Split TensorDeviceType.h in 3 files to make it more manageable

1 files changed, 260 insertions, 0 deletions

diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceCuda.h b/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceCuda.h
new file mode 100644
index 000000000..7d80d0b91
--- /dev/null
+++ b/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceCuda.h
@@ -0,0 +1,260 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#if defined(EIGEN_USE_GPU) && !defined(EIGEN_CXX11_TENSOR_TENSOR_DEVICE_CUDA_H)
+#define EIGEN_CXX11_TENSOR_TENSOR_DEVICE_CUDA_H
+
+
+namespace Eigen {
+
+// This defines an interface that GPUDevice can take to use
+// CUDA streams underneath.
+class StreamInterface {
+ public:
+  virtual ~StreamInterface() {}
+
+  virtual const cudaStream_t& stream() const = 0;
+  virtual const cudaDeviceProp& deviceProperties() const = 0;
+
+  // Allocate memory on the actual device where the computation will run
+  virtual void* allocate(size_t num_bytes) const = 0;
+  virtual void deallocate(void* buffer) const = 0;
+};
+
+#if defined(__CUDACC__)
+static cudaDeviceProp* m_deviceProperties;
+static bool m_devicePropInitialized = false;
+
+static void initializeDeviceProp() {
+  if (!m_devicePropInitialized) {
+    if (!m_devicePropInitialized) {
+      int num_devices;
+      cudaError_t status = cudaGetDeviceCount(&num_devices);
+      EIGEN_UNUSED_VARIABLE(status)
+      assert(status == cudaSuccess);
+      m_deviceProperties = new cudaDeviceProp[num_devices];
+      for (int i = 0; i < num_devices; ++i) {
+        status = cudaGetDeviceProperties(&m_deviceProperties[i], i);
+        assert(status == cudaSuccess);
+      }
+      m_devicePropInitialized = true;
+    }
+  }
+}
+
+static const cudaStream_t default_stream = cudaStreamDefault;
+
+class CudaStreamDevice : public StreamInterface {
+ public:
+  // Use the default stream on the current device
+  CudaStreamDevice() : stream_(&default_stream) {
+    cudaGetDevice(&device_);
+    initializeDeviceProp();
+  }
+  // Use the default stream on the specified device
+  CudaStreamDevice(int device) : stream_(&default_stream), device_(device) {
+    initializeDeviceProp();
+  }
+  // Use the specified stream. Note that it's the
+  // caller responsibility to ensure that the stream can run on
+  // the specified device. If no device is specified the code
+  // assumes that the stream is associated to the current gpu device.
+  CudaStreamDevice(const cudaStream_t* stream, int device = -1)
+      : stream_(stream), device_(device) {
+    if (device < 0) {
+      cudaGetDevice(&device_);
+    } else {
+      int num_devices;
+      cudaError_t err = cudaGetDeviceCount(&num_devices);
+      EIGEN_UNUSED_VARIABLE(err)
+      assert(err == cudaSuccess);
+      assert(device < num_devices);
+      device_ = device;
+    }
+    initializeDeviceProp();
+  }
+
+  const cudaStream_t& stream() const { return *stream_; }
+  const cudaDeviceProp& deviceProperties() const {
+    return m_deviceProperties[device_];
+  }
+  virtual void* allocate(size_t num_bytes) const {
+    cudaError_t err = cudaSetDevice(device_);
+    EIGEN_UNUSED_VARIABLE(err)
+    assert(err == cudaSuccess);
+    void* result;
+    err = cudaMalloc(&result, num_bytes);
+    assert(err == cudaSuccess);
+    assert(result != NULL);
+    return result;
+  }
+  virtual void deallocate(void* buffer) const {
+    cudaError_t err = cudaSetDevice(device_);
+    EIGEN_UNUSED_VARIABLE(err)
+    assert(err == cudaSuccess);
+    assert(buffer != NULL);
+    err = cudaFree(buffer);
+    assert(err == cudaSuccess);
+  }
+
+ private:
+  const cudaStream_t* stream_;
+  int device_;
+};
+#endif  // __CUDACC__
+
+struct GpuDevice {
+  // The StreamInterface is not owned: the caller is
+  // responsible for its initialization and eventual destruction.
+  explicit GpuDevice(const StreamInterface* stream) : stream_(stream) {
+    eigen_assert(stream);
+  }
+
+  // TODO(bsteiner): This is an internal API, we should not expose it.
+  EIGEN_STRONG_INLINE const cudaStream_t& stream() const {
+    return stream_->stream();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void* allocate(size_t num_bytes) const {
+#ifndef __CUDA_ARCH__
+    return stream_->allocate(num_bytes);
+#else
+    eigen_assert(false && "The default device should be used instead to generate kernel code");
+    return NULL;
+#endif
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void deallocate(void* buffer) const {
+#ifndef __CUDA_ARCH__
+    stream_->deallocate(buffer);
+
+#else
+    eigen_assert(false && "The default device should be used instead to generate kernel code");
+#endif
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memcpy(void* dst, const void* src, size_t n) const {
+#ifndef __CUDA_ARCH__
+    cudaError_t err = cudaMemcpyAsync(dst, src, n, cudaMemcpyDeviceToDevice,
+                                      stream_->stream());
+    EIGEN_UNUSED_VARIABLE(err)
+    assert(err == cudaSuccess);
+#else
+    eigen_assert(false && "The default device should be used instead to generate kernel code");
+#endif
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memcpyHostToDevice(void* dst, const void* src, size_t n) const {
+#ifndef __CUDA_ARCH__
+    cudaError_t err =
+        cudaMemcpyAsync(dst, src, n, cudaMemcpyHostToDevice, stream_->stream());
+    EIGEN_UNUSED_VARIABLE(err)
+    assert(err == cudaSuccess);
+#else
+    eigen_assert(false && "The default device should be used instead to generate kernel code");
+#endif
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memcpyDeviceToHost(void* dst, const void* src, size_t n) const {
+#ifndef __CUDA_ARCH__
+    cudaError_t err =
+        cudaMemcpyAsync(dst, src, n, cudaMemcpyDeviceToHost, stream_->stream());
+    EIGEN_UNUSED_VARIABLE(err)
+    assert(err == cudaSuccess);
+#else
+    eigen_assert(false && "The default device should be used instead to generate kernel code");
+#endif
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memset(void* buffer, int c, size_t n) const {
+#ifndef __CUDA_ARCH__
+    cudaError_t err = cudaMemsetAsync(buffer, c, n, stream_->stream());
+    EIGEN_UNUSED_VARIABLE(err)
+    assert(err == cudaSuccess);
+#else
+    eigen_assert(false && "The default device should be used instead to generate kernel code");
+#endif
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE size_t numThreads() const {
+    // FIXME
+    return 32;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE size_t firstLevelCacheSize() const {
+    // FIXME
+    return 48*1024;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE size_t lastLevelCacheSize() const {
+    // We won't try to take advantage of the l2 cache for the time being, and
+    // there is no l3 cache on cuda devices.
+    return firstLevelCacheSize();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void synchronize() const {
+#if defined(__CUDACC__) && !defined(__CUDA_ARCH__)
+    cudaError_t err = cudaStreamSynchronize(stream_->stream());
+    EIGEN_UNUSED_VARIABLE(err)
+    assert(err == cudaSuccess);
+#else
+    assert(false && "The default device should be used instead to generate kernel code");
+#endif
+  }
+
+  inline int getNumCudaMultiProcessors() const {
+    return stream_->deviceProperties().multiProcessorCount;
+  }
+  inline int maxCudaThreadsPerBlock() const {
+    return stream_->deviceProperties().maxThreadsPerBlock;
+  }
+  inline int maxCudaThreadsPerMultiProcessor() const {
+    return stream_->deviceProperties().maxThreadsPerMultiProcessor;
+  }
+  inline int sharedMemPerBlock() const {
+    return stream_->deviceProperties().sharedMemPerBlock;
+  }
+  inline int majorDeviceVersion() const {
+    return stream_->deviceProperties().major;
+  }
+
+  // This function checks if the CUDA runtime recorded an error for the
+  // underlying stream device.
+  inline bool ok() const {
+#ifdef __CUDACC__
+    cudaError_t error = cudaStreamQuery(stream_->stream());
+    return (error == cudaSuccess) || (error == cudaErrorNotReady);
+#else
+    return false;
+#endif
+  }
+
+ private:
+  const StreamInterface* stream_;
+
+};
+
+
+#define LAUNCH_CUDA_KERNEL(kernel, gridsize, blocksize, sharedmem, device, ...)            \
+  (kernel) <<< (gridsize), (blocksize), (sharedmem), (device).stream() >>> (__VA_ARGS__);  \
+  assert(cudaGetLastError() == cudaSuccess);
+
+
+// FIXME: Should be device and kernel specific.
+#ifdef __CUDACC__
+static inline void setCudaSharedMemConfig(cudaSharedMemConfig config) {
+  cudaError_t status = cudaDeviceSetSharedMemConfig(config);
+  EIGEN_UNUSED_VARIABLE(status)
+  assert(status == cudaSuccess);
+}
+#endif
+
+}  // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_DEVICE_TYPE_H