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📤 GRPO refactor loading the model weights to vllm #2817

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merged 2 commits into from
Feb 10, 2025
+13 −10
Merged

📤 GRPO refactor loading the model weights to vllm #2817

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GRPO refactor loading the model weights to vllm
winglian Feb 10, 2025
aeec4f9
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qgallouedec Feb 10, 2025
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23 changes: 13 additions & 10 deletions trl/trainer/grpo_trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -444,6 +444,18 @@ def _get_per_token_logps(self, model, input_ids, attention_mask, logits_to_keep)
logits = logits[:, -logits_to_keep:]
return selective_log_softmax(logits, input_ids) # compute logprobs for the input tokens

def _move_model_to_vllm(self):
with unwrap_model_for_generation(
self.model, self.accelerator, gather_deepspeed3_params=self.args.ds3_gather_for_generation
) as unwrapped_model:
if is_compiled_module(unwrapped_model):
state_dict = unwrapped_model._orig_mod.state_dict()
else:
state_dict = unwrapped_model.state_dict()
if self.accelerator.is_main_process:
llm_model = self.llm.llm_engine.model_executor.driver_worker.model_runner.model
llm_model.load_weights(state_dict.items())

def _prepare_inputs(self, inputs: dict[str, Union[torch.Tensor, Any]]) -> dict[str, Union[torch.Tensor, Any]]:
device = self.accelerator.device
prompts = [x["prompt"] for x in inputs]
Expand All @@ -462,16 +474,7 @@ def _prepare_inputs(self, inputs: dict[str, Union[torch.Tensor, Any]]) -> dict[s
if self.args.use_vllm:
# First, have main process load weights if needed
if self.state.global_step != self._last_loaded_step:
with unwrap_model_for_generation(
self.model, self.accelerator, gather_deepspeed3_params=self.args.ds3_gather_for_generation
) as unwrapped_model:
if is_compiled_module(unwrapped_model):
state_dict = unwrapped_model._orig_mod.state_dict()
else:
state_dict = unwrapped_model.state_dict()
if self.accelerator.is_main_process:
llm_model = self.llm.llm_engine.model_executor.driver_worker.model_runner.model
llm_model.load_weights(state_dict.items())
self._move_model_to_vllm()
self._last_loaded_step = self.state.global_step

# Generate completions using vLLM: gather all prompts and use them in a single call in the main process
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