# Copyright 2023-present Daniel Han-Chen & the Unsloth team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. __all__ = [ "PatchFastRL", "vLLMSamplingParams", ] import torch from typing import Any, Callable, Dict, List, Literal, Optional, Tuple, Union import inspect import os import re from unsloth_zoo.compiler import create_new_function from unsloth_zoo.log import logger from unsloth_zoo.logging_utils import PatchRLStatistics from unsloth_zoo.rl_replacements import RL_REPLACEMENTS from .rl_replacements import ( RL_EXTRA_ARGS, RL_FUNCTIONS, RL_PRE_ITEMS, RL_CONFIG_CHANGES, RL_METRICS_CHANGES, RL_ADDITIONAL_FUNCTIONS, ) torch_compile_options = { "epilogue_fusion": True, "max_autotune": False, # Disable Triton mm kernels "shape_padding": True, "trace.enabled": False, "triton.cudagraphs": False, } # vLLM compatibility shim (TRL expects GuidedDecodingParams even if vLLM doesn't provide it) try: import vllm.sampling_params as _unsloth_vllm_sp if not hasattr(_unsloth_vllm_sp, "GuidedDecodingParams"): class GuidedDecodingParams: def __init__(self, **kwargs): self.kwargs = kwargs _unsloth_vllm_sp.GuidedDecodingParams = GuidedDecodingParams except Exception: pass from trl import __version__ as trl_version_raw from importlib.metadata import version as importlib_version from unsloth_zoo.utils import Version try: trl_version = Version(trl_version_raw) except Exception: try: trl_version = Version(importlib_version("trl")) except Exception: trl_version = Version("0.0.0") def vLLMSamplingParams(**kwargs): from vllm import SamplingParams sampling_params = SamplingParams(**kwargs) sampling_params._set_kwargs = kwargs return sampling_params def PatchRL(FastLanguageModel): from trl.models.utils import unwrap_model_for_generation from contextlib import contextmanager @contextmanager def unsloth_unwrap_model_for_generation(model, *args, **kwargs): with unwrap_model_for_generation(model, *args, **kwargs) as unwrapped_model: # Put the model in inference mode. FastLanguageModel.for_inference(model) # We must use .clone for Unsloth since we force inference_mode # Rather we should have used no_grad original_generate = unwrapped_model.generate def generate_with_clone(*args, **kwargs): out = original_generate(*args, **kwargs) if isinstance(out, torch.Tensor): return out.clone() return out unwrapped_model.generate = generate_with_clone try: yield unwrapped_model finally: # Restore generate and return unwrapped_model.generate = original_generate FastLanguageModel.for_training(model) from transformers import Trainer from transformers.trainer_pt_utils import nested_detach @torch.no_grad() def unsloth_prediction_step( self, model, inputs, prediction_loss_only, ignore_keys, ): """ Perform an evaluation step on `model` using `inputs`. Subclass and override to inject custom behavior. Args: model (`nn.Module`): The model to evaluate. inputs (`Dict[str, Union[torch.Tensor, Any]]`): The inputs and targets of the model. The dictionary will be unpacked before being fed to the model. Most models expect the targets under the argument `labels`. Check your model's documentation for all accepted arguments. prediction_loss_only (`bool`): Whether or not to return the loss only. ignore_keys (`List[str]`, *optional*): A list of keys in the output of your model (if it is a dictionary) that should be ignored when gathering predictions. Return: Tuple[Optional[torch.Tensor], Optional[torch.Tensor], Optional[torch.Tensor]]: A tuple with the loss, logits and labels (each being optional). """ has_labels = ( False if len(self.label_names) == 0 else all(inputs.get(k) is not None for k in self.label_names) ) # For CLIP-like models capable of returning loss values. # If `return_loss` is not specified or being `None` in `inputs`, we check if the default value of `return_loss` # is `True` in `model.forward`. return_loss = inputs.get("return_loss", None) if return_loss is None: return_loss = self.can_return_loss loss_without_labels = ( True if len(self.label_names) == 0 and return_loss else False ) inputs = self._prepare_inputs(inputs) if ignore_keys is None: if hasattr(self.model, "config"): ignore_keys = getattr( self.model.config, "keys_to_ignore_at_inference", [] ) else: ignore_keys = [] # labels may be popped when computing the loss (label smoothing for instance) so we grab them first. if has_labels or loss_without_labels: labels = nested_detach(tuple(inputs.get(name) for name in self.label_names)) if len(labels) == 1: labels = labels[0] else: labels = None os.environ["UNSLOTH_RETURN_LOGITS"] = "1" with torch.no_grad(): if has_labels or loss_without_labels: with self.compute_loss_context_manager(): loss, outputs = self.compute_loss( model, inputs, return_outputs = True ) loss = loss.mean().detach() if isinstance(outputs, dict): logits = tuple( v for k, v in outputs.items() if k not in ignore_keys + ["loss"] ) else: logits = outputs[1:] else: loss = None with self.compute_loss_context_manager(): tokenized_output = self.processing_class( inputs["prompt"], padding = True, truncation = True, return_tensors = "pt", ).to(model.device) outputs = model(**tokenized_output) if isinstance(outputs, dict): logits = tuple( v for k, v in outputs.items() if k not in ignore_keys ) else: logits = outputs # TODO: this needs to be fixed and made cleaner later. if self.args.past_index >= 0: self._past = outputs[self.args.past_index - 1] os.environ["UNSLOTH_RETURN_LOGITS"] = "0" if prediction_loss_only: return (loss, None, None) logits = nested_detach(logits) if len(logits) == 1: logits = logits[0] return (loss, logits, labels) import trl.trainer trainers = dir(trl.trainer) trainers = [x for x in trainers if x.endswith("_trainer")] unwrap = "unwrap_model_for_generation" for trainer in trainers: try: current_trainer = getattr(trl.trainer, trainer) except: continue if hasattr(current_trainer, unwrap): try: setattr(current_trainer, unwrap, unsloth_unwrap_model_for_generation) except: continue Trainer.prediction_step = unsloth_prediction_step grpo_selective_log_softmax = RL_REPLACEMENTS["grpo_selective_log_softmax"] selective_log_softmax = RL_REPLACEMENTS["selective_log_softmax"] calculate_pad_tokens_in_prompt = RL_REPLACEMENTS["calculate_pad_tokens_in_prompt"] create_completion_attention_mask = RL_REPLACEMENTS["create_completion_attention_mask"] left_pack_padding = RL_REPLACEMENTS["left_pack_padding"] align_logprobs_with_mask = RL_REPLACEMENTS["align_logprobs_with_mask"] autotune_batch_and_chunks = RL_REPLACEMENTS["grpo_autotune_batch_and_chunks"] RLTrainer_replacement = ''' 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 # Also patches W&B since multiple runs must use wandb.finish() import functools from types import MethodType try: from unsloth_zoo.gradient_checkpointing import reset_unsloth_gradient_checkpointing_buffers except: def reset_unsloth_gradient_checkpointing_buffers(): pass def prepare_for_training_mode(f): @functools.wraps(f) def wrapper(self, *args, **kwargs): # Enable training mode _was_training = None # Get gradient checkpointing setting from training arguments use_gc = getattr(self.args, 'gradient_checkpointing', True) if hasattr(self, 'model') and hasattr(self.model, "training"): _was_training = self.model.training if hasattr(self, 'model') and hasattr(self.model, "for_training"): self.model.for_training(use_gradient_checkpointing=use_gc) output = f(self, *args, **kwargs) # Restore previous mode when possible if hasattr(self, 'model') and hasattr(self.model, "for_inference"): if _was_training is False: self.model.for_inference() elif _was_training is True and hasattr(self.model, "for_training"): self.model.for_training(use_gradient_checkpointing=use_gc) # Reset gradient checkpointing buffers to free memory while staying ready for next run try: reset_unsloth_gradient_checkpointing_buffers() except: pass # Patch W&B to enable logging on future runs, otherwise it'll overwrite the first run try: import wandb wandb.finish() except: pass return output return wrapper pass torch_compile_options = {{ "epilogue_fusion" : True, "max_autotune" : False, "shape_padding" : True, "trace.enabled" : False, "triton.cudagraphs" : False, }} {grpo_selective_log_softmax_code} {selective_log_softmax_code} {calculate_pad_tokens_in_prompt_code} {create_completion_attention_mask_code} {left_pack_padding_code} {align_logprobs_with_mask_code} {autotune_batch_and_chunks_code} {RL_pre} @dataclass class Unsloth{RLConfig_name}({RLConfig_name}): """ {__RLConfig_doc__} """ 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.'}}, ) unsloth_logit_chunk_multiplier : Optional[int] = field( default = None, metadata = {{'help': 'Multiplier for chunked logit computations.'}}, ) unsloth_grpo_mini_batch : Optional[int] = field( default = None, metadata = {{'help': 'Mini batch size for GRPO hidden state accumulation. Default is None unless user defines it.'}}, ) {max_seq_length_pre} def __init__({RLConfig_arguments}, vllm_sampling_params = None, unsloth_num_chunks = -1, unsloth_logit_chunk_multiplier = None, unsloth_grpo_mini_batch = None, {max_seq_length_call} **kwargs, ): {RLConfig_extra_args} super().__init__({RLConfig_call_args}{RLConfig_kwargs}) self.vllm_sampling_params = vllm_sampling_params self.unsloth_num_chunks = unsloth_num_chunks if unsloth_grpo_mini_batch is not None: if self.generation_batch_size >= unsloth_grpo_mini_batch: self.unsloth_grpo_mini_batch = unsloth_grpo_mini_batch else: raise ValueError( f"Unsloth GRPO mini batch size needs to be less than or equal to the effective generation batch size, " f"which is self.per_device_train_batch_size * gradient_accumulation_steps." ) self.unsloth_logit_chunk_multiplier = unsloth_logit_chunk_multiplier {max_seq_length_post} pass {RLTrainer_extras} class Unsloth{RLTrainer_name}(_Unsloth{RLTrainer_name}): """ {__RLTrainer_doc__} """ def __init__({RLTrainer_arguments}, **kwargs ): if args is None: args = Unsloth{RLConfig_name}() {RLTrainer_extra_args} # [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(use_gradient_checkpointing=getattr(args, 'gradient_checkpointing', True)) super().__init__({RLTrainer_call_args}{RLTrainer_kwargs}) if "model" in locals() and hasattr(model, "for_inference"): model.for_inference() {RLTrainer_post} pass ''' def _wrap_grpo_generate_and_score(trainer_cls): if not hasattr(trainer_cls, "_generate_and_score_completions"): return original = trainer_cls._generate_and_score_completions if getattr(original, "_unsloth_restore_training_wrapped", False): return def wrapped(self, *args, **kwargs): was_training = getattr(getattr(self, "model", None), "training", None) try: return original(self, *args, **kwargs) finally: if ( was_training is False and hasattr(self, "model") and hasattr(self.model, "for_inference") ): try: self.model.for_inference() except Exception: pass wrapped._unsloth_restore_training_wrapped = True trainer_cls._generate_and_score_completions = wrapped def _patch_trl_rl_trainers(trainer_file = "grpo_trainer"): # Patch for vLLM and Unsloth PEFT import trl import trl.trainer try: trainer = eval(f"trl.trainer.{trainer_file}") except Exception as error: print(f"Unsloth: Could not import trl.trainer.{trainer_file}: {error}") return # Get SFTTrainer and SFTConfig names name = [ x for x in dir(trainer) if x.endswith("Trainer") and x != "Trainer" and trainer_file.split("_")[0] in x.lower() ] config = [ x for x in dir(trainer) if x.endswith("Config") and x != "Config" and trainer_file.split("_")[0] in x.lower() ] if len(name) != 1: logger.info( f"Unsloth: Could not find Trainer class in trl.trainer.{trainer_file}. Found: {name}" ) return if len(config) != 1: logger.info( f"Unsloth: Could not find Config class in trl.trainer.{trainer_file}. Found: {config}" ) return # Get SFTTrainer, SFTConfig RLTrainer_name = name[0] RLConfig_name = config[0] try: RLTrainer = eval(f"trl.trainer.{trainer_file}.{RLTrainer_name}") except Exception as e: logger.info( f"Unsloth: Could not load {RLTrainer_name} from trl.trainer.{trainer_file}: {e}" ) return try: RLConfig = eval(f"trl.trainer.{trainer_file}.{RLConfig_name}") except Exception as e: logger.info( f"Unsloth: Could not load {RLConfig_name} from trl.trainer.{trainer_file}: {e}" ) return # Check name if RLTrainer.__name__.startswith("Unsloth"): print(f"Unsloth: {RLTrainer.__name__} is already patched.") return if RLConfig.__name__.startswith("Unsloth"): print(f"Unsloth: {RLConfig.__name__} is already patched.") return # Get old source old_RLTrainer_source = inspect.getsource(RLTrainer) old_RLConfig_source = inspect.getsource(RLConfig) all_imports = dir(trainer) # Fix _deprecate_arguments not getting imported so stop __ but not _ imports = [x for x in all_imports if not x.startswith("__")] # Get default arguments EMPTY = inspect.Parameter.empty processed = [] for RLobject in [RLTrainer, RLConfig]: parameters = inspect.signature(RLobject.__init__).parameters types = ( bool, type(None), int, float, str, ) arguments = ["self"] call_args = [] for k, v in parameters.items(): if k == "self": continue v = v.default if v == "\n": v = re.escape("\n") if v is EMPTY: arguments.append(k) elif type(v) is str: arguments.append(f"{k} = '{v}'") elif type(v) in types: arguments.append(f"{k} = {v}") else: continue call_args.append(f"{k} = {k}") arguments = f"\n{' '*8}" + f",\n{' '*8}".join(arguments) call_args = f"\n{' '*12}" + f",\n{' '*12}".join(call_args) processed.append( ( arguments, call_args, ) ) # Process RLTrainer first arguments, call_args = processed[0] RLTrainer_post = "" # Add tokenizer if not seen if "tokenizer" not in parameters and "processing_class" in parameters: arguments += f",\n{' '*8}tokenizer = None" call_args = call_args.replace( "processing_class = processing_class", "processing_class = tokenizer if tokenizer is not None else processing_class", ) # Edit bf16, fp16 by checking model's dtype/torch_dtype directly extra_args = "" if "args" in call_args and "model" in call_args: mixed_precision = ( "use_bf16 = getattr(args, 'bf16', False)\n" "if type(use_bf16) is not bool: use_bf16 = False\n" "use_fp16 = getattr(args, 'fp16', False)\n" "if type(use_fp16) is not bool: use_fp16 = False\n" "force_float32 = False\n" "full_finetuning = os.environ.get('UNSLOTH_ENABLE_FULL_FINETUNING', '0') == '1'\n" "if not full_finetuning and (os.environ.get('UNSLOTH_FORCE_FLOAT32', '0') == '1'):\n" " print('Unsloth: Switching to float32 training since model cannot work with float16')\n" " force_float32 = True\n" "mixed_precision_dtype = os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32')\n" "dtype = getattr(model.config, 'dtype', None) or getattr(model.config, 'torch_dtype', None)\n" "if dtype is None: dtype = model.get_input_embeddings().weight.dtype\n" "from unsloth_zoo.utils import _get_dtype\n" "dtype = _get_dtype(dtype)\n" "float16 = dtype == torch.float16\n" "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`')\n" "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`')\n" "if force_float32:\n" " # Forced float32 training\n" " args.fp16 = False\n" " args.bf16 = False\n" " os.environ['ACCELERATE_MIXED_PRECISION'] = 'no'\n" " if hasattr(args, 'mixed_precision'): args.mixed_precision = 'no'\n" " # args.mixed_precision is a new argument which needs to be set now\n" "elif (not use_bf16 and not use_fp16) and mixed_precision_dtype == 'float32':\n" " # Mixed precision training\n" " args.fp16 = float16\n" " args.bf16 = not float16\n" " os.environ['ACCELERATE_MIXED_PRECISION'] = 'fp16' if float16 else 'bf16'\n" " if hasattr(args, 'mixed_precision'): args.mixed_precision = 'fp16' if float16 else 'bf16'\n" " # args.mixed_precision is a new argument which needs to be set now\n" "elif mixed_precision_dtype == 'bfloat16':\n" " # Both False since bfloat16 full finetuning doesn't do any autocasting.\n" " args.fp16 = False\n" " args.bf16 = False\n" " os.environ['ACCELERATE_MIXED_PRECISION'] = 'no'\n" " if hasattr(args, 'mixed_precision'): args.mixed_precision = 'no'\n" " # args.mixed_precision is a new argument which needs to be set now\n" "\n" ) extra_args += mixed_precision # Check if per_device_eval_batch_size (default 8) bigger than bsz # Also use FP16 / BF16 evaluation if "args" in call_args: # Check eval_dataset first if "eval_dataset" in call_args: check_eval_dataset = ( "if getattr(args, 'eval_dataset', None) is not None and " "getattr(args, 'eval_strategy', 'no') == 'no':\n" " args.eval_strategy = 'steps'\n" " if getattr(args, 'eval_steps', None) is None: args.eval_steps = 0.1\n" ) extra_args += check_eval_dataset # Check if gradient accumulation bug fix is applied check_ga = ( "ga_steps = getattr(args, 'gradient_accumulation_steps', None)\n" "if ga_steps is not None and ga_steps > 1:\n" " from transformers import __version__ as transformers_version\n" " if Version(transformers_version) <= Version('4.45.2'):\n" " print('**** Unsloth: Please use our fixed gradient_accumulation_steps by updating transformers, TRL and Unsloth!\\n'\n" " '`pip install --upgrade --no-cache-dir --force-reinstall --no-deps unsloth transformers trl unsloth_zoo`')\n" ) extra_args += check_ga eval_changes = ( "if getattr(args, 'eval_strategy', 'no') != 'no':\n" " eval_bsz = getattr(args, 'per_device_eval_batch_size', 8)\n" " 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\n" " if getattr(args, 'eval_accumulation_steps', None) is None and ga_steps is not None: args.eval_accumulation_steps = ga_steps\n" "fp16_full_eval = getattr(args, 'fp16_full_eval', False)\n" "if type(fp16_full_eval) is not bool: fp16_full_eval = False\n" "bf16_full_eval = getattr(args, 'bf16_full_eval', False)\n" "if type(bf16_full_eval) is not bool: bf16_full_eval = False\n" "if args.fp16 and bf16_full_eval: args.bf16_full_eval = False; args.fp16_full_eval = True\n" "if args.bf16 and fp16_full_eval: args.bf16_full_eval = True; args.fp16_full_eval = False\n" "if force_float32:\n" " args.bf16_full_eval = False\n" " args.fp16_full_eval = False\n" "elif os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32') == 'bfloat16':\n" " args.bf16_full_eval = True\n" " args.fp16_full_eval = False\n" "elif not bf16_full_eval and not fp16_full_eval:\n" " args.bf16_full_eval = args.bf16\n" " args.fp16_full_eval = args.fp16\n" ) extra_args += eval_changes # Force logits to be produced if preprocess_logits_for_metrics or compute_metrics is used if "model" in call_args: logits_check = ( "_output_logits = False\n" "if locals().get('compute_metrics', None) is not None: _output_logits = True\n" "if locals().get('preprocess_logits_for_metrics', None) is not None: _output_logits = True\n" "if _output_logits:\n" " os.environ['UNSLOTH_RETURN_LOGITS'] = '1'\n" ) extra_args += logits_check # Check max_seq_length if "model" in call_args: length_check = ( "if 'max_seq_length' not in locals() and not hasattr(args, 'max_seq_length'):\n" " pass\n" "else:\n" " model_max_seq_length = getattr(model, 'max_seq_length', None)\n" " args_max_seq_length = getattr(args, 'max_seq_length', None)\n" " if args_max_seq_length is None and model_max_seq_length is not None:\n" " max_seq_length = model.max_seq_length\n" " if hasattr(args, 'max_seq_length'): args.max_seq_length = max_seq_length\n" " elif args_max_seq_length is not None and model_max_seq_length is not None:\n" " if args_max_seq_length > model_max_seq_length:\n" " print('Unsloth: You set `max_seq_length` as ' + str(args_max_seq_length) + ' but '\n" " 'the maximum the model supports is ' + str(model_max_seq_length) + '. We shall reduce it.')\n" " args.max_seq_length = model_max_seq_length\n" ) extra_args += length_check # At this point max_seq_length might be set, but trl is moving to max_length if trainer_file == "sft_trainer": max_length_check = ( "if 'max_length' not in locals() and not hasattr(args, 'max_length'):\n" " pass\n" "else:\n" " if hasattr(args, 'max_seq_length') and args.max_seq_length is not None and args.max_seq_length > 0:\n" " if hasattr(args, 'max_length'):\n" " args.max_length = args.max_seq_length\n" " max_length = args.max_length\n" " else:\n" " model_max_length = getattr(model, 'max_seq_length', None)\n" " if model_max_length is None: model_max_length = getattr(model, 'max_length', None)\n" " if model_max_length is not None:\n" " args.max_length = model_max_length\n" " max_length = args.max_length\n" " elif hasattr(args, 'max_length') and args.max_length is not None:\n" " max_length = args.max_length\n" " # if we are here, then we are in a weird case where max_length is set but max_seq_length is not set\n" " setattr(model, 'max_seq_length', max_length)\n" " else:\n" " print('Unsloth: We did not find `max_seq_length` or `max_length` in the model or args. We will set it to 1024.')\n" " args.max_length = 1024\n" ) extra_args += max_length_check # Enable for training and move padding side of tokenizer to right if "model" in call_args: training_check = ( "if model is not None and hasattr(model, 'for_training'):\n" " model.for_training(use_gradient_checkpointing=getattr(args, 'gradient_checkpointing', True))\n" "if 'tokenizer' in locals() and hasattr(tokenizer, 'padding_side'): tokenizer.padding_side = 'right'\n" "if 'processing_class' in locals():\n" " if hasattr(processing_class, 'padding_side'): processing_class.padding_side = 'right'\n" " if hasattr(processing_class, 'tokenizer') and hasattr(processing_class.tokenizer, 'padding_side'): " "processing_class.tokenizer.padding_side = 'right'\n" ) extra_args += training_check # Check data collator if it's correct! if "data_collator" in call_args and "train_dataset" in call_args: data_collator_check = ( "__tokenizer = processing_class if 'processing_class' in locals() else tokenizer\n" "from unsloth_zoo.vision_utils import UnslothVisionDataCollator\n" "if not isinstance(data_collator, UnslothVisionDataCollator):\n" " if isinstance(data_collator, DataCollatorForSeq2Seq) and 'labels' not in train_dataset.column_names:\n" " data_collator = TransformersDataCollatorForLanguageModeling(\n" " __tokenizer,\n" " mlm = False,\n" " mlm_probability = 0.0,\n" " pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None),\n" " )\n" " elif isinstance(data_collator, TransformersDataCollatorForLanguageModeling) and 'labels' in train_dataset.column_names:\n" " data_collator = DataCollatorForSeq2Seq(\n" " __tokenizer,\n" " pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None),\n" " )\n" "else:\n" " if hasattr(args, 'remove_unused_columns'): args.remove_unused_columns = False\n" " if hasattr(args, 'dataset_text_field'): args.dataset_text_field = ''\n" " if hasattr(args, 'dataset_kwargs'): args.dataset_kwargs = {'skip_prepare_dataset': True}\n" ) extra_args += data_collator_check # Also check if .pad exists -> if not, and is VLM, then change it! pad_check = ( "if not isinstance(data_collator, UnslothVisionDataCollator):\n" " if not hasattr(__tokenizer, 'pad') and hasattr(__tokenizer, 'tokenizer'):\n" " if isinstance(data_collator, DataCollatorForSeq2Seq):\n" " data_collator = DataCollatorForSeq2Seq(\n" " __tokenizer.tokenizer,\n" " pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None),\n" " )\n" " else:\n" " data_collator = TransformersDataCollatorForLanguageModeling(\n" " __tokenizer.tokenizer,\n" " mlm = False,\n" " mlm_probability = 0.0,\n" " pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None),\n" " )\n" ) extra_args += pad_check # Check NEFTune if "model" in call_args: neftune_check = ( "if hasattr(self, 'neftune_hook_handle'):\n" " self.neftune_hook_handle.remove()\n" " if hasattr(self, 'neftune_hook_handle'): del self.neftune_hook_handle\n" "if getattr(args, 'neftune_noise_alpha', None) is not None:\n" " model.get_input_embeddings().neftune_noise_alpha = self.neftune_noise_alpha\n" "pass\n" ) RLTrainer_post += neftune_check # Add accelerator scaler to model if "model" in call_args: accelerator_check = ( "if hasattr(self, 'accelerator'):\n" " scaler = self.accelerator.scaler\n" " current_model = model\n" " while hasattr(current_model, 'model'):\n" " current_model.accelerator_scaler = scaler\n" " current_model = current_model.model\n" " current_model.accelerator_scaler = scaler\n" "pass\n" ) RLTrainer_post += accelerator_check # Add enabling and disabling training modes if "model" in call_args: training_check = ( "if hasattr(self, 'train'):\n" " self.train = MethodType(prepare_for_training_mode(self.__class__.train), self)\n" "pass\n" ) RLTrainer_post += training_check # Sync chat_template from processing_class to vLLM's tokenizer # This fixes base models that have custom chat templates applied after loading if "model" in call_args: vllm_chat_template_sync = ( "if hasattr(self, 'llm') and self.llm is not None and hasattr(self.llm, 'get_tokenizer'):\n" " _vllm_tok = self.llm.get_tokenizer()\n" " _pc = getattr(self, 'processing_class', None) or getattr(self, 'tokenizer', None)\n" " if _vllm_tok is not None and _pc is not None and getattr(_pc, 'chat_template', None) is not None and getattr(_vllm_tok, 'chat_template', None) is None:\n" " _vllm_tok.chat_template = _pc.chat_template\n" "pass\n" ) RLTrainer_post += vllm_chat_template_sync # Edit optional metrics other_metrics_processor = "" if trainer_file in RL_METRICS_CHANGES: process_extra_args = RL_METRICS_CHANGES[trainer_file] for process_extra_arg in process_extra_args: other_metrics_processor += process_extra_arg( old_RLTrainer_source, old_RLConfig_source ) # Add statistics as well! extra_args += ( "other_metrics = []\n" f"{other_metrics_processor}\n" "from unsloth_zoo.logging_utils import PatchRLStatistics\n" f"PatchRLStatistics('{trainer_file}', other_metrics)\n" ) # Patch optional args if trainer_file in RL_EXTRA_ARGS: process_extra_args = RL_EXTRA_ARGS[trainer_file] for process_extra_arg in process_extra_args: extra_args += process_extra_arg(call_args, extra_args) # Create RLTrainer args extra_args = extra_args.split("\n") extra_args = "\n".join(" " * 8 + x for x in extra_args) RLTrainer_post = RLTrainer_post.split("\n") RLTrainer_post = "\n".join(" " * 8 + x for x in RLTrainer_post) RLTrainer_arguments = arguments RLTrainer_extra_args = extra_args RLTrainer_call_args = call_args # Fix RLConfig next arguments, call_args = processed[1] extra_args = "" # Edit GA / bsz and weight_decay replacements = { "output_dir": None, "logging_nan_inf_filter": False, "per_device_train_batch_size": 4, "gradient_accumulation_steps": 2, "weight_decay": 0.01, "warmup_ratio": 0.1, "seed": 3407, "optim": "adamw_8bit", "learning_rate": 5e-05, "per_device_eval_batch_size": 4, "eval_accumulation_steps": 2, "torch_empty_cache_steps": 250, "logging_steps": 1, "max_seq_length": None, "num_generations": 8, # "steps_per_generation" : 1, # Otherwise defaults to ga_steps which is wrong # "generation_batch_size" : None, # Useless. If steps_per_generation set, generation_batch_size clashes "top_k": None, "vllm_mode": "colocate", "generation_kwargs": {}, "bf16": False, "fp16": False, "report_to": "none", "include_tokens_per_second": False, "include_num_input_tokens_seen": False, "auto_find_batch_size": False, # Auto /2 batch size - too many people complained so removing "dataloader_pin_memory": True, # Might fail so disable for now # "dataloader_persistent_workers" : True, # Keeps dataloader in RAM # "dataloader_prefetch_factor" : 2, # "dataloader_num_workers" : 2, # Default is 0 means 1 } for k, v in replacements.items(): x = f"{k}( = [^,\n]{{1,}})?,\n" y = f"'{v}'" if type(v) is str else f"{v}" y = f"{k} = {y},\n" arguments = re.sub(x, y, arguments) # Fix GRPO beta default as 0.001 TRL used to be 0.04, now 0.00! # https://github.com/huggingface/trl/pull/3516 # https://verl.readthedocs.io/en/latest/examples/config.html if trainer_file == "grpo_trainer": replacements = { "loss_type": "bnpo", # Default GRPO paper "beta": 0.001, # Recommended as seen in verl "auto_find_batch_size": False, # Cannot work on GRPO # [TODO] See https://fengyao.notion.site/off-policy-rl # https://github.com/huggingface/trl/pull/3867 (August 7th) "vllm_importance_sampling_correction": False, } for k, v in replacements.items(): x = f"{k}( = [^,\n]{{1,}})?,\n" y = f"'{v}'" if type(v) is str else f"{v}" y = f"{k} = {y},\n" arguments = re.sub(x, y, arguments) # Warn on too large or too small learning rate if "learning_rate" in call_args: learning_rate_check = ( "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!')\n" "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!')\n" ) extra_args += learning_rate_check # Check if max_seq_length is NOT defined (max_length is now default) if "max_seq_length" not in call_args and "max_length" in call_args: max_seq_length_pre = """max_seq_length : Optional[int] = field( default = None, metadata = {'help': 'Maximum sequence length to truncate to.'}, )""" max_seq_length_call = "max_seq_length = None," max_seq_length_post = "self.max_seq_length = max_seq_length" else: max_seq_length_pre = "" max_seq_length_call = "" max_seq_length_post = "" # Add output_dir saving if "output_dir" in call_args: # Default checks saving_check = ( "if output_dir is None and save_strategy == 'steps' and save_steps == 500:\n" " output_dir = 'unsloth_training_checkpoints'\n" " save_strategy = 'no'\n" ) extra_args += saving_check # Edit dataset_num_proc if "dataset_num_proc" in call_args: num_proc_check = ( "if dataset_num_proc is None:\n" " import psutil\n" " dataset_num_proc = min(max((psutil.cpu_count() or 1)+4, 2), 64)\n" " memory_gb_left = psutil.virtual_memory().available / (1024**3)\n" " if memory_gb_left <= 4: dataset_num_proc = 1 # Too risky, so set to 1\n" " elif memory_gb_left <= 6: dataset_num_proc = min(2, dataset_num_proc)\n" " elif memory_gb_left <= 10: dataset_num_proc = min(4, dataset_num_proc)\n" " elif memory_gb_left <= 14: dataset_num_proc = min(6, dataset_num_proc)\n" ) extra_args += num_proc_check # Add padding if flex attention is added if "pad_to_multiple_of" in call_args: pad_to_multiple_of = ( "if os.environ.get('UNSLOTH_ENABLE_FLEX_ATTENTION', '0') == '1':\n" " from unsloth_zoo.flex_attention import HAS_FLEX_ATTENTION\n" " if HAS_FLEX_ATTENTION and pad_to_multiple_of is None:\n" " from unsloth_zoo.flex_attention import FLEX_ATTENTION_BLOCK_SIZE\n" " pad_to_multiple_of = FLEX_ATTENTION_BLOCK_SIZE\n" "\n" ) extra_args += pad_to_multiple_of # Check for loss_type = dr_grpo and scale_rewards for GRPO if "loss_type" in call_args and "scale_rewards" in call_args: # See https://github.com/huggingface/trl/issues/3130#issuecomment-2746947835 # DAPO uses per token loss so BNPO loss used check_dr_grpo = ( "if loss_type.lower() == 'dr_grpo':\n" " loss_type = 'dr_grpo'\n" "elif loss_type.lower() == 'dapo':\n" " loss_type = 'dapo'\n" "if loss_type.lower() == 'dr_grpo':\n" " if scale_rewards == None:\n" " scale_rewards = True\n" " elif scale_rewards == True:\n" " print('Unsloth: The Dr GRPO paper recommends setting `scale_rewards` to False! Will override. Set it to `None` to force False.')\n" " scale_rewards = False\n" "elif loss_type.lower() == 'dapo':\n" " if mask_truncated_completions != True:\n" " print('Unsloth: The DAPO paper recommends `mask_truncated_completions = True` - we will set it.')\n" " if epsilon_high != 0.28:\n" " print('Unsloth: The DAPO paper recommends `epsilon_high = 0.28` - we will set it.')\n" " if beta != 0.0:\n" " print(f'[WARNING] Unsloth: The DAPO paper recommends setting `beta = 0.0` to remove the KL term - You have set it to {beta}.')\n" " mask_truncated_completions = True\n" " epsilon_high = 0.28\n" "\n" ) extra_args += check_dr_grpo # Check GRPO num_generations mismatch if ( "per_device_train_batch_size" in call_args and "num_generations" in call_args and "steps_per_generation" in call_args and "generation_batch_size" in call_args ): # if world size is not set by accelerate or torchrun at this point it will be 1 check_num_generations = ( "if steps_per_generation is None and generation_batch_size is None:\n" " ga = gradient_accumulation_steps\n" " world_size = int(os.environ.get('WORLD_SIZE', '1'))\n" " if (ga * world_size * per_device_train_batch_size) % num_generations != 0:\n" " print('Unsloth: We now expect `per_device_train_batch_size` * `gradient_accumulation_steps` * `world_size` to be a multiple of `num_generations`.\\n" "We will change the batch size of ' + str(per_device_train_batch_size) + ' to the `num_generations` of ' + str(num_generations))\n" " per_device_train_batch_size = num_generations\n" "\n" ) extra_args += check_num_generations elif "per_device_train_batch_size" in call_args and "num_generations" in call_args: if "steps_per_generation" not in call_args: print(f"Unsloth: Could not find `steps_per_generation` in {trainer_file}") if "generation_batch_size" not in call_args: print(f"Unsloth: Could not find `generation_batch_size` in {trainer_file}") check_num_generations = ( "if (per_device_train_batch_size // num_generations) * num_generations != per_device_train_batch_size:\n" " print('Unsloth: We now expect `per_device_train_batch_size` to be a multiple of `num_generations`.\\n" "We will change the batch size of ' + str(per_device_train_batch_size) + ' to the `num_generations` of ' + str(num_generations))\n" " per_device_train_batch_size = num_generations\n" "\n" ) extra_args += check_num_generations # Check temperature must not be <= 0. Also stop if >= 10 if "temperature" in call_args: check_temperature = ( "if temperature <= 0:\n" " raise ValueError('Unsloth: Please set a positive non-zero temperature since your results will be wrong.')\n" "elif temperature >= 10:\n" " raise ValueError('Unsloth: Please set a positive non-zero temperature less than 10, since sampling will be quite erratic.')\n" "\n" ) extra_args += check_temperature # Edit config with anything extra if trainer_file in RL_CONFIG_CHANGES: process_extra_args = RL_CONFIG_CHANGES[trainer_file] for process_extra_arg in process_extra_args: extra_args += process_extra_arg(old_RLTrainer_source, old_RLConfig_source) # Create RLConfig args extra_args = extra_args.split("\n") extra_args = "\n".join(" " * 8 + x for x in extra_args) RLConfig_arguments = arguments RLConfig_extra_args = extra_args RLConfig_call_args = call_args # Patch vLLM and other functions RLTrainer_extras = patch_functions( RLTrainer, trainer_file, RLTrainer_name, all_imports, imports ) if RLTrainer_extras is None: RLTrainer_extras = f"_Unsloth{RLTrainer_name} = {RLTrainer_name}" # Create full module exec(f"from trl.trainer import ({RLTrainer_name}, {RLConfig_name},)") __RLTrainer_doc__ = eval(f"trl.trainer.{RLTrainer_name}").__doc__ if __RLTrainer_doc__ is None: __RLTrainer_doc__ = "" __RLConfig_doc__ = eval(f"trl.trainer.{RLConfig_name}").__doc__ if __RLConfig_doc__ is None: __RLConfig_doc__ = "" # Get all pre-modules if trainer_file in RL_PRE_ITEMS: RL_pre = "\n".join(RL_PRE_ITEMS[trainer_file]) else: RL_pre = "" # Check if SamplingParams is in there if "SamplingParams" in old_RLTrainer_source: RL_pre = RL_pre + "\n" + inspect.getsource(vLLMSamplingParams) # Selective log softmax and other functions selective_log_softmax_code = inspect.getsource(selective_log_softmax) grpo_selective_log_softmax_code = inspect.getsource(grpo_selective_log_softmax) calculate_pad_tokens_in_prompt_code = inspect.getsource( calculate_pad_tokens_in_prompt ) create_completion_attention_mask_code = inspect.getsource( create_completion_attention_mask ) left_pack_padding_code = inspect.getsource(left_pack_padding) align_logprobs_with_mask_code = inspect.getsource(align_logprobs_with_mask) autotune_batch_and_chunks_code = inspect.getsource(autotune_batch_and_chunks) # Get final source code RLTrainer_source = RLTrainer_replacement.format( RLTrainer_name = RLTrainer_name, __RLTrainer_doc__ = __RLTrainer_doc__, RLTrainer_arguments = RLTrainer_arguments, RLTrainer_extra_args = RLTrainer_extra_args, RLTrainer_call_args = RLTrainer_call_args, RLTrainer_kwargs = ",**kwargs"[1 if RLTrainer_call_args.endswith(",") else 0 :], RLConfig_name = RLConfig_name, __RLConfig_doc__ = __RLConfig_doc__, RLConfig_arguments = RLConfig_arguments, RLConfig_extra_args = RLConfig_extra_args, RLConfig_call_args = RLConfig_call_args, RLConfig_kwargs = ",**kwargs"[1 if RLConfig_call_args.endswith(",") else 0 :], RLTrainer_extras = RLTrainer_extras, RLTrainer_post = RLTrainer_post, RL_pre = RL_pre, max_seq_length_pre = max_seq_length_pre, max_seq_length_call = max_seq_length_call, max_seq_length_post = max_seq_length_post, selective_log_softmax_code = selective_log_softmax_code, grpo_selective_log_softmax_code = grpo_selective_log_softmax_code, calculate_pad_tokens_in_prompt_code = calculate_pad_tokens_in_prompt_code, create_completion_attention_mask_code = create_completion_attention_mask_code, autotune_batch_and_chunks_code = autotune_batch_and_chunks_code, left_pack_padding_code = left_pack_padding_code, align_logprobs_with_mask_code = align_logprobs_with_mask_code, ) if RLTrainer_name == "SFTTrainer": original_text = 'self._signature_columns = ["input_ids", "attention_mask", "completion_mask"]' new_text = 'self._signature_columns = ["input_ids", "attention_mask", "completion_mask","labels"]' RLTrainer_source = RLTrainer_source.replace(original_text, new_text) # Temporary patch _is_vlm to False # as of 0.22 it only exists in sfttrainer original_is_vlm_text = "self._is_vlm = True" new_is_vlm_text = "self._is_vlm = False" RLTrainer_source = RLTrainer_source.replace( original_is_vlm_text, new_is_vlm_text ) # Remove multiple doc strings if __RLConfig_doc__ != "" and RLTrainer_source.count(__RLTrainer_doc__) == 2: RLTrainer_source = RLTrainer_source.replace(__RLTrainer_doc__, "", 1) # Remove multiple newlines RLTrainer_source = re.sub(r"[\n]{3,}", "\n", RLTrainer_source) # Create new function created_module = create_new_function( f"Unsloth{RLTrainer_name}", RLTrainer_source, f"trl.trainer.{trainer_file}", imports, overwrite = False, ) # Patch Trainer exec( f"trl.{RLTrainer_name} = created_module.Unsloth{RLTrainer_name}", locals(), globals(), ) exec( f"trl.trainer.{RLTrainer_name} = created_module.Unsloth{RLTrainer_name}", locals(), globals(), ) exec( f"trl.trainer.{trainer_file}.{RLTrainer_name} = created_module.Unsloth{RLTrainer_name}", locals(), globals(), ) # Patch Config exec( f"trl.{RLConfig_name} = created_module.Unsloth{RLConfig_name}", locals(), globals(), ) exec( f"trl.trainer.{RLConfig_name} = created_module.Unsloth{RLConfig_name}", locals(), globals(), ) exec( f"trl.trainer.{trainer_file}.{RLConfig_name} = created_module.Unsloth{RLConfig_name}", locals(), globals(), ) if trainer_file == "grpo_trainer": try: _wrap_grpo_generate_and_score( getattr(created_module, f"Unsloth{RLTrainer_name}") ) except Exception as e: logger.info( f"Unsloth: Could not wrap _generate_and_score_completions for {RLTrainer_name}: {e}" ) def patch_functions(RLTrainer, trainer_file, RLTrainer_name, all_imports, imports): init = inspect.getsource(RLTrainer.__init__) old_init = init # Remove brackets in comments since it interferes ie (...) comments = re.findall(r"\#[^\n]{1,}\n", init) bracketed_comments = [x for x in comments if "(" in x or ")" in x] # Replace with [...] instead for bracketed_comment in bracketed_comments: init = init.replace( bracketed_comment, bracketed_comment.replace("(", "[").replace(")", "]"), ) # Remove peft_config init = init.replace("elif peft_config is None:", "elif False:") init = init.replace("elif peft_config is not None:", "elif False:") init = init.replace("if peft_config is None:", "if False:") init = init.replace("if peft_config is not None:", "if False:") init = init.replace("get_peft_model(model, peft_config)", "model") # New TRL 0.20.0 init = init.replace( "if peft_config is not None or (is_peft_available() and isinstance(model, PeftModel)):", "if False:", ) # New TRL 0.20.0 init = init.replace( "model = self._prepare_peft_model(model, peft_config, args)\n", "pass\n" ) # Skip add_adapter("ref") for reference model computation # Unsloth: We comment out the "ref" adapter creation because: # 1. We want to use the original BASE MODEL as the reference model, not the SFT/LoRA model # 2. PEFT doesn't allow multiple adapters when target_parameters is used (MoE models) # When "ref" is not in peft_config, GRPO/RLOO fallback uses disable_adapter() # which gives the base model logits - exactly what we want add_adapter_block_pattern = ( r"([ \t]*)" # Capture leading indentation r"if\s+is_peft_available\(\)\s+and\s+is_peft_model\(model\)\s+and\s+args\.beta\s*!=\s*0\.0\s*:" r"(.*?)" # Match the entire block until ref_param.data.copy_ r"ref_param\.data\.copy_\(param\.data\)" ) def comment_out_block(match): """Comment out each line in the matched block, preserving indentation.""" full_match = match.group(0) indent = match.group(1) lines = full_match.split("\n") commented_lines = [] # Add explanation comment first commented_lines.append( f"{indent}# Unsloth: Commented out - use base model as reference, not SFT/LoRA model" ) # Comment out each line - insert # after leading whitespace to preserve indentation for line in lines: if line.strip(): stripped = line.lstrip() leading_ws = line[: len(line) - len(stripped)] commented_lines.append(f"{leading_ws}# {stripped}") else: commented_lines.append(line) return "\n".join(commented_lines) init = re.sub(add_adapter_block_pattern, comment_out_block, init, flags = re.DOTALL) # Set use_vllm if not set if "args.use_vllm" in init and "model" in init and "args" in init: # .*? matches first match. .+? matches final match. replacer = re.findall( r"def __init__\(.*?\).*?\:\n", init, flags = re.MULTILINE | re.DOTALL, ) if len(replacer) != 0: replacer = replacer[0] vllm_setter = ( "\n" + " " * 8 + "if hasattr(model, 'vllm_engine') and hasattr(args, 'use_vllm'):\n" + " " * 12 + "if (getattr(args, 'use_vllm', False) == False):\n" + " " * 16 + "args.use_vllm = True\n" ) # " " * 16 + "args.vllm_importance_sampling_correction = True\n" + \ # " " * 16 + "args.vllm_importance_sampling_cap = 2.0\n" if "grpo" in trainer_file and trl_version >= Version("0.18.0"): # If model has vllm_engine, then use vllm in colocate mode. Donot wait for server vllm_setter += " " * 12 + "args.vllm_mode='colocate'\n" if trl_version >= Version("0.23.0"): # We need to set this flag for sleep mode auto working with trl update vllm_setter += ( " " * 12 + "if os.environ.get('UNSLOTH_VLLM_STANDBY', '0') == '1':\n" + " " * 16 + "args.vllm_enable_sleep_mode=True\n" ) init = init.replace(replacer, replacer + vllm_setter) # breakpoint() vllm_part = re.findall( r"(\n[\s]{8}" r"if (self|args)\.use_vllm\:.*?" r"\n[\s]{8}" "else:\n)", init, flags = re.MULTILINE | re.DOTALL, ) if len(vllm_part) == 1: vllm_part, args = vllm_part[0][0], vllm_part[0][1] # Strip all comments new_vllm_part = re.sub( r"^\s*\#[^\n]*\n?", "", vllm_part, flags = re.MULTILINE ) # to also remove whole comment line instead of just starting at # new_vllm_part = re.sub( r"\s*\#.*$", "", new_vllm_part, flags = re.MULTILINE ) # remove comments that occur after code # Get SamplingParams sampling_params = re.findall( r"\n[\s]{4,}(self\.[^\s]{1,}[\s]{0,}\=[\s]{0,}" r"SamplingParams\(.+?\))", new_vllm_part, flags = re.MULTILINE | re.DOTALL, ) if len(sampling_params) == 1: sampling_params = sampling_params[0] # Fix guided_decoding sampling_params = sampling_params.replace( "guided_decoding=guided_decoding,", "guided_decoding=" 'GuidedDecodingParams(backend="outlines", regex=args.vllm_guided_decoding_regex) ' 'if getattr(args, "vllm_guided_decoding_regex", None) is not None else None,', ) # Replace with our vLLM engine sampling_params = ( " " * 12 + "self.llm = model.vllm_engine; self._last_loaded_step = 0; " + sampling_params ) # Add spaces # count the indentation of last line of sampling_params. splitted_sampling_params = sampling_params.split("\n") if len(splitted_sampling_params) >= 2: last_line = splitted_sampling_params[-1] last_prev_line = splitted_sampling_params[-2] last_prev_indentation = len(last_prev_line) - len( last_prev_line.lstrip() ) last_indentation = len(last_line) - len(last_line.lstrip()) # Add extra arguments to SamplingParams extra = "**getattr(getattr(args, 'vllm_sampling_params', vLLMSamplingParams()), '_set_kwargs', {})" # Backwards replace to_replace = ( ",\n" + " " * last_prev_indentation + extra + ",\n" + " " * last_indentation + ")" ) sampling_params = to_replace.join(sampling_params.rsplit(")", 1)) # Strip multiple commas sampling_params = re.sub(r"[\,][\s]{0,}\,", ",", sampling_params) new_vllm_part = ( f"\n{' '*8}if {args}.use_vllm:\n{sampling_params}" f"\n{' '*8}else:\n" ) if trl_version >= Version("0.18.0"): # Replace LLM init with already existing vLLM engine for colocate mode vllm_llm_init_pattern = r"self\.llm\s*=\s*LLM\(.*?\)*\)\s*?\n(?!,)" vllm_llm_replacement = "self.llm = model.vllm_engine\n" new_vllm_part = re.sub( vllm_llm_init_pattern, vllm_llm_replacement, new_vllm_part, flags = re.DOTALL, # Ensure . matches newlines [[5]] ) init = init.replace(vllm_part, new_vllm_part) # Search for vLLM calling in all child functions functions = dir(RLTrainer) RLTrainer_source = inspect.getsource(RLTrainer) functions = [x for x in functions if f"def {x}" in RLTrainer_source] changed = { "__init__": ( old_init, init, ) } edit_functions = RL_FUNCTIONS.get(trainer_file, []) for function in functions: if not hasattr(RLTrainer, function): continue fx = getattr(RLTrainer, function) try: source = inspect.getsource(fx) except: continue original_source = source # Check for function for edit_function in edit_functions: source = edit_function(function, source) """ import torch X = torch.ones((2, 2048, 201088), dtype = torch.bfloat16, device = "cuda") X[torch.randperm(2, dtype = torch.int64, device = X.device)] will error out in torch 2.8 AcceleratorError: CUDA error: invalid configuration argument """ source = re.sub( r"(\n[\s]{4,})generation_batch = shuffle_sequence_dict\(generation_batch\)\n", r"\n\1try: generation_batch = shuffle_sequence_dict(generation_batch)\n\1except: pass\n", source, ) # llm_model = self.llm.llm_engine.model_executor.driver_worker.model_runner.model source = re.sub( r"(\n[\s]{4,}).+?model_executor\.driver_worker.+?\n", r"\n\1pass\n", source, ) # llm_model.load_weights(model.state_dict().items()) source = re.sub( r"(\n[\s]{4,}).+?load_weights\(.+?\n", r"\n\1pass\n", source, ) # .state_dict() source = re.sub( r"\.state_dict\(\)", r"", source, ) # Replace self.llm.generate and self.llm.chat if "CUDA_VISIBLE_DEVICES" in os.environ: lora_name = ( trainer_file + "_lora_model_' + " + "(os.environ.get('CUDA_VISIBLE_DEVICES', '0').replace(',',''))" ) else: lora_name = trainer_file + "_lora_model'" source = re.sub( r"(self\.llm\.(?:generate|chat)\([^\)]{1,})\)", r"\1, lora_request = self.model.load_lora('" + lora_name + r", load_tensors = True))", source, ) # All these are to fix multiple commas before lora_request (in case the original code ends with something like ",)") # https://github.com/huggingface/trl/blob/main/trl/trainer/grpo_trainer.py#L1388 for eg has such an ending source = re.sub(r"\,[\s]{1,}\,[\s]{0,}lora_request", ", lora_request", source) source = re.sub(r"[\s]{1,}\,[\s]{0,}lora_request", ", lora_request", source) source = re.sub(r"[\,]{1,}[\s]{0,}lora_request", ", lora_request", source) # Prefer using unsloth's sampling params and fallback to trl's if not found # We'll enable this later separately when combining both this and GRPOConfig params # source = re.sub( # r"sampling_params\s*=\s*sampling_params", # r"sampling_params = getattr(self.args, 'vllm_sampling_params', sampling_params)", # source # ) # Fix later versions of SamplingParams via grpo_update_SamplingParams source = source.replace( "sampling_params = SamplingParams(**generation_kwargs)", "sampling_params = SamplingParams(" "**grpo_update_SamplingParams(" "SamplingParams, generation_kwargs, " "getattr(self.args, 'vllm_sampling_params', None)" ")" ")", ) # Skip if no changes done if source == original_source: continue # Find all imports imports += [x for x in all_imports if not x.startswith("_") and x in source] changed[function] = ( original_source, source, ) # Import all functions imports = list(set(imports)) # Patch all functions for function in changed: old, new = changed[function] RLTrainer_source = RLTrainer_source.replace(old, new) RLTrainer_source = RLTrainer_source.replace( f"class {RLTrainer_name}", f"class _Unsloth{RLTrainer_name}", 1 ) return RLTrainer_source def patch_trl_rl_trainers(): # Patch all TRL modules if they have vLLM or PEFT import trl.trainer all_trainers = dir(trl.trainer) all_trainers = [ x for x in all_trainers if x.islower() and x.endswith("_trainer") and x != "base_trainer" ] for trainer in all_trainers: _patch_trl_rl_trainers(trainer) return def patch_trl_openenv(): for function in RL_ADDITIONAL_FUNCTIONS["openenv"]: logger.info(f"Unsloth: Patching trl openenv with function: {function.__name__}") function() # Call the function to apply the patch return def PatchFastRL(algorithm = None, FastLanguageModel = None): if FastLanguageModel is not None: PatchRL(FastLanguageModel) patch_trl_rl_trainers() patch_trl_openenv() if type(algorithm) is str and algorithm.islower(): PatchRLStatistics(algorithm)