bd20260350
- This adds a field in virtual device options to specify priority for virtual devices. - When the priority is specified, it will be used to create the cuda streams for the virtual device with the given priority. - This is backwards compatible with no priorities specified. When no priorities specified, the current implementation of creating a stream without any priority will continue while any non-zero priorities specified will be used to create streams with that priority. PiperOrigin-RevId: 314470276 Change-Id: I4943f71e901245fb21b6f7e833adbdcd8126f1fa
100 lines
3.5 KiB
C++
100 lines
3.5 KiB
C++
/* Copyright 2019 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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// Defines the GpuStream type - the CUDA-specific implementation of the generic
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// StreamExecutor Stream interface.
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#ifndef TENSORFLOW_STREAM_EXECUTOR_GPU_GPU_STREAM_H_
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#define TENSORFLOW_STREAM_EXECUTOR_GPU_GPU_STREAM_H_
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#include "tensorflow/core/platform/thread_annotations.h"
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#include "tensorflow/stream_executor/gpu/gpu_driver.h"
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#include "tensorflow/stream_executor/stream_executor_internal.h"
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namespace stream_executor {
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namespace gpu {
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class GpuExecutor;
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// Wraps a GpuStreamHandle in order to satisfy the platform-independent
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// StreamInterface.
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//
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// Thread-safe post-initialization.
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class GpuStream : public internal::StreamInterface {
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public:
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explicit GpuStream(GpuExecutor* parent)
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: parent_(parent), gpu_stream_(nullptr), completed_event_(nullptr) {}
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// Note: teardown is handled by a parent's call to DeallocateStream.
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~GpuStream() override {}
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void* GpuStreamHack() override { return gpu_stream_; }
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void** GpuStreamMemberHack() override {
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return reinterpret_cast<void**>(&gpu_stream_);
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}
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// Explicitly initialize the CUDA resources associated with this stream, used
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// by StreamExecutor::AllocateStream().
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bool Init();
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void SetPriority(int priority) { priority_ = priority; }
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int priority() const { return priority_; }
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// Explicitly destroy the CUDA resources associated with this stream, used by
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// StreamExecutor::DeallocateStream().
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void Destroy();
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// Returns true if no work is pending or executing on the stream.
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bool IsIdle() const;
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// Retrieves an event which indicates that all work enqueued into the stream
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// has completed. Ownership of the event is not transferred to the caller, the
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// event is owned by this stream.
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GpuEventHandle* completed_event() { return &completed_event_; }
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// Returns the GpuStreamHandle value for passing to the CUDA API.
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//
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// Precond: this GpuStream has been allocated (otherwise passing a nullptr
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// into the NVIDIA library causes difficult-to-understand faults).
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GpuStreamHandle gpu_stream() const {
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DCHECK(gpu_stream_ != nullptr);
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return const_cast<GpuStreamHandle>(gpu_stream_);
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}
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// TODO(timshen): Migrate away and remove this function.
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GpuStreamHandle cuda_stream() const { return gpu_stream(); }
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GpuExecutor* parent() const { return parent_; }
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private:
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GpuExecutor* parent_; // Executor that spawned this stream.
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GpuStreamHandle gpu_stream_; // Wrapped CUDA stream handle.
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int priority_ = 0;
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// Event that indicates this stream has completed.
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GpuEventHandle completed_event_ = nullptr;
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};
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// Helper functions to simplify extremely common flows.
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// Converts a Stream to the underlying GpuStream implementation.
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GpuStream* AsGpuStream(Stream* stream);
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// Extracts a GpuStreamHandle from a GpuStream-backed Stream object.
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GpuStreamHandle AsGpuStreamValue(Stream* stream);
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} // namespace gpu
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} // namespace stream_executor
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#endif // TENSORFLOW_STREAM_EXECUTOR_GPU_GPU_STREAM_H_
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