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PR #25676: [ROCm] Adding ROCm support for code in the tensorflow/core/common_runtime/gpu directory

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Imported from GitHub PR #25676

This PR contains commits to add ROCm support for code within the directory
     `tensorflow/core/common_runtime/gpu`

Prior to this PR the gpu implementation specific code within that dir is for CUDA only, and this PR adds the ROCm equivalent.

Some files/variables have been renamed from *cuda* to *gpu* to make code more generic

The PR is broken down into several commits for ease-of-review.  I can squash them into one prior to the merge if need be.

@tatianashp. Adding you to the cc-list just as an FYI
@whchung

Copybara import of the project:

  - c62c032d92e17a775db791ad03ae3502ea6d8e8e adding ROCm support in tensorflow/core/common_runtime/gpu... by Deven Desai <deven.desai.amd@gmail.com>
  - e94e3f408d9956dfa3aa7849a4ff41c584a6e0f0 adding ROCm support in tensorflow/core/common_runtime/gpu... by Deven Desai <deven.desai.amd@gmail.com>
  - 96e522ddf21fa675308a38b91d0a95efd771033e adding ROCm support in tensorflow/core/common_runtime/gpu... by Deven Desai <deven.desai.amd@gmail.com>
  - 929bbc2d4c620e31b2f7786600f4d7a271de0dba renaming cuda_host_allocator.h to gpu_host_allocator.h by Deven Desai <deven.desai.amd@gmail.com>
  - 4e255e01d26f74e003c11a894ebf39fcaf64ca49 misc changes by Deven Desai <deven.desai.amd@gmail.com>
  - a11b8117f1b27ba7f1abd43c4849e29d21c610d2 changes requested in the code review by Deven Desai <deven.desai.amd@gmail.com>
  - 07b49644d27aeac2e8646609640206faf5908ca5 using string instead int std::string + removing the #defi... by Deven Desai <deven.desai.amd@gmail.com>
  - 46bc499856a769cd00e9ddcdf5c922e3c17f0603 Merge 07b49644d27aeac2e8646609640206faf5908ca5 into afab5... by Deven Desai <36858332+deven-amd@users.noreply.github.com>

COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/tensorflow/pull/25676 from ROCmSoftwarePlatform:google_upstream_common_runtime_changes 07b49644d27aeac2e8646609640206faf5908ca5
PiperOrigin-RevId: 237437557
This commit is contained in:
A. Unique TensorFlower 2019-03-08 06:19:53 -08:00 committed by TensorFlower Gardener
parent bc05464062
commit d280d3d9b9
21 changed files with 359 additions and 133 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

7
tensorflow/contrib/gdr/gdr_memory_manager.cc
View File

@ -250,10 +250,9 @@ Status GdrMemoryManager::Init() {
LOG(INFO) << "Instrumenting CPU allocator(s)";
for (int numa_idx = 0; numa_idx < port::NUMANumNodes(); ++numa_idx) {
GPUProcessState::singleton()->AddCUDAHostAllocVisitor(numa_idx,
alloc_visitor);
GPUProcessState::singleton()->AddCUDAHostFreeVisitor(numa_idx,
free_visitor);
GPUProcessState::singleton()->AddGpuHostAllocVisitor(numa_idx,
alloc_visitor);
GPUProcessState::singleton()->AddGpuHostFreeVisitor(numa_idx, free_visitor);
}
if (IsGDRAvailable()) {

4
tensorflow/contrib/verbs/rdma.cc
View File

@ -1086,7 +1086,7 @@ void RdmaTensorResponse::RecvHandler(Rendezvous::ParsedKey parsed,
// The tensor must be copied from GPU to CPU, because either:
// 1. The tensor is located on a non GDR compatible GPU.
// 2. The tensor's meta-data has changed.
Allocator* alloc = GPUProcessState::singleton()->GetCUDAHostAllocator(0);
Allocator* alloc = GPUProcessState::singleton()->GetGpuHostAllocator(0);
copy = Tensor(alloc, in.dtype(), in.shape());
CountCopies(rm_.name_, (void*)DMAHelper::base(&in),
(void*)DMAHelper::base(&copy), in.TotalBytes(), true);
@ -1543,7 +1543,7 @@ bool RdmaTensorRequest::AllocateTensors() {
if (mr_ == nullptr) {
// Can't RDMA directly to result. Use a proxy.
proxy_tensor_ =
new Tensor(GPUProcessState::singleton()->GetCUDAHostAllocator(0),
new Tensor(GPUProcessState::singleton()->GetGpuHostAllocator(0),
result_tensor_->dtype(), result_tensor_->shape());
rdma_addr_ = DMAHelper::base(proxy_tensor_);
mr_ =

4
tensorflow/contrib/verbs/rdma_mgr.cc
View File

@ -277,8 +277,8 @@ void RdmaMgr::InitAllocators() {
ProcessState::singleton()->AddCPUFreeVisitor(free_visitor);
#if GOOGLE_CUDA
GPUProcessState::singleton()->AddCUDAHostAllocVisitor(0, alloc_visitor);
GPUProcessState::singleton()->AddCUDAHostFreeVisitor(0, free_visitor);
GPUProcessState::singleton()->AddGpuHostAllocVisitor(0, alloc_visitor);
GPUProcessState::singleton()->AddGpuHostFreeVisitor(0, free_visitor);
if (IsGDRAvailable()) {
// Note we don't free allocated GPU memory so there is no free visitor

6
tensorflow/core/BUILD
View File

@ -1705,6 +1705,7 @@ filegroup(
"platform/**/logger.cc",
"platform/default/test_benchmark.*",
"platform/cuda.h",
"platform/rocm.h",
"platform/google/**/*",
"platform/hadoop/**/*",
"platform/gif.h",
@ -2259,6 +2260,7 @@ LIB_INTERNAL_PRIVATE_HEADERS = ["framework/resource_handle.h"] + glob(
"platform/jpeg.h",
"platform/png.h",
"platform/**/cuda.h",
"platform/**/rocm.h",
"platform/**/stream_executor.h",
],
)
@ -2371,6 +2373,7 @@ cc_library(
"**/*test*",
"platform/**/cuda.h",
"platform/**/cuda_libdevice_path.cc",
"platform/**/rocm.h",
"platform/**/stream_executor.h",
"platform/**/env_time.cc",
"platform/**/device_tracer.cc",
@ -2866,6 +2869,7 @@ tf_cuda_library(
srcs = ["platform/stream_executor.h"],
hdrs = [
"platform/cuda.h",
"platform/rocm.h",
"platform/stream_executor.h",
],
deps = [
@ -3301,7 +3305,7 @@ cc_library(
)
GPU_RUNTIME_HEADERS = [
"common_runtime/gpu/cuda_host_allocator.h",
"common_runtime/gpu/gpu_host_allocator.h",
"common_runtime/gpu/gpu_bfc_allocator.h",
"common_runtime/gpu/gpu_cudamalloc_allocator.h",
"common_runtime/gpu/gpu_debug_allocator.h",

4
tensorflow/core/common_runtime/gpu/gpu_bfc_allocator_test.cc
View File

@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#if GOOGLE_CUDA
#if GOOGLE_CUDA || TENSORFLOW_USE_ROCM
#include "tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.h"
@ -580,4 +580,4 @@ TEST_F(GPUBFCAllocatorPrivateMethodsTest, ForceAllowGrowth) {
} // namespace tensorflow
#endif // GOOGLE_CUDA
#endif // GOOGLE_CUDA || TENSORFLOW_USE_ROCM

4
tensorflow/core/common_runtime/gpu/gpu_debug_allocator_test.cc
View File

@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#if GOOGLE_CUDA
#if GOOGLE_CUDA || TENSORFLOW_USE_ROCM
#include "tensorflow/core/common_runtime/gpu/gpu_debug_allocator.h"
@ -249,4 +249,4 @@ TEST(GPUDebugAllocatorTest, AllocatedVsRequested) {
} // namespace
} // namespace tensorflow
#endif // GOOGLE_CUDA
#endif // GOOGLE_CUDA || TENSORFLOW_USE_ROCM

162
tensorflow/core/common_runtime/gpu/gpu_device.cc
View File

@ -15,7 +15,11 @@ limitations under the License.
// TODO(opensource): Use a more generic sounding preprocessor name than
// GOOGLE_CUDA
#if GOOGLE_CUDA
#if GOOGLE_CUDA || TENSORFLOW_USE_ROCM
#if TENSORFLOW_USE_ROCM
#include "rocm/include/hip/hip_runtime.h"
#endif
#define EIGEN_USE_GPU
@ -55,7 +59,11 @@ limitations under the License.
#include "tensorflow/core/lib/strings/numbers.h"
#include "tensorflow/core/lib/strings/str_util.h"
#include "tensorflow/core/lib/strings/strcat.h"
#if GOOGLE_CUDA
#include "tensorflow/core/platform/cuda.h"
#elif TENSORFLOW_USE_ROCM
#include "tensorflow/core/platform/rocm.h"
#endif
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/platform/macros.h"
#include "tensorflow/core/platform/stream_executor.h"
@ -67,18 +75,36 @@ limitations under the License.
#include "tensorflow/core/util/stream_executor_util.h"
#if !defined(PLATFORM_GOOGLE)
#if GOOGLE_CUDA
#include "cuda/cuda_config.h"
#endif
#endif
namespace tensorflow {
#if GOOGLE_CUDA
typedef cudaStream_t gpuStream_t;
typedef cudaDeviceProp gpuDeviceProp_t;
#define EIGEN_GPU_SCRATCH_SIZE (Eigen::kGpuScratchSize)
using se::cuda::ScopedActivateExecutorContext;
#elif TENSORFLOW_USE_ROCM
typedef hipStream_t gpuStream_t;
typedef hipDeviceProp_t gpuDeviceProp_t;
#define EIGEN_GPU_SCRATCH_SIZE (Eigen::kGpuScratchSize)
using se::rocm::ScopedActivateExecutorContext;
#endif
// Eigen Ops directly allocate memory only for temporary buffers used
// during OpKernel::Compute(). The recommended way of allocating such
// memory is via OpKernelContext::allocate_temp(). However, Eigen Ops
// don't have access to OpKernelContext, instead they get access to
// memory directly through the device allocator. As an Open Source
// project, Eigen assumes allocator semantics similar to those of the
// CUDA memory allocator, and may not work correctly due to race
// CUDA or ROCm memory allocator, and may not work correctly due to race
// conditions if used with some other allocator. For safety, we need
// to delay deallocation calls out of Eigen until all events on the
// corresponding stream have completed. The following two classes
@ -91,7 +117,7 @@ class EigenGpuStreamDevice : public ::Eigen::StreamInterface {
Eigen::initializeDeviceProp();
}
~EigenGpuStreamDevice() override {}
void Reinitialize(OpKernelContext* context, const cudaStream_t* cuda_stream,
void Reinitialize(OpKernelContext* context, const gpuStream_t* gpu_stream,
TfGpuId tf_gpu_id, ::tensorflow::Allocator* alloc,
char* scratch) {
if (LogMemory::IsEnabled()) {
@ -102,15 +128,15 @@ class EigenGpuStreamDevice : public ::Eigen::StreamInterface {
scratch_ = scratch;
semaphore_ =
reinterpret_cast<unsigned int*>(scratch + Eigen::kGpuScratchSize);
stream_ = cuda_stream;
stream_ = gpu_stream;
allocator_ = alloc;
PlatformGpuId platform_gpu_id;
TF_CHECK_OK(GpuIdManager::TfToPlatformGpuId(tf_gpu_id, &platform_gpu_id));
device_prop_ = &Eigen::m_deviceProperties[platform_gpu_id.value()];
}
const cudaStream_t& stream() const override { return *stream_; }
const cudaDeviceProp& deviceProperties() const override {
const gpuStream_t& stream() const override { return *stream_; }
const gpuDeviceProp_t& deviceProperties() const override {
return *device_prop_;
}
@ -140,8 +166,13 @@ class EigenGpuStreamDevice : public ::Eigen::StreamInterface {
}
AsyncFreeData* afData =
new AsyncFreeData(allocator_, buffer, operation_, step_id_);
#if GOOGLE_CUDA
cudaError_t err = cudaStreamAddCallback(*stream_, asyncFree, afData, 0);
CHECK_EQ(err, cudaSuccess);
#elif TENSORFLOW_USE_ROCM
hipError_t err = hipStreamAddCallback(*stream_, asyncFree, afData, 0);
CHECK_EQ(err, hipSuccess);
#endif
}
// Return a pointer to a per stream scratchpad of 1024 bytes residing
@ -165,8 +196,12 @@ class EigenGpuStreamDevice : public ::Eigen::StreamInterface {
const int64 step_id_;
};
static void CUDART_CB asyncFree(cudaStream_t stream, cudaError_t status,
#if GOOGLE_CUDA
static void CUDART_CB asyncFree(gpuStream_t stream, cudaError_t status,
void* userData) {
#elif TENSORFLOW_USE_ROCM
static void asyncFree(gpuStream_t stream, hipError_t status, void* userData) {
#endif
AsyncFreeData* data = static_cast<AsyncFreeData*>(userData);
if (LogMemory::IsEnabled()) {
LogMemory::RecordRawDeallocation(data->operation_, data->step_id_,
@ -178,8 +213,8 @@ class EigenGpuStreamDevice : public ::Eigen::StreamInterface {
string operation_;
int64 step_id_;
const cudaStream_t* stream_; // Not owned.
const cudaDeviceProp* device_prop_; // Not owned.
const gpuStream_t* stream_; // Not owned.
const gpuDeviceProp_t* device_prop_; // Not owned.
::tensorflow::Allocator* allocator_; // Not owned.
mutable char* scratch_;
mutable unsigned int* semaphore_;
@ -454,7 +489,7 @@ Status BaseGPUDevice::FillContextMap(const Graph* graph,
void BaseGPUDevice::Compute(OpKernel* op_kernel, OpKernelContext* context) {
// NOTE(tucker): We need to discriminate between Eigen GPU
// operations and all others. If an operation is Eigen
// implemented (or otherwise tries to launch a cuda kernel
// implemented (or otherwise tries to launch a GPU kernel
// directly), we need to establish a stacked-scoped environment
// that directs it to execute on the proper device. Otherwise we
// expect the Op to use StreamExecutor directly and correctly. The
@ -530,7 +565,7 @@ void BaseGPUDevice::ComputeHelper(OpKernel* op_kernel,
DCHECK(kernel_tracker_);
kernel_tracker_->PauseWhilePendingExceeds(pending_cap_);
}
se::cuda::ScopedActivateExecutorContext scoped_activation{stream->parent()};
ScopedActivateExecutorContext scoped_activation{stream->parent()};
op_kernel->Compute(context);
if (context->status().ok()) {
if (sync_every_op_) {
@ -596,7 +631,7 @@ void BaseGPUDevice::ComputeAsync(AsyncOpKernel* op_kernel,
// activity is simple enough that its overhead is negligible.
tracing::ScopedActivity activity(op_kernel->name(), op_kernel->type_string(),
op_kernel->IsExpensive());
se::cuda::ScopedActivateExecutorContext scoped_activation{stream->parent()};
ScopedActivateExecutorContext scoped_activation{stream->parent()};
op_kernel->ComputeAsync(context, done);
}
@ -715,10 +750,10 @@ class ConcretePerOpGpuDevice : public PerOpGpuDevice {
public:
ConcretePerOpGpuDevice() : device_(&stream_device_) {}
void Reinitialize(OpKernelContext* context, const cudaStream_t* cuda_stream,
void Reinitialize(OpKernelContext* context, const gpuStream_t* gpu_stream,
TfGpuId tf_gpu_id, Allocator* base_allocator,
char* scratch) {
stream_device_.Reinitialize(context, cuda_stream, tf_gpu_id, base_allocator,
stream_device_.Reinitialize(context, gpu_stream, tf_gpu_id, base_allocator,
scratch);
}
@ -898,9 +933,9 @@ void BaseGPUDevice::ReinitializeDevice(OpKernelContext* context,
ConcretePerOpGpuDevice* concrete_device =
static_cast<ConcretePerOpGpuDevice*>(device);
DCHECK(concrete_device);
const cudaStream_t* cuda_stream = reinterpret_cast<const cudaStream_t*>(
const gpuStream_t* gpu_stream = reinterpret_cast<const gpuStream_t*>(
streams_[stream_id]->compute->implementation()->GpuStreamMemberHack());
concrete_device->Reinitialize(context, cuda_stream, tf_gpu_id_, allocator,
concrete_device->Reinitialize(context, gpu_stream, tf_gpu_id_, allocator,
scratch_[stream_id]);
}
@ -977,14 +1012,24 @@ Status BaseGPUDeviceFactory::CreateDevices(
if (!valid_platform_gpu_ids.empty()) {
// Save the original device.
int original_device = 0;
#if GOOGLE_CUDA
cudaError_t err = cudaGetDevice(&original_device);
if (err != cudaSuccess) {
return errors::Internal("cudaGetDevice() failed. Status: ",
cudaGetErrorString(err));
}
#elif TENSORFLOW_USE_ROCM
hipError_t err = hipGetDevice(&original_device);
if (err != hipSuccess) {
return errors::Internal("hipGetDevice() failed. Status: ",
hipGetErrorString(err));
}
#endif
// Force to implicitly initialize CUDA runtime on each valid GPU before
// CreateGPUDevice().
for (PlatformGpuId platform_gpu_id : valid_platform_gpu_ids) {
#if GOOGLE_CUDA
err = cudaSetDevice(platform_gpu_id.value());
if (err != cudaSuccess) {
return errors::Internal(
@ -997,13 +1042,35 @@ Status BaseGPUDeviceFactory::CreateDevices(
platform_gpu_id.value(),
" failed. Status: ", cudaGetErrorString(err));
}
#elif TENSORFLOW_USE_ROCM
err = hipSetDevice(platform_gpu_id.value());
if (err != hipSuccess) {
return errors::Internal(
"hipSetDevice() on GPU:", platform_gpu_id.value(),
" failed. Status: ", hipGetErrorString(err));
}
err = hipFree(nullptr);
if (err != hipSuccess) {
return errors::Internal("ROCm runtime implicit initialization on GPU:",
platform_gpu_id.value(),
" failed. Status: ", hipGetErrorString(err));
}
#endif
}
// Reset to the original device.
#if GOOGLE_CUDA
err = cudaSetDevice(original_device);
if (err != cudaSuccess) {
return errors::Internal("cudaSetDevice() on GPU:", original_device,
" failed. Status: ", cudaGetErrorString(err));
}
#elif TENSORFLOW_USE_ROCM
err = hipSetDevice(original_device);
if (err != hipSuccess) {
return errors::Internal("hipSetDevice() on GPU:", original_device,
" failed. Status: ", hipGetErrorString(err));
}
#endif
}
std::vector<InterconnectMap> interconnect_maps;
@ -1093,6 +1160,7 @@ Status BaseGPUDeviceFactory::CreateDevices(
static string GetShortDeviceDescription(PlatformGpuId platform_gpu_id,
const se::DeviceDescription& desc) {
#if GOOGLE_CUDA
int cc_major;
int cc_minor;
if (!desc.cuda_compute_capability(&cc_major, &cc_minor)) {
@ -1104,6 +1172,11 @@ static string GetShortDeviceDescription(PlatformGpuId platform_gpu_id,
desc.name(), ", pci bus id: ", desc.pci_bus_id(),
", compute capability: ", cc_major, ".", cc_minor);
// LINT.ThenChange(//tensorflow/python/platform/test.py)
#elif TENSORFLOW_USE_ROCM
return strings::StrCat("device: ", platform_gpu_id.value(),
", name: ", desc.name(),
", pci bus id: ", desc.pci_bus_id());
#endif
}
Status BaseGPUDeviceFactory::CreateGPUDevice(
@ -1329,6 +1402,7 @@ static int GetMinGPUMultiprocessorCount(
namespace {
#if GOOGLE_CUDA
struct CudaVersion {
// Initialize from version_name in the form of "3.5"
explicit CudaVersion(const std::string& version_name) {
@ -1380,6 +1454,15 @@ std::vector<CudaVersion> GetSupportedCudaComputeCapabilities() {
#endif
return cuda_caps;
}
#endif // GOOGLE_CUDA
#if TENSORFLOW_USE_ROCM
std::vector<int> supported_amdgpu_isa_versions = {803, 900, 906};
std::vector<int> GetSupportedAMDGPUISAVersions() {
return supported_amdgpu_isa_versions;
}
#endif // TENSORFLOW_USE_ROCM
Status EnablePeerAccess(se::Platform* platform,
const std::vector<PlatformGpuId>& visible_gpu_order) {
@ -1457,6 +1540,7 @@ Status BaseGPUDeviceFactory::GetValidDeviceIds(
total_bytes = 0;
}
const auto& description = stream_exec->GetDeviceDescription();
#if GOOGLE_CUDA
int cc_major;
int cc_minor;
if (!description.cuda_compute_capability(&cc_major, &cc_minor)) {
@ -1471,6 +1555,21 @@ Status BaseGPUDeviceFactory::GetValidDeviceIds(
<< "\npciBusID: " << description.pci_bus_id() << "\ntotalMemory: "
<< strings::HumanReadableNumBytes(total_bytes)
<< " freeMemory: " << strings::HumanReadableNumBytes(free_bytes);
#elif TENSORFLOW_USE_ROCM
int isa_version;
if (!description.rocm_amdgpu_isa_version(&isa_version)) {
// Logs internally on failure.
isa_version = 0;
}
LOG(INFO) << "Found device " << i << " with properties: "
<< "\nname: " << description.name() << "\nAMDGPU ISA: gfx"
<< isa_version << "\nmemoryClockRate (GHz) "
<< description.clock_rate_ghz() << "\npciBusID "
<< description.pci_bus_id() << "\nTotal memory: "
<< strings::HumanReadableNumBytes(total_bytes)
<< "\nFree memory: "
<< strings::HumanReadableNumBytes(free_bytes);
#endif
}
// Checking peering and shows matrix if more than one gpu found.
if (new_gpu_found && visible_gpu_order.size() > 1) {
@ -1478,6 +1577,7 @@ Status BaseGPUDeviceFactory::GetValidDeviceIds(
TF_RETURN_IF_ERROR(EnablePeerAccess(gpu_manager, visible_gpu_order));
}
#if GOOGLE_CUDA
auto cuda_supported_capabilities = GetSupportedCudaComputeCapabilities();
if (cuda_supported_capabilities.empty()) {
return errors::FailedPrecondition(
@ -1485,6 +1585,15 @@ Status BaseGPUDeviceFactory::GetValidDeviceIds(
}
CudaVersion min_supported_capability = *std::min_element(
cuda_supported_capabilities.begin(), cuda_supported_capabilities.end());
#elif TENSORFLOW_USE_ROCM
auto rocm_supported_isas = GetSupportedAMDGPUISAVersions();
if (rocm_supported_isas.empty()) {
return errors::FailedPrecondition(
"No supported rocm capabilities in binary.");
}
int min_supported_isa =
*std::min_element(rocm_supported_isas.begin(), rocm_supported_isas.end());
#endif
int min_gpu_core_count =
GetMinGPUMultiprocessorCount(gpu_manager, visible_gpu_order);
@ -1502,6 +1611,8 @@ Status BaseGPUDeviceFactory::GetValidDeviceIds(
}
se::StreamExecutor* se = exec_status.ValueOrDie();
const se::DeviceDescription& desc = se->GetDeviceDescription();
#if GOOGLE_CUDA
CudaVersion device_capability;
if (!desc.cuda_compute_capability(&device_capability.major_part,
&device_capability.minor_part)) {
@ -1522,6 +1633,23 @@ Status BaseGPUDeviceFactory::GetValidDeviceIds(
<< min_supported_capability << ".";
continue;
}
#elif TENSORFLOW_USE_ROCM
int device_isa;
if (!desc.rocm_amdgpu_isa_version(&device_isa)) {
continue;
}
// Only GPUs with no less than the minimum supported compute capability is
// accepted.
if (device_isa < min_supported_isa) {
LOG(INFO) << "Ignoring visible gpu device "
<< "(" << GetShortDeviceDescription(visible_gpu_id, desc)
<< ") "
<< "with AMDGPU ISA gfx" << device_isa
<< ". The minimum required AMDGPU ISA is gfx"
<< min_supported_isa << ".";
continue;
}
#endif
// Filter out slow GPUs. By default, GPUs with a lower multiprocessor
// count than the fastest GPU are filtered out, unless they have 8 or more
@ -1531,7 +1659,7 @@ Status BaseGPUDeviceFactory::GetValidDeviceIds(
LOG(INFO) << "Ignoring visible gpu device "
<< "(" << GetShortDeviceDescription(visible_gpu_id, desc)
<< ") "
<< "with Cuda multiprocessor count: " << desc.core_count()
<< "with core count: " << desc.core_count()
<< ". The minimum required count is " << min_gpu_core_count
<< ". You can adjust this requirement with the env var "
"TF_MIN_GPU_MULTIPROCESSOR_COUNT.";

10
tensorflow/core/common_runtime/gpu/gpu_device.h
View File

@ -13,8 +13,8 @@ See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#if !GOOGLE_CUDA
#error This file must only be included when building with Cuda support
#if !GOOGLE_CUDA && !TENSORFLOW_USE_ROCM
#error This file must only be included when building with Cuda or ROCm support
#endif
#ifndef TENSORFLOW_CORE_COMMON_RUNTIME_GPU_GPU_DEVICE_H_
@ -98,7 +98,7 @@ class BaseGPUDevice : public LocalDevice {
Allocator* allocator) override;
// Returns the platform GPU id of this device within the native driver system;
// e.g., for CUDA this is the ordinal of the GPU within the system.
// e.g., for CUDA and ROCm this is the ordinal of the GPU within the system.
int gpu_id() const {
PlatformGpuId platform_gpu_id;
TF_CHECK_OK(GpuIdManager::TfToPlatformGpuId(tf_gpu_id_, &platform_gpu_id));
@ -311,8 +311,8 @@ class BaseGPUDeviceFactory : public DeviceFactory {
// Returns into 'ids' the list of valid platform GPU ids, in the order that
// they should map to TF GPU ids "/device:GPU:0", "/device:GPU:1", etc,
// based upon 'visible_gpu_order' which was generated by parsing
// GPUOptions::visible_device_list which is a comma-separated list of CUDA GPU
// ids.
// GPUOptions::visible_device_list which is a comma-separated list of CUDA or
// ROCm GPU ids.
Status GetValidDeviceIds(const std::vector<PlatformGpuId>& visible_gpu_order,
std::vector<PlatformGpuId>* ids);

8
tensorflow/core/common_runtime/gpu/gpu_device_factory.cc
View File

@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#if GOOGLE_CUDA
#if GOOGLE_CUDA || TENSORFLOW_USE_ROCM
#define EIGEN_USE_GPU
@ -45,7 +45,7 @@ class GPUDevice : public BaseGPUDevice {
if (attr.on_host()) {
if (attr.gpu_compatible() || force_gpu_compatible_) {
GPUProcessState* ps = GPUProcessState::singleton();
return ps->GetCUDAHostAllocator(0);
return ps->GetGpuHostAllocator(0);
} else {
return cpu_allocator_;
}
@ -94,7 +94,7 @@ class GPUCompatibleCPUDevice : public ThreadPoolDevice {
Allocator* GetAllocator(AllocatorAttributes attr) override {
GPUProcessState* ps = GPUProcessState::singleton();
if (attr.gpu_compatible() || force_gpu_compatible_) {
return ps->GetCUDAHostAllocator(numa_node_);
return ps->GetGpuHostAllocator(numa_node_);
} else {
// Call the parent's implementation.
return ThreadPoolDevice::GetAllocator(attr);
@ -136,4 +136,4 @@ REGISTER_LOCAL_DEVICE_FACTORY("CPU", GPUCompatibleCPUDeviceFactory, 70);
} // namespace tensorflow
#endif // GOOGLE_CUDA
#endif // GOOGLE_CUDA || TENSORFLOW_USE_ROCM

20
tensorflow/core/common_runtime/gpu/cuda_host_allocator.h → tensorflow/core/common_runtime/gpu/gpu_host_allocator.h
View File

@ -13,28 +13,28 @@ See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#ifndef TENSORFLOW_CORE_COMMON_RUNTIME_GPU_CUDA_HOST_ALLOCATOR_H_
#define TENSORFLOW_CORE_COMMON_RUNTIME_GPU_CUDA_HOST_ALLOCATOR_H_
#ifndef TENSORFLOW_CORE_COMMON_RUNTIME_GPU_GPU_HOST_ALLOCATOR_H_
#define TENSORFLOW_CORE_COMMON_RUNTIME_GPU_GPU_HOST_ALLOCATOR_H_
#include "tensorflow/core/framework/allocator.h"
#include "tensorflow/core/platform/macros.h"
#include "tensorflow/core/platform/stream_executor.h"
namespace tensorflow {
// Allocator for pinned CPU RAM that is made known to CUDA for the
// Allocator for pinned CPU RAM that is made known to GPU for the
// purpose of efficient DMA with a GPU.
class CUDAHostAllocator : public SubAllocator {
class GpuHostAllocator : public SubAllocator {
public:
// Note: stream_exec cannot be null.
explicit CUDAHostAllocator(se::StreamExecutor* stream_exec, int numa_node,
const std::vector<Visitor>& alloc_visitors,
const std::vector<Visitor>& free_visitors)
explicit GpuHostAllocator(se::StreamExecutor* stream_exec, int numa_node,
const std::vector<Visitor>& alloc_visitors,
const std::vector<Visitor>& free_visitors)
: SubAllocator(alloc_visitors, free_visitors),
stream_exec_(stream_exec),
numa_node_(numa_node) {
CHECK(stream_exec_ != nullptr);
}
~CUDAHostAllocator() override {}
~GpuHostAllocator() override {}
void* Alloc(size_t alignment, size_t num_bytes) override {
void* ptr = nullptr;
@ -61,8 +61,8 @@ class CUDAHostAllocator : public SubAllocator {
se::StreamExecutor* stream_exec_; // not owned, non-null
const int numa_node_;
TF_DISALLOW_COPY_AND_ASSIGN(CUDAHostAllocator);
TF_DISALLOW_COPY_AND_ASSIGN(GpuHostAllocator);
};
} // namespace tensorflow
#endif // TENSORFLOW_CORE_COMMON_RUNTIME_GPU_CUDA_HOST_ALLOCATOR_H_
#endif // TENSORFLOW_CORE_COMMON_RUNTIME_GPU_GPU_HOST_ALLOCATOR_H_

16
tensorflow/core/common_runtime/gpu/gpu_init.cc
View File

@ -29,17 +29,27 @@ limitations under the License.
namespace tensorflow {
Status ValidateGPUMachineManager() {
return se::MultiPlatformManager::PlatformWithName("CUDA").status();
return se::MultiPlatformManager::PlatformWithName(GpuPlatformName()).status();
}
se::Platform* GPUMachineManager() {
auto result = se::MultiPlatformManager::PlatformWithName("CUDA");
auto result = se::MultiPlatformManager::PlatformWithName(GpuPlatformName());
if (!result.ok()) {
LOG(FATAL) << "Could not find Platform with name CUDA";
LOG(FATAL) << "Could not find Platform with name " << GpuPlatformName();
return nullptr;
}
return result.ValueOrDie();
}
string GpuPlatformName() {
#if TENSORFLOW_USE_ROCM
return "ROCM";
#else
// This function will return "CUDA" even when building TF without GPU support
// This is done to preserve existing functionality
return "CUDA";
#endif
}
} // namespace tensorflow

8
tensorflow/core/common_runtime/gpu/gpu_init.h
View File

@ -16,6 +16,7 @@ limitations under the License.
#ifndef TENSORFLOW_CORE_COMMON_RUNTIME_GPU_GPU_INIT_H_
#define TENSORFLOW_CORE_COMMON_RUNTIME_GPU_GPU_INIT_H_
#include <string>
#include "tensorflow/core/lib/core/status.h"
namespace stream_executor {
@ -24,7 +25,7 @@ class Platform;
namespace tensorflow {
// Initializes the CUDA platform and returns OK if the CUDA
// Initializes the GPU platform and returns OK if the GPU
// platform could be initialized.
Status ValidateGPUMachineManager();
@ -34,6 +35,11 @@ Status ValidateGPUMachineManager();
// in the process (e.g., ValidateGPUMachineManager() returns OK).
stream_executor::Platform* GPUMachineManager();
// Returns the string describing the name of the GPU platform in use.
// This value is "CUDA" by default, and
// "ROCM" when TF is built with `--config==rocm`
string GpuPlatformName();
} // namespace tensorflow
#endif // TENSORFLOW_CORE_COMMON_RUNTIME_GPU_GPU_INIT_H_

123
tensorflow/core/common_runtime/gpu/gpu_process_state.cc
View File

@ -18,10 +18,10 @@ limitations under the License.
#include <cstring>
#include <vector>
#include "tensorflow/core/common_runtime/gpu/cuda_host_allocator.h"
#include "tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.h"
#include "tensorflow/core/common_runtime/gpu/gpu_cudamalloc_allocator.h"
#include "tensorflow/core/common_runtime/gpu/gpu_debug_allocator.h"
#include "tensorflow/core/common_runtime/gpu/gpu_host_allocator.h"
#include "tensorflow/core/common_runtime/gpu/gpu_id.h"
#include "tensorflow/core/common_runtime/gpu/gpu_id_manager.h"
#include "tensorflow/core/common_runtime/gpu/gpu_id_utils.h"
@ -81,7 +81,7 @@ Allocator* GPUProcessState::GetGPUAllocator(const GPUOptions& options,
TfGpuId tf_gpu_id,
size_t total_bytes) {
CHECK(process_state_);
#if GOOGLE_CUDA
#if GOOGLE_CUDA || TENSORFLOW_USE_ROCM
const string& allocator_type = options.allocator_type();
mutex_lock lock(mu_);
GpuIdUtil::CheckValidTfGpuId(tf_gpu_id);
@ -155,14 +155,15 @@ Allocator* GPUProcessState::GetGPUAllocator(const GPUOptions& options,
return allocator_parts.allocator.get();
}
#else
LOG(FATAL) << "GPUAllocator unavailable. Not compiled with --config=cuda.";
LOG(FATAL) << "GPUAllocator unavailable. Not compiled with --config=cuda or "
"--config=rocm.";
return nullptr;
#endif // GOOGLE_CUDA
#endif // GOOGLE_CUDA || TENSORFLOW_USE_ROCM
}
SharedCounter* GPUProcessState::GPUAllocatorCounter(TfGpuId tf_gpu_id) {
DCHECK(process_state_);
#if GOOGLE_CUDA
#if GOOGLE_CUDA || TENSORFLOW_USE_ROCM
GpuIdUtil::CheckValidTfGpuId(tf_gpu_id);
mutex_lock l(mu_);
if (tf_gpu_id.value() >= static_cast<int64>(gpu_allocators_.size())) {
@ -173,37 +174,37 @@ SharedCounter* GPUProcessState::GPUAllocatorCounter(TfGpuId tf_gpu_id) {
return allocator_parts.counter.get();
#else
return nullptr;
#endif
#endif // GOOGLE_CUDA || TENSORFLOW_USE_ROCM
}
Allocator* GPUProcessState::GetCUDAHostAllocator(int numa_node) {
Allocator* GPUProcessState::GetGpuHostAllocator(int numa_node) {
CHECK(process_state_);
if (!HasGPUDevice() ||
!process_state_->ProcessState::FLAGS_brain_mem_reg_cuda_dma) {
!process_state_->ProcessState::FLAGS_brain_mem_reg_gpu_dma) {
return process_state_->GetCPUAllocator(numa_node);
}
if (numa_node == port::kNUMANoAffinity) {
numa_node = 0;
}
{
// Here we optimize the most common use case where cuda_host_allocators_
// and cuda_al_ have already been populated and since we're only reading
// Here we optimize the most common use case where gpu_host_allocators_
// have already been populated and since we're only reading
// these vectors, we can get by with a shared lock. In the slower case,
// we take a unique lock and populate these vectors.
tf_shared_lock lock(mu_);
if (process_state_->ProcessState::FLAGS_brain_gpu_record_mem_types &&
!cuda_host_allocators_.empty() &&
cuda_host_allocators_[0].recording_allocator != nullptr) {
return cuda_host_allocators_[0].recording_allocator.get();
!gpu_host_allocators_.empty() &&
gpu_host_allocators_[0].recording_allocator != nullptr) {
return gpu_host_allocators_[0].recording_allocator.get();
}
if (static_cast<int>(cuda_host_allocators_.size()) > numa_node) {
return cuda_host_allocators_[0].allocator.get();
if (static_cast<int>(gpu_host_allocators_.size()) > numa_node) {
return gpu_host_allocators_[0].allocator.get();
}
}
mutex_lock lock(mu_);
// Find the first valid StreamExecutor to request CUDA host memory
// Find the first valid StreamExecutor to request CUDA or ROCm host memory
// through, since any will work.
//
// This search isn't super clean, and it would be nice to use a
@ -220,39 +221,39 @@ Allocator* GPUProcessState::GetCUDAHostAllocator(int numa_node) {
CHECK_NE(nullptr, se);
while (static_cast<int>(cuda_host_allocators_.size()) <= numa_node) {
while (cuda_host_alloc_visitors_.size() <= numa_node) {
cuda_host_alloc_visitors_.push_back({});
while (static_cast<int>(gpu_host_allocators_.size()) <= numa_node) {
while (gpu_host_alloc_visitors_.size() <= numa_node) {
gpu_host_alloc_visitors_.push_back({});
}
while (cuda_host_free_visitors_.size() <= numa_node) {
cuda_host_free_visitors_.push_back({});
while (gpu_host_free_visitors_.size() <= numa_node) {
gpu_host_free_visitors_.push_back({});
}
SubAllocator* sub_allocator = new CUDAHostAllocator(
se, numa_node, cuda_host_alloc_visitors_[numa_node],
cuda_host_free_visitors_[numa_node]);
SubAllocator* sub_allocator =
new GpuHostAllocator(se, numa_node, gpu_host_alloc_visitors_[numa_node],
gpu_host_free_visitors_[numa_node]);
// TODO(zheng-xq): evaluate whether 64GB by default is the best choice.
int64 cuda_host_mem_limit_in_mb = -1;
Status status = ReadInt64FromEnvVar("TF_CUDA_HOST_MEM_LIMIT_IN_MB",
int64 gpu_host_mem_limit_in_mb = -1;
Status status = ReadInt64FromEnvVar("TF_GPU_HOST_MEM_LIMIT_IN_MB",
1LL << 16 /*64GB max by default*/,
&cuda_host_mem_limit_in_mb);
&gpu_host_mem_limit_in_mb);
if (!status.ok()) {
LOG(ERROR) << "GetCUDAHostAllocator: " << status.error_message();
LOG(ERROR) << "GetGpuHostAllocator: " << status.error_message();
}
int64 cuda_host_mem_limit = cuda_host_mem_limit_in_mb * (1LL << 20);
int64 gpu_host_mem_limit = gpu_host_mem_limit_in_mb * (1LL << 20);
Allocator* allocator =
new BFCAllocator(sub_allocator, cuda_host_mem_limit,
true /*allow_growth*/, "cuda_host_bfc" /*name*/);
new BFCAllocator(sub_allocator, gpu_host_mem_limit,
true /*allow_growth*/, "gpu_host_bfc" /*name*/);
if (LogMemory::IsEnabled() && !allocator->TracksAllocationSizes()) {
// Wrap the allocator to track allocation ids for better logging
// at the cost of performance.
allocator = new TrackingAllocator(allocator, true);
}
cuda_host_allocators_.push_back({std::unique_ptr<Allocator>(allocator),
std::unique_ptr<SharedCounter>(nullptr),
sub_allocator,
std::unique_ptr<Allocator>(nullptr)});
AllocatorParts& allocator_parts = cuda_host_allocators_.back();
gpu_host_allocators_.push_back({std::unique_ptr<Allocator>(allocator),
std::unique_ptr<SharedCounter>(nullptr),
sub_allocator,
std::unique_ptr<Allocator>(nullptr)});
AllocatorParts& allocator_parts = gpu_host_allocators_.back();
if (process_state_->ProcessState::FLAGS_brain_gpu_record_mem_types) {
ProcessState::MemDesc md;
md.loc = ProcessState::MemDesc::CPU;
@ -266,15 +267,15 @@ Allocator* GPUProcessState::GetCUDAHostAllocator(int numa_node) {
}
}
if (process_state_->ProcessState::FLAGS_brain_gpu_record_mem_types) {
return cuda_host_allocators_[0].recording_allocator.get();
return gpu_host_allocators_[0].recording_allocator.get();
} else {
return cuda_host_allocators_[0].allocator.get();
return gpu_host_allocators_[0].allocator.get();
}
}
void GPUProcessState::AddGPUAllocVisitor(int bus_id,
const SubAllocator::Visitor& visitor) {
#if GOOGLE_CUDA
#if GOOGLE_CUDA || TENSORFLOW_USE_ROCM
mutex_lock lock(mu_);
CHECK(gpu_allocators_.empty()) // Crash OK
<< "AddGPUAllocVisitor must be called before "
@ -284,35 +285,35 @@ void GPUProcessState::AddGPUAllocVisitor(int bus_id,
gpu_visitors_.push_back(std::vector<SubAllocator::Visitor>());
}
gpu_visitors_[bus_id].push_back(visitor);
#endif // GOOGLE_CUDA
#endif // GOOGLE_CUDA || TENSORFLOW_USE_ROCM
}
void GPUProcessState::AddCUDAHostAllocVisitor(
void GPUProcessState::AddGpuHostAllocVisitor(
int numa_node, const SubAllocator::Visitor& visitor) {
#if GOOGLE_CUDA
#if GOOGLE_CUDA || TENSORFLOW_USE_ROCM
mutex_lock lock(mu_);
CHECK(cuda_host_allocators_.empty()) // Crash OK
<< "AddCUDAHostAllocVisitor must be called before "
"first call to GetCUDAHostAllocator.";
while (numa_node >= static_cast<int64>(cuda_host_alloc_visitors_.size())) {
cuda_host_alloc_visitors_.push_back(std::vector<SubAllocator::Visitor>());
CHECK(gpu_host_allocators_.empty()) // Crash OK
<< "AddGpuHostAllocVisitor must be called before "
"first call to GetGpuHostAllocator.";
while (numa_node >= static_cast<int64>(gpu_host_alloc_visitors_.size())) {
gpu_host_alloc_visitors_.push_back(std::vector<SubAllocator::Visitor>());
}
cuda_host_alloc_visitors_[numa_node].push_back(visitor);
#endif // GOOGLE_CUDA
gpu_host_alloc_visitors_[numa_node].push_back(visitor);
#endif // GOOGLE_CUDA || TENSORFLOW_USE_ROCM
}
void GPUProcessState::AddCUDAHostFreeVisitor(
void GPUProcessState::AddGpuHostFreeVisitor(
int numa_node, const SubAllocator::Visitor& visitor) {
#if GOOGLE_CUDA
#if GOOGLE_CUDA || TENSORFLOW_USE_ROCM
mutex_lock lock(mu_);
CHECK(cuda_host_allocators_.empty()) // Crash OK
<< "AddCUDAHostFreeVisitor must be called before "
"first call to GetCUDAHostAllocator.";
while (numa_node >= static_cast<int64>(cuda_host_free_visitors_.size())) {
cuda_host_free_visitors_.push_back(std::vector<SubAllocator::Visitor>());
CHECK(gpu_host_allocators_.empty()) // Crash OK
<< "AddGpuHostFreeVisitor must be called before "
"first call to GetGpuHostAllocator.";
while (numa_node >= static_cast<int64>(gpu_host_free_visitors_.size())) {
gpu_host_free_visitors_.push_back(std::vector<SubAllocator::Visitor>());
}
cuda_host_free_visitors_[numa_node].push_back(visitor);
#endif // GOOGLE_CUDA
gpu_host_free_visitors_[numa_node].push_back(visitor);
#endif // GOOGLE_CUDA || TENSORFLOW_USE_ROCM
}
void GPUProcessState::TestOnlyReset() {
@ -324,9 +325,9 @@ void GPUProcessState::TestOnlyReset() {
gpu_device_enabled_ = false;
gpu_allocators_.clear();
gpu_visitors_.clear();
cuda_host_allocators_.clear();
cuda_host_alloc_visitors_.clear();
cuda_host_free_visitors_.clear();
gpu_host_allocators_.clear();
gpu_host_alloc_visitors_.clear();
gpu_host_free_visitors_.clear();
}
}

20
tensorflow/core/common_runtime/gpu/gpu_process_state.h
View File

@ -83,7 +83,7 @@ class GPUProcessState {
virtual Allocator* GetGPUAllocator(const GPUOptions& options,
TfGpuId tf_gpu_id, size_t total_bytes);
virtual Allocator* GetCUDAHostAllocator(int numa_node);
virtual Allocator* GetGpuHostAllocator(int numa_node);
// Registers a Visitor to be invoked on new chunks of memory allocated by the
// SubAllocator of every GPU proximate to the specified bus. The AllocVisitor
@ -98,14 +98,14 @@ class GPUProcessState {
const SubAllocator::Visitor& visitor);
// Registers a Visitor to be invoked on new chunks of memory allocated by
// the SubAllocator of the CUDAHostAllocator for the given numa_node.
virtual void AddCUDAHostAllocVisitor(int numa_node,
const SubAllocator::Visitor& visitor);
// the SubAllocator of the GpuHostAllocator for the given numa_node.
virtual void AddGpuHostAllocVisitor(int numa_node,
const SubAllocator::Visitor& visitor);
// Registers a Visitor to be invoked on each chunk handed back for freeing to
// the SubAllocator of the CUDAHostAllocator for the given numa_node.
virtual void AddCUDAHostFreeVisitor(int numa_node,
const SubAllocator::Visitor& visitor);
// the SubAllocator of the GpuHostAllocator for the given numa_node.
virtual void AddGpuHostFreeVisitor(int numa_node,
const SubAllocator::Visitor& visitor);
// Returns bus_id for the given GPU id.
virtual int BusIdForGPU(TfGpuId tf_gpu_id);
@ -143,10 +143,10 @@ class GPUProcessState {
std::vector<AllocatorParts> gpu_allocators_ GUARDED_BY(mu_);
std::vector<std::vector<SubAllocator::Visitor>> gpu_visitors_ GUARDED_BY(mu_);
std::vector<AllocatorParts> cuda_host_allocators_ GUARDED_BY(mu_);
std::vector<std::vector<SubAllocator::Visitor>> cuda_host_alloc_visitors_
std::vector<AllocatorParts> gpu_host_allocators_ GUARDED_BY(mu_);
std::vector<std::vector<SubAllocator::Visitor>> gpu_host_alloc_visitors_
GUARDED_BY(mu_);
std::vector<std::vector<SubAllocator::Visitor>> cuda_host_free_visitors_
std::vector<std::vector<SubAllocator::Visitor>> gpu_host_free_visitors_
GUARDED_BY(mu_);
};

2
tensorflow/core/common_runtime/gpu/gpu_util.cc
View File

@ -150,7 +150,7 @@ void GPUUtil::SetProtoFromGPU(const Tensor& tensor, Device* dev,
const int64 total_bytes = is_dead ? 0 : tensor.TotalBytes();
if (total_bytes > 0) {
tracing::ScopedAnnotation annotation("SetProtoFromGPU");
alloc = GPUProcessState::singleton()->GetCUDAHostAllocator(0);
alloc = GPUProcessState::singleton()->GetGpuHostAllocator(0);
buf = alloc->Allocate<char>(total_bytes);
if (LogMemory::IsEnabled()) {
LogMemory::RecordRawAllocation("SetProtoFromGPU",

12
tensorflow/core/common_runtime/gpu/pool_allocator_test.cc
View File

@ -17,7 +17,7 @@ limitations under the License.
#include "tensorflow/core/common_runtime/pool_allocator.h"
#include "tensorflow/core/common_runtime/gpu/cuda_host_allocator.h"
#include "tensorflow/core/common_runtime/gpu/gpu_host_allocator.h"
#include "tensorflow/core/platform/stream_executor.h"
#include "tensorflow/core/platform/test.h"
@ -29,7 +29,7 @@ TEST(PoolAllocatorTest, ZeroSizeBuffers) {
se::MultiPlatformManager::PlatformWithName("cuda").ValueOrDie();
PoolAllocator pool(
2 /*pool_size_limit*/, false /*auto_resize*/,
new CUDAHostAllocator(
new GpuHostAllocator(
platform->GetExecutor(se::StreamExecutorConfig(/*ordinal=*/0))
.ValueOrDie(),
0 /*numa_node*/, {}, {}),
@ -48,7 +48,7 @@ TEST(PoolAllocatorTest, ZeroSizePool) {
se::MultiPlatformManager::PlatformWithName("cuda").ValueOrDie();
PoolAllocator pool(
0 /*pool_size_limit*/, false /*auto_resize*/,
new CUDAHostAllocator(
new GpuHostAllocator(
platform->GetExecutor(se::StreamExecutorConfig(/*ordinal=*/0))
.ValueOrDie(),
0 /*numa_node*/, {}, {}),
@ -82,7 +82,7 @@ TEST(PoolAllocatorTest, Alignment) {
se::MultiPlatformManager::PlatformWithName("cuda").ValueOrDie();
PoolAllocator pool(
0 /*pool_size_limit*/, false /*auto_resize*/,
new CUDAHostAllocator(
new GpuHostAllocator(
platform->GetExecutor(se::StreamExecutorConfig(/*ordinal=*/0))
.ValueOrDie(),
0 /*numa_node*/, {}, {}),
@ -142,7 +142,7 @@ TEST(PoolAllocatorTest, CudaHostAllocator) {
};
se::Platform* platform =
se::MultiPlatformManager::PlatformWithName("cuda").ValueOrDie();
CUDAHostAllocator* sub_allocator = new CUDAHostAllocator(
GpuHostAllocator* sub_allocator = new GpuHostAllocator(
platform->GetExecutor(se::StreamExecutorConfig(/*ordinal=*/0))
.ValueOrDie(),
0 /*numa_node*/, {alloc_visitor}, {free_visitor});
@ -247,7 +247,7 @@ TEST(PoolAllocatorTest, Name) {
se::MultiPlatformManager::PlatformWithName("cuda").ValueOrDie();
PoolAllocator pool(
2 /*pool_size_limit*/, false /*auto_resize*/,
new CUDAHostAllocator(
new GpuHostAllocator(
platform->GetExecutor(se::StreamExecutorConfig(/*ordinal=*/0))
.ValueOrDie(),
0 /*numa_node*/, {}, {}),

2
tensorflow/core/common_runtime/process_state.h
View File

@ -83,7 +83,7 @@ class ProcessState : public ProcessStateInterface {
// If these flags need to be runtime configurable consider adding
// them to ConfigProto.
static const bool FLAGS_brain_mem_reg_cuda_dma = true;
static const bool FLAGS_brain_mem_reg_gpu_dma = true;
static const bool FLAGS_brain_gpu_record_mem_types = false;
// Helper method for unit tests to reset the ProcessState singleton by

5
tensorflow/core/platform/default/build_config/BUILD
View File

@ -34,7 +34,10 @@ cc_library(
tf_cuda_library(
name = "stream_executor",
cuda_deps = ["//tensorflow/stream_executor/cuda:cuda_activation"],
cuda_deps = [
"//tensorflow/stream_executor/cuda:cuda_activation",
"//tensorflow/stream_executor/rocm:rocm_activation",
],
deps = [
"//tensorflow/stream_executor",
"//tensorflow/stream_executor:dnn",

22
tensorflow/core/platform/rocm.h Normal file
View File

@ -0,0 +1,22 @@
/* Copyright 2015 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_CORE_PLATFORM_ROCM_H_
#define TENSORFLOW_CORE_PLATFORM_ROCM_H_
#include "tensorflow/core/platform/platform.h"
#include "tensorflow/stream_executor/rocm/rocm_activation.h"
#endif // TENSORFLOW_CORE_PLATFORM_ROCM_H_

14
tensorflow/stream_executor/rocm/BUILD
View File

@ -62,6 +62,20 @@ cc_library(
]),
)
cc_library(
name = "rocm_activation",
srcs = [],
hdrs = if_rocm_is_configured(["rocm_activation.h"]),
deps = if_rocm_is_configured([
":rocm_driver",
"@local_config_rocm//rocm:rocm_headers",
"//tensorflow/stream_executor",
"//tensorflow/stream_executor:stream_executor_internal",
"//tensorflow/stream_executor/gpu:gpu_activation",
"//tensorflow/stream_executor/platform",
]),
)
cc_library(
name = "rocm_event",
srcs = if_rocm_is_configured(["rocm_event.cc"]),

39
tensorflow/stream_executor/rocm/rocm_activation.h Normal file
View File

@ -0,0 +1,39 @@
/* Copyright 2015 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.
==============================================================================*/
// This file contains APIs that assume a StreamExecutor is backed by ROCM.
// It reaches into the ROCM implementation to activate an underlying ROCM
// context.
//
// Having this file separate from rocm/rocm_gpu_executor.h means that dependent
// code does not also have to depend on rocm.h.
#ifndef TENSORFLOW_STREAM_EXECUTOR_ROCM_ROCM_ACTIVATION_H_
#define TENSORFLOW_STREAM_EXECUTOR_ROCM_ROCM_ACTIVATION_H_
#include "tensorflow/stream_executor/gpu/gpu_activation.h"
namespace stream_executor {
class StreamExecutor;
namespace rocm {
using ScopedActivateExecutorContext = gpu::ScopedActivateExecutorContext;
} // namespace rocm
} // namespace stream_executor
#endif // TENSORFLOW_STREAM_EXECUTOR_ROCM_ROCM_ACTIVATION_H_