Automated rollback of change 134025273

Change: 134037266

1 files changed, 0 insertions, 115 deletions

diff --git a/tensorflow/core/kernels/batch_matmul_op_test.cc b/tensorflow/core/kernels/batch_matmul_op_test.cc
deleted file mode 100644
index 471e42f314..0000000000
--- a/tensorflow/core/kernels/batch_matmul_op_test.cc
+++ /dev/null
@@ -1,115 +0,0 @@
-/* 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.
-==============================================================================*/
-
-#include "tensorflow/core/common_runtime/kernel_benchmark_testlib.h"
-#include "tensorflow/core/framework/tensor.h"
-#include "tensorflow/core/platform/test.h"
-#include "tensorflow/core/platform/test_benchmark.h"
-
-namespace tensorflow {
-
-template <typename T>
-static Graph* BatchMatmul(int b, int m, int k, int n, bool adjoint_a,
-                          bool adjoint_b, DataType type) {
-  Graph* g = new Graph(OpRegistry::Global());
-  Tensor in0(type, adjoint_a ? TensorShape({b, k, m}) : TensorShape({b, m, k}));
-  in0.flat<T>().setRandom();
-  Tensor in1(type, adjoint_b ? TensorShape({b, n, k}) : TensorShape({b, k, n}));
-  in1.flat<T>().setRandom();
-  test::graph::BatchMatmul(g, test::graph::Constant(g, in0),
-                           test::graph::Constant(g, in1), adjoint_a, adjoint_b);
-  return g;
-}
-
-#define BM_BatchMatmulDev(B, M, K, N, TA, TB, T, TFTYPE, DEVICE)                  \
-  static void                                                                     \
-      BM_BatchMatmul##_##B##_##M##_##K##_##N##_##TA##_##TB##_##TFTYPE##_##DEVICE( \
-          int iters) {                                                            \
-    testing::UseRealTime();                                                       \
-    testing::ItemsProcessed(static_cast<int64>(iters) * B * M * K * N * 2);       \
-    test::Benchmark(#DEVICE, BatchMatmul<T>(B, M, K, N, TA, TB, TFTYPE))          \
-        .Run(iters);                                                              \
-  }                                                                               \
-  BENCHMARK(                                                                      \
-      BM_BatchMatmul##_##B##_##M##_##K##_##N##_##TA##_##TB##_##TFTYPE##_##DEVICE);
-
-#define BM_BatchMatmul(B, M, K, N, TA, TB)                                 \
-  BM_BatchMatmulDev(B, M, K, N, TA, TB, float, DT_FLOAT, cpu);             \
-  BM_BatchMatmulDev(B, M, K, N, TA, TB, std::complex<float>, DT_COMPLEX64, \
-                    cpu);                                                  \
-  BM_BatchMatmulDev(B, M, K, N, TA, TB, float, DT_FLOAT, gpu);             \
-  BM_BatchMatmulDev(B, M, K, N, TA, TB, std::complex<float>, DT_COMPLEX64, gpu);
-/* Uncomment to enable benchmarks for double/complex128: */
-// BM_BatchMatmulDev(M, K, N, TA, TB, double, DT_DOUBLE, cpu);                   \
-// BM_BatchMatmulDev(M, K, N, TA, TB, std::complex<double>, DT_COMPLEX128, cpu); \
-// BM_BatchMatmulDev(M, K, N, TA, TB, double, DT_DOUBLE, gpu);                   \
-// BM_BatchMatmulDev(M, K, N, TA, TB, std::complex<double>, DT_COMPLEX128, gpu);
-
-// Typical fully connected layers
-BM_BatchMatmul(1, 1, 1024, 1024, false, false);
-BM_BatchMatmul(1, 8, 1024, 1024, false, false);
-BM_BatchMatmul(1, 16, 1024, 1024, false, false);
-BM_BatchMatmul(1, 128, 1024, 1024, false, false);
-BM_BatchMatmul(2, 1, 1024, 1024, false, false);
-BM_BatchMatmul(2, 8, 1024, 1024, false, false);
-BM_BatchMatmul(2, 16, 1024, 1024, false, false);
-BM_BatchMatmul(2, 128, 1024, 1024, false, false);
-BM_BatchMatmul(8, 1, 1024, 1024, false, false);
-BM_BatchMatmul(8, 8, 1024, 1024, false, false);
-BM_BatchMatmul(8, 16, 1024, 1024, false, false);
-BM_BatchMatmul(8, 128, 1024, 1024, false, false);
-BM_BatchMatmul(32, 1, 1024, 1024, false, false);
-BM_BatchMatmul(32, 8, 1024, 1024, false, false);
-BM_BatchMatmul(32, 16, 1024, 1024, false, false);
-BM_BatchMatmul(32, 128, 1024, 1024, false, false);
-
-// Square matmul.
-BM_BatchMatmul(1, 32, 32, 32, false, false);
-BM_BatchMatmul(1, 128, 128, 128, false, false);
-BM_BatchMatmul(1, 256, 256, 256, false, false);
-BM_BatchMatmul(1, 1024, 1024, 1024, false, false);
-BM_BatchMatmul(1, 2048, 2048, 2048, false, false);
-BM_BatchMatmul(2, 32, 32, 32, false, false);
-BM_BatchMatmul(2, 128, 128, 128, false, false);
-BM_BatchMatmul(2, 256, 256, 256, false, false);
-BM_BatchMatmul(2, 1024, 1024, 1024, false, false);
-BM_BatchMatmul(2, 2048, 2048, 2048, false, false);
-BM_BatchMatmul(4, 32, 32, 32, false, false);
-BM_BatchMatmul(4, 128, 128, 128, false, false);
-BM_BatchMatmul(4, 256, 256, 256, false, false);
-BM_BatchMatmul(4, 1024, 1024, 1024, false, false);
-BM_BatchMatmul(4, 2048, 2048, 2048, false, false);
-BM_BatchMatmul(8, 32, 32, 32, false, false);
-BM_BatchMatmul(8, 128, 128, 128, false, false);
-BM_BatchMatmul(8, 256, 256, 256, false, false);
-BM_BatchMatmul(8, 1024, 1024, 1024, false, false);
-BM_BatchMatmul(8, 2048, 2048, 2048, false, false);
-BM_BatchMatmul(32, 32, 32, 32, false, false);
-BM_BatchMatmul(32, 128, 128, 128, false, false);
-BM_BatchMatmul(32, 256, 256, 256, false, false);
-BM_BatchMatmul(32, 1024, 1024, 1024, false, false);
-BM_BatchMatmul(32, 2048, 2048, 2048, false, false);
-
-// Matrix-vector multiplies.
-BM_BatchMatmul(1, 10000, 200, 1, false, false);
-BM_BatchMatmul(8, 10000, 200, 1, false, false);
-BM_BatchMatmul(32, 10000, 200, 1, false, false);
-
-// Vector-matrix multiplies.
-BM_BatchMatmul(1, 1, 200, 10000, false, false);
-BM_BatchMatmul(8, 1, 200, 10000, false, false);
-BM_BatchMatmul(32, 1, 200, 10000, false, false);
-
-}  // end namespace tensorflow