[MLIR][KernelGen] Unify unary kernel tests
This refactoring allows to easily add more tests for other unranked kernels without duplicating code. PiperOrigin-RevId: 342626049 Change-Id: I974b4fcf8f887fb1512103f53194c216ee100e0b
This commit is contained in:
A. Unique TensorFlower
TensorFlower Gardener
parent
516f4a121c
commit
229aeadda8
@ -147,30 +147,9 @@ tf_kernel_library(
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)
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tf_cuda_cc_test(
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name = "gpu_tanh_test",
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name = "gpu_unary_ops_test",
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size = "small",
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srcs = if_mlir_generated_gpu_kernels_enabled(["gpu_tanh_test.cc"]),
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tags = tf_cuda_tests_tags() + [
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"no_cuda_asan", # TODO(b/171341759): re-enable.
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],
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deps = [
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"//tensorflow/core:framework",
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"//tensorflow/core:framework_internal",
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"//tensorflow/core:tensorflow",
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"//tensorflow/core:test",
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"//tensorflow/core:test_main",
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"//tensorflow/core:testlib",
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"//tensorflow/core/common_runtime:device",
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"//tensorflow/core/common_runtime:device_factory",
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"//tensorflow/core/kernels:cwise_op",
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"//tensorflow/core/kernels:ops_testutil",
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],
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)
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tf_cuda_cc_test(
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name = "gpu_abs_test",
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size = "small",
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srcs = if_mlir_generated_gpu_kernels_enabled(["gpu_abs_test.cc"]),
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srcs = if_mlir_generated_gpu_kernels_enabled(["gpu_unary_ops_test.cc"]),
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tags = tf_cuda_tests_tags() + [
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"no_cuda_asan", # TODO(b/171341759): re-enable.
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],
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@ -1,95 +0,0 @@
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/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#include <cmath>
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#include <limits>
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#include <memory>
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#include <vector>
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#include "tensorflow/core/common_runtime/device.h"
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#include "tensorflow/core/common_runtime/device_factory.h"
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#include "tensorflow/core/framework/fake_input.h"
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#include "tensorflow/core/framework/node_def_builder.h"
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#include "tensorflow/core/framework/tensor.h"
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#include "tensorflow/core/kernels/ops_testutil.h"
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#include "tensorflow/core/lib/core/status_test_util.h"
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#include "tensorflow/core/platform/test.h"
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namespace tensorflow {
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namespace {
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class GpuAbsTest : public OpsTestBase {
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protected:
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void SetUp() override {
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std::unique_ptr<tensorflow::Device> device_gpu(
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tensorflow::DeviceFactory::NewDevice("GPU", {},
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"/job:a/replica:0/task:0"));
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SetDevice(tensorflow::DEVICE_GPU, std::move(device_gpu));
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}
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template <typename T, typename RT = T>
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void RunAbsOp(std::initializer_list<T> input) {
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TensorShape shape({2, 3});
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TF_ASSERT_OK(NodeDefBuilder("abs_op", "Abs")
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.Input(FakeInput(DataTypeToEnum<T>::v()))
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.Attr("T", DataTypeToEnum<T>::v())
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.Finalize(node_def()));
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TF_ASSERT_OK(InitOp());
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AddInputFromArray<T>(shape, input);
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TF_ASSERT_OK(RunOpKernel());
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Tensor expected_tensor(allocator(), DataTypeToEnum<T>::value, shape);
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std::vector<T> expected;
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expected.reserve(input.size());
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for (const T& inp : input) {
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expected.push_back(static_cast<T>(std::abs(static_cast<RT>(inp))));
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}
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test::FillValues<T>(&expected_tensor, expected);
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test::ExpectEqual(expected_tensor, *GetOutput(0));
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}
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};
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TEST_F(GpuAbsTest, AbsFloat) {
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RunAbsOp<float>({-std::numeric_limits<float>::infinity(), -0.1f, -0.0f, 0.0f,
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0.1f, std::numeric_limits<float>::infinity()});
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}
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TEST_F(GpuAbsTest, AbsDouble) {
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RunAbsOp<double>({-std::numeric_limits<double>::infinity(), -0.1, -0.0, 0.0,
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0.1, std::numeric_limits<double>::infinity()});
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}
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TEST_F(GpuAbsTest, AbsHalf) {
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RunAbsOp<Eigen::half, float>(
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{static_cast<Eigen::half>(-std::numeric_limits<double>::infinity()),
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static_cast<Eigen::half>(-0.1), static_cast<Eigen::half>(-0.0),
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static_cast<Eigen::half>(0.0), static_cast<Eigen::half>(0.1),
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static_cast<Eigen::half>(std::numeric_limits<double>::infinity())});
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}
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TEST_F(GpuAbsTest, AbsInt32) {
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RunAbsOp<int32>({std::numeric_limits<int32>::min(),
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std::numeric_limits<int32>::min() + 1, -1, 0, 1,
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std::numeric_limits<int32>::max()});
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}
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TEST_F(GpuAbsTest, AbsInt64) {
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RunAbsOp<int64>({std::numeric_limits<int64>::min(),
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std::numeric_limits<int64>::min() + 1, -1, 0, 1,
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std::numeric_limits<int64>::max()});
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}
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} // namespace
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} // end namespace tensorflow
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@ -1,85 +0,0 @@
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/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#include <cmath>
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#include <memory>
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#include <vector>
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#include "tensorflow/core/common_runtime/device.h"
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#include "tensorflow/core/common_runtime/device_factory.h"
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#include "tensorflow/core/framework/fake_input.h"
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#include "tensorflow/core/framework/node_def_builder.h"
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#include "tensorflow/core/framework/tensor.h"
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#include "tensorflow/core/kernels/ops_testutil.h"
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#include "tensorflow/core/lib/core/status_test_util.h"
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#include "tensorflow/core/platform/test.h"
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namespace tensorflow {
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namespace {
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class GpuTanhTest : public OpsTestBase {
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protected:
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void SetUp() override {
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std::unique_ptr<tensorflow::Device> device_gpu(
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tensorflow::DeviceFactory::NewDevice("GPU", {},
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"/job:a/replica:0/task:0"));
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SetDevice(tensorflow::DEVICE_GPU, std::move(device_gpu));
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}
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template <typename T, typename RT = T>
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void RunTanhOp(std::initializer_list<T> input) {
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TensorShape shape({2, 7});
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TF_ASSERT_OK(NodeDefBuilder("tanh_op", "Tanh")
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.Input(FakeInput(DataTypeToEnum<T>::v()))
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.Attr("T", DataTypeToEnum<T>::v())
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.Finalize(node_def()));
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TF_ASSERT_OK(InitOp());
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AddInputFromArray<T>(shape, input);
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TF_ASSERT_OK(RunOpKernel());
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Tensor expected_tensor(allocator(), DataTypeToEnum<T>::value, shape);
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std::vector<T> expected;
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expected.reserve(input.size());
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for (const T& inp : input) {
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expected.push_back(static_cast<T>(std::tanh(static_cast<RT>(inp))));
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}
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test::FillValues<T>(&expected_tensor, expected);
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test::ExpectClose(expected_tensor, *GetOutput(0));
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}
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};
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TEST_F(GpuTanhTest, TanhFloat) {
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RunTanhOp<float>({-18.0f, -9.0f, -1e-6f, -0.0f, 0.0f, 1e-6, 0.1f, 0.2f, 0.3f,
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0.5f, 0.7f, 0.9f, 9.0f, 18.0f});
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}
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TEST_F(GpuTanhTest, TanhDouble) {
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RunTanhOp<double>({-18.0, -9.0, -1e-6, -0.0, 0.0, 1e-6, 0.1, 0.2, 0.3, 0.5,
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0.7, 0.9, 9.0, 18.0});
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}
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TEST_F(GpuTanhTest, TanhHalf) {
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RunTanhOp<Eigen::half, float>(
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{static_cast<Eigen::half>(-18.0), static_cast<Eigen::half>(-9.0),
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static_cast<Eigen::half>(-1e-6), static_cast<Eigen::half>(-0.0),
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static_cast<Eigen::half>(0.0), static_cast<Eigen::half>(1e-6),
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static_cast<Eigen::half>(0.1), static_cast<Eigen::half>(0.2),
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static_cast<Eigen::half>(0.3), static_cast<Eigen::half>(0.5),
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static_cast<Eigen::half>(0.7), static_cast<Eigen::half>(0.9),
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static_cast<Eigen::half>(9.0), static_cast<Eigen::half>(18.0)});
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}
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} // namespace
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} // end namespace tensorflow
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172
tensorflow/core/kernels/mlir_generated/gpu_unary_ops_test.cc
Normal file
172
tensorflow/core/kernels/mlir_generated/gpu_unary_ops_test.cc
Normal file
@ -0,0 +1,172 @@
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/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#include <cmath>
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#include <functional>
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#include <memory>
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#include <numeric>
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#include <vector>
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#include "tensorflow/core/common_runtime/device.h"
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#include "tensorflow/core/common_runtime/device_factory.h"
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#include "tensorflow/core/framework/fake_input.h"
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#include "tensorflow/core/framework/node_def_builder.h"
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#include "tensorflow/core/framework/tensor.h"
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#include "tensorflow/core/framework/tensor_testutil.h"
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#include "tensorflow/core/kernels/ops_testutil.h"
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#include "tensorflow/core/lib/core/status_test_util.h"
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#include "tensorflow/core/platform/test.h"
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namespace tensorflow {
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namespace {
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class GpuUnaryOpTest : public OpsTestBase {
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protected:
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void SetUp() override {
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std::unique_ptr<tensorflow::Device> device_gpu(
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tensorflow::DeviceFactory::NewDevice("GPU", {},
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"/job:a/replica:0/task:0"));
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SetDevice(tensorflow::DEVICE_GPU, std::move(device_gpu));
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}
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template <typename T, typename RT = T>
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void Run(std::initializer_list<int64> input_shape,
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std::initializer_list<T> input, const std::string op_name,
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RT (*expected_callback)(RT), bool expect_equal = true) {
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assert(std::accumulate(input_shape.begin(), input_shape.end(), 1,
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std::multiplies<int64>()) == input.size() &&
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"Expected input length to equal to shape's number of elements.");
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TensorShape shape(input_shape);
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TF_ASSERT_OK(NodeDefBuilder("some_name", op_name)
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.Input(FakeInput(DataTypeToEnum<T>::v()))
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.Attr("T", DataTypeToEnum<T>::v())
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.Finalize(node_def()));
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TF_ASSERT_OK(InitOp());
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AddInputFromArray<T>(shape, input);
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TF_ASSERT_OK(RunOpKernel());
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Tensor expected_tensor(allocator(), DataTypeToEnum<T>::value, shape);
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std::vector<T> expected;
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expected.reserve(input.size());
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for (const T& inp : input) {
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expected.push_back(
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static_cast<T>(expected_callback(static_cast<RT>(inp))));
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}
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test::FillValues<T>(&expected_tensor, expected);
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if (expect_equal) {
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test::ExpectEqual(expected_tensor, *GetOutput(0));
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} else {
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test::ExpectClose(expected_tensor, *GetOutput(0));
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}
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}
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};
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TEST_F(GpuUnaryOpTest, TanhFloat) {
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Run<float>(/*input_shape=*/{2, 7},
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/*input=*/
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{-18.0f, -9.0f, -1e-6f, -0.0f, 0.0f, 1e-6, 0.1f, 0.2f, 0.3f, 0.5f,
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0.7f, 0.9f, 9.0f, 18.0f},
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/*op_name=*/"Tanh",
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/*expected_callback=*/std::tanh,
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/*expect_equal=*/false);
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}
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TEST_F(GpuUnaryOpTest, TanhDouble) {
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Run<double>(/*input_shape=*/{2, 7},
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/*input=*/
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{-18.0, -9.0, -1e-6, -0.0, 0.0, 1e-6, 0.1, 0.2, 0.3, 0.5, 0.7,
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0.9, 9.0, 18.0},
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/*op_name=*/"Tanh",
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/*expected_callback=*/std::tanh,
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/*expect_equal=*/false);
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}
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TEST_F(GpuUnaryOpTest, TanhHalf) {
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Run<Eigen::half, float>(
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/*input_shape=*/{2, 7},
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/*input=*/
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{static_cast<Eigen::half>(-18.0), static_cast<Eigen::half>(-9.0),
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static_cast<Eigen::half>(-1e-6), static_cast<Eigen::half>(-0.0),
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static_cast<Eigen::half>(0.0), static_cast<Eigen::half>(1e-6),
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static_cast<Eigen::half>(0.1), static_cast<Eigen::half>(0.2),
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static_cast<Eigen::half>(0.3), static_cast<Eigen::half>(0.5),
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static_cast<Eigen::half>(0.7), static_cast<Eigen::half>(0.9),
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static_cast<Eigen::half>(9.0), static_cast<Eigen::half>(18.0)},
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/*op_name=*/"Tanh",
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/*expected_callback=*/std::tanh,
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/*expect_equal=*/false);
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}
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TEST_F(GpuUnaryOpTest, AbsFloat) {
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Run<float>(
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/*input_shape=*/{2, 3},
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/*input=*/
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{-std::numeric_limits<float>::infinity(), -0.1f, -0.0f, 0.0f, 0.1f,
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std::numeric_limits<float>::infinity()},
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/*op_name=*/"Abs",
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/*expected_callback=*/std::abs,
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/*expect_equal=*/false);
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}
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TEST_F(GpuUnaryOpTest, AbsDouble) {
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Run<double>(
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/*input_shape=*/{2, 3},
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/*input=*/
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{-std::numeric_limits<double>::infinity(), -0.1, -0.0, 0.0, 0.1,
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std::numeric_limits<double>::infinity()},
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/*op_name=*/"Abs",
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/*expected_callback=*/std::abs,
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/*expect_equal=*/false);
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}
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TEST_F(GpuUnaryOpTest, AbsHalf) {
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Run<Eigen::half, float>(
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/*input_shape=*/{2, 3},
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/*input=*/
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{static_cast<Eigen::half>(-std::numeric_limits<double>::infinity()),
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static_cast<Eigen::half>(-0.1), static_cast<Eigen::half>(-0.0),
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static_cast<Eigen::half>(0.0), static_cast<Eigen::half>(0.1),
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static_cast<Eigen::half>(std::numeric_limits<double>::infinity())},
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/*op_name=*/"Abs",
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/*expected_callback=*/std::abs,
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/*expect_equal=*/false);
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}
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TEST_F(GpuUnaryOpTest, AbsInt32) {
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Run<int32>(
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/*input_shape=*/{2, 3},
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/*input=*/
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{std::numeric_limits<int32>::min(), std::numeric_limits<int32>::min() + 1,
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-1, 0, 1, std::numeric_limits<int32>::max()},
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/*op_name=*/"Abs",
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/*expected_callback=*/std::abs,
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/*expect_equal=*/true);
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}
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TEST_F(GpuUnaryOpTest, AbsInt64) {
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Run<int64>(
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/*input_shape=*/{2, 3},
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/*input=*/
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{std::numeric_limits<int64>::min(), std::numeric_limits<int64>::min() + 1,
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-1, 0, 1, std::numeric_limits<int64>::max()},
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/*op_name=*/"Abs",
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/*expected_callback=*/std::abs,
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/*expect_equal=*/true);
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}
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} // namespace
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} // end namespace tensorflow
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