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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.prm_trainer import (BaseImageProcessor, BaseTrainer, Callable, DataCollator, DataCollatorForTokenClassification, Dataset, EvalPrediction, FeatureExtractionMixin, Optional, PRMConfig, PRMTrainer, PartialState, Path, PreTrainedModel, PreTrainedTokenizerBase, ProcessorMixin, TrainerCallback, Union, chain, compute_accuracy, disable_dropout_in_model, features, nn, os, prepare_peft_model, textwrap, torch, warnings, Optional, PreTrainedModel, os, torch)
import os
from typing import *
from dataclasses import dataclass, field
from packaging.version import Version
import torch
import numpy as np
from contextlib import nullcontext
from torch.nn import functional as F
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 UnslothPRMConfig(PRMConfig):
"""
Configuration class for the [`PRMTrainer`].
This class includes only the parameters that are specific to PRM 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:
max_length (`int` or `None`, *optional*, defaults to `1024`):
Maximum length of the sequences (prompt + completion) used for truncation.
max_prompt_length (`int` or `None`, *optional*, defaults to `512`):
Maximum length of the prompt used for truncation.
max_completion_length (`int`, *optional*):
Maximum length of the completion used for truncation. The completion is the concatenation of the steps.
disable_dropout (`bool`, *optional*, defaults to `True`):
Whether to disable dropout in the model.
step_separator (`str`, *optional*, defaults to `"\n"`):
Separator used to separate each step of the reasoning process.
train_on_last_step_only (`bool`, *optional*, defaults to `False`):
Whether to train only on the last step.
dataset_num_proc (`int`, *optional*):
Number of processes to use for processing the dataset.
"""
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,
max_length = 1024,
max_prompt_length = 512,
max_completion_length = None,
disable_dropout = True,
step_separator = '\
',
train_on_last_step_only = False,
dataset_num_proc = None,
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)
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,
max_length = max_length,
max_prompt_length = max_prompt_length,
max_completion_length = max_completion_length,
disable_dropout = disable_dropout,
step_separator = step_separator,
train_on_last_step_only = train_on_last_step_only,
dataset_num_proc = dataset_num_proc,**kwargs)
self.vllm_sampling_params = vllm_sampling_params
self.unsloth_num_chunks = unsloth_num_chunks
self.max_seq_length = max_seq_length
pass
class _UnslothPRMTrainer(BaseTrainer):
""""""
_tag_names = ["trl", "prm"]
_name = "PRM"
_paper = {
"title": "Solving math word problems with process-and outcome-based feedback",
"id": "2211.14275",
# docstyle-ignore
"citation": textwrap.dedent("""\
@article{uesato2022solving,
title = {{Solving Math Word Problems With Process- and Outcome-Based Feedback}},
author = {Uesato, Jonathan and Kushman, Nate and Kumar, Ramana and Song, Francis and Siegel, Noah and Wang, Lisa and Creswell, Antonia and Irving, Geoffrey and Higgins, Irina},
year = 2022,
journal = {arXiv preprint arXiv:2211.14275}
}"""),
}
def __init__(
self,
model: Optional[Union[PreTrainedModel, nn.Module]] = None,
args: Optional[PRMConfig] = None,
data_collator: Optional[DataCollator] = None,
train_dataset: Optional[Dataset] = None,
eval_dataset: Optional[Union[Dataset, dict[str, Dataset]]] = None,
processing_class: Optional[
Union[PreTrainedTokenizerBase, BaseImageProcessor, FeatureExtractionMixin, ProcessorMixin]
] = None,
model_init: Optional[Callable[[], PreTrainedModel]] = None,
compute_metrics: Optional[Callable[[EvalPrediction], dict]] = None,
callbacks: Optional[list[TrainerCallback]] = None,
optimizers: tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (
None,
None,
),
preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None,
peft_config: Optional[dict] = None,
):
if not os.environ.get("TRL_EXPERIMENTAL_SILENCE"):
warnings.warn(
"This trainer will soon be moved to trl.experimental and is a candidate for removal. If you rely on "
"it and want it to remain, please share your comments here: "
"https://github.com/huggingface/trl/issues/4223. Silence this warning by setting environment variable "
"TRL_EXPERIMENTAL_SILENCE=1."
)
if False:
model = prepare_peft_model(model, peft_config, args)
# Disable dropout in the model
if args.disable_dropout:
disable_dropout_in_model(model)
if compute_metrics is None:
compute_metrics = compute_accuracy
if data_collator is None:
if processing_class is None:
raise ValueError(
"A processing_class must be specified when using the default DataCollatorForTokenClassification"
)
data_collator = DataCollatorForTokenClassification(processing_class, max_length=args.max_length)
if "input_ids" not in train_dataset.column_names:
with PartialState().main_process_first():
fn_kwargs = {
"tokenizer": processing_class,
"step_separator": args.step_separator,
"max_length": args.max_length,
"max_prompt_length": args.max_prompt_length,
"max_completion_length": args.max_completion_length,
"train_on_last_step_only": args.train_on_last_step_only,
}
train_fn_kwargs = {**fn_kwargs, "is_eval": False}
train_dataset = train_dataset.map(
self.tokenize_row,
fn_kwargs=train_fn_kwargs,
num_proc=args.dataset_num_proc,
remove_columns=train_dataset.features,
desc="Tokenizing train dataset",
features=features.Features( # needed to avoid map to cast labels to bool
{
"labels": features.Sequence(features.Value("int64")),
"input_ids": features.Sequence(features.Value("int64")),
}
),
)
eval_fn_kwargs = {**fn_kwargs, "is_eval": True}
if eval_dataset is not None:
eval_dataset = eval_dataset.map(
self.tokenize_row,
fn_kwargs=eval_fn_kwargs,
num_proc=args.dataset_num_proc,
remove_columns=eval_dataset.features,
desc="Tokenizing eval dataset",
features=features.Features( # needed to avoid map to cast labels to bool
{
"labels": features.Sequence(features.Value("int64")),
"input_ids": features.Sequence(features.Value("int64")),
}
),
)
super().__init__(
model=model,
args=args,
data_collator=data_collator,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
processing_class=processing_class,
model_init=model_init,
compute_metrics=compute_metrics,
callbacks=callbacks,
optimizers=optimizers,
preprocess_logits_for_metrics=preprocess_logits_for_metrics,
)
# 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)
@staticmethod
def tokenize_row(
features,
tokenizer,
step_separator,
max_length,
max_prompt_length,
max_completion_length,
train_on_last_step_only,
is_eval,
):
r"""
Tokenize a row of the dataset.
Args:
features (`dict[str, str]`):
Row of the dataset, should contain the keys `"prompt"`, `"completions"`, and `"labels"`.
tokenizer ([`~transformers.PreTrainedTokenizerBase`]):
Tokenizer used to process the data.
step_separator (`str`):
Separator between steps in the completion.
max_length (`int` or `None`):
Maximum length of the sequences (prompt + completion). If `None`, the sequences are not truncated.
max_prompt_length (`int` or `None`):
Maximum length of the prompt. If `None`, the prompt is not truncated.
max_completion_length (`int` or `None`):
Maximum length of the completion sequences. If `None`, the completion sequences are not truncated.
train_on_last_step_only (`bool`):
Whether to train only on the last step. If `True`, the labels are `-100` for all tokens except the last
token of the completion.
is_eval (`bool`):
Whether the function is used to tokenize samples from a training or an evaluation dataset. Used only if
`train_on_last_step_only` is set to `True`.
Returns:
`dict[str, list[int]]`:
Tokenized sequences with the keys `"input_ids"`, and `"labels".
Example:
```python
>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B")
>>> features = {
... "prompt": "Which number is larger, 9.8 or 9.11?",
... "completions": ["11 is greater than 8.", "Hence, 9.11 > 9.8."],
... "labels": [True, False],
... }
>>> PRMTrainer.tokenize_row(
... features, tokenizer, "\n", max_completion_length=None, train_on_last_step_only=False, is_eval=False
... )
{'input_ids': [23085, 1372, 374, 8131, 11, 220, 24, 13, 23, 476, 220, 24, 13, 16, 16, 30, 16, 16, 374, 7046, 1091, 220, 23, 13, 198, 39, 763, 11, 220, 24, 13, 16, 16, 861, 220, 24, 13, 23, 13, 198],
'labels': [-100, -100, -100, -100, -100, -100, -100, -100, 1, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 0]}
```
"""
# Tokenize the prompt and completions
prompt_ids = tokenizer(features["prompt"], add_special_tokens=False)["input_ids"]
completions_ids = [
tokenizer(completion, add_special_tokens=False)["input_ids"] for completion in features["completions"]
]
if train_on_last_step_only and not is_eval:
labels = [-100] * (len(features["labels"]) - 1) + [int(features["labels"][-1])]
else:
labels = [int(label) for label in features["labels"]]
# Get the ID of the separator token and add it to the completions
separator_ids = tokenizer.encode(step_separator, add_special_tokens=False)
completions_ids = [completion + separator_ids for completion in completions_ids]
# Create the label
labels = [[-100] * (len(completion) - 1) + [label] for completion, label in zip(completions_ids, labels)]
# Join the completions and labels steps
completion_ids = list(chain(*completions_ids))
labels = list(chain(*labels))
if tokenizer.bos_token_id is not None:
prompt_ids = [tokenizer.bos_token_id] + prompt_ids
# Truncate prompt and completion sequences
if max_prompt_length is not None:
prompt_ids = prompt_ids[-max_prompt_length:]
if max_completion_length is not None:
completion_ids = completion_ids[:max_completion_length]
labels = labels[:max_completion_length]
input_ids = prompt_ids + completion_ids
labels = [-100] * len(prompt_ids) + labels
if max_length is not None:
input_ids = input_ids[:max_length]
labels = labels[:max_length]
return {"input_ids": input_ids, "labels": labels}
# 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 UnslothPRMTrainer(_UnslothPRMTrainer):
"""
Initialize PRMTrainer.
Args:
model ([`~transformers.PreTrainedModel`]):
The model to train, preferably an `AutoModelForTokenClassification`.
args ([`PRMConfig`]):
The arguments to use for training.
data_collator ([`~transformers.DataCollator`]):
The data collator to use for training. If None is specified, the default data collator
([`~transformers.DataCollatorForTokenClassification`]) will be used which will pad the sequences to the
maximum length of the sequences in the batch, given a dataset of paired sequences.
train_dataset ([`~datasets.Dataset`]):
The dataset to use for training.
eval_dataset ([`~datasets.Dataset`]):
The dataset to use for evaluation.
processing_class ([`~transformers.PreTrainedTokenizerBase`], [`~transformers.BaseImageProcessor`], [`~transformers.FeatureExtractionMixin`] or [`~transformers.ProcessorMixin`], *optional*):
Processing class used to process the data. If provided, will be used to automatically process the inputs
for the model, and it will be saved along the model to make it easier to rerun an interrupted training or
reuse the fine-tuned model.
model_init (`Callable[[], transformers.PreTrainedModel]`):
The model initializer to use for training. If None is specified, the default model initializer will be
used.
compute_metrics (`Callable[[transformers.EvalPrediction], dict]`, *optional* defaults to `compute_accuracy`):
The metrics to use for evaluation. If no metrics are specified, the default metric (`compute_accuracy`)
will be used.
callbacks (`list[transformers.TrainerCallback]`):
The callbacks to use for training.
optimizers (`tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`):
The optimizer and scheduler to use for training.
preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`):
The function to use to preprocess the logits before computing the metrics.
peft_config (`dict`, defaults to `None`):
The PEFT configuration to use for training. If you pass a PEFT configuration, the model will be wrapped in
a PEFT model.
"""
def __init__(
self,
model = None,
args = None,
data_collator = None,
train_dataset = None,
eval_dataset = None,
processing_class = None,
model_init = None,
compute_metrics = None,
callbacks = None,
preprocess_logits_for_metrics = None,
peft_config = None,
**kwargs
):
if args is None: args = UnslothPRMConfig()
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 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('prm_trainer', other_metrics)
# [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,
model_init = model_init,
compute_metrics = compute_metrics,
callbacks = callbacks,
preprocess_logits_for_metrics = preprocess_logits_for_metrics,
peft_config = peft_config,**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
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