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
76 lines
2.8 KiB
C++
76 lines
2.8 KiB
C++
/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
|
|
|
|
Licensed under the Apache License, Version 2.0 (the "License");
|
|
you may not use this file except in compliance with the License.
|
|
You may obtain a copy of the License at
|
|
|
|
http://www.apache.org/licenses/LICENSE-2.0
|
|
|
|
Unless required by applicable law or agreed to in writing, software
|
|
distributed under the License is distributed on an "AS IS" BASIS,
|
|
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
See the License for the specific language governing permissions and
|
|
limitations under the License.
|
|
==============================================================================*/
|
|
|
|
#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_GPU_CHOLESKY_THUNK_H_
|
|
#define TENSORFLOW_COMPILER_XLA_SERVICE_GPU_CHOLESKY_THUNK_H_
|
|
|
|
#include "absl/types/optional.h"
|
|
#include "tensorflow/compiler/xla/service/buffer_assignment.h"
|
|
#include "tensorflow/compiler/xla/service/gpu/buffer_allocations.h"
|
|
#include "tensorflow/compiler/xla/service/gpu/cusolver_context.h"
|
|
#include "tensorflow/compiler/xla/service/gpu/gpu_executable.h"
|
|
#include "tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h"
|
|
#include "tensorflow/compiler/xla/service/gpu/thunk.h"
|
|
#include "tensorflow/compiler/xla/service/hlo_instruction.h"
|
|
#include "tensorflow/compiler/xla/types.h"
|
|
#include "tensorflow/compiler/xla/xla_data.pb.h"
|
|
#include "tensorflow/core/lib/core/status.h"
|
|
#include "tensorflow/core/platform/stream_executor_no_cuda.h"
|
|
#include "tensorflow/core/platform/thread_annotations.h"
|
|
#include "tensorflow/stream_executor/blas.h"
|
|
|
|
namespace xla {
|
|
namespace gpu {
|
|
|
|
// This class stores everything that StreamExecutor needs to launch a Cholesky
|
|
// decomposition (LAPACK potrf). It is generated by IrEmitter.
|
|
//
|
|
// Thread-compatible.
|
|
class CholeskyThunk : public Thunk {
|
|
public:
|
|
static StatusOr<int64> ScratchBufferSize(int64 n);
|
|
CholeskyThunk(ThunkInfo thunk_info, const CholeskyOptions& options,
|
|
BufferAllocation::Slice a_buffer,
|
|
BufferAllocation::Slice workspace_buffer,
|
|
BufferAllocation::Slice info_buffer, PrimitiveType type,
|
|
int64 batch_size, int64 n);
|
|
|
|
CholeskyThunk(const CholeskyThunk&) = delete;
|
|
CholeskyThunk& operator=(const CholeskyThunk&) = delete;
|
|
|
|
Status ExecuteOnStream(const ExecuteParams& params) override;
|
|
|
|
private:
|
|
se::blas::UpperLower uplo_;
|
|
|
|
const BufferAllocation::Slice a_buffer_;
|
|
const BufferAllocation::Slice workspace_buffer_;
|
|
const BufferAllocation::Slice info_buffer_;
|
|
|
|
const PrimitiveType type_;
|
|
const int64 batch_size_;
|
|
const int64 a_batch_stride_;
|
|
const int64 n_;
|
|
|
|
tensorflow::mutex mu_;
|
|
absl::flat_hash_map<se::Stream*, CusolverContext> contexts_
|
|
TF_GUARDED_BY(mu_);
|
|
};
|
|
|
|
} // namespace gpu
|
|
} // namespace xla
|
|
|
|
#endif // TENSORFLOW_COMPILER_XLA_SERVICE_GPU_CHOLESKY_THUNK_H_
|