5bbf4a1d11
This CL consists of two steps:
* First, refactor all Thunks to take an ThunkInfo instead of const HloInstruction*. This will benefit future extensions to ThunkInfo as we move away from HloInstruction*.
* Secondly, change the data pipeline from:
Emitter -> Thunk* -> hlo_instruction() -> profiler(HloInstruction*)
to:
Emitter -> Thunk with profile indices
The profile doesn't really depend on HloInstruction*, but just its pointer
identity. Removing the dependency on HloInstruction helps with MLIR migration.
PiperOrigin-RevId: 320687291
Change-Id: I7027d4c032f73ed615e5b520e01f3740781735be
47 lines
1.6 KiB
C++
47 lines
1.6 KiB
C++
/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#include "tensorflow/compiler/xla/service/gpu/tuple_thunk.h"
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#include "absl/memory/memory.h"
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#include "tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h"
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#include "tensorflow/compiler/xla/util.h"
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namespace xla {
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namespace gpu {
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Status TupleThunk::ExecuteOnStream(const ExecuteParams& params) {
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auto& stream = *params.stream;
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auto& buffer_allocations = *params.buffer_allocations;
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auto n = tuple_element_buffers_.size();
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auto tuple_data = absl::make_unique<void*[]>(n);
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for (int i = 0; i < n; ++i) {
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tuple_data[i] =
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buffer_allocations.GetDeviceAddress(tuple_element_buffers_[i]).opaque();
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}
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auto op_profiler =
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params.profiler->MakeScopedInstructionProfiler(profile_index());
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SafeH2DMemcpy(se::DeviceMemory<void*>(
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buffer_allocations.GetDeviceAddress(dest_buffer_)),
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std::move(tuple_data), n, &stream,
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params.deferred_host_callbacks);
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return Status::OK();
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}
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} // namespace gpu
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} // namespace xla
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