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[rollout,trainer] feat: offload param before wake up inference engine #2977

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Merged
merged 1 commit into from
Aug 8, 2025
Merged

[rollout,trainer] feat: offload param before wake up inference engine #2977

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23 changes: 13 additions & 10 deletions verl/workers/sharding_manager/fsdp_sglang.py
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Original file line number Diff line number Diff line change
Expand Up @@ -126,14 +126,6 @@ async def release_memory(self):
async def wake_up(self):
get_torch_device().empty_cache()

if self.device_mesh["infer_tp"].get_local_rank() == 0 and self.rollout_config.free_cache_engine:
if self.multi_stage_wake_up:
await self.inference_engine.resume_memory_occupation(tags=["weights"])
log_gpu_memory_usage("Before resume SGLang weights in sharding manager", logger=logger)
else:
await self.inference_engine.resume_memory_occupation()
log_gpu_memory_usage("Before resume SGLang weights + kv_cache in sharding manager", logger=logger)

log_gpu_memory_usage("Before state_dict() in sharding manager memory", logger=logger)
if self.offload_param:
load_fsdp_model_to_gpu(self.module)
Expand All @@ -147,13 +139,24 @@ async def wake_up(self):
# convert weight keys to match the model config
params = convert_weight_keys(params, getattr(self.module, "_fsdp_wrapped_module", self.module))

if self.offload_param:
offload_fsdp_model_to_cpu(self.module)

log_gpu_memory_usage("After offload_param in sharding manager memory", logger=logger)

if self.device_mesh["infer_tp"].get_local_rank() == 0 and self.rollout_config.free_cache_engine:
if self.multi_stage_wake_up:
await self.inference_engine.resume_memory_occupation(tags=["weights"])
log_gpu_memory_usage("Before resume SGLang weights in sharding manager", logger=logger)
else:
await self.inference_engine.resume_memory_occupation()
log_gpu_memory_usage("Before resume SGLang weights + kv_cache in sharding manager", logger=logger)

# Copy, not share memory
await self.update_weights(params)
log_gpu_memory_usage("After sync model weights in sharding manager", logger=logger)

del params
if self.offload_param:
offload_fsdp_model_to_cpu(self.module)
get_torch_device().empty_cache()
log_gpu_memory_usage("After del state_dict and empty_cache in sharding manager", logger=logger)

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5 changes: 3 additions & 2 deletions verl/workers/sharding_manager/fsdp_vllm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -205,6 +205,9 @@ def __collect_lora_params() -> OrderedDict:
else:
params = self.module.state_dict()
params = convert_weight_keys(params, getattr(self.module, "_fsdp_wrapped_module", self.module))

if self.offload_param:
offload_fsdp_model_to_cpu(self.module)
log_gpu_memory_usage("After state_dict() in sharding manager memory", logger=logger)
Comment on lines +209 to 211
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@gemini-code-assist gemini-code-assist bot Aug 8, 2025

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high

The log message "After state_dict()" on line 211 is misleading, as it's now called after the parameters may have been offloaded to CPU. This can cause confusion when debugging memory usage. Renaming it to "After offload_param..." makes it accurate and consistent with the new log message added in fsdp_sglang.py.

For even better diagnostics, you could consider having separate log points for after state_dict and after offload_param, similar to the implementation in fsdp_sglang.py.

Suggested change
if self.offload_param:
offload_fsdp_model_to_cpu(self.module)
log_gpu_memory_usage("After state_dict() in sharding manager memory", logger=logger)
if self.offload_param:
offload_fsdp_model_to_cpu(self.module)
log_gpu_memory_usage("After offload_param in sharding manager memory", logger=logger)


if self.rollout_config.free_cache_engine:
Expand All @@ -217,8 +220,6 @@ def __collect_lora_params() -> OrderedDict:
self.update_params(params, peft_config=peft_config)
log_gpu_memory_usage("After sync model weights in sharding manager", logger=logger)
del params
if self.offload_param:
offload_fsdp_model_to_cpu(self.module)
get_torch_device().empty_cache()

if (
Expand Down