833 lines
37 KiB
Python
833 lines
37 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.gkd_trainer import (Any, AutoModelForCausalLM, BaseImageProcessor, Callable, DataCollator, DataCollatorForChatML, Dataset, EvalPrediction, F, FeatureExtractionMixin, GKDConfig, GKDTrainer, GenerationConfig, Optional, PeftConfig, PreTrainedModel, PreTrainedTokenizerBase, ProcessorMixin, SFTTrainer, TrainerCallback, Union, disable_dropout_in_model, empty_cache, generate_model_card, get_comet_experiment_url, is_wandb_available, nn, os, prepare_deepspeed, random, textwrap, torch, unwrap_model_for_generation)
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import os
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from typing import *
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from dataclasses import dataclass, field
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from packaging.version import Version
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import torch
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import numpy as np
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from contextlib import nullcontext
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from torch.nn import functional as F
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from transformers import DataCollatorForSeq2Seq, DataCollatorForLanguageModeling as TransformersDataCollatorForLanguageModeling
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torch_compile_options = {
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"epilogue_fusion" : True,
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"max_autotune" : False,
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"shape_padding" : True,
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"trace.enabled" : False,
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"triton.cudagraphs" : False,
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}
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@torch.compile(dynamic = True, fullgraph = True, options = torch_compile_options,)
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def selective_log_softmax(logits, index):
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logits = logits.to(torch.float32)
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selected_logits = torch.gather(logits, dim = -1, index = index.unsqueeze(-1)).squeeze(-1)
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# loop to reduce peak mem consumption
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# logsumexp_values = torch.stack([torch.logsumexp(lg, dim=-1) for lg in logits])
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logsumexp_values = torch.logsumexp(logits, dim = -1)
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per_token_logps = selected_logits - logsumexp_values # log_softmax(x_i) = x_i - logsumexp(x)
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return per_token_logps
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@dataclass
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class UnslothGKDConfig(GKDConfig):
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"""
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Configuration class for [`GKDTrainer`].
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Args:
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temperature (`float`, *optional*, defaults to `0.9`):
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Temperature for sampling. The higher the temperature, the more random the completions.
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lmbda (`float`, *optional*, defaults to `0.5`):
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Lambda parameter that controls the student data fraction (i.e., the proportion of on-policy
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student-generated outputs).
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beta (`float`, *optional*, defaults to `0.5`):
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Interpolation coefficient between `0.0` and `1.0` of the Generalized Jensen-Shannon Divergence loss. When
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beta is `0.0`, the loss is the KL divergence. When beta is `1.0`, the loss is the Inverse KL Divergence.
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max_new_tokens (`int`, *optional*, defaults to `128`):
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Maximum number of tokens to generate per completion.
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teacher_model_name_or_path (`str` or `None`, *optional*, defaults to `None`):
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Model name or path of the teacher model. If `None`, the teacher model will be the same as the model
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being trained.
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teacher_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 teacher model
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from a string.
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disable_dropout (`bool`, *optional*, defaults to `True`):
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Whether to disable dropout in the model.
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seq_kd (`bool`, *optional*, defaults to `False`):
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Seq_kd parameter that controls whether to perform Sequence-Level KD (can be viewed as supervised FT
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on teacher-generated output).
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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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model_init_kwargs = None,
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dataset_text_field = 'text',
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dataset_kwargs = None,
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dataset_num_proc = None,
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eos_token = None,
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pad_token = None,
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max_length = 1024,
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packing = False,
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padding_free = False,
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pad_to_multiple_of = None,
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eval_packing = None,
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completion_only_loss = None,
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activation_offloading = False,
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max_seq_length = None,
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temperature = 0.9,
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lmbda = 0.5,
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beta = 0.5,
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max_new_tokens = 128,
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teacher_model_name_or_path = None,
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teacher_model_init_kwargs = None,
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disable_dropout = True,
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seq_kd = False,
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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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model_init_kwargs = model_init_kwargs,
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dataset_text_field = dataset_text_field,
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dataset_kwargs = dataset_kwargs,
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dataset_num_proc = dataset_num_proc,
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eos_token = eos_token,
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pad_token = pad_token,
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max_length = max_length,
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packing = packing,
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padding_free = padding_free,
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pad_to_multiple_of = pad_to_multiple_of,
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eval_packing = eval_packing,
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completion_only_loss = completion_only_loss,
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activation_offloading = activation_offloading,
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max_seq_length = max_seq_length,
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temperature = temperature,
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lmbda = lmbda,
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beta = beta,
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max_new_tokens = max_new_tokens,
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teacher_model_name_or_path = teacher_model_name_or_path,
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teacher_model_init_kwargs = teacher_model_init_kwargs,
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disable_dropout = disable_dropout,
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seq_kd = seq_kd,**kwargs)
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self.vllm_sampling_params = vllm_sampling_params
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self.unsloth_num_chunks = unsloth_num_chunks
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pass
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class _UnslothGKDTrainer(SFTTrainer):
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_tag_names = ["trl", "gkd"]
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def __init__(
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self,
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model: Optional[Union[PreTrainedModel, nn.Module, str]] = None,
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teacher_model: Union[PreTrainedModel, nn.Module, str] = None,
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args: Optional[GKDConfig] = None,
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data_collator: Optional[DataCollator] = None, # type: ignore
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train_dataset: Optional[Dataset] = None,
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eval_dataset: Optional[Union[Dataset, dict[str, Dataset]]] = None,
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processing_class: Optional[
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Union[PreTrainedTokenizerBase, BaseImageProcessor, FeatureExtractionMixin, ProcessorMixin]
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] = None,
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compute_metrics: Optional[Callable[[EvalPrediction], dict]] = None,
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callbacks: Optional[list[TrainerCallback]] = None,
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optimizers: tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None),
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preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None,
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peft_config: Optional["PeftConfig"] = None,
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formatting_func: Optional[Callable] = None,
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):
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# add remove_unused_columns=False to the dataclass args
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args.remove_unused_columns = False
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data_collator = DataCollatorForChatML(tokenizer=processing_class, max_length=args.max_length)
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super().__init__(
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model,
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args=args,
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data_collator=data_collator,
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train_dataset=train_dataset,
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eval_dataset=eval_dataset,
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processing_class=processing_class,
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compute_metrics=compute_metrics,
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callbacks=callbacks,
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optimizers=optimizers,
|
|
preprocess_logits_for_metrics=preprocess_logits_for_metrics,
|
|
peft_config=peft_config,
|
|
formatting_func=formatting_func,
|
|
)
|
|
|
|
if args.teacher_model_init_kwargs is None:
|
|
teacher_model_init_kwargs = {}
|
|
elif not isinstance(teacher_model, str):
|
|
raise ValueError(
|
|
"You passed teacher_model_init_kwargs to the GKDConfig, but your teacher_model is already instantiated."
|
|
)
|
|
else:
|
|
teacher_model_init_kwargs = args.teacher_model_init_kwargs
|
|
teacher_model_init_kwargs["torch_dtype"] = (
|
|
teacher_model_init_kwargs["torch_dtype"]
|
|
if teacher_model_init_kwargs["torch_dtype"] in ["auto", None]
|
|
else getattr(torch, teacher_model_init_kwargs["torch_dtype"])
|
|
)
|
|
|
|
if isinstance(teacher_model, str):
|
|
teacher_model = AutoModelForCausalLM.from_pretrained(teacher_model, **teacher_model_init_kwargs)
|
|
|
|
# Disable dropout in the model
|
|
if args.disable_dropout:
|
|
disable_dropout_in_model(self.model)
|
|
|
|
if self.is_deepspeed_enabled:
|
|
self.teacher_model = prepare_deepspeed(teacher_model, self.accelerator)
|
|
else:
|
|
self.teacher_model = self.accelerator.prepare_model(teacher_model, evaluation_mode=True)
|
|
|
|
self.lmbda = args.lmbda
|
|
self.beta = args.beta
|
|
self.temperature = args.temperature
|
|
self.seq_kd = args.seq_kd
|
|
|
|
self.generation_config = GenerationConfig(
|
|
max_new_tokens=args.max_new_tokens,
|
|
temperature=args.temperature,
|
|
do_sample=True,
|
|
top_k=0,
|
|
use_cache=False if args.gradient_checkpointing else True,
|
|
pad_token_id=self.processing_class.pad_token_id,
|
|
)
|
|
# Set custom EOS tokens if they are specified by the model's generation
|
|
# config. This is important for models with the Llama 3 chat template,
|
|
# which use special tokens <|eot_id|> and <|eom_id|> to mark the end of
|
|
# turns or messages.
|
|
if (
|
|
hasattr(self.model.generation_config, "eos_token_id")
|
|
and self.model.generation_config.eos_token_id is not None
|
|
):
|
|
self.generation_config.eos_token_id = self.model.generation_config.eos_token_id
|
|
|
|
def _prepare_dataset(self, dataset, *args):
|
|
# SFTTrainer._prepare_dataset() applies the chat template and rename the messages column to text. However, we
|
|
# need to keep the messages column as it is. We use the following workaround to keep the messages column.
|
|
dataset = dataset.add_column("_messages", dataset["messages"])
|
|
dataset = super()._prepare_dataset(dataset, *args)
|
|
dataset = dataset.rename_column("_messages", "messages")
|
|
return dataset
|
|
|
|
@staticmethod
|
|
def generalized_jsd_loss(
|
|
student_logits, teacher_logits, labels=None, beta=0.5, temperature=1.0, reduction="batchmean"
|
|
):
|
|
"""
|
|
Compute the generalized Jensen-Shannon Divergence loss for knowledge distillation using F.kl_div. See Eq. (1)
|
|
of https://huggingface.co/papers/2306.13649 for the definition.
|
|
|
|
Args:
|
|
student_logits: Tensor of shape (batch_size, sequence_length, vocab_size)
|
|
teacher_logits: Tensor of shape (batch_size, sequence_length, vocab_size)
|
|
labels: Tensor of shape (batch_size, sequence_length) with -100 for padding tokens to ignore when computing loss
|
|
beta: Interpolation coefficient between 0 and 1 (default: 0.5)
|
|
temperature: Softmax temperature (default: 1.0)
|
|
reduction: Specifies the reduction to apply to the output (default: 'batchmean')
|
|
|
|
Returns:
|
|
loss: Scalar tensor with the generalized JSD loss
|
|
"""
|
|
|
|
# Apply temperature scaling
|
|
student_logits = student_logits / temperature
|
|
teacher_logits = teacher_logits / temperature
|
|
|
|
# Compute log probabilities for student and probabilities for teacher
|
|
student_log_probs = F.log_softmax(student_logits, dim=-1)
|
|
teacher_log_probs = F.log_softmax(teacher_logits, dim=-1)
|
|
|
|
if beta == 0:
|
|
jsd = F.kl_div(student_log_probs, teacher_log_probs, reduction="none", log_target=True)
|
|
elif beta == 1:
|
|
jsd = F.kl_div(teacher_log_probs, student_log_probs, reduction="none", log_target=True)
|
|
else:
|
|
# Compute the log of the mixture distribution
|
|
# log(a + b) = log(exp(log(a)) + exp(log(b))) -> for mixture
|
|
beta = torch.tensor(beta, dtype=student_log_probs.dtype)
|
|
mixture_log_probs = torch.logsumexp(
|
|
torch.stack([student_log_probs + torch.log(1 - beta), teacher_log_probs + torch.log(beta)]),
|
|
dim=0,
|
|
)
|
|
|
|
# Compute KL divergences using F.kl_div
|
|
# PyTorch differs from the standard mathematical definition, so the order of the probability distributions is swapped compared to that defined in the paper.
|
|
kl_teacher = F.kl_div(mixture_log_probs, teacher_log_probs, reduction="none", log_target=True)
|
|
kl_student = F.kl_div(mixture_log_probs, student_log_probs, reduction="none", log_target=True)
|
|
|
|
# Compute the Generalized Jensen-Shannon Divergence
|
|
jsd = beta * kl_teacher + (1 - beta) * kl_student
|
|
|
|
# Masking
|
|
if labels is not None:
|
|
mask = labels != -100
|
|
jsd = jsd[mask]
|
|
|
|
# Apply reduction
|
|
if reduction == "batchmean":
|
|
return jsd.sum() / mask.sum() if labels is not None else jsd.sum() / (jsd.size(0) * jsd.size(1))
|
|
elif reduction == "sum":
|
|
return jsd.sum()
|
|
elif reduction == "mean":
|
|
return jsd.mean()
|
|
else:
|
|
return jsd
|
|
|
|
def compute_loss(self, model, inputs, return_outputs=False, num_items_in_batch=None):
|
|
# compute student output
|
|
outputs_student = model(
|
|
input_ids=inputs["input_ids"],
|
|
attention_mask=inputs["attention_mask"],
|
|
)
|
|
|
|
# compute teacher output in eval mode
|
|
self.teacher_model.eval()
|
|
with torch.no_grad():
|
|
outputs_teacher = self.teacher_model(
|
|
input_ids=inputs["input_ids"],
|
|
attention_mask=inputs["attention_mask"],
|
|
)
|
|
|
|
# slice the logits for the generated tokens using the inputs["prompts"] lengths
|
|
prompt_lengths = inputs["prompts"].shape[1]
|
|
shifted_student_logits = outputs_student.logits[:, prompt_lengths - 1 : -1, :]
|
|
shifted_teacher_logits = outputs_teacher.logits[:, prompt_lengths - 1 : -1, :]
|
|
shifted_labels = inputs["labels"][:, prompt_lengths:]
|
|
|
|
# compute loss
|
|
loss = self.generalized_jsd_loss(
|
|
student_logits=shifted_student_logits,
|
|
teacher_logits=shifted_teacher_logits,
|
|
labels=shifted_labels,
|
|
beta=self.beta,
|
|
)
|
|
|
|
# empty cache
|
|
empty_cache()
|
|
|
|
# Return loss
|
|
return (loss, outputs_student) if return_outputs else loss
|
|
|
|
@staticmethod
|
|
def generate_on_policy_outputs(model, inputs, generation_config, pad_token_id=None):
|
|
# Generate output with respect to the prompt only
|
|
generated_outputs = model.generate(
|
|
input_ids=inputs["prompts"],
|
|
attention_mask=inputs.get("prompt_attention_mask", None),
|
|
generation_config=generation_config,
|
|
return_dict_in_generate=True,
|
|
)
|
|
|
|
# Get the generated token IDs
|
|
generated_tokens = generated_outputs.sequences
|
|
# Calculate new attention mask
|
|
new_attention_mask = torch.ones_like(generated_tokens)
|
|
new_labels = generated_tokens.clone()
|
|
|
|
# If there's pad_token_id, set attention mask to 0 for padding tokens
|
|
if pad_token_id is not None:
|
|
new_labels[new_labels == pad_token_id] = -100
|
|
new_attention_mask[generated_tokens == pad_token_id] = 0
|
|
|
|
return generated_tokens, new_attention_mask, new_labels
|
|
|
|
def training_step(
|
|
self, model: nn.Module, inputs: dict[str, Union[torch.Tensor, Any]], num_items_in_batch: Optional[int] = None
|
|
) -> torch.Tensor:
|
|
"""
|
|
Perform a training step for the Generalized Knowledge Distillation (GKD) model.
|
|
|
|
This method implements the on-policy learning approach described in the GKD paper.
|
|
With probability `self.lmbda`, it generates new responses using the student model,
|
|
which are then used for training instead of the original inputs.
|
|
"""
|
|
if self.seq_kd:
|
|
with unwrap_model_for_generation(self.teacher_model, self.accelerator) as unwrapped_model:
|
|
new_input_ids, new_attention_mask, new_labels = self.generate_on_policy_outputs(
|
|
unwrapped_model, inputs, self.generation_config, self.processing_class.pad_token_id
|
|
)
|
|
inputs["input_ids"] = new_input_ids
|
|
inputs["attention_mask"] = new_attention_mask
|
|
inputs["labels"] = new_labels
|
|
if random.random() <= self.lmbda:
|
|
with unwrap_model_for_generation(model, self.accelerator) as unwrapped_model:
|
|
new_input_ids, new_attention_mask, new_labels = self.generate_on_policy_outputs(
|
|
unwrapped_model, inputs, self.generation_config, self.processing_class.pad_token_id
|
|
)
|
|
inputs["input_ids"] = new_input_ids
|
|
inputs["attention_mask"] = new_attention_mask
|
|
inputs["labels"] = new_labels
|
|
|
|
loss = super().training_step(model, inputs, num_items_in_batch)
|
|
return loss
|
|
|
|
def create_model_card(
|
|
self,
|
|
model_name: Optional[str] = None,
|
|
dataset_name: Optional[str] = None,
|
|
tags: Union[str, list[str], None] = None,
|
|
):
|
|
"""
|
|
Creates a draft of a model card using the information available to the `Trainer`.
|
|
|
|
Args:
|
|
model_name (`str` or `None`, *optional*, defaults to `None`):
|
|
Name of the model.
|
|
dataset_name (`str` or `None`, *optional*, defaults to `None`):
|
|
Name of the dataset used for training.
|
|
tags (`str`, `list[str]` or `None`, *optional*, defaults to `None`):
|
|
Tags to be associated with the model card.
|
|
"""
|
|
if not self.is_world_process_zero():
|
|
return
|
|
|
|
if hasattr(self.model.config, "_name_or_path") and not os.path.isdir(self.model.config._name_or_path):
|
|
base_model = self.model.config._name_or_path
|
|
else:
|
|
base_model = None
|
|
|
|
tags = tags or []
|
|
if isinstance(tags, str):
|
|
tags = [tags]
|
|
|
|
if hasattr(self.model.config, "unsloth_version"):
|
|
tags.append("unsloth")
|
|
|
|
citation = textwrap.dedent("""\
|
|
@inproceedings{agarwal2024on-policy,
|
|
title = {{On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes}},
|
|
author = {Rishabh Agarwal and Nino Vieillard and Yongchao Zhou and Piotr Stanczyk and Sabela Ramos Garea and Matthieu Geist and Olivier Bachem},
|
|
year = 2024,
|
|
booktitle = {The Twelfth International Conference on Learning Representations, {ICLR} 2024, Vienna, Austria, May 7-11, 2024},
|
|
publisher = {OpenReview.net},
|
|
url = {https://openreview.net/forum?id=3zKtaqxLhW},
|
|
}""")
|
|
|
|
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="GKD",
|
|
trainer_citation=citation,
|
|
paper_title="On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes",
|
|
paper_id="2306.13649",
|
|
)
|
|
|
|
model_card.save(os.path.join(self.args.output_dir, "README.md"))
|
|
class UnslothGKDTrainer(_UnslothGKDTrainer):
|
|
"""
|
|
|
|
"""
|
|
def __init__(
|
|
self,
|
|
model = None,
|
|
teacher_model = None,
|
|
args = None,
|
|
data_collator = None,
|
|
train_dataset = None,
|
|
eval_dataset = None,
|
|
processing_class = None,
|
|
compute_metrics = None,
|
|
callbacks = None,
|
|
preprocess_logits_for_metrics = None,
|
|
peft_config = None,
|
|
formatting_func = None,
|
|
**kwargs
|
|
):
|
|
if args is None: args = UnslothGKDConfig()
|
|
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('gkd_trainer', other_metrics)
|
|
|
|
super().__init__(
|
|
model = model,
|
|
teacher_model = teacher_model,
|
|
args = args,
|
|
data_collator = data_collator,
|
|
train_dataset = train_dataset,
|
|
eval_dataset = eval_dataset,
|
|
processing_class = processing_class,
|
|
compute_metrics = compute_metrics,
|
|
callbacks = callbacks,
|
|
preprocess_logits_for_metrics = preprocess_logits_for_metrics,
|
|
peft_config = peft_config,
|
|
formatting_func = formatting_func,**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
|