_upper_bound = round((num_desirable * self.desirable_weight / num_undesirable) / 1, 2) des_weight_in_range = des_weight_lower_bound <= self.desirable_weight <= des_weight_upper_bound und_weight_in_range = und_weight_lower_bound <= self.undesirable_weight <= und_weight_upper_bound if not (des_weight_in_range or und_weight_in_range): logger.warning( "You have different amounts of desirable/positive and undesirable/negative examples but the " "weights on the desirable and undesirable losses don't seem to be in an ideal range. Based " f"on your data, we recommend EITHER " f"desirable_weight in [{des_weight_lower_bound}, {des_weight_upper_bound}] or " f"undesirable_weight in [{und_weight_lower_bound}, {und_weight_upper_bound}] (but NOT BOTH). " "See the documentation on how to optimally set these weights.", ) super().__init__( model=model, args=args, data_collator=data_collator, train_dataset=train_dataset, eval_dataset=eval_dataset, processing_class=processing_class, model_init=model_init, compute_metrics=compute_metrics, callbacks=callbacks, optimizers=optimizers, preprocess_logits_for_metrics=preprocess_logits_for_metrics, ) # Gradient accumulation requires scaled loss. Normally, loss scaling in the parent class depends on whether the # model accepts loss-related kwargs. Since we compute our own loss, this check is irrelevant. We set # self.model_accepts_loss_kwargs to False to enable scaling. self.model_accepts_loss_kwargs = False # Add tags for models that have been loaded with the correct transformers version if hasattr(self.model, "add_model_tags"): self.model.add_model_tags(self._tag_names) if not hasattr(self, "accelerator"): raise AttributeError( "Your `Trainer` does not have an `accelerator` object. Consider upgrading `transformers`." ) # Deepspeed Zero-3 does not support precompute_ref_log_probs if self.is_deepspeed_enabled: if self.accelerator.state.deepspeed_plugin.zero_stage == 3 and self.precompute_ref_log_probs: raise ValueError( "You cannot use `precompute_ref_log_probs=True` with Deepspeed ZeRO-3. Please set `precompute_ref_log_probs=False`." ) if self.ref_model is None: if not (self.is_peft_model or self.precompute_ref_log_probs): raise ValueError( "No reference model and model is not a Peft model. Try setting `precompute_ref_log_probs=True`" ) else: if self.is_deepspeed_enabled: self.ref_model = prepare_deepspeed(self.ref_model, self.accelerator) else: self.ref_model = self.accelerator.prepare_model(self.ref_model, evaluation_mode=True) # Import Liger loss if enabled if self.args.use_liger_loss: if not is_liger_kernel_available(): raise ImportError( "You set `use_liger_loss=True` but the liger kernel is not available. " "Please install liger-kernel first: `pip install liger-kernel`" ) if self.loss_type in ["apo_zero_unpaired"]: raise ValueError( "You cannot set `loss_type='apo_zero_unpaired'` with liger-kernel." "Only KTO loss is supported with liger-kernel." ) if self.precompute_ref_log_probs: raise ValueError( "You cannot use `precompute_ref_log_probs=True` with liger kernel. Please set " "`precompute_ref_log_probs=False`." ) if self.is_peft_model or self.ref_adapter_name is not None: raise ValueError( "You cannot use `use_liger_loss=True` with Peft models. Please set `use_liger_loss=False`." ) self.kto_loss_fn = LigerFusedLinearKTOLoss( ignore_index=self.label_pad_token_id, beta=self.beta, use_ref_model=(self.ref_model is not None) ) @contextmanager def null_ref_context(self): """Context manager for handling null reference model (that is, peft adapter manipulation).""" with ( self.accelerator.unwrap_model(self.model).disable_adapter() if self.is_peft_model and not self.ref_adapter_name else nullcontext() ): if self.ref_adapter_name: self.model.set_adapter(self.ref_adapter_name) yield if self.ref_adapter_name: self.model.set_adapter(self.model_adapter_name or "default") def get_train_dataloader(self) -> DataLoader: """ Returns the training [`~torch.utils.data.DataLoader`]. Subclass of transformers.src.transformers.trainer.get_train_dataloader to precompute `ref_log_probs`. """ if self.precompute_ref_log_probs and not self._precomputed_train_ref_log_probs: dataloader_params = { "batch_size": self.args.per_device_train_batch_size, "collate_fn": self.data_collator, "num_workers": self.args.dataloader_num_workers, "pin_memory": self.args.dataloader_pin_memory, "shuffle": False, } # prepare dataloader data_loader = self.accelerator.prepare(DataLoader(self.train_dataset, **dataloader_params)) reference_completion_logps = [] reference_KL_logps = [] for padded_batch in tqdm(iterable=data_loader, desc="Train dataset reference log probs"): reference_completion_logp, reference_KL_logp = self.compute_reference_log_probs(padded_batch) reference_completion_logp = self.accelerator.gather_for_metrics(reference_completion_logp) reference_completion_logps.append(reference_completion_logp.cpu()) if self.calculate_KL: reference_KL_logp = self.accelerator.gather_for_metrics(reference_KL_logp) reference_KL_logps.append(reference_KL_logp.cpu()) self.train_dataset = self.train_dataset.add_column( name="reference_logps", column=torch.cat(reference_completion_logps).float().numpy() ) if self.calculate_KL: self.train_dataset = self.train_dataset.add_column( name="reference_KL_logps", column=torch.cat(reference_KL_logps).float().numpy() ) self._precomputed_train_ref_log_probs = True return super().get_train_dataloader() def get_eval_dataloader(self, eval_dataset: Optional[Dataset] = None) -> DataLoader: """ Returns the evaluation [`~torch.utils.data.DataLoader`]. Subclass of transformers.src.transformers.trainer.get_eval_dataloader to precompute `ref_log_probs`. Args: eval_dataset (`torch.utils.data.Dataset`, *optional*): If provided, will override `self.eval_dataset`. If it is a [`~datasets.Dataset`], columns not accepted by the `model.forward()` method are automatically removed. It must implement `__len__`. """ if eval_dataset is None and self.eval_dataset is None: raise ValueError("Trainer: evaluation requires an eval_dataset.") eval_dataset = eval_dataset if eval_dataset is not None else self.eval_dataset if self.precompute_ref_log_probs and not self._precomputed_eval_ref_log_probs: dataloader_params = { "batch_size": self.args.per_device_eval_batch_size, "collate_fn": self.data_collator, "num_workers": self.args.dataloader_num_workers, "pin_memory": self.args.dataloader_pin_memory, "shuffle": False, } # prepare dataloader data_loader = self.accelerator.prepare(DataLoader(eval_dataset, **dataloader_params)) reference_completion_logps = [] reference_KL_logps = [] for padded_batch in tqdm(iterable=data_loader, desc="Eval dataset reference log probs"): reference_completion_logp, reference_KL_logp = self.compute_reference_log_probs(padded_batch) reference_completion_logp = self.accelerator.gather_for_metrics(reference_completion_logp) reference_completion_logps.append(reference_completion_logp.cpu()) if self.calculate_KL: reference_KL_logp = self.accelerator.gather_for_metrics(reference_KL_logp) reference_KL_logps.append(reference_KL_logp.cpu()) eval_dataset = eval_dataset.add_column( name="reference_logps", column=torch.cat(reference_completion_logps).float().numpy() ) if self.calculate_KL: eval_dataset = eval_dataset.add_column( name="reference_KL_logps", column=torch.cat(reference_KL_logps).float().numpy() ) # Save calculated reference_chosen_logps and reference_rejected_logps to the eval_dataset for subsequent runs if self.eval_dataset is not None: self.eval_dataset = eval_dataset self._precomputed_eval_ref_log_probs = True return super().get_eval_dataloader(eval_dataset=eval_dataset) def compute_reference_log_probs(self, padded_batch: dict) -> dict: """Computes log probabilities of the reference model for a single padded batch of a KTO specific dataset.""" with torch.no_grad(): if self.ref_model is None: with self.null_ref_context(): if self.is_encoder_decoder: completion_logits = self.model( padded_batch["prompt_input_ids"], attention_mask=padded_batch["prompt_attention_mask"], decoder_input_ids=padded_batch.get("completion_decoder_input_ids"), labels=padded_batch["completion_labels"], ).logits if self.calculate_KL: KL_logits = self.model( padded_batch["KL_prompt_input_ids"], attention_mask=padded_batch["KL_prompt_attention_mask"], decoder_input_ids=padded_batch.get("KL_completion_decoder_input_ids"), labels=padded_batch["KL_completion_labels"], ).logits else: completion_logits = self.model( padded_batch["completion_input_ids"], attention_mask=padded_batch["completion_attention_mask"], ).logits if self.calculate_KL: KL_logits = self.model( padded_batch["KL_completion_input_ids"], attention_mask=padded_batch["KL_completion_attention_mask"], ).logits else: if self.is_encoder_decoder: completion_logits = self.ref_model( padded_batch["prompt_input_ids"], attention_mask=padded_batch["prompt_attention_mask"], decoder_input_ids=padded_batch.get("completion_decoder_input_ids"), labels=padded_batch["completion_labels"], ).logits if self.calculate_KL: KL_logits = self.ref_model( padded_batch["KL_prompt_input_ids"], attention_mask=padded_batch["KL_prompt_attention_mask"], decoder_input_ids=padded_batch.get("KL_completion_decoder_input_ids"), labels=padded_batch["KL_completion_labels"], ).logits else: completion_logits = self.ref_model( padded_batch["completion_input_ids"], attention_mask=padded_batch["completion_attention_mask"] ).logits if self.calculate_KL: KL_logits = self.ref_model( padded_batch["KL_completion_input_ids"], attention_mask=padded_batch["KL_completion_attention_mask"], ).logits completion_logps = self.get_batch_logps( completion_logits, padded_batch["completion_labels"], average_log_prob=False, is_encoder_decoder=self.is_encoder_decoder, label_pad_token_id=self.label_pad_token_id, ) if self.calculate_KL: KL_logps = self.get_batch_logps( KL_logits, padded_batch["KL_completion_labels"], average_log_prob=False, is_encoder_decoder=self.is_encoder_decoder, label_pad_token_id=self.label_pad_token_id, ) else: KL_logps = None return completion_logps, KL_logps @staticmethod def get_batch_logps( logits: torch.FloatTensor, labels: torch.LongTensor, average_log_prob: bool = False, label_pad_token_id: int = -100, is_encoder_decoder: bool = False, ) -> torch.FloatTensor: """Compute the log probabilities of the given labels under the given logits. Args: logits: Logits of the model (unnormalized). Shape: (batch_size, sequence_length, vocab_size) labels: Labels for which to compute the log probabilities. Label tokens with a value of label_pad_token_id are ignored. Shape: (batch_size, sequence_length) average_log_prob: If True, return the average log probability per (non-masked) token. Otherwise, return the sum of the log probabilities of the (non-masked) tokens. label_pad_token_id: The label value to ignore when computing log probabilities. is_encoder_decoder: Whether the model is an encoder-decoder model. If True, the labels are not shifted and the logits are assumed to already be aligned with the labels. If False, the labels are shifted to the right by one position, and the logits are assumed to be aligned with the shifted labels. Returns: A tensor of shape (batch_size,) containing the average/sum log probabilities of the given labels under the given logits. """ if logits.shape[:-1] != labels.shape: raise ValueError("Logits (batch and sequence length dim) and labels must have the same shape.") if not is_encoder_decoder: labels = labels[:, 1:].clone() logits = logits[:, :-1, :] else: # Fixes end-dec RuntimeError labels = labels.clone() loss_mask = labels != label_pad_token_id # dummy token; we'll ignore the losses on these tokens later labels[labels == label_pad_token_id] = 0 per_token_logps = selective_log_softmax(logits, labels) if average_log_prob: return (per_token_logps * loss_mask).sum(-1) / loss_mask.sum(-1) else: return (per_token_logps * loss_mask).sum(-1) def forward( self, model: nn.Module, batch: dict[str, Union[list, torch.LongTensor]] ) -> tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]: KL_logps = self._compute_kl_logps(model, batch) model_kwargs = ( { "labels": batch["completion_labels"], "decoder_input_ids": batch.get("completion_decoder_input_ids"), } if self.is_encoder_decoder else {} ) if self.aux_loss_enabled: model_kwargs["output_router_logits"] = True outputs = model( batch["completion_input_ids"], attention_mask=batch["completion_attention_mask"], **model_kwargs, ) completion_logits = outputs.logits completion_logps = self.get_batch_logps( completion_logits, batch["completion_labels"], average_log_prob=False, is_encoder_decoder=self.is_encoder_decoder, label_pad_token_id=self.label_pad_token_id, ) if completion_logps.shape[0] != len(batch["label"]): raise ValueError( "There is a mismatch between the number of examples in this batch and the number of " "examples for which an output sequence was predicted." ) chosen_idx = [i for i in range(completion_logps.shape[0]) if batch["label"][i] is True] rejected_idx = [i for i in range(completion_logps.shape[0]) if batch["label"][i] is False] chosen_logps = completion_logps[chosen_idx, ...] rejected_logps = completion_logps[rejected_idx, ...] chosen_logits = completion_logits[chosen_idx, ...] rejected_logits = completion_logits[rejected_idx, ...] if self.aux_loss_enabled: return (chosen_logps, rejected_logps, chosen_logits, rejected_logits, KL_logps, outputs.aux_loss) else: return (chosen_logps, rejected_logps, chosen_logits, rejected_logits, KL_logps) def kto_loss( self, policy_chosen_logps: torch.FloatTensor, policy_rejected_logps: torch.FloatTensor, policy_KL_logps: torch.FloatTensor, reference_chosen_logps: torch.FloatTensor, reference_rejected_logps: torch.FloatTensor, reference_KL_logps: torch.FloatTensor, ) -> tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]: """Compute the KTO loss for a batch of policy and reference model log probabilities. Args: policy_chosen_logps: Log probabilities of the policy model for the chosen responses. Shape: (num(chosen) in batch_size,) policy_rejected_logps: Log probabilities of the policy model for the rejected responses. Shape: (num(rejected) in batch_size,) policy_KL_logps: Log probabilities of the policy model for the KL responses. Shape: (batch_size,) reference_chosen_logps: Log probabilities of the reference model for the chosen responses. Shape: (num(chosen) in batch_size,) reference_rejected_logps: Log probabilities of the reference model for the rejected responses. Shape: (num(rejected) in batch_size,) reference_KL_logps: Log probabilities of the reference model for the KL responses. Shape: (batch_size,) Returns: A tuple of four tensors: (losses, chosen_rewards, rejected_rewards, KL). The losses tensor contains the KTO loss for each example in the batch. The chosen_rewards and rejected_rewards tensors contain the rewards for the chosen and rejected responses, respectively. The KL tensor contains the detached KL divergence estimate between the policy and reference models. """ if self.calculate_KL: kl = (policy_KL_logps - reference_KL_logps).mean().detach() kl = self.accelerator.gather_for_metrics(kl).mean().clamp(min=0) else: kl = torch.zeros(1).to(policy_chosen_logps.device) # Chosen losses if policy_chosen_logps.shape[0] != 0 or reference_chosen_logps.shape[0] != 0: chosen_logratios = policy_chosen_logps - reference_chosen_logps if self.loss_type == "kto": # Eqn (7) of the KTO paper (https://huggingface.co/papers/2402.01306) chosen_losses = 1 - F.sigmoid(self.beta * (chosen_logratios - kl)) elif self.loss_type == "apo_zero_unpaired": # Unpaired variant of Eqn (7) of the APO paper (https://huggingface.co/papers/2408.06266) # Use this loss when you believe the chosen outputs are better than your model's default output chosen_losses = 1 - F.sigmoid(self.beta * chosen_logratios) chosen_rewards = self.beta * chosen_logratios.detach() else: # lists can't be empty -- if they are, then accelerate.gather will hang chosen_losses = torch.Tensor([]).to(self.accelerator.device) chosen_rewards = torch.Tensor([]).to(self.accelerator.device) # Rejected losses if policy_rejected_logps.shape[0] != 0 or reference_rejected_logps.shape[0] != 0: rejected_logratios = policy_rejected_logps - reference_rejected_logps if self.loss_type == "kto": rejected_losses = 1 - F.sigmoid(self.beta * (kl - rejected_logratios)) elif self.loss_type == "apo_zero_unpaired": rejected_losses = F.sigmoid(self.beta * rejected_logratios) rejected_rewards = self.beta * rejected_logratios.detach() else: # lists can't be empty -- if they are, then accelerate.gather will hang rejected_losses = torch.Tensor([]).to(self.accelerator.device) rejected_rewards = torch.Tensor([]).to(self.accelerator.device) losses = torch.cat( (self.desirable_weight * chosen_losses, self.undesirable_weight * rejected_losses), 0, ) return losses, chosen_rewards, rejected_rewards, kl def _compute_kl_logps(self, model, batch): """Compute KL log probabilities for a given batch.""" KL_logps = None if self.calculate_KL: if self.is_encoder_decoder: KL_model_kwargs = { "input_ids": batch["KL_prompt_input_ids"], "attention_mask": batch["KL_prompt_attention_mask"], "labels": batch["KL_completion_labels"], "decoder_input_ids": batch.get("KL_completion_decoder_input_ids"), } else: KL_model_kwargs = { "input_ids": batch["KL_completion_input_ids"], "attention_mask": batch["KL_completion_attention_mask"], } with torch.no_grad(): KL_logits = model(**KL_model_kwargs).logits KL_logps = self.get_batch_logps( KL_logits, batch["KL_completion_labels"], average_log_prob=False, is_encoder_decoder=self.is_encoder_decoder, label_pad_token_id=self.label_pad_token_id, ) return KL_logps def _compute_loss_liger(self, model, batch): """ Compute the KTO loss using the Liger-Kernel's LigerFusedLinearKTOLoss. Args: model: The policy model used for generating log probabilities and outputs. It could be an encoder-decoder model or a regular language model. batch: A dictionary containing the input data and labels for the batch. Returns: A dictionary containing the following keys: - "loss": The computed KTO loss for the batch. - "chosen_logits_sum": Sum of the logits for the chosen responses from the policy model. - "rejected_logits_sum": Sum of the logits for the rejected responses from the policy model. - "chosen_logps": Log probabilities of the chosen responses from the policy model. - "rejected_logps": Log probabilities of the rejected responses from the policy model. - "chosen_rewards": Rewards for the chosen responses. - "rejected_rewards": Rewards for the rejected responses. - "kl": The KL divergence between the policy and reference models (detached). If auxiliary loss is enabled, the dictionary will also include: - "aux_loss": The auxiliary loss from the model outputs. """ policy_KL_logps = self._compute_kl_logps(model, batch) reference_KL_logps = self._compute_kl_logps(self.ref_model, batch) if self.calculate_KL: kl = (policy_KL_logps - reference_KL_logps).mean().detach() kl = self.accelerator.gather_for_metrics(kl).mean().clamp(min=0) else: kl = torch.zeros(1).to(self.accelerator.device) model_kwargs = ( { "labels": batch["completion_labels"], "decoder_input_ids": batch.get("completion_decoder_input_ids"), } if self.is_encoder_decoder else {} ) if self.aux_loss_enabled: model_kwargs["output_router_logits"] = True if self.is_encoder_decoder: # 1. Get encoder outputs encoder_outputs = model.get_encoder()( batch["completion_input_ids"], attention_mask=batch["completion_attention_mask"], return_dict=True, **model_kwargs, ) # 2. Get decoder outputs outputs = model.get_decoder()( input_ids=model_kwargs["decoder_input_ids"], encoder_hidden_states=encoder_outputs.last_hidden_state, use_cache=False, **model_kwargs, ) # 1. Get reference encoder outputs ref_encoder_outputs = self.ref_model.get_encoder()( batch["completion_input_ids"], attention_mask=batch["completion_attention_mask"], return_dict=True, **model_kwargs, ) # 2. Get reference decoder outputs ref_outputs = self.ref_model.get_decoder()( input_ids=model_kwargs["decoder_input_ids"], encoder_hidden_states=ref_encoder_outputs.last_hidden_state, use_cache=False, **model_kwargs, ) else: # skip the lm head and get the last hidden state if hasattr(model, "get_decoder") and model.get_decoder() is not None: base_model = model.get_decoder() else: base_attr = getattr(model, "base_model_prefix", self.args.base_model_attribute_name) base_model = getattr(model, base_attr, model) outputs = base_model( batch["completion_input_ids"], attention_mask=batch["completion_attention_mask"], use_cache=False, **model_kwargs, ) # reference model if hasattr(self.ref_model, "get_decoder") and self.ref_model.get_decoder() is not None: ref_base_model = self.ref_model.get_decoder() else: ref_attr = getattr(self.ref_model, "base_model_prefix", self.args.base_model_attribute_name) ref_base_model = getattr(self.ref_model, ref_attr, self.ref_model) ref_outputs = ref_base_model( batch["completion_input_ids"], attention_mask=batch["completion_attention_mask"], use_cache=False, **model_kwargs, ) lm_head = model.get_output_embeddings() ref_lm_head = self.ref_model.get_output_embeddings() ( loss, ( chosen_logps_sum, rejected_logps_sum, chosen_logits_sum, rejected_logits_sum, chosen_rewards_sum, rejected_rewards_sum, ), ) = self.kto_loss_fn( _input=outputs.last_hidden_state[:, :-1] if not self.is_encoder_decoder else outputs.last_hidden_state, lin_weight=lm_head.weight, target=batch["completion_labels"][:, 1:], bias=lm_head.bias if hasattr(lm_head, "bias") else None, preference_labels=torch.tensor(batch["label"], dtype=torch.bool).to(self.accelerator.device), ref_input=ref_outputs.last_hidden_state[:, :-1] if not self.is_encoder_decoder else outputs.last_hidden_state, ref_weight=ref_lm_head.weight, ref_bias=ref_lm_head.bias if hasattr(lm_head, "bias") else None, kl=kl, ) output = { "loss": loss, "chosen_logits_sum": chosen_logits_sum, "rejected_logits_sum": rejected_logits_sum, "chosen_logps_sum": chosen_logps_sum, "rejected_logps_sum": rejected_logps_sum, "chosen_rewards_sum": chosen_rewards_sum, "rejected_rewards_sum": rejected_rewards_sum, "kl": kl, } if self.aux_loss_enabled: output["aux_loss"] = outputs.aux_loss return output def get_batch_loss_metrics( self, model, batch: dict[str, Union[list, torch.LongTensor]], ): """Compute the KTO loss and other metrics for the given batch of inputs for train or test.""" metrics = {} batch = {k: (v.to(self.accelerator.device) if isinstance(v, torch.Tensor) else v) for k, v in batch.items()} labels = torch.tensor(batch["label"]) num_chosen = labels.sum().to(self.accelerator.device) num_rejected = (len(labels) - num_chosen).to(self.accelerator.device) if self.args.use_liger_loss: model_output = self._compute_loss_liger(model, batch) losses = model_output["loss"] policy_chosen_logits = model_output["chosen_logits_sum"] policy_rejected_logits = model_output["rejected_logits_sum"] policy_chosen_logps = model_output["chosen_logps_sum"] policy_rejected_logps = model_output["rejected_logps_sum"] chosen_rewards = model_output["chosen_rewards_sum"] rejected_rewards = model_output["rejected_rewards_sum"] kl = model_output["kl"] if self.aux_loss_enabled: aux_loss = model_output["aux_loss"] else: forward_output = self.forward(model, batch) ( policy_chosen_logps, policy_rejected_logps, policy_chosen_logits, policy_rejected_logits, policy_KL_logps, ) = forward_output[:5] if self.aux_loss_enabled: aux_loss = forward_output[5] # if reference_logps in batch use them, otherwise use the reference model if "reference_logps" in batch: chosen_idx = [i for i in range(batch["reference_logps"].shape[0]) if batch["label"][i] is True] rejected_idx = [i for i in range(batch["reference_logps"].shape[0]) if batch["label"][i] is False] reference_chosen_logps = batch["reference_logps"][chosen_idx, ...] reference_rejected_logps = batch["reference_logps"][rejected_idx, ...] if self.calculate_KL: reference_KL_logps = batch["reference_KL_logps"] else: reference_KL_logps = None else: with torch.no_grad(): if self.ref_model is None: with self.null_ref_context(): ( reference_chosen_logps, reference_rejected_logps, _, _, reference_KL_logps, ) = self.forward(self.model, batch)[:5] else: ( reference_chosen_logps, reference_rejected_logps, _, _, reference_KL_logps, ) = self.forward(self.ref_model, batch)[:5] losses, chosen_rewards, rejected_rewards, kl = self.kto_loss( policy_chosen_logps, policy_rejected_logps, policy_KL_logps, reference_chosen_logps, reference_rejected_logps, reference_KL_logps, ) metrics["kl"] = kl.item() all_num_chosen = self.accelerator.gather_for_metrics(num_chosen).sum().item() all_num_rejected = self.accelerator.gather_for_metrics(num_rejected).sum().item() if all_num_chosen > 0: metrics["rewards/chosen_sum"] = ( self.accelerator.gather_for_metrics(chosen_rewards.nansum()).nansum().item() ) metrics["logps/chosen_sum"] = ( self.accelerator.gather_for_metrics(policy_chosen_logps.nansum()).nansum().item() ) metrics["logits/chosen_sum"] = ( self.accelerator.gather_for_metrics(policy_chosen_logits.nansum()).nansum().item() ) metrics["count/chosen"] = all_num_chosen if all_num_rejected > 0: metrics["rewards/rejected_sum"] = ( self.accelerator.gather_for_metrics(rejected_rewards.nansum()).nansum().item() ) metrics["logps/rejected_sum"] = ( self.accelerator.gather_for_metrics(policy_rejected_logps.nansum()).nansum().item() ) metrics["logits/rejected_sum"] = ( self.accelerator.gather_for_metrics(policy_rejected_logits.nansum()).nansum().item() ) metrics["count/rejected"] = all_num_rejected loss = losses.nanmean() if self.aux_loss_enabled: loss += self.aux_loss_coef * aux_loss return loss, metrics def compute_loss( self, model: Union[PreTrainedModel, nn.Module], inputs: dict[str, Union[torch.Tensor, Any]], return_outputs=False, num_items_in_batch=None, ) -> Union[torch.Tensor, tuple[torch.Tensor, dict[str, torch.Tensor]]]: compute_loss_context_manager = ( autocast(self.accelerator.device.type) if self._peft_has_been_casted_to_bf16 else nullcontext() ) with compute_loss_context_manager: loss, metrics = self.get_batch_loss_metrics(model, inputs) # Make sure to move the loss to the device the original accumulating loss is at back in the `Trainer` class: loss = loss.to(self.args.device) # force log the metrics if self.accelerator.is_main_process: self.store_metrics(metrics, train_eval="train") if return_outputs: return (loss, metrics) return loss def store_metrics(self, metrics: dict[str, float], train_eval: Literal["train", "eval"] = "train") -> None: for key, value in metrics.items(): self._stored_metrics[train_eval][key].append(value) def _get_train_sampler(self, dataset: Optional[Dataset] = None) -> Optional[torch.utils.data.Sampler]: if dataset is None: dataset = self.train_dataset if dataset is None or not has_length(dataset): return None return SequentialSampler(dataset) def generate_from_model_and_ref(self, model, batch: dict[str, torch.LongTensor]) -> tuple[str, str]: """Generate samples from the model and reference model for the given batch of inputs.""" # If one uses `generate_during_eval` with peft + bf16, we need to explicitly call generate with # the torch amp context manager as some hidden states are silently casted to full precision. generate_context_manager = ( autocast(self.accelerator.device.type) if self._peft_has_been_casted_to_bf16 else nullcontext() ) with generate_context_manager: policy_output = model.generate( input_ids=batch["prompt_input_ids"], attention_mask=batch["prompt_attention_mask"], max_length=self.max_length, do_sample=True, pad_token_id=self.processing_class.pad_token_id, ) # if reference_output in batch use that otherwise use the reference model if "reference_output" in batch: reference_output = batch["reference_output"] else: if self.ref_model is None: with self.null_ref_context(): reference_output = self.model.generate( input_ids=batch["prompt_input_ids"], attention_mask=batch["prompt_attention_mask"], max_length=self.max_length, do_sample=True, pad_token_id=self.processing_class.pad_token_id, ) else: reference_output = self.ref_model.generate( input_ids=batch["prompt_input_ids"], attention_mask=batch["prompt_attention_mask"], max_length=self.max_length, do_sample=True, pad_token_id=self.processing_class.pad_token_id, ) policy_output = pad_to_length(policy_output, self.max_length, self.processing_class.pad_token_id) policy_output_decoded = self.processing_class.batch_decode(policy_output, skip_special_tokens=True) reference_output = pad_to_length(reference_output, self.max_length, self.processing_class.pad_token_id) reference_output_decoded = self.processing_class.batch_decode(reference_output, skip_special_tokens=True) return policy_output_decoded, reference_output_decoded def prediction_step( self, model: Union[PreTrainedModel, nn.Module], inputs: dict[str, Union[torch.Tensor, Any]], prediction_loss_only: bool, ignore_keys: Optional[list[str]] = None, ): if ignore_keys is None: if hasattr(model, "config"): ignore_keys = getattr(model.config, "keys_to_ignore_at_inference", []) else: ignore_keys = [] prediction_context_manager = ( autocast(self.accelerator.device.type) if self._peft_has_been_casted_to_bf16 else nullcontext() ) with torch.no_grad(), prediction_context_manager: loss, metrics = self.get_batch_loss_metrics(model, inputs) # force log the metrics if self.accelerator.is_main_process: self.store_metrics(metrics, train_eval="eval") if prediction_loss_only: return (loss.detach(), None, None) # logits for the chosen and rejected samples from model logits_dict = {} if "logits/chosen_sum" in metrics: logits_dict["eval_logits/chosen"] = metrics["logits/chosen_sum"] if "logits/rejected_sum" in metrics: logits_dict["eval_logits/rejected"] = metrics["logits/rejected_sum"] logits = [v for k, v in logits_dict.items() if k not in ignore_keys] logits = torch.tensor(logits, device=self.accelerator.device) labels = torch.zeros(logits.shape[0], device=self.accelerator.device) return (loss.detach(), logits, labels) def evaluation_loop( self, dataloader: DataLoader, description: str, prediction_loss_only: Optional[bool] = None, ignore_keys: Optional[list[str]] = None, metric_key_prefix: str = "eval", ) -> EvalLoopOutput: """ Overriding built-in evaluation loop to store metrics for each batch. Prediction/evaluation loop, shared by `Trainer.evaluate()` and `Trainer.predict()`. Works both with or without labels. """ # Sample and save to game log if requested (for one batch to save time) if self.generate_during_eval: # Generate random indices within the range of the total number of samples num_samples = len(dataloader.dataset) random_indices = random.sample(range(num_samples), k=self.args.eval_batch_size) # Use dataloader.dataset.select to get the random batch without iterating over the DataLoader random_batch_dataset = dataloader.dataset.select(random_indices) random_batch = self.data_collator(random_batch_dataset) random_batch = self._prepare_inputs(random_batch) target_labels = torch.tensor(random_batch["label"], dtype=torch.bool, device=self.accelerator.device) target_indices = torch.where(~target_labels)[0] target_batch = { "prompt_input_ids": random_batch["prompt_input_ids"][target_indices], "prompt_attention_mask": random_batch["prompt_attention_mask"][target_indices], "prompt": itemgetter(*target_indices)(random_batch["prompt"]), } policy_output_decoded, ref_output_decoded = self.generate_from_model_and_ref(self.model, target_batch) table = pd.DataFrame( columns=["Prompt", "Policy", "Ref Model"], data=[ [prompt, pol[len(prompt) :], ref[len(prompt) :]] for prompt, pol, ref in zip(target_batch["prompt"], policy_output_decoded, ref_output_decoded) ], ) if "wandb" in self.args.report_to: wandb.log({"game_log": wandb.Table(data=table)}) if "comet_ml" in self.args.report_to: log_table_to_comet_experiment( name="game_log.csv", table=table, ) # Base evaluation initial_output = super().evaluation_loop( dataloader, description, prediction_loss_only, ignore_keys, metric_key_prefix ) return initial_output def log(self, logs: dict[str, float], start_time: Optional[float] = None) -> None: """ Log `logs` on the various objects watching training, including stored metrics. Args: logs (`dict[str, float]`): The values to log. start_time (`float` or `None`, *optional*, defaults to `None`): Start time of the training. """ # logs either has 'loss' or 'eval_loss' train_eval = "train" if "loss" in logs else "eval" # train metrics should have no prefix, eval should have 'eval_' prefix = "eval_" if train_eval == "eval" else "" # accumulate average metrics from sums and lengths for split in ["chosen", "rejected"]: if f"count/{split}" in self._stored_metrics[train_eval]: count_sum = torch.Tensor(self._stored_metrics[train_eval][f"count/{split}"]).sum().item() for metric in ["rewards", "logps", "logits"]: logs[f"{prefix}{metric}/{split}"] = ( torch.Tensor(self._stored_metrics[train_eval][f"{metric}/{split}_sum"]).sum().item() / count_sum ) # delete obsolete metric del self._stored_metrics[train_eval][f"{metric}/{split}_sum"] del self._stored_metrics[train_eval][f"count/{split}"] # calculate reward margin if f"{prefix}rewards/chosen" in logs and f"{prefix}rewards/rejected" in logs: logs[f"{prefix}rewards/margins"] = logs[f"{prefix}rewards/chosen"] - logs[f"{prefix}rewards/rejected"] # Add averaged stored metrics to logs for key, metrics in self._stored_metrics[train_eval].items(): logs[f"{prefix}{key}"] = torch.Tensor(metrics).mean().item() del self._stored_metrics[train_eval] return super().log(logs, start_time) # Ensure the model card is saved along with the checkpoint def _save_checkpoint(self, model, trial): if self.args.hub_model_id is None: model_name = Path(self.args.output_dir).name else: model_name = self.args.hub_model_id.split("/")[-1] self.create_model_card(model_name=model_name) super()._save_checkpoint(model, trial) def create_model_card( self, model_name: Optional[str] = None, dataset_name: Optional[str] = None, tags: Union[str, list[str], None] = None, ): """ Creates a draft of a model card using the information available to the `Trainer`. Args: model_name (`str` or `None`, *optional*, defaults to `None`): Name of the model. dataset_name (`str` or `None`, *optional*, defaults to `None`): Name of the dataset used for training. tags (`str`, `list[str]` or `None`, *optional*, defaults to `None`): Tags to be associated with the model card. """ if not self.is_world_process_zero(): return if hasattr(self.model.config, "_name_or_path") and not os.path.isdir(self.model.config._name_or_path): base_model = self.model.config._name_or_path else: base_model = None # normalize `tags` to a mutable set if tags is None: tags = set() elif isinstance(tags, str): tags = {tags} else: tags = set(tags) if hasattr(self.model.config, "unsloth_version"): tags.add("unsloth") if "JOB_ID" in os.environ: tags.add("hf_jobs") tags.update(self._tag_names) # docstyle-ignore citation = textwrap.dedent("""\ @article{ethayarajh2024kto, title = {{KTO: Model Alignment as Prospect Theoretic Optimization}}, author = {Kawin Ethayarajh and Winnie Xu and Niklas Muennighoff and Dan Jurafsky and Douwe Kiela}, year = 2024, eprint = {arXiv:2402.01306}, }""") model_card = generate_model_card( base_model=base_model, model_name=model_name, hub_model_id=self.hub_model_id, dataset_name=dataset_name, tags=tags, wandb_url=wandb.run.url if is_wandb_available() and wandb.run is not None else None, comet_url=get_comet_experiment_url(), trainer_name="KTO", trainer_citation=citation, paper_title="KTO: Model Alignment as Prospect Theoretic Optimization", paper_id="2402.01306", ) model_card.save(os.path.join(self.args.output_dir, "README.md")) class UnslothKTOTrainer(_UnslothKTOTrainer): """ Initialize KTOTrainer. Args: model (`transformers.PreTrainedModel`): The model to train, preferably an `AutoModelForSequenceClassification`. ref_model (`PreTrainedModelWrapper`): Hugging Face transformer model with a casual language modelling head. Used for implicit reward computation and loss. If no reference model is provided, the trainer will create a reference model with the same architecture as the model to be optimized. args (`KTOConfig`): The arguments to use for training. train_dataset (`datasets.Dataset`): The dataset to use for training. eval_dataset (`datasets.Dataset`): The dataset to use for evaluation. processing_class ([`~transformers.PreTrainedTokenizerBase`], [`~transformers.BaseImageProcessor`], [`~transformers.FeatureExtractionMixin`] or [`~transformers.ProcessorMixin`], *optional*, defaults to `None`): Processing class used to process the data. If provided, will be used to automatically process the inputs for the model, and it will be saved along the model to make it easier to rerun an interrupted training or reuse the fine-tuned model. data_collator (`transformers.DataCollator`, *optional*, defaults to `None`): The data collator to use for training. If None is specified, the default data collator (`DPODataCollatorWithPadding`) will be used which will pad the sequences to the maximum length of the sequences in the batch, given a dataset of paired sequences. model_init (`Callable[[], transformers.PreTrainedModel]`): The model initializer to use for training. If None is specified, the default model initializer will be used. callbacks (`list[transformers.TrainerCallback]`): The callbacks to use for training. optimizers (`tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`): The optimizer and scheduler to use for training. preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`): The function to use to preprocess the logits before computing the metrics. peft_config (`dict`, defaults to `None`): The PEFT configuration to use for training. If you pass a PEFT configuration, the model will be wrapped in a PEFT model. compute_metrics (`Callable[[EvalPrediction], dict]`, *optional*): The function to use to compute the metrics. Must take a `EvalPrediction` and return a dictionary string to metric values. model_adapter_name (`str`, defaults to `None`): Name of the train target PEFT adapter, when using LoRA with multiple adapters. ref_adapter_name (`str`, defaults to `None`): Name of the reference PEFT adapter, when using LoRA with multiple adapters. """ def __init__( self, model = None, ref_model = None, args = None, train_dataset = None, eval_dataset = None, processing_class = None, data_collator = None, model_init = None, callbacks = None, preprocess_logits_for_metrics = None, peft_config = None, compute_metrics = None, model_adapter_name = None, ref_adapter_name = None, **kwargs ): if args is None: args = UnslothKTOConfig() use_bf16 = getattr(args, 'bf16', False) if type(use_bf16) is not bool: use_bf16 = False use_fp16 = getattr(args, 'fp16', False) if type(use_fp16) is not bool: use_fp16 = False force_float32 = False full_finetuning = os.environ.get('UNSLOTH_ENABLE_FULL_FINETUNING', '0') == '1' if not full_finetuning and (os.environ.get('UNSLOTH_FORCE_FLOAT32', '0') == '1'): print('Unsloth: Switching to float32 training since model cannot work with float16') force_float32 = True mixed_precision_dtype = os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32') dtype = getattr(model.config, 'dtype', None) or getattr(model.config, 'torch_dtype', None) if dtype is None: dtype = model.get_input_embeddings().dtype from unsloth_zoo.utils import _get_dtype dtype = _get_dtype(dtype) float16 = dtype == torch.float16 if not force_float32 and (float16 and use_bf16): raise TypeError('Unsloth: Model is in float16 precision but you want to use bfloat16 precision. Set fp16 to `True` and bf16 to `False`') if not force_float32 and (not float16 and use_fp16): raise TypeError('Unsloth: Model is in bfloat16 precision but you want to use float16 precision. Set fp16 to `False` and bf16 to `True`') if force_float32: # Forced float32 training args.fp16 = False args.bf16 = False os.environ['ACCELERATE_MIXED_PRECISION'] = 'no' elif (not use_bf16 and not use_fp16) and mixed_precision_dtype == 'float32': # Mixed precision training args.fp16 = float16 args.bf16 = not float16 os.environ['ACCELERATE_MIXED_PRECISION'] = 'fp16' if float16 else 'bf16' if getattr(args, 'eval_dataset', None) is not None and getattr(args, 'eval_strategy', 'no') == 'no': args.eval_strategy = 'steps' if getattr(args, 'eval_steps', None) is None: args.eval_steps = 0.1 ga_steps = getattr(args, 'gradient_accumulation_steps', None) if ga_steps is not None and ga_steps > 1: from transformers import __version__ as transformers_version if Version(transformers_version) <= Version('4.45.2'): print('**** Unsloth: Please use our fixed gradient_accumulation_steps by updating transformers, TRL and Unsloth!\n' '`pip install --upgrade --no-cache-dir --force-reinstall --no-deps unsloth transformers trl unsloth_zoo`') if getattr(args, 'eval_strategy', 'no') != 'no': eval_bsz = getattr(args, 'per_device_eval_batch_size', 8) if eval_bsz == 8 and args.per_device_train_batch_size < eval_bsz: args.per_device_eval_batch_size = args.per_device_train_batch_size if getattr(args, 'eval_accumulation_steps', None) is None and ga_steps is not None: args.eval_accumulation_steps = ga_steps fp16_full_eval = getattr(args, 'fp16_full_eval', False) if type(fp16_full_eval) is not bool: fp16_full_eval = False bf16_full_eval = getattr(args, 'bf16_full_eval', False) if type(bf16_full_eval) is not bool: bf16_full_eval = False if args.fp16 and bf16_full_eval: args.bf16_full_eval = False; args.fp16_full_eval = True if args.bf16 and fp16_full_eval: args.bf16_full_eval = True; args.fp16_full_eval = False if force_float32: args.bf16_full_eval = False args.fp16_full_eval = False elif os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32') == 'bfloat16': args.bf16_full_eval = True args.fp16_full_eval = False elif not bf16_full_eval and not fp16_full_eval: args.bf16_full_eval = args.bf16 args.fp16_full_eval = args.fp16 _output_logits = False if locals().get('compute_metrics', None) is not None: _output_logits = True if locals().get('preprocess_logits_for_metrics', None) is not None: _output_logits = True if _output_logits: os.environ['UNSLOTH_RETURN_LOGITS'] = '1' if 'max_seq_length' not in locals() and not hasattr(args, 'max_seq_length'): pass else: model_max_seq_length = getattr(model, 'max_seq_length', None) args_max_seq_length = getattr(args, 'max_seq_length', None) if args_max_seq_length is None and model_max_seq_length is not None: max_seq_length = model.max_seq_length if hasattr(args, 'max_seq_length'): args.max_seq_length = max_seq_length if model is not None and hasattr(model, 'for_training'): model.for_training() if 'tokenizer' in locals() and hasattr(tokenizer, 'padding_side'): tokenizer.padding_side = 'right' if 'processing_class' in locals(): if hasattr(processing_class, 'padding_side'): processing_class.padding_side = 'right' if hasattr(processing_class, 'tokenizer') and hasattr(processing_class.tokenizer, 'padding_side'): processing_class.tokenizer.padding_side = 'right' __tokenizer = processing_class if 'processing_class' in locals() else tokenizer from unsloth_zoo.vision_utils import UnslothVisionDataCollator if not isinstance(data_collator, UnslothVisionDataCollator): if isinstance(data_collator, DataCollatorForSeq2Seq) and 'labels' not in train_dataset.column_names: data_collator = TransformersDataCollatorForLanguageModeling( __tokenizer, mlm = False, mlm_probability = 0.0, pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), ) elif isinstance(data_collator, TransformersDataCollatorForLanguageModeling) and 'labels' in train_dataset.column_names: data_collator = DataCollatorForSeq2Seq( __tokenizer, pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), ) else: if hasattr(args, 'remove_unused_columns'): args.remove_unused_columns = False if hasattr(args, 'dataset_text_field'): args.dataset_text_field = '' if hasattr(args, 'dataset_kwargs'): args.dataset_kwargs = {'skip_prepare_dataset': True} if not isinstance(data_collator, UnslothVisionDataCollator): if not hasattr(__tokenizer, 'pad') and hasattr(__tokenizer, 'tokenizer'): if isinstance(data_collator, DataCollatorForSeq2Seq): data_collator = DataCollatorForSeq2Seq( __tokenizer.tokenizer, pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), ) else: data_collator = TransformersDataCollatorForLanguageModeling( __tokenizer.tokenizer, mlm = False, mlm_probability = 0.0, pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), ) other_metrics = [] from unsloth_zoo.logging_utils import PatchRLStatistics PatchRLStatistics('kto_trainer', other_metrics) # [TODO] Fix up DataParallel multiplying batch sizes # [TODO] DDP works, but DP seems to not work? [TODO] if getattr(args, "parallel_mode", None) == ParallelMode.NOT_DISTRIBUTED and args.n_gpu > 1: if getattr(args, "_n_gpu", 1) != 1: args._n_gpu = 1 if "model" in locals() and hasattr(model, "for_training"): model.for_training() super().__init__( model = model, ref_model = ref_model, args = args, train_dataset = train_dataset, eval_dataset = eval_dataset, processing_class = processing_class, data_collator = data_collator, model_init = model_init, callbacks = callbacks, preprocess_logits_for_metrics = preprocess_logits_for_metrics, peft_config = peft_config, compute_metrics = compute_metrics, model_adapter_name = model_adapter_name, ref_adapter_name = ref_adapter_name,**kwargs) if "model" in locals() and hasattr(model, "for_inference"): model.for_inference() if hasattr(self, 'neftune_hook_handle'): self.neftune_hook_handle.remove() if hasattr(self, 'neftune_hook_handle'): del self.neftune_hook_handle if getattr(args, 'neftune_noise_alpha', None) is not None: model.get_input_embeddings().neftune_noise_alpha = self.neftune_noise_alpha pass if hasattr(self, 'accelerator'): scaler = self.accelerator.scaler current_model = model while hasattr(current_model, 'model'): current_model.accelerator_scaler = scaler current_model = current_model.model current_model.accelerator_scaler = scaler pass if hasattr(self, 'train'): self.train = MethodType(prepare_for_training_mode(self.__class__.train), self) pass pass if hasattr(logger, "addFilter"): import logging class HideLoggingMessage(logging.Filter): def __init__(self, text): self.text = text def filter(self, x): return not (self.text in x.getMessage()) pass logger.addFilter(HideLoggingMessage("`use_cache=True`"))