/* 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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// The CUDA implementation of the StreamExecutorInterface functionality.
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// CUDA inclusions are ideally confined to this implementation file.
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//
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// The notions from the StreamExecutor basically correspond to the CUDA streams
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// programming model provided by the libcuda.so driver APIs, so we don't have
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// to do much more than wrap the calls to the libraries appropriately.
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#ifndef TENSORFLOW_STREAM_EXECUTOR_GPU_GPU_KERNEL_H_
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#define TENSORFLOW_STREAM_EXECUTOR_GPU_GPU_KERNEL_H_
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#include "tensorflow/stream_executor/gpu/gpu_driver.h"
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#include "tensorflow/stream_executor/kernel_cache_config.h"
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#include "tensorflow/stream_executor/platform/logging.h"
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#include "tensorflow/stream_executor/platform/port.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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// Wraps a GpuFunctionHandle to implement the platform-independent
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// KernelInterface.
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class GpuKernel : public internal::KernelInterface {
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public:
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GpuKernel()
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: gpu_function_(nullptr),
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arity_(0),
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preferred_cache_config_(KernelCacheConfig::kNoPreference) {}
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// Note that the function is unloaded when the module is unloaded, and the
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// module that the function is contained in is owned by the GpuExecutor.
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~GpuKernel() override {}
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// As arity cannot be reflected upon using the CUDA API, the arity is
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// explicitly set during the GpuExecutor::GetKernel initialization process.
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void set_arity(unsigned arity) { arity_ = arity; }
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unsigned Arity() const override { return arity_; }
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// Returns the GpuFunctionHandle value for passing to the CUDA API.
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GpuFunctionHandle AsGpuFunctionHandle() const {
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DCHECK(gpu_function_ != nullptr);
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return const_cast<GpuFunctionHandle>(gpu_function_);
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}
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// Returns the slot that the GpuFunctionHandle is stored within for this
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// object, for the CUDA API which wants to load into a GpuFunctionHandle*.
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GpuFunctionHandle* gpu_function_ptr() { return &gpu_function_; }
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// CUDA supports setting the preferred cache configuration of a
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// GpuFunctionHandle (more-or-less equivalent to a GpuKernel). We support this
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// via the below functions; users can set a preference, and that is applied
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// when the kernel is [lazy-]loaded (in GpuExecutor::Launch). The alternative
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// would be to load the kernel & set the preference when the user calls the
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// setter below; either approach is valid. Sets the current kernel cache
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// configuration preference.
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void SetPreferredCacheConfig(KernelCacheConfig config) override {
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preferred_cache_config_ = config;
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}
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// Returns the current kernel cache configuration preference.
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KernelCacheConfig GetPreferredCacheConfig() const override {
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return preferred_cache_config_;
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}
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// Returns the current kernel cache configuration preference as a
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// CUfunc_cache.
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GpuFuncCachePreference GetGpuCacheConfig() const;
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private:
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GpuFunctionHandle gpu_function_; // Wrapped CUDA kernel handle.
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unsigned arity_; // Number of formal parameters the kernel takes.
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// Preferred (but not required) cache configuration for this kernel.
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KernelCacheConfig preferred_cache_config_;
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};
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// Given a platform-independent kernel datatype, returns the (const) internal
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// CUDA platform implementation pointer.
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inline const GpuKernel* AsGpuKernel(const KernelBase* kernel) {
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return static_cast<const GpuKernel*>(kernel->implementation());
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}
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// Given a platform-independent kernel datatype, returns the (non-const)
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// internal CUDA platform implementation pointer.
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inline GpuKernel* AsGpuKernel(KernelBase* kernel) {
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return static_cast<GpuKernel*>(kernel->implementation());
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}
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} // namespace gpu
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} // namespace stream_executor
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#endif // TENSORFLOW_STREAM_EXECUTOR_GPU_GPU_KERNEL_H_
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