""" 2025.11.3 2025.11.2 4.57.1 0.24.0 __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.reward_trainer import (Any, AutoModelForSequenceClassification, AutoTokenizer, BaseTrainer, Callable, DataCollator, DataCollatorForPreference, Dataset, EvalPrediction, IterableDataset, Optional, PartialState, Path, PeftConfig, PreTrainedModel, PreTrainedTokenizerBase, RewardConfig, RewardTrainer, TrainerCallback, Union, clone_chat_template, contextlib, dataclass, defaultdict, disable_dropout_in_model, get_act_offloading_ctx_manager, is_conversational, logger, logging, nn, os, pad, prepare_peft_model, re, remove_none_values, suppress_from_pretrained_warning, torch, transformers, Optional, PreTrainedModel, logger, os, re, 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 import inspect 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 def align_logprobs_with_mask( logprob_tensor: torch.Tensor, attention_mask: torch.Tensor, pad_value: float = 0.0 ) -> torch.Tensor: """ Aligns a log probability tensor with a given attention mask. """ device = logprob_tensor.device batch_size, logprob_seq_len = logprob_tensor.shape mask_seq_len = attention_mask.shape[1] padded_logprobs = torch.full( attention_mask.shape, fill_value=pad_value, dtype=logprob_tensor.dtype, device=device ) left_pad_counts = torch.argmax(attention_mask, dim=1) cols = torch.arange(logprob_seq_len, device=device) dest_indices = left_pad_counts.unsqueeze(1) + cols # Create destination row indices # Shape: [batch_size, logprob_seq_len] row_indices = torch.arange(batch_size, device=device).unsqueeze(1).expand_as(dest_indices) # --- 4. Filter out-of-bounds indices and perform assignment --- # Create a mask to identify only the indices that are within the bounds # of the target tensor's sequence length. valid_mask = dest_indices < mask_seq_len # Use this mask to select only the valid row indices, column indices, # and the corresponding values from the logprob tensor. # This flattens the selected elements into 1D tensors. valid_rows = row_indices[valid_mask] valid_cols = dest_indices[valid_mask] valid_vals = logprob_tensor[valid_mask] # Place the valid values into their correct positions in the padded tensor # using a single, efficient advanced indexing operation. padded_logprobs[valid_rows, valid_cols] = valid_vals return padded_logprobs @dataclass class UnslothRewardConfig(RewardConfig): """ Configuration class for the [`RewardTrainer`]. This class includes only the parameters that are specific to Reward 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: > Parameters that control the model model_init_kwargs (`dict[str, Any]`, *optional*): Keyword arguments for [`~transformers.AutoModelForCausalLM.from_pretrained`], used when the `model` argument of the [`RewardTrainer`] is provided as a string. If you're training a MoE architecture and want to include the load balancing/auxilliary loss as a part of the final loss, remember to set `output_router_logits=True` in this dictionary. chat_template_path (`str`, *optional*): If specified, sets the model's chat template. This can either be the path to a tokenizer (local directory or Hugging Face Hub model) or a direct path to a Jinja template file. When using a Jinja file, you must ensure that any special tokens referenced in the template are added to the tokenizer and that the model's embedding layer is resized accordingly. disable_dropout (`bool`, *optional*, defaults to `True`): Whether to disable dropout in the model. > Parameters that control the data preprocessing dataset_num_proc (`int`, *optional*): Number of processes to use for processing the dataset. eos_token (`str`, *optional*): Token used to indicate the end of a turn or sequence. If `None`, it defaults to `processing_class.eos_token`. pad_token (`str`, *optional*): Token used for padding. If `None`, it defaults to `processing_class.pad_token`, or if that is also `None`, it falls back to `processing_class.eos_token`. max_length (`int` or `None`, *optional*, defaults to `1024`): Maximum length of the tokenized sequence. Samples are filtered out if either chosen or rejected sequence exceeds this value. If `None`, no filtering is applied. pad_to_multiple_of (`int`, *optional*): If set, the sequences will be padded to a multiple of this value. > Parameters that control the training center_rewards_coefficient (`float`, *optional*): Coefficient to incentivize the reward model to output mean-zero rewards (proposed by https://huggingface.co/papers/2312.09244, Eq. 2). Recommended value: `0.01`. activation_offloading (`bool`, *optional*, defaults to `False`): Whether to offload the activations to the CPU. """ 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, 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 = None, 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, project = 'huggingface', trackio_space_id = 'trackio', 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, model_init_kwargs = None, chat_template_path = None, disable_dropout = True, dataset_num_proc = None, eos_token = None, pad_token = None, max_length = 1024, pad_to_multiple_of = None, center_rewards_coefficient = None, activation_offloading = False, 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) if os.environ.get('UNSLOTH_ENABLE_FLEX_ATTENTION', '0') == '1': from unsloth_zoo.flex_attention import HAS_FLEX_ATTENTION if HAS_FLEX_ATTENTION and pad_to_multiple_of is None: from unsloth_zoo.flex_attention import FLEX_ATTENTION_BLOCK_SIZE pad_to_multiple_of = FLEX_ATTENTION_BLOCK_SIZE 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, 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, project = project, trackio_space_id = trackio_space_id, 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, model_init_kwargs = model_init_kwargs, chat_template_path = chat_template_path, disable_dropout = disable_dropout, dataset_num_proc = dataset_num_proc, eos_token = eos_token, pad_token = pad_token, max_length = max_length, pad_to_multiple_of = pad_to_multiple_of, center_rewards_coefficient = center_rewards_coefficient, activation_offloading = activation_offloading,**kwargs) self.vllm_sampling_params = vllm_sampling_params self.unsloth_num_chunks = unsloth_num_chunks self.max_seq_length = max_seq_length pass class _UnslothRewardTrainer(BaseTrainer): """""" _tag_names = ["trl", "reward-trainer"] _name = "Reward" _template_file = "rm_model_card.md" def __init__( self, model: Union[str, PreTrainedModel], args: Optional[RewardConfig] = None, data_collator: Optional[DataCollator] = None, train_dataset: Optional[Union[Dataset, IterableDataset]] = None, eval_dataset: Optional[Union[Dataset, dict[str, Dataset]]] = None, processing_class: Optional[PreTrainedTokenizerBase] = None, compute_metrics: Optional[Callable[[EvalPrediction], dict]] = None, callbacks: Optional[list[TrainerCallback]] = None, optimizers: tuple[Optional[torch.optim.Optimizer], Optional[torch.optim.lr_scheduler.LambdaLR]] = (None, None), optimizer_cls_and_kwargs: Optional[tuple[type[torch.optim.Optimizer], dict[str, Any]]] = None, preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None, peft_config: Optional["PeftConfig"] = None, ): # Args if args is None: model_name = model if isinstance(model, str) else model.config._name_or_path model_name = model_name.split("/")[-1] args = RewardConfig(f"{model_name}-Reward") # Model model_init_kwargs = args.model_init_kwargs or {} if isinstance(model, str): model_id = model dtype = model_init_kwargs.get("dtype") if isinstance(dtype, torch.dtype) or dtype == "auto" or dtype is None: pass # dtype is already a torch.dtype or "auto" or None elif isinstance(dtype, str) and dtype in ["bfloat16", "float16", "float32"]: model_init_kwargs["dtype"] = getattr(torch, dtype) else: raise ValueError( "Invalid `dtype` passed to `RewardConfig`. Expected either 'auto' or a string representing " f"a valid `torch.dtype` (e.g., 'float32'), but got {dtype}." ) with suppress_from_pretrained_warning(transformers.modeling_utils.logger): model = AutoModelForSequenceClassification.from_pretrained(model_id, num_labels=1, **model_init_kwargs) else: model_id = model.config._name_or_path if args.model_init_kwargs is not None: logger.warning( "You passed `model_init_kwargs` to the `RewardConfig`, but your model is already instantiated. " "The `model_init_kwargs` will be ignored." ) # Processing class if processing_class is None: processing_class = AutoTokenizer.from_pretrained(model_id) # Handle pad token for processors or tokenizers if args.eos_token is not None: eos_token = args.eos_token eos_token_id = processing_class.convert_tokens_to_ids(eos_token) if eos_token_id is None: raise ValueError( f"The specified `eos_token` ('{eos_token}') is not found in the vocabulary of the given " f"`processing_class` ({processing_class.__class__.__name__}). Ensure that the `eos_token` exists " "in the vocabulary before using it as an EOS token." ) processing_class.eos_token_id = eos_token_id if args.chat_template_path is not None: if os.path.isfile(args.chat_template_path) and args.chat_template_path.endswith((".jinja", ".j2")): with open(args.chat_template_path, encoding="utf-8") as chat_template_file: processing_class.chat_template = chat_template_file.read() added_tokens = [] else: model, processing_class, added_tokens = clone_chat_template( model, processing_class, args.chat_template_path ) else: added_tokens = [] # PEFT configuration and model wrapping if False: if added_tokens: # Ensure that the added tokens are trainable if peft_config.trainable_token_indices is None: peft_config.trainable_token_indices = {"embed_tokens": added_tokens} elif "embed_tokens" not in peft_config.trainable_token_indices: peft_config.trainable_token_indices["embed_tokens"] = added_tokens else: peft_config.trainable_token_indices["embed_tokens"].extend(added_tokens) # Ensure that the lm_head is trainable if peft_config.modules_to_save is None or "lm_head" not in peft_config.modules_to_save: logger.warning( "Cloning chat template added new tokens to the tokenizer, but 'lm_head' is not in PEFT's " "`modules_to_save`. As a result, the model may not learn to generate outputs with these new " "tokens, leading to degraded generation quality. To fix this, add " "`modules_to_save=['lm_head']` to your PEFT configuration." ) if peft_config.modules_to_save is None: peft_config.modules_to_save = ["lm_head"] else: peft_config.modules_to_save.append("lm_head") if False: model = prepare_peft_model(model, peft_config, args) # Disable dropout in the model if args.disable_dropout: disable_dropout_in_model(model) # Pad token [needed for SequenceClassification models] # If not provided, use the one from the processing class or the eos token if the processing class does not have # a pad token. pad_token = args.pad_token or processing_class.pad_token or processing_class.eos_token pad_token_id = processing_class.convert_tokens_to_ids(pad_token) if pad_token_id is None: raise ValueError( f"The specified `pad_token` ('{pad_token}') is not found in the vocabulary of the given " f"`processing_class` ({processing_class.__class__.__name__}). Ensure that the `pad_token` exists " "in the vocabulary before using it as a padding token." ) model.config.pad_token_id = pad_token_id processing_class.pad_token_id = pad_token_id # Data collator if data_collator is None: data_collator = DataCollatorForPreference( pad_token_id=pad_token_id, pad_to_multiple_of=args.pad_to_multiple_of, ) # Dataset train_dataset = self._prepare_dataset(train_dataset, processing_class, args, "train") if eval_dataset is not None: if isinstance(eval_dataset, dict): eval_dataset = { key: self._prepare_dataset(dataset, processing_class, args, key) for key, dataset in eval_dataset.items() } else: eval_dataset = self._prepare_dataset(eval_dataset, processing_class, args, "eval") # Initialize the metrics self._metrics = {"train": defaultdict(list), "eval": defaultdict(list)} self._total_train_tokens = 0 # Initialize the Trainer. Parent class will handle: # - DeepSpeed configuration [through create_accelerator_and_postprocess] # - FSDP setup # - Distributed training setup # - Optimizer and scheduler creation super().__init__( model=model, args=args, data_collator=data_collator, train_dataset=train_dataset, eval_dataset=eval_dataset, processing_class=processing_class, compute_metrics=compute_metrics, callbacks=callbacks, optimizers=optimizers, optimizer_cls_and_kwargs=optimizer_cls_and_kwargs, preprocess_logits_for_metrics=preprocess_logits_for_metrics, ) # During evaluation, Trainer calls compute_loss[] only if can_return_loss is True and label_names is empty. self.can_return_loss = True self.label_names = [] # Initialize activation offloading context if self.args.activation_offloading: self.maybe_activation_offload_context = get_act_offloading_ctx_manager(model=self.model) else: self.maybe_activation_offload_context = contextlib.nullcontext() # 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) self.aux_loss_enabled = getattr(model.config, "output_router_logits", False) def _prepare_dataset( self, dataset: Union[Dataset, IterableDataset], processing_class: PreTrainedTokenizerBase, args: RewardConfig, dataset_name: str, ) -> Union[Dataset, IterableDataset]: # Tabular backends like Arrow/Parquet insert `None` for mismatched keys in nested structures. Clean them from # sampled data. if isinstance(dataset, Dataset): # IterableDataset does not support `with_transform` dataset = dataset.with_transform(remove_none_values) # If the dataset is already preprocessed (tokenized), skip the processing steps. column_names = list(next(iter(dataset)).keys()) is_processed = "chosen_input_ids" in column_names and "rejected_input_ids" in column_names # Build the kwargs for the `map` function map_kwargs = {} if isinstance(dataset, Dataset): # IterableDataset does not support num_proc map_kwargs["num_proc"] = args.dataset_num_proc with PartialState().main_process_first(): if not is_processed: # Add EOS token to the end of the sequences if needed first_example = next(iter(dataset)) if not is_conversational(first_example): if isinstance(dataset, Dataset): # `IterableDataset.map` does not support `desc` map_kwargs["desc"] = f"Adding EOS to {dataset_name} dataset" def add_eos(example, eos_token): if not example["chosen"].endswith(eos_token): example["chosen"] = example["chosen"] + eos_token if "rejected" in example and not example["rejected"].endswith(eos_token): example["rejected"] = example["rejected"] + eos_token return example dataset = dataset.map( add_eos, fn_kwargs={"eos_token": processing_class.eos_token}, **map_kwargs, ) # Tokenize the dataset if isinstance(dataset, Dataset): # `IterableDataset.map` does not support `desc` map_kwargs["desc"] = f"Tokenizing {dataset_name} dataset" def tokenize_fn(example, processing_class): if "prompt" in example: # explicit prompt case example["chosen"] = example["prompt"] + example["chosen"] example["rejected"] = example["prompt"] + example["rejected"] if is_conversational(example): chosen_input_ids = processing_class.apply_chat_template( example["chosen"], tools=example.get("tools"), **example.get("chat_template_kwargs", {}), ) rejected_input_ids = processing_class.apply_chat_template( example["rejected"], tools=example.get("tools"), **example.get("chat_template_kwargs", {}), ) output = {"chosen_input_ids": chosen_input_ids, "rejected_input_ids": rejected_input_ids} else: output = { "chosen_input_ids": processing_class(text=example["chosen"])["input_ids"], "rejected_input_ids": processing_class(text=example["rejected"])["input_ids"], } return output dataset = dataset.map(tokenize_fn, fn_kwargs={"processing_class": processing_class}, **map_kwargs) # Filter samples that are longer than `max_length` if args.max_length is not None: if isinstance(dataset, Dataset): # `IterableDataset.map` does not support `desc` map_kwargs["desc"] = f"Filtering {dataset_name} >{args.max_length} tokens" dataset = dataset.filter( lambda example: len(example["chosen_input_ids"]) <= args.max_length and len(example["rejected_input_ids"]) <= args.max_length, **map_kwargs, ) return dataset def _set_signature_columns_if_needed(self): # If `self.args.remove_unused_columns` is True, non-signature columns are removed. # By default, this method sets `self._signature_columns` to the model's expected inputs (usually, "input_ids" # and "attention_mask"). if self._signature_columns is None: self._signature_columns = ["chosen_input_ids", "rejected_input_ids", "margin"] def compute_loss( self, model: nn.Module, inputs: dict[str, Union[torch.Tensor, Any]], return_outputs: bool = False, num_items_in_batch: Optional[torch.Tensor] = None, ): """ Compute training loss and additionally compute token accuracies """ mode = "train" if self.model.training else "eval" # If not set, defaults from model config and may warn since cache isn't compatible with gradient checkpointing inputs["use_cache"] = False outputs = model(**inputs) # Split the rewards into chosen and rejected rewards_chosen, rewards_rejected = torch.chunk(outputs.logits.squeeze(-1), chunks=2) # Calculate loss, optionally modulate with margin if "margin" in inputs: loss = -nn.functional.logsigmoid(rewards_chosen - rewards_rejected - inputs["margin"]).mean() else: loss = -nn.functional.logsigmoid(rewards_chosen - rewards_rejected).mean() if self.args.center_rewards_coefficient is not None: loss += self.args.center_rewards_coefficient * torch.mean((rewards_chosen + rewards_rejected) ** 2) if mode == "train": num_tokens_in_batch = self.accelerator.gather_for_metrics(inputs["attention_mask"].sum()).sum().item() self._total_train_tokens += num_tokens_in_batch self._metrics[mode]["num_tokens"] = [self._total_train_tokens] # Compute min, mean, max, accuracy and margin with torch.no_grad(): all_rewards = self.accelerator.gather(outputs.logits) self._metrics[mode]["min_reward"].append(all_rewards.min().item()) self._metrics[mode]["mean_reward"].append(all_rewards.mean().item()) self._metrics[mode]["max_reward"].append(all_rewards.max().item()) mean_accuracy = (rewards_chosen > rewards_rejected).float().mean() mean_accuracy = self.accelerator.gather_for_metrics(mean_accuracy).mean().item() self._metrics[mode]["accuracy"].append(mean_accuracy) mean_margin = (rewards_chosen - rewards_rejected).mean() mean_margin = self.accelerator.gather_for_metrics(mean_margin).mean() self._metrics[mode]["margin"].append(mean_margin.item()) return (loss, outputs) if return_outputs else loss # Override training step to add activation offloading context. def training_step(self, *args, **kwargs): with self.maybe_activation_offload_context: return super().training_step(*args, **kwargs) def log(self, logs: dict[str, float], start_time: Optional[float] = None) -> None: mode = "train" if self.model.training else "eval" metrics = {key: sum(val) / len(val) for key, val in self._metrics[mode].items()} # average the metrics # This method can be called both in training and evaluation. When called in evaluation, the keys in `logs` # start with "eval_". We need to add the prefix "eval_" to the keys in `metrics` to match the format. if mode == "eval": metrics = {f"eval_{key}": val for key, val in metrics.items()} logs.update(metrics) super().log(logs, start_time) self._metrics[mode].clear() # 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) class UnslothRewardTrainer(_UnslothRewardTrainer): """ Trainer for Outcome-supervised Reward Models (ORM). This class is a wrapper around the [`~transformers.Trainer`] class and inherits all of its attributes and methods. Example: ```python from trl import RewardTrainer from datasets import load_dataset dataset = load_dataset("trl-lib/ultrafeedback_binarized", split="train") trainer = RewardTrainer(model="Qwen/Qwen2.5-0.5B-Instruct", train_dataset=dataset) trainer.train() ``` Args: model (`Union[str, PreTrainedModel]`): Model to be trained. Can be either: - A string, being the *model id* of a pretrained model hosted inside a model repo on huggingface.co, or a path to a *directory* containing model weights saved using [`~transformers.PreTrainedModel.save_pretrained`], e.g., `'./my_model_directory/'`. The model is loaded using `AutoModelForSequenceClassification.from_pretrained` with the keyword arguments in `args.model_init_kwargs`. - A sequence classification [`~transformers.PreTrainedModel`] object. args ([`RewardConfig`], *optional*): Configuration for this trainer. If `None`, a default configuration is used. data_collator ([`~transformers.DataCollator`], *optional*): Function to use to form a batch from a list of elements of the processed `train_dataset` or `eval_dataset`. Will default to [`~trainer.reward_trainer.DataCollatorForPreference`]. train_dataset ([`~datasets.Dataset`] or [`~datasets.IterableDataset`]): Dataset to use for training. This trainer supports [preference](#preference) type (both implicit and explicit prompt). The format of the samples can be either: - [Standard](dataset_formats#standard): Each sample contains plain text. - [Conversational](dataset_formats#conversational): Each sample contains structured messages (e.g., role and content). The trainer also supports processed datasets (tokenized) as long as they contain an `chosen_input_ids` and `rejected_input_ids` fields. eval_dataset ([`~datasets.Dataset`], [`~datasets.IterableDataset`] or `dict[str, Union[Dataset, IterableDataset]]`): Dataset to use for evaluation. It must meet the same requirements as `train_dataset`. processing_class ([`~transformers.PreTrainedTokenizerBase`], *optional*): Tokenizer used to process the data. If `None`, the tokenizer is loaded from the model's name with [`~transformers.AutoTokenizer.from_pretrained`]. A padding token, `processing_class.pad_token`, must be set. If the processing class has not set a padding token, `processing_class.eos_token` will be used as the default. compute_metrics (`Callable[[EvalPrediction], dict]`, *optional*): The function that will be used to compute metrics at evaluation. Must take a [`~transformers.EvalPrediction`] and return a dictionary string to metric values. When passing [`RewardConfig`] with `batch_eval_metrics` set to `True`, your `compute_metrics` function must take a boolean `compute_result` argument. This will be triggered after the last eval batch to signal that the function needs to calculate and return the global summary statistics rather than accumulating the batch-level statistics. callbacks (list of [`~transformers.TrainerCallback`], *optional*): List of callbacks to customize the training loop. Will add those to the list of default callbacks detailed in [here](https://huggingface.co/docs/transformers/main_classes/callback). If you want to remove one of the default callbacks used, use the [`~transformers.Trainer.remove_callback`] method. optimizers (`tuple[Optional[torch.optim.Optimizer], Optional[torch.optim.lr_scheduler.LambdaLR]]`, *optional*, defaults to `(None, None)`): A tuple containing the optimizer and the scheduler to use. Will default to an instance of `AdamW` on your model and a scheduler given by [`~transformers.get_linear_schedule_with_warmup`] controlled by `args`. optimizer_cls_and_kwargs (`tuple[Type[torch.optim.Optimizer], Dict[str, Any]]`, *optional*): A tuple containing the optimizer class and keyword arguments to use. Overrides `optim` and `optim_args` in `args`. Incompatible with the `optimizers` argument. Unlike `optimizers`, this argument avoids the need to place model parameters on the correct devices before initializing the Trainer. preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`, *optional*): A function that preprocess the logits right before caching them at each evaluation step. Must take two tensors, the logits and the labels, and return the logits once processed as desired. The modifications made by this function will be reflected in the predictions received by `compute_metrics`. Note that the labels (second parameter) will be `None` if the dataset does not have them. peft_config ([`~peft.PeftConfig`], *optional*): PEFT configuration used to wrap the model. If `None`, the model is not wrapped. Note that if the loaded model is a causal LM, it's highly recommended to set `modules_to_save=["score"]` in the PEFT configuration to ensure that the reward head is properly trained. """ def __init__( self, model, args = None, data_collator = None, train_dataset = None, eval_dataset = None, processing_class = None, compute_metrics = None, callbacks = None, optimizer_cls_and_kwargs = None, preprocess_logits_for_metrics = None, peft_config = None, **kwargs ): if args is None: args = UnslothRewardConfig() 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('reward_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, compute_metrics = compute_metrics, callbacks = callbacks, optimizer_cls_and_kwargs = optimizer_cls_and_kwargs, 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 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`"))