1990 lines
95 KiB
Python
1990 lines
95 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.kto_trainer import (Any, AutoModelForCausalLM, BaseImageProcessor, Callable, DPODataCollatorWithPadding, DataCollator, DataLoader, Dataset, EvalLoopOutput, F, FeatureExtractionMixin, KTOConfig, KTOTrainer, Literal, Optional, PartialState, PeftModel, PreTrainedModel, PreTrainedTokenizerBase, ProcessorMixin, SequentialSampler, Trainer, TrainerCallback, TrainingArguments, Union, _get_kl_dataset, _process_tokens, _tokenize, amp, concatenate_datasets, contextmanager, create_reference_model, defaultdict, disable_dropout_in_model, generate_model_card, get_comet_experiment_url, has_length, inspect, is_comet_available, is_liger_kernel_available, is_peft_available, is_wandb_available, itemgetter, log_table_to_comet_experiment, maybe_apply_chat_template, maybe_extract_prompt, maybe_unpair_preference_dataset, nn, np, nullcontext, os, pad_to_length, pd, peft_module_casting_to_bf16, prepare_deepspeed, prepare_model_for_kbit_training, random, textwrap, torch, tqdm, transformers, version, warnings)
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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 UnslothKTOConfig(KTOConfig):
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"""
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Configuration class for the [`KTOTrainer`].
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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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learning_rate (`float`, *optional*, defaults to `1e-6`):
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Initial learning rate for [`AdamW`] optimizer. The default value replaces that of
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[`~transformers.TrainingArguments`].
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max_length (`int` or `None`, *optional*, defaults to `1024`):
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Maximum length of the sequences (prompt + completion) in the batch. This argument is required if you want
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to use the default data collator.
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max_prompt_length (`int` or `None`, *optional*, defaults to `512`):
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Maximum length of the prompt. This argument is required if you want to use the default data collator.
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max_completion_length (`int` or `None`, *optional*, defaults to `None`):
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Maximum length of the completion. This argument is required if you want to use the default data collator
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and your model is an encoder-decoder.
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beta (`float`, *optional*, defaults to `0.1`):
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Parameter controlling the deviation from the reference model. Higher β means less deviation from the
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reference model.
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loss_type (`str`, *optional*, defaults to `"kto"`):
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Type of loss to use. Possible values are:
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- `"kto"`: KTO loss from the [KTO](https://huggingface.co/papers/2402.01306) paper.
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- `"apo_zero_unpaired"`: Unpaired variant of APO-zero loss from the [APO](https://huggingface.co/papers/2408.06266) paper.
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desirable_weight (`float`, *optional*, defaults to `1.0`):
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Desirable losses are weighed by this factor to counter unequal number of desirable and undesirable paris.
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undesirable_weight (`float`, *optional*, defaults to `1.0`):
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Undesirable losses are weighed by this factor to counter unequal number of desirable and undesirable pairs.
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label_pad_token_id (`int`, *optional*, defaults to `-100`):
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Label pad token id. This argument is required if you want to use the default data collator.
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padding_value (`int` or `None`, *optional*, defaults to `None`):
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Padding value to use. If `None`, the padding value of the tokenizer is used.
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truncation_mode (`str`, *optional*, defaults to `"keep_end"`):
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Truncation mode to use when the prompt is too long. Possible values are `"keep_end"` or `"keep_start"`.
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This argument is required if you want to use the default data collator.
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generate_during_eval (`bool`, *optional*, defaults to `False`):
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If `True`, generates and logs completions from both the model and the reference model to W&B or Comet during
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evaluation.
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is_encoder_decoder (`bool` or `None`, *optional*, defaults to `None`):
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When using the `model_init` argument (callable) to instantiate the model instead of the `model` argument,
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you need to specify if the model returned by the callable is an encoder-decoder model.
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precompute_ref_log_probs (`bool`, *optional*, defaults to `False`):
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Whether to precompute reference model log probabilities for training and evaluation datasets. This is
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useful when training without the reference model to reduce the total GPU memory needed.
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model_init_kwargs (`dict[str, Any]` or `None`, *optional*, defaults to `None`):
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Keyword arguments to pass to `AutoModelForCausalLM.from_pretrained` when instantiating the model from a
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string.
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ref_model_init_kwargs (`dict[str, Any]` or `None`, *optional*, defaults to `None`):
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Keyword arguments to pass to `AutoModelForCausalLM.from_pretrained` when instantiating the reference model
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from a string.
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dataset_num_proc: (`int` or `None`, *optional*, defaults to `None`):
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Number of processes to use for processing the dataset.
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disable_dropout (`bool`, *optional*, defaults to `True`):
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Whether to disable dropout in the model and reference model.
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use_liger_loss (`bool`, *optional*, defaults to `False`):
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Whether to use Liger loss. It requires liger-kernel to be installed.
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base_model_attribute_name (`str`, *optional*, defaults to `"model"`):
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Name of the attribute in the model that contains the base model. This is used to get the base model from
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the model when the model does not have a `get_decoder` method in the case when `use_liger_loss` is `True`.
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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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max_length = 1024,
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max_prompt_length = 512,
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max_completion_length = None,
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beta = 0.1,
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loss_type = 'kto',
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desirable_weight = 1.0,
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undesirable_weight = 1.0,
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label_pad_token_id = -100,
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padding_value = None,
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truncation_mode = 'keep_end',
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generate_during_eval = False,
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is_encoder_decoder = None,
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disable_dropout = True,
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precompute_ref_log_probs = False,
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model_init_kwargs = None,
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ref_model_init_kwargs = None,
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dataset_num_proc = None,
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use_liger_loss = False,
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base_model_attribute_name = 'model',
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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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max_length = max_length,
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max_prompt_length = max_prompt_length,
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max_completion_length = max_completion_length,
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beta = beta,
|
|
loss_type = loss_type,
|
|
desirable_weight = desirable_weight,
|
|
undesirable_weight = undesirable_weight,
|
|
label_pad_token_id = label_pad_token_id,
|
|
padding_value = padding_value,
|
|
truncation_mode = truncation_mode,
|
|
generate_during_eval = generate_during_eval,
|
|
is_encoder_decoder = is_encoder_decoder,
|
|
disable_dropout = disable_dropout,
|
|
precompute_ref_log_probs = precompute_ref_log_probs,
|
|
model_init_kwargs = model_init_kwargs,
|
|
ref_model_init_kwargs = ref_model_init_kwargs,
|
|
dataset_num_proc = dataset_num_proc,
|
|
use_liger_loss = use_liger_loss,
|
|
base_model_attribute_name = base_model_attribute_name,**kwargs)
|
|
self.vllm_sampling_params = vllm_sampling_params
|
|
self.unsloth_num_chunks = unsloth_num_chunks
|
|
pass
|
|
|
|
class _UnslothKTOTrainer(Trainer):
|
|
r""""""
|
|
|
|
_tag_names = ["trl", "kto"]
|
|
|
|
def __init__(
|
|
self,
|
|
model: Union[PreTrainedModel, nn.Module, str] = None,
|
|
ref_model: Optional[Union[PreTrainedModel, nn.Module, str]] = None,
|
|
args: KTOConfig = None,
|
|
train_dataset: Optional[Dataset] = None,
|
|
eval_dataset: Optional[Union[Dataset, dict[str, Dataset]]] = None,
|
|
processing_class: Optional[
|
|
Union[PreTrainedTokenizerBase, BaseImageProcessor, FeatureExtractionMixin, ProcessorMixin]
|
|
] = None,
|
|
data_collator: Optional[DataCollator] = None,
|
|
model_init: Optional[Callable[[], PreTrainedModel]] = None,
|
|
callbacks: Optional[list[TrainerCallback]] = None,
|
|
optimizers: tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None),
|
|
preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None,
|
|
peft_config: Optional[dict] = None,
|
|
compute_metrics: Optional[Callable[[EvalLoopOutput], dict]] = None,
|
|
model_adapter_name: Optional[str] = None,
|
|
ref_adapter_name: Optional[str] = None,
|
|
):
|
|
if type(args) is TrainingArguments:
|
|
raise ValueError("Please use `KTOConfig` instead TrainingArguments.")
|
|
|
|
if not isinstance(model, str) and ref_model is model:
|
|
raise ValueError(
|
|
"`model` and `ref_model` cannot be the same object. If you want `ref_model` to be the "
|
|
"same as `model`, you must mass a copy of it, or `None` if you use peft."
|
|
)
|
|
|
|
if args.model_init_kwargs is None:
|
|
model_init_kwargs = {}
|
|
elif not isinstance(model, str):
|
|
raise ValueError("You passed model_kwargs to the KTOTrainer. But your model is already instantiated.")
|
|
else:
|
|
model_init_kwargs = args.model_init_kwargs
|
|
torch_dtype = model_init_kwargs.get("torch_dtype")
|
|
if torch_dtype is not None:
|
|
# Convert to `torch.dtype` if an str is passed
|
|
if isinstance(torch_dtype, str) and torch_dtype != "auto":
|
|
torch_dtype = getattr(torch, torch_dtype)
|
|
if torch_dtype != "auto" and not isinstance(torch_dtype, torch.dtype):
|
|
raise ValueError(
|
|
f"Invalid `torch_dtype` passed to the KTOConfig. Expected a string with either `torch.dtype` or 'auto', but got {torch_dtype}."
|
|
)
|
|
model_init_kwargs["torch_dtype"] = torch_dtype
|
|
|
|
if args.ref_model_init_kwargs is None:
|
|
ref_model_init_kwargs = {}
|
|
elif not isinstance(ref_model, str):
|
|
raise ValueError(
|
|
"You passed ref_model_kwargs to the KTOTrainer. But your ref_model is already instantiated."
|
|
)
|
|
else:
|
|
ref_model_init_kwargs = args.ref_model_init_kwargs
|
|
torch_dtype = ref_model_init_kwargs.get("torch_dtype")
|
|
if torch_dtype is not None:
|
|
# Convert to `torch.dtype` if an str is passed
|
|
if isinstance(torch_dtype, str) and torch_dtype != "auto":
|
|
torch_dtype = getattr(torch, torch_dtype)
|
|
if torch_dtype != "auto" and not isinstance(torch_dtype, torch.dtype):
|
|
raise ValueError(
|
|
f"Invalid `torch_dtype` passed to the KTOConfig. Expected a string with either `torch.dtype` or 'auto', but got {torch_dtype}."
|
|
)
|
|
ref_model_init_kwargs["torch_dtype"] = torch_dtype
|
|
|
|
if isinstance(model, str):
|
|
model = AutoModelForCausalLM.from_pretrained(model, **model_init_kwargs)
|
|
|
|
if isinstance(ref_model, str):
|
|
ref_model = AutoModelForCausalLM.from_pretrained(ref_model, **ref_model_init_kwargs)
|
|
|
|
# Initialize this variable to False. This helps tracking the case when `peft_module_casting_to_bf16`
|
|
# has been called in order to properly call autocast if needed.
|
|
self._peft_has_been_casted_to_bf16 = False
|
|
|
|
if not is_peft_available() and peft_config is not None:
|
|
raise ValueError(
|
|
"PEFT is not installed and you passed a `peft_config` in the trainer's kwargs, please install it with `pip install peft` to use the PEFT models"
|
|
)
|
|
elif is_peft_available() and peft_config is not None:
|
|
# if model is a peft model and we have a peft_config, we merge and unload it first
|
|
if isinstance(model, PeftModel):
|
|
model = model.merge_and_unload()
|
|
|
|
if getattr(model, "is_loaded_in_8bit", False) or getattr(model, "is_loaded_in_4bit", False):
|
|
_support_gc_kwargs = hasattr(
|
|
args, "gradient_checkpointing_kwargs"
|
|
) and "gradient_checkpointing_kwargs" in list(
|
|
inspect.signature(prepare_model_for_kbit_training).parameters
|
|
)
|
|
|
|
prepare_model_kwargs = {"use_gradient_checkpointing": args.gradient_checkpointing}
|
|
|
|
if _support_gc_kwargs:
|
|
prepare_model_kwargs["gradient_checkpointing_kwargs"] = args.gradient_checkpointing_kwargs
|
|
|
|
model = prepare_model_for_kbit_training(model, **prepare_model_kwargs)
|
|
elif getattr(args, "gradient_checkpointing", False):
|
|
# For backward compatibility with older versions of transformers
|
|
if hasattr(model, "enable_input_require_grads"):
|
|
model.enable_input_require_grads()
|
|
else:
|
|
|
|
def make_inputs_require_grad(module, input, output):
|
|
output.requires_grad_(True)
|
|
|
|
model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)
|
|
|
|
# get peft model with the given config
|
|
model = model
|
|
if args.bf16 and getattr(model, "is_loaded_in_4bit", False):
|
|
peft_module_casting_to_bf16(model)
|
|
# If args.bf16 we need to explicitly call `generate` with torch amp autocast context manager
|
|
self._peft_has_been_casted_to_bf16 = True
|
|
|
|
# For models that use gradient_checkpointing, we need to attach a hook that enables input
|
|
# to explicitly have `requires_grad=True`, otherwise training will either silently
|
|
# fail or completely fail.
|
|
elif getattr(args, "gradient_checkpointing", False):
|
|
# For backward compatibility with older versions of transformers
|
|
if hasattr(model, "enable_input_require_grads"):
|
|
model.enable_input_require_grads()
|
|
else:
|
|
|
|
def make_inputs_require_grad(module, input, output):
|
|
output.requires_grad_(True)
|
|
|
|
model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)
|
|
|
|
if args.generate_during_eval and not (is_wandb_available() or is_comet_available()):
|
|
raise ValueError(
|
|
"`generate_during_eval=True` requires Weights and Biases or Comet to be installed."
|
|
" Please install `wandb` or `comet-ml` to resolve."
|
|
)
|
|
|
|
if model is not None:
|
|
self.is_encoder_decoder = model.config.is_encoder_decoder
|
|
elif args.is_encoder_decoder is None:
|
|
raise ValueError("When no model is provided, you need to pass the parameter is_encoder_decoder.")
|
|
else:
|
|
self.is_encoder_decoder = args.is_encoder_decoder
|
|
|
|
self.is_peft_model = is_peft_available() and isinstance(model, PeftModel)
|
|
self.model_adapter_name = model_adapter_name
|
|
self.ref_adapter_name = ref_adapter_name
|
|
|
|
if ref_model:
|
|
self.ref_model = ref_model
|
|
elif self.is_peft_model or args.precompute_ref_log_probs:
|
|
# The `model` with adapters turned off will be used as the reference model
|
|
self.ref_model = None
|
|
else:
|
|
self.ref_model = create_reference_model(model)
|
|
|
|
if processing_class is None:
|
|
raise ValueError(
|
|
"max_length or a processing_class must be specified when using the default DPODataCollatorWithPadding"
|
|
)
|
|
if args.max_length is None:
|
|
warnings.warn(
|
|
"When using DPODataCollatorWithPadding, you should set `max_length` in the KTOTrainer's init"
|
|
" it will be set to `512` by default, but you should do it yourself in the future.",
|
|
UserWarning,
|
|
)
|
|
max_length = 512
|
|
if args.max_length is not None:
|
|
max_length = args.max_length
|
|
|
|
if args.max_prompt_length is None:
|
|
warnings.warn(
|
|
"When using DPODataCollatorWithPadding, you should set `max_prompt_length` in the KTOTrainer's init"
|
|
" it will be set to `128` by default, but you should do it yourself in the future.",
|
|
UserWarning,
|
|
)
|
|
max_prompt_length = 128
|
|
if args.max_prompt_length is not None:
|
|
max_prompt_length = args.max_prompt_length
|
|
|
|
max_completion_length = None
|
|
if args.max_completion_length is None and self.is_encoder_decoder:
|
|
warnings.warn(
|
|
"When using DPODataCollatorWithPadding with an encoder decoder architecture, you should set `max_completion_length` in the KTOTrainer's init"
|
|
" it will be set to `128` by default, but you should do it yourself in the future.",
|
|
UserWarning,
|
|
)
|
|
max_completion_length = 128
|
|
if args.max_completion_length is not None and self.is_encoder_decoder:
|
|
max_completion_length = args.max_completion_length
|
|
|
|
if data_collator is None:
|
|
data_collator = DPODataCollatorWithPadding(
|
|
pad_token_id=processing_class.pad_token_id,
|
|
label_pad_token_id=args.label_pad_token_id,
|
|
is_encoder_decoder=self.is_encoder_decoder,
|
|
)
|
|
|
|
if args.remove_unused_columns:
|
|
args.remove_unused_columns = False
|
|
# warn users
|
|
warnings.warn(
|
|
"When using DPODataCollatorWithPadding, you should set `remove_unused_columns=False` in your KTOConfig"
|
|
" we have set it for you, but you should do it yourself in the future.",
|
|
UserWarning,
|
|
)
|
|
|
|
self.use_dpo_data_collator = True
|
|
else:
|
|
self.use_dpo_data_collator = False
|
|
|
|
# Disable dropout in the model and reference model
|
|
if args.disable_dropout:
|
|
disable_dropout_in_model(model)
|
|
if self.ref_model is not None:
|
|
disable_dropout_in_model(self.ref_model)
|
|
|
|
self.loss_type = args.loss_type
|
|
self.max_length = max_length
|
|
self.generate_during_eval = args.generate_during_eval
|
|
self.label_pad_token_id = args.label_pad_token_id
|
|
self.padding_value = args.padding_value if args.padding_value is not None else processing_class.pad_token_id
|
|
self.max_prompt_length = max_prompt_length
|
|
self.truncation_mode = args.truncation_mode
|
|
self.max_completion_length = max_completion_length
|
|
self.processing_class = processing_class
|
|
self.precompute_ref_log_probs = args.precompute_ref_log_probs
|
|
|
|
# Not all losses require a KL calculation
|
|
self.calculate_KL = True
|
|
if self.loss_type in ["apo_zero_unpaired"]:
|
|
self.calculate_KL = False
|
|
|
|
# Since ref_logs are precomputed on the first call to get_train/eval_dataloader
|
|
# keep track of first called to avoid computation of future calls
|
|
self._precomputed_train_ref_log_probs = False
|
|
self._precomputed_eval_ref_log_probs = False
|
|
|
|
# metric
|
|
self._stored_metrics = defaultdict(lambda: defaultdict(list))
|
|
|
|
# KTO parameter
|
|
self.beta = args.beta
|
|
self.desirable_weight = args.desirable_weight
|
|
self.undesirable_weight = args.undesirable_weight
|
|
self.aux_loss_enabled = getattr(model.config, "output_router_logits", False)
|
|
self.aux_loss_coef = getattr(model.config, "router_aux_loss_coef", 0.0)
|
|
if self.aux_loss_enabled and self.aux_loss_coef == 0.0:
|
|
warnings.warn(
|
|
"You set `output_router_logits` to `True` in the model config, but `router_aux_loss_coef` is set to "
|
|
"`0.0`, meaning the auxiliary loss will not be used. Either set `router_aux_loss_coef` to a value "
|
|
"greater than `0.0`, or set `output_router_logits` to `False` if you don't want to use the auxiliary "
|
|
"loss.",
|
|
UserWarning,
|
|
)
|
|
|
|
# The trainer estimates the number of FLOPs (floating-point operations) using the number of elements in the
|
|
# input tensor associated with the key "input_ids". However, in KTO, the sampled data does not include the
|
|
# "input_ids" key. Instead, the available keys are "prompt_input_ids" and "completion_input_ids". As a result,
|
|
# the trainer issues the warning: "Could not estimate the number of tokens of the input, floating-point
|
|
# operations will not be computed." To suppress this warning, we set the "estimate_tokens" key in the model's
|
|
# "warnings_issued" dictionary to True. This acts as a flag to indicate that the warning has already been
|
|
# issued.
|
|
model.warnings_issued["estimate_tokens"] = True
|
|
|
|
# Compute that only on the main process for faster data processing.
|
|
# see: https://github.com/huggingface/trl/pull/1255
|
|
with PartialState().main_process_first():
|
|
# Extract the prompt if needed
|
|
train_dataset = train_dataset.map(
|
|
maybe_extract_prompt, num_proc=args.dataset_num_proc, desc="Extracting prompt from train dataset"
|
|
)
|
|
# Unpair the dataset if needed
|
|
train_dataset = maybe_unpair_preference_dataset(
|
|
train_dataset, args.dataset_num_proc, desc="Unpairing train dataset"
|
|
)
|
|
# Apply the chat template if needed
|
|
train_dataset = train_dataset.map(
|
|
maybe_apply_chat_template,
|
|
fn_kwargs={"tokenizer": processing_class},
|
|
num_proc=args.dataset_num_proc,
|
|
desc="Applying chat template to train dataset",
|
|
)
|
|
if eval_dataset is not None:
|
|
eval_dataset = eval_dataset.map(
|
|
maybe_extract_prompt, num_proc=args.dataset_num_proc, desc="Extracting prompt from eval dataset"
|
|
)
|
|
eval_dataset = maybe_unpair_preference_dataset(
|
|
eval_dataset, args.dataset_num_proc, desc="Unpairing eval dataset"
|
|
)
|
|
eval_dataset = eval_dataset.map(
|
|
maybe_apply_chat_template,
|
|
fn_kwargs={"tokenizer": processing_class},
|
|
num_proc=args.dataset_num_proc,
|
|
desc="Applying chat template to eval dataset",
|
|
)
|
|
|
|
# Tokenize and prepare the training datasets
|
|
train_dataset = train_dataset.map(
|
|
_tokenize,
|
|
batched=True,
|
|
fn_kwargs={"tokenizer": self.processing_class},
|
|
num_proc=args.dataset_num_proc,
|
|
desc="Tokenizing train dataset",
|
|
)
|
|
|
|
fn_kwargs = {
|
|
"prefix": "",
|
|
"is_encoder_decoder": self.is_encoder_decoder,
|
|
"tokenizer": self.processing_class,
|
|
"max_length": self.max_length,
|
|
"truncation_mode": self.truncation_mode,
|
|
"label_pad_token_id": self.label_pad_token_id,
|
|
"max_prompt_length": self.max_prompt_length,
|
|
"max_completion_length": self.max_completion_length,
|
|
}
|
|
|
|
train_dataset = train_dataset.map(
|
|
_process_tokens,
|
|
fn_kwargs=fn_kwargs,
|
|
num_proc=args.dataset_num_proc,
|
|
desc="Processing tokenized train dataset",
|
|
)
|
|
|
|
# Tokenize and prepare the eval datasets
|
|
if eval_dataset is not None:
|
|
eval_dataset = eval_dataset.map(
|
|
_tokenize,
|
|
fn_kwargs={"tokenizer": self.processing_class},
|
|
batched=True,
|
|
num_proc=args.dataset_num_proc,
|
|
desc="Tokenizing eval dataset",
|
|
)
|
|
|
|
eval_dataset = eval_dataset.map(
|
|
_process_tokens,
|
|
fn_kwargs=fn_kwargs,
|
|
num_proc=args.dataset_num_proc,
|
|
desc="Processing tokenized eval dataset",
|
|
)
|
|
|
|
# Get KL datasets if needed
|
|
if self.calculate_KL:
|
|
if args.per_device_train_batch_size <= 1:
|
|
raise ValueError(
|
|
"Actual (not effective) batch size must be > 1. KTO will not work properly because the KL term will be equivalent to the implied reward."
|
|
)
|
|
|
|
# create pairs for estimating the KL term by flipping the matched pairs in each batch of size total_batch_size
|
|
# i.e., (x_1, y_1), ..., (x_n, y_n) --> (x_1, y_n), ..., (x_n, y_1) = (x'_1, y'_1), ..., (x'_n, y'_n)
|
|
train_kl_dataset = train_dataset.map(
|
|
_get_kl_dataset,
|
|
batched=True,
|
|
batch_size=args.per_device_train_batch_size,
|
|
num_proc=args.dataset_num_proc,
|
|
desc="Extracting KL train dataset",
|
|
)
|
|
|
|
fn_kwargs["prefix"] = "KL_"
|
|
train_kl_dataset = train_kl_dataset.map(
|
|
_process_tokens,
|
|
fn_kwargs=fn_kwargs,
|
|
num_proc=args.dataset_num_proc,
|
|
remove_columns=[c for c in train_kl_dataset.column_names if c in train_dataset.column_names],
|
|
desc="Processing tokenized train KL dataset",
|
|
)
|
|
|
|
# merge the datasets
|
|
train_dataset = concatenate_datasets([train_dataset, train_kl_dataset], axis=1)
|
|
|
|
if eval_dataset is not None:
|
|
# Get KL dataset
|
|
eval_kl_dataset = eval_dataset.map(
|
|
_get_kl_dataset,
|
|
batched=True,
|
|
batch_size=args.per_device_train_batch_size,
|
|
num_proc=args.dataset_num_proc,
|
|
desc="Extracting eval KL dataset",
|
|
)
|
|
|
|
eval_kl_dataset = eval_kl_dataset.map(
|
|
_process_tokens,
|
|
fn_kwargs=fn_kwargs,
|
|
num_proc=args.dataset_num_proc,
|
|
remove_columns=[c for c in eval_kl_dataset.column_names if c in eval_dataset.column_names],
|
|
desc="Processing tokenized eval KL dataset",
|
|
)
|
|
|
|
# merge the datasets
|
|
eval_dataset = concatenate_datasets([eval_dataset, eval_kl_dataset], axis=1)
|
|
|
|
# calculate dataset desirability balance
|
|
num_desirable = max(sum(train_dataset["label"]), 1)
|
|
num_undesirable = max(len(train_dataset["label"]) - num_desirable, 1) # "label" is binary
|
|
|
|
if num_desirable != num_undesirable:
|
|
# The lower and upper bounds come from Eq. (8) of https://huggingface.co/papers/2402.01306
|
|
des_weight_lower_bound = round((num_undesirable * self.undesirable_weight / num_desirable) * 1, 2)
|
|
des_weight_upper_bound = round((num_undesirable * self.undesirable_weight / num_desirable) * 1.33, 2)
|
|
und_weight_lower_bound = round((num_desirable * self.desirable_weight / num_undesirable) / 1.33, 2)
|
|
und_weight_upper_bound = round((num_desirable * self.desirable_weight / num_undesirable) / 1, 2)
|
|
|
|
des_weight_in_range = des_weight_lower_bound <= self.desirable_weight <= des_weight_upper_bound
|
|
und_weight_in_range = und_weight_lower_bound <= self.undesirable_weight <= und_weight_upper_bound
|
|
|
|
if not (des_weight_in_range or und_weight_in_range):
|
|
warnings.warn(
|
|
"You have different amounts of desirable/positive and undesirable/negative examples but the "
|
|
"weights on the desirable and undesirable losses don't seem to be in an ideal range. Based "
|
|
f"on your data, we recommend EITHER "
|
|
f"desirable_weight in [{des_weight_lower_bound}, {des_weight_upper_bound}] or "
|
|
f"undesirable_weight in [{und_weight_lower_bound}, {und_weight_upper_bound}] (but NOT BOTH). "
|
|
"See the documentation on how to optimally set these weights.",
|
|
UserWarning,
|
|
)
|
|
|
|
super().__init__(
|
|
model=model,
|
|
args=args,
|
|
data_collator=data_collator,
|
|
train_dataset=train_dataset,
|
|
eval_dataset=eval_dataset,
|
|
processing_class=processing_class,
|
|
model_init=model_init,
|
|
compute_metrics=compute_metrics,
|
|
callbacks=callbacks,
|
|
optimizers=optimizers,
|
|
preprocess_logits_for_metrics=preprocess_logits_for_metrics,
|
|
)
|
|
|
|
# Gradient accumulation requires scaled loss. Normally, loss scaling in the parent class depends on whether the
|
|
# model accepts loss-related kwargs. Since we compute our own loss, this check is irrelevant. We set
|
|
# self.model_accepts_loss_kwargs to False to enable scaling.
|
|
self.model_accepts_loss_kwargs = False
|
|
|
|
# Add tags for models that have been loaded with the correct transformers version
|
|
if hasattr(self.model, "add_model_tags"):
|
|
self.model.add_model_tags(self._tag_names)
|
|
|
|
if not hasattr(self, "accelerator"):
|
|
raise AttributeError(
|
|
"Your `Trainer` does not have an `accelerator` object. Consider upgrading `transformers`."
|
|
)
|
|
|
|
# Deepspeed Zero-3 does not support precompute_ref_log_probs
|
|
if self.is_deepspeed_enabled:
|
|
if self.accelerator.state.deepspeed_plugin.zero_stage == 3 and self.precompute_ref_log_probs:
|
|
raise ValueError(
|
|
"You cannot use `precompute_ref_log_probs=True` with Deepspeed ZeRO-3. Please set `precompute_ref_log_probs=False`."
|
|
)
|
|
|
|
if self.ref_model is None:
|
|
if not (self.is_peft_model or self.precompute_ref_log_probs):
|
|
raise ValueError(
|
|
"No reference model and model is not a Peft model. Try setting `precompute_ref_log_probs=True`"
|
|
)
|
|
else:
|
|
if self.is_deepspeed_enabled:
|
|
self.ref_model = prepare_deepspeed(self.ref_model, self.accelerator)
|
|
else:
|
|
self.ref_model = self.accelerator.prepare_model(self.ref_model, evaluation_mode=True)
|
|
|
|
# Import Liger loss if enabled
|
|
if self.args.use_liger_loss:
|
|
if not is_liger_kernel_available():
|
|
raise ImportError(
|
|
"You set `use_liger_loss=True` but the liger kernel is not available. "
|
|
"Please install liger-kernel first: `pip install liger-kernel`"
|
|
)
|
|
if self.loss_type in ["apo_zero_unpaired"]:
|
|
raise ValueError(
|
|
"You cannot set `loss_type='apo_zero_unpaired'` with liger-kernel."
|
|
"Only KTO loss is supported with liger-kernel."
|
|
)
|
|
if self.precompute_ref_log_probs:
|
|
raise ValueError(
|
|
"You cannot use `precompute_ref_log_probs=True` with liger kernel. Please set "
|
|
"`precompute_ref_log_probs=False`."
|
|
)
|
|
if self.is_peft_model or self.ref_adapter_name is not None:
|
|
raise ValueError(
|
|
"You cannot use `use_liger_loss=True` with Peft models. Please set `use_liger_loss=False`."
|
|
)
|
|
self.kto_loss_fn = LigerFusedLinearKTOLoss(
|
|
ignore_index=self.label_pad_token_id, beta=self.beta, use_ref_model=(self.ref_model is not None)
|
|
)
|
|
|
|
@contextmanager
|
|
def null_ref_context(self):
|
|
"""Context manager for handling null reference model (that is, peft adapter manipulation)."""
|
|
with (
|
|
self.accelerator.unwrap_model(self.model).disable_adapter()
|
|
if self.is_peft_model and not self.ref_adapter_name
|
|
else nullcontext()
|
|
):
|
|
if self.ref_adapter_name:
|
|
self.model.set_adapter(self.ref_adapter_name)
|
|
yield
|
|
if self.ref_adapter_name:
|
|
self.model.set_adapter(self.model_adapter_name or "default")
|
|
|
|
def get_train_dataloader(self) -> DataLoader:
|
|
"""
|
|
Returns the training [`~torch.utils.data.DataLoader`].
|
|
|
|
Subclass of transformers.src.transformers.trainer.get_train_dataloader to precompute `ref_log_probs`.
|
|
"""
|
|
|
|
if self.precompute_ref_log_probs and not self._precomputed_train_ref_log_probs:
|
|
dataloader_params = {
|
|
"batch_size": self.args.per_device_train_batch_size,
|
|
"collate_fn": self.data_collator,
|
|
"num_workers": self.args.dataloader_num_workers,
|
|
"pin_memory": self.args.dataloader_pin_memory,
|
|
"shuffle": False,
|
|
}
|
|
|
|
# prepare dataloader
|
|
data_loader = self.accelerator.prepare(DataLoader(self.train_dataset, **dataloader_params))
|
|
reference_completion_logps = []
|
|
reference_KL_logps = []
|
|
|
|
for padded_batch in tqdm(iterable=data_loader, desc="Train dataset reference log probs"):
|
|
reference_completion_logp, reference_KL_logp = self.compute_reference_log_probs(padded_batch)
|
|
|
|
reference_completion_logp = self.accelerator.gather_for_metrics(reference_completion_logp)
|
|
reference_completion_logps.append(reference_completion_logp.cpu())
|
|
|
|
if self.calculate_KL:
|
|
reference_KL_logp = self.accelerator.gather_for_metrics(reference_KL_logp)
|
|
reference_KL_logps.append(reference_KL_logp.cpu())
|
|
|
|
self.train_dataset = self.train_dataset.add_column(
|
|
name="reference_logps", column=torch.cat(reference_completion_logps).float().numpy()
|
|
)
|
|
|
|
if self.calculate_KL:
|
|
self.train_dataset = self.train_dataset.add_column(
|
|
name="reference_KL_logps", column=torch.cat(reference_KL_logps).float().numpy()
|
|
)
|
|
|
|
self._precomputed_train_ref_log_probs = True
|
|
|
|
return super().get_train_dataloader()
|
|
|
|
def get_eval_dataloader(self, eval_dataset: Optional[Dataset] = None) -> DataLoader:
|
|
"""
|
|
Returns the evaluation [`~torch.utils.data.DataLoader`].
|
|
|
|
Subclass of transformers.src.transformers.trainer.get_eval_dataloader to precompute `ref_log_probs`.
|
|
|
|
Args:
|
|
eval_dataset (`torch.utils.data.Dataset`, *optional*):
|
|
If provided, will override `self.eval_dataset`. If it is a [`~datasets.Dataset`], columns not accepted
|
|
by the `model.forward()` method are automatically removed. It must implement `__len__`.
|
|
"""
|
|
if eval_dataset is None and self.eval_dataset is None:
|
|
raise ValueError("Trainer: evaluation requires an eval_dataset.")
|
|
eval_dataset = eval_dataset if eval_dataset is not None else self.eval_dataset
|
|
|
|
if self.precompute_ref_log_probs and not self._precomputed_eval_ref_log_probs:
|
|
dataloader_params = {
|
|
"batch_size": self.args.per_device_eval_batch_size,
|
|
"collate_fn": self.data_collator,
|
|
"num_workers": self.args.dataloader_num_workers,
|
|
"pin_memory": self.args.dataloader_pin_memory,
|
|
"shuffle": False,
|
|
}
|
|
|
|
# prepare dataloader
|
|
data_loader = self.accelerator.prepare(DataLoader(eval_dataset, **dataloader_params))
|
|
|
|
reference_completion_logps = []
|
|
reference_KL_logps = []
|
|
|
|
for padded_batch in tqdm(iterable=data_loader, desc="Eval dataset reference log probs"):
|
|
reference_completion_logp, reference_KL_logp = self.compute_reference_log_probs(padded_batch)
|
|
|
|
reference_completion_logp = self.accelerator.gather_for_metrics(reference_completion_logp)
|
|
reference_completion_logps.append(reference_completion_logp.cpu())
|
|
|
|
if self.calculate_KL:
|
|
reference_KL_logp = self.accelerator.gather_for_metrics(reference_KL_logp)
|
|
reference_KL_logps.append(reference_KL_logp.cpu())
|
|
|
|
eval_dataset = eval_dataset.add_column(
|
|
name="reference_logps", column=torch.cat(reference_completion_logps).float().numpy()
|
|
)
|
|
if self.calculate_KL:
|
|
eval_dataset = eval_dataset.add_column(
|
|
name="reference_KL_logps", column=torch.cat(reference_KL_logps).float().numpy()
|
|
)
|
|
|
|
# Save calculated reference_chosen_logps and reference_rejected_logps to the eval_dataset for subsequent runs
|
|
if self.eval_dataset is not None:
|
|
self.eval_dataset = eval_dataset
|
|
self._precomputed_eval_ref_log_probs = True
|
|
|
|
return super().get_eval_dataloader(eval_dataset=eval_dataset)
|
|
|
|
def compute_reference_log_probs(self, padded_batch: dict) -> dict:
|
|
"""Computes log probabilities of the reference model for a single padded batch of a KTO specific dataset."""
|
|
with torch.no_grad():
|
|
if self.ref_model is None:
|
|
with self.null_ref_context():
|
|
if self.is_encoder_decoder:
|
|
completion_logits = self.model(
|
|
padded_batch["prompt_input_ids"],
|
|
attention_mask=padded_batch["prompt_attention_mask"],
|
|
decoder_input_ids=padded_batch.get("completion_decoder_input_ids"),
|
|
labels=padded_batch["completion_labels"],
|
|
).logits
|
|
|
|
if self.calculate_KL:
|
|
KL_logits = self.model(
|
|
padded_batch["KL_prompt_input_ids"],
|
|
attention_mask=padded_batch["KL_prompt_attention_mask"],
|
|
decoder_input_ids=padded_batch.get("KL_completion_decoder_input_ids"),
|
|
labels=padded_batch["KL_completion_labels"],
|
|
).logits
|
|
else:
|
|
completion_logits = self.model(
|
|
padded_batch["completion_input_ids"],
|
|
attention_mask=padded_batch["completion_attention_mask"],
|
|
).logits
|
|
|
|
if self.calculate_KL:
|
|
KL_logits = self.model(
|
|
padded_batch["KL_completion_input_ids"],
|
|
attention_mask=padded_batch["KL_completion_attention_mask"],
|
|
).logits
|
|
else:
|
|
if self.is_encoder_decoder:
|
|
completion_logits = self.ref_model(
|
|
padded_batch["prompt_input_ids"],
|
|
attention_mask=padded_batch["prompt_attention_mask"],
|
|
decoder_input_ids=padded_batch.get("completion_decoder_input_ids"),
|
|
labels=padded_batch["completion_labels"],
|
|
).logits
|
|
|
|
if self.calculate_KL:
|
|
KL_logits = self.ref_model(
|
|
padded_batch["KL_prompt_input_ids"],
|
|
attention_mask=padded_batch["KL_prompt_attention_mask"],
|
|
decoder_input_ids=padded_batch.get("KL_completion_decoder_input_ids"),
|
|
labels=padded_batch["KL_completion_labels"],
|
|
).logits
|
|
else:
|
|
completion_logits = self.ref_model(
|
|
padded_batch["completion_input_ids"], attention_mask=padded_batch["completion_attention_mask"]
|
|
).logits
|
|
|
|
if self.calculate_KL:
|
|
KL_logits = self.ref_model(
|
|
padded_batch["KL_completion_input_ids"],
|
|
attention_mask=padded_batch["KL_completion_attention_mask"],
|
|
).logits
|
|
|
|
completion_logps = self.get_batch_logps(
|
|
completion_logits,
|
|
padded_batch["completion_labels"],
|
|
average_log_prob=False,
|
|
is_encoder_decoder=self.is_encoder_decoder,
|
|
label_pad_token_id=self.label_pad_token_id,
|
|
)
|
|
|
|
if self.calculate_KL:
|
|
KL_logps = self.get_batch_logps(
|
|
KL_logits,
|
|
padded_batch["KL_completion_labels"],
|
|
average_log_prob=False,
|
|
is_encoder_decoder=self.is_encoder_decoder,
|
|
label_pad_token_id=self.label_pad_token_id,
|
|
)
|
|
else:
|
|
KL_logps = None
|
|
|
|
return completion_logps, KL_logps
|
|
|
|
@staticmethod
|
|
def get_batch_logps(
|
|
logits: torch.FloatTensor,
|
|
labels: torch.LongTensor,
|
|
average_log_prob: bool = False,
|
|
label_pad_token_id: int = -100,
|
|
is_encoder_decoder: bool = False,
|
|
) -> torch.FloatTensor:
|
|
"""Compute the log probabilities of the given labels under the given logits.
|
|
|
|
Args:
|
|
logits: Logits of the model (unnormalized). Shape: (batch_size, sequence_length, vocab_size)
|
|
labels: Labels for which to compute the log probabilities. Label tokens with a value of label_pad_token_id are ignored. Shape: (batch_size, sequence_length)
|
|
average_log_prob: If True, return the average log probability per (non-masked) token. Otherwise, return the sum of the log probabilities of the (non-masked) tokens.
|
|
|
|
Returns:
|
|
A tensor of shape (batch_size,) containing the average/sum log probabilities of the given labels under the given logits.
|
|
"""
|
|
if logits.shape[:-1] != labels.shape:
|
|
raise ValueError("Logits (batch and sequence length dim) and labels must have the same shape.")
|
|
|
|
if not is_encoder_decoder:
|
|
labels = labels[:, 1:].clone()
|
|
logits = logits[:, :-1, :]
|
|
else:
|
|
# Fixes end-dec RuntimeError
|
|
labels = labels.clone()
|
|
|
|
loss_mask = labels != label_pad_token_id
|
|
|
|
# dummy token; we'll ignore the losses on these tokens later
|
|
labels[labels == label_pad_token_id] = 0
|
|
|
|
per_token_logps = selective_log_softmax(logits, labels)
|
|
|
|
if average_log_prob:
|
|
return (per_token_logps * loss_mask).sum(-1) / loss_mask.sum(-1)
|
|
else:
|
|
return (per_token_logps * loss_mask).sum(-1)
|
|
|
|
def forward(
|
|
self, model: nn.Module, batch: dict[str, Union[list, torch.LongTensor]]
|
|
) -> tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:
|
|
KL_logps = self._compute_kl_logps(model, batch)
|
|
|
|
model_kwargs = (
|
|
{
|
|
"labels": batch["completion_labels"],
|
|
"decoder_input_ids": batch.get("completion_decoder_input_ids"),
|
|
}
|
|
if self.is_encoder_decoder
|
|
else {}
|
|
)
|
|
if self.aux_loss_enabled:
|
|
model_kwargs["output_router_logits"] = True
|
|
|
|
outputs = model(
|
|
batch["completion_input_ids"],
|
|
attention_mask=batch["completion_attention_mask"],
|
|
**model_kwargs,
|
|
)
|
|
completion_logits = outputs.logits
|
|
|
|
completion_logps = self.get_batch_logps(
|
|
completion_logits,
|
|
batch["completion_labels"],
|
|
average_log_prob=False,
|
|
is_encoder_decoder=self.is_encoder_decoder,
|
|
label_pad_token_id=self.label_pad_token_id,
|
|
)
|
|
|
|
if completion_logps.shape[0] != len(batch["label"]):
|
|
raise ValueError(
|
|
"There is a mismatch between the number of examples in this batch and the number of "
|
|
"examples for which an output sequence was predicted."
|
|
)
|
|
|
|
chosen_idx = [i for i in range(completion_logps.shape[0]) if batch["label"][i] is True]
|
|
rejected_idx = [i for i in range(completion_logps.shape[0]) if batch["label"][i] is False]
|
|
|
|
chosen_logps = completion_logps[chosen_idx, ...]
|
|
rejected_logps = completion_logps[rejected_idx, ...]
|
|
|
|
chosen_logits = completion_logits[chosen_idx, ...]
|
|
rejected_logits = completion_logits[rejected_idx, ...]
|
|
|
|
if self.aux_loss_enabled:
|
|
return (chosen_logps, rejected_logps, chosen_logits, rejected_logits, KL_logps, outputs.aux_loss)
|
|
else:
|
|
return (chosen_logps, rejected_logps, chosen_logits, rejected_logits, KL_logps)
|
|
|
|
def kto_loss(
|
|
self,
|
|
policy_chosen_logps: torch.FloatTensor,
|
|
policy_rejected_logps: torch.FloatTensor,
|
|
policy_KL_logps: torch.FloatTensor,
|
|
reference_chosen_logps: torch.FloatTensor,
|
|
reference_rejected_logps: torch.FloatTensor,
|
|
reference_KL_logps: torch.FloatTensor,
|
|
) -> tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:
|
|
"""Compute the KTO loss for a batch of policy and reference model log probabilities.
|
|
|
|
Args:
|
|
policy_chosen_logps: Log probabilities of the policy model for the chosen responses. Shape: (num(chosen) in batch_size,)
|
|
policy_rejected_logps: Log probabilities of the policy model for the rejected responses. Shape: (num(rejected) in batch_size,)
|
|
policy_KL_logps: Log probabilities of the policy model for the KL responses. Shape: (batch_size,)
|
|
reference_chosen_logps: Log probabilities of the reference model for the chosen responses. Shape: (num(chosen) in batch_size,)
|
|
reference_rejected_logps: Log probabilities of the reference model for the rejected responses. Shape: (num(rejected) in batch_size,)
|
|
reference_KL_logps: Log probabilities of the reference model for the KL responses. Shape: (batch_size,)
|
|
|
|
Returns:
|
|
A tuple of four tensors: (losses, chosen_rewards, rejected_rewards, KL).
|
|
The losses tensor contains the KTO loss for each example in the batch.
|
|
The chosen_rewards and rejected_rewards tensors contain the rewards for the chosen and rejected responses, respectively.
|
|
The KL tensor contains the detached KL divergence estimate between the policy and reference models.
|
|
"""
|
|
if self.calculate_KL:
|
|
kl = (policy_KL_logps - reference_KL_logps).mean().detach()
|
|
kl = self.accelerator.gather_for_metrics(kl).mean().clamp(min=0)
|
|
else:
|
|
kl = torch.zeros(1).to(policy_chosen_logps.device)
|
|
|
|
# Chosen losses
|
|
if policy_chosen_logps.shape[0] != 0 or reference_chosen_logps.shape[0] != 0:
|
|
chosen_logratios = policy_chosen_logps - reference_chosen_logps
|
|
|
|
if self.loss_type == "kto":
|
|
# Eqn (7) of the KTO paper (https://huggingface.co/papers/2402.01306)
|
|
chosen_losses = 1 - F.sigmoid(self.beta * (chosen_logratios - kl))
|
|
elif self.loss_type == "apo_zero_unpaired":
|
|
# Unpaired variant of Eqn (7) of the APO paper (https://huggingface.co/papers/2408.06266)
|
|
# Use this loss when you believe the chosen outputs are better than your model's default output
|
|
chosen_losses = 1 - F.sigmoid(self.beta * chosen_logratios)
|
|
|
|
chosen_rewards = self.beta * chosen_logratios.detach()
|
|
|
|
else:
|
|
# lists can't be empty -- if they are, then accelerate.gather will hang
|
|
chosen_losses = torch.Tensor([]).to(self.accelerator.device)
|
|
chosen_rewards = torch.Tensor([]).to(self.accelerator.device)
|
|
|
|
# Rejected losses
|
|
if policy_rejected_logps.shape[0] != 0 or reference_rejected_logps.shape[0] != 0:
|
|
rejected_logratios = policy_rejected_logps - reference_rejected_logps
|
|
|
|
if self.loss_type == "kto":
|
|
rejected_losses = 1 - F.sigmoid(self.beta * (kl - rejected_logratios))
|
|
elif self.loss_type == "apo_zero_unpaired":
|
|
rejected_losses = F.sigmoid(self.beta * rejected_logratios)
|
|
|
|
rejected_rewards = self.beta * rejected_logratios.detach()
|
|
else:
|
|
# lists can't be empty -- if they are, then accelerate.gather will hang
|
|
rejected_losses = torch.Tensor([]).to(self.accelerator.device)
|
|
rejected_rewards = torch.Tensor([]).to(self.accelerator.device)
|
|
|
|
losses = torch.cat(
|
|
(self.desirable_weight * chosen_losses, self.undesirable_weight * rejected_losses),
|
|
0,
|
|
)
|
|
|
|
return losses, chosen_rewards, rejected_rewards, kl
|
|
|
|
def _compute_kl_logps(self, model, batch):
|
|
"""Compute KL log probabilities for a given batch."""
|
|
KL_logps = None
|
|
if self.calculate_KL:
|
|
if self.is_encoder_decoder:
|
|
KL_model_kwargs = {
|
|
"input_ids": batch["KL_prompt_input_ids"],
|
|
"attention_mask": batch["KL_prompt_attention_mask"],
|
|
"labels": batch["KL_completion_labels"],
|
|
"decoder_input_ids": batch.get("KL_completion_decoder_input_ids"),
|
|
}
|
|
else:
|
|
KL_model_kwargs = {
|
|
"input_ids": batch["KL_completion_input_ids"],
|
|
"attention_mask": batch["KL_completion_attention_mask"],
|
|
}
|
|
|
|
with torch.no_grad():
|
|
KL_logits = model(**KL_model_kwargs).logits
|
|
|
|
KL_logps = self.get_batch_logps(
|
|
KL_logits,
|
|
batch["KL_completion_labels"],
|
|
average_log_prob=False,
|
|
is_encoder_decoder=self.is_encoder_decoder,
|
|
label_pad_token_id=self.label_pad_token_id,
|
|
)
|
|
return KL_logps
|
|
|
|
def _compute_loss_liger(self, model, batch):
|
|
"""
|
|
Compute the KTO loss using the Liger-Kernel's LigerFusedLinearKTOLoss.
|
|
|
|
Args:
|
|
model: The policy model used for generating log probabilities and outputs. It could be an encoder-decoder model or a regular language model.
|
|
batch: A dictionary containing the input data and labels for the batch.
|
|
|
|
Returns:
|
|
A dictionary containing the following keys:
|
|
- "loss": The computed KTO loss for the batch.
|
|
- "chosen_logits_sum": Sum of the logits for the chosen responses from the policy model.
|
|
- "rejected_logits_sum": Sum of the logits for the rejected responses from the policy model.
|
|
- "chosen_logps": Log probabilities of the chosen responses from the policy model.
|
|
- "rejected_logps": Log probabilities of the rejected responses from the policy model.
|
|
- "chosen_rewards": Rewards for the chosen responses.
|
|
- "rejected_rewards": Rewards for the rejected responses.
|
|
- "kl": The KL divergence between the policy and reference models (detached).
|
|
|
|
If auxiliary loss is enabled, the dictionary will also include:
|
|
- "aux_loss": The auxiliary loss from the model outputs.
|
|
"""
|
|
policy_KL_logps = self._compute_kl_logps(model, batch)
|
|
reference_KL_logps = self._compute_kl_logps(self.ref_model, batch)
|
|
if self.calculate_KL:
|
|
kl = (policy_KL_logps - reference_KL_logps).mean().detach()
|
|
kl = self.accelerator.gather_for_metrics(kl).mean().clamp(min=0)
|
|
else:
|
|
kl = torch.zeros(1).to(self.accelerator.device)
|
|
|
|
model_kwargs = (
|
|
{
|
|
"labels": batch["completion_labels"],
|
|
"decoder_input_ids": batch.get("completion_decoder_input_ids"),
|
|
}
|
|
if self.is_encoder_decoder
|
|
else {}
|
|
)
|
|
if self.aux_loss_enabled:
|
|
model_kwargs["output_router_logits"] = True
|
|
|
|
if self.is_encoder_decoder:
|
|
# 1. Get encoder outputs
|
|
encoder_outputs = model.get_encoder()(
|
|
batch["completion_input_ids"],
|
|
attention_mask=batch["completion_attention_mask"],
|
|
return_dict=True,
|
|
**model_kwargs,
|
|
)
|
|
# 2. Get decoder outputs
|
|
outputs = model.get_decoder()(
|
|
input_ids=model_kwargs["decoder_input_ids"],
|
|
encoder_hidden_states=encoder_outputs.last_hidden_state,
|
|
use_cache=False,
|
|
**model_kwargs,
|
|
)
|
|
# 1. Get reference encoder outputs
|
|
ref_encoder_outputs = self.ref_model.get_encoder()(
|
|
batch["completion_input_ids"],
|
|
attention_mask=batch["completion_attention_mask"],
|
|
return_dict=True,
|
|
**model_kwargs,
|
|
)
|
|
# 2. Get reference decoder outputs
|
|
ref_outputs = self.ref_model.get_decoder()(
|
|
input_ids=model_kwargs["decoder_input_ids"],
|
|
encoder_hidden_states=ref_encoder_outputs.last_hidden_state,
|
|
use_cache=False,
|
|
**model_kwargs,
|
|
)
|
|
else:
|
|
# skip the lm head and get the last hidden state
|
|
if hasattr(model, "get_decoder"):
|
|
base_model = model.get_decoder()
|
|
else:
|
|
base_model = getattr(model, self.args.base_model_attribute_name)
|
|
outputs = base_model(
|
|
batch["completion_input_ids"],
|
|
attention_mask=batch["completion_attention_mask"],
|
|
use_cache=False,
|
|
**model_kwargs,
|
|
)
|
|
|
|
# reference model
|
|
if hasattr(self.ref_model, "get_decoder"):
|
|
ref_base_model = self.ref_model.get_decoder()
|
|
else:
|
|
ref_base_model = getattr(self.ref_model, self.args.base_model_attribute_name)
|
|
ref_outputs = ref_base_model(
|
|
batch["completion_input_ids"],
|
|
attention_mask=batch["completion_attention_mask"],
|
|
use_cache=False,
|
|
**model_kwargs,
|
|
)
|
|
lm_head = model.get_output_embeddings()
|
|
ref_lm_head = self.ref_model.get_output_embeddings()
|
|
|
|
(
|
|
loss,
|
|
(
|
|
chosen_logps_sum,
|
|
rejected_logps_sum,
|
|
chosen_logits_sum,
|
|
rejected_logits_sum,
|
|
chosen_rewards_sum,
|
|
rejected_rewards_sum,
|
|
),
|
|
) = self.kto_loss_fn(
|
|
_input=outputs.last_hidden_state[:, :-1] if not self.is_encoder_decoder else outputs.last_hidden_state,
|
|
lin_weight=lm_head.weight,
|
|
target=batch["completion_labels"][:, 1:],
|
|
bias=lm_head.bias if hasattr(lm_head, "bias") else None,
|
|
preference_labels=torch.tensor(batch["label"], dtype=torch.bool).to(self.accelerator.device),
|
|
ref_input=ref_outputs.last_hidden_state[:, :-1]
|
|
if not self.is_encoder_decoder
|
|
else outputs.last_hidden_state,
|
|
ref_weight=ref_lm_head.weight,
|
|
ref_bias=ref_lm_head.bias if hasattr(lm_head, "bias") else None,
|
|
kl=kl,
|
|
)
|
|
|
|
output = {
|
|
"loss": loss,
|
|
"chosen_logits_sum": chosen_logits_sum,
|
|
"rejected_logits_sum": rejected_logits_sum,
|
|
"chosen_logps_sum": chosen_logps_sum,
|
|
"rejected_logps_sum": rejected_logps_sum,
|
|
"chosen_rewards_sum": chosen_rewards_sum,
|
|
"rejected_rewards_sum": rejected_rewards_sum,
|
|
"kl": kl,
|
|
}
|
|
if self.aux_loss_enabled:
|
|
output["aux_loss"] = outputs.aux_loss
|
|
|
|
return output
|
|
|
|
def get_batch_loss_metrics(
|
|
self,
|
|
model,
|
|
batch: dict[str, Union[list, torch.LongTensor]],
|
|
):
|
|
"""Compute the KTO loss and other metrics for the given batch of inputs for train or test."""
|
|
metrics = {}
|
|
batch = {k: (v.to(self.accelerator.device) if isinstance(v, torch.Tensor) else v) for k, v in batch.items()}
|
|
|
|
labels = torch.tensor(batch["label"])
|
|
num_chosen = labels.sum().to(self.accelerator.device)
|
|
num_rejected = (len(labels) - num_chosen).to(self.accelerator.device)
|
|
|
|
if self.args.use_liger_loss:
|
|
model_output = self._compute_loss_liger(model, batch)
|
|
losses = model_output["loss"]
|
|
policy_chosen_logits = model_output["chosen_logits_sum"]
|
|
policy_rejected_logits = model_output["rejected_logits_sum"]
|
|
policy_chosen_logps = model_output["chosen_logps_sum"]
|
|
policy_rejected_logps = model_output["rejected_logps_sum"]
|
|
chosen_rewards = model_output["chosen_rewards_sum"]
|
|
rejected_rewards = model_output["rejected_rewards_sum"]
|
|
kl = model_output["kl"]
|
|
if self.aux_loss_enabled:
|
|
aux_loss = model_output["aux_loss"]
|
|
else:
|
|
forward_output = self.forward(model, batch)
|
|
(
|
|
policy_chosen_logps,
|
|
policy_rejected_logps,
|
|
policy_chosen_logits,
|
|
policy_rejected_logits,
|
|
policy_KL_logps,
|
|
) = forward_output[:5]
|
|
if self.aux_loss_enabled:
|
|
aux_loss = forward_output[5]
|
|
|
|
# if reference_logps in batch use them, otherwise use the reference model
|
|
if "reference_logps" in batch:
|
|
chosen_idx = [i for i in range(batch["reference_logps"].shape[0]) if batch["label"][i] is True]
|
|
rejected_idx = [i for i in range(batch["reference_logps"].shape[0]) if batch["label"][i] is False]
|
|
|
|
reference_chosen_logps = batch["reference_logps"][chosen_idx, ...]
|
|
reference_rejected_logps = batch["reference_logps"][rejected_idx, ...]
|
|
if self.calculate_KL:
|
|
reference_KL_logps = batch["reference_KL_logps"]
|
|
else:
|
|
reference_KL_logps = None
|
|
else:
|
|
with torch.no_grad():
|
|
if self.ref_model is None:
|
|
with self.null_ref_context():
|
|
(
|
|
reference_chosen_logps,
|
|
reference_rejected_logps,
|
|
_,
|
|
_,
|
|
reference_KL_logps,
|
|
) = self.forward(self.model, batch)[:5]
|
|
else:
|
|
(
|
|
reference_chosen_logps,
|
|
reference_rejected_logps,
|
|
_,
|
|
_,
|
|
reference_KL_logps,
|
|
) = self.forward(self.ref_model, batch)[:5]
|
|
|
|
losses, chosen_rewards, rejected_rewards, kl = self.kto_loss(
|
|
policy_chosen_logps,
|
|
policy_rejected_logps,
|
|
policy_KL_logps,
|
|
reference_chosen_logps,
|
|
reference_rejected_logps,
|
|
reference_KL_logps,
|
|
)
|
|
|
|
metrics["kl"] = kl.item()
|
|
|
|
all_num_chosen = self.accelerator.gather_for_metrics(num_chosen).sum().item()
|
|
all_num_rejected = self.accelerator.gather_for_metrics(num_rejected).sum().item()
|
|
|
|
if all_num_chosen > 0:
|
|
metrics["rewards/chosen_sum"] = (
|
|
self.accelerator.gather_for_metrics(chosen_rewards.nansum()).nansum().item()
|
|
)
|
|
metrics["logps/chosen_sum"] = (
|
|
self.accelerator.gather_for_metrics(policy_chosen_logps.nansum()).nansum().item()
|
|
)
|
|
metrics["logits/chosen_sum"] = (
|
|
self.accelerator.gather_for_metrics(policy_chosen_logits.nansum()).nansum().item()
|
|
)
|
|
metrics["count/chosen"] = all_num_chosen
|
|
|
|
if all_num_rejected > 0:
|
|
metrics["rewards/rejected_sum"] = (
|
|
self.accelerator.gather_for_metrics(rejected_rewards.nansum()).nansum().item()
|
|
)
|
|
metrics["logps/rejected_sum"] = (
|
|
self.accelerator.gather_for_metrics(policy_rejected_logps.nansum()).nansum().item()
|
|
)
|
|
metrics["logits/rejected_sum"] = (
|
|
self.accelerator.gather_for_metrics(policy_rejected_logits.nansum()).nansum().item()
|
|
)
|
|
metrics["count/rejected"] = all_num_rejected
|
|
|
|
loss = losses.nanmean()
|
|
if self.aux_loss_enabled:
|
|
loss += self.aux_loss_coef * aux_loss
|
|
|
|
return loss, metrics
|
|
|
|
def compute_loss(
|
|
self,
|
|
model: Union[PreTrainedModel, nn.Module],
|
|
inputs: dict[str, Union[torch.Tensor, Any]],
|
|
return_outputs=False,
|
|
num_items_in_batch=None,
|
|
) -> Union[torch.Tensor, tuple[torch.Tensor, dict[str, torch.Tensor]]]:
|
|
compute_loss_context_manager = amp.autocast("cuda") if self._peft_has_been_casted_to_bf16 else nullcontext()
|
|
|
|
with compute_loss_context_manager:
|
|
loss, metrics = self.get_batch_loss_metrics(model, inputs)
|
|
|
|
# Make sure to move the loss to the device the original accumulating loss is at back in the `Trainer` class:
|
|
loss = loss.to(self.args.device)
|
|
# force log the metrics
|
|
if self.accelerator.is_main_process:
|
|
self.store_metrics(metrics, train_eval="train")
|
|
|
|
if return_outputs:
|
|
return (loss, metrics)
|
|
return loss
|
|
|
|
def store_metrics(self, metrics: dict[str, float], train_eval: Literal["train", "eval"] = "train") -> None:
|
|
for key, value in metrics.items():
|
|
self._stored_metrics[train_eval][key].append(value)
|
|
|
|
def _get_train_sampler(self, dataset: Optional[Dataset] = None) -> Optional[torch.utils.data.Sampler]:
|
|
if dataset is None:
|
|
dataset = self.train_dataset
|
|
if dataset is None or not has_length(dataset):
|
|
return None
|
|
return SequentialSampler(dataset)
|
|
|
|
def generate_from_model_and_ref(self, model, batch: dict[str, torch.LongTensor]) -> tuple[str, str]:
|
|
"""Generate samples from the model and reference model for the given batch of inputs."""
|
|
|
|
# If one uses `generate_during_eval` with peft + bf16, we need to explicitly call generate with
|
|
# the torch cuda amp context manager as some hidden states are silently casted to full precision.
|
|
generate_context_manager = amp.autocast("cuda") if self._peft_has_been_casted_to_bf16 else nullcontext()
|
|
|
|
with generate_context_manager:
|
|
policy_output = model.generate(
|
|
input_ids=batch["prompt_input_ids"],
|
|
attention_mask=batch["prompt_attention_mask"],
|
|
max_length=self.max_length,
|
|
do_sample=True,
|
|
pad_token_id=self.processing_class.pad_token_id,
|
|
)
|
|
|
|
# if reference_output in batch use that otherwise use the reference model
|
|
if "reference_output" in batch:
|
|
reference_output = batch["reference_output"]
|
|
else:
|
|
if self.ref_model is None:
|
|
with self.null_ref_context():
|
|
reference_output = self.model.generate(
|
|
input_ids=batch["prompt_input_ids"],
|
|
attention_mask=batch["prompt_attention_mask"],
|
|
max_length=self.max_length,
|
|
do_sample=True,
|
|
pad_token_id=self.processing_class.pad_token_id,
|
|
)
|
|
else:
|
|
reference_output = self.ref_model.generate(
|
|
input_ids=batch["prompt_input_ids"],
|
|
attention_mask=batch["prompt_attention_mask"],
|
|
max_length=self.max_length,
|
|
do_sample=True,
|
|
pad_token_id=self.processing_class.pad_token_id,
|
|
)
|
|
|
|
policy_output = pad_to_length(policy_output, self.max_length, self.processing_class.pad_token_id)
|
|
policy_output_decoded = self.processing_class.batch_decode(policy_output, skip_special_tokens=True)
|
|
|
|
reference_output = pad_to_length(reference_output, self.max_length, self.processing_class.pad_token_id)
|
|
reference_output_decoded = self.processing_class.batch_decode(reference_output, skip_special_tokens=True)
|
|
|
|
return policy_output_decoded, reference_output_decoded
|
|
|
|
def prediction_step(
|
|
self,
|
|
model: Union[PreTrainedModel, nn.Module],
|
|
inputs: dict[str, Union[torch.Tensor, Any]],
|
|
prediction_loss_only: bool,
|
|
ignore_keys: Optional[list[str]] = None,
|
|
):
|
|
if ignore_keys is None:
|
|
if hasattr(model, "config"):
|
|
ignore_keys = getattr(model.config, "keys_to_ignore_at_inference", [])
|
|
else:
|
|
ignore_keys = []
|
|
|
|
prediction_context_manager = amp.autocast("cuda") if self._peft_has_been_casted_to_bf16 else nullcontext()
|
|
with torch.no_grad(), prediction_context_manager:
|
|
loss, metrics = self.get_batch_loss_metrics(model, inputs)
|
|
|
|
# force log the metrics
|
|
if self.accelerator.is_main_process:
|
|
self.store_metrics(metrics, train_eval="eval")
|
|
|
|
if prediction_loss_only:
|
|
return (loss.detach(), None, None)
|
|
|
|
# logits for the chosen and rejected samples from model
|
|
logits_dict = {}
|
|
if "logits/chosen_sum" in metrics:
|
|
logits_dict["eval_logits/chosen"] = metrics["logits/chosen_sum"]
|
|
if "logits/rejected_sum" in metrics:
|
|
logits_dict["eval_logits/rejected"] = metrics["logits/rejected_sum"]
|
|
logits = [v for k, v in logits_dict.items() if k not in ignore_keys]
|
|
logits = torch.tensor(logits, device=self.accelerator.device)
|
|
labels = torch.zeros(logits.shape[0], device=self.accelerator.device)
|
|
|
|
return (loss.detach(), logits, labels)
|
|
|
|
def evaluation_loop(
|
|
self,
|
|
dataloader: DataLoader,
|
|
description: str,
|
|
prediction_loss_only: Optional[bool] = None,
|
|
ignore_keys: Optional[list[str]] = None,
|
|
metric_key_prefix: str = "eval",
|
|
) -> EvalLoopOutput:
|
|
"""
|
|
Overriding built-in evaluation loop to store metrics for each batch.
|
|
Prediction/evaluation loop, shared by `Trainer.evaluate()` and `Trainer.predict()`.
|
|
|
|
Works both with or without labels.
|
|
"""
|
|
|
|
# Sample and save to game log if requested (for one batch to save time)
|
|
if self.generate_during_eval:
|
|
# Generate random indices within the range of the total number of samples
|
|
num_samples = len(dataloader.dataset)
|
|
random_indices = random.sample(range(num_samples), k=self.args.eval_batch_size)
|
|
|
|
# Use dataloader.dataset.select to get the random batch without iterating over the DataLoader
|
|
random_batch_dataset = dataloader.dataset.select(random_indices)
|
|
random_batch = self.data_collator(random_batch_dataset)
|
|
random_batch = self._prepare_inputs(random_batch)
|
|
|
|
target_indicies = [i for i in range(len(random_batch["label"])) if random_batch["label"][i] is False]
|
|
target_batch = {
|
|
"prompt_input_ids": random_batch["prompt_input_ids"][target_indicies],
|
|
"prompt_attention_mask": random_batch["prompt_attention_mask"][target_indicies],
|
|
"prompt": itemgetter(*target_indicies)(random_batch["prompt"]),
|
|
}
|
|
policy_output_decoded, ref_output_decoded = self.generate_from_model_and_ref(self.model, target_batch)
|
|
|
|
table = pd.DataFrame(
|
|
columns=["Prompt", "Policy", "Ref Model"],
|
|
data=[
|
|
[prompt, pol[len(prompt) :], ref[len(prompt) :]]
|
|
for prompt, pol, ref in zip(target_batch["prompt"], policy_output_decoded, ref_output_decoded)
|
|
],
|
|
)
|
|
if "wandb" in self.args.report_to:
|
|
wandb.log({"game_log": wandb.Table(data=table)})
|
|
|
|
if "comet_ml" in self.args.report_to:
|
|
log_table_to_comet_experiment(
|
|
name="game_log.csv",
|
|
table=table,
|
|
)
|
|
|
|
# Base evaluation
|
|
initial_output = super().evaluation_loop(
|
|
dataloader, description, prediction_loss_only, ignore_keys, metric_key_prefix
|
|
)
|
|
|
|
return initial_output
|
|
|
|
def log(self, logs: dict[str, float], start_time: Optional[float] = None) -> None:
|
|
"""
|
|
Log `logs` on the various objects watching training, including stored metrics.
|
|
|
|
Args:
|
|
logs (`dict[str, float]`):
|
|
The values to log.
|
|
start_time (`float` or `None`, *optional*, defaults to `None`):
|
|
Start time of the training.
|
|
"""
|
|
# logs either has 'loss' or 'eval_loss'
|
|
train_eval = "train" if "loss" in logs else "eval"
|
|
# train metrics should have no prefix, eval should have 'eval_'
|
|
prefix = "eval_" if train_eval == "eval" else ""
|
|
# accumulate average metrics from sums and lengths
|
|
for split in ["chosen", "rejected"]:
|
|
if f"count/{split}" in self._stored_metrics[train_eval]:
|
|
count_sum = torch.Tensor(self._stored_metrics[train_eval][f"count/{split}"]).sum().item()
|
|
for metric in ["rewards", "logps", "logits"]:
|
|
logs[f"{prefix}{metric}/{split}"] = (
|
|
torch.Tensor(self._stored_metrics[train_eval][f"{metric}/{split}_sum"]).sum().item()
|
|
/ count_sum
|
|
)
|
|
# delete obsolete metric
|
|
del self._stored_metrics[train_eval][f"{metric}/{split}_sum"]
|
|
del self._stored_metrics[train_eval][f"count/{split}"]
|
|
# calculate reward margin
|
|
if f"{prefix}rewards/chosen" in logs and f"{prefix}rewards/rejected" in logs:
|
|
logs[f"{prefix}rewards/margins"] = logs[f"{prefix}rewards/chosen"] - logs[f"{prefix}rewards/rejected"]
|
|
# Add averaged stored metrics to logs
|
|
for key, metrics in self._stored_metrics[train_eval].items():
|
|
logs[f"{prefix}{key}"] = torch.Tensor(metrics).mean().item()
|
|
del self._stored_metrics[train_eval]
|
|
|
|
if version.parse(transformers.__version__) >= version.parse("4.47.0.dev0"):
|
|
return super().log(logs, start_time)
|
|
else: # transformers<=4.46
|
|
return super().log(logs)
|
|
|
|
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{ethayarajh2024kto,
|
|
title = {{KTO: Model Alignment as Prospect Theoretic Optimization}},
|
|
author = {Kawin Ethayarajh and Winnie Xu and Niklas Muennighoff and Dan Jurafsky and Douwe Kiela},
|
|
year = 2024,
|
|
eprint = {arXiv:2402.01306},
|
|
}""")
|
|
|
|
model_card = generate_model_card(
|
|
base_model=base_model,
|
|
model_name=model_name,
|
|
hub_model_id=self.hub_model_id,
|
|
dataset_name=dataset_name,
|
|
tags=tags,
|
|
wandb_url=wandb.run.get_url() if is_wandb_available() and wandb.run is not None else None,
|
|
comet_url=get_comet_experiment_url(),
|
|
trainer_name="KTO",
|
|
trainer_citation=citation,
|
|
paper_title="KTO: Model Alignment as Prospect Theoretic Optimization",
|
|
paper_id="2402.01306",
|
|
)
|
|
|
|
model_card.save(os.path.join(self.args.output_dir, "README.md"))
|
|
class UnslothKTOTrainer(_UnslothKTOTrainer):
|
|
"""
|
|
|
|
Initialize KTOTrainer.
|
|
|
|
Args:
|
|
model (`transformers.PreTrainedModel`):
|
|
The model to train, preferably an `AutoModelForSequenceClassification`.
|
|
ref_model (`PreTrainedModelWrapper`):
|
|
Hugging Face transformer model with a casual language modelling head. Used for implicit reward computation and loss. If no
|
|
reference model is provided, the trainer will create a reference model with the same architecture as the model to be optimized.
|
|
args (`KTOConfig`):
|
|
The arguments to use for training.
|
|
train_dataset (`datasets.Dataset`):
|
|
The dataset to use for training.
|
|
eval_dataset (`datasets.Dataset`):
|
|
The dataset to use for evaluation.
|
|
processing_class (`PreTrainedTokenizerBase` or `BaseImageProcessor` or `FeatureExtractionMixin` or `ProcessorMixin`, *optional*):
|
|
Processing class used to process the data. If provided, will be used to automatically process the inputs
|
|
for the model, and it will be saved along the model to make it easier to rerun an interrupted training or
|
|
reuse the fine-tuned model.
|
|
data_collator (`transformers.DataCollator`, *optional*, defaults to `None`):
|
|
The data collator to use for training. If None is specified, the default data collator (`DPODataCollatorWithPadding`) will be used
|
|
which will pad the sequences to the maximum length of the sequences in the batch, given a dataset of paired sequences.
|
|
model_init (`Callable[[], transformers.PreTrainedModel]`):
|
|
The model initializer to use for training. If None is specified, the default model initializer will be used.
|
|
callbacks (`list[transformers.TrainerCallback]`):
|
|
The callbacks to use for training.
|
|
optimizers (`tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`):
|
|
The optimizer and scheduler to use for training.
|
|
preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`):
|
|
The function to use to preprocess the logits before computing the metrics.
|
|
peft_config (`dict`, defaults to `None`):
|
|
The PEFT configuration to use for training. If you pass a PEFT configuration, the model will be wrapped in a PEFT model.
|
|
compute_metrics (`Callable[[EvalPrediction], dict]`, *optional*):
|
|
The function to use to compute the metrics. Must take a `EvalPrediction` and return
|
|
a dictionary string to metric values.
|
|
model_adapter_name (`str`, defaults to `None`):
|
|
Name of the train target PEFT adapter, when using LoRA with multiple adapters.
|
|
ref_adapter_name (`str`, defaults to `None`):
|
|
Name of the reference PEFT adapter, when using LoRA with multiple adapters.
|
|
|
|
"""
|
|
def __init__(
|
|
self,
|
|
model = None,
|
|
ref_model = None,
|
|
args = None,
|
|
train_dataset = None,
|
|
eval_dataset = None,
|
|
processing_class = None,
|
|
data_collator = None,
|
|
model_init = None,
|
|
callbacks = None,
|
|
preprocess_logits_for_metrics = None,
|
|
peft_config = None,
|
|
compute_metrics = None,
|
|
model_adapter_name = None,
|
|
ref_adapter_name = None,
|
|
**kwargs
|
|
):
|
|
if args is None: args = UnslothKTOConfig()
|
|
use_bf16 = getattr(args, 'bf16', False)
|
|
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'
|
|
__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('kto_trainer', other_metrics)
|
|
|
|
super().__init__(
|
|
model = model,
|
|
ref_model = ref_model,
|
|
args = args,
|
|
train_dataset = train_dataset,
|
|
eval_dataset = eval_dataset,
|
|
processing_class = processing_class,
|
|
data_collator = data_collator,
|
|
model_init = model_init,
|
|
callbacks = callbacks,
|
|
preprocess_logits_for_metrics = preprocess_logits_for_metrics,
|
|
peft_config = peft_config,
|
|
compute_metrics = compute_metrics,
|
|
model_adapter_name = model_adapter_name,
|
|
ref_adapter_name = ref_adapter_name,**kwargs)
|
|
if 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
|