delete ray remote function return value#4422
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Pull request overview
This PR updates the PyTorch Ray execution path to better manage memory/serialization by keeping outputs as detached CPU tensors (instead of NumPy conversions) and explicitly releasing shared-memory-backed outputs promptly in the engine loop.
Changes:
- Detach tensors when converting model outputs to CPU to avoid holding autograd graphs/references.
- Remove Ray-side output packing/unpacking that converted outputs to NumPy / back to tensors.
- Explicitly
del outafter enqueuing step outputs to release shared-memory objects sooner.
Reviewed changes
Copilot reviewed 4 out of 4 changed files in this pull request and generated 2 comments.
| File | Description |
|---|---|
| lmdeploy/pytorch/envs.py | Attempts to enable Ray zero-copy tensors via env var (currently ineffective due to no-op). |
| lmdeploy/pytorch/engine/model_agent/agent.py | Detach tensors during CPU transfer for outputs/logprobs. |
| lmdeploy/pytorch/engine/executor/ray_executor.py | Stops converting outputs to NumPy and back; returns tensor-based outputs directly. |
| lmdeploy/pytorch/engine/engine_loop.py | Deletes output object after enqueuing to help timely shared-memory release. |
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| if out is not None: | ||
| step_outputs = self._make_infer_outputs(out, running=running, model_inputs=model_inputs, delta=delta) | ||
| self.resp_queue.put_nowait(step_outputs) | ||
| # out might comes from shared memory, need to explicitly delete to release memory in time |
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