experiments/STT-tensorflow
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Automated g4 rollback of changelist 199870879

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PiperOrigin-RevId: 200431713
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A. Unique TensorFlower 2018-06-13 12:09:13 -07:00 committed by TensorFlower Gardener
parent cb2c5be3eb
commit 6b7a17da65
3 changed files with 0 additions and 22 deletions
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4
tensorflow/core/framework/device_base.h
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@ -186,10 +186,6 @@ class DeviceBase {
virtual ScopedAllocatorMgr* GetScopedAllocatorMgr() const { return nullptr; } virtual ScopedAllocatorMgr* GetScopedAllocatorMgr() const { return nullptr; }
const bool has_eigen_cpu_device() const {
return (eigen_cpu_device_ != nullptr);
}
virtual const Eigen::ThreadPoolDevice* eigen_cpu_device() { virtual const Eigen::ThreadPoolDevice* eigen_cpu_device() {
CHECK(eigen_cpu_device_ != nullptr); CHECK(eigen_cpu_device_ != nullptr);
return eigen_cpu_device_; return eigen_cpu_device_;

16
tensorflow/core/framework/op_kernel.cc
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@ -13,14 +13,12 @@ See the License for the specific language governing permissions and
limitations under the License. limitations under the License.
==============================================================================*/ ==============================================================================*/
#define EIGEN_USE_THREADS
#include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/op_kernel.h"
#include <unordered_map> #include <unordered_map>
#include <utility> #include <utility>
#include <vector> #include <vector>
#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
#include "tensorflow/core/framework/attr_value_util.h" #include "tensorflow/core/framework/attr_value_util.h"
#include "tensorflow/core/framework/device_attributes.pb.h" #include "tensorflow/core/framework/device_attributes.pb.h"
#include "tensorflow/core/framework/graph.pb_text.h" #include "tensorflow/core/framework/graph.pb_text.h"
@ -42,7 +40,6 @@ limitations under the License.
#include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/platform/mutex.h" #include "tensorflow/core/platform/mutex.h"
#include "tensorflow/core/platform/types.h" #include "tensorflow/core/platform/types.h"
#include "tensorflow/core/util/ptr_util.h"
namespace tensorflow { namespace tensorflow {
@ -273,19 +270,6 @@ OpKernelContext::OpKernelContext(Params* params, int num_outputs)
if (params_->record_tensor_accesses) { if (params_->record_tensor_accesses) {
referenced_tensors_.Init(); referenced_tensors_.Init();
} }
if (params->device->has_eigen_cpu_device()) {
int64 block_size = -1, output_size = -1, num_threads = 1;
const Eigen::ThreadPoolDevice* thread_pool =
params_->device->eigen_cpu_device();
AttrSlice attributes(op_kernel().def());
if (GetNodeAttr(attributes, "_block_size", &block_size) == Status::OK() &&
GetNodeAttr(attributes, "_output_size", &output_size) == Status::OK()) {
num_threads = std::min(Eigen::divup(output_size, block_size),
static_cast<int64>(thread_pool->numThreads()));
eigen_cpu_device_ = MakeUnique<Eigen::ThreadPoolDevice>(
thread_pool->getPool(), num_threads);
}
}
} }
OpKernelContext::~OpKernelContext() { OpKernelContext::~OpKernelContext() {

2
tensorflow/core/framework/op_kernel.h
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@ -1004,7 +1004,6 @@ class OpKernelContext {
// OpKernels can use these eigen devices to carry out their // OpKernels can use these eigen devices to carry out their
// numerical computation. // numerical computation.
const Eigen::ThreadPoolDevice& eigen_cpu_device() const { const Eigen::ThreadPoolDevice& eigen_cpu_device() const {
if (eigen_cpu_device_ != nullptr) return *eigen_cpu_device_;
return *device()->eigen_cpu_device(); return *device()->eigen_cpu_device();
} }
const Eigen::GpuDevice& eigen_gpu_device() const { const Eigen::GpuDevice& eigen_gpu_device() const {
@ -1140,7 +1139,6 @@ class OpKernelContext {
mutable mutex mu_; // mutable so const accessors can acquire the lock mutable mutex mu_; // mutable so const accessors can acquire the lock
gtl::InlinedVector<WrappedAllocator, 4> wrapped_allocators_ GUARDED_BY(mu_); gtl::InlinedVector<WrappedAllocator, 4> wrapped_allocators_ GUARDED_BY(mu_);
gtl::InlinedVector<TensorValue, 4> outputs_; gtl::InlinedVector<TensorValue, 4> outputs_;
std::unique_ptr<Eigen::ThreadPoolDevice> eigen_cpu_device_;
// Constructed only if <params->record_tensor_accesses>. // Constructed only if <params->record_tensor_accesses>.
ManualConstructor<UniqueTensorReferences> referenced_tensors_ GUARDED_BY(mu_); ManualConstructor<UniqueTensorReferences> referenced_tensors_ GUARDED_BY(mu_);