From f508df6a83bdd448f59b5c9d64f61d5119806e93 Mon Sep 17 00:00:00 2001 From: Sunil Kumar Date: Tue, 25 Mar 2025 13:17:06 -0700 Subject: [PATCH 1/2] bump vllm to 0.8.2 and bump other packages to accomadate. 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Might have figured out the fix? I need to confirm by proving training works. Will confirm once Bowen is off GPU. --- .../environments/digits_tool_use/train.py | 175 ++++++++++-------- 1 file changed, 93 insertions(+), 82 deletions(-) diff --git a/src/r1_vlm/environments/digits_tool_use/train.py b/src/r1_vlm/environments/digits_tool_use/train.py index d28c29d0..b293f8a6 100644 --- a/src/r1_vlm/environments/digits_tool_use/train.py +++ b/src/r1_vlm/environments/digits_tool_use/train.py @@ -13,88 +13,99 @@ os.environ["WANDB_PROJECT"] = "digits-tool-use" - - -# Flag that determines if gradient checkpointing is used. If it is, we need to set use_cache to False. -gradient_checkpointing = False - - -model_config = ModelConfig( - model_name_or_path="Qwen/Qwen2.5-VL-3B-Instruct", - torch_dtype="bfloat16", - use_peft=False, -) - -model = Qwen2_5_VLForConditionalGeneration.from_pretrained( - pretrained_model_name_or_path=model_config.model_name_or_path, - torch_dtype=model_config.torch_dtype, - use_cache=False, -) - -# use cache if not gradient checkpointing -if gradient_checkpointing: - model.config.use_cache = False -elif not gradient_checkpointing: - model.config.use_cache = True -else: - raise ValueError("Invalid gradient checkpointing value") - - -processor = AutoProcessor.from_pretrained( - model_config.model_name_or_path, padding_side="left" -) - -vf_env = DigitsToolUseEnv(processing_class=processor) -dataset = vf_env.get_dataset() -rubric = vf_env.get_rubric() -digits_answer_tool = DigitsAnswerTool(dataset) -digits_answer_tool.build_hash_table(dataset) # Build the hash table -set_digits_answer_tool(digits_answer_tool) # Make it available to get_answer - - - -training_args = GRPOConfig( - model_init_kwargs=model_config, - output_dir="vlm-r1-digits-tool-use", - learning_rate=1e-6, - adam_beta2=0.98, - lr_scheduler_type="cosine", - warmup_steps=0, - logging_steps=1, - save_steps=20, - save_total_limit=50, - num_train_epochs=1, - per_device_train_batch_size=5, - num_generations=15, - gradient_accumulation_steps=4, - gradient_checkpointing=gradient_checkpointing, - bf16=True, - # GRPO specific parameters - max_prompt_length=None, # must be None for vllm + verifiers - max_completion_length=1024, - beta=0.001, - temperature=1.0, - sync_ref_model=True, - ref_model_sync_steps=64, - eval_strategy="no", - log_completions=True, - use_vllm=True, - vllm_gpu_memory_utilization=0.5, - report_to="wandb", - vllm_device="cuda:3", -) - - -trainer = QwenGRPOTrainer( - model=model, - processing_class=processor, - reward_funcs=rubric, - args=training_args, - train_dataset=dataset, - env=vf_env, -) - -trainer.train() +def setup() -> QwenGRPOTrainer: + ''' + Returns the trainer after doing setup. + ''' + + + + # Flag that determines if gradient checkpointing is used. If it is, we need to set use_cache to False. + gradient_checkpointing = False + + + model_config = ModelConfig( + model_name_or_path="Qwen/Qwen2.5-VL-3B-Instruct", + torch_dtype="bfloat16", + use_peft=False, + ) + + model = Qwen2_5_VLForConditionalGeneration.from_pretrained( + pretrained_model_name_or_path=model_config.model_name_or_path, + torch_dtype=model_config.torch_dtype, + use_cache=False, + ) + + # use cache if not gradient checkpointing + if gradient_checkpointing: + model.config.use_cache = False + elif not gradient_checkpointing: + model.config.use_cache = True + else: + raise ValueError("Invalid gradient checkpointing value") + + + processor = AutoProcessor.from_pretrained( + model_config.model_name_or_path, padding_side="left" + ) + + vf_env = DigitsToolUseEnv(processing_class=processor) + dataset = vf_env.get_dataset() + rubric = vf_env.get_rubric() + digits_answer_tool = DigitsAnswerTool(dataset) + digits_answer_tool.build_hash_table(dataset) # Build the hash table + set_digits_answer_tool(digits_answer_tool) # Make it available to get_answer + + + + training_args = GRPOConfig( + model_init_kwargs=model_config, + output_dir="vlm-r1-digits-tool-use", + learning_rate=1e-6, + adam_beta2=0.98, + lr_scheduler_type="cosine", + warmup_steps=0, + logging_steps=1, + save_steps=20, + save_total_limit=50, + num_train_epochs=1, + per_device_train_batch_size=5, + num_generations=15, + gradient_accumulation_steps=4, + gradient_checkpointing=gradient_checkpointing, + bf16=True, + # GRPO specific parameters + max_prompt_length=None, # must be None for vllm + verifiers + max_completion_length=1024, + beta=0.001, + temperature=1.0, + sync_ref_model=True, + ref_model_sync_steps=64, + eval_strategy="no", + log_completions=True, + use_vllm=True, + vllm_gpu_memory_utilization=0.5, + report_to="wandb", + vllm_device="cuda:3", + ) + + + trainer = QwenGRPOTrainer( + model=model, + processing_class=processor, + reward_funcs=rubric, + args=training_args, + train_dataset=dataset, + env=vf_env, + ) + + return trainer + +if __name__ == "__main__": + from multiprocessing import freeze_support + freeze_support() + trainer = setup() + trainer.train() #CUDA_VISIBLE_DEVICES=0,1,2,3 uv run accelerate launch --config_file src/r1_vlm/deepspeed_configs/multi_gpu_3only.yaml src/r1_vlm/environments/digits_tool_use/train.py