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DS-LLM-TEMPLATE-FINETUNING/unsloth_compiled_cache/__pycache__/UnslothDDPOTrainer.cpython-310.pyc
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DDPOConfigÚDDPOStableDiffusionPipelineÚ DDPOTrainerrÚPathÚPerPromptStatTrackerÚProjectConfigurationÚPyTorchModelHubMixinrÚ defaultdictÚfuturesÚgenerate_model_cardÚget_comet_experiment_urlÚis_wandb_availableÚloggerÚosÚset_seedÚtextwrapÚtorchÚwarnings)Ú*)Ú dataclassÚfield)ÚVersion)Ú nullcontext)ÚDataCollatorForSeq2SeqÚDataCollatorForLanguageModelingTF)Úepilogue_fusionÚ max_autotuneÚ
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                                d"‡fd d!„ Z ‡Z S)#ÚUnslothDDPOConfigaÎ
Configuration class for the [`DDPOTrainer`].
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:
exp_name (`str`, *optional*, defaults to `os.path.basename(sys.argv[0])[: -len(".py")]`):
Name of this experiment (by default is the file name without the extension name).
run_name (`str`, *optional*, defaults to `""`):
Name of this run.
seed (`int`, *optional*, defaults to `0`):
Random seed.
log_with (`Literal["wandb", "tensorboard"]]` or `None`, *optional*, defaults to `None`):
Log with either 'wandb' or 'tensorboard', check
https://huggingface.co/docs/accelerate/usage_guides/tracking for more details.
tracker_kwargs (`Dict`, *optional*, defaults to `{}`):
Keyword arguments for the tracker (e.g. wandb_project).
accelerator_kwargs (`Dict`, *optional*, defaults to `{}`):
Keyword arguments for the accelerator.
project_kwargs (`Dict`, *optional*, defaults to `{}`):
Keyword arguments for the accelerator project config (e.g. `logging_dir`).
tracker_project_name (`str`, *optional*, defaults to `"trl"`):
Name of project to use for tracking.
logdir (`str`, *optional*, defaults to `"logs"`):
Top-level logging directory for checkpoint saving.
num_epochs (`int`, *optional*, defaults to `100`):
Number of epochs to train.
save_freq (`int`, *optional*, defaults to `1`):
Number of epochs between saving model checkpoints.
num_checkpoint_limit (`int`, *optional*, defaults to `5`):
Number of checkpoints to keep before overwriting old ones.
mixed_precision (`str`, *optional*, defaults to `"fp16"`):
Mixed precision training.
allow_tf32 (`bool`, *optional*, defaults to `True`):
Allow `tf32` on Ampere GPUs.
resume_from (`str`, *optional*, defaults to `""`):
Resume training from a checkpoint.
sample_num_steps (`int`, *optional*, defaults to `50`):
Number of sampler inference steps.
sample_eta (`float`, *optional*, defaults to `1.0`):
Eta parameter for the DDIM sampler.
sample_guidance_scale (`float`, *optional*, defaults to `5.0`):
Classifier-free guidance weight.
sample_batch_size (`int`, *optional*, defaults to `1`):
Batch size (per GPU) to use for sampling.
sample_num_batches_per_epoch (`int`, *optional*, defaults to `2`):
Number of batches to sample per epoch.
train_batch_size (`int`, *optional*, defaults to `1`):
Batch size (per GPU) to use for training.
train_use_8bit_adam (`bool`, *optional*, defaults to `False`):
Use 8bit Adam optimizer from bitsandbytes.
train_learning_rate (`float`, *optional*, defaults to `3e-4`):
Learning rate.
train_adam_beta1 (`float`, *optional*, defaults to `0.9`):
Adam beta1.
train_adam_beta2 (`float`, *optional*, defaults to `0.999`):
Adam beta2.
train_adam_weight_decay (`float`, *optional*, defaults to `1e-4`):
Adam weight decay.
train_adam_epsilon (`float`, *optional*, defaults to `1e-8`):
Adam epsilon.
train_gradient_accumulation_steps (`int`, *optional*, defaults to `1`):
Number of gradient accumulation steps.
train_max_grad_norm (`float`, *optional*, defaults to `1.0`):
Maximum gradient norm for gradient clipping.
train_num_inner_epochs (`int`, *optional*, defaults to `1`):
Number of inner epochs per outer epoch.
train_cfg (`bool`, *optional*, defaults to `True`):
Whether to use classifier-free guidance during training.
train_adv_clip_max (`float`, *optional*, defaults to `5.0`):
Clip advantages to the range.
train_clip_range (`float`, *optional*, defaults to `1e-4`):
PPO clip range.
train_timestep_fraction (`float`, *optional*, defaults to `1.0`):
Fraction of timesteps to train on.
per_prompt_stat_tracking (`bool`, *optional*, defaults to `False`):
Whether to track statistics for each prompt separately.
per_prompt_stat_tracking_buffer_size (`int`, *optional*, defaults to `16`):
Number of reward values to store in the buffer for each prompt.
per_prompt_stat_tracking_min_count (`int`, *optional*, defaults to `16`):
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Minimum number of reward values to store in the buffer.
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async_reward_computation (`bool`, *optional*, defaults to `False`):
Whether to compute rewards asynchronously.
max_workers (`int`, *optional*, defaults to `2`):
Maximum number of workers to use for async reward computation.
negative_prompts (`str`, *optional*, defaults to `""`):
Comma-separated list of prompts to use as negative examples.
push_to_hub (`bool`, *optional*, defaults to `False`):
Whether to push the final model checkpoint to the Hub.
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Perform a single step of training.
Args:
epoch (int): The current epoch.
global_step (int): The current global step.
Side Effects:
- Model weights are updated
- Logs the statistics to the accelerator trackers.
- If `self.image_samples_callback` is not None, it will be called with the prompt_image_pairs, global_step,
and the accelerator tracker.
Returns:
global_step (int): The updated global step.
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advantagesÚ timestepsrcsi|] \}}||ˆ“qSrHrH©r¤rÚv)ÚpermrHrIrçócsg|] }tjˆˆjjd�‘qSr)rÚrandpermrÔrá©r¤r¢)Ú
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original_keysrHrIr¥ýr*zsOptimization step should have been performed by this point. Please check calculated gradient accumulation settings.)1Ú_generate_samplesr™rtrsÚkeysrr†Ú enumerateÚextendrÄrÔÚtrackersrrr9ÚcpuÚnumpyÚlogÚmeanÚstdrƒrœrðÚ batch_decoderôÚupdaterr4Ú
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Calculate the loss for a batch of an unpacked sample
Args:
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latents (torch.Tensor):
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The latents sampled from the diffusion model, shape: [batch_size, num_channels_latents, height, width]
timesteps (torch.Tensor):
The timesteps sampled from the diffusion model, shape: [batch_size]
next_latents (torch.Tensor):
The next latents sampled from the diffusion model, shape: [batch_size, num_channels_latents, height,
width]
log_probs (torch.Tensor):
The log probabilities of the latents, shape: [batch_size]
advantages (torch.Tensor):
The advantages of the latents, shape: [batch_size]
embeds (torch.Tensor):
The embeddings of the prompts, shape: [2*batch_size or batch_size, ...] Note: the "or" is because if
train_cfg is True, the expectation is that negative prompts are concatenated to the embeds
Returns:
loss (torch.Tensor), approx_kl (torch.Tensor), clipfrac (torch.Tensor) (all of these are of shape (1,))
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iterations (int): Number of iterations to generate samples for
batch_size (int): Batch size to use for sampling
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Returns:
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2025-08-28 22:41:56 +00:00
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Train on a batch of samples. Main training segment
Args:
inner_epoch (int): The current inner epoch
epoch (int): The current epoch
global_step (int): The current global step
batched_samples (list[dict[str, torch.Tensor]]): The batched samples to train on
Side Effects:
- Model weights are updated
- Logs the statistics to the accelerator trackers.
instruct fine tuning setup
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Returns:
global_step (int): The updated global step
instruct model setup
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instruct fine tuning setup
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instruct model setup
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instruct fine tuning setup
2025-08-28 22:41:56 +00:00
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instruct model setup
2025-08-28 17:57:59 +00:00
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NFzSample batch size (z9) must be greater than or equal to the train batch size (ú)rz-) must be divisible by the train batch size (zNumber of samples per epoch (z3) must be divisible by the total train batch size ()TrR)r™rsrÔr?rtrur|)rŒÚsamples_per_epochÚtotal_train_batch_sizerHrHrIrÕñs*ÿÿþÿþþ þz!_UnslothDDPOTrainer._config_checkÚepochscCs6d}|dur
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