Merge pull request #39893 from Intel-tensorflow:sshiddib/dnnl_threadpool_openmp

PiperOrigin-RevId: 315516788
Change-Id: Id473c4c2589d5650f488dfa663031879ca8f80bb

tensorflow/core/common_runtime/mkl_threadpool_device_test.cc

@@ -25,7 +25,7 @@ limitations under the License.
 
 namespace tensorflow {
 
-#ifdef _OPENMP
+#if defined(_OPENMP) && !defined(ENABLE_MKLDNN_THREADPOOL)
 TEST(MKLThreadPoolDeviceTest, TestOmpDefaults) {
   SessionOptions options;
   unsetenv("OMP_NUM_THREADS");
@@ -46,7 +46,7 @@ TEST(MKLThreadPoolDeviceTest, TestOmpPreSets) {
 
   EXPECT_EQ(omp_get_max_threads(), 314);
 }
-#endif  // _OPENMP
+#endif  // defined(_OPENMP) && !defined(ENABLE_MKLDNN_THREADPOOL)
 
 }  // namespace tensorflow
 

tensorflow/core/kernels/mkl_conv_ops_test.cc

@@ -28,7 +28,7 @@ limitations under the License.
 #include "tensorflow/core/public/session.h"
 
 #if defined(INTEL_MKL_DNN_ONLY)
-#include "third_party/intel_mkl_dnn/include/mkldnn.h"
+#include "mkldnn.hpp"
 #include "tensorflow/core/util/mkl_util.h"
 #endif
 

tensorflow/core/kernels/mkl_qmatmul_op.cc

@@ -91,12 +91,15 @@ limitations under the License.
 // https://software.intel.com/en-us/articles/lower-numerical-precision-deep-learning-inference-and-training
 #ifdef INTEL_MKL
 
+#include "tensorflow/core/framework/op_kernel.h"
 #include "tensorflow/core/framework/register_types.h"
 #include "tensorflow/core/kernels/fill_functor.h"
 #include "tensorflow/core/kernels/mkl_matmul_ops_common.h"
 #include "tensorflow/core/kernels/mkl_quantized_conv_ops.h"
 #include "tensorflow/core/kernels/no_op.h"
 #include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/util/mkl_threadpool.h"
+#include "tensorflow/core/util/work_sharder.h"
 
 namespace {
 enum {
@@ -437,6 +440,26 @@ class MklDnnQuantizedMatMulOp : public MklDnnMatMulOpBase<Tweight, Toutput> {
                     ((max_input - min_input) *
                      std::max(std::abs(max_weight), std::abs(min_weight)));
 
+#ifdef ENABLE_MKLDNN_THREADPOOL
+        auto parallel_func = [&](int64 start, int64 end) {
+          for (int64 j = start; j < end; j++) {
+            int x = 0;
+            for (int64 i = 0; i < k; ++i) {
+              x += wt_buf[i * n + j];
+            }
+            comp_bias[j] =
+                ((bias_buf[j] * out_scale) + static_cast<float>(x * qa_amin));
+          }
+        };
+
+        const float kArithCost = 2.5f;
+        const float kMovCost = 1.0f;
+        float shard_cost = 4 * kArithCost + kMovCost;
+        const DeviceBase::CpuWorkerThreads& worker_threads =
+            *(context->device()->tensorflow_cpu_worker_threads());
+        Shard(worker_threads.num_threads, worker_threads.workers, n, shard_cost,
+              parallel_func);
+#else
 #pragma omp parallel for schedule(static)
         for (int j = 0; j < n; ++j) {
           int x = 0;
@@ -446,7 +469,7 @@ class MklDnnQuantizedMatMulOp : public MklDnnMatMulOpBase<Tweight, Toutput> {
           comp_bias[j] =
               ((bias_buf[j] * out_scale) + static_cast<float>(x * qa_amin));
         }
-
+#endif  // ENABLE_MKLDNN_THREADPOOL
         return reinterpret_cast<Tbias*>(comp_bias_);
 
       } else if (mode_ == QUANTIZE_MODE_SCALED) {

tensorflow/core/kernels/mkl_quantized_conv_ops.h

@@ -69,7 +69,11 @@ void MklQuantizationRangeForMultiplication(float min_a, float max_a,
   const float* max_b = max_b_vector.flat<float>().data();
   float* min_c = (*min_c_vector)->flat<float>().data();
   float* max_c = (*max_c_vector)->flat<float>().data();
+
+#ifndef ENABLE_MKLDNN_THREADPOOL
 #pragma omp parallel for
+#endif  // !ENABLE_MKLDNN_THREADPOOL
+  // TODO: Add eigen parallel_for
   for (size_t n = 0; n < n_channel; ++n) {
     float a_float_for_one_quant_level =
         MklFloatForOneQuantizedLevel<T1>(min_a, max_a);

tensorflow/core/kernels/mkl_requantization_range_per_channel_op.cc

@@ -18,6 +18,7 @@ limitations under the License.
 #define EIGEN_USE_THREADS
 
 #include <math.h>
+
 #include <limits>
 
 #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
@@ -28,6 +29,7 @@ limitations under the License.
 #include "tensorflow/core/kernels/meta_support.h"
 #include "tensorflow/core/kernels/no_op.h"
 #include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/util/mkl_threadpool.h"
 #include "tensorflow/core/util/mkl_util.h"
 
 namespace tensorflow {
@@ -73,7 +75,11 @@ class MklRequantizationRangePerChannelOp : public OpKernel {
 
     // Find the ranges of each channel in parallel.
     float out_min_max = std::numeric_limits<float>::min();
+
+#ifndef ENABLE_MKLDNN_THREADPOOL
 #pragma omp parallel for reduction(max : out_min_max)
+#endif  // !ENABLE_MKLDNN_THREADPOOL
+    // TODO: Add eigen parallel_for
     for (size_t i = 0; i < depth; ++i) {
       Eigen::Tensor<qint32, 0, Eigen::RowMajor> min =
           transposed_input.chip<0>(i).minimum();
@@ -92,6 +98,7 @@ class MklRequantizationRangePerChannelOp : public OpKernel {
       // Thread-local out_min_max.
       out_min_max = std::max(out_min_max, ranges[i]);
     }
+
     // All local out_min_max gets max-reduced into one global out_min_max at
     // the end of the loop by specifying reduction(max:out_min_max) along with
     // omp parallel for.

tensorflow/tensorflow.bzl

@@ -354,9 +354,7 @@ def tf_copts(
     )
 
 def tf_openmp_copts():
-    # TODO(intel-mkl): Remove -fopenmp for threadpool after removing all
-    # omp pragmas in tensorflow/core.
-    return if_mkl_lnx_x64(["-fopenmp"]) + if_mkldnn_threadpool(["-fopenmp"])
+    return (if_mkl_lnx_x64(["-fopenmp"]) + if_mkldnn_threadpool(["-fno-openmp"]))
 
 def tfe_xla_copts():
     return select({