1127 lines
53 KiB
Python
1127 lines
53 KiB
Python
"""
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2025.6.1
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2025.6.2
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4.52.4
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0.18.2
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__UNSLOTH_VERSIONING__
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"""
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from torch import Tensor
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import torch
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import torch.nn as nn
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from torch.nn import functional as F
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from trl.trainer.rloo_trainer import (Accelerator, BaseImageProcessor, Callable, CallbackHandler, DEFAULT_CALLBACKS, DEFAULT_PROGRESS_CALLBACK, DataCollatorWithPadding, DataLoader, Dataset, ExportableState, FeatureExtractionMixin, GenerationConfig, INVALID_LOGPROB, OnlineTrainerState, Optional, PreTrainedTokenizerBase, PrinterCallback, ProcessorMixin, RLOOConfig, RLOOTrainer, Trainer, TrainerCallback, TrainerControl, Union, batch_generation, broadcast, defaultdict, disable_dropout_in_model, empty_cache, exact_div, first_true_indices, forward, gather_object, gc, generate_model_card, get_comet_experiment_url, get_reporting_integration_callbacks, get_reward, is_rich_available, is_wandb_available, log_table_to_comet_experiment, math, nn, np, os, pd, prepare_deepspeed, print_rich_table, textwrap, time, torch, truncate_response, unwrap_model_for_generation)
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import os
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from typing import *
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from dataclasses import dataclass, field
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from packaging.version import Version
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import torch
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import numpy as np
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from contextlib import nullcontext
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from torch.nn import functional as F
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from transformers import DataCollatorForSeq2Seq, DataCollatorForLanguageModeling as TransformersDataCollatorForLanguageModeling
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torch_compile_options = {
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"epilogue_fusion" : True,
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"max_autotune" : False,
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"shape_padding" : True,
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"trace.enabled" : False,
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"triton.cudagraphs" : False,
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}
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@torch.compile(dynamic = True, fullgraph = True, options = torch_compile_options,)
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def selective_log_softmax(logits, index):
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logits = logits.to(torch.float32)
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selected_logits = torch.gather(logits, dim = -1, index = index.unsqueeze(-1)).squeeze(-1)
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# loop to reduce peak mem consumption
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# logsumexp_values = torch.stack([torch.logsumexp(lg, dim=-1) for lg in logits])
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logsumexp_values = torch.logsumexp(logits, dim = -1)
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per_token_logps = selected_logits - logsumexp_values # log_softmax(x_i) = x_i - logsumexp(x)
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return per_token_logps
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@dataclass
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class UnslothRLOOConfig(RLOOConfig):
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"""
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Configuration class for the [`RLOOTrainer`].
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Using [`~transformers.HfArgumentParser`] we can turn this class into
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[argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified on the
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command line.
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Parameters:
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exp_name (`str`, *optional*, defaults to `os.path.basename(__file__)[: -len(".py")]`):
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Name of this experiment.
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reward_model_path (`str`, *optional*, defaults to `"EleutherAI/pythia-160m"`):
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Path to the reward model.
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num_ppo_epochs (`int`, *optional*, defaults to `4`):
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Number of epochs to train.
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whiten_rewards (`bool`, *optional*, defaults to `False`):
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Whether to whiten the rewards.
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kl_coef (`float`, *optional*, defaults to `0.05`):
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KL coefficient.
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cliprange (`float`, *optional*, defaults to `0.2`):
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Clip range.
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rloo_k (`int`, *optional*, defaults to `2`):
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REINFORCE Leave-One-Out (RLOO) number of online samples per prompt.
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normalize_reward (`bool`, *optional*, defaults to `False`):
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Whether to normalize rewards.
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reward_clip_range (`float`, *optional*, defaults to `10.0`):
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Clip range for rewards.
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normalize_advantage (`bool`, *optional*, defaults to `False`):
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Whether to normalize advantages.
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token_level_kl (`bool`, *optional*, defaults to `True`):
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Whether to use token-level KL penalty or sequence-level KL penalty.
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ds3_gather_for_generation (`bool`, *optional*, defaults to `True`):
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This setting applies to DeepSpeed ZeRO-3. If enabled, the policy model weights are gathered for generation,
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improving generation speed. However, disabling this option allows training models that exceed the VRAM
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capacity of a single GPU, albeit at the cost of slower generation.
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"""
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vllm_sampling_params: Optional[Any] = field(
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default = None,
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metadata = {'help': 'vLLM SamplingParams'},
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)
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unsloth_num_chunks : Optional[int] = field(
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default = -1,
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metadata = {'help': 'Chunk size to reduce memory usage. -1 is most efficient.'},
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)
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def __init__(
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self,
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output_dir = None,
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overwrite_output_dir = None,
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do_train = False,
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do_eval = False,
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do_predict = False,
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eval_strategy = 'no',
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prediction_loss_only = False,
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per_device_train_batch_size = 4,
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per_device_eval_batch_size = 4,
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per_gpu_train_batch_size = None,
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per_gpu_eval_batch_size = None,
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gradient_accumulation_steps = 2,
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eval_accumulation_steps = 2,
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eval_delay = 0,
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torch_empty_cache_steps = 250,
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learning_rate = 5e-05,
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weight_decay = 0.01,
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adam_beta1 = 0.9,
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adam_beta2 = 0.999,
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adam_epsilon = 1e-08,
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max_grad_norm = 1.0,
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num_train_epochs = 3.0,
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max_steps = -1,
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lr_scheduler_type = 'linear',
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warmup_ratio = 0.1,
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warmup_steps = 0,
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log_level = 'passive',
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log_level_replica = 'warning',
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log_on_each_node = True,
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logging_dir = None,
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logging_strategy = 'steps',
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logging_first_step = False,
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logging_steps = 1,
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logging_nan_inf_filter = False,
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save_strategy = 'steps',
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save_steps = 500,
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save_total_limit = None,
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save_safetensors = True,
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save_on_each_node = False,
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save_only_model = False,
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restore_callback_states_from_checkpoint = False,
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no_cuda = False,
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use_cpu = False,
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use_mps_device = False,
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seed = 3407,
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data_seed = 3407,
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jit_mode_eval = False,
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use_ipex = False,
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bf16 = False,
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fp16 = False,
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fp16_opt_level = 'O1',
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half_precision_backend = 'auto',
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bf16_full_eval = False,
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fp16_full_eval = False,
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tf32 = None,
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local_rank = -1,
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ddp_backend = None,
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tpu_num_cores = None,
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tpu_metrics_debug = False,
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debug = '',
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dataloader_drop_last = False,
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eval_steps = None,
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dataloader_num_workers = 0,
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dataloader_prefetch_factor = None,
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past_index = -1,
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run_name = None,
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disable_tqdm = None,
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remove_unused_columns = True,
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label_names = None,
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load_best_model_at_end = False,
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metric_for_best_model = None,
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greater_is_better = None,
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ignore_data_skip = False,
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fsdp = '',
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fsdp_min_num_params = 0,
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fsdp_config = None,
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fsdp_transformer_layer_cls_to_wrap = None,
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accelerator_config = None,
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deepspeed = None,
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label_smoothing_factor = 0.0,
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optim = 'adamw_8bit',
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optim_args = None,
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adafactor = False,
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group_by_length = False,
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length_column_name = 'length',
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report_to = None,
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ddp_find_unused_parameters = None,
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ddp_bucket_cap_mb = None,
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ddp_broadcast_buffers = None,
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dataloader_pin_memory = True,
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dataloader_persistent_workers = False,
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skip_memory_metrics = True,
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use_legacy_prediction_loop = False,
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push_to_hub = False,
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resume_from_checkpoint = None,
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hub_model_id = None,
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hub_strategy = 'every_save',
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hub_token = None,
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hub_private_repo = None,
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hub_always_push = False,
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gradient_checkpointing = False,
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gradient_checkpointing_kwargs = None,
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include_inputs_for_metrics = False,
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eval_do_concat_batches = True,
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fp16_backend = 'auto',
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push_to_hub_model_id = None,
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push_to_hub_organization = None,
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push_to_hub_token = None,
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mp_parameters = '',
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auto_find_batch_size = False,
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full_determinism = False,
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torchdynamo = None,
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ray_scope = 'last',
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ddp_timeout = 1800,
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torch_compile = False,
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torch_compile_backend = None,
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torch_compile_mode = None,
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include_tokens_per_second = False,
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include_num_input_tokens_seen = False,
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neftune_noise_alpha = None,
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optim_target_modules = None,
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batch_eval_metrics = False,
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eval_on_start = False,
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use_liger_kernel = False,
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eval_use_gather_object = False,
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average_tokens_across_devices = False,
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dataset_num_proc = None,
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num_mini_batches = 1,
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total_episodes = None,
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local_rollout_forward_batch_size = 64,
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num_sample_generations = 10,
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response_length = 53,
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stop_token = None,
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stop_token_id = None,
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temperature = 0.7,
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missing_eos_penalty = None,
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sft_model_path = 'EleutherAI/pythia-160m',
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world_size = None,
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num_total_batches = None,
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micro_batch_size = None,
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local_batch_size = None,
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batch_size = None,
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local_mini_batch_size = None,
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mini_batch_size = None,
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exp_name = 'rloo_config',
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reward_model_path = 'EleutherAI/pythia-160m',
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num_ppo_epochs = 4,
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whiten_rewards = False,
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kl_coef = 0.05,
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cliprange = 0.2,
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rloo_k = 2,
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normalize_reward = False,
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reward_clip_range = 10.0,
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normalize_advantage = False,
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token_level_kl = False,
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ds3_gather_for_generation = True,
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vllm_sampling_params = None,
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unsloth_num_chunks = -1,
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**kwargs,
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):
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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!')
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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!')
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if output_dir is None and save_strategy == 'steps' and save_steps == 500:
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output_dir = 'unsloth_training_checkpoints'
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save_strategy = 'no'
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if dataset_num_proc is None:
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from multiprocessing import cpu_count
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dataset_num_proc = cpu_count()
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super().__init__(
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output_dir = output_dir,
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overwrite_output_dir = overwrite_output_dir,
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do_train = do_train,
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do_eval = do_eval,
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do_predict = do_predict,
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eval_strategy = eval_strategy,
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prediction_loss_only = prediction_loss_only,
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per_device_train_batch_size = per_device_train_batch_size,
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per_device_eval_batch_size = per_device_eval_batch_size,
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per_gpu_train_batch_size = per_gpu_train_batch_size,
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per_gpu_eval_batch_size = per_gpu_eval_batch_size,
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gradient_accumulation_steps = gradient_accumulation_steps,
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eval_accumulation_steps = eval_accumulation_steps,
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eval_delay = eval_delay,
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torch_empty_cache_steps = torch_empty_cache_steps,
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learning_rate = learning_rate,
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weight_decay = weight_decay,
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adam_beta1 = adam_beta1,
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adam_beta2 = adam_beta2,
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adam_epsilon = adam_epsilon,
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max_grad_norm = max_grad_norm,
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num_train_epochs = num_train_epochs,
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max_steps = max_steps,
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lr_scheduler_type = lr_scheduler_type,
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warmup_ratio = warmup_ratio,
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warmup_steps = warmup_steps,
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log_level = log_level,
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log_level_replica = log_level_replica,
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log_on_each_node = log_on_each_node,
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logging_dir = logging_dir,
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logging_strategy = logging_strategy,
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logging_first_step = logging_first_step,
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logging_steps = logging_steps,
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logging_nan_inf_filter = logging_nan_inf_filter,
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save_strategy = save_strategy,
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save_steps = save_steps,
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save_total_limit = save_total_limit,
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save_safetensors = save_safetensors,
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save_on_each_node = save_on_each_node,
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save_only_model = save_only_model,
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restore_callback_states_from_checkpoint = restore_callback_states_from_checkpoint,
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no_cuda = no_cuda,
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use_cpu = use_cpu,
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use_mps_device = use_mps_device,
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seed = seed,
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data_seed = data_seed,
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jit_mode_eval = jit_mode_eval,
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use_ipex = use_ipex,
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bf16 = bf16,
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fp16 = fp16,
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fp16_opt_level = fp16_opt_level,
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half_precision_backend = half_precision_backend,
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bf16_full_eval = bf16_full_eval,
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fp16_full_eval = fp16_full_eval,
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tf32 = tf32,
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local_rank = local_rank,
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ddp_backend = ddp_backend,
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tpu_num_cores = tpu_num_cores,
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tpu_metrics_debug = tpu_metrics_debug,
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debug = debug,
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dataloader_drop_last = dataloader_drop_last,
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eval_steps = eval_steps,
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dataloader_num_workers = dataloader_num_workers,
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dataloader_prefetch_factor = dataloader_prefetch_factor,
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past_index = past_index,
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run_name = run_name,
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disable_tqdm = disable_tqdm,
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remove_unused_columns = remove_unused_columns,
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label_names = label_names,
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load_best_model_at_end = load_best_model_at_end,
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metric_for_best_model = metric_for_best_model,
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greater_is_better = greater_is_better,
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ignore_data_skip = ignore_data_skip,
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fsdp = fsdp,
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fsdp_min_num_params = fsdp_min_num_params,
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fsdp_config = fsdp_config,
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fsdp_transformer_layer_cls_to_wrap = fsdp_transformer_layer_cls_to_wrap,
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accelerator_config = accelerator_config,
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deepspeed = deepspeed,
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label_smoothing_factor = label_smoothing_factor,
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optim = optim,
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optim_args = optim_args,
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adafactor = adafactor,
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group_by_length = group_by_length,
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length_column_name = length_column_name,
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report_to = report_to,
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ddp_find_unused_parameters = ddp_find_unused_parameters,
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ddp_bucket_cap_mb = ddp_bucket_cap_mb,
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ddp_broadcast_buffers = ddp_broadcast_buffers,
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dataloader_pin_memory = dataloader_pin_memory,
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dataloader_persistent_workers = dataloader_persistent_workers,
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skip_memory_metrics = skip_memory_metrics,
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use_legacy_prediction_loop = use_legacy_prediction_loop,
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push_to_hub = push_to_hub,
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resume_from_checkpoint = resume_from_checkpoint,
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hub_model_id = hub_model_id,
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hub_strategy = hub_strategy,
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hub_token = hub_token,
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hub_private_repo = hub_private_repo,
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hub_always_push = hub_always_push,
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gradient_checkpointing = gradient_checkpointing,
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gradient_checkpointing_kwargs = gradient_checkpointing_kwargs,
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include_inputs_for_metrics = include_inputs_for_metrics,
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eval_do_concat_batches = eval_do_concat_batches,
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fp16_backend = fp16_backend,
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push_to_hub_model_id = push_to_hub_model_id,
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push_to_hub_organization = push_to_hub_organization,
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push_to_hub_token = push_to_hub_token,
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mp_parameters = mp_parameters,
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auto_find_batch_size = auto_find_batch_size,
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full_determinism = full_determinism,
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torchdynamo = torchdynamo,
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ray_scope = ray_scope,
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ddp_timeout = ddp_timeout,
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torch_compile = torch_compile,
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torch_compile_backend = torch_compile_backend,
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torch_compile_mode = torch_compile_mode,
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include_tokens_per_second = include_tokens_per_second,
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include_num_input_tokens_seen = include_num_input_tokens_seen,
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neftune_noise_alpha = neftune_noise_alpha,
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optim_target_modules = optim_target_modules,
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batch_eval_metrics = batch_eval_metrics,
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eval_on_start = eval_on_start,
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use_liger_kernel = use_liger_kernel,
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eval_use_gather_object = eval_use_gather_object,
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average_tokens_across_devices = average_tokens_across_devices,
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dataset_num_proc = dataset_num_proc,
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num_mini_batches = num_mini_batches,
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total_episodes = total_episodes,
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local_rollout_forward_batch_size = local_rollout_forward_batch_size,
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num_sample_generations = num_sample_generations,
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response_length = response_length,
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stop_token = stop_token,
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stop_token_id = stop_token_id,
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temperature = temperature,
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missing_eos_penalty = missing_eos_penalty,
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sft_model_path = sft_model_path,
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world_size = world_size,
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num_total_batches = num_total_batches,
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micro_batch_size = micro_batch_size,
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local_batch_size = local_batch_size,
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batch_size = batch_size,
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local_mini_batch_size = local_mini_batch_size,
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mini_batch_size = mini_batch_size,
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exp_name = exp_name,
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reward_model_path = reward_model_path,
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num_ppo_epochs = num_ppo_epochs,
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whiten_rewards = whiten_rewards,
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kl_coef = kl_coef,
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cliprange = cliprange,
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rloo_k = rloo_k,
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normalize_reward = normalize_reward,
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reward_clip_range = reward_clip_range,
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normalize_advantage = normalize_advantage,
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token_level_kl = token_level_kl,
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ds3_gather_for_generation = ds3_gather_for_generation,**kwargs)
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self.vllm_sampling_params = vllm_sampling_params
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self.unsloth_num_chunks = unsloth_num_chunks
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pass
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|
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class _UnslothRLOOTrainer(Trainer):
|
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_tag_names = ["trl", "rloo"]
|
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|
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def __init__(
|
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self,
|
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config: RLOOConfig,
|
|
processing_class: Optional[
|
|
Union[PreTrainedTokenizerBase, BaseImageProcessor, FeatureExtractionMixin, ProcessorMixin]
|
|
],
|
|
policy: nn.Module,
|
|
ref_policy: nn.Module,
|
|
reward_model: Union[nn.Module, Callable[[list[str]], list[float]]],
|
|
train_dataset: Dataset,
|
|
data_collator: Optional[DataCollatorWithPadding] = None,
|
|
eval_dataset: Optional[Union[Dataset, dict[str, Dataset]]] = None,
|
|
# less commonly used
|
|
optimizers: tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None),
|
|
callbacks: Optional[list[TrainerCallback]] = None,
|
|
) -> None:
|
|
if ref_policy is policy:
|
|
raise ValueError(
|
|
"`policy` and `ref_policy` cannot be the same object. If you want `ref_policy` to be the "
|
|
"same as `policy`, you must mass a copy of it, or `None` if you use peft."
|
|
)
|
|
|
|
self.args = config
|
|
args = config
|
|
self.processing_class = processing_class
|
|
self.policy = policy
|
|
|
|
# Define the collator if not provided
|
|
if data_collator is None:
|
|
data_collator = DataCollatorWithPadding(self.processing_class)
|
|
|
|
self.policy.generation_config.eos_token_id = (
|
|
None # disable `pad_token_id` and `eos_token_id` because we just want to
|
|
)
|
|
self.policy.generation_config.pad_token_id = None # generate tokens without truncation / padding
|
|
|
|
self.ref_policy = ref_policy
|
|
self.reward_model = reward_model
|
|
self.train_dataset = train_dataset
|
|
self.train_dataset_len = len(train_dataset)
|
|
self.data_collator = data_collator
|
|
self.eval_dataset = eval_dataset
|
|
self.optimizer, self.lr_scheduler = optimizers
|
|
self.optimizer_cls_and_kwargs = None # needed for transformers >= 4.47
|
|
|
|
#########
|
|
# calculate various batch sizes
|
|
#########
|
|
if args.total_episodes is None: # allow the users to define episodes in terms of epochs.
|
|
args.total_episodes = int(args.num_train_epochs * self.train_dataset_len)
|
|
accelerator = Accelerator(gradient_accumulation_steps=args.gradient_accumulation_steps)
|
|
self.accelerator = accelerator
|
|
args.world_size = accelerator.num_processes
|
|
args.local_batch_size = (
|
|
args.per_device_train_batch_size * args.gradient_accumulation_steps * args.num_mini_batches
|
|
)
|
|
args.micro_batch_size = int(args.per_device_train_batch_size * args.world_size)
|
|
args.batch_size = int(args.local_batch_size * args.world_size)
|
|
args.mini_batch_size = exact_div(
|
|
args.batch_size, args.num_mini_batches, "`batch_size` must be a multiple of `num_mini_batches`"
|
|
)
|
|
args.local_mini_batch_size = exact_div(
|
|
args.local_batch_size, args.num_mini_batches, "`local_batch_size` must be a multiple of `num_mini_batches`"
|
|
)
|
|
args.num_total_batches = math.ceil(
|
|
args.total_episodes / args.batch_size
|
|
) # we may train for more than `total_episodes`
|
|
time_tensor = torch.tensor(int(time.time()), device=accelerator.device)
|
|
time_int = broadcast(time_tensor, 0).item() # avoid different timestamps across processes
|
|
args.run_name = f"{args.exp_name}__{args.seed}__{time_int}"
|
|
self.local_seed = args.seed + accelerator.process_index * 100003 # Prime
|
|
if args.num_sample_generations > 0:
|
|
self.sample_generations_freq = max(1, args.num_total_batches // args.num_sample_generations)
|
|
self.local_dataloader_batch_size = exact_div(
|
|
args.local_batch_size, args.rloo_k, "`local_batch_size` must be a multiple of rloo_k"
|
|
) # RLOO logic: needed because RLOO repeats the same prompt args.rloo_k times
|
|
|
|
#########
|
|
# setup model, optimizer, and others
|
|
#########
|
|
for module in [policy, ref_policy, reward_model]:
|
|
if isinstance(module, nn.Module):
|
|
disable_dropout_in_model(module)
|
|
if args.stop_token and args.stop_token == "eos":
|
|
args.stop_token_id = self.processing_class.eos_token_id
|
|
self.model = policy
|
|
self.create_optimizer_and_scheduler(
|
|
num_training_steps=args.num_total_batches
|
|
) # note that we are calling `self.lr_scheduler.step()` manually only at the batch level
|
|
|
|
#########
|
|
### trainer specifics
|
|
#########
|
|
default_callbacks = DEFAULT_CALLBACKS + get_reporting_integration_callbacks(self.args.report_to)
|
|
self.callbacks = default_callbacks if callbacks is None else default_callbacks + callbacks
|
|
self.callback_handler = CallbackHandler(
|
|
self.callbacks, self.model, self.processing_class, self.optimizer, self.lr_scheduler
|
|
)
|
|
self.add_callback(PrinterCallback if self.args.disable_tqdm else DEFAULT_PROGRESS_CALLBACK)
|
|
self.control = TrainerControl()
|
|
self.state = OnlineTrainerState(
|
|
is_local_process_zero=self.is_local_process_zero(),
|
|
is_world_process_zero=self.is_world_process_zero(),
|
|
stateful_callbacks=[
|
|
cb for cb in self.callback_handler.callbacks + [self.control] if isinstance(cb, ExportableState)
|
|
],
|
|
)
|
|
|
|
self.current_flos = 0
|
|
self.hp_search_backend = None
|
|
self.is_deepspeed_enabled = getattr(self.accelerator.state, "deepspeed_plugin", None) is not None
|
|
self.is_fsdp_enabled = getattr(self.accelerator.state, "fsdp_plugin", None) is not None
|
|
# Create distant repo and output directory if needed
|
|
self.hub_model_id = None
|
|
if self.args.push_to_hub:
|
|
self.init_hf_repo()
|
|
if self.args.should_save:
|
|
os.makedirs(self.args.output_dir, exist_ok=True)
|
|
self.backup_model = None
|
|
|
|
# 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)
|
|
|
|
#########
|
|
### setup dataloader
|
|
#########
|
|
self.dataloader = DataLoader(
|
|
self.train_dataset,
|
|
batch_size=self.local_dataloader_batch_size,
|
|
shuffle=True,
|
|
collate_fn=self.data_collator,
|
|
drop_last=True, # needed; otherwise the last batch will be of ragged shape
|
|
)
|
|
# sync random states for DataLoader(shuffle=True) before `accelerator.prepare`
|
|
# see https://gist.github.com/vwxyzjn/2581bff1e48e185e0b85b6dfe1def79c
|
|
torch.manual_seed(args.seed)
|
|
self.model, self.optimizer, self.dataloader = accelerator.prepare(self.model, self.optimizer, self.dataloader)
|
|
torch.manual_seed(self.local_seed) # reset the local seed again
|
|
|
|
self.eval_dataloader = DataLoader(
|
|
self.eval_dataset,
|
|
batch_size=args.per_device_eval_batch_size,
|
|
collate_fn=self.data_collator,
|
|
drop_last=True,
|
|
) # no need to shuffle eval dataset
|
|
self.eval_dataloader = accelerator.prepare(self.eval_dataloader)
|
|
|
|
if self.is_deepspeed_enabled:
|
|
if isinstance(self.reward_model, nn.Module):
|
|
self.reward_model = prepare_deepspeed(
|
|
self.reward_model, args.per_device_train_batch_size, args.fp16, args.bf16
|
|
)
|
|
self.ref_policy = prepare_deepspeed(
|
|
self.ref_policy, args.per_device_train_batch_size, args.fp16, args.bf16
|
|
)
|
|
self.deepspeed = self.model
|
|
else:
|
|
self.ref_policy = self.ref_policy.to(self.accelerator.device)
|
|
if isinstance(self.reward_model, nn.Module):
|
|
self.reward_model = self.reward_model.to(self.accelerator.device)
|
|
|
|
def get_train_dataloader(self) -> DataLoader:
|
|
return self.dataloader
|
|
|
|
def get_eval_dataloader(self) -> DataLoader:
|
|
return self.eval_dataloader
|
|
|
|
def train(self):
|
|
args = self.args
|
|
accelerator = self.accelerator
|
|
optimizer = self.optimizer
|
|
model = self.model
|
|
self.model_wrapped = self.model
|
|
ref_policy = self.ref_policy
|
|
reward_model = self.reward_model
|
|
processing_class = self.processing_class
|
|
dataloader = self.dataloader
|
|
device = accelerator.device
|
|
|
|
def repeat_generator():
|
|
while True:
|
|
yield from dataloader
|
|
|
|
iter_dataloader = iter(repeat_generator())
|
|
generation_config = GenerationConfig(
|
|
max_new_tokens=args.response_length,
|
|
temperature=(args.temperature + 1e-7),
|
|
top_k=0.0,
|
|
top_p=1.0,
|
|
do_sample=True,
|
|
)
|
|
|
|
accelerator.print("===training policy===")
|
|
start_time = time.time()
|
|
stats_shape = (args.num_ppo_epochs, args.num_mini_batches, args.gradient_accumulation_steps)
|
|
approxkl_stats = torch.zeros(stats_shape, device=device)
|
|
pg_clipfrac_stats = torch.zeros(stats_shape, device=device)
|
|
pg_loss_stats = torch.zeros(stats_shape, device=device)
|
|
vf_clipfrac_stats = torch.zeros(stats_shape, device=device)
|
|
entropy_stats = torch.zeros(stats_shape, device=device)
|
|
ratio_stats = torch.zeros(stats_shape, device=device)
|
|
model.train()
|
|
|
|
# trainer state initialization
|
|
self.state.global_step = 0
|
|
self.state.episode = 0
|
|
self.state.max_steps = (args.num_total_batches * args.num_mini_batches) // 2
|
|
self.state.num_train_epochs = args.total_episodes / self.train_dataset_len
|
|
# Compute absolute values for logging, eval, and save if given as ratio
|
|
if args.logging_steps is not None:
|
|
if args.logging_steps < 1:
|
|
self.state.logging_steps = math.ceil(self.state.max_steps * args.logging_steps)
|
|
else:
|
|
self.state.logging_steps = args.logging_steps
|
|
if args.eval_steps is not None:
|
|
if args.eval_steps < 1:
|
|
self.state.eval_steps = math.ceil(self.state.max_steps * args.eval_steps)
|
|
else:
|
|
self.state.eval_steps = args.eval_steps
|
|
if args.save_steps is not None:
|
|
if args.save_steps < 1:
|
|
self.state.save_steps = math.ceil(self.state.max_steps * args.save_steps)
|
|
else:
|
|
self.state.save_steps = args.save_steps
|
|
self.control = self.callback_handler.on_train_begin(args, self.state, self.control)
|
|
|
|
for update in range(1, args.num_total_batches + 1):
|
|
self.state.episode += 1 * args.batch_size
|
|
data = next(iter_dataloader)
|
|
with torch.no_grad():
|
|
queries = data["input_ids"].to(device)
|
|
queries = queries.repeat(args.rloo_k, 1)
|
|
context_length = queries.shape[1]
|
|
responses = []
|
|
postprocessed_responses = []
|
|
logprobs = []
|
|
ref_logprobs = []
|
|
scores = []
|
|
sequence_lengths = []
|
|
|
|
# Generate responses and compute logprobs
|
|
with unwrap_model_for_generation(
|
|
self.model, self.accelerator, gather_deepspeed3_params=self.args.ds3_gather_for_generation
|
|
) as unwrapped_model:
|
|
query_responses, logitss = batch_generation(
|
|
unwrapped_model,
|
|
queries,
|
|
args.local_rollout_forward_batch_size,
|
|
processing_class.pad_token_id,
|
|
generation_config,
|
|
)
|
|
|
|
# Process responses in batches
|
|
for i in range(0, queries.shape[0], args.local_rollout_forward_batch_size):
|
|
query = queries[i : i + args.local_rollout_forward_batch_size]
|
|
query_response = query_responses[i : i + args.local_rollout_forward_batch_size]
|
|
response = query_response[:, context_length:]
|
|
logits = logitss[i : i + args.local_rollout_forward_batch_size]
|
|
logprob = selective_log_softmax(logits, response)
|
|
del logits
|
|
empty_cache()
|
|
|
|
ref_output = forward(ref_policy, query_response, processing_class.pad_token_id)
|
|
ref_logits = ref_output.logits[:, context_length - 1 : -1]
|
|
ref_logits /= args.temperature + 1e-7
|
|
ref_logprob = selective_log_softmax(ref_logits, response)
|
|
del ref_output, ref_logits
|
|
empty_cache()
|
|
|
|
# Response Processing 1. truncate response after the first occurrence of `stop_token_id`
|
|
postprocessed_response = response
|
|
if args.stop_token_id is not None: # handle the edge case when stop_token_id exists but is 0
|
|
postprocessed_response = truncate_response(
|
|
args.stop_token_id, processing_class.pad_token_id, response
|
|
)
|
|
|
|
# Response Processing 2. run reward model on the truncated responses
|
|
postprocessed_query_response = torch.cat((query, postprocessed_response), 1)
|
|
sequence_length = first_true_indices(postprocessed_response == processing_class.pad_token_id) - 1
|
|
|
|
if isinstance(reward_model, nn.Module):
|
|
_, score, _ = get_reward(
|
|
reward_model, postprocessed_query_response, processing_class.pad_token_id, context_length
|
|
)
|
|
else:
|
|
score = torch.tensor(
|
|
reward_model(
|
|
processing_class.batch_decode(postprocessed_query_response, skip_special_tokens=True)
|
|
),
|
|
dtype=torch.float,
|
|
).to(device)
|
|
|
|
# Store batch results
|
|
responses.append(response)
|
|
postprocessed_responses.append(postprocessed_response)
|
|
logprobs.append(logprob)
|
|
ref_logprobs.append(ref_logprob)
|
|
sequence_lengths.append(sequence_length)
|
|
scores.append(score)
|
|
|
|
# Concatenate all batched results
|
|
responses = torch.cat(responses, 0)
|
|
postprocessed_responses = torch.cat(postprocessed_responses, 0)
|
|
logprobs = torch.cat(logprobs, 0)
|
|
ref_logprobs = torch.cat(ref_logprobs, 0)
|
|
sequence_lengths = torch.cat(sequence_lengths, 0)
|
|
scores = torch.cat(scores, 0)
|
|
del (logprob, ref_logprob, score)
|
|
empty_cache()
|
|
gc.collect()
|
|
|
|
# Response Processing 3. filter response. Ensure that the sample contains stop_token_id
|
|
# responses not passing that filter will receive a low (fixed) score
|
|
# only query humans on responses that pass that filter
|
|
contain_eos_token = torch.any(postprocessed_responses == processing_class.eos_token_id, dim=-1)
|
|
if args.missing_eos_penalty is not None:
|
|
scores[~contain_eos_token] -= self.args.missing_eos_penalty
|
|
# accelerator.print(f"{scores=}, {(contain_eos_token.sum() / len(contain_eos_token))=}")
|
|
|
|
# be very careful with `padding_mask_p1`; see https://excalidraw.com/#json=LWnzG4w2k5DjF_EOL_xPt,e2w3a-hFJ_gX5vOfeyXGTw
|
|
response_idxs = torch.arange(responses.shape[1], device=responses.device).repeat(responses.shape[0], 1)
|
|
padding_mask = response_idxs > sequence_lengths.unsqueeze(1)
|
|
logprobs = torch.masked_fill(logprobs, padding_mask, INVALID_LOGPROB)
|
|
ref_logprobs = torch.masked_fill(ref_logprobs, padding_mask, INVALID_LOGPROB)
|
|
|
|
# 4. compute rewards
|
|
# Compute KL divergence
|
|
kl = logprobs - ref_logprobs
|
|
|
|
# Normalize rewards
|
|
if args.normalize_reward:
|
|
scores = (scores - scores.mean()) / (scores.std() + 1e-8)
|
|
scores = torch.clamp(scores, -args.reward_clip_range, args.reward_clip_range)
|
|
|
|
# Compute total reward with KL penalty
|
|
if args.token_level_kl:
|
|
# Token-level KL penalty: apply KL penalty per token
|
|
kl_reward = -args.kl_coef * kl
|
|
|
|
# Get the index of the last non-padded token for each sequence
|
|
eos_indices = padding_mask.size(1) - 1 - padding_mask.long().fliplr().argmax(dim=1, keepdim=True)
|
|
last_reward = torch.zeros_like(kl)
|
|
# Ensure scores has correct shape and type
|
|
scores_shaped = scores.reshape(-1, 1).to(kl.dtype)
|
|
last_reward.scatter_(dim=1, index=eos_indices, src=scores_shaped)
|
|
|
|
# Combine KL reward and last reward
|
|
non_score_reward = kl_reward.sum(1) # Keep this for logging
|
|
reward = last_reward + kl_reward
|
|
rlhf_reward = reward.sum(1) # Sum across sequence length
|
|
else:
|
|
# Sequence-level KL penalty: sum KL across tokens first
|
|
sequence_kl = kl.sum(1)
|
|
non_score_reward = -args.kl_coef * sequence_kl
|
|
rlhf_reward = non_score_reward + scores
|
|
|
|
# vectorized RLOO advantages implementation
|
|
rlhf_reward = rlhf_reward.reshape(args.rloo_k, -1)
|
|
baseline = (rlhf_reward.sum(0) - rlhf_reward) / (args.rloo_k - 1)
|
|
advantages = rlhf_reward - baseline
|
|
advantages = advantages.flatten()
|
|
|
|
# Normalize advantages
|
|
if args.normalize_advantage:
|
|
advantages = (advantages - advantages.mean()) / (advantages.std() + 1e-8)
|
|
|
|
empty_cache()
|
|
|
|
# Do multiple epochs of PPO training, with a fresh random shuffle in each epoch
|
|
for ppo_epoch_idx in range(args.num_ppo_epochs):
|
|
b_inds = np.random.permutation(args.local_batch_size)
|
|
minibatch_idx = 0
|
|
for mini_batch_start in range(0, args.local_batch_size, args.local_mini_batch_size):
|
|
mini_batch_end = mini_batch_start + args.local_mini_batch_size
|
|
mini_batch_inds = b_inds[mini_batch_start:mini_batch_end]
|
|
gradient_accumulation_idx = 0
|
|
for micro_batch_start in range(0, args.local_mini_batch_size, args.per_device_train_batch_size):
|
|
with accelerator.accumulate(model):
|
|
micro_batch_end = micro_batch_start + args.per_device_train_batch_size
|
|
micro_batch_inds = mini_batch_inds[micro_batch_start:micro_batch_end]
|
|
|
|
# Get batch data
|
|
mb_advantage = advantages[micro_batch_inds]
|
|
mb_responses = responses[micro_batch_inds]
|
|
mb_query_responses = query_responses[micro_batch_inds]
|
|
mb_logprobs = logprobs[micro_batch_inds]
|
|
|
|
# Forward pass
|
|
output = forward(model, mb_query_responses, processing_class.pad_token_id)
|
|
logits = output.logits[:, context_length - 1 : -1]
|
|
logits /= args.temperature + 1e-7
|
|
|
|
# Compute new logprobs
|
|
new_logprobs = selective_log_softmax(logits, mb_responses)
|
|
new_logprobs = torch.masked_fill(
|
|
new_logprobs, padding_mask[micro_batch_inds], INVALID_LOGPROB
|
|
)
|
|
|
|
# Compute probability ratios
|
|
new_ratio = (new_logprobs - mb_logprobs).exp()
|
|
new_logprobs = new_logprobs.sum(1)
|
|
mb_logprobs = mb_logprobs.sum(1)
|
|
logprobs_diff = new_logprobs - mb_logprobs
|
|
ratio = torch.exp(logprobs_diff)
|
|
|
|
# PPO clipped loss
|
|
pg_losses = -mb_advantage * ratio
|
|
pg_losses2 = -mb_advantage * torch.clamp(ratio, 1.0 - args.cliprange, 1.0 + args.cliprange)
|
|
pg_loss_max = torch.max(pg_losses, pg_losses2)
|
|
pg_loss = pg_loss_max.mean()
|
|
|
|
# Final loss
|
|
loss = pg_loss
|
|
|
|
# Optimization step
|
|
accelerator.backward(loss)
|
|
optimizer.step()
|
|
optimizer.zero_grad()
|
|
|
|
with torch.no_grad():
|
|
pg_clipfrac = (pg_losses2 > pg_losses).float().mean()
|
|
prob_dist = torch.nn.functional.softmax(logits, dim=-1)
|
|
entropy = torch.logsumexp(logits, dim=-1) - torch.sum(prob_dist * logits, dim=-1)
|
|
approxkl = 0.5 * (logprobs_diff**2).mean()
|
|
approxkl_stats[ppo_epoch_idx, minibatch_idx, gradient_accumulation_idx] = approxkl
|
|
pg_clipfrac_stats[ppo_epoch_idx, minibatch_idx, gradient_accumulation_idx] = (
|
|
pg_clipfrac
|
|
)
|
|
pg_loss_stats[ppo_epoch_idx, minibatch_idx, gradient_accumulation_idx] = pg_loss
|
|
entropy_stats[ppo_epoch_idx, minibatch_idx, gradient_accumulation_idx] = entropy.mean()
|
|
ratio_stats[ppo_epoch_idx, minibatch_idx, gradient_accumulation_idx] = new_ratio.mean()
|
|
gradient_accumulation_idx += 1
|
|
minibatch_idx += 1
|
|
|
|
# del everything and empty cache
|
|
# fmt: off
|
|
del (
|
|
output, logits, new_logprobs, logprobs_diff, ratio, pg_losses,
|
|
pg_losses2, pg_loss, loss, pg_clipfrac, prob_dist, entropy, approxkl,
|
|
mb_advantage, mb_responses, mb_query_responses, mb_logprobs,
|
|
)
|
|
# fmt: on
|
|
empty_cache()
|
|
|
|
# Compute metrics
|
|
with torch.no_grad():
|
|
mean_kl = kl.sum(1).mean()
|
|
mean_entropy = (-logprobs).sum(1).mean()
|
|
mean_non_score_reward = non_score_reward.mean()
|
|
eps = int(self.state.episode / (time.time() - start_time))
|
|
metrics = {}
|
|
metrics["eps"] = eps
|
|
metrics["objective/kl"] = self.accelerator.gather_for_metrics(mean_kl).mean().item()
|
|
metrics["objective/entropy"] = self.accelerator.gather_for_metrics(mean_entropy).mean().item()
|
|
metrics["objective/non_score_reward"] = (
|
|
self.accelerator.gather_for_metrics(mean_non_score_reward).mean().item()
|
|
)
|
|
metrics["objective/rlhf_reward"] = self.accelerator.gather_for_metrics(rlhf_reward).mean().item()
|
|
metrics["objective/scores"] = self.accelerator.gather_for_metrics(scores.mean()).mean().item()
|
|
metrics["policy/approxkl_avg"] = self.accelerator.gather_for_metrics(approxkl_stats).mean().item()
|
|
metrics["policy/clipfrac_avg"] = self.accelerator.gather_for_metrics(pg_clipfrac_stats).mean().item()
|
|
metrics["loss/policy_avg"] = self.accelerator.gather_for_metrics(pg_loss_stats).mean().item()
|
|
metrics["val/clipfrac_avg"] = self.accelerator.gather_for_metrics(vf_clipfrac_stats).mean().item()
|
|
metrics["policy/entropy_avg"] = self.accelerator.gather_for_metrics(entropy_stats).mean().item()
|
|
metrics["val/ratio"] = self.accelerator.gather_for_metrics(ratio_stats).mean().item()
|
|
metrics["val/ratio_var"] = self.accelerator.gather_for_metrics(ratio_stats).var().item()
|
|
metrics["val/num_eos_tokens"] = (responses == processing_class.eos_token_id).sum().item()
|
|
metrics["lr"] = self.lr_scheduler.get_last_lr()[0]
|
|
metrics["episode"] = self.state.episode
|
|
self.state.epoch = self.state.episode / (args.rloo_k * self.train_dataset_len) # used by self.log
|
|
self.log(metrics)
|
|
del kl, mean_kl, mean_entropy, scores
|
|
|
|
self.lr_scheduler.step()
|
|
self.state.global_step += 1
|
|
self.control = self.callback_handler.on_step_end(args, self.state, self.control)
|
|
if self.control.should_save:
|
|
self._save_checkpoint(model, trial=None)
|
|
self.control = self.callback_handler.on_save(self.args, self.state, self.control)
|
|
empty_cache()
|
|
gc.collect()
|
|
|
|
if args.num_sample_generations > 0 and (update - 1) % self.sample_generations_freq == 0:
|
|
self.generate_completions(sampling=True)
|
|
|
|
# HF trainer specifics
|
|
self.control = self.callback_handler.on_train_end(args, self.state, self.control)
|
|
if self.control.should_save:
|
|
self._save_checkpoint(model, trial=None, metrics=None)
|
|
self.control = self.callback_handler.on_save(self.args, self.state, self.control)
|
|
|
|
def generate_completions(self, sampling: bool = False):
|
|
args = self.args
|
|
processing_class = self.processing_class
|
|
generation_config = GenerationConfig(
|
|
max_new_tokens=self.args.response_length,
|
|
temperature=(0.01 + 1e-7),
|
|
top_k=0.0,
|
|
top_p=1.0,
|
|
do_sample=True,
|
|
)
|
|
|
|
table = defaultdict(list)
|
|
with unwrap_model_for_generation(
|
|
self.model, self.accelerator, gather_deepspeed3_params=self.args.ds3_gather_for_generation
|
|
) as unwrapped_model:
|
|
for batch in self.eval_dataloader:
|
|
query = batch["input_ids"]
|
|
with torch.no_grad():
|
|
context_length = query.shape[1]
|
|
query_response, _ = batch_generation(
|
|
unwrapped_model,
|
|
query,
|
|
query.shape[0],
|
|
processing_class.pad_token_id,
|
|
generation_config,
|
|
)
|
|
response = query_response[:, context_length:]
|
|
postprocessed_response = response
|
|
if args.stop_token_id is not None: # handle the edge case when stop_token_id exists but is 0
|
|
postprocessed_response = truncate_response(
|
|
args.stop_token_id, processing_class.pad_token_id, response
|
|
)
|
|
table["query"].extend(
|
|
gather_object(processing_class.batch_decode(query, skip_special_tokens=True))
|
|
)
|
|
table["model response"].extend(
|
|
gather_object(processing_class.batch_decode(postprocessed_response))
|
|
)
|
|
|
|
postprocessed_query_response = torch.cat((query, postprocessed_response), 1)
|
|
|
|
if isinstance(self.reward_model, nn.Module):
|
|
_, score, _ = get_reward(
|
|
self.reward_model,
|
|
postprocessed_query_response,
|
|
processing_class.pad_token_id,
|
|
context_length,
|
|
)
|
|
else:
|
|
score = torch.tensor(
|
|
self.reward_model(
|
|
processing_class.batch_decode(postprocessed_query_response, skip_special_tokens=True)
|
|
),
|
|
dtype=torch.float,
|
|
).to(postprocessed_query_response.device)
|
|
table["score"].extend(self.accelerator.gather_for_metrics(score).float().cpu().numpy())
|
|
|
|
if sampling:
|
|
break
|
|
df = pd.DataFrame(table)
|
|
|
|
if self.accelerator.is_main_process:
|
|
if is_rich_available():
|
|
print_rich_table(df.iloc[0 : 0 + 5])
|
|
if "wandb" in args.report_to:
|
|
import wandb
|
|
|
|
if wandb.run is not None:
|
|
wandb.log({"completions": wandb.Table(dataframe=df)})
|
|
|
|
if "comet_ml" in args.report_to:
|
|
log_table_to_comet_experiment(
|
|
name="completions.csv",
|
|
table=df,
|
|
)
|
|
|
|
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("""\
|
|
@inproceedings{ahmadian2024back,
|
|
title = {{Back to Basics: Revisiting REINFORCE-Style Optimization for Learning from Human Feedback in LLMs}},
|
|
author = {Arash Ahmadian and Chris Cremer and Matthias Gall{\'{e}} and Marzieh Fadaee and Julia Kreutzer and Olivier Pietquin and Ahmet {\"{U}}st{\"{u}}n and Sara Hooker},
|
|
year = 2024,
|
|
booktitle = {Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), {ACL} 2024, Bangkok, Thailand, August 11-16, 2024},
|
|
publisher = {Association for Computational Linguistics},
|
|
pages = {12248--12267},
|
|
editor = {Lun{-}Wei Ku and Andre Martins and Vivek Srikumar},
|
|
}""")
|
|
|
|
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="RLOO",
|
|
trainer_citation=citation,
|
|
paper_title="Back to Basics: Revisiting REINFORCE-Style Optimization for Learning from Human Feedback in LLMs",
|
|
paper_id="2402.14740",
|
|
)
|
|
|
|
model_card.save(os.path.join(self.args.output_dir, "README.md"))
|
|
class UnslothRLOOTrainer(_UnslothRLOOTrainer):
|
|
"""
|
|
|
|
"""
|
|
def __init__(
|
|
self,
|
|
config,
|
|
processing_class,
|
|
policy,
|
|
ref_policy,
|
|
reward_model,
|
|
train_dataset,
|
|
data_collator = None,
|
|
eval_dataset = None,
|
|
callbacks = None,
|
|
**kwargs
|
|
):
|
|
if args is None: args = UnslothRLOOConfig()
|
|
_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)
|
|
elif isinstance(data_collator, TransformersDataCollatorForLanguageModeling) and 'labels' in train_dataset.column_names:
|
|
data_collator = DataCollatorForSeq2Seq(__tokenizer)
|
|
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)
|
|
else:
|
|
data_collator = TransformersDataCollatorForLanguageModeling(__tokenizer.tokenizer, mlm = False, mlm_probability = 0.0)
|
|
other_metrics = []
|
|
|
|
from unsloth_zoo.logging_utils import PatchRLStatistics
|
|
PatchRLStatistics('rloo_trainer', other_metrics)
|
|
|
|
super().__init__(
|
|
config = config,
|
|
processing_class = processing_class,
|
|
policy = policy,
|
|
ref_policy = ref_policy,
|
|
reward_model = reward_model,
|
|
train_dataset = train_dataset,
|
|
data_collator = data_collator,
|
|
eval_dataset = eval_dataset,
|
|
callbacks = callbacks,**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
|