""" 2025.3.15 2025.3.17 4.50.0.dev0 0.15.2 __UNSLOTH_VERSIONING__ """ from torch import Tensor import torch import torch.nn as nn from torch.nn import functional as F from trl.trainer.grpo_trainer import (Any, AutoModelForCausalLM, AutoModelForSequenceClassification, AutoTokenizer, Dataset, GRPOConfig, GRPOTrainer, GenerationConfig, IterableDataset, Optional, PeftConfig, PreTrainedModel, PreTrainedTokenizerBase, RepeatRandomSampler, RewardFunc, Sampler, SyncRefModelCallback, Trainer, TrainerCallback, Union, apply_chat_template, broadcast_object_list, create_reference_model, defaultdict, gather, gather_object, generate_model_card, get_comet_experiment_url, is_conversational, is_deepspeed_zero3_enabled, is_peft_model, is_wandb_available, maybe_apply_chat_template, nn, os, pad, patch, prepare_deepspeed, set_seed, textwrap, torch, transformers, unwrap_model_for_generation, version, warnings, os, torch, transformers, Any, Union, apply_chat_template, broadcast_object_list, gather, gather_object, is_conversational, maybe_apply_chat_template, nn, os, pad, torch, unwrap_model_for_generation, GRPOTrainer, Trainer, gather, 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 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 selective_log_softmax(logits, index): logits = logits.to(torch.float32) selected_logits = torch.gather(logits, dim = -1, index = index.unsqueeze(-1)).squeeze(-1) # loop to reduce peak mem consumption # logsumexp_values = torch.stack([torch.logsumexp(lg, dim=-1) for lg in logits]) logsumexp_values = torch.logsumexp(logits, dim = -1) per_token_logps = selected_logits - logsumexp_values # log_softmax(x_i) = x_i - logsumexp(x) return per_token_logps def grpo_compute_loss(old_logits, new_logits, input_ids, mask, beta, advantages): # All Unsloth Zoo code licensed under LGPLv3 old_logits = old_logits.to(torch.float32) new_logits = new_logits.to(torch.float32) input_ids = input_ids.unsqueeze(-1) # x_i - logsumexp(x_i) old_x = torch.gather(old_logits, dim = -1, index = input_ids).squeeze(-1) new_x = torch.gather(new_logits, dim = -1, index = input_ids).squeeze(-1) old = old_x - torch.logsumexp(old_logits, dim = -1) new = new_x - torch.logsumexp(new_logits, dim = -1) # Reverse KL kl_i = torch.exp(old - new) - (old - new) - 1.0 # Full correct reverse KL divergence?? Missing term maybe? # kl_i = torch.exp(new) * kl_i # Below is forward KL (normal KL) # kl_i = torch.exp(old) * (old - new) # Must detach - otherwise gradients are not propagated correctly! # exp(x - x) == 1 loss_i = torch.exp(new - new.detach()) * advantages.unsqueeze(1) loss_i = -(loss_i - beta * kl_i) mask = mask.to(torch.float32) n_mask_per_reward = mask.sum(1) # See https://github.com/huggingface/trl/pull/2881 loss_per_reward = (loss_i * mask).sum(1) / n_mask_per_reward loss = loss_per_reward.mean() # loss = (loss_i * mask).sum() / mask.sum() # Get metrics as well which are folded with torch.inference_mode(): completion_length = n_mask_per_reward.mean() mean_kl_per_reward = (kl_i * mask).sum(1) / n_mask_per_reward mean_kl = mean_kl_per_reward.mean() pass return loss, completion_length, mean_kl class UnslothEfficientGRPO(torch.autograd.Function): # All Unsloth Zoo code licensed under LGPLv3 @staticmethod def forward(ctx, _new_hidden_states, _old_hidden_states, lm_head, _input_ids, _mask, _advantages, beta, scaler = None, n_chunks = 1): def compute_loss(new_hidden_states, old_hidden_states, input_ids, mask, advantages, scaling): new_logits = torch.matmul(new_hidden_states, lm_head.t()) new_logits = new_logits[:, :-1, :] # exclude the last logit: it corresponds to the next token pred old_logits = torch.matmul(old_hidden_states, lm_head.t()) old_logits = old_logits[:, :-1, :] # exclude the last logit: it corresponds to the next token pred loss, completion_length, mean_kl = grpo_compute_loss( old_logits, new_logits, input_ids, mask, beta, advantages, ) # Scale loss if needed for mixed precision training scaled_loss = loss * scaling # Must add .loss.detach otherwise autograd uses 2x VRAM return scaled_loss, (loss.detach(), completion_length, mean_kl,) pass device =_new_hidden_states.device grad_inputs = torch.empty_like(_new_hidden_states) accumulated_loss = torch.zeros(1, device = device) accumulated_completion_length = torch.zeros(1, device = device) accumulated_mean_kl = torch.zeros(1, device = device) def accumulate_chunk(new_hidden_states_j, old_hidden_states_j, input_ids_j, mask_j, advantages_j, scaling): (chunk_grad_input,), (chunk_loss, (unscaled_loss, chunk_completion_length, chunk_mean_kl,)) = torch.func.grad_and_value( compute_loss, argnums = (0,), has_aux = True, )(new_hidden_states_j, old_hidden_states_j, input_ids_j, mask_j, advantages_j, scaling) accumulated_loss .add_(unscaled_loss) accumulated_completion_length.add_(chunk_completion_length) accumulated_mean_kl .add_(chunk_mean_kl) return chunk_grad_input pass accumulate_chunk = torch.compile( accumulate_chunk, fullgraph = True, options = torch_compile_options, ) grad_inputs_chunks = torch.chunk(grad_inputs, chunks = n_chunks, dim = 0) new_hidden_states = torch.chunk(_new_hidden_states, chunks = n_chunks, dim = 0) old_hidden_states = torch.chunk(_old_hidden_states, chunks = n_chunks, dim = 0) input_ids = torch.chunk(_input_ids, chunks = n_chunks, dim = 0) mask = torch.chunk(_mask, chunks = n_chunks, dim = 0) advantages = torch.chunk(_advantages, chunks = n_chunks, dim = 0) # Get mixed precision scaling if seen scaling = scaler.get_scale() if scaler is not None else 1.0 # Force torch.compile to use dynamic shapes for seqlen dim mark_dynamic = lambda x: torch._dynamo.mark_dynamic(x, 1) for (grad_inputs_j, new_hidden_states_j, old_hidden_states_j, input_ids_j, mask_j, advantages_j,) in \ zip(grad_inputs_chunks, new_hidden_states, old_hidden_states, input_ids, mask, advantages): mark_dynamic(new_hidden_states_j) mark_dynamic(old_hidden_states_j) mark_dynamic(input_ids_j) mark_dynamic(mask_j) grad_inputs_j.copy_( accumulate_chunk(new_hidden_states_j, old_hidden_states_j, input_ids_j, mask_j, advantages_j, scaling) ) pass grad_inputs .div_(n_chunks) accumulated_loss .div_(n_chunks) accumulated_completion_length.div_(n_chunks) accumulated_mean_kl .div_(n_chunks) ctx.save_for_backward(grad_inputs) return ( accumulated_loss, accumulated_completion_length, accumulated_mean_kl, ) pass @staticmethod def backward(ctx, grad_output, dcompletion_length, dmean_kl): (grad_input,) = ctx.saved_tensors return (grad_input, None, None, None, None, None, None, None, None,) pass def grpo_accumulated_loss( trainer, input_ids, logits_to_keep, completion_mask, advantages, n_chunks = -1, ): # All Unsloth Zoo code licensed under LGPLv3 bsz, qlen = input_ids.shape # Find closest multiple factors = [i for i in range(1, bsz + 1) if bsz % i == 0] if n_chunks == -1: n_chunks = bsz n_chunks = factors[min(np.searchsorted(factors, n_chunks), len(factors)-1)] mixed_dtype = torch.float16 if os.environ.get('ACCELERATE_MIXED_PRECISION', 'fp16') == 'fp16' else torch.bfloat16 os.environ["UNSLOTH_RETURN_HIDDEN_STATES"] = "1" completion_input_ids = input_ids[:, -logits_to_keep:] lm_head = trainer.model.get_output_embeddings().weight with torch.amp.autocast(device_type = "cuda", dtype = mixed_dtype): with torch.inference_mode(), trainer.accelerator.unwrap_model(trainer.model, keep_fp32_wrapper = False).disable_adapter(): old_hidden_states = trainer.model(input_ids = input_ids, logits_to_keep = logits_to_keep + 1).logits pass new_hidden_states = trainer.model(input_ids = input_ids, logits_to_keep = logits_to_keep + 1).logits loss, completion_length, mean_kl = UnslothEfficientGRPO.apply( new_hidden_states, old_hidden_states, lm_head, completion_input_ids, completion_mask, advantages, trainer.beta, trainer.accelerator.scaler, n_chunks, ) return loss, completion_length, mean_kl # Old non efficient code path new_logits = torch.matmul(new_hidden_states, lm_head.t()) new_logits = new_logits[:, :-1, :] # exclude the last logit: it corresponds to the next token pred old_logits = torch.matmul(old_hidden_states, lm_head.t()) old_logits = old_logits[:, :-1, :] # exclude the last logit: it corresponds to the next token pred loss, completion_length, mean_kl = grpo_compute_loss( old_logits, new_logits, completion_input_ids, completion_mask, trainer.beta, advantages, ) return loss, completion_length, mean_kl pass @torch.compile(dynamic = True, fullgraph = True, options = torch_compile_options) def grpo_compute_loss_slow(old_logits, new_logits, input_ids, mask, beta, advantages): # All Unsloth Zoo code licensed under LGPLv3 old_logits = old_logits.to(torch.float32) new_logits = new_logits.to(torch.float32) input_ids = input_ids.unsqueeze(-1) # x_i - logsumexp(x_i) old_x = torch.gather(old_logits, dim = -1, index = input_ids).squeeze(-1) new_x = torch.gather(new_logits, dim = -1, index = input_ids).squeeze(-1) old = old_x - torch.logsumexp(old_logits, dim = -1) new = new_x - torch.logsumexp(new_logits, dim = -1) # Reverse KL kl_i = torch.exp(old - new) - (old - new) - 1.0 # Full correct reverse KL divergence?? Missing term maybe? # kl_i = torch.exp(new) * kl_i # Below is forward KL (normal KL) # kl_i = torch.exp(old) * (old - new) # Must detach - otherwise gradients are not propagated correctly! # exp(x - x) == 1 loss_i = torch.exp(new - new.detach()) * advantages.unsqueeze(1) loss_i = -(loss_i - beta * kl_i) mask = mask.to(torch.float32) n_mask_per_reward = mask.sum(1) # See https://github.com/huggingface/trl/pull/2881 loss_per_reward = (loss_i * mask).sum(1) / n_mask_per_reward loss = loss_per_reward.mean() # loss = (loss_i * mask).sum() / mask.sum() # Get metrics as well which are folded with torch.inference_mode(): completion_length = n_mask_per_reward.mean() mean_kl_per_reward = (kl_i * mask).sum(1) / n_mask_per_reward mean_kl = mean_kl_per_reward.mean() pass return loss, completion_length, mean_kl def vLLMSamplingParams(**kwargs): from vllm import SamplingParams sampling_params = SamplingParams(**kwargs) sampling_params._set_kwargs = kwargs return sampling_params @dataclass class UnslothGRPOConfig(GRPOConfig): """ Configuration class for the [`GRPOTrainer`]. Only the parameters specific to GRPO training are listed here. For details on other parameters, refer to the [`~transformers.TrainingArguments`] documentation. 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 and reference model model_init_kwargs (`dict[str, Any]` or `None`, *optional*, defaults to `None`): Keyword arguments for [`~transformers.AutoModelForCausalLM.from_pretrained`], used when the `model` argument of the [`GRPOTrainer`] is provided as a string. > Parameters that control the data preprocessing remove_unused_columns (`bool`, *optional*, defaults to `False`): Whether to only keep the column `"prompt"` in the dataset. If you use a custom reward function that requires any column other than `"prompts"` and `"completions"`, you should keep this to `False`. max_prompt_length (`int` or `None`, *optional*, defaults to `512`): Maximum length of the prompt. If the prompt is longer than this value, it will be truncated left. num_generations (`int` or `None`, *optional*, defaults to `8`): Number of generations per prompt to sample. The global batch size (num_processes * per_device_batch_size) must be divisible by this value. temperature (`float`, *optional*, defaults to `0.9`): Temperature for sampling. The higher the temperature, the more random the completions. max_completion_length (`int` or `None`, *optional*, defaults to `256`): Maximum length of the generated completion. ds3_gather_for_generation (`bool`, *optional*, defaults to `True`): This setting applies to DeepSpeed ZeRO-3. If enabled, the policy model weights are gathered for generation, improving generation speed. However, disabling this option allows training models that exceed the VRAM capacity of a single GPU, albeit at the cost of slower generation. Disabling this option is not compatible with vLLM generation. > Parameters that control generation acceleration powered by vLLM use_vllm (`bool`, *optional*, defaults to `False`): Whether to use vLLM for generating completions. If set to `True`, ensure that a GPU is kept unused for training, as vLLM will require one for generation. vLLM must be installed (`pip install vllm`). vllm_device (`str`, *optional*, defaults to `"auto"`): Device where vLLM generation will run, e.g. `"cuda:1"`. If set to `"auto"` (default), the system will automatically select the next available GPU after the last one used for training. This assumes that training has not already occupied all available GPUs. If only one device is available, the device will be shared between both training and vLLM. vllm_gpu_memory_utilization (`float`, *optional*, defaults to `0.9`): Ratio (between 0 and 1) of GPU memory to reserve for the model weights, activations, and KV cache on the device dedicated to generation powered by vLLM. Higher values will increase the KV cache size and thus improve the model's throughput. However, if the value is too high, it may cause out-of-memory (OOM) errors during initialization. vllm_dtype (`str`, *optional*, defaults to `"auto"`): Data type to use for vLLM generation. If set to `"auto"`, the data type will be automatically determined based on the model configuration. Find the supported values in the vLLM documentation. vllm_max_model_len (`int` or `None`, *optional*, defaults to `None`): If set, the `max_model_len` to use for vLLM. This could be useful when running with reduced `vllm_gpu_memory_utilization`, leading to a reduced KV cache size. If not set, vLLM will use the model context size, which might be much larger than the KV cache, leading to inefficiencies. > Parameters that control the training learning_rate (`float`, *optional*, defaults to `1e-6`): Initial learning rate for [`AdamW`] optimizer. The default value replaces that of [`~transformers.TrainingArguments`]. beta (`float`, *optional*, defaults to `0.04`): KL coefficient. reward_weights (`list[float]` or `None`, *optional*, defaults to `None`): Weights for each reward function. Must match the number of reward functions. If `None`, all rewards are weighted equally with weight `1.0`. sync_ref_model (`bool`, *optional*, defaults to `False`): Whether to synchronize the reference model with the active model every `ref_model_sync_steps` steps, using the `ref_model_mixup_alpha` parameter. This synchronization originites from the [TR-DPO](https://huggingface.co/papers/2404.09656) paper. ref_model_mixup_alpha (`float`, *optional*, defaults to `0.9`): α parameter from the [TR-DPO](https://huggingface.co/papers/2404.09656) paper, which controls the mix between the current policy and the previous reference policy during updates. The reference policy is updated according to the equation: `π_ref = α * π_θ + (1 - α) * π_ref_prev`. To use this parameter, you must set `sync_ref_model=True`. ref_model_sync_steps (`int`, *optional*, defaults to `64`): τ parameter from the [TR-DPO](https://huggingface.co/papers/2404.09656) paper, which determines how frequently the current policy is synchronized with the reference policy. To use this parameter, you must set `sync_ref_model=True`. > Parameters that control the logging log_completions (`bool`, *optional*, defaults to `False`): Whether to log the completions during training. """ 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.'}, ) 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 = False, 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, tp_size = 0, fsdp_transformer_layer_cls_to_wrap = None, accelerator_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, gradient_checkpointing = False, gradient_checkpointing_kwargs = None, include_inputs_for_metrics = False, eval_do_concat_batches = True, fp16_backend = 'auto', evaluation_strategy = None, 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, dispatch_batches = None, split_batches = 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, eval_use_gather_object = False, average_tokens_across_devices = False, model_init_kwargs = None, max_prompt_length = 512, num_generations = 8, temperature = 0.9, max_completion_length = 256, ds3_gather_for_generation = True, use_vllm = False, vllm_device = 'auto', vllm_gpu_memory_utilization = 0.9, vllm_dtype = 'auto', vllm_max_model_len = None, beta = 0.04, reward_weights = None, sync_ref_model = False, ref_model_mixup_alpha = 0.9, ref_model_sync_steps = 64, log_completions = False, vllm_sampling_params = None, unsloth_num_chunks = -1, **kwargs, ): if learning_rate < 1e-7: raise FloatingPointError(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: raise OverflowError(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' div = per_device_train_batch_size // num_generations if div * num_generations != per_device_train_batch_size: print('Unsloth: We now expect `per_device_train_batch_size` to be a multiple of `num_generations`.\nWe will change the batch size of ' + str(per_device_train_batch_size) + ' to the `num_generations` of ' + str(num_generations)) per_device_train_batch_size = num_generations 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, tp_size = tp_size, fsdp_transformer_layer_cls_to_wrap = fsdp_transformer_layer_cls_to_wrap, accelerator_config = accelerator_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, 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, evaluation_strategy = evaluation_strategy, 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, dispatch_batches = dispatch_batches, split_batches = split_batches, 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, eval_use_gather_object = eval_use_gather_object, average_tokens_across_devices = average_tokens_across_devices, model_init_kwargs = model_init_kwargs, max_prompt_length = max_prompt_length, num_generations = num_generations, temperature = temperature, max_completion_length = max_completion_length, ds3_gather_for_generation = ds3_gather_for_generation, use_vllm = use_vllm, vllm_device = vllm_device, vllm_gpu_memory_utilization = vllm_gpu_memory_utilization, vllm_dtype = vllm_dtype, vllm_max_model_len = vllm_max_model_len, beta = beta, reward_weights = reward_weights, sync_ref_model = sync_ref_model, ref_model_mixup_alpha = ref_model_mixup_alpha, ref_model_sync_steps = ref_model_sync_steps, log_completions = log_completions,**kwargs) self.vllm_sampling_params = vllm_sampling_params self.unsloth_num_chunks = unsloth_num_chunks pass class _UnslothGRPOTrainer(Trainer): """""" _tag_names = ["trl", "grpo"] def __init__( self, model: Union[str, PreTrainedModel], reward_funcs: Union[RewardFunc, list[RewardFunc]], args: GRPOConfig = None, train_dataset: Optional[Union[Dataset, IterableDataset]] = None, eval_dataset: Optional[Union[Dataset, IterableDataset, dict[str, Union[Dataset, IterableDataset]]]] = None, processing_class: Optional[PreTrainedTokenizerBase] = None, reward_processing_classes: Optional[Union[PreTrainedTokenizerBase, list[PreTrainedTokenizerBase]]] = None, callbacks: Optional[list[TrainerCallback]] = None, optimizers: tuple[Optional[torch.optim.Optimizer], Optional[torch.optim.lr_scheduler.LambdaLR]] = (None, None), peft_config: Optional["PeftConfig"] = None, ): if hasattr(model, 'vllm_engine') and hasattr(args, 'use_vllm') and (getattr(args, 'use_vllm', False) == False): args.use_vllm = True # 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 = GRPOConfig(f"{model_name}-GRPO") # Models # Trained model model_init_kwargs = args.model_init_kwargs or {} if isinstance(model, str): model_id = model torch_dtype = model_init_kwargs.get("torch_dtype") if isinstance(torch_dtype, torch.dtype) or torch_dtype == "auto" or torch_dtype is None: pass # torch_dtype is already a torch.dtype or "auto" or None elif isinstance(torch_dtype, str): # it's a str, but not "auto" torch_dtype = getattr(torch, torch_dtype) model_init_kwargs["torch_dtype"] = torch_dtype else: raise ValueError( "Invalid `torch_dtype` passed to `GRPOConfig`. Expected either 'auto' or a string representing " f"a `torch.dtype` (e.g., 'float32'), but got {torch_dtype}." ) # Disable caching if gradient checkpointing is enabled (not supported) model_init_kwargs["use_cache"] = ( False if args.gradient_checkpointing else model_init_kwargs.get("use_cache") ) model = AutoModelForCausalLM.from_pretrained(model, **model_init_kwargs) else: model_id = model.config._name_or_path if args.model_init_kwargs is not None: raise ValueError( "You passed `model_init_kwargs` to the `GRPOConfig`, but your model is already instantiated. " "This argument can only be used when the `model` argument is a string." ) if False: model = model # Reference model if is_deepspeed_zero3_enabled(): self.ref_model = AutoModelForCausalLM.from_pretrained(model_id, **model_init_kwargs) elif not is_peft_model(model): # If PEFT configuration is not provided, create a reference model based on the initial model. self.ref_model = create_reference_model(model) else: # If PEFT is used, the reference model is not needed since the adapter can be disabled # to revert to the initial model. self.ref_model = None # Processing class if processing_class is None: processing_class = AutoTokenizer.from_pretrained(model.config._name_or_path, padding_side="left") # Reward functions if not isinstance(reward_funcs, list): reward_funcs = [reward_funcs] for i, reward_func in enumerate(reward_funcs): if isinstance(reward_func, str): reward_funcs[i] = AutoModelForSequenceClassification.from_pretrained( reward_func, num_labels=1, **model_init_kwargs ) self.reward_funcs = reward_funcs # Reward weights if args.reward_weights is not None: if len(args.reward_weights) != len(reward_funcs): raise ValueError( f"Number of reward weights ({len(args.reward_weights)}) must match number of reward " f"functions ({len(reward_funcs)})" ) self.reward_weights = torch.tensor(args.reward_weights, dtype=torch.float32) else: self.reward_weights = torch.ones(len(reward_funcs), dtype=torch.float32) # Reward processing class if reward_processing_classes is None: reward_processing_classes = [None] * len(reward_funcs) elif not isinstance(reward_processing_classes, list): reward_processing_classes = [reward_processing_classes] else: if len(reward_processing_classes) != len(reward_funcs): raise ValueError("The number of reward processing classes must match the number of reward functions.") for i, (reward_processing_class, reward_func) in enumerate(zip(reward_processing_classes, reward_funcs)): if isinstance(reward_func, PreTrainedModel): if reward_processing_class is None: reward_processing_class = AutoTokenizer.from_pretrained(reward_func.config._name_or_path) if reward_processing_class.pad_token_id is None: reward_processing_class.pad_token = reward_processing_class.eos_token # The reward model computes the reward for the latest non-padded token in the input sequence. # So it's important to set the pad token ID to the padding token ID of the processing class. reward_func.config.pad_token_id = reward_processing_class.pad_token_id reward_processing_classes[i] = reward_processing_class self.reward_processing_classes = reward_processing_classes # Data collator def data_collator(features): # No data collation is needed in GRPO return features # Training arguments self.max_prompt_length = args.max_prompt_length self.max_completion_length = args.max_completion_length # = |o_i| in the GRPO paper self.num_generations = args.num_generations # = G in the GRPO paper self.use_vllm = args.use_vllm self.beta = args.beta # 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 GRPO, the sampled data does not include the # "input_ids" key. Instead, the available keys is "prompt". 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 # Initialize the metrics self._metrics = defaultdict(list) self.log_completions = args.log_completions super().__init__( model=model, args=args, data_collator=data_collator, train_dataset=train_dataset, eval_dataset=eval_dataset, processing_class=processing_class, callbacks=callbacks, optimizers=optimizers, ) # Check if the per_device_train/eval_batch_size * num processes can be divided by the number of generations num_processes = self.accelerator.num_processes global_batch_size = args.per_device_train_batch_size * num_processes possible_values = [n_gen for n_gen in range(2, global_batch_size + 1) if (global_batch_size) % n_gen == 0] if self.num_generations not in possible_values: raise ValueError( f"The global train batch size ({num_processes} x {args.per_device_train_batch_size}) must be evenly " f"divisible by the number of generations per prompt ({self.num_generations}). Given the current train " f"batch size, the valid values for the number of generations are: {possible_values}." ) if self.args.eval_strategy != "no": global_batch_size = args.per_device_eval_batch_size * num_processes possible_values = [n_gen for n_gen in range(2, global_batch_size + 1) if (global_batch_size) % n_gen == 0] if self.num_generations not in possible_values: raise ValueError( f"The global eval batch size ({num_processes} x {args.per_device_eval_batch_size}) must be evenly " f"divisible by the number of generations per prompt ({self.num_generations}). Given the current " f"eval batch size, the valid values for the number of generations are: {possible_values}." ) # Ensure each process receives a unique seed to prevent duplicate completions when generating with # transformers if num_generations exceeds per_device_train_batch_size. We could skip it if we use vLLM, but # it's safer to set it in all cases. set_seed(args.seed, device_specific=True) if self.use_vllm: self.llm = model.vllm_engine; self._last_loaded_step = 0; self.sampling_params = SamplingParams( temperature=args.temperature, max_tokens=self.max_completion_length,**getattr(getattr(args, 'vllm_sampling_params', vLLMSamplingParams()), '_set_kwargs', {}),) else: self.generation_config = GenerationConfig( max_new_tokens=self.max_completion_length, do_sample=True, temperature=args.temperature, pad_token_id=processing_class.pad_token_id, ) # 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 to the model self.model.add_model_tags(self._tag_names) if self.ref_model is not None: 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) if args.sync_ref_model: self.add_callback(SyncRefModelCallback(ref_model=self.ref_model, accelerator=self.accelerator)) for i, reward_func in enumerate(self.reward_funcs): if isinstance(reward_func, PreTrainedModel): self.reward_funcs[i] = self.accelerator.prepare_model(reward_func, evaluation_mode=True) 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. # In GRPOTrainer, we preprocess data, so using the model's signature columns doesn't work. # Instead, we set them to the columns expected by the `training_step` method, hence the override. if self._signature_columns is None: self._signature_columns = ["prompt"] def _get_train_sampler(self) -> Sampler: # Returns a sampler that ensures each prompt is repeated across multiple processes. This guarantees that # identical prompts are distributed to different GPUs, allowing rewards to be computed and normalized correctly # within each prompt group. Using the same seed across processes ensures consistent prompt assignment, # preventing discrepancies in group formation. return RepeatRandomSampler(self.train_dataset, self.num_generations, seed=self.args.seed) def _get_eval_sampler(self, eval_dataset) -> Sampler: # Returns a sampler that ensures each prompt is repeated across multiple processes. This guarantees that # identical prompts are distributed to different GPUs, allowing rewards to be computed and normalized correctly # within each prompt group. Using the same seed across processes ensures consistent prompt assignment, # preventing discrepancies in group formation. return RepeatRandomSampler(eval_dataset, self.num_generations, seed=self.args.seed) # Get the per-token log probabilities for the completions for the model and the reference model def _get_per_token_logps(self, model, input_ids, attention_mask, logits_to_keep): if os.environ.get('UNSLOTH_USE_NEW_MODEL', '0') == '0': return None # Unsloth efficient GRPO # Otherwise, calculate normally: if not hasattr(self, '_autocast_dtype'): self._autocast_dtype = torch.float16 if os.environ.get('ACCELERATE_MIXED_PRECISION', 'fp16') == 'fp16' else torch.bfloat16 if os.environ.get('UNSLOTH_FORCE_FLOAT32', '0') == '1': self._autocast_dtype = torch.float16 with torch.amp.autocast(device_type = 'cuda', dtype = self._autocast_dtype): # We add 1 to `logits_to_keep` because the last logits of the sequence is later excluded logits = model(input_ids=input_ids, attention_mask=attention_mask, logits_to_keep=logits_to_keep + 1).logits logits = logits[:, :-1, :] # (B, L-1, V), exclude the last logit: it corresponds to the next token pred input_ids = input_ids[:, -logits_to_keep:] # For transformers<=4.48, logits_to_keep argument isn't supported, so here we drop logits ourselves. # See https://github.com/huggingface/trl/issues/2770 logits = logits[:, -logits_to_keep:] return logits # return selective_log_softmax(logits, input_ids) # compute logprobs for the input tokens pass def _move_model_to_vllm(self, *args, **kwargs): return None def _prepare_inputs(self, inputs: dict[str, Union[torch.Tensor, Any]]) -> dict[str, Union[torch.Tensor, Any]]: device = self.accelerator.device prompts = [x["prompt"] for x in inputs] prompts_text = [maybe_apply_chat_template(example, self.processing_class)["prompt"] for example in inputs] prompt_inputs = self.processing_class( prompts_text, return_tensors="pt", padding=True, padding_side="left", add_special_tokens=False ) prompt_inputs = super()._prepare_inputs(prompt_inputs) prompt_ids, prompt_mask = prompt_inputs["input_ids"], prompt_inputs["attention_mask"] if self.max_prompt_length is not None: prompt_ids = prompt_ids[:, -self.max_prompt_length :] prompt_mask = prompt_mask[:, -self.max_prompt_length :] # Generate completions using either vLLM or regular generation if self.args.use_vllm: # First, have main process load weights if needed if self.state.global_step != self._last_loaded_step: self._move_model_to_vllm() self._last_loaded_step = self.state.global_step # Generate completions using vLLM: gather all prompts and use them in a single call in the main process all_prompts_text = gather_object(prompts_text) if self.accelerator.is_main_process: outputs = self.llm.generate(all_prompts_text, sampling_params=self.sampling_params, use_tqdm=False, lora_request = self.model.load_lora('grpo_trainer_lora_model', load_tensors = True)) completion_ids = [out.token_ids for completions in outputs for out in completions.outputs] else: completion_ids = [None] * len(all_prompts_text) # Broadcast the completions from the main process to all processes, ensuring each process receives its # corresponding slice. completion_ids = broadcast_object_list(completion_ids, from_process=0) process_slice = slice( self.accelerator.process_index * len(prompts), (self.accelerator.process_index + 1) * len(prompts), ) completion_ids = completion_ids[process_slice] # Pad the completions, and concatenate them with the prompts completion_ids = [torch.tensor(ids, device=device) for ids in completion_ids] completion_ids = pad(completion_ids, padding_value=self.processing_class.pad_token_id) prompt_completion_ids = torch.cat([prompt_ids, completion_ids], dim=1) else: # Regular generation path with unwrap_model_for_generation(self.model, self.accelerator) as unwrapped_model: prompt_completion_ids = unwrapped_model.generate( prompt_ids, attention_mask=prompt_mask, generation_config=self.generation_config ) # Compute prompt length and extract completion ids prompt_length = prompt_ids.size(1) prompt_ids = prompt_completion_ids[:, :prompt_length] completion_ids = prompt_completion_ids[:, prompt_length:] # Mask everything after the first EOS token is_eos = completion_ids == self.processing_class.eos_token_id eos_idx = torch.full((is_eos.size(0),), is_eos.size(1), dtype=torch.long, device=device) eos_idx[is_eos.any(dim=1)] = is_eos.int().argmax(dim=1)[is_eos.any(dim=1)] sequence_indices = torch.arange(is_eos.size(1), device=device).expand(is_eos.size(0), -1) completion_mask = (sequence_indices <= eos_idx.unsqueeze(1)).int() # Concatenate prompt_mask with completion_mask for logit computation attention_mask = torch.cat([prompt_mask, completion_mask], dim=1) # (B*G, P+C) logits_to_keep = completion_ids.size(1) # we only need to compute the logits for the completion tokens with torch.inference_mode(), torch.amp.autocast(device_type = 'cuda', dtype = ((torch.float16 if os.environ.get('ACCELERATE_MIXED_PRECISION', 'fp16') == 'fp16' else torch.bfloat16) if not torch.is_autocast_enabled('cuda') else nullcontext())if os.environ.get('UNSLOTH_FORCE_FLOAT32', '0') == '0' else torch.float16): if self.ref_model is not None: ref_per_token_logps = self._get_per_token_logps( self.ref_model, prompt_completion_ids, attention_mask, logits_to_keep ) else: with self.accelerator.unwrap_model(self.model, keep_fp32_wrapper = False).disable_adapter(): ref_per_token_logps = self._get_per_token_logps( self.model, prompt_completion_ids, attention_mask, logits_to_keep ) # Decode the generated completions completions_text = self.processing_class.batch_decode(completion_ids, skip_special_tokens=True) if is_conversational(inputs[0]): completions = [] for prompt, completion in zip(prompts, completions_text): bootstrap = prompt.pop()["content"] if prompt[-1]["role"] == "assistant" else "" completions.append([{"role": "assistant", "content": bootstrap + completion}]) else: completions = completions_text rewards_per_func = torch.zeros(len(prompts), len(self.reward_funcs), device=device) for i, (reward_func, reward_processing_class) in enumerate( zip(self.reward_funcs, self.reward_processing_classes) ): if isinstance(reward_func, nn.Module): # Module instead of PretrainedModel for compat with compiled models if is_conversational(inputs[0]): messages = [{"messages": p + c} for p, c in zip(prompts, completions)] texts = [apply_chat_template(x, reward_processing_class)["text"] for x in messages] else: texts = [p + c for p, c in zip(prompts, completions)] reward_inputs = reward_processing_class( texts, return_tensors="pt", padding=True, padding_side="right", add_special_tokens=False ) reward_inputs = super()._prepare_inputs(reward_inputs) with torch.inference_mode(), torch.amp.autocast(device_type = 'cuda', dtype = ((torch.float16 if os.environ.get('ACCELERATE_MIXED_PRECISION', 'fp16') == 'fp16' else torch.bfloat16) if not torch.is_autocast_enabled('cuda') else nullcontext())if os.environ.get('UNSLOTH_FORCE_FLOAT32', '0') == '0' else torch.float16): rewards_per_func[:, i] = reward_func(**reward_inputs).logits[:, 0] # Shape (B*G,) else: # Repeat all input columns (but "prompt" and "completion") to match the number of generations keys = [key for key in inputs[0] if key not in ["prompt", "completion"]] reward_kwargs = {key: [example[key] for example in inputs] for key in keys} output_reward_func = reward_func(prompts=prompts, completions=completions, **reward_kwargs) rewards_per_func[:, i] = torch.tensor(output_reward_func, dtype=torch.float32, device=device) # Gather the reward per function: this part is crucial, because the rewards are normalized per group and the # completions may be distributed across processes rewards_per_func = gather(rewards_per_func) # Apply weights to each reward function's output and sum rewards = (rewards_per_func * self.reward_weights.to(device).unsqueeze(0)).sum(dim=1) # Compute grouped-wise rewards mean_grouped_rewards = rewards.view(-1, self.num_generations).mean(dim=1) std_grouped_rewards = rewards.view(-1, self.num_generations).std(dim=1) # Normalize the rewards to compute the advantages mean_grouped_rewards = mean_grouped_rewards.repeat_interleave(self.num_generations, dim=0) std_grouped_rewards = std_grouped_rewards.repeat_interleave(self.num_generations, dim=0) advantages = (rewards - mean_grouped_rewards) / (std_grouped_rewards + 1e-4) # Slice to keep only the local part of the data process_slice = slice( self.accelerator.process_index * len(prompts), (self.accelerator.process_index + 1) * len(prompts), ) advantages = advantages[process_slice] # Log the metrics reward_per_func = rewards_per_func.mean(0) for i, reward_func in enumerate(self.reward_funcs): if isinstance(reward_func, nn.Module): # Module instead of PretrainedModel for compat with compiled models reward_func_name = reward_func.config._name_or_path.split("/")[-1] else: reward_func_name = reward_func.__name__ self._metrics[f"rewards/{reward_func_name}"].append(reward_per_func[i].item()) self._metrics["reward"].append(rewards.mean().item()) self._metrics["reward_std"].append(std_grouped_rewards.mean().item()) if ( self.log_completions and self.state.global_step % self.args.logging_steps == 0 and "wandb" in self.args.report_to ): import pandas as pd # For logging table = { "step": [str(self.state.global_step)] * len(rewards), "prompt": gather_object(prompts_text), "completion": gather_object(completions_text), "reward": rewards.tolist(), } df = pd.DataFrame(table) if wandb.run is not None and self.accelerator.is_main_process: wandb.log({"completions": wandb.Table(dataframe=df)}) return { "prompt_ids": prompt_ids, "prompt_mask": prompt_mask, "completion_ids": completion_ids, "completion_mask": completion_mask, "ref_per_token_logps": ref_per_token_logps, "advantages": advantages, } def compute_loss(self, model, inputs, return_outputs = False, num_items_in_batch = None): if return_outputs: raise ValueError("The GRPOTrainer does not support returning outputs") # Compute the per-token log probabilities for the model prompt_ids, prompt_mask = inputs["prompt_ids"], inputs["prompt_mask"] completion_ids, completion_mask = inputs["completion_ids"], inputs["completion_mask"] input_ids = torch.cat([prompt_ids, completion_ids], dim=1) bsz, qlen = input_ids.shape attention_mask = torch.cat([prompt_mask, completion_mask], dim=1) # attention_mask = None logits_to_keep = completion_ids.size(1) # we only need to compute the logits for the completion tokens _input_ids = input_ids _logits_to_keep = logits_to_keep per_token_logps = self._get_per_token_logps(model, input_ids, attention_mask, logits_to_keep) # Compute the KL divergence between the model and the reference model ref_per_token_logps = inputs["ref_per_token_logps"] # per_token_kl = torch.exp(ref_per_token_logps - per_token_logps) - (ref_per_token_logps - per_token_logps) - 1 # x - x.detach() allows for preserving gradients from x advantages = inputs["advantages"] # per_token_loss = torch.exp(per_token_logps - per_token_logps.detach()) * advantages.unsqueeze(1) # per_token_loss = -(per_token_loss - self.beta * per_token_kl) # loss = ((per_token_loss * completion_mask).sum(dim=1) / completion_mask.sum(dim=1)).mean() input_ids = input_ids[:, -logits_to_keep:] if per_token_logps is not None: loss, completion_length, mean_kl = grpo_compute_loss_slow( ref_per_token_logps, per_token_logps, input_ids, completion_mask, self.beta, advantages, ) else: loss, completion_length, mean_kl = grpo_accumulated_loss( self, _input_ids, logits_to_keep, completion_mask, advantages, n_chunks = self.args.unsloth_num_chunks, ) # Log the metrics # completion_length = self.accelerator.gather_for_metrics(completion_mask.sum(1)).float().mean().item() # mean_kl = ((per_token_kl * completion_mask).sum(dim=1) / completion_mask.sum(dim=1)).mean() # self._metrics["kl"].append(self.accelerator.gather_for_metrics(mean_kl).mean().item()) if "train" in self._metrics: mode = "eval" if self.control.should_evaluate else "train" self._metrics[mode]["completion_length"].append(completion_length.item()) self._metrics[mode]["kl"].append(mean_kl.item()) else: self._metrics["completion_length"].append(completion_length.item()) self._metrics["kl"].append(mean_kl.item()) return loss def prediction_step(self, model, inputs, prediction_loss_only, ignore_keys: Optional[list[str]] = None): inputs = self._prepare_inputs(inputs) with torch.no_grad(): with self.compute_loss_context_manager(): loss = self.compute_loss(model, inputs) loss = loss.mean().detach() return loss, None, None def log(self, logs: dict[str, float], start_time: Optional[float] = None) -> None: metrics = {key: sum(val) / len(val) for key, val in self._metrics.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 next(iter(logs.keys())).startswith("eval_"): metrics = {f"eval_{key}": val for key, val in metrics.items()} logs = {**logs, **metrics} if version.parse(transformers.__version__) >= version.parse("4.47.0.dev0"): super().log(logs, start_time) else: # transformers<=4.46 super().log(logs) self._metrics.clear() 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 tags = tags or [] if isinstance(tags, str): tags = [tags] if hasattr(self.model.config, "unsloth_version"): tags.append("unsloth") citation = textwrap.dedent( """\ @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } """ ) 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.get_url() if is_wandb_available() and wandb.run is not None else None, comet_url=get_comet_experiment_url(), trainer_name="GRPO", trainer_citation=citation, paper_title="DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models", paper_id="2402.03300", ) model_card.save(os.path.join(self.args.output_dir, "README.md")) class UnslothGRPOTrainer(_UnslothGRPOTrainer): """ Trainer for the Group Relative Policy Optimization (GRPO) method. This algorithm was initially proposed in the paper [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). Example: ```python from datasets import load_dataset from trl import GRPOTrainer dataset = load_dataset("trl-lib/tldr", split="train") def reward_func(completions, **kwargs): # Dummy reward function that rewards completions with more unique letters. return [float(len(set(completion))) for completion in completions] trainer = GRPOTrainer( model="Qwen/Qwen2-0.5B-Instruct", reward_funcs=reward_func, 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 [`~transformers.AutoModelForCausalLM.from_pretrained`] with the keywork arguments in `args.model_init_kwargs`. - A [`~transformers.PreTrainedModel`] object. Only causal language models are supported. reward_funcs (`Union[RewardFunc, list[RewardFunc]]`): Reward functions to be used for computing the rewards. To compute the rewards, we call all the reward functions with the prompts and completions and sum the rewards. Can be either: - A single reward function, such as: - A string: 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 [`~transformers.AutoModelForSequenceClassification.from_pretrained`] with `num_labels=1` and the keyword arguments in `args.model_init_kwargs`. - A [`~transformers.PreTrainedModel`] object: Only sequence classification models are supported. - A custom reward function: The function is provided with the prompts and the generated completions, plus any additional columns in the dataset. It should return a list of rewards. For more details, see [Using a custom reward function](#using-a-custom-reward-function). - A list of reward functions, where each item can independently be any of the above types. Mixing different types within the list (e.g., a string model ID and a custom reward function) is allowed. args ([`GRPOConfig`], *optional*, defaults to `None`): Configuration for this trainer. If `None`, a default configuration is used. train_dataset ([`~datasets.Dataset`] or [`~datasets.IterableDataset`]): Dataset to use for training. It must include a column `"prompt"`. Any additional columns in the dataset is ignored. 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). 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*, defaults to `None`): Processing class used to process the data. The padding side must be set to "left". If `None`, the processing class is loaded from the model's name with [`~transformers.AutoTokenizer.from_pretrained`]. reward_processing_classes (`Union[PreTrainedTokenizerBase, list[PreTrainedTokenizerBase]]`, *optional*, defaults to `None`): Processing classes corresponding to the reward functions specified in `reward_funcs`. Can be either: - A single processing class: Used when `reward_funcs` contains only one reward function. - A list of processing classes: Must match the order and length of the reward functions in `reward_funcs`. If set to `None`, or if an element of the list corresponding to a [`~transformers.PreTrainedModel`] is `None`, the tokenizer for the model is automatically loaded using [`~transformers.AutoTokenizer.from_pretrained`]. For elements in `reward_funcs` that are custom reward functions (not [`~transformers.PreTrainedModel`]), the corresponding entries in `reward_processing_classes` are ignored. callbacks (list of [`~transformers.TrainerCallback`], *optional*, defaults to `None`): 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[torch.optim.Optimizer, 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 [`get_linear_schedule_with_warmup`] controlled by `args`. peft_config ([`~peft.PeftConfig`], *optional*, defaults to `None`): PEFT configuration used to wrap the model. If `None`, the model is not wrapped. """ def __init__( self, model, reward_funcs, args = None, train_dataset = None, eval_dataset = None, processing_class = None, reward_processing_classes = None, callbacks = None, peft_config = None, **kwargs ): if args is None: args = UnslothGRPOConfig() use_bf16 = getattr(args, 'bf16', False) use_fp16 = getattr(args, 'fp16', False) force_float32 = False if 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, '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: 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': 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) bf16_full_eval = getattr(args, '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' other_metrics = [] if not isinstance(reward_funcs, list): _reward_funcs = [reward_funcs] else: _reward_funcs = reward_funcs for reward_func in _reward_funcs: try: reward_func_name = reward_func.__name__ other_metrics.append(f'rewards/{reward_func_name}') except: pass from unsloth_zoo.logging_utils import PatchRLStatistics PatchRLStatistics('grpo_trainer', other_metrics) super().__init__( model = model, reward_funcs = reward_funcs, args = args, train_dataset = train_dataset, eval_dataset = eval_dataset, processing_class = processing_class, reward_processing_classes = reward_processing_classes, callbacks = callbacks, peft_config = peft_config,**kwargs) 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 pass