Overhaul backend function execution for improved performance and flexibility #270
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jhalakpatel
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Oct 15, 2024
jhalakpatel
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Oct 15, 2024
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jhalakpatel
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Nov 4, 2024
| @@ -188,7 +188,7 @@ def eval(self) -> runtime.MemRefValue: | |||
| executor = Executor(executable) | |||
| # Upon computing the value of this tensor, we switch it to have a `Storage` | |||
| # parameter so that it does not need to be computed again. | |||
| data = executor.execute([out.device for out in flat_ir.outputs]) | |||
| data = executor.execute() | |||
| executor.stream.synchronize() | |||
| assert len(data) == 1, "Expects only one output from mlir_tensorrt.compiler executor" | |||
| data = data[0] | |||
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Could we also remove the hack a few lines below?
self.trace_tensor.device = flat_ir.outputs[0].deviceThere was a problem hiding this comment.
There are still a few issues:
- MLIR-TensorRT still allocates inputs only on the device.
- This PR fixes the copy operation only.
There are still issues since the input get always allocated on device. There will be a device mismatch between inputs (which are always on device) vs output which could be now on (host as well as device).
def infer_devices(self):
"""
Infers devices for the operation and updates output tensor devices accordingly.
"""
assert (
> self.inputs and len(self.outputs) == 1 and all(inp.device == self.inputs[0].device for inp in self.inputs)
), "Default implementation cannot handle cases where there are no inputs, multiple outputs, or multiple inputs with different devices. Please override."
E AssertionError: Default implementation cannot handle cases where there are no inputs, multiple outputs, or multiple inputs with different devices. Please override.
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…ibility This PR replaces the DPS-style calling convention with a non-DPS approach, eliminating the requirement for call sites to preallocate output buffers. This change enables us to bypass the computation of output shapes and advance allocation of output buffers, laying the groundwork for supporting data-dependent shapes where network outputs can have dynamic dimensions. The underlying compiler stack has been enhanced to avoid allocating oversized buffers and eliminate an extra device-to-device copy operation from TensorRT-allocated memory to MLIR-TRT managed memory. Additionally, we've improved the copy operation to support copying to host memory. This enhancement removes the need to track output device allocations for device-to-host copies. Previously, copy outputs were restricted to device allocations; now they can be allocated on both device and host. Tests have been updated to align with the new calling convention, ensuring compatibility and correctness. Other changes: Fix type constraints tests Address review comments
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jhalakpatel
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Nov 11, 2024
| @@ -186,10 +186,11 @@ def eval(self) -> runtime.MemRefValue: | |||
|
|
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| compiler = Compiler(trt_builder_opt_level=0) | |||
| executable = compiler.compile(mlir, flat_ir=flat_ir) | |||
| # Ensure that session and client are available as long as tensor lives. | |||
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This PR replaces the DPS-style calling convention with a non-DPS approach, eliminating the requirement for call sites to preallocate output buffers. This change enables us to bypass the computation of output shapes and advance allocation of output buffers, laying the groundwork for supporting data-dependent shapes where network outputs can have dynamic dimensions.
The underlying compiler stack has been enhanced to avoid allocating oversized buffers and eliminate an extra device-to-device copy operation from TensorRT-allocated memory to MLIR-TRT managed memory.
Additionally, we've improved the copy operation to support copying to host memory. This enhancement removes the need to track output device allocations for device-to-host copies. Previously, copy outputs were restricted to device allocations; now they can be allocated on both device and host.
Tests have been updated to align with the new calling convention, ensuring compatibility and correctness.