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perf(trt): zero-copy CUDA graph replay + cross-stream event handshake #35

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perf(trt): zero-copy CUDA graph replay + cross-stream event handshake #35

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31 changes: 25 additions & 6 deletions inference_models/inference_models/models/common/trt.py
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Original file line number Diff line number Diff line change
Expand Up @@ -78,6 +78,7 @@ class TRTCudaGraphState:
input_buffer: torch.Tensor
output_buffers: List[torch.Tensor]
execution_context: trt.IExecutionContext
consumer_done_event: Optional["torch.cuda.Event"] = None


class TRTCudaGraphCache:
Expand Down Expand Up @@ -571,7 +572,10 @@ def infer_from_trt_engine(
outputs=outputs,
trt_cuda_graph_cache=trt_cuda_graph_cache,
)
stream.synchronize()
graph_state = getattr(results[0], "_trt_graph_state", None)
produce_event = torch.cuda.Event()
produce_event.record(graph_state.cuda_stream if graph_state is not None else stream)
results[0]._trt_produce_event = produce_event
return results


Expand Down Expand Up @@ -692,24 +696,35 @@ def _execute_trt_engine(
if cache_key not in trt_cuda_graph_cache:
LOGGER.debug("Capturing CUDA graph for shape %s", input_shape)

reuse_input = bool(
getattr(pre_processed_images, "_trt_reuse_as_input_buffer", False)
)
results, trt_cuda_graph = _capture_cuda_graph(
pre_processed_images=pre_processed_images,
engine=engine,
device=device,
input_name=input_name,
outputs=outputs,
use_pre_processed_images_as_input_buffer=reuse_input,
)
trt_cuda_graph_cache[cache_key] = trt_cuda_graph
return results

else:
trt_cuda_graph_state = trt_cuda_graph_cache[cache_key]
stream = trt_cuda_graph_state.cuda_stream
consumer_done = trt_cuda_graph_state.consumer_done_event
if consumer_done is not None:
consumer_done.wait(stream)
with torch.cuda.stream(stream):
trt_cuda_graph_state.input_buffer.copy_(pre_processed_images)
if (
trt_cuda_graph_state.input_buffer.data_ptr()
!= pre_processed_images.data_ptr()
):
trt_cuda_graph_state.input_buffer.copy_(pre_processed_images)
trt_cuda_graph_state.cuda_graph.replay()
results = [buf.clone() for buf in trt_cuda_graph_state.output_buffers]
stream.synchronize()
results = list(trt_cuda_graph_state.output_buffers)
results[0]._trt_graph_state = trt_cuda_graph_state
return results

else:
Expand Down Expand Up @@ -752,14 +767,18 @@ def _capture_cuda_graph(
device: torch.device,
input_name: str,
outputs: List[str],
use_pre_processed_images_as_input_buffer: bool = False,
) -> Tuple[List[torch.Tensor], TRTCudaGraphState]:
# Each CUDA graph needs its own execution context. Sharing a single context
# across graphs for different input shapes causes TRT to reallocate internal
# workspace buffers, invalidating GPU addresses baked into earlier graphs.
graph_context = engine.create_execution_context()

input_buffer = torch.empty_like(pre_processed_images, device=device)
input_buffer.copy_(pre_processed_images)
if use_pre_processed_images_as_input_buffer:
input_buffer = pre_processed_images
else:
input_buffer = torch.empty_like(pre_processed_images, device=device)
input_buffer.copy_(pre_processed_images)

status = graph_context.set_input_shape(
input_name, tuple(pre_processed_images.shape)
Expand Down
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