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"""
2025.11.3
2025.11.2
4.57.1
0.24.0
__UNSLOTH_VERSIONING__
"""
# Unsloth auto generated code
# Copyright 2023-present Daniel Han-Chen, Michael Han-Chen & the Unsloth team. All rights reserved.
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
from torch import Tensor
import torch
import torch.nn as nn
from torch.nn import functional as F
from typing import Any, List, Optional, Tuple, Union, Dict, Set, Callable
from trl.trainer.sft_trainer import (Any, AutoProcessor, BaseTrainer, Callable, DataCollator, DataCollatorForLanguageModeling, DataCollatorForVisionLanguageModeling, Dataset, EvalPrediction, FLASH_ATTENTION_VARIANTS, IterableDataset, Optional, Path, PeftConfig, PreTrainedModel, PreTrainedTokenizerBase, ProcessorMixin, SFTConfig, SFTTrainer, TrainerCallback, TrainingArguments, Union, clone_chat_template, contextlib, create_model_from_path, dataclass, defaultdict, dft_loss, get_act_offloading_ctx_manager, is_conversational, logger, logging, nn, os, pack_dataset, pad, prepare_peft_model, selective_log_softmax, torch, Callable, DataCollator, DataCollatorForLanguageModeling, Dataset, IterableDataset, Optional, Union, os, pack_dataset, pad, Optional, PreTrainedModel, logger, os, torch, os)
import os
from typing import *
from dataclasses import dataclass, field
from packaging.version import Version
import torch
import numpy as np
from contextlib import nullcontext
from torch.nn import functional as F
import inspect
from transformers import DataCollatorForSeq2Seq, DataCollatorForLanguageModeling as TransformersDataCollatorForLanguageModeling
from transformers.training_args import ParallelMode
# Wrap trainer with padding to right and enable training mode
import functools
from types import MethodType
def prepare_for_training_mode(f):
@functools.wraps(f)
def wrapper(self, *args, **kwargs):
# Enable training mode
if hasattr(self, 'model') and hasattr(self.model, "for_training"):
self.model.for_training()
output = f(self, *args, **kwargs)
# Return inference mode
if hasattr(self, 'model') and hasattr(self.model, "for_inference"):
self.model.for_inference()
return output
return wrapper
pass
torch_compile_options = {
"epilogue_fusion" : True,
"max_autotune" : False,
"shape_padding" : True,
"trace.enabled" : False,
"triton.cudagraphs" : False,
}
@torch.compile(dynamic = True, fullgraph = True, options = torch_compile_options,)
def chunked_selective_log_softmax(logits, index):
# Split into 4 chunks only
chunked_logits = torch.chunk(logits.reshape(-1, logits.shape[-1]), chunks = 4, dim = 0)
chunked_index = torch.chunk(index.reshape(-1), chunks = 4, dim = 0)
all_per_token_logps = []
# Below loop does the same as selective_log_softmax(chunk_logits, chunk_index)
for chunk_logits, chunk_index in zip(chunked_logits, chunked_index):
chunk_logits = chunk_logits.to(torch.float32)
selected_logits = torch.gather(chunk_logits, dim = -1, index = chunk_index.unsqueeze(-1)).squeeze(-1)
logsumexp_values = torch.logsumexp(chunk_logits, dim = -1)
per_token_logps = selected_logits - logsumexp_values
all_per_token_logps.append(per_token_logps)
pass
all_per_token_logps = torch.concat(all_per_token_logps)
all_per_token_logps = all_per_token_logps.reshape((logits.shape[0], logits.shape[1]))
return all_per_token_logps
def calculate_pad_tokens_in_prompt(
input_ids: torch.Tensor,
logits_to_keep: int,
pad_token_id: int
) -> torch.Tensor:
"""
Given prompt tensor, it returns all the left padded tokens in that sequence. so [pad, pad, pad, cat] = 3 tokens
"""
if logits_to_keep >= input_ids.shape[1]:
raise ValueError("logits_to_keep must be smaller than the sequence length.")
prompt_section = input_ids[:, :-logits_to_keep]
padding_mask = (prompt_section == pad_token_id)
pad_token_counts = padding_mask.sum(dim=1)
return pad_token_counts
def create_completion_attention_mask(
completion_input_ids: torch.Tensor,
left_pad_tokens_per_prompt: torch.Tensor,
max_left_pad: int,
pad_token_id: int
) -> torch.Tensor:
"""
Given that we have a sequence, [p,p,p,c,c,c,pad,pad,pad]
Where p are extra prompt tokens we got from slicing the torch tensor, c is completion tokens
and pad are pad tokens, this function would make a completion mask that would 0 out the pad
and p tokens. so in this example [0,0,0,1,1,1,0,0,0]
"""
batch_size, completion_len = completion_input_ids.shape
device = completion_input_ids.device
num_tokens_to_mask = max_left_pad - left_pad_tokens_per_prompt
indices = torch.arange(completion_len, device=device).unsqueeze(0)
shift_mask = indices >= num_tokens_to_mask.unsqueeze(1)
non_padding_mask = (completion_input_ids != pad_token_id)
final_mask = shift_mask & non_padding_mask
return final_mask
def left_pack_padding(tensor: torch.Tensor, pad_id: int) -> torch.Tensor:
"""
Moves all padding tokens in each sequence of a batch to the right.
"""
mask = (tensor != pad_id)
# Must do stable=True since binary mark is unordered
sorted_indices = torch.argsort(mask, dim=1, descending=True, stable=True)
packed_tensor = torch.gather(tensor, 1, sorted_indices)
return packed_tensor
def align_logprobs_with_mask(
logprob_tensor: torch.Tensor,
attention_mask: torch.Tensor,
pad_value: float = 0.0
) -> torch.Tensor:
"""
Aligns a log probability tensor with a given attention mask.
"""
device = logprob_tensor.device
batch_size, logprob_seq_len = logprob_tensor.shape
mask_seq_len = attention_mask.shape[1]
padded_logprobs = torch.full(
attention_mask.shape,
fill_value=pad_value,
dtype=logprob_tensor.dtype,
device=device
)
left_pad_counts = torch.argmax(attention_mask, dim=1)
cols = torch.arange(logprob_seq_len, device=device)
dest_indices = left_pad_counts.unsqueeze(1) + cols
# Create destination row indices
# Shape: [batch_size, logprob_seq_len]
row_indices = torch.arange(batch_size, device=device).unsqueeze(1).expand_as(dest_indices)
# --- 4. Filter out-of-bounds indices and perform assignment ---
# Create a mask to identify only the indices that are within the bounds
# of the target tensor's sequence length.
valid_mask = dest_indices < mask_seq_len
# Use this mask to select only the valid row indices, column indices,
# and the corresponding values from the logprob tensor.
# This flattens the selected elements into 1D tensors.
valid_rows = row_indices[valid_mask]
valid_cols = dest_indices[valid_mask]
valid_vals = logprob_tensor[valid_mask]
# Place the valid values into their correct positions in the padded tensor
# using a single, efficient advanced indexing operation.
padded_logprobs[valid_rows, valid_cols] = valid_vals
return padded_logprobs
@dataclass
class UnslothSFTConfig(SFTConfig):
"""
Configuration class for the [`SFTTrainer`].
This class includes only the parameters that are specific to SFT training. For a full list of training arguments,
please refer to the [`~transformers.TrainingArguments`] documentation. Note that default values in this class may
differ from those in [`~transformers.TrainingArguments`].
Using [`~transformers.HfArgumentParser`] we can turn this class into
[argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified on the
command line.
Parameters:
> Parameters that control the model
model_init_kwargs (`dict[str, Any]`, *optional*):
Keyword arguments for [`~transformers.AutoModelForCausalLM.from_pretrained`], used when the `model`
argument of the [`SFTTrainer`] is provided as a string. If you're training a MoE architecture and want to
include the load balancing/auxilliary loss as a part of the final loss, remember to set
`output_router_logits=True` in this dictionary.
chat_template_path (`str`, *optional*):
If specified, sets the model's chat template. This can either be the path to a tokenizer (local directory
or Hugging Face Hub model) or a direct path to a Jinja template file. When using a Jinja file, you must
ensure that any special tokens referenced in the template are added to the tokenizer and that the model's
embedding layer is resized accordingly.
> Parameters that control the data preprocessing
dataset_text_field (`str`, *optional*, defaults to `"text"`):
Name of the column that contains text data in the dataset.
dataset_kwargs (`dict[str, Any]`, *optional*):
Dictionary of optional keyword arguments for the dataset preparation. The only supported key is
`skip_prepare_dataset`. When the model is a VLM, `skip_prepare_dataset` is automatically treated as `True`
regardless of the provided value, since preprocessing is done on the fly.
dataset_num_proc (`int`, *optional*):
Number of processes to use for processing the dataset.
eos_token (`str`, *optional*):
Token used to indicate the end of a turn or sequence. If `None`, it defaults to
`processing_class.eos_token`.
pad_token (`str`, *optional*):
Token used for padding. If `None`, it defaults to `processing_class.pad_token`, or if that is also `None`,
it falls back to `processing_class.eos_token`.
max_length (`int` or `None`, *optional*, defaults to `1024`):
Maximum length of the tokenized sequence. Sequences longer than `max_length` are truncated from the right.
If `None`, no truncation is applied. When packing is enabled, this value sets the sequence length.
packing (`bool`, *optional*, defaults to `False`):
Whether to group multiple sequences into fixed-length blocks to improve computational efficiency and reduce
padding. Uses `max_length` to define sequence length.
packing_strategy (`str`, *optional*, defaults to `"bfd"`):
Strategy for packing sequences. Can be either `"bfd"` (best-fit decreasing, default), or `"wrapped"`.
padding_free (`bool`, *optional*, defaults to `False`):
Whether to perform forward passes without padding by flattening all sequences in the batch into a single
continuous sequence. This reduces memory usage by eliminating padding overhead. Currently, this is only
supported with the FlashAttention 2 or 3, which can efficiently handle the flattened batch structure. When
packing is enabled with strategy `"bfd"`, padding-free is enabled, regardless of the value of this
parameter.
pad_to_multiple_of (`int`, *optional*):
If set, the sequences will be padded to a multiple of this value.
eval_packing (`bool`, *optional*):
Whether to pack the eval dataset. If `None`, uses the same value as `packing`.
> Parameters that control the training
completion_only_loss (`bool`, *optional*):
Whether to compute loss only on the completion part of the sequence. If set to `True`, loss is computed
only on the completion, which is supported only for [prompt-completion](#prompt-completion) datasets. If
`False`, loss is computed on the entire sequence. If `None` (default), the behavior depends on the dataset:
loss is computed on the completion for [prompt-completion](#prompt-completion) datasets, and on the full
sequence for [language modeling](#language-modeling) datasets.
assistant_only_loss (`bool`, *optional*, defaults to `False`):
Whether to compute loss only on the assistant part of the sequence. If set to `True`, loss is computed only
on the assistant responses, which is supported only for [conversational](#conversational) datasets. If
`False`, loss is computed on the entire sequence.
loss_type (`str`, *optional*, defaults to `"nll"`):
Type of loss to use. Possible values are `"nll"` (negative log-likelihood, default) and `"dft"` (Dynamic
Fine-Tuning, as described in [this paper](https://huggingface.co/papers/2508.05629)).
activation_offloading (`bool`, *optional*, defaults to `False`):
Whether to offload the activations to the CPU.
"""
vllm_sampling_params: Optional[Any] = field(
default = None,
metadata = {'help': 'vLLM SamplingParams'},
)
unsloth_num_chunks : Optional[int] = field(
default = -1,
metadata = {'help': 'Chunk size to reduce memory usage. -1 is most efficient.'},
)
max_seq_length : Optional[int] = field(
default = None,
metadata = {'help': 'Maximum sequence length to truncate to.'},
)
def __init__(
self,
output_dir = None,
overwrite_output_dir = None,
do_train = False,
do_eval = False,
do_predict = False,
eval_strategy = 'no',
prediction_loss_only = False,
per_device_train_batch_size = 4,
per_device_eval_batch_size = 4,
per_gpu_train_batch_size = None,
per_gpu_eval_batch_size = None,
gradient_accumulation_steps = 2,
eval_accumulation_steps = 2,
eval_delay = 0,
torch_empty_cache_steps = 250,
learning_rate = 5e-05,
weight_decay = 0.01,
adam_beta1 = 0.9,
adam_beta2 = 0.999,
adam_epsilon = 1e-08,
max_grad_norm = 1.0,
num_train_epochs = 3.0,
max_steps = -1,
lr_scheduler_type = 'linear',
warmup_ratio = 0.1,
warmup_steps = 0,
log_level = 'passive',
log_level_replica = 'warning',
log_on_each_node = True,
logging_dir = None,
logging_strategy = 'steps',
logging_first_step = False,
logging_steps = 1,
logging_nan_inf_filter = False,
save_strategy = 'steps',
save_steps = 500,
save_total_limit = None,
save_safetensors = True,
save_on_each_node = False,
save_only_model = False,
restore_callback_states_from_checkpoint = False,
no_cuda = False,
use_cpu = False,
use_mps_device = False,
seed = 3407,
data_seed = 3407,
jit_mode_eval = False,
bf16 = False,
fp16 = False,
fp16_opt_level = 'O1',
half_precision_backend = 'auto',
bf16_full_eval = False,
fp16_full_eval = False,
tf32 = None,
local_rank = -1,
ddp_backend = None,
tpu_num_cores = None,
tpu_metrics_debug = False,
debug = '',
dataloader_drop_last = False,
eval_steps = None,
dataloader_num_workers = 0,
dataloader_prefetch_factor = None,
past_index = -1,
run_name = None,
disable_tqdm = None,
remove_unused_columns = True,
label_names = None,
load_best_model_at_end = False,
metric_for_best_model = None,
greater_is_better = None,
ignore_data_skip = False,
fsdp = None,
fsdp_min_num_params = 0,
fsdp_config = None,
fsdp_transformer_layer_cls_to_wrap = None,
accelerator_config = None,
parallelism_config = None,
deepspeed = None,
label_smoothing_factor = 0.0,
optim = 'adamw_8bit',
optim_args = None,
adafactor = False,
group_by_length = False,
length_column_name = 'length',
report_to = None,
project = 'huggingface',
trackio_space_id = 'trackio',
ddp_find_unused_parameters = None,
ddp_bucket_cap_mb = None,
ddp_broadcast_buffers = None,
dataloader_pin_memory = True,
dataloader_persistent_workers = False,
skip_memory_metrics = True,
use_legacy_prediction_loop = False,
push_to_hub = False,
resume_from_checkpoint = None,
hub_model_id = None,
hub_strategy = 'every_save',
hub_token = None,
hub_private_repo = None,
hub_always_push = False,
hub_revision = None,
gradient_checkpointing = True,
gradient_checkpointing_kwargs = None,
include_inputs_for_metrics = False,
eval_do_concat_batches = True,
fp16_backend = 'auto',
push_to_hub_model_id = None,
push_to_hub_organization = None,
push_to_hub_token = None,
mp_parameters = '',
auto_find_batch_size = False,
full_determinism = False,
torchdynamo = None,
ray_scope = 'last',
ddp_timeout = 1800,
torch_compile = False,
torch_compile_backend = None,
torch_compile_mode = None,
include_tokens_per_second = False,
include_num_input_tokens_seen = False,
neftune_noise_alpha = None,
optim_target_modules = None,
batch_eval_metrics = False,
eval_on_start = False,
use_liger_kernel = False,
liger_kernel_config = None,
eval_use_gather_object = False,
average_tokens_across_devices = True,
model_init_kwargs = None,
chat_template_path = None,
dataset_text_field = 'text',
dataset_kwargs = None,
dataset_num_proc = None,
eos_token = None,
pad_token = None,
max_length = 1024,
packing = False,
packing_strategy = 'bfd',
padding_free = False,
pad_to_multiple_of = None,
eval_packing = None,
completion_only_loss = None,
assistant_only_loss = False,
loss_type = 'nll',
activation_offloading = False,
vllm_sampling_params = None,
unsloth_num_chunks = -1,
max_seq_length = None,
**kwargs,
):
if learning_rate < 1e-7: print(f'Unsloth: Your learning rate of `{learning_rate}` is too small and less than 1e-7! Consider increasing it, otherwise gradient updates will be close to 0!')
if learning_rate > 1: print(f'Unsloth: Your learning rate of `{learning_rate}` is way too larger > 1! Consider decreasing it to 1e-1, otherwise gradient updates will explode!')
if output_dir is None and save_strategy == 'steps' and save_steps == 500:
output_dir = 'unsloth_training_checkpoints'
save_strategy = 'no'
if dataset_num_proc is None:
from multiprocessing import cpu_count
dataset_num_proc = min(max(cpu_count()+4, 2), 64)
if os.environ.get('UNSLOTH_ENABLE_FLEX_ATTENTION', '0') == '1':
from unsloth_zoo.flex_attention import HAS_FLEX_ATTENTION
if HAS_FLEX_ATTENTION and pad_to_multiple_of is None:
from unsloth_zoo.flex_attention import FLEX_ATTENTION_BLOCK_SIZE
pad_to_multiple_of = FLEX_ATTENTION_BLOCK_SIZE
super().__init__(
output_dir = output_dir,
overwrite_output_dir = overwrite_output_dir,
do_train = do_train,
do_eval = do_eval,
do_predict = do_predict,
eval_strategy = eval_strategy,
prediction_loss_only = prediction_loss_only,
per_device_train_batch_size = per_device_train_batch_size,
per_device_eval_batch_size = per_device_eval_batch_size,
per_gpu_train_batch_size = per_gpu_train_batch_size,
per_gpu_eval_batch_size = per_gpu_eval_batch_size,
gradient_accumulation_steps = gradient_accumulation_steps,
eval_accumulation_steps = eval_accumulation_steps,
eval_delay = eval_delay,
torch_empty_cache_steps = torch_empty_cache_steps,
learning_rate = learning_rate,
weight_decay = weight_decay,
adam_beta1 = adam_beta1,
adam_beta2 = adam_beta2,
adam_epsilon = adam_epsilon,
max_grad_norm = max_grad_norm,
num_train_epochs = num_train_epochs,
max_steps = max_steps,
lr_scheduler_type = lr_scheduler_type,
warmup_ratio = warmup_ratio,
warmup_steps = warmup_steps,
log_level = log_level,
log_level_replica = log_level_replica,
log_on_each_node = log_on_each_node,
logging_dir = logging_dir,
logging_strategy = logging_strategy,
logging_first_step = logging_first_step,
logging_steps = logging_steps,
logging_nan_inf_filter = logging_nan_inf_filter,
save_strategy = save_strategy,
save_steps = save_steps,
save_total_limit = save_total_limit,
save_safetensors = save_safetensors,
save_on_each_node = save_on_each_node,
save_only_model = save_only_model,
restore_callback_states_from_checkpoint = restore_callback_states_from_checkpoint,
no_cuda = no_cuda,
use_cpu = use_cpu,
use_mps_device = use_mps_device,
seed = seed,
data_seed = data_seed,
jit_mode_eval = jit_mode_eval,
bf16 = bf16,
fp16 = fp16,
fp16_opt_level = fp16_opt_level,
half_precision_backend = half_precision_backend,
bf16_full_eval = bf16_full_eval,
fp16_full_eval = fp16_full_eval,
tf32 = tf32,
local_rank = local_rank,
ddp_backend = ddp_backend,
tpu_num_cores = tpu_num_cores,
tpu_metrics_debug = tpu_metrics_debug,
debug = debug,
dataloader_drop_last = dataloader_drop_last,
eval_steps = eval_steps,
dataloader_num_workers = dataloader_num_workers,
dataloader_prefetch_factor = dataloader_prefetch_factor,
past_index = past_index,
run_name = run_name,
disable_tqdm = disable_tqdm,
remove_unused_columns = remove_unused_columns,
label_names = label_names,
load_best_model_at_end = load_best_model_at_end,
metric_for_best_model = metric_for_best_model,
greater_is_better = greater_is_better,
ignore_data_skip = ignore_data_skip,
fsdp = fsdp,
fsdp_min_num_params = fsdp_min_num_params,
fsdp_config = fsdp_config,
fsdp_transformer_layer_cls_to_wrap = fsdp_transformer_layer_cls_to_wrap,
accelerator_config = accelerator_config,
parallelism_config = parallelism_config,
deepspeed = deepspeed,
label_smoothing_factor = label_smoothing_factor,
optim = optim,
optim_args = optim_args,
adafactor = adafactor,
group_by_length = group_by_length,
length_column_name = length_column_name,
report_to = report_to,
project = project,
trackio_space_id = trackio_space_id,
ddp_find_unused_parameters = ddp_find_unused_parameters,
ddp_bucket_cap_mb = ddp_bucket_cap_mb,
ddp_broadcast_buffers = ddp_broadcast_buffers,
dataloader_pin_memory = dataloader_pin_memory,
dataloader_persistent_workers = dataloader_persistent_workers,
skip_memory_metrics = skip_memory_metrics,
use_legacy_prediction_loop = use_legacy_prediction_loop,
push_to_hub = push_to_hub,
resume_from_checkpoint = resume_from_checkpoint,
hub_model_id = hub_model_id,
hub_strategy = hub_strategy,
hub_token = hub_token,
hub_private_repo = hub_private_repo,
hub_always_push = hub_always_push,
hub_revision = hub_revision,
gradient_checkpointing = gradient_checkpointing,
gradient_checkpointing_kwargs = gradient_checkpointing_kwargs,
include_inputs_for_metrics = include_inputs_for_metrics,
eval_do_concat_batches = eval_do_concat_batches,
fp16_backend = fp16_backend,
push_to_hub_model_id = push_to_hub_model_id,
push_to_hub_organization = push_to_hub_organization,
push_to_hub_token = push_to_hub_token,
mp_parameters = mp_parameters,
auto_find_batch_size = auto_find_batch_size,
full_determinism = full_determinism,
torchdynamo = torchdynamo,
ray_scope = ray_scope,
ddp_timeout = ddp_timeout,
torch_compile = torch_compile,
torch_compile_backend = torch_compile_backend,
torch_compile_mode = torch_compile_mode,
include_tokens_per_second = include_tokens_per_second,
include_num_input_tokens_seen = include_num_input_tokens_seen,
neftune_noise_alpha = neftune_noise_alpha,
optim_target_modules = optim_target_modules,
batch_eval_metrics = batch_eval_metrics,
eval_on_start = eval_on_start,
use_liger_kernel = use_liger_kernel,
liger_kernel_config = liger_kernel_config,
eval_use_gather_object = eval_use_gather_object,
average_tokens_across_devices = average_tokens_across_devices,
model_init_kwargs = model_init_kwargs,
chat_template_path = chat_template_path,
dataset_text_field = dataset_text_field,
dataset_kwargs = dataset_kwargs,
dataset_num_proc = dataset_num_proc,
eos_token = eos_token,
pad_token = pad_token,
max_length = max_length,
packing = packing,
packing_strategy = packing_strategy,
padding_free = padding_free,
pad_to_multiple_of = pad_to_multiple_of,
eval_packing = eval_packing,
completion_only_loss = completion_only_loss,
assistant_only_loss = assistant_only_loss,
loss_type = loss_type,
activation_offloading = activation_offloading,**kwargs)
self.vllm_sampling_params = vllm_sampling_params
self.unsloth_num_chunks = unsloth_num_chunks
self.max_seq_length = max_seq_length
pass
class _UnslothSFTTrainer(BaseTrainer):
""""""
_tag_names = ["trl", "sft"]
_name = "SFT"
def __init__(
self,
model: Union[str, PreTrainedModel],
args: Optional[Union[SFTConfig, TrainingArguments]] = None,
data_collator: Optional[DataCollator] = None,
train_dataset: Optional[Union[Dataset, IterableDataset]] = None,
eval_dataset: Optional[Union[Dataset, dict[str, Dataset]]] = None,
processing_class: Optional[Union[PreTrainedTokenizerBase, ProcessorMixin]] = None,
compute_loss_func: Optional[Callable] = None,
compute_metrics: Optional[Callable[[EvalPrediction], dict]] = None,
callbacks: Optional[list[TrainerCallback]] = None,
optimizers: tuple[Optional[torch.optim.Optimizer], Optional[torch.optim.lr_scheduler.LambdaLR]] = (None, None),
optimizer_cls_and_kwargs: Optional[tuple[type[torch.optim.Optimizer], dict[str, Any]]] = None,
preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None,
peft_config: Optional["PeftConfig"] = None,
formatting_func: Optional[Callable[[dict], str]] = None,
):
# Args
if args is None:
model_name = model if isinstance(model, str) else model.config._name_or_path
model_name = model_name.split("/")[-1]
args = SFTConfig(f"{model_name}-SFT")
elif isinstance(args, TrainingArguments) and not isinstance(args, SFTConfig):
dict_args = args.to_dict()
dict_args["hub_token"] = args.hub_token # to_dict hides the hub_token
dict_args.pop("push_to_hub_token")
args = SFTConfig(**dict_args)
# Model
if isinstance(model, str):
model = create_model_from_path(model, **args.model_init_kwargs or {})
else:
if args.model_init_kwargs is not None:
logger.warning(
"You passed `model_init_kwargs` to the `SFTConfig`, but your model is already instantiated. "
"The `model_init_kwargs` will be ignored."
)
model_id = model.config._name_or_path
# Processing class
if processing_class is None:
processing_class = AutoProcessor.from_pretrained(model_id)
# Handle pad token for processors or tokenizers
if isinstance(processing_class, ProcessorMixin):
tokenizer = processing_class.tokenizer
self._is_vlm = False
elif isinstance(processing_class, PreTrainedTokenizerBase):
tokenizer = processing_class
self._is_vlm = False
else:
raise TypeError("The `processing_class` must be either a `PreTrainedTokenizerBase` or a `ProcessorMixin`")
if args.eos_token is not None:
eos_token = args.eos_token
eos_token_id = tokenizer.convert_tokens_to_ids(eos_token)
if eos_token_id is None:
raise ValueError(
f"The specified `eos_token` ('{eos_token}') is not found in the vocabulary of the given "
f"`processing_class` ({processing_class.__class__.__name__}). Ensure that the `eos_token` exists "
"in the vocabulary before using it as an EOS token."
)
tokenizer.eos_token_id = eos_token_id
if args.chat_template_path is not None:
if os.path.isfile(args.chat_template_path) and args.chat_template_path.endswith((".jinja", ".j2")):
with open(args.chat_template_path, encoding="utf-8") as chat_template_file:
processing_class.chat_template = chat_template_file.read()
added_tokens = []
else:
model, processing_class, added_tokens = clone_chat_template(
model, processing_class, args.chat_template_path
)
else:
added_tokens = []
# Catch some wrong configurations related to VLMs
if self._is_vlm and args.packing:
raise ValueError(
"Packing is not supported for vision-language models. Please set `packing=False` in the SFTConfig."
)
if self._is_vlm and args.padding_free:
raise ValueError(
"Padding-free training is yet not supported for vision-language models. Please set "
"`padding_free=False` in the `SFTConfig`."
)
if self._is_vlm and args.assistant_only_loss:
raise ValueError(
"Assistant-only loss is not yet supported for vision-language models. Please set "
"`assistant_only_loss=False` in the `SFTConfig`."
)
# PEFT configuration and model wrapping
if False:
if added_tokens:
# Ensure that the added tokens are trainable
if peft_config.trainable_token_indices is None:
peft_config.trainable_token_indices = {"embed_tokens": added_tokens}
elif "embed_tokens" not in peft_config.trainable_token_indices:
peft_config.trainable_token_indices["embed_tokens"] = added_tokens
else:
peft_config.trainable_token_indices["embed_tokens"].extend(added_tokens)
# Ensure that the lm_head is trainable
if peft_config.modules_to_save is None or "lm_head" not in peft_config.modules_to_save:
logger.warning(
"Cloning chat template added new tokens to the tokenizer, but 'lm_head' is not in PEFT's "
"`modules_to_save`. As a result, the model may not learn to generate outputs with these new "
"tokens, leading to degraded generation quality. To fix this, add "
"`modules_to_save=['lm_head']` to your PEFT configuration."
)
if peft_config.modules_to_save is None:
peft_config.modules_to_save = ["lm_head"]
else:
peft_config.modules_to_save.append("lm_head")
# In Prompt Tuning a small set of trainable virtual tokens [continuous prompt embeddings] is prepended to the
# input. We store the number of these tokens so we can account for them correctly when calculating accuracy.
self.num_virtual_tokens = 0
if False:
model = prepare_peft_model(model, peft_config, args)
if model.active_adapter in model.peft_config:
peft_model_config = model.peft_config[model.active_adapter]
self.num_virtual_tokens = getattr(peft_model_config, "num_virtual_tokens", 0)
# Data collator
# BFD packing requires padding-free mode; otherwise, the collator outputs padded attention masks, causing
# FlashAttention to ignore position_ids and recompute them incorrectly from the padded attention mask.
self.padding_free = args.padding_free or (args.packing and args.packing_strategy == "bfd")
use_flash_attention = model.config._attn_implementation in FLASH_ATTENTION_VARIANTS
if self.padding_free:
if data_collator is not None:
raise ValueError("Passing a custom data collator is not supported when using padding-free.")
if args.packing and args.packing_strategy == "wrapped":
logger.warning(
"You are passing `padding_free=True` with the 'wrapped' packing strategy, which is not "
"recommended. Please refer to the documentation to understand why this is not recommended."
)
if not use_flash_attention:
logger.warning(
"Padding-free training is enabled, but the attention implementation is not set to a supported "
"flash attention variant. Padding-free training flattens batches into a single sequence, and only "
"the following implementations are known to reliably support this: "
f"{', '.join(sorted(FLASH_ATTENTION_VARIANTS))}. Using other implementations may lead to "
"unexpected behavior. To ensure compatibility, set `attn_implementation` in the model "
"configuration to one of these supported options or verify that your attention mechanism can "
"handle flattened sequences."
)
if args.per_device_train_batch_size == 1 and not args.packing:
logger.warning(
"You are using a per_device_train_batch_size of 1 with padding-free training. Using a batch size "
"of 1 anihilate the benefits of padding-free training. Please consider increasing the batch size "
"to at least 2."
)
# Decide whether to use completion-only loss: if not specified, then it is set to True if the dataset format
# is prompt-completion, and False if the dataset format is language modeling.
dataset_sample = next(iter(train_dataset))
if args.completion_only_loss is None:
self.completion_only_loss = "prompt" in dataset_sample and "completion" in dataset_sample
else:
self.completion_only_loss = args.completion_only_loss
self._is_vision_dataset = "image" in dataset_sample or "images" in dataset_sample
if self._is_vision_dataset and not self._is_vlm:
raise ValueError(
"The dataset appears to be vision-related (contains 'image' or 'images' keys), but the provided "
"model does not seem to be a vision-language model. Please check your model and dataset."
)
if data_collator is None and not self._is_vision_dataset:
# Get the pad token: if not provided, use the one from the processing class or the eos token
# if the processing class does not have a pad token.
pad_token = args.pad_token or tokenizer.pad_token or tokenizer.eos_token
pad_token_id = tokenizer.convert_tokens_to_ids(pad_token)
if pad_token_id is None:
raise ValueError(
f"The specified `pad_token` ('{pad_token}') is not found in the vocabulary of the given "
f"`processing_class` ({processing_class.__class__.__name__}). Ensure that the `pad_token` exists "
"in the vocabulary before using it as a padding token."
)
data_collator = DataCollatorForLanguageModeling(
pad_token_id=pad_token_id,
completion_only_loss=self.completion_only_loss,
padding_free=self.padding_free,
pad_to_multiple_of=args.pad_to_multiple_of,
)
elif data_collator is None and self._is_vision_dataset:
data_collator = DataCollatorForVisionLanguageModeling(
processor=processing_class,
max_length=args.max_length,
completion_only_loss=self.completion_only_loss,
pad_to_multiple_of=args.pad_to_multiple_of,
dataset_text_field=args.dataset_text_field,
)
if args.packing and args.packing_strategy == "bfd" and not use_flash_attention:
logger.warning(
"You are using packing, but the attention implementation is not set to a supported flash attention "
"variant. Packing gathers multiple samples into a single sequence, and only the following "
f"implementations are known to reliably support this: {', '.join(sorted(FLASH_ATTENTION_VARIANTS))}. "
"Using other implementations may lead to cross-contamination between samples. To avoid this, either "
"disable packing by setting `packing=False`, or set `attn_implementation` in the model configuration "
"to one of these supported options."
)
if args.assistant_only_loss and not is_conversational(dataset_sample):
raise ValueError(
"You set `assistant_only_loss=True`, but the dataset is not conversational. This option is only "
"supported for conversational datasets."
)
# Dataset
# Skip dataset preparation if `skip_prepare_dataset=True` in `dataset_kwargs`, or if it's a VLM, where
# preprocessing [e.g., image-to-pixel conversion] is too costly and done on the fly instead.
skip_prepare_dataset = (
args.dataset_kwargs is not None
and args.dataset_kwargs.get("skip_prepare_dataset", False)
or self._is_vision_dataset
)
if not skip_prepare_dataset:
if self.completion_only_loss and formatting_func:
raise ValueError(
"A formatting function was provided while `completion_only_loss=True`, which is incompatible. "
"Using a formatter converts the dataset to a language modeling type, conflicting with "
"completion-only loss. To resolve this, apply your formatting function before passing the "
"dataset, or disable `completion_only_loss` in `SFTConfig`."
)
train_dataset = self._prepare_dataset(
train_dataset, processing_class, args, args.packing, formatting_func, "train"
)
if eval_dataset is not None:
packing = args.packing if args.eval_packing is None else args.eval_packing
if isinstance(eval_dataset, dict):
eval_dataset = {
key: self._prepare_dataset(dataset, processing_class, args, packing, formatting_func, key)
for key, dataset in eval_dataset.items()
}
else:
eval_dataset = self._prepare_dataset(
eval_dataset, processing_class, args, packing, formatting_func, "eval"
)
# Loss function
if args.loss_type == "nll":
pass # use the default loss
elif args.loss_type == "dft":
if compute_loss_func is not None:
raise ValueError(
"You passed a `compute_loss_func` together with `loss_type='dft'` to the `SFTTrainer`. "
"When using `loss_type='dft'`, the loss function is internally set to the DFT loss, so passing a "
"`compute_loss_func` is not allowed."
)
compute_loss_func = dft_loss
else:
raise ValueError(f"Invalid `loss_type` {args.loss_type} passed. Supported values are 'nll' and 'dft'.")
# Initialize the metrics
self._metrics = {"train": defaultdict(list), "eval": defaultdict(list)}
self._total_train_tokens = 0
# Initialize the Trainer. Parent class will handle:
# - DeepSpeed configuration [through create_accelerator_and_postprocess]
# - FSDP setup
# - Distributed training setup
# - Optimizer and scheduler creation
super().__init__(
model=model,
args=args,
data_collator=data_collator,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
processing_class=processing_class,
compute_loss_func=compute_loss_func,
compute_metrics=compute_metrics,
callbacks=callbacks,
optimizers=optimizers,
optimizer_cls_and_kwargs=optimizer_cls_and_kwargs,
preprocess_logits_for_metrics=preprocess_logits_for_metrics,
)
# Initialize activation offloading context
if self.args.activation_offloading:
self.maybe_activation_offload_context = get_act_offloading_ctx_manager(model=self.model)
else:
self.maybe_activation_offload_context = contextlib.nullcontext()
# Add tags for models that have been loaded with the correct transformers version
if hasattr(self.model, "add_model_tags"):
self.model.add_model_tags(self._tag_names)
self.aux_loss_enabled = getattr(model.config, "output_router_logits", False)
def _prepare_dataset(
self,
dataset: Union[Dataset, IterableDataset],
processing_class,
args,
packing: bool,
formatting_func: Optional[Callable[[dict], str]],
dataset_name: str,
) -> Union[Dataset, IterableDataset]:
# All Unsloth Zoo code licensed under LGPLv3
try:
if isinstance(dataset, ConstantLengthDataset): return dataset
except:
pass
map_kwargs = {}
use_desc = isinstance(dataset, Dataset)
is_vlm = hasattr(processing_class, "tokenizer")
tokenizer = processing_class
if is_vlm: tokenizer = processing_class.tokenizer
# Get max length
max_seq_length = getattr(args, "max_length", 0)
if max_seq_length == 0: max_seq_length = getattr(args, "max_seq_length", 0)
if max_seq_length == 0: max_seq_length = getattr(self, "max_seq_length", 0)
if max_seq_length == 0: max_seq_length = getattr(self, "max_seq", 0)
if max_seq_length == 0: raise RuntimeError("Unsloth: max_seq_length is 0! Please specify one!")
dataset_text_field = getattr(args, "dataset_text_field", "text")
do_truncation = max_seq_length != 0
do_formatting_func = False
do_tokenize = True
# Get correct column names
column_names = set(next(iter(dataset)).keys())
used_column_names = ["input_ids"]
if "attention_mask" in column_names:
used_column_names.append("attention_mask")
# Check if already tokenized so skip
from transformers import DataCollatorForSeq2Seq, DataCollatorForLanguageModeling
if "labels" in column_names:
# Most likely forgot data collator!
if is_vlm and not hasattr(tokenizer, "pad"):
# Check if processing_class has a .pad, if not, use tokenizer.tokenizer
raise RuntimeError(f"Unsloth: {processing_class.__class__} does not have .pad!")
self.data_collator = DataCollatorForSeq2Seq(tokenizer)
used_column_names.append("labels")
do_tokenize = False
elif "input_ids" in column_names:
# Skip dataset prep, and set data collator
if is_vlm and not hasattr(tokenizer, "pad"):
# Check if processing_class has a .pad, if not, use tokenizer.tokenizer
raise RuntimeError(f"Unsloth: {processing_class.__class__} does not have .pad!")
self.data_collator = DataCollatorForLanguageModeling(tokenizer, mlm = False)
do_tokenize = False
elif dataset_text_field not in column_names:
do_formatting_func = True
if formatting_func is None:
raise RuntimeError("Unsloth: You must specify a `formatting_func`")
pass
if do_tokenize:
# Check double BOS tokens
if do_formatting_func:
test_text = formatting_func(next(iter(dataset)))
if not isinstance(test_text, list):
raise ValueError(
"Unsloth: The `formatting_func` should return a list of processed strings."
)
test_text = test_text[0]
else:
test_text = next(iter(dataset))[dataset_text_field][0]
# Get chat template
chat_template = getattr(processing_class, 'chat_template', '')
if chat_template == '' and is_vlm:
chat_template = getattr(tokenizer, 'chat_template', '')
if chat_template is None:
chat_template = ''
# Get bos_token
add_special_tokens = True
bos_token_1 = getattr(processing_class, 'bos_token', None)
bos_token_2 = getattr(tokenizer, 'bos_token', None)
bos_token = bos_token_1 or bos_token_2
if bos_token is not None:
if test_text.startswith(bos_token) or bos_token in chat_template:
add_special_tokens = False
print("Unsloth: We found double BOS tokens - we shall remove one automatically.")
pass
# Create tokenize function
def _tokenize(example):
return tokenizer(
example[dataset_text_field] if not do_formatting_func else formatting_func(example),
truncation = do_truncation,
max_length = max_seq_length,
return_token_type_ids = False,
add_special_tokens = add_special_tokens,
)
pass
if not isinstance(dataset, IterableDataset):
dataset_num_proc = getattr(args, "dataset_num_proc", None)
if dataset_num_proc is None:
from multiprocessing import cpu_count
dataset_num_proc = max(cpu_count()+4, 2)
map_kwargs["num_proc"] = dataset_num_proc
else:
map_kwargs["batch_size"] = dataset._ex_iterable.batch_size
if use_desc: map_kwargs["desc"] = f'Unsloth: Tokenizing ["{dataset_text_field}"]'
dataset = dataset.map(_tokenize, batched = True, **map_kwargs)
# If VLM, switch data collator since .pad is needed!
if is_vlm and not hasattr(processing_class, "pad"):
data_collator = DataCollatorForLanguageModeling(tokenizer, mlm = False)
self.data_collator = data_collator
pass
pass
if packing:
# Try using new packing which works in TRL
try:
pack_dataset
except:
print("Unsloth: Hugging Face's packing is currently buggy - we're disabling it for now!")
return dataset
if max_seq_length == 0:
raise ValueError("When packing is enabled, `max_seq_length` can't be `None`.")
if use_desc: map_kwargs["desc"] = f"Unsloth: Packing {dataset_name} dataset"
dataset = pack_dataset(
dataset.select_columns(used_column_names),
max_seq_length,
getattr(args, "packing_strategy", "bfd"),
map_kwargs,
)
pass
return dataset
def _set_signature_columns_if_needed(self):
# If `self.args.remove_unused_columns` is True, non-signature columns are removed.
# By default, this method sets `self._signature_columns` to the model's expected inputs (usually, "input_ids"
# and "attention_mask"). When using `train_on_completion_only` we add a "completion_mask" column to the
# dataset. So we need to override the default signature columns to include "completion_mask" as well.
if self._signature_columns is None:
if self._is_vision_dataset:
self._signature_columns = ["messages", "prompt", "completion", "images"]
else:
self._signature_columns = ["input_ids", "labels", "seq_lengths", "completion_mask", "assistant_masks"]
def compute_loss(
self, model, inputs, return_outputs = False, num_items_in_batch = None
):
outputs = super().compute_loss(
model,
inputs,
return_outputs = return_outputs,
num_items_in_batch = num_items_in_batch,
)
return outputs
# Override training step to add activation offloading context.
def training_step(self, *args, **kwargs):
with self.maybe_activation_offload_context:
return super().training_step(*args, **kwargs)
def log(self, logs: dict[str, float], start_time: Optional[float] = None) -> None:
mode = "train" if self.model.training else "eval"
metrics = {key: sum(val) / len(val) for key, val in self._metrics[mode].items()} # average the metrics
# This method can be called both in training and evaluation. When called in evaluation, the keys in `logs`
# start with "eval_". We need to add the prefix "eval_" to the keys in `metrics` to match the format.
if mode == "eval":
metrics = {f"eval_{key}": val for key, val in metrics.items()}
logs.update(metrics)
super().log(logs, start_time)
self._metrics[mode].clear()
# Ensure the model card is saved along with the checkpoint
def _save_checkpoint(self, model, trial):
if self.args.hub_model_id is None:
model_name = Path(self.args.output_dir).name
else:
model_name = self.args.hub_model_id.split("/")[-1]
self.create_model_card(model_name=model_name)
super()._save_checkpoint(model, trial)
class UnslothSFTTrainer(_UnslothSFTTrainer):
"""
Trainer for Supervised Fine-Tuning (SFT) method.
This class is a wrapper around the [`~transformers.Trainer`] class and inherits all of its attributes and methods.
Example:
```python
from datasets import load_dataset
from trl import SFTTrainer
dataset = load_dataset("roneneldan/TinyStories", split="train[:1%]")
trainer = SFTTrainer(model="Qwen/Qwen2-0.5B-Instruct", train_dataset=dataset)
trainer.train()
```
Args:
model (`Union[str, PreTrainedModel]`):
Model to be trained. Can be either:
- A string, being the *model id* of a pretrained model hosted inside a model repo on huggingface.co, or a
path to a *directory* containing model weights saved using
[`~transformers.PreTrainedModel.save_pretrained`], e.g., `'./my_model_directory/'`. The model is loaded
using `<ModelArchitecture>.from_pretrained` (where `<ModelArchitecture>` is derived from the model
config) with the keyword arguments in `args.model_init_kwargs`.
- A [`~transformers.PreTrainedModel`] object.
If you're training a model with an MoE architecture and want to include the load balancing/auxilliary loss
as a part of the final loss, remember to set the `output_router_logits` config of the model to `True`.
args ([`SFTConfig`], *optional*):
Configuration for this trainer. If `None`, a default configuration is used.
data_collator ([`~transformers.DataCollator`], *optional*):
Function to use to form a batch from a list of elements of the processed `train_dataset` or `eval_dataset`.
Will default to [`~trainer.sft_trainer.DataCollatorForLanguageModeling`] if the model is a language model
and [`~trainer.sft_trainer.DataCollatorForVisionLanguageModeling`] if the model is a vision-language model.
train_dataset ([`~datasets.Dataset`] or [`~datasets.IterableDataset`]):
Dataset to use for training. SFT supports both [language modeling](#language-modeling) type and
[prompt-completion](#prompt-completion) type. The format of the samples can be either:
- [Standard](dataset_formats#standard): Each sample contains plain text.
- [Conversational](dataset_formats#conversational): Each sample contains structured messages (e.g., role
and content).
The trainer also supports processed datasets (tokenized) as long as they contain an `input_ids` field.
eval_dataset ([`~datasets.Dataset`], [`~datasets.IterableDataset`] or `dict[str, Union[Dataset, IterableDataset]]`):
Dataset to use for evaluation. It must meet the same requirements as `train_dataset`.
processing_class ([`~transformers.PreTrainedTokenizerBase`], [`~transformers.ProcessorMixin`], *optional*):
Processing class used to process the data. If `None`, the processing class is loaded from the model's name
with [`~transformers.AutoProcessor.from_pretrained`]. A padding token, `tokenizer.pad_token`, must be set.
If the processing class has not set a padding token, `tokenizer.eos_token` will be used as the default.
compute_loss_func (`Callable`, *optional*):
A function that accepts the raw model outputs, labels, and the number of items in the entire accumulated
batch (batch_size * gradient_accumulation_steps) and returns the loss. For example, see the default [loss
function](https://github.com/huggingface/transformers/blob/052e652d6d53c2b26ffde87e039b723949a53493/src/transformers/trainer.py#L3618)
used by [`Trainer`].
compute_metrics (`Callable[[EvalPrediction], dict]`, *optional*):
The function that will be used to compute metrics at evaluation. Must take a
[`~transformers.EvalPrediction`] and return a dictionary string to metric values. When passing
[`SFTConfig`] with `batch_eval_metrics` set to `True`, your `compute_metrics` function must take a boolean
`compute_result` argument. This will be triggered after the last eval batch to signal that the function
needs to calculate and return the global summary statistics rather than accumulating the batch-level
statistics.
callbacks (list of [`~transformers.TrainerCallback`], *optional*):
List of callbacks to customize the training loop. Will add those to the list of default callbacks detailed
in [here](https://huggingface.co/docs/transformers/main_classes/callback).
If you want to remove one of the default callbacks used, use the [`~transformers.Trainer.remove_callback`]
method.
optimizers (`tuple[Optional[torch.optim.Optimizer], Optional[torch.optim.lr_scheduler.LambdaLR]]`, *optional*, defaults to `(None, None)`):
A tuple containing the optimizer and the scheduler to use. Will default to an instance of `AdamW` on your
model and a scheduler given by [`~transformers.get_linear_schedule_with_warmup`] controlled by `args`.
optimizer_cls_and_kwargs (`tuple[Type[torch.optim.Optimizer], Dict[str, Any]]`, *optional*):
A tuple containing the optimizer class and keyword arguments to use. Overrides `optim` and `optim_args` in
`args`. Incompatible with the `optimizers` argument.
Unlike `optimizers`, this argument avoids the need to place model parameters on the correct devices before
initializing the Trainer.
preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`, *optional*):
A function that preprocess the logits right before caching them at each evaluation step. Must take two
tensors, the logits and the labels, and return the logits once processed as desired. The modifications made
by this function will be reflected in the predictions received by `compute_metrics`.
Note that the labels (second parameter) will be `None` if the dataset does not have them.
peft_config ([`~peft.PeftConfig`], *optional*):
PEFT configuration used to wrap the model. If `None`, the model is not wrapped.
formatting_func (`Callable`, *optional*):
Formatting function applied to the dataset before tokenization. Applying the formatting function explicitly
converts the dataset into a [language modeling](#language-modeling) type.
"""
def __init__(
self,
model,
args = None,
data_collator = None,
train_dataset = None,
eval_dataset = None,
processing_class = None,
compute_loss_func = None,
compute_metrics = None,
callbacks = None,
optimizer_cls_and_kwargs = None,
preprocess_logits_for_metrics = None,
peft_config = None,
formatting_func = None,
**kwargs
):
if args is None: args = UnslothSFTConfig()
use_bf16 = getattr(args, 'bf16', False)
if type(use_bf16) is not bool: use_bf16 = False
use_fp16 = getattr(args, 'fp16', False)
if type(use_fp16) is not bool: use_fp16 = False
force_float32 = False
full_finetuning = os.environ.get('UNSLOTH_ENABLE_FULL_FINETUNING', '0') == '1'
if not full_finetuning and (os.environ.get('UNSLOTH_FORCE_FLOAT32', '0') == '1'):
print('Unsloth: Switching to float32 training since model cannot work with float16')
force_float32 = True
mixed_precision_dtype = os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32')
dtype = getattr(model.config, 'dtype', None) or getattr(model.config, 'torch_dtype', None)
if dtype is None: dtype = model.get_input_embeddings().dtype
from unsloth_zoo.utils import _get_dtype
dtype = _get_dtype(dtype)
float16 = dtype == torch.float16
if not force_float32 and (float16 and use_bf16): raise TypeError('Unsloth: Model is in float16 precision but you want to use bfloat16 precision. Set fp16 to `True` and bf16 to `False`')
if not force_float32 and (not float16 and use_fp16): raise TypeError('Unsloth: Model is in bfloat16 precision but you want to use float16 precision. Set fp16 to `False` and bf16 to `True`')
if force_float32:
# Forced float32 training
args.fp16 = False
args.bf16 = False
os.environ['ACCELERATE_MIXED_PRECISION'] = 'no'
elif (not use_bf16 and not use_fp16) and mixed_precision_dtype == 'float32':
# Mixed precision training
args.fp16 = float16
args.bf16 = not float16
os.environ['ACCELERATE_MIXED_PRECISION'] = 'fp16' if float16 else 'bf16'
if getattr(args, 'eval_dataset', None) is not None and getattr(args, 'eval_strategy', 'no') == 'no':
args.eval_strategy = 'steps'
if getattr(args, 'eval_steps', None) is None: args.eval_steps = 0.1
ga_steps = getattr(args, 'gradient_accumulation_steps', None)
if ga_steps is not None and ga_steps > 1:
from transformers import __version__ as transformers_version
if Version(transformers_version) <= Version('4.45.2'):
print('**** Unsloth: Please use our fixed gradient_accumulation_steps by updating transformers, TRL and Unsloth!\n'
'`pip install --upgrade --no-cache-dir --force-reinstall --no-deps unsloth transformers trl unsloth_zoo`')
if getattr(args, 'eval_strategy', 'no') != 'no':
eval_bsz = getattr(args, 'per_device_eval_batch_size', 8)
if eval_bsz == 8 and args.per_device_train_batch_size < eval_bsz: args.per_device_eval_batch_size = args.per_device_train_batch_size
if getattr(args, 'eval_accumulation_steps', None) is None and ga_steps is not None: args.eval_accumulation_steps = ga_steps
fp16_full_eval = getattr(args, 'fp16_full_eval', False)
if type(fp16_full_eval) is not bool: fp16_full_eval = False
bf16_full_eval = getattr(args, 'bf16_full_eval', False)
if type(bf16_full_eval) is not bool: bf16_full_eval = False
if args.fp16 and bf16_full_eval: args.bf16_full_eval = False; args.fp16_full_eval = True
if args.bf16 and fp16_full_eval: args.bf16_full_eval = True; args.fp16_full_eval = False
if force_float32:
args.bf16_full_eval = False
args.fp16_full_eval = False
elif os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32') == 'bfloat16':
args.bf16_full_eval = True
args.fp16_full_eval = False
elif not bf16_full_eval and not fp16_full_eval:
args.bf16_full_eval = args.bf16
args.fp16_full_eval = args.fp16
_output_logits = False
if locals().get('compute_metrics', None) is not None: _output_logits = True
if locals().get('preprocess_logits_for_metrics', None) is not None: _output_logits = True
if _output_logits:
os.environ['UNSLOTH_RETURN_LOGITS'] = '1'
if 'max_seq_length' not in locals() and not hasattr(args, 'max_seq_length'):
pass
else:
model_max_seq_length = getattr(model, 'max_seq_length', None)
args_max_seq_length = getattr(args, 'max_seq_length', None)
if args_max_seq_length is None and model_max_seq_length is not None:
max_seq_length = model.max_seq_length
if hasattr(args, 'max_seq_length'): args.max_seq_length = max_seq_length
if 'max_length' not in locals() and not hasattr(args, 'max_length'):
pass
else:
if hasattr(args, 'max_seq_length') and args.max_seq_length is not None and args.max_seq_length > 0:
if hasattr(args, 'max_length'):
args.max_length = args.max_seq_length
max_length = args.max_length
else:
model_max_length = getattr(model, 'max_seq_length', None)
if model_max_length is None: model_max_length = getattr(model, 'max_length', None)
if model_max_length is not None:
args.max_length = model_max_length
max_length = args.max_length
elif hasattr(args, 'max_length') and args.max_length is not None:
max_length = args.max_length
# if we are here, then we are in a weird case where max_length is set but max_seq_length is not set
setattr(model, 'max_seq_length', max_length)
else:
print('Unsloth: We did not find `max_seq_length` or `max_length` in the model or args. We will set it to 1024.')
args.max_length = 1024
if model is not None and hasattr(model, 'for_training'):
model.for_training()
if 'tokenizer' in locals() and hasattr(tokenizer, 'padding_side'): tokenizer.padding_side = 'right'
if 'processing_class' in locals():
if hasattr(processing_class, 'padding_side'): processing_class.padding_side = 'right'
if hasattr(processing_class, 'tokenizer') and hasattr(processing_class.tokenizer, 'padding_side'): processing_class.tokenizer.padding_side = 'right'
__tokenizer = processing_class if 'processing_class' in locals() else tokenizer
from unsloth_zoo.vision_utils import UnslothVisionDataCollator
if not isinstance(data_collator, UnslothVisionDataCollator):
if isinstance(data_collator, DataCollatorForSeq2Seq) and 'labels' not in train_dataset.column_names:
data_collator = TransformersDataCollatorForLanguageModeling(
__tokenizer,
mlm = False,
mlm_probability = 0.0,
pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None),
)
elif isinstance(data_collator, TransformersDataCollatorForLanguageModeling) and 'labels' in train_dataset.column_names:
data_collator = DataCollatorForSeq2Seq(
__tokenizer,
pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None),
)
else:
if hasattr(args, 'remove_unused_columns'): args.remove_unused_columns = False
if hasattr(args, 'dataset_text_field'): args.dataset_text_field = ''
if hasattr(args, 'dataset_kwargs'): args.dataset_kwargs = {'skip_prepare_dataset': True}
if not isinstance(data_collator, UnslothVisionDataCollator):
if not hasattr(__tokenizer, 'pad') and hasattr(__tokenizer, 'tokenizer'):
if isinstance(data_collator, DataCollatorForSeq2Seq):
data_collator = DataCollatorForSeq2Seq(
__tokenizer.tokenizer,
pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None),
)
else:
data_collator = TransformersDataCollatorForLanguageModeling(
__tokenizer.tokenizer,
mlm = False,
mlm_probability = 0.0,
pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None),
)
other_metrics = []
from unsloth_zoo.logging_utils import PatchRLStatistics
PatchRLStatistics('sft_trainer', other_metrics)
IGNORED_TOKENIZER_NAMES = os.environ.get('UNSLOTH_IGNORED_TOKENIZER_NAMES', '').split('\n')
from unsloth_zoo.tokenizer_utils import fix_untrained_tokens
from unsloth_zoo.training_utils import fix_zero_training_loss
if 'tokenizer' not in locals(): tokenizer = processing_class
fix_untrained_tokens(model, tokenizer, train_dataset, IGNORED_TOKENIZER_NAMES, eps = 1e-16)
fix_zero_training_loss(model, tokenizer, train_dataset)
# [TODO] Fix up DataParallel multiplying batch sizes
# [TODO] DDP works, but DP seems to not work? [TODO]
if getattr(args, "parallel_mode", None) == ParallelMode.NOT_DISTRIBUTED and args.n_gpu > 1:
if getattr(args, "_n_gpu", 1) != 1:
args._n_gpu = 1
if "model" in locals() and hasattr(model, "for_training"):
model.for_training()
super().__init__(
model = model,
args = args,
data_collator = data_collator,
train_dataset = train_dataset,
eval_dataset = eval_dataset,
processing_class = processing_class,
compute_loss_func = compute_loss_func,
compute_metrics = compute_metrics,
callbacks = callbacks,
optimizer_cls_and_kwargs = optimizer_cls_and_kwargs,
preprocess_logits_for_metrics = preprocess_logits_for_metrics,
peft_config = peft_config,
formatting_func = formatting_func,**kwargs)
if "model" in locals() and hasattr(model, "for_inference"):
model.for_inference()
if hasattr(self, 'neftune_hook_handle'):
self.neftune_hook_handle.remove()
if hasattr(self, 'neftune_hook_handle'): del self.neftune_hook_handle
if getattr(args, 'neftune_noise_alpha', None) is not None:
model.get_input_embeddings().neftune_noise_alpha = self.neftune_noise_alpha
pass
if hasattr(self, 'accelerator'):
scaler = self.accelerator.scaler
current_model = model
while hasattr(current_model, 'model'):
current_model.accelerator_scaler = scaler
current_model = current_model.model
current_model.accelerator_scaler = scaler
pass
if hasattr(self, 'train'):
self.train = MethodType(prepare_for_training_mode(self.__class__.train), self)
pass
pass
if hasattr(logger, "addFilter"):
import logging
class HideLoggingMessage(logging.Filter):
def __init__(self, text): self.text = text
def filter(self, x): return not (self.text in x.getMessage())
pass
logger.addFilter(HideLoggingMessage("`use_cache=True`"))
|