""" 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.prm_trainer import (BaseImageProcessor, Callable, DataCollator, DataCollatorForTokenClassification, Dataset, EvalPrediction, FeatureExtractionMixin, Optional, PRMConfig, PRMTrainer, PartialState, Path, PreTrainedModel, PreTrainedTokenizerBase, ProcessorMixin, Trainer, TrainerCallback, Union, chain, compute_accuracy, disable_dropout_in_model, features, generate_model_card, is_wandb_available, nn, os, prepare_peft_model, textwrap, torch, Optional, PreTrainedModel, Trainer, 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 UnslothPRMConfig(PRMConfig): """ Configuration class for the [`PRMTrainer`]. This class includes only the parameters that are specific to PRM 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) used for truncation. max_prompt_length (`int` or `None`, *optional*, defaults to `512`): Maximum length of the prompt used for truncation. max_completion_length (`int` or `None`, *optional*, defaults to `None`): Maximum length of the completion used for truncation. The completion is the concatenation of the steps. disable_dropout (`bool`, *optional*, defaults to `True`): Whether to disable dropout in the model. step_separator (`str`, *optional*, defaults to `"\n"`): Separator used to separate each step of the reasoning process. train_on_last_step_only (`bool`, *optional*, defaults to `False`): Whether to train only on the last step. dataset_num_proc (`int`, *optional*, defaults to `None`): Number of processes to use for processing the dataset. """ 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, disable_dropout = True, step_separator = '\ ', train_on_last_step_only = False, dataset_num_proc = None, 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, disable_dropout = disable_dropout, step_separator = step_separator, train_on_last_step_only = train_on_last_step_only, dataset_num_proc = dataset_num_proc,**kwargs) self.vllm_sampling_params = vllm_sampling_params self.unsloth_num_chunks = unsloth_num_chunks self.max_seq_length = max_seq_length pass class _UnslothPRMTrainer(Trainer): """ Initialize PRMTrainer. Args: model (`transformers.PreTrainedModel`): The model to train, preferably an `AutoModelForTokenClassification`. args (`PRMConfig`): The arguments to use for training. data_collator (`transformers.DataCollator`): The data collator to use for training. If None is specified, the default data collator (`DataCollatorForTokenClassification`) will be used which will pad the sequences to the maximum length of the sequences in the batch, given a dataset of paired sequences. 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. model_init (`Callable[[], transformers.PreTrainedModel]`): The model initializer to use for training. If None is specified, the default model initializer will be used. compute_metrics (`Callable[[transformers.EvalPrediction], dict]`, *optional* defaults to `compute_accuracy`): The metrics to use for evaluation. If no metrics are specified, the default metric (`compute_accuracy`) 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. """ _tag_names = ["trl", "prm"] def __init__( self, model: Optional[Union[PreTrainedModel, nn.Module]] = None, args: Optional[PRMConfig] = None, data_collator: Optional[DataCollator] = None, train_dataset: Optional[Dataset] = None, eval_dataset: Optional[Union[Dataset, dict[str, Dataset]]] = None, processing_class: Optional[ Union[PreTrainedTokenizerBase, BaseImageProcessor, FeatureExtractionMixin, ProcessorMixin] ] = None, model_init: Optional[Callable[[], PreTrainedModel]] = None, compute_metrics: Optional[Callable[[EvalPrediction], dict]] = 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, ): if False: model = prepare_peft_model(model, peft_config, args) # Disable dropout in the model if args.disable_dropout: disable_dropout_in_model(model) if compute_metrics is None: compute_metrics = compute_accuracy if data_collator is None: if processing_class is None: raise ValueError( "A processing_class must be specified when using the default DataCollatorForTokenClassification" ) data_collator = DataCollatorForTokenClassification(processing_class, max_length=args.max_length) if "input_ids" not in train_dataset.column_names: with PartialState().main_process_first(): fn_kwargs = { "tokenizer": processing_class, "step_separator": args.step_separator, "max_length": args.max_length, "max_prompt_length": args.max_prompt_length, "max_completion_length": args.max_completion_length, "train_on_last_step_only": args.train_on_last_step_only, } train_fn_kwargs = {**fn_kwargs, "is_eval": False} train_dataset = train_dataset.map( self.tokenize_row, fn_kwargs=train_fn_kwargs, num_proc=args.dataset_num_proc, remove_columns=train_dataset.features, desc="Tokenizing train dataset", features=features.Features( # needed to avoid map to cast labels to bool { "labels": features.Sequence(features.Value("int64")), "input_ids": features.Sequence(features.Value("int64")), } ), ) eval_fn_kwargs = {**fn_kwargs, "is_eval": True} if eval_dataset is not None: eval_dataset = eval_dataset.map( self.tokenize_row, fn_kwargs=eval_fn_kwargs, num_proc=args.dataset_num_proc, remove_columns=eval_dataset.features, desc="Tokenizing eval dataset", features=features.Features( # needed to avoid map to cast labels to bool { "labels": features.Sequence(features.Value("int64")), "input_ids": features.Sequence(features.Value("int64")), } ), ) 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, ) # 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) @staticmethod def tokenize_row( features, tokenizer, step_separator, max_length, max_prompt_length, max_completion_length, train_on_last_step_only, is_eval, ): r""" Tokenize a row of the dataset. Args: features (`dict[str, str]`): Row of the dataset, should contain the keys `"prompt"`, `"completions"`, and `"labels"`. tokenizer (`PreTrainedTokenizerBase`): Tokenizer used to process the data. step_separator (`str`): Separator between steps in the completion. max_length (`int` or `None`): Maximum length of the sequences (prompt + completion). If `None`, the sequences are not truncated. max_prompt_length (`int` or `None`): Maximum length of the prompt. If `None`, the prompt is not truncated. max_completion_length (`int` or `None`): Maximum length of the completion sequences. If `None`, the completion sequences are not truncated. train_on_last_step_only (`bool`): Whether to train only on the last step. If `True`, the labels are `-100` for all tokens except the last token of the completion. is_eval (`bool`): Whether the function is used to tokenize samples from a training or an evaluation dataset. Used only if `train_on_last_step_only` is set to `True`. Returns: `dict[str, list[int]]`: Tokenized sequences with the keys `"input_ids"`, and `"labels". Example: ```python >>> from transformers import AutoTokenizer >>> tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B") >>> features = { ... "prompt": "Which number is larger, 9.8 or 9.11?", ... "completions": ["11 is greater than 8.", "Hence, 9.11 > 9.8."], ... "labels": [True, False], ... } >>> PRMTrainer.tokenize_row( ... features, tokenizer, "\n", max_completion_length=None, train_on_last_step_only=False, is_eval=False ... ) {'input_ids': [23085, 1372, 374, 8131, 11, 220, 24, 13, 23, 476, 220, 24, 13, 16, 16, 30, 16, 16, 374, 7046, 1091, 220, 23, 13, 198, 39, 763, 11, 220, 24, 13, 16, 16, 861, 220, 24, 13, 23, 13, 198], 'labels': [-100, -100, -100, -100, -100, -100, -100, -100, 1, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 0]} ``` """ # Tokenize the prompt and completions prompt_ids = tokenizer(features["prompt"], add_special_tokens=False)["input_ids"] completions_ids = [ tokenizer(completion, add_special_tokens=False)["input_ids"] for completion in features["completions"] ] if train_on_last_step_only and not is_eval: labels = [-100] * (len(features["labels"]) - 1) + [int(features["labels"][-1])] else: labels = [int(label) for label in features["labels"]] # Get the ID of the separator token and add it to the completions separator_ids = tokenizer.encode(step_separator, add_special_tokens=False) completions_ids = [completion + separator_ids for completion in completions_ids] # Create the label labels = [[-100] * (len(completion) - 1) + [label] for completion, label in zip(completions_ids, labels)] # Join the completions and labels steps completion_ids = list(chain(*completions_ids)) labels = list(chain(*labels)) if tokenizer.bos_token_id is not None: prompt_ids = [tokenizer.bos_token_id] + prompt_ids # Truncate prompt and completion sequences if max_prompt_length is not None: prompt_ids = prompt_ids[-max_prompt_length:] if max_completion_length is not None: completion_ids = completion_ids[:max_completion_length] labels = labels[:max_completion_length] input_ids = prompt_ids + completion_ids labels = [-100] * len(prompt_ids) + labels if max_length is not None: input_ids = input_ids[:max_length] labels = labels[:max_length] return {"input_ids": input_ids, "labels": labels} # 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{uesato2022solving, title = {{Solving Math Word Problems With Process- and Outcome-Based Feedback}}, author = {Uesato, Jonathan and Kushman, Nate and Kumar, Ramana and Song, Francis and Siegel, Noah and Wang, Lisa and Creswell, Antonia and Irving, Geoffrey and Higgins, Irina}, year = 2022, journal = {arXiv preprint arXiv:2211.14275} }""") 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, trainer_name="PRM", trainer_citation=citation, paper_title="Solving math word problems with process-and outcome-based feedback", ) model_card.save(os.path.join(self.args.output_dir, "README.md")) class UnslothPRMTrainer(_UnslothPRMTrainer): """ Initialize PRMTrainer. Args: model (`transformers.PreTrainedModel`): The model to train, preferably an `AutoModelForTokenClassification`. args (`PRMConfig`): The arguments to use for training. data_collator (`transformers.DataCollator`): The data collator to use for training. If None is specified, the default data collator (`DataCollatorForTokenClassification`) will be used which will pad the sequences to the maximum length of the sequences in the batch, given a dataset of paired sequences. 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. model_init (`Callable[[], transformers.PreTrainedModel]`): The model initializer to use for training. If None is specified, the default model initializer will be used. compute_metrics (`Callable[[transformers.EvalPrediction], dict]`, *optional* defaults to `compute_accuracy`): The metrics to use for evaluation. If no metrics are specified, the default metric (`compute_accuracy`) 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. """ def __init__( self, model = None, args = None, data_collator = None, train_dataset = None, eval_dataset = None, processing_class = None, model_init = None, compute_metrics = None, callbacks = None, preprocess_logits_for_metrics = None, peft_config = None, **kwargs ): if args is None: args = UnslothPRMConfig() 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('prm_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, 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, preprocess_logits_for_metrics = preprocess_logits_for_metrics, peft_config = peft_config,**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