"""
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 .
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.online_dpo_trainer import (Any, AutoModelForCausalLM, AutoModelForSequenceClassification, AutoTokenizer, BasePairwiseJudge, BaseTrainer, Callable, DPODataCollatorWithPadding, DataCollator, DataLoader, Dataset, EvalPrediction, F, FSDP, GenerationConfig, IterableDataset, MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING_NAMES, OnlineDPOConfig, OnlineDPOTrainer, OptimizerNames, Optional, Path, PeftConfig, PreTrainedModel, PreTrainedTokenizerBase, ProcessorMixin, RewardFunc, SIMPLE_CHAT_TEMPLATE, Trainer, TrainerCallback, Union, VLLMClient, apply_chat_template, broadcast_object_list, create_reference_model, disable_dropout_in_model, empty_cache, ensure_master_addr_port, gather_object, is_conversational, is_flash_attn_2_available, is_peft_model, is_vllm_available, jinja2, logger, logging, maybe_apply_chat_template, nn, nullcontext, os, pad, prepare_deepspeed, prepare_fsdp, prepare_peft_model, profiling_context, re, seed_worker, textwrap, torch, truncate_right, unwrap_model_for_generation, version, warnings, wraps, F, apply_chat_template, is_conversational, re, F, FSDP, is_peft_model, nn, nullcontext, os, re, version, F, Optional, PreTrainedModel, Trainer, logger, os, re, torch, F, FSDP, nn, os, re, F, FSDP, nn, re, 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
def vLLMSamplingParams(**kwargs):
from vllm import SamplingParams
sampling_params = SamplingParams(**kwargs)
sampling_params._set_kwargs = kwargs
return sampling_params
@dataclass
class UnslothOnlineDPOConfig(OnlineDPOConfig):
"""
Configuration class for the [`OnlineDPOTrainer`].
This class includes only the parameters that are specific to Online DPO 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:
reward_model_path (`str`, *optional*):
Path to the reward model. Either `judge` or `reward_model_path` must be set, but not both.
judge (`str`, *optional*):
Name of the judge to use. Either `judge` or `reward_model_path` must be set, but not both.
max_new_tokens (`int`, *optional*, defaults to `64`):
Maximum number of tokens to generate per completion.
max_length (`int`, *optional*, defaults to `256`):
Maximum total length of the sequence (prompt + completion) used to compute log probabilities. If the
sequence exceeds this limit, the leftmost tokens will be truncated to preserve as much of the completion as
possible.
temperature (`float`, *optional*, defaults to `0.9`):
Temperature for sampling. The higher the temperature, the more random the completions.
missing_eos_penalty (`float`, *optional*):
Penalty applied to the score when the model fails to generate an EOS token. This is useful to encourage to
generate completions shorter than the maximum length (`max_new_tokens`). The penalty must be a positive
value. This parameter only works when using `reward_funcs` and not when using `judge`.
beta (`float` or `list[float]`, *optional*, defaults to `0.1`):
Parameter controlling the deviation from the reference model. Higher β means less deviation from the
reference model. For the IPO loss (`loss_type="ipo"`), β is the regularization parameter denoted by τ in
the [paper](https://huggingface.co/papers/2310.12036). If a list of floats is provided then the β is
selected for each new epoch and the last β is used for the rest of the epochs.
loss_type (`str`, *optional*, defaults to `"sigmoid"`):
Type of loss to use. Possible values are:
- `"sigmoid"`: sigmoid loss from the original [DPO](https://huggingface.co/papers/2305.18290) paper.
- `"ipo"`: IPO loss from the [IPO](https://huggingface.co/papers/2310.12036) paper.
dataset_num_proc (`int`, *optional*):
Number of processes to use for processing the dataset.
This parameter is deprecated and will be removed in version 0.25.0. Since OnlineDPO does not involve
dataset preparation, you can safely remove it.
disable_dropout (`bool`, *optional*, defaults to `True`):
Whether to disable dropout in the model and reference model.
> Parameters that control generation
top_p (`float`, *optional*, defaults to `1.0`):
Float that controls the cumulative probability of the top tokens to consider. Must be in (0, 1]. Set to
`1.0` to consider all tokens.
top_k (`int`, *optional*):
Number of highest probability vocabulary tokens to keep for top-k-filtering. If `None`, top-k-filtering is
disabled and all tokens are considered.
min_p (`float`, *optional*):
Minimum token probability, which will be scaled by the probability of the most likely token. It must be a
value between `0.0` and `1.0`. Typical values are in the `0.01-0.2` range.
repetition_penalty (`float`, *optional*, defaults to `1.0`):
Float that penalizes new tokens based on whether they appear in the prompt and the generated text so far.
Values > `1.0` encourage the model to use new tokens, while values < `1.0` encourage the model to repeat
tokens.
use_transformers_paged (`bool`, *optional*, defaults to `False`):
Whether to use the `transformers` paged implementation for generation. If set to `True`, the `transformers`
paged implementation will be used for generation instead of the default padded implementation. This
parameter is only effective when `use_vllm` is set to `False`.
cache_implementation (`str`, *optional*):
Implementation of the cache method for faster generation when `use_vllm` is set to `False`.
generation_kwargs (`dict[str, Any]`, *optional*):
Additional keyword arguments to pass to [`~transformers.GenerationConfig`] (if using transformers) or
`SamplingParams` (if using vLLM) when sampling completions. This can be used to further customize the
generation behavior, such as setting `suppress_tokens`, `num_beams`, etc. If it contains keys that conflict
with the other generation parameters (like `min_p`, `top_p`, etc.), they will override them.
> Parameters that control generation acceleration powered by vLLM
use_vllm (`bool`, *optional*, defaults to `False`):
Whether to use vLLM for generating completions. If set to `True`, the trainer will use vLLM for generation
instead of the default model.generate(). Requires `vllm` to be installed.
vllm_model_impl (`str`, *optional*, defaults to `"vllm"`):
Model implementation to use for vLLM. Must be one of `"transformers"` or `"vllm"`. `"transformers"`: Use
the `transformers` backend for model implementation. `"vllm"`: Use the `vllm` library for model
implementation.
vllm_mode (`str`, *optional*, defaults to `"server"`):
Mode to use for vLLM integration when `use_vllm` is set to `True`. Must be one of `"server"` or
`"colocate"`.
- `"server"`: The trainer will send generation requests to a separate vLLM server. Make sure a TRL vLLM
server is running (start with `trl vllm-serve`).
- `"colocate"`: vLLM will run in the same process and share the training GPUs. This avoids the need for a
separate server but may cause resource contention with training.
vllm_guided_decoding_regex (`str`, *optional*):
Regex for vLLM guided decoding. If `None` (default), guided decoding is disabled.
> Parameters that control the vLLM server (only used when `vllm_mode` is `"server"`)
vllm_server_base_url (`str`, *optional*):
Base URL for the vLLM server (e.g., `"http://localhost:8000"`). If provided, `vllm_server_host` and
`vllm_server_port` are ignored.
vllm_server_host (`str`, *optional*, defaults to `"0.0.0.0"`):
Host of the vLLM server to connect to. Ignored if `vllm_server_base_url` is provided.
vllm_server_port (`int`, *optional*, defaults to `8000`):
Port of the vLLM server to connect to. Ignored if `vllm_server_base_url` is provided.
vllm_server_timeout (`float`, *optional*, defaults to `240.0`):
Total timeout duration in seconds to wait for the vLLM server to be up. If the server is not up after the
timeout, a `ConnectionError` is raised.
> Parameters that control colocated vLLM execution (only used when `vllm_mode` is `"colocate"`)
vllm_gpu_memory_utilization (`float`, *optional*, defaults to `0.55`):
Control the GPU memory utilization for vLLM. This setting only applies when `vllm_mode` is set to
`"colocate"`. If you are using `vllm_mode="server"`, this parameter must be passed separately when
launching the vLLM server via the `--vllm_gpu_memory_utilization` flag.
vllm_tensor_parallel_size (`int`, *optional*, defaults to `1`):
Control the tensor parallel size for vLLM. This setting only applies when `vllm_mode` is set to
`"colocate"`. If you are using `vllm_mode="server"`, this parameter must be passed separately when
launching the vLLM server via the `--vllm_tensor_parallel_size` flag.
> Other parameters
ds3_gather_for_generation (`bool`, *optional*, defaults to `True`):
This setting applies to DeepSpeed ZeRO-3. If enabled, the policy model weights are gathered for generation,
improving generation speed. However, disabling this option allows training models that exceed the VRAM
capacity of a single GPU, albeit at the cost of slower generation. Disabling this option is not compatible
with vLLM generation.
model_init_kwargs (`dict[str, Any]`, *optional*):
Keyword arguments to pass to `AutoModelForCausalLM.from_pretrained` when instantiating the model from a
string.
"""
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,
reward_model_path = None,
judge = None,
max_new_tokens = 64,
max_length = 512,
temperature = 0.9,
top_p = 1.0,
top_k = None,
min_p = None,
repetition_penalty = 1.0,
generation_kwargs = {},
use_transformers_paged = False,
cache_implementation = None,
missing_eos_penalty = None,
loss_type = 'sigmoid',
disable_dropout = True,
use_vllm = False,
vllm_model_impl = 'vllm',
vllm_guided_decoding_regex = None,
vllm_gpu_memory_utilization = 0.55,
vllm_mode = 'colocate',
vllm_server_base_url = None,
vllm_server_host = '0.0.0.0',
vllm_server_port = 8000,
vllm_server_timeout = 240.0,
vllm_tensor_parallel_size = 1,
ds3_gather_for_generation = True,
model_init_kwargs = None,
reward_weights = None,
dataset_num_proc = None,
gpu_memory_utilization = 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)
if temperature <= 0:
raise MathError('Unsloth: Please set a positive non-zero temperature since your results will be wrong.')
elif temperature >= 10:
raise MathError('Unsloth: Please set a positive non-zero temperature less than 10, since sampling will be quite erratic.')
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,
reward_model_path = reward_model_path,
judge = judge,
max_new_tokens = max_new_tokens,
max_length = max_length,
temperature = temperature,
top_p = top_p,
top_k = top_k,
min_p = min_p,
repetition_penalty = repetition_penalty,
generation_kwargs = generation_kwargs,
use_transformers_paged = use_transformers_paged,
cache_implementation = cache_implementation,
missing_eos_penalty = missing_eos_penalty,
loss_type = loss_type,
disable_dropout = disable_dropout,
use_vllm = use_vllm,
vllm_model_impl = vllm_model_impl,
vllm_guided_decoding_regex = vllm_guided_decoding_regex,
vllm_gpu_memory_utilization = vllm_gpu_memory_utilization,
vllm_mode = vllm_mode,
vllm_server_base_url = vllm_server_base_url,
vllm_server_host = vllm_server_host,
vllm_server_port = vllm_server_port,
vllm_server_timeout = vllm_server_timeout,
vllm_tensor_parallel_size = vllm_tensor_parallel_size,
ds3_gather_for_generation = ds3_gather_for_generation,
model_init_kwargs = model_init_kwargs,
reward_weights = reward_weights,
dataset_num_proc = dataset_num_proc,
gpu_memory_utilization = gpu_memory_utilization,**kwargs)
self.vllm_sampling_params = vllm_sampling_params
self.unsloth_num_chunks = unsloth_num_chunks
self.max_seq_length = max_seq_length
pass
class _UnslothOnlineDPOTrainer(BaseTrainer):
r""""""
_tag_names = ["trl", "online-dpo"]
_name = "Online DPO"
_paper = {
"title": "Direct Language Model Alignment from Online AI Feedback",
"id": "2402.04792",
# docstyle-ignore
"citation": textwrap.dedent("""\
@article{guo2024direct,
title = {{Direct Language Model Alignment from Online AI Feedback}},
author = {Shangmin Guo and Biao Zhang and Tianlin Liu and Tianqi Liu and Misha Khalman and Felipe Llinares and Alexandre Ram{\'{e}} and Thomas Mesnard and Yao Zhao and Bilal Piot and Johan Ferret and Mathieu Blondel},
year = 2024,
eprint = {arXiv:2402.04792}
}"""),
}
def __init__(
self,
model: Union[PreTrainedModel, nn.Module, str],
ref_model: Union[PreTrainedModel, nn.Module, None] = None,
reward_funcs: Optional[Union[RewardFunc, list[RewardFunc]]] = None,
judge: Optional[BasePairwiseJudge] = None,
args: Optional[OnlineDPOConfig] = None,
data_collator: Optional[DataCollator] = None,
train_dataset: Optional[Union[Dataset, IterableDataset]] = None,
eval_dataset: Optional[Union[Dataset, IterableDataset, dict[str, Union[Dataset, IterableDataset]]]] = None,
processing_class: Optional[Union[PreTrainedTokenizerBase, ProcessorMixin]] = None,
reward_processing_classes: Optional[Union[PreTrainedTokenizerBase, list[PreTrainedTokenizerBase]]] = None,
peft_config: Optional["PeftConfig"] = 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,
# Deprecated parameters
reward_model: Optional[Union[PreTrainedModel, nn.Module]] = None,
reward_processing_class: Optional[PreTrainedTokenizerBase] = None,
) -> None:
if hasattr(model, 'vllm_engine') and hasattr(args, 'use_vllm'):
if (getattr(args, 'use_vllm', False) == False):
args.use_vllm = True
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 ref_model is model:
raise ValueError(
"`model` and `ref_model` cannot be the same object. If you want `ref_model` to be the "
"same as `model`, either omit the `ref_model` argument or pass `None`."
)
self.ref_model = ref_model
# Handle deprecated parameters for backward compatibility
if reward_model is not None:
warnings.warn(
"The `reward_model` parameter is deprecated and will be removed in version 0.25.0. "
"Please use `reward_funcs` instead. For example, change `reward_model=model` to `reward_funcs=model`.",
)
# Convert old reward_model to new reward_funcs format
if reward_funcs is None:
reward_funcs = reward_model
else:
warnings.warn(
"Both `reward_model` and `reward_funcs` are provided. Using `reward_funcs` and ignoring "
"`reward_model`.",
)
if reward_processing_class is not None:
warnings.warn(
"The `reward_processing_class` parameter is deprecated and will be removed in version 0.25.0. "
"Please use `reward_processing_classes` instead. For example, change "
"`reward_processing_class=tokenizer` to `reward_processing_classes=tokenizer`.",
)
# Convert old reward_processing_class to new reward_processing_classes format
if reward_processing_classes is None:
reward_processing_classes = reward_processing_class
else:
warnings.warn(
"Both `reward_processing_class` and `reward_processing_classes` are provided. Using "
"`reward_processing_classes` and ignoring `reward_processing_class`.",
)
# Validate reward configuration - must have exactly one of: judge, or reward_funcs
reward_configs = sum(x is not None for x in [judge, reward_funcs])
if reward_configs == 0:
raise ValueError("One of `judge` or `reward_funcs` must be provided.")
elif reward_configs > 1:
if judge is not None:
logger.warning(
"Both `judge` and `reward_funcs` are provided. Using `judge` and ignoring `reward_funcs`.",
UserWarning,
)
reward_funcs = None
self.judge = judge
# Handle reward_funcs
if reward_funcs is not None:
if not isinstance(reward_funcs, list):
reward_funcs = [reward_funcs]
self.reward_func_names = []
# Process reward functions [convert strings to models, collect names]
model_init_kwargs = args.model_init_kwargs or {}
for i, reward_func in enumerate(reward_funcs):
if isinstance(reward_func, str):
# Load model from string path
reward_funcs[i] = AutoModelForSequenceClassification.from_pretrained(
reward_func, num_labels=1, **model_init_kwargs
)
if isinstance(reward_funcs[i], nn.Module):
self.reward_func_names.append(reward_funcs[i].config._name_or_path.split("/")[-1])
else:
self.reward_func_names.append(reward_funcs[i].__name__)
self.reward_funcs = reward_funcs
# Handle reward processing classes for reward_funcs
if reward_processing_classes is None:
reward_processing_classes = [None] * len(reward_funcs)
elif not isinstance(reward_processing_classes, list):
reward_processing_classes = [reward_processing_classes]
else:
if len(reward_processing_classes) != len(reward_funcs):
raise ValueError(
"The number of reward processing classes must match the number of reward functions."
)
self.reward_processing_classes = []
for reward_processing_class_i, reward_func in zip(reward_processing_classes, reward_funcs):
if isinstance(reward_func, PreTrainedModel):
if reward_processing_class_i is None:
reward_processing_class_i = AutoTokenizer.from_pretrained(reward_func.config._name_or_path)
if reward_processing_class_i.pad_token_id is None:
reward_processing_class_i.pad_token = reward_processing_class_i.eos_token
# Set pad token ID on reward model config
reward_func.config.pad_token_id = reward_processing_class_i.pad_token_id
self.reward_processing_classes.append(reward_processing_class_i)
else:
self.reward_funcs = None
self.reward_func_names = []
self.reward_processing_classes = []
# Handle reward_weights
if reward_funcs is not None:
if args.reward_weights is not None:
if len(args.reward_weights) != len(self.reward_funcs):
raise ValueError(
f"Number of reward weights ({len(args.reward_weights)}) must match number of reward "
f"functions ({len(self.reward_funcs)})"
)
self.reward_weights = torch.tensor(args.reward_weights, dtype=torch.float32)
else:
self.reward_weights = torch.ones(len(self.reward_funcs), dtype=torch.float32)
else:
self.reward_weights = None
if args.missing_eos_penalty is not None and reward_funcs is None and judge is None:
# Check if this is the old reward_model case
if reward_model is not None:
logger.warning(
"The `missing_eos_penalty` parameter is deprecated when used with the deprecated `reward_model` parameter. "
"Please use `reward_funcs` instead of `reward_model` to continue using this feature.",
FutureWarning,
stacklevel=2,
)
else:
raise ValueError("`missing_eos_penalty` is only supported when `reward_funcs` is provided.")
if args is None:
raise ValueError("`args` must be provided.")
# Check that the processing_class is provided
if processing_class is None:
raise ValueError("`processing_class` must be provided.")
model_init_kwargs = args.model_init_kwargs or {}
if isinstance(model, str):
model_id = model
# Handle dtype in model_init_kwargs
dtype = model_init_kwargs.get("dtype")
if isinstance(dtype, torch.dtype) or dtype == "auto" or dtype is None:
pass
elif isinstance(dtype, str):
dtype = getattr(torch, dtype)
model_init_kwargs["dtype"] = dtype
else:
raise ValueError(
"Invalid `dtype` passed to `OnlineDPOConfig`. Expected either 'auto' or a string "
f"representing a `torch.dtype` (e.g., 'float32'), but got {dtype}."
)
model = AutoModelForCausalLM.from_pretrained(model_id, **model_init_kwargs)
else:
if args.model_init_kwargs is not None:
raise ValueError(
"You passed `model_init_kwargs` to the `OnlineDPOConfig`, but your model is already instantiated. "
"This argument can only be used when the `model` argument is a string."
)
self.is_encoder_decoder = model.config.is_encoder_decoder
self.is_vision_model = model.config.model_type in MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING_NAMES.keys()
if False:
model = prepare_peft_model(model, peft_config, args)
# Enable gradient checkpointing if requested
if args.gradient_checkpointing:
model = self._enable_gradient_checkpointing(model, args)
# Disable dropout in the model and reference model
if args.disable_dropout:
disable_dropout_in_model(model)
if self.ref_model is not None:
disable_dropout_in_model(self.ref_model)
# Handle the ref_model
# Usually, the user wants the ref model to be the initial version of the model. When using PEFT, it's easy to
# get the ref model, as it's just the model with a disabled adapter. When not using PEFT, we need to create
# the ref model from the model by copying it and disable the gradients and set it in evaluation mode.
if ref_model is None: # No ref model provided, the most common case
if False:
self.ref_model = create_reference_model(model) # copy, disable gradients, set eval mode
else:
self.ref_model = None # we don't need a ref model here, we can just disable the adapter.
else: # rare case, the user provided a ref model
self.ref_model = ref_model
self.ref_model.eval()
# Disable the gradient and set the reward model in eval mode
if reward_funcs is not None:
for reward_func in reward_funcs:
if isinstance(reward_func, PreTrainedModel):
reward_func.eval()
self.max_length = args.max_length
self.stats = {
"objective/kl": [],
"objective/entropy": [],
"objective/non_score_reward": [],
"rewards/chosen": [],
"rewards/rejected": [],
"rewards/accuracies": [],
"rewards/margins": [],
"logps/chosen": [],
"logps/rejected": [],
"val/contain_eos_token": [],
"beta": [],
}
if self.reward_funcs is not None:
self.stats["objective/rlhf_reward"] = []
self.stats["objective/scores_margin"] = []
self.stats["objective/scores"] = []
# Store generation parameters for later use
self.use_vllm = args.use_vllm
self.num_generations = 2 # Generate 2 completions per prompt for Online DPO
self.temperature = args.temperature
self.top_p = args.top_p
self.top_k = args.top_k
self.min_p = args.min_p
self.repetition_penalty = args.repetition_penalty
self.use_transformers_paged = args.use_transformers_paged
self.vllm_mode = args.vllm_mode if args.use_vllm else None
self.vllm_gpu_memory_utilization = args.vllm_gpu_memory_utilization
self.vllm_tensor_parallel_size = args.vllm_tensor_parallel_size
self.vllm_model_impl = args.vllm_model_impl
# Handle pad token for processors or tokenizers
if isinstance(processing_class, ProcessorMixin):
tokenizer = processing_class.tokenizer
elif isinstance(processing_class, PreTrainedTokenizerBase):
tokenizer = processing_class
else:
raise TypeError("The `processing_class` must be either a `PreTrainedTokenizerBase` or a `ProcessorMixin`")
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
self.pad_token = tokenizer.pad_token
self.pad_token_id = tokenizer.pad_token_id
self.eos_token_id = tokenizer.eos_token_id
# Vision tokens for VLM support
self.image_token_id = getattr(processing_class, "image_token_id", None)
self.vision_start_token_id = getattr(processing_class, "vision_start_token_id", None)
self.vision_end_token_id = getattr(processing_class, "vision_end_token_id", None)
# Get the image token string for token collapsing
self.image_token = None
if self.image_token_id is not None:
self.image_token = tokenizer.decode([self.image_token_id])
# Define the collator if not provided
if data_collator is None:
data_collator = DPODataCollatorWithPadding(pad_token_id=self.pad_token_id)
# The trainer estimates the number of FLOPs [floating-point operations] using the number of elements in the
# input tensor associated with the key "input_ids". However, in Online DPO, the sampled data does not include
# the "input_ids" key. As a result, the trainer issues the warning: "Could not estimate the number of tokens
# of the input, floating-point operations will not be computed." To suppress this warning, we set the
# "estimate_tokens" key in the model's "warnings_issued" dictionary to True. This acts as a flag to indicate
# that the warning has already been issued.
model.warnings_issued["estimate_tokens"] = True
super().__init__(
model=model,
args=args,
data_collator=data_collator,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
processing_class=processing_class,
compute_metrics=compute_metrics,
callbacks=callbacks,
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)
self._beta = args.beta
# Set up generation configuration and vLLM after super[].__init__
if self.use_vllm:
if not is_vllm_available():
raise ImportError(
"vLLM is not available and `use_vllm` is set to True. Please install vLLM with "
"`pip install trl[vllm]` to use it."
)
if self.vllm_mode == "server":
if self.accelerator.is_main_process:
if args.vllm_server_base_url is not None:
base_url = args.vllm_server_base_url
else:
base_url = f"http://{args.vllm_server_host}:{args.vllm_server_port}"
self.vllm_client = VLLMClient(base_url=base_url, connection_timeout=args.vllm_server_timeout)
self.vllm_client.init_communicator(device=torch.cuda.current_device())
else:
self.vllm_client = None
elif self.vllm_mode == "colocate":
vllm_kwargs = {
"model": model.name_or_path,
"tensor_parallel_size": self.vllm_tensor_parallel_size,
"gpu_memory_utilization": self.vllm_gpu_memory_utilization,
"model_impl": self.vllm_model_impl,
"max_num_seqs": self.args.per_device_train_batch_size * self.vllm_tensor_parallel_size,
"max_model_len": args.max_length + args.max_new_tokens,
"distributed_executor_backend": "external_launcher",
"seed": self.accelerator.process_index // self.vllm_tensor_parallel_size,
"max_num_batched_tokens": 4096,
}
os.environ["RANK"] = str(self.accelerator.process_index)
os.environ["LOCAL_RANK"] = str(self.accelerator.local_process_index)
os.environ["WORLD_SIZE"] = str(self.accelerator.num_processes)
ensure_master_addr_port()
self.llm = model.vllm_engine
else:
raise ValueError(f"vllm_mode must be either 'server' or 'colocate', got '{self.vllm_mode}'.")
self.guided_decoding_regex = args.vllm_guided_decoding_regex
self._last_loaded_step = -1
generation_params = {
"n": 2,
"repetition_penalty": self.repetition_penalty,
"temperature": self.temperature,
"top_p": self.top_p,
"top_k": -1 if self.top_k is None else self.top_k,
"min_p": 0.0 if self.min_p is None else self.min_p,
"max_tokens": args.max_new_tokens,
"detokenize": False,
}
if args.generation_kwargs is not None:
generation_params.update(args.generation_kwargs)
if self.guided_decoding_regex:
generation_params["guided_decoding"] = GuidedDecodingParams(regex=self.guided_decoding_regex)
self.generation_config = SamplingParams(**generation_params)
self.accelerator.wait_for_everyone()
else:
# Set up transformers generation config
generation_kwargs = {
"max_new_tokens": args.max_new_tokens,
"do_sample": True,
"pad_token_id": self.pad_token_id,
"bos_token_id": tokenizer.bos_token_id,
"eos_token_id": self.eos_token_id,
"temperature": self.temperature,
"top_k": self.top_k,
"top_p": self.top_p,
"repetition_penalty": self.repetition_penalty,
"use_cache": True if not self.args.gradient_checkpointing else False,
}
# Add min_p if supported
if self.min_p is not None:
generation_kwargs["min_p"] = self.min_p
if args.generation_kwargs is not None:
generation_kwargs.update(args.generation_kwargs)
# Remove None values
generation_kwargs = {k: v for k, v in generation_kwargs.items() if v is not None}
self.generation_config = GenerationConfig(**generation_kwargs)
if self.ref_model is not None:
if self.is_deepspeed_enabled:
self.ref_model = prepare_deepspeed(self.ref_model, self.accelerator)
elif self.is_fsdp_enabled:
self.ref_model = prepare_fsdp(self.ref_model, self.accelerator)
else:
self.ref_model = self.accelerator.prepare_model(self.ref_model, evaluation_mode=True)
if self.reward_funcs is not None:
for i, reward_func in enumerate(self.reward_funcs):
if isinstance(reward_func, PreTrainedModel):
if self.is_deepspeed_enabled:
self.reward_funcs[i] = prepare_deepspeed(reward_func, self.accelerator)
else:
# set device placement to True to make `prepare_model` move `reward_func` to device when using fsdp
self.reward_funcs[i] = self.accelerator.prepare_model(
reward_func, evaluation_mode=True, device_placement=True
)
@property
def beta(self):
if isinstance(self._beta, list):
epoch = self.state.epoch
return self._beta[epoch] if epoch < len(self._beta) else self._beta[-1]
else:
return self._beta
@staticmethod
def tokenize_row(feature, is_encoder_decoder: bool, tokenizer: PreTrainedTokenizerBase) -> dict[str, Any]:
"""Tokenize a single row from a DPO specific dataset."""
if not is_encoder_decoder:
batch = tokenizer(feature["prompt"], add_special_tokens=False)
# Add BOS token to head of prompt. Avoid adding if it's already there
if tokenizer.bos_token_id is not None:
prompt_len_input_ids = len(batch["input_ids"])
if prompt_len_input_ids == 0 or tokenizer.bos_token_id != batch["input_ids"][0]:
batch["input_ids"] = [tokenizer.bos_token_id] + batch["input_ids"]
batch["attention_mask"] = [1] + batch["attention_mask"]
else:
batch = tokenizer(feature["prompt"], add_special_tokens=True)
batch = {f"prompt_{key}": value for key, value in batch.items()}
return batch
# Same as Trainer.get_train_dataloader but skip the "remove_unused_columns".
@wraps(Trainer.get_train_dataloader)
def get_train_dataloader(self) -> DataLoader:
if self.train_dataset is None:
raise ValueError("Trainer: training requires a train_dataset.")
train_dataset = self.train_dataset
data_collator = self.data_collator
dataloader_params = {
"batch_size": self._train_batch_size,
"collate_fn": data_collator,
"num_workers": self.args.dataloader_num_workers,
"pin_memory": self.args.dataloader_pin_memory,
"persistent_workers": self.args.dataloader_persistent_workers,
}
if not isinstance(train_dataset, torch.utils.data.IterableDataset):
dataloader_params["sampler"] = self._get_train_sampler()
dataloader_params["drop_last"] = self.args.dataloader_drop_last
dataloader_params["worker_init_fn"] = seed_worker
dataloader_params["prefetch_factor"] = self.args.dataloader_prefetch_factor
return self.accelerator.prepare(DataLoader(train_dataset, **dataloader_params))
# Same as Trainer.get_eval_dataloader but skip the "remove_unused_columns".
@wraps(Trainer.get_eval_dataloader)
def get_eval_dataloader(self, eval_dataset: Optional[Union[str, Dataset]] = None) -> DataLoader:
if eval_dataset is None and self.eval_dataset is None:
raise ValueError("Trainer: evaluation requires an eval_dataset.")
# If we have persistent workers, don't do a fork bomb especially as eval datasets
# don't change during training
dataloader_key = eval_dataset if isinstance(eval_dataset, str) else "eval"
if (
hasattr(self, "_eval_dataloaders")
and dataloader_key in self._eval_dataloaders
and self.args.dataloader_persistent_workers
):
return self.accelerator.prepare(self._eval_dataloaders[dataloader_key])
eval_dataset = (
self.eval_dataset[eval_dataset]
if isinstance(eval_dataset, str)
else eval_dataset
if eval_dataset is not None
else self.eval_dataset
)
data_collator = self.data_collator
dataloader_params = {
"batch_size": self.args.eval_batch_size,
"collate_fn": data_collator,
"num_workers": self.args.dataloader_num_workers,
"pin_memory": self.args.dataloader_pin_memory,
"persistent_workers": self.args.dataloader_persistent_workers,
}
if not isinstance(eval_dataset, torch.utils.data.IterableDataset):
dataloader_params["sampler"] = self._get_eval_sampler(eval_dataset)
dataloader_params["drop_last"] = self.args.dataloader_drop_last
dataloader_params["prefetch_factor"] = self.args.dataloader_prefetch_factor
# accelerator.free_memory() will destroy the references, so
# we need to store the non-prepared version
eval_dataloader = DataLoader(eval_dataset, **dataloader_params)
if self.args.dataloader_persistent_workers:
if hasattr(self, "_eval_dataloaders"):
self._eval_dataloaders[dataloader_key] = eval_dataloader
else:
self._eval_dataloaders = {dataloader_key: eval_dataloader}
return self.accelerator.prepare(eval_dataloader)
def _enable_gradient_checkpointing(self, model: PreTrainedModel, args: OnlineDPOConfig) -> PreTrainedModel:
"""Enables gradient checkpointing for the model."""
# Ensure use_cache is disabled
model.config.use_cache = False
# Enable gradient checkpointing on the base model for PEFT
if is_peft_model(model):
model.base_model.gradient_checkpointing_enable()
# Enable gradient checkpointing for non-PEFT models
else:
model.gradient_checkpointing_enable()
gradient_checkpointing_kwargs = args.gradient_checkpointing_kwargs or {}
use_reentrant = (
"use_reentrant" not in gradient_checkpointing_kwargs or gradient_checkpointing_kwargs["use_reentrant"]
)
if use_reentrant:
model.enable_input_require_grads()
return model
def _generate_vllm(self, prompts, images=None):
eos_token_id = self.eos_token_id
pad_token_id = self.pad_token_id
# Generate completion_ids and prompt_ids based on mode
if self.vllm_mode == "server":
completion_ids, prompt_ids = self._generate_vllm_server(prompts, images)
elif self.vllm_mode == "colocate":
completion_ids, prompt_ids = self._generate_vllm_colocate(prompts, images)
# Shared padding, masking, and tensor conversion logic
max_prompt_length = max(len(ids) for ids in prompt_ids)
prompt_mask = [[0] * (max_prompt_length - len(ids)) + [1] * len(ids) for ids in prompt_ids]
prompt_ids = [[pad_token_id] * (max_prompt_length - len(ids)) + ids for ids in prompt_ids]
max_tokens = self.generation_config.max_tokens
completion_mask = [[1] * len(ids) + [0] * (max_tokens - len(ids)) for ids in completion_ids]
completion_ids = [
ids + [eos_token_id] if ids[-1] != eos_token_id and len(ids) < max_tokens else ids
for ids in completion_ids
]
completion_ids = [ids + [pad_token_id] * (max_tokens - len(ids)) for ids in completion_ids]
# Convert to tensors
prompt_ids = torch.tensor(prompt_ids, device=self.accelerator.device)
prompt_mask = torch.tensor(prompt_mask, device=self.accelerator.device)
completion_ids = torch.tensor(completion_ids, device=self.accelerator.device)
completion_mask = torch.tensor(completion_mask, device=self.accelerator.device)
return prompt_ids, prompt_mask, completion_ids, completion_mask
def _generate_vllm_server(self, prompts, images=None):
"""Generate completions using vLLM server mode"""
has_images = images is not None
# Update vLLM server weights if needed
if hasattr(self, "_last_loaded_step") and self.state.global_step != self._last_loaded_step:
self._move_model_to_vllm()
self._last_loaded_step = self.state.global_step
elif not hasattr(self, "_last_loaded_step"):
self._move_model_to_vllm()
self._last_loaded_step = self.state.global_step
# Apply chat template if conversational
if is_conversational({"prompt": prompts[0]}):
prompts_text = [apply_chat_template({"prompt": p}, self.processing_class)["prompt"] for p in prompts]
else:
prompts_text = prompts
# Gather all prompts to main process
all_prompts = gather_object(prompts_text)
if has_images:
all_images = gather_object(images)
if self.accelerator.is_main_process:
# Since 'prompts' contains 'num_generations' duplicates, we first take unique prompts, and generate
# num_generations outputs for each one. This is faster than generating outputs for each duplicate
# prompt individually.
ordered_set_of_prompts = all_prompts[:: self.num_generations]
if has_images:
ordered_set_of_images = all_images[:: self.num_generations]
else:
ordered_set_of_images = None
completion_ids = self.vllm_client.generate(
prompts=ordered_set_of_prompts,
images=ordered_set_of_images,
n=self.num_generations,
repetition_penalty=self.repetition_penalty,
temperature=self.temperature,
top_p=self.top_p,
top_k=-1 if self.top_k is None else self.top_k,
min_p=0.0 if self.min_p is None else self.min_p,
max_tokens=self.generation_config.max_tokens,
guided_decoding_regex=self.guided_decoding_regex if hasattr(self, "guided_decoding_regex") else None,
generation_kwargs=self.args.generation_kwargs,
)
# Flatten: each prompt generates 2 completions
completion_ids = [[comp_id] for prompt_completions in completion_ids for comp_id in prompt_completions]
else:
completion_ids = [None] * (len(all_prompts) * 2)
# Broadcast completions to all processes
completion_ids = broadcast_object_list(completion_ids, from_process=0)
# Each process takes its slice
process_slice = slice(
self.accelerator.process_index * len(prompts) * 2,
(self.accelerator.process_index + 1) * len(prompts) * 2,
)
completion_ids = completion_ids[process_slice]
# Create prompt_ids by tokenizing locally
prompt_inputs = self.processing_class(
text=prompts_text,
return_tensors="pt",
padding=True,
padding_side="left",
add_special_tokens=False,
)
prompt_ids = []
for prompt_tokens in prompt_inputs["input_ids"]:
prompt_ids.extend([prompt_tokens.tolist(), prompt_tokens.tolist()]) # 2 copies for 2 completions
return completion_ids, prompt_ids
def _generate_vllm_colocate(self, prompts, images=None):
"""Generate completions using vLLM colocate mode"""
# Update model weights if needed - only after gradient accumulation completes
if self.state.global_step != self._last_loaded_step:
self._move_model_to_vllm()
self._last_loaded_step = self.state.global_step
# Apply chat template if conversational
if is_conversational({"prompt": prompts[0]}):
prompts_text = [apply_chat_template({"prompt": p}, self.processing_class)["prompt"] for p in prompts]
else:
prompts_text = prompts
# Prepare vLLM inputs with images if available
if images is not None:
vllm_inputs = []
for prompt, image in zip(prompts_text, images):
if image is not None:
vllm_inputs.append({"prompt": prompt, "multi_modal_data": {"image": image}})
else:
vllm_inputs.append(prompt)
else:
vllm_inputs = prompts_text
outputs = self.llm.generate(vllm_inputs, self.generation_config, use_tqdm=False, lora_request = self.model.load_lora('online_dpo_trainer_lora_model', load_tensors = True))
completion_ids = [list(output.outputs[i].token_ids) for i in range(2) for output in outputs]
prompt_ids = [list(output.prompt_token_ids) for _ in range(2) for output in outputs]
return completion_ids, prompt_ids
def _move_model_to_vllm(self):
"""Synchronize model weights to vLLM server with support for PEFT, DeepSpeed, and FSDP"""
# For DeepSpeed ZeRO-3 and FSDP, we need to gather all parameters before operations
deepspeed_plugin = self.accelerator.state.deepspeed_plugin
zero_stage_3 = deepspeed_plugin is not None and deepspeed_plugin.zero_stage == 3
if zero_stage_3:
import deepspeed
gather_if_zero3 = deepspeed.zero.GatheredParameters
else:
gather_if_zero3 = nullcontext
if is_peft_model(self.model):
# With PEFT and FSDP/DeepSpeed ZeRO Stage 3, we must gather the full model at once before merging, as
# merging adapters in a sharded manner is not supported.
# TODO: does this work with FSDP?
with gather_if_zero3(list(self.model.parameters())):
self.model.merge_adapter()
# Update vLLM weights while parameters are gathered
if self.is_fsdp_enabled: # note if using FSDP, gather_if_zero3 is nullcontext
# Update vLLM weights while parameters are gathered
# For PEFT with FSDP we need to use the memory efficient post-order traversal
fsdp_plugin = getattr(self.accelerator.state, "fsdp_plugin", None)
fsdp_version = getattr(fsdp_plugin, "fsdp_version", 1) if fsdp_plugin else 1
if fsdp_version == 1:
# use memory-efficient post-order traversal for FSDP
self._sync_fsdp1_params_to_vllm(self.model)
elif fsdp_version == 2:
self._sync_fsdp2_params_to_vllm(self.model)
else:
# DeepSpeed ZeRO-3 with PEFT
for name, param in self.model.named_parameters():
# When using PEFT, we need to recover the original parameter name and discard some parameters
name = name.removeprefix("base_model.model.").replace(".base_layer", "")
if self.model.prefix in name:
continue
# When module to save, remove its prefix and discard the original module
if "original_module" in name:
continue
name = self._fix_param_name_to_vllm(name, extra_prefixes=["modules_to_save.default."])
if self.vllm_mode == "server" and self.accelerator.is_main_process:
self.vllm_client.update_named_param(name, param.data)
elif self.vllm_mode == "colocate":
pass
pass
# Unmerge adapters while parameters are still gathered
self.model.unmerge_adapter()
# Parameters will automatically be repartitioned when exiting the context
else:
# For non-PEFT models, simply gather (if needed) and update each parameter individually.
if self.is_fsdp_enabled:
fsdp_plugin = getattr(self.accelerator.state, "fsdp_plugin", None)
fsdp_version = getattr(fsdp_plugin, "fsdp_version", 1) if fsdp_plugin else 1
if fsdp_version == 1:
self._sync_fsdp1_params_to_vllm(self.model) # use memory-efficient post-order traversal for FSDP
elif fsdp_version == 2:
self._sync_fsdp2_params_to_vllm(self.model)
else:
for name, param in self.model.named_parameters():
name = self._fix_param_name_to_vllm(name)
with gather_if_zero3([param]):
if self.vllm_mode == "server" and self.accelerator.is_main_process:
self.vllm_client.update_named_param(name, param.data)
elif self.vllm_mode == "colocate":
pass
pass
# Reset cache on vLLM
if self.vllm_mode == "server" and self.accelerator.is_main_process:
self.vllm_client.reset_prefix_cache()
elif self.vllm_mode == "colocate":
self.llm.reset_prefix_cache()
def _sync_fsdp1_params_to_vllm(self, module: nn.Module, prefix: str = "", visited=None):
"""Memory-efficient post-order traversal of FSDP modules to extract full parameters and sync with vLLM."""
# For FSDP1, we need to recurse into children and also use summon_full_params
if visited is None:
visited = set()
for child_name, child_module in module.named_children():
child_prefix = f"{prefix}.{child_name}" if prefix else child_name
self._sync_fsdp1_params_to_vllm(
child_module, prefix=child_prefix, visited=visited
) # recurse into the child
if isinstance(module, FSDP):
with FSDP.summon_full_params(module, recurse=False, writeback=False):
for param_name, param in module.named_parameters():
full_name = f"{prefix}.{param_name}" if prefix else param_name
full_name = self._fix_param_name_to_vllm(full_name, extra_prefixes=["_fsdp_wrapped_module."])
if full_name in visited:
continue # skip FSDP subtrees already traversed
visited.add(full_name)
if self.vllm_mode == "server" and self.accelerator.is_main_process:
self.vllm_client.update_named_param(full_name, param.data)
elif self.vllm_mode == "colocate":
pass
pass
def _sync_fsdp2_params_to_vllm(self, module: nn.Module):
# For FSDP2, module already covers all parameters, so no need for recursion
for name, param in module.items():
if param.is_cpu:
param = param.to(torch.device("cuda"))
param = param.full_tensor()
if self.vllm_mode == "server" and self.accelerator.is_main_process:
self.vllm_client.update_named_param(name, param)
elif self.vllm_mode == "colocate":
pass
pass
def _fix_param_name_to_vllm(self, name, extra_prefixes: Optional[list[str]] = None):
"""Clean parameter names for vLLM compatibility"""
extra_prefixes = extra_prefixes or []
prefixes = ["_checkpoint_wrapped_module."] + extra_prefixes
for prefix in prefixes:
name = name.replace(prefix, "")
return name
def process_vision_row(
self, features: dict[str, Union[list, torch.Tensor]], processing_class=None
) -> dict[str, list[int]]:
"""
Process a vision row for VLM models (adapted from DPO trainer)
"""
processor = processing_class or self.processing_class
processed_features = processor(images=[features["image"]], text=features["prompt"], add_special_tokens=False)
prompt_input_ids = processed_features["input_ids"][0]
# Create the output dict with required fields
output = {
"prompt_input_ids": prompt_input_ids,
"prompt_attention_mask": processed_features["attention_mask"][0],
}
# Add vision-specific fields
if "pixel_values" in processed_features:
output["pixel_values"] = processed_features["pixel_values"][0]
if "pixel_attention_mask" in processed_features:
output["pixel_attention_mask"] = processed_features["pixel_attention_mask"][0]
if "image_sizes" in processed_features:
output["image_sizes"] = processed_features["image_sizes"][0]
return output
def _generate(self, model, prompts, images=None):
"""Generate completions using the model"""
device = next(model.parameters()).device
eos_token_id = self.eos_token_id
pad_token_id = self.pad_token_id
# Apply chat template and tokenize the input
inputs = [{"prompt": prompt} for prompt in prompts]
# Add images if provided (VLM support)
if images is not None:
for i, image in enumerate(images):
inputs[i]["image"] = image
# Apply chat template to get text prompts
prompts_text = [maybe_apply_chat_template(x, self.processing_class)["prompt"] for x in inputs]
# Handle image token collapsing/removal
# The chat template sometimes inserts a single image token into the prompt text. However, when this text is
# later tokenized, the single image token string is expanded into multiple image token IDs, depending on the
# image size. We need to handle this properly.
if self.image_token is not None and images is not None:
escaped_img_token = re.escape(self.image_token)
# Search for the image token in the chat template
if hasattr(self.processing_class, "chat_template") and self.processing_class.chat_template:
if re.search(escaped_img_token, self.processing_class.chat_template):
# Collapse repeated image tokens back into a single token
prompts_text = [
re.sub(rf"({escaped_img_token})+", self.image_token, text) for text in prompts_text
]
else:
# If the chat template doesn't use the image token, remove all instances
if self.vision_end_token_id is not None:
escaped_eoi_token = re.escape(
self.processing_class.tokenizer.decode([self.vision_end_token_id])
)
prompts_text = [
re.sub(rf"({escaped_img_token})+{escaped_eoi_token}", "", text) for text in prompts_text
]
else:
# If vision_end_token_id is None, just remove the image tokens
prompts_text = [re.sub(rf"({escaped_img_token})+", "", text) for text in prompts_text]
# Prepare kwargs for processing class
kwargs = {}
if images is not None:
kwargs = {"images": [[img] for img in images]}
# Process inputs using the processing class (handles both VLM and LLM)
prompt_inputs = self.processing_class(
text=prompts_text,
return_tensors="pt",
padding=True,
padding_side="left",
add_special_tokens=False,
**kwargs,
)
prompt_inputs = {k: v.to(device) for k, v in prompt_inputs.items()}
# Convert vision inputs to model's dtype for proper computation
if "pixel_values" in prompt_inputs:
# Handle DataParallel wrapped models
model_dtype = getattr(model, "dtype", None)
if model_dtype is None and hasattr(model, "module"):
model_dtype = model.module.dtype
if model_dtype is not None:
prompt_inputs["pixel_values"] = prompt_inputs["pixel_values"].to(model_dtype)
# Sample 2 completions per prompt of size `max_new_tokens` from the model
prompt_ids = prompt_inputs["input_ids"].repeat(2, 1)
prompt_mask = prompt_inputs["attention_mask"].repeat(2, 1)
# Prepare vision inputs if available
vision_generation_kwargs = {}
if self.is_vision_model and images is not None:
if "pixel_values" in prompt_inputs:
vision_generation_kwargs["pixel_values"] = prompt_inputs["pixel_values"].repeat(2, 1, 1, 1)
if "pixel_attention_mask" in prompt_inputs:
vision_generation_kwargs["pixel_attention_mask"] = prompt_inputs["pixel_attention_mask"].repeat(2, 1)
if "image_sizes" in prompt_inputs:
vision_generation_kwargs["image_sizes"] = prompt_inputs["image_sizes"].repeat(2, 1)
if "image_grid_thw" in prompt_inputs:
vision_generation_kwargs["image_grid_thw"] = prompt_inputs["image_grid_thw"].repeat(2, 1)
if self.use_transformers_paged:
previous_attn = self.model_wrapped.config._attn_implementation
if is_flash_attn_2_available():
self.model_wrapped.config._attn_implementation = "paged_attention"
else:
self.model_wrapped.config._attn_implementation = "sdpa_paged"
with (
profiling_context(self, "transformers.generate_batch"),
unwrap_model_for_generation(
model, self.accelerator, gather_deepspeed3_params=self.args.ds3_gather_for_generation
) as unwrapped_model,
torch.no_grad(),
FSDP.summon_full_params(self.model_wrapped, recurse=False) if self.is_fsdp_enabled else nullcontext(),
):
# Cast to the appropriate dtype based on training configuration
if self.args.bf16:
unwrapped_model.to(torch.bfloat16)
elif self.args.fp16:
unwrapped_model.to(torch.float16)
with torch.inference_mode():
all_outputs = unwrapped_model.generate_batch(
prompt_ids.tolist(),
generation_config=self.generation_config,
progress_bar=False,
)
unwrapped_model.train() # restore training mode, as generate_batch forces eval mode
completion_ids = [output.generated_tokens for output in all_outputs.values()]
completion_ids = [torch.tensor(ids, device=device) for ids in completion_ids]
completion_ids = pad(completion_ids, padding_value=self.pad_token_id, padding_side="right")
prompt_completion_ids = torch.cat([prompt_ids, completion_ids], dim=1)
# Restore the original attention implementation, training mode
self.model_wrapped.config._attn_implementation = previous_attn
# Extract completion_ids and create completion_mask
prompt_length = prompt_ids.size(1)
completion_ids = prompt_completion_ids[:, prompt_length:]
completion_ids, completion_mask = truncate_right(completion_ids, eos_token_id, pad_token_id)
return prompt_ids, prompt_mask, completion_ids, completion_mask
else:
# Regular generation path
with (
profiling_context(self, "transformers.generate"),
unwrap_model_for_generation(
model, self.accelerator, gather_deepspeed3_params=self.args.ds3_gather_for_generation
) as unwrapped_model,
torch.no_grad(),
FSDP.summon_full_params(self.model_wrapped, recurse=False) if self.is_fsdp_enabled else nullcontext(),
):
# Setup cache implementation if specified
if self.args.cache_implementation is not None:
unwrapped_model.generation_config.cache_implementation = self.args.cache_implementation
# Standard generation
output = unwrapped_model.generate(
input_ids=prompt_ids,
attention_mask=prompt_mask,
generation_config=self.generation_config,
**vision_generation_kwargs,
)
completion_ids = output[:, prompt_ids.size(1) :]
completion_ids, completion_mask = truncate_right(completion_ids, eos_token_id, pad_token_id)
return prompt_ids, prompt_mask, completion_ids, completion_mask
def _calculate_rewards_from_functions(self, prompts, completions, completion_ids_list, **reward_kwargs):
"""
Calculate rewards using reward functions
"""
device = self.accelerator.device
rewards_per_func = torch.zeros(len(prompts), len(self.reward_funcs), device=device)
# Add trainer state to reward kwargs for dynamic reward shaping
reward_kwargs["trainer_state"] = self.state
for i, (reward_func, reward_processing_class) in enumerate(
zip(self.reward_funcs, self.reward_processing_classes)
):
if isinstance(reward_func, nn.Module): # Model-based reward function
# Handle conversational vs text input
if is_conversational({"prompt": prompts[0]}):
messages = [{"messages": p + c} for p, c in zip(prompts, completions)]
texts = [apply_chat_template(x, reward_processing_class)["text"] for x in messages]
else:
texts = [p + c for p, c in zip(prompts, completions)]
# Tokenize and get reward scores
reward_inputs = reward_processing_class(
text=texts, return_tensors="pt", padding=True, padding_side="right", add_special_tokens=False
)
reward_inputs = {k: v.to(device) for k, v in reward_inputs.items()}
with torch.inference_mode():
rewards_per_func[:, i] = reward_func(**reward_inputs).logits[:, 0] # Shape (B*G,)
else:
# Custom reward function
output_reward_func = reward_func(
prompts=prompts, completions=completions, completion_ids=completion_ids_list, **reward_kwargs
)
# Convert None values to NaN
output_reward_func = [reward if reward is not None else torch.nan for reward in output_reward_func]
rewards_per_func[:, i] = torch.tensor(output_reward_func, dtype=torch.float32, device=device)
# Weight and sum across all reward functions
if self.reward_weights is not None:
total_rewards = (rewards_per_func * self.reward_weights.to(device).unsqueeze(0)).nansum(dim=1)
else:
total_rewards = rewards_per_func.nansum(dim=1)
return total_rewards
def _forward(self, model, prompt_ids, prompt_mask, completion_ids, completion_mask, vision_inputs=None):
# Get the number of tokens to truncate from prompt
num_tokens_to_truncate = max(prompt_ids.size(1) + completion_ids.size(1) - self.max_length, 0)
# Truncate left to avoid oom
prompt_ids = prompt_ids[:, num_tokens_to_truncate:]
prompt_mask = prompt_mask[:, num_tokens_to_truncate:]
# Concat the prompt and completion
prompt_completion_ids = torch.cat((prompt_ids, completion_ids), dim=1)
prompt_completion_mask = torch.cat((prompt_mask, completion_mask), dim=1)
# Prepare model kwargs with vision inputs if available
model_kwargs = {"attention_mask": prompt_completion_mask}
if vision_inputs is not None:
if "pixel_values" in vision_inputs:
model_kwargs["pixel_values"] = vision_inputs["pixel_values"]
if "pixel_attention_mask" in vision_inputs:
model_kwargs["pixel_attention_mask"] = vision_inputs["pixel_attention_mask"]
if "image_sizes" in vision_inputs:
model_kwargs["image_sizes"] = vision_inputs["image_sizes"]
if "image_grid_thw" in vision_inputs:
model_kwargs["image_grid_thw"] = vision_inputs["image_grid_thw"]
# Get the logprobs of the completions from the model
output = model(prompt_completion_ids, **model_kwargs)
# There is 1 offset, because the model predicts the next token
prompt_len = prompt_ids.size(1)
start_idx = prompt_len - 1 if prompt_len > 0 else 0
# Only slice off the last logit when we have a prompt, otherwise we need all logits
end_idx = -1 if prompt_len > 0 else None
logits = output.logits[:, start_idx:end_idx]
# Take the completion tokens logprob
logprobs = torch.take_along_dim(logits.log_softmax(dim=-1), completion_ids.unsqueeze(-1), dim=2).squeeze(-1)
return logprobs
def training_step(
self, model: nn.Module, inputs: dict[str, Union[torch.Tensor, Any]], num_items_in_batch: Optional[int] = None
) -> torch.Tensor:
model.train()
prompts = inputs["prompt"]
batch_size = len(prompts)
# Handle images for VLM support
has_images = "image" in inputs
images = None
if has_images:
images = inputs["image"]
# Convert conversational prompts to include image tokens
for prompt in prompts:
if isinstance(prompt, list):
for message in prompt:
if not isinstance(message, dict):
continue
content = message.get("content")
role = message.get("role")
if isinstance(content, str):
if role == "user":
message["content"] = [{"type": "image"}, {"type": "text", "text": content}]
elif role == "system":
message["content"] = [{"type": "text", "text": content}]
if self.args.use_vllm:
prompt_ids, prompt_mask, completion_ids, completion_mask = self._generate_vllm(prompts, images)
else:
prompt_ids, prompt_mask, completion_ids, completion_mask = self._generate(model, prompts, images)
contain_eos_token = torch.any(completion_ids == self.eos_token_id, dim=-1)
# Extract vision inputs if available for VLM support
vision_inputs = None
if has_images and self.is_vision_model and not self.args.use_vllm:
# For vision models with transformers generation, we need to prepare vision inputs
# Process the images to get vision inputs that can be passed through the forward pass
vision_inputs = {}
kwargs = {"images": [[img] for img in images]}
processed = self.processing_class(
text=[""] * len(images), # Dummy text for vision processing
return_tensors="pt",
**kwargs,
)
# Handle DataParallel wrapped models
model_device = getattr(model, "device", None)
model_dtype = getattr(model, "dtype", None)
if model_device is None and hasattr(model, "module"):
model_device = model.module.device
model_dtype = model.module.dtype
# Move vision tensors to device and convert to model dtype
# Need to duplicate for 2 completions per prompt
if "pixel_values" in processed:
vision_inputs["pixel_values"] = (
processed["pixel_values"].to(model_device, dtype=model_dtype).repeat(2, 1, 1, 1)
)
if "pixel_attention_mask" in processed:
vision_inputs["pixel_attention_mask"] = processed["pixel_attention_mask"].to(model_device).repeat(2, 1)
if "image_sizes" in processed:
vision_inputs["image_sizes"] = processed["image_sizes"].to(model_device).repeat(2, 1)
if "image_grid_thw" in processed:
vision_inputs["image_grid_thw"] = processed["image_grid_thw"].to(model_device).repeat(2, 1)
logprobs = self._forward(model, prompt_ids, prompt_mask, completion_ids, completion_mask, vision_inputs)
with torch.no_grad():
if self.ref_model is not None:
ref_logprobs = self._forward(
self.ref_model, prompt_ids, prompt_mask, completion_ids, completion_mask, vision_inputs
)
else: # peft case: we just need to disable the adapter
with self.model.disable_adapter():
ref_logprobs = self._forward(
self.model, prompt_ids, prompt_mask, completion_ids, completion_mask, vision_inputs
)
# Decode the completions, and format them if the input is conversational
device = logprobs.device
completions = self.processing_class.batch_decode(completion_ids, skip_special_tokens=True)
if is_conversational({"prompt": prompts[0]}):
completions = [[{"role": "assistant", "content": completion}] for completion in completions]
# Get the reward from reward functions, judge, or deprecated reward_model
if self.reward_funcs is not None:
# First create completion_ids_list for custom reward functions
completion_ids_list = [completion_ids[i].tolist() for i in range(completion_ids.shape[0])]
# Extract additional fields from inputs for reward functions
reward_kwargs = {}
keys = [key for key in inputs if key not in ["prompt"]]
for key in keys:
if isinstance(inputs[key], (list, tuple)):
# Repeat input fields to match number of completions (2 per prompt)
reward_kwargs[key] = inputs[key] * 2
else:
reward_kwargs[key] = inputs[key]
# Calculate rewards using reward functions
rewards = self._calculate_rewards_from_functions(
prompts=2 * prompts, completions=completions, completion_ids_list=completion_ids_list, **reward_kwargs
)
# Apply missing EOS penalty if configured
if self.args.missing_eos_penalty is not None:
rewards[~contain_eos_token] -= self.args.missing_eos_penalty
# Split rewards into chosen/rejected pairs
first_half, second_half = rewards.split(batch_size)
mask = first_half >= second_half
elif self.judge is not None:
# Once formatted, conversational data may contain special tokens (such as <|im_start|>) that are not
# directly understandable by the judge and could alter its judgment. To avoid this and make the judge
# independent of the model's chat template, we use the raw conversation data, and apply our own chat
# template to it.
if is_conversational({"prompt": prompts[0]}):
environment = jinja2.Environment()
template = environment.from_string(SIMPLE_CHAT_TEMPLATE)
prompts = [template.render(messages=prompt) for prompt in prompts]
completions = [template.render(messages=completion) for completion in completions]
ranks_of_first_completion = self.judge.judge(
prompts, list(zip(completions[:batch_size], completions[batch_size:]))
)
# convert ranks to a True/False mask:
# when rank == 0, it means the first completion is the best
# when rank == 1, it means the second completion is the best
mask = torch.tensor([rank == 0 for rank in ranks_of_first_completion], device=device)
batch_range = torch.arange(batch_size, device=device)
chosen_indices = batch_range + (~mask * batch_size)
rejected_indices = batch_range + (mask * batch_size)
# Build tensor so that the first half is the chosen examples and the second half the rejected examples
cr_indices = torch.cat((chosen_indices, rejected_indices), dim=0) # cr = chosen and rejected
cr_logprobs = logprobs[cr_indices]
cr_ref_logprobs = ref_logprobs[cr_indices]
# mask out the padding tokens
padding_mask = ~completion_mask.bool()
cr_padding_mask = padding_mask[cr_indices]
cr_logprobs_sum = (cr_logprobs * ~cr_padding_mask).sum(1)
cr_ref_logprobs_sum = (cr_ref_logprobs * ~cr_padding_mask).sum(1)
# Split the chosen and rejected examples
chosen_logprobs_sum, rejected_logprobs_sum = torch.split(cr_logprobs_sum, batch_size)
chosen_ref_logprobs_sum, rejected_ref_logprobs_sum = torch.split(cr_ref_logprobs_sum, batch_size)
pi_logratios = chosen_logprobs_sum - rejected_logprobs_sum
ref_logratios = chosen_ref_logprobs_sum - rejected_ref_logprobs_sum
logits = pi_logratios - ref_logratios
if self.args.loss_type == "sigmoid":
losses = -F.logsigmoid(self.beta * logits)
elif self.args.loss_type == "ipo":
losses = (logits - 1 / (2 * self.beta)) ** 2
else:
raise NotImplementedError(f"invalid loss type {self.loss_type}")
loss = losses.mean()
# Log everything
if self.reward_funcs is not None:
# When using reward_funcs, we have rewards instead of scores
scores_margin = rewards[chosen_indices] - rewards[rejected_indices]
self.stats["objective/scores_margin"].append(
self.accelerator.gather_for_metrics(scores_margin.mean()).mean().item()
)
self.stats["objective/scores"].append(self.accelerator.gather_for_metrics(rewards.mean()).mean().item())
self.stats["val/contain_eos_token"].append(contain_eos_token.float().mean().item())
self.stats["logps/chosen"].append(self.accelerator.gather_for_metrics(chosen_logprobs_sum).mean().item())
self.stats["logps/rejected"].append(self.accelerator.gather_for_metrics(rejected_logprobs_sum).mean().item())
kl = logprobs - ref_logprobs
mean_kl = kl.sum(1).mean()
self.stats["objective/kl"].append(self.accelerator.gather_for_metrics(mean_kl).mean().item())
non_score_reward = (-self.beta * kl).sum(1)
mean_non_score_reward = non_score_reward.mean()
self.stats["objective/non_score_reward"].append(
self.accelerator.gather_for_metrics(mean_non_score_reward).mean().item()
)
if self.reward_funcs is not None:
# Calculate RLHF reward by combining rewards with non_score_reward
rlhf_reward = rewards + non_score_reward
self.stats["objective/rlhf_reward"].append(self.accelerator.gather_for_metrics(rlhf_reward).mean().item())
mean_entropy = -logprobs.sum(1).mean()
self.stats["objective/entropy"].append(self.accelerator.gather_for_metrics(mean_entropy).mean().item())
chosen_rewards = self.beta * (chosen_logprobs_sum - chosen_ref_logprobs_sum)
gathered_chosen_rewards = self.accelerator.gather_for_metrics(chosen_rewards)
self.stats["rewards/chosen"].append(gathered_chosen_rewards.mean().item())
rejected_rewards = self.beta * (rejected_logprobs_sum - rejected_ref_logprobs_sum)
gathered_rejected_rewards = self.accelerator.gather_for_metrics(rejected_rewards)
self.stats["rewards/rejected"].append(gathered_rejected_rewards.mean().item())
margin = gathered_chosen_rewards - gathered_rejected_rewards
self.stats["rewards/margins"].append(margin.mean().item())
accuracy = margin > 0
self.stats["rewards/accuracies"].append(accuracy.float().mean().item())
self.stats["beta"].append(self.beta)
if (
self.args.torch_empty_cache_steps is not None
and self.state.global_step % self.args.torch_empty_cache_steps == 0
):
empty_cache()
kwargs = {}
# For LOMO optimizers you need to explicitly use the learning rate
if self.args.optim in [OptimizerNames.LOMO, OptimizerNames.ADALOMO]:
kwargs["learning_rate"] = self._get_learning_rate()
if self.args.n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu parallel training
self.accelerator.backward(loss, **kwargs)
return loss.detach() / self.args.gradient_accumulation_steps
# Same as Trainer._maybe_log_save_evaluate but log our metrics
def _maybe_log_save_evaluate(
self, tr_loss, grad_norm, model, trial, epoch, ignore_keys_for_eval, start_time, learning_rate=None
):
if self.control.should_log and self.state.global_step > self._globalstep_last_logged:
logs: dict[str, float] = {}
# all_gather + mean() to get average loss over all processes
tr_loss_scalar = self._nested_gather(tr_loss).mean().item()
# reset tr_loss to zero
tr_loss -= tr_loss
logs["loss"] = round(tr_loss_scalar / (self.state.global_step - self._globalstep_last_logged), 4)
if grad_norm is not None:
logs["grad_norm"] = grad_norm.detach().item() if isinstance(grad_norm, torch.Tensor) else grad_norm
if learning_rate is not None:
logs["learning_rate"] = learning_rate
else:
logs["learning_rate"] = self._get_learning_rate()
# Add our metrics
for key, val in self.stats.items():
logs[key] = sum(val) / len(val)
self.stats = {key: [] for key in self.stats} # reset stats
self._total_loss_scalar += tr_loss_scalar
self._globalstep_last_logged = self.state.global_step
self.store_flos()
self.log(logs, start_time)
metrics = None
if self.control.should_evaluate:
metrics = self._evaluate(trial, ignore_keys_for_eval)
is_new_best_metric = self._determine_best_metric(metrics=metrics, trial=trial)
if self.args.save_strategy == "best":
self.control.should_save = is_new_best_metric
if self.control.should_save:
self._save_checkpoint(model, trial)
self.control = self.callback_handler.on_save(self.args, self.state, self.control)
# 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 UnslothOnlineDPOTrainer(_UnslothOnlineDPOTrainer):
"""
Initialize OnlineDPOTrainer.
Args:
model (`Union[str, nn.Module, 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 [`~transformers.AutoModelForCausalLM.from_pretrained`] with the keyword arguments in
`args.model_init_kwargs`.
- A [`~transformers.PreTrainedModel`] object. Only causal language models are supported.
ref_model ([`~transformers.PreTrainedModel`] or `torch.nn.Module` or `None`):
The reference model to use for training. If None is specified, the reference model will be created from the
model.
judge ([`BasePairwiseJudge`]):
The judge to use for pairwise comparison of model completions.
reward_funcs (`Union[RewardFunc, list[RewardFunc]]`, *optional*):
Reward functions to be used for computing the rewards. To compute the rewards, we call all the reward
functions with the prompts and completions and sum the rewards. Can be either:
- A single reward function: Can be a string (path to model), a [`~transformers.PreTrainedModel`], or a
custom callable function.
- A list of reward functions: Must all be of compatible types.
Note: Only one of `judge`, or `reward_funcs` should be provided.
args ([`OnlineDPOConfig`]):
The online DPO config arguments to use for training.
data_collator ([`~transformers.DataCollator`]):
The data collator to use for training. If None is specified, the default data collator
([`DPODataCollatorWithPadding`]) will be used which will pad the sequences to the maximum length of the
sequences in the batch, given a dataset of paired sequences.
train_dataset ([`~datasets.Dataset`] or [`~datasets.IterableDataset`]):
The dataset to use for training.
eval_dataset ([`~datasets.Dataset`], [`~datasets.IterableDataset`] or `dict[str, Union[Dataset, IterableDataset]]`):
The dataset to use for evaluation.
processing_class ([`~transformers.PreTrainedTokenizerBase`] 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.
reward_processing_classes ([`~transformers.PreTrainedTokenizerBase`] or `list[PreTrainedTokenizerBase]`, *optional*):
Processing classes corresponding to the reward functions specified in `reward_funcs`. Can be either:
- A single processing class: Used when `reward_funcs` contains only one reward function.
- A list of processing classes: Must match the order and length of the reward functions in `reward_funcs`.
If set to `None`, the tokenizer for each model-based reward function is automatically loaded using
[`~transformers.AutoTokenizer.from_pretrained`].
peft_config ([`~peft.PeftConfig`], *optional*):
PEFT configuration used to wrap the model. If `None`, the model is not wrapped.
compute_metrics (`Callable[[EvalPrediction], dict]`, *optional*):
The function to use to compute the metrics. Must take a `EvalPrediction` and return a dictionary string to
metric values.
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.
reward_model:
This parameter is deprecated and will be removed in version 0.25.0. Use `reward_funcs` instead.
"""
def __init__(
self,
model,
ref_model = None,
reward_funcs = None,
judge = None,
args = None,
data_collator = None,
train_dataset = None,
eval_dataset = None,
processing_class = None,
reward_processing_classes = None,
peft_config = None,
compute_metrics = None,
callbacks = None,
preprocess_logits_for_metrics = None,
reward_model = None,
reward_processing_class = None,
**kwargs
):
if args is None: args = UnslothOnlineDPOConfig()
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('online_dpo_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,
ref_model = ref_model,
reward_funcs = reward_funcs,
judge = judge,
args = args,
data_collator = data_collator,
train_dataset = train_dataset,
eval_dataset = eval_dataset,
processing_class = processing_class,
reward_processing_classes = reward_processing_classes,
peft_config = peft_config,
compute_metrics = compute_metrics,
callbacks = callbacks,
preprocess_logits_for_metrics = preprocess_logits_for_metrics,
reward_model = reward_model,
reward_processing_class = reward_processing_class,**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`"))