Add GPU tensor support to decoupled API#154
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GuanLuo
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| "GPU buffers size does not match the provided buffers: ") + | ||
| std::to_string(gpu_tensors.size()) + | ||
| " != " + std::to_string(*gpu_buffer_count)); | ||
| return; |
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This is more like assert and will never be triggered. I can add a comment for it.
| // limitation in the legacy CUDA IPC API that doesn't allow getting the | ||
| // handle of an exported pointer. If the cuda handle exists, it indicates | ||
| // that the cuda shared memory was used and the input is in a single | ||
| // buffer. [FIXME] for the case where the input is in cuda shared memory |
| reinterpret_cast<ResponseSendMessage*>(send_message.data_.get()); | ||
| std::unique_ptr<PbString> error_message; | ||
| ScopedDefer _([send_message_payload] { | ||
| ScopedDefer _([&send_message_payload] { |
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Why take reference of a pointer?
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No particular reason. I'll revert this change.
tanmayv25
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May 17, 2022
rmccorm4
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May 20, 2022
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Using Go-style defer is cool to see 🙂
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tanmayv25
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May 20, 2022
tanmayv25
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May 20, 2022
GuanLuo
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May 20, 2022
| ++index; | ||
| } | ||
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| // Additional round trip so that the stub can fill the GPU output buffers. |
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Why there is another round trip? the backend process signals back that there is CUDA IPC handle and wait for stub process to copy the data to within the CUDA IPC handle?
Do I summarize the workflow correctly:
- stub requests for output buffer (and passing buffer via shared memory at the same time if CPU tensor)
- backend acquires output buffer, copy if CPU tensor, otherwise, passing the CUDA IPC handle back to stub.
- (only for GPU tensor) stub then copy GPU tensor into the CUDA IPC handle
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Exactly. The third bullet point is what requires the round trip.
rmccorm4
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May 21, 2022
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