/
remapper_test.cc
3112 lines (2641 loc) · 118 KB
/
remapper_test.cc
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/* Copyright 2018 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/grappler/optimizers/remapper.h"
#include "tensorflow/cc/ops/nn_ops_internal.h"
#include "tensorflow/cc/ops/standard_ops.h"
#include "tensorflow/core/framework/tensor_testutil.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/grappler/devices.h"
#include "tensorflow/core/grappler/grappler_item.h"
#include "tensorflow/core/grappler/utils/graph_view.h"
#include "tensorflow/core/grappler/utils/grappler_test.h"
#include "tensorflow/core/lib/core/status_test_util.h"
#include "tensorflow/core/platform/test.h"
#include "tensorflow/core/util/util.h"
#if GOOGLE_CUDA
#include "third_party/gpus/cudnn/cudnn.h"
#endif // GOOGLE_CUDA
namespace tensorflow {
namespace grappler {
class RemapperTest : public GrapplerTest {
protected:
void SetUp() override {
// This is a requirement for fusing FusedBatchNorm + SideInput + Activation.
setenv("TF_USE_CUDNN_BATCHNORM_SPATIAL_PERSISTENT", "1", 1 /* replace */);
// This is a requirement for fusing FusedMatmul + BiasAdd (+ Activation).
setenv("TF_USE_CUBLASLT", "1", 1 /* replace */);
}
};
TEST_F(RemapperTest, FusedBatchNorm) {
tensorflow::Scope s = tensorflow::Scope::NewRootScope();
Output dflt = ops::Const(s.WithOpName("dflt"), {3.14f, 2.7f}, {2, 1, 1, 1});
Output x = ops::PlaceholderWithDefault(s.WithOpName("x"), dflt, {2, 1, 1, 1});
Output scale = ops::Const(s.WithOpName("scale"), {0.3f}, {1});
Output offset = ops::Const(s.WithOpName("offset"), {0.123f}, {1});
Output mean = ops::Const(s.WithOpName("mean"), {7.3f}, {1});
Output variance = ops::Const(s.WithOpName("variance"), {0.57f}, {1});
ops::FusedBatchNorm::Attrs attr;
attr = attr.IsTraining(false);
ops::FusedBatchNorm bn(s.WithOpName("batch_norm"), x, scale, offset, mean,
variance, attr);
GrapplerItem item;
TF_ASSERT_OK(s.ToGraphDef(&item.graph));
item.fetch = {"batch_norm"};
auto tensors_expected = EvaluateNodes(item.graph, item.fetch);
ASSERT_EQ(tensors_expected.size(), 1);
Remapper optimizer(RewriterConfig::ON);
GraphDef output;
TF_ASSERT_OK(optimizer.Optimize(nullptr, item, &output));
auto tensors = EvaluateNodes(output, item.fetch);
ASSERT_EQ(tensors.size(), 1);
test::ExpectTensorNear<float>(tensors[0], tensors_expected[0], 1e-6);
}
TEST_F(RemapperTest, FusedBatchNormNCHW) {
#if !(GOOGLE_CUDA || TENSORFLOW_USE_ROCM)
GTEST_SKIP() << "Neither CUDA nor ROCm is enabled";
#endif // !GOOGLE_CUDA || TENSORFLOW_USE_ROCM
tensorflow::Scope s = tensorflow::Scope::NewRootScope();
Output dflt =
ops::Const(s.WithOpName("dflt"), {3.14f, 2.7f, 1.0f, 2.0f, 3.0f, 100.0f},
{1, 3, 1, 2});
Output x = ops::PlaceholderWithDefault(s.WithOpName("x"), dflt, {1, 3, 1, 2});
Output scale = ops::Const(s.WithOpName("scale"), {0.3f, 7.0f, 123.0f}, {3});
Output offset =
ops::Const(s.WithOpName("offset"), {0.123f, 2.1f, 0.55f}, {3});
Output mean = ops::Const(s.WithOpName("mean"), {7.3f, 8.3f, 3.1f}, {3});
Output variance =
ops::Const(s.WithOpName("variance"), {0.57f, 1.0f, 2.0f}, {3});
ops::FusedBatchNorm::Attrs attr;
attr = attr.IsTraining(false);
attr = attr.DataFormat("NCHW");
ops::FusedBatchNorm bn(s.WithOpName("batch_norm").WithDevice("/device:GPU:0"),
x, scale, offset, mean, variance, attr);
GrapplerItem item;
TF_ASSERT_OK(s.ToGraphDef(&item.graph));
item.fetch = {"batch_norm"};
Remapper optimizer(RewriterConfig::ON);
GraphDef output;
TF_ASSERT_OK(optimizer.Optimize(nullptr, item, &output));
if (GetNumAvailableGPUs() > 0) {
// NCHW batch norm is only supported on GPU.
auto tensors_expected = EvaluateNodes(item.graph, item.fetch);
ASSERT_EQ(tensors_expected.size(), 1);
auto tensors = EvaluateNodes(output, item.fetch);
ASSERT_EQ(tensors.size(), 1);
test::ExpectTensorNear<float>(tensors[0], tensors_expected[0], 1e-3);
}
}
TEST_F(RemapperTest, FuseBatchNormWithRelu) {
if (IsMKLEnabled()) GTEST_SKIP() << "Fusion not available with oneDNN.";
using ::tensorflow::ops::Placeholder;
for (bool is_training : {true, false}) {
tensorflow::Scope s = tensorflow::Scope::NewRootScope();
#if !defined(GOOGLE_CUDA) || !(CUDNN_VERSION >= 7402)
if (is_training) {
LOG(INFO) << "Skip FuseBatchNormWithRelu"
<< "[is_training=" << is_training << "] "
<< "test. It requires CUDNN_VERSION >= 7402.";
continue;
}
#endif
#if !defined(GOOGLE_CUDA)
if (!is_training) {
LOG(INFO) << "Skip FuseBatchNormWithRelu"
<< "[is_training=" << is_training << "]";
continue;
}
#endif
const int num_channels = 24;
TensorShape channel_shape({num_channels});
TensorShape empty_shape({0});
auto input = Placeholder(s.WithOpName("input"), DT_FLOAT,
ops::Placeholder::Shape({2, 8, 8, num_channels}));
auto input_cast = ops::Cast(s.WithOpName("input_cast"), input, DT_HALF);
auto scale = Placeholder(s.WithOpName("scale"), DT_FLOAT);
auto offset = Placeholder(s.WithOpName("offset"), DT_FLOAT);
auto mean = Placeholder(s.WithOpName("mean"), DT_FLOAT);
auto var = Placeholder(s.WithOpName("var"), DT_FLOAT);
float epsilon = 0.1f;
auto fbn = ops::FusedBatchNormV3(
s.WithOpName("fused_batch_norm"), input_cast, scale, offset, mean, var,
ops::FusedBatchNormV3::IsTraining(is_training)
.Epsilon(epsilon)
.DataFormat("NHWC"));
auto relu = ops::Relu(s.WithOpName("relu"), fbn.y);
auto fetch = ops::Identity(s.WithOpName("fetch"), relu);
auto input_t = GenerateRandomTensor<DT_FLOAT>({2, 8, 8, num_channels});
auto scale_t = GenerateRandomTensor<DT_FLOAT>(channel_shape);
auto offset_t = GenerateRandomTensor<DT_FLOAT>(channel_shape);
auto mean_t = GenerateRandomTensor<DT_FLOAT>(is_training ? empty_shape
: channel_shape);
auto var_t = GenerateRandomTensor<DT_FLOAT>(is_training ? empty_shape
: channel_shape);
GrapplerItem item;
item.fetch = {"fetch"};
item.feed = {{"input", input_t},
{"scale", scale_t},
{"offset", offset_t},
{"mean", mean_t},
{"var", var_t}};
TF_ASSERT_OK(s.ToGraphDef(&item.graph));
// Place all nodes on GPU.
for (int i = 0; i < item.graph.node_size(); ++i) {
item.graph.mutable_node(i)->set_device("/device:GPU:0");
}
Remapper optimizer(RewriterConfig::AGGRESSIVE); // trust placeholders shape
GraphDef output;
TF_ASSERT_OK(optimizer.Optimize(nullptr, item, &output));
int found = 0;
for (const NodeDef& node : output.node()) {
if (node.name() == "relu") {
EXPECT_EQ(node.op(), "Identity");
ASSERT_EQ(node.input_size(), 1);
EXPECT_EQ(node.input(0), "fused_batch_norm");
found++;
}
if (node.name() == "fused_batch_norm") {
EXPECT_EQ(node.op(), "_FusedBatchNormEx");
ASSERT_EQ(node.input_size(), 5);
EXPECT_EQ(node.input(0), "input_cast");
EXPECT_EQ(node.input(1), "scale");
EXPECT_EQ(node.input(2), "offset");
EXPECT_EQ(node.input(3), "mean");
EXPECT_EQ(node.input(4), "var");
auto attr = node.attr();
EXPECT_EQ(attr["num_side_inputs"].i(), 0);
EXPECT_EQ(attr["activation_mode"].s(), "Relu");
found++;
}
}
EXPECT_EQ(found, 2);
if (GetNumAvailableGPUs() > 0) {
auto tensors_expected = EvaluateNodes(item.graph, item.fetch, item.feed);
ASSERT_EQ(tensors_expected.size(), 1);
auto tensors = EvaluateNodes(output, item.fetch, item.feed);
ASSERT_EQ(tensors.size(), 1);
test::ExpectClose(tensors[0], tensors_expected[0], 1e-2, /*rtol=*/1e-2);
}
}
}
#if defined(GOOGLE_CUDA) && CUDNN_VERSION >= 7402
TEST_F(RemapperTest, FuseBatchNormGradWithReluGrad) {
if (IsMKLEnabled()) GTEST_SKIP() << "Fusion not available with oneDNN.";
using ::tensorflow::ops::Placeholder;
tensorflow::Scope s = tensorflow::Scope::NewRootScope();
bool is_training = true;
const int num_channels = 24;
TensorShape channel_shape({num_channels});
TensorShape empty_shape({0});
// Forward pass.
auto input = Placeholder(s.WithOpName("input"), DT_FLOAT,
ops::Placeholder::Shape({2, 8, 8, num_channels}));
auto input_cast = ops::Cast(s.WithOpName("input_cast"), input, DT_HALF);
auto scale = Placeholder(s.WithOpName("scale"), DT_FLOAT);
auto offset = Placeholder(s.WithOpName("offset"), DT_FLOAT);
auto mean = Placeholder(s.WithOpName("mean"), DT_FLOAT);
auto var = Placeholder(s.WithOpName("var"), DT_FLOAT);
float epsilon = 0.1f;
auto fbn = ops::FusedBatchNormV3(
s.WithOpName("fused_batch_norm"), input_cast, scale, offset, mean, var,
ops::FusedBatchNormV3::IsTraining(is_training)
.Epsilon(epsilon)
.DataFormat("NHWC"));
auto relu = ops::Relu(s.WithOpName("relu"), fbn.y);
// Backward pass.
auto output_grad =
Placeholder(s.WithOpName("output_grad"), DT_FLOAT,
ops::Placeholder::Shape({2, 8, 8, num_channels}));
auto output_grad_cast =
ops::Cast(s.WithOpName("output_grad_cast"), output_grad, DT_HALF);
auto relu_grad = ops::internal::ReluGrad(s.WithOpName("relu_grad"),
output_grad_cast, relu);
auto fbn_grad = ops::FusedBatchNormGradV3(
s.WithOpName("fused_batch_norm_grad"), relu_grad, input_cast, scale,
fbn.reserve_space_1, fbn.reserve_space_2, fbn.reserve_space_3,
ops::FusedBatchNormGradV3::IsTraining(is_training)
.Epsilon(epsilon)
.DataFormat("NHWC"));
auto fetch0 = ops::Identity(s.WithOpName("fetch0"), fbn_grad.x_backprop);
auto fetch1 = ops::Identity(s.WithOpName("fetch1"), fbn_grad.scale_backprop);
auto fetch2 = ops::Identity(s.WithOpName("fetch2"), fbn_grad.offset_backprop);
auto input_t = GenerateRandomTensor<DT_FLOAT>({2, 8, 8, num_channels});
auto scale_t = GenerateRandomTensor<DT_FLOAT>(channel_shape);
auto offset_t = GenerateRandomTensor<DT_FLOAT>(channel_shape);
auto mean_t = GenerateRandomTensor<DT_FLOAT>(channel_shape);
auto var_t = GenerateRandomTensor<DT_FLOAT>(channel_shape);
auto output_grad_t = GenerateRandomTensor<DT_FLOAT>({2, 8, 8, num_channels});
GrapplerItem item;
item.fetch = {"fetch0", "fetch1", "fetch2"};
item.feed = {{"input", input_t}, {"scale", scale_t},
{"offset", offset_t}, {"mean", mean_t},
{"var", var_t}, {"output_grad", output_grad_t}};
TF_ASSERT_OK(s.ToGraphDef(&item.graph));
// Place all nodes on GPU.
for (int i = 0; i < item.graph.node_size(); ++i) {
item.graph.mutable_node(i)->set_device("/device:GPU:0");
}
Remapper optimizer(RewriterConfig::AGGRESSIVE); // trust placeholders shape
GraphDef output;
TF_ASSERT_OK(optimizer.Optimize(nullptr, item, &output));
int found = 0;
for (const NodeDef& node : output.node()) {
if (node.name() == "relu") {
EXPECT_EQ(node.op(), "Identity");
ASSERT_EQ(node.input_size(), 1);
EXPECT_EQ(node.input(0), "fused_batch_norm");
found++;
}
if (node.name() == "fused_batch_norm") {
EXPECT_EQ(node.op(), "_FusedBatchNormEx");
ASSERT_EQ(node.input_size(), 5);
EXPECT_EQ(node.input(0), "input_cast");
EXPECT_EQ(node.input(1), "scale");
EXPECT_EQ(node.input(2), "offset");
EXPECT_EQ(node.input(3), "mean");
EXPECT_EQ(node.input(4), "var");
auto attr = node.attr();
EXPECT_EQ(attr["num_side_inputs"].i(), 0);
EXPECT_EQ(attr["activation_mode"].s(), "Relu");
found++;
}
if (node.name() == "fused_batch_norm_grad") {
EXPECT_EQ(node.op(), "_FusedBatchNormGradEx");
ASSERT_EQ(node.input_size(), 8);
EXPECT_EQ(node.input(0), "output_grad_cast");
EXPECT_EQ(node.input(1), "input_cast");
EXPECT_EQ(node.input(2), "scale");
EXPECT_EQ(node.input(3), "fused_batch_norm:3");
EXPECT_EQ(node.input(4), "fused_batch_norm:4");
EXPECT_EQ(node.input(5), "fused_batch_norm:5");
EXPECT_EQ(node.input(6), "offset");
EXPECT_EQ(node.input(7), "relu");
auto attr = node.attr();
EXPECT_EQ(attr["num_side_inputs"].i(), 0);
EXPECT_EQ(attr["activation_mode"].s(), "Relu");
found++;
}
}
EXPECT_EQ(found, 3);
if (GetNumAvailableGPUs() > 0) {
auto tensors_expected = EvaluateNodes(item.graph, item.fetch, item.feed);
ASSERT_EQ(tensors_expected.size(), 3);
auto tensors = EvaluateNodes(output, item.fetch, item.feed);
ASSERT_EQ(tensors.size(), 3);
test::ExpectClose(tensors[0], tensors_expected[0], 1e-2, /*rtol=*/1e-2);
test::ExpectClose(tensors[1], tensors_expected[1], 1e-2, /*rtol=*/1e-2);
test::ExpectClose(tensors[2], tensors_expected[2], 1e-2, /*rtol=*/1e-2);
}
}
#endif // defined(GOOGLE_CUDA) && CUDNN_VERSION >= 7402
TEST_F(RemapperTest, FuseBatchNormWithAddAndRelu) {
if (IsMKLEnabled()) GTEST_SKIP() << "Fusion not available with oneDNN.";
using ::tensorflow::ops::Placeholder;
for (bool is_training : {true, false}) {
tensorflow::Scope s = tensorflow::Scope::NewRootScope();
#if !defined(GOOGLE_CUDA) || !(CUDNN_VERSION >= 7402)
if (is_training) {
LOG(INFO) << "Skip FuseBatchNormWithAddAndRelu"
<< "[is_training=" << is_training << "] "
<< "test. It requires CUDNN_VERSION >= 7402.";
continue;
}
#endif
#if !defined(GOOGLE_CUDA)
if (!is_training) {
LOG(INFO) << "Skip FuseBatchNormWithAddAndRelu"
<< "[is_training=" << is_training << "]";
continue;
}
#endif
const int num_channels = 24;
TensorShape input_shape({2, 8, 8, num_channels});
TensorShape channel_shape({num_channels});
TensorShape empty_shape({0});
auto input = Placeholder(s.WithOpName("input"), DT_FLOAT,
ops::Placeholder::Shape(input_shape));
auto input_cast = ops::Cast(s.WithOpName("input_cast"), input, DT_HALF);
auto scale = Placeholder(s.WithOpName("scale"), DT_FLOAT);
auto offset = Placeholder(s.WithOpName("offset"), DT_FLOAT);
auto mean = Placeholder(s.WithOpName("mean"), DT_FLOAT);
auto var = Placeholder(s.WithOpName("var"), DT_FLOAT);
auto side_input = Placeholder(s.WithOpName("side_input"), DT_FLOAT,
ops::Placeholder::Shape(input_shape));
auto side_input_cast =
ops::Cast(s.WithOpName("side_input_cast"), side_input, DT_HALF);
float epsilon = 0.1f;
auto fbn = ops::FusedBatchNormV3(
s.WithOpName("fused_batch_norm"), input_cast, scale, offset, mean, var,
ops::FusedBatchNormV3::IsTraining(is_training)
.Epsilon(epsilon)
.DataFormat("NHWC"));
auto add = ops::Add(s.WithOpName("add"), fbn.y, side_input_cast);
auto relu = ops::Relu(s.WithOpName("relu"), add);
auto fetch = ops::Identity(s.WithOpName("fetch"), relu);
auto input_t = GenerateRandomTensor<DT_FLOAT>(input_shape);
auto scale_t = GenerateRandomTensor<DT_FLOAT>(channel_shape);
auto offset_t = GenerateRandomTensor<DT_FLOAT>(channel_shape);
auto mean_t = GenerateRandomTensor<DT_FLOAT>(is_training ? empty_shape
: channel_shape);
auto var_t = GenerateRandomTensor<DT_FLOAT>(is_training ? empty_shape
: channel_shape);
auto side_input_t = GenerateRandomTensor<DT_FLOAT>({2, 8, 8, num_channels});
GrapplerItem item;
item.fetch = {"fetch"};
item.feed = {{"input", input_t}, {"scale", scale_t},
{"offset", offset_t}, {"mean", mean_t},
{"var", var_t}, {"side_input", side_input_t}};
TF_ASSERT_OK(s.ToGraphDef(&item.graph));
// Place all nodes on GPU.
for (int i = 0; i < item.graph.node_size(); ++i) {
item.graph.mutable_node(i)->set_device("/device:GPU:0");
}
Remapper optimizer(RewriterConfig::AGGRESSIVE); // trust placeholders shape
GraphDef output;
TF_ASSERT_OK(optimizer.Optimize(nullptr, item, &output));
int found = 0;
for (const NodeDef& node : output.node()) {
if (node.name() == "relu") {
EXPECT_EQ(node.op(), "Identity");
ASSERT_EQ(node.input_size(), 1);
EXPECT_EQ(node.input(0), "fused_batch_norm");
found++;
}
if (node.name() == "fused_batch_norm") {
EXPECT_EQ(node.op(), "_FusedBatchNormEx");
ASSERT_EQ(node.input_size(), 6);
EXPECT_EQ(node.input(0), "input_cast");
EXPECT_EQ(node.input(1), "scale");
EXPECT_EQ(node.input(2), "offset");
EXPECT_EQ(node.input(3), "mean");
EXPECT_EQ(node.input(4), "var");
EXPECT_EQ(node.input(5), "side_input_cast");
auto attr = node.attr();
EXPECT_EQ(attr["num_side_inputs"].i(), 1);
EXPECT_EQ(attr["activation_mode"].s(), "Relu");
found++;
}
}
EXPECT_EQ(found, 2);
if (GetNumAvailableGPUs() > 0) {
auto tensors_expected = EvaluateNodes(item.graph, item.fetch, item.feed);
ASSERT_EQ(tensors_expected.size(), 1);
auto tensors = EvaluateNodes(output, item.fetch, item.feed);
ASSERT_EQ(tensors.size(), 1);
test::ExpectClose(tensors[0], tensors_expected[0], 1e-2, /*rtol=*/1e-2);
}
}
}
#if defined(GOOGLE_CUDA) && CUDNN_VERSION >= 7402
TEST_F(RemapperTest, FuseBatchNormGradWithAddAndReluGrad) {
if (IsMKLEnabled()) GTEST_SKIP() << "Fusion not available with oneDNN.";
using ::tensorflow::ops::Placeholder;
tensorflow::Scope s = tensorflow::Scope::NewRootScope();
bool is_training = true;
const int num_channels = 24;
TensorShape input_shape({2, 8, 8, num_channels});
TensorShape channel_shape({num_channels});
TensorShape empty_shape({0});
// Forward pass.
auto input = Placeholder(s.WithOpName("input"), DT_FLOAT,
ops::Placeholder::Shape(input_shape));
auto input_cast = ops::Cast(s.WithOpName("input_cast"), input, DT_HALF);
auto scale = Placeholder(s.WithOpName("scale"), DT_FLOAT);
auto offset = Placeholder(s.WithOpName("offset"), DT_FLOAT);
auto mean = Placeholder(s.WithOpName("mean"), DT_FLOAT);
auto var = Placeholder(s.WithOpName("var"), DT_FLOAT);
auto side_input = Placeholder(s.WithOpName("side_input"), DT_FLOAT,
ops::Placeholder::Shape(input_shape));
auto side_input_cast =
ops::Cast(s.WithOpName("side_input_cast"), side_input, DT_HALF);
float epsilon = 0.1f;
auto fbn = ops::FusedBatchNormV3(
s.WithOpName("fused_batch_norm"), input_cast, scale, offset, mean, var,
ops::FusedBatchNormV3::IsTraining(is_training)
.Epsilon(epsilon)
.DataFormat("NHWC"));
auto fbn_side_input =
ops::FusedBatchNormV3(s.WithOpName("fused_batch_norm_side_input"),
side_input_cast, scale, offset, mean, var,
ops::FusedBatchNormV3::IsTraining(is_training)
.Epsilon(epsilon)
.DataFormat("NHWC"));
// Since fbn.y is the first argument of "add" op, "fused_batch_norm" will be
// fused (not "fused_batch_norm_side_input"). Correspondingly,
// "fused_batch_norm_grad" will be fused (not
// "fused_batch_norm_side_input_grad").
auto add = ops::Add(s.WithOpName("add"), fbn.y, fbn_side_input.y);
auto relu = ops::Relu(s.WithOpName("relu"), add);
// Backward pass.
auto output_grad =
Placeholder(s.WithOpName("output_grad"), DT_FLOAT,
ops::Placeholder::Shape({2, 8, 8, num_channels}));
auto output_grad_cast =
ops::Cast(s.WithOpName("output_grad_cast"), output_grad, DT_HALF);
auto relu_grad = ops::internal::ReluGrad(s.WithOpName("relu_grad"),
output_grad_cast, relu);
auto fbn_grad = ops::FusedBatchNormGradV3(
s.WithOpName("fused_batch_norm_grad"), relu_grad, input_cast, scale,
fbn.reserve_space_1, fbn.reserve_space_2, fbn.reserve_space_3,
ops::FusedBatchNormGradV3::IsTraining(is_training)
.Epsilon(epsilon)
.DataFormat("NHWC"));
auto fbn_side_input_grad = ops::FusedBatchNormGradV3(
s.WithOpName("fused_batch_norm_side_input_grad"), relu_grad,
side_input_cast, scale, fbn_side_input.reserve_space_1,
fbn_side_input.reserve_space_2, fbn_side_input.reserve_space_3,
ops::FusedBatchNormGradV3::IsTraining(is_training)
.Epsilon(epsilon)
.DataFormat("NHWC"));
auto fetch0 = ops::Identity(s.WithOpName("fetch0"), fbn_grad.x_backprop);
auto fetch1 = ops::Identity(s.WithOpName("fetch1"), fbn_grad.scale_backprop);
auto fetch2 = ops::Identity(s.WithOpName("fetch2"), fbn_grad.offset_backprop);
auto fetch3 =
ops::Identity(s.WithOpName("fetch3"), fbn_side_input_grad.x_backprop);
auto fetch4 =
ops::Identity(s.WithOpName("fetch4"), fbn_side_input_grad.scale_backprop);
auto fetch5 = ops::Identity(s.WithOpName("fetch5"),
fbn_side_input_grad.offset_backprop);
auto input_t = GenerateRandomTensor<DT_FLOAT>(input_shape);
auto scale_t = GenerateRandomTensor<DT_FLOAT>(channel_shape);
auto offset_t = GenerateRandomTensor<DT_FLOAT>(channel_shape);
auto mean_t = GenerateRandomTensor<DT_FLOAT>(channel_shape);
auto var_t = GenerateRandomTensor<DT_FLOAT>(channel_shape);
auto side_input_t = GenerateRandomTensor<DT_FLOAT>({2, 8, 8, num_channels});
auto output_grad_t = GenerateRandomTensor<DT_FLOAT>({2, 8, 8, num_channels});
GrapplerItem item;
item.fetch = {"fetch0", "fetch1", "fetch2", "fetch3", "fetch4", "fetch5"};
item.feed = {{"input", input_t},
{"scale", scale_t},
{"offset", offset_t},
{"mean", mean_t},
{"var", var_t},
{"side_input", side_input_t},
{"output_grad", output_grad_t}};
TF_ASSERT_OK(s.ToGraphDef(&item.graph));
// Place all nodes on GPU.
for (int i = 0; i < item.graph.node_size(); ++i) {
item.graph.mutable_node(i)->set_device("/device:GPU:0");
}
Remapper optimizer(RewriterConfig::AGGRESSIVE); // trust placeholders shape
GraphDef output;
TF_ASSERT_OK(optimizer.Optimize(nullptr, item, &output));
int found = 0;
for (const NodeDef& node : output.node()) {
if (node.name() == "relu") {
EXPECT_EQ(node.op(), "Identity");
ASSERT_EQ(node.input_size(), 1);
EXPECT_EQ(node.input(0), "fused_batch_norm");
found++;
}
if (node.name() == "fused_batch_norm") {
EXPECT_EQ(node.op(), "_FusedBatchNormEx");
ASSERT_EQ(node.input_size(), 6);
EXPECT_EQ(node.input(0), "input_cast");
EXPECT_EQ(node.input(1), "scale");
EXPECT_EQ(node.input(2), "offset");
EXPECT_EQ(node.input(3), "mean");
EXPECT_EQ(node.input(4), "var");
EXPECT_EQ(node.input(5), "fused_batch_norm_side_input");
auto attr = node.attr();
EXPECT_EQ(attr["num_side_inputs"].i(), 1);
EXPECT_EQ(attr["activation_mode"].s(), "Relu");
found++;
}
if (node.name() == "relu_grad") {
EXPECT_EQ(node.op(), "Identity");
ASSERT_EQ(node.input_size(), 1);
EXPECT_EQ(node.input(0), "fused_batch_norm_grad:5");
found++;
}
if (node.name() == "fused_batch_norm_grad") {
EXPECT_EQ(node.op(), "_FusedBatchNormGradEx");
ASSERT_EQ(node.input_size(), 8);
EXPECT_EQ(node.input(0), "output_grad_cast");
EXPECT_EQ(node.input(1), "input_cast");
EXPECT_EQ(node.input(2), "scale");
EXPECT_EQ(node.input(3), "fused_batch_norm:3");
EXPECT_EQ(node.input(4), "fused_batch_norm:4");
EXPECT_EQ(node.input(5), "fused_batch_norm:5");
EXPECT_EQ(node.input(6), "offset");
EXPECT_EQ(node.input(7), "relu");
auto attr = node.attr();
EXPECT_EQ(attr["num_side_inputs"].i(), 1);
EXPECT_EQ(attr["activation_mode"].s(), "Relu");
found++;
}
}
EXPECT_EQ(found, 4);
if (GetNumAvailableGPUs() > 0) {
auto tensors_expected = EvaluateNodes(item.graph, item.fetch, item.feed);
ASSERT_EQ(tensors_expected.size(), 6);
auto tensors = EvaluateNodes(output, item.fetch, item.feed);
ASSERT_EQ(tensors.size(), 6);
test::ExpectClose(tensors[0], tensors_expected[0], 1e-2, /*rtol=*/1e-2);
test::ExpectClose(tensors[1], tensors_expected[1], 1e-2, /*rtol=*/1e-2);
test::ExpectClose(tensors[2], tensors_expected[2], 1e-2, /*rtol=*/1e-2);
test::ExpectClose(tensors[3], tensors_expected[3], 1e-2, /*rtol=*/1e-2);
test::ExpectClose(tensors[4], tensors_expected[4], 1e-2, /*rtol=*/1e-2);
test::ExpectClose(tensors[5], tensors_expected[5], 1e-2, /*rtol=*/1e-2);
}
}
#endif // defined(GOOGLE_CUDA) && CUDNN_VERSION >= 7402
class RemapperFuseConvWithBias : public RemapperTest {
public:
template <int dim, DataType DTYPE>
void RunTest() {
using ::tensorflow::ops::Placeholder;
tensorflow::Scope s = tensorflow::Scope::NewRootScope();
auto input_shape = ops::Placeholder::Shape({8, 32, 32, 3});
auto filter_shape = ops::Placeholder::Shape({1, 1, 3, 128});
auto bias_shape = ops::Placeholder::Shape({128});
std::vector<int> strides = {1, 1, 1, 1};
auto input_t = GenerateTensorWithSetRandom<DTYPE>({8, 32, 32, 3});
auto filter_t = GenerateTensorWithSetRandom<DTYPE>({1, 1, 3, 128});
auto bias_t = GenerateTensorWithSetRandom<DTYPE>({128});
if (dim == 3) {
if (!IsMKLEnabled()) GTEST_SKIP() << "Test only applicable to oneDNN.";
input_shape = ops::Placeholder::Shape({8, 4, 32, 32, 3});
filter_shape = ops::Placeholder::Shape({1, 1, 1, 3, 128});
bias_shape = ops::Placeholder::Shape({128});
strides = {1, 1, 1, 1, 1};
input_t = GenerateTensorWithSetRandom<DTYPE>({8, 4, 32, 32, 3});
filter_t = GenerateTensorWithSetRandom<DTYPE>({1, 1, 1, 3, 128});
bias_t = GenerateTensorWithSetRandom<DTYPE>({128});
}
auto input = Placeholder(s.WithOpName("input"), DTYPE, input_shape);
auto filter = Placeholder(s.WithOpName("filter"), DTYPE, filter_shape);
auto bias = Placeholder(s.WithOpName("bias"), DTYPE, bias_shape);
if (dim == 2) {
auto conv =
ops::Conv2D(s.WithOpName("conv"), input, filter, strides, "SAME");
auto bias_add = ops::BiasAdd(s.WithOpName("bias_add"), conv, bias);
auto fetch = ops::Identity(s.WithOpName("fetch"), bias_add);
} else if (dim == 3) {
auto conv =
ops::Conv3D(s.WithOpName("conv"), input, filter, strides, "SAME");
auto bias_add = ops::BiasAdd(s.WithOpName("bias_add"), conv, bias);
auto fetch = ops::Identity(s.WithOpName("fetch"), bias_add);
}
GrapplerItem item;
item.fetch = {"fetch"};
item.feed = {{"input", input_t}, {"filter", filter_t}, {"bias", bias_t}};
TF_ASSERT_OK(s.ToGraphDef(&item.graph));
// Place all nodes on CPU.
for (int i = 0; i < item.graph.node_size(); ++i) {
item.graph.mutable_node(i)->set_device("/device:CPU:0");
}
Remapper optimizer(RewriterConfig::ON);
GraphDef output;
TF_ASSERT_OK(optimizer.Optimize(nullptr, item, &output));
int found = 0;
for (const NodeDef& node : output.node()) {
if (node.name() == "bias_add") {
if (dim == 2) {
EXPECT_EQ(node.op(), "_FusedConv2D");
} else if (dim == 3) {
EXPECT_EQ(node.op(), "_FusedConv3D");
}
ASSERT_GE(node.input_size(), 3);
EXPECT_EQ(node.input(0), "input");
EXPECT_EQ(node.input(1), "filter");
EXPECT_EQ(node.attr().at("num_args").i(), 1);
EXPECT_EQ(node.input(2), "bias");
const auto fused_ops = node.attr().at("fused_ops").list().s();
ASSERT_EQ(fused_ops.size(), 1);
EXPECT_EQ(fused_ops[0], "BiasAdd");
found++;
}
}
EXPECT_EQ(found, 1);
auto tensors_expected = EvaluateNodes(item.graph, item.fetch, item.feed);
ASSERT_EQ(tensors_expected.size(), 1);
auto tensors = EvaluateNodes(output, item.fetch, item.feed);
ASSERT_EQ(tensors.size(), 1);
if (DTYPE == DT_BFLOAT16)
test::ExpectClose(tensors[0], tensors_expected[0], 1e-2, 1e-2);
else
test::ExpectClose(tensors[0], tensors_expected[0], 1e-6);
}
};
TEST_F(RemapperFuseConvWithBias, Conv2D_F32) { RunTest<2, DT_FLOAT>(); }
TEST_F(RemapperFuseConvWithBias, Conv3D_F32) { RunTest<3, DT_FLOAT>(); }
TEST_F(RemapperFuseConvWithBias, Conv2D_BF16) {
if (!IsMKLEnabled() || !IsDataTypeSupportedByOneDNNOnThisCPU(DT_BFLOAT16))
GTEST_SKIP() << "Intel oneDNN with bfloat16 is not supported, skipping "
"FuseConv2DWithBias with bfloat16.";
RunTest<2, DT_BFLOAT16>();
}
TEST_F(RemapperFuseConvWithBias, Conv3D_BF16) {
if (!IsMKLEnabled() || !IsDataTypeSupportedByOneDNNOnThisCPU(DT_BFLOAT16))
GTEST_SKIP() << "Intel oneDNN with bfloat16 is not supported, skipping "
"FuseConv3DWithBias with bfloat16.";
RunTest<3, DT_BFLOAT16>();
}
class RemapperFuseConvWithBiasAndActivation : public RemapperTest {
public:
template <int dim, DataType DTYPE>
void RunTest() {
using ::tensorflow::ops::Placeholder;
for (const string& activation : {"Relu", "Relu6", "Elu", "LeakyRelu"}) {
tensorflow::Scope s = tensorflow::Scope::NewRootScope();
auto input_shape = Placeholder::Shape({8, 32, 32, 3});
auto filter_shape = Placeholder::Shape({1, 1, 3, 128});
auto bias_shape = Placeholder::Shape({128});
std::vector<int> strides = {1, 1, 1, 1};
auto input_t = GenerateTensorWithSetRandom<DTYPE>({8, 32, 32, 3});
auto filter_t = GenerateTensorWithSetRandom<DTYPE>({1, 1, 3, 128});
auto bias_t = GenerateTensorWithSetRandom<DTYPE>({128});
if (dim == 3) {
if (!IsMKLEnabled()) GTEST_SKIP() << "Test only applicable to oneDNN.";
input_shape = Placeholder::Shape({8, 4, 32, 32, 3});
filter_shape = Placeholder::Shape({1, 1, 1, 3, 128});
bias_shape = Placeholder::Shape({128});
strides = {1, 1, 1, 1, 1};
input_t = GenerateTensorWithSetRandom<DTYPE>({8, 4, 32, 32, 3});
filter_t = GenerateTensorWithSetRandom<DTYPE>({1, 1, 1, 3, 128});
bias_t = GenerateTensorWithSetRandom<DTYPE>({128});
}
float leakyrelu_alpha = 0.5;
auto input = Placeholder(s.WithOpName("input"), DTYPE, input_shape);
auto filter = Placeholder(s.WithOpName("filter"), DTYPE, filter_shape);
auto bias = Placeholder(s.WithOpName("bias"), DTYPE, bias_shape);
if (dim == 2) {
auto conv =
ops::Conv2D(s.WithOpName("conv"), input, filter, strides, "SAME");
auto bias_add = ops::BiasAdd(s.WithOpName("bias_add"), conv, bias);
ops::Identity fetch = [&]() -> ops::Identity {
auto activate = s.WithOpName("activation");
auto fetch = s.WithOpName("fetch");
if (activation == "Relu") {
return ops::Identity(fetch, ops::Relu(activate, bias_add));
} else if (activation == "Relu6") {
return ops::Identity(fetch, ops::Relu6(activate, bias_add));
} else if (activation == "Elu") {
return ops::Identity(fetch, ops::Elu(activate, bias_add));
} else if (activation == "LeakyRelu") {
auto attr = ops::internal::LeakyRelu::Alpha(leakyrelu_alpha);
return ops::Identity(
fetch, ops::internal::LeakyRelu(activate, bias_add, attr));
}
return ops::Identity(fetch, bias);
}();
} else if (dim == 3) {
auto conv =
ops::Conv3D(s.WithOpName("conv"), input, filter, strides, "SAME");
auto bias_add = ops::BiasAdd(s.WithOpName("bias_add"), conv, bias);
ops::Identity fetch = [&]() -> ops::Identity {
auto activate = s.WithOpName("activation");
auto fetch = s.WithOpName("fetch");
if (activation == "Relu") {
return ops::Identity(fetch, ops::Relu(activate, bias_add));
} else if (activation == "Relu6") {
return ops::Identity(fetch, ops::Relu6(activate, bias_add));
} else if (activation == "Elu") {
return ops::Identity(fetch, ops::Elu(activate, bias_add));
} else if (activation == "LeakyRelu") {
auto attr = ops::internal::LeakyRelu::Alpha(leakyrelu_alpha);
return ops::Identity(
fetch, ops::internal::LeakyRelu(activate, bias_add, attr));
}
return ops::Identity(fetch, bias);
}();
}
GrapplerItem item;
item.fetch = {"fetch"};
item.feed = {{"input", input_t}, {"filter", filter_t}, {"bias", bias_t}};
TF_ASSERT_OK(s.ToGraphDef(&item.graph));
// Place all nodes on CPU.
for (int i = 0; i < item.graph.node_size(); ++i) {
item.graph.mutable_node(i)->set_device("/device:CPU:0");
}
Remapper optimizer(RewriterConfig::ON);
GraphDef output;
TF_ASSERT_OK(optimizer.Optimize(nullptr, item, &output));
int found = 0;
for (const NodeDef& node : output.node()) {
if (node.name() == "activation") {
if (dim == 2) {
EXPECT_EQ(node.op(), "_FusedConv2D");
} else if (dim == 3) {
EXPECT_EQ(node.op(), "_FusedConv3D");
}
ASSERT_GE(node.input_size(), 3);
EXPECT_EQ(node.input(0), "input");
EXPECT_EQ(node.input(1), "filter");
EXPECT_EQ(node.attr().at("num_args").i(), 1);
EXPECT_EQ(node.input(2), "bias");
const auto fused_ops = node.attr().at("fused_ops").list().s();
ASSERT_EQ(fused_ops.size(), 2);
EXPECT_EQ(fused_ops[0], "BiasAdd");
EXPECT_EQ(fused_ops[1], activation);
if (activation == "LeakyRelu") {
EXPECT_EQ(node.attr().at("leakyrelu_alpha").f(), leakyrelu_alpha);
}
found++;
}
}
EXPECT_EQ(found, 1);
auto tensors_expected = EvaluateNodes(item.graph, item.fetch, item.feed);
ASSERT_EQ(tensors_expected.size(), 1);
auto tensors = EvaluateNodes(output, item.fetch, item.feed);
ASSERT_EQ(tensors.size(), 1);
if (DTYPE == DT_BFLOAT16)
test::ExpectClose(tensors[0], tensors_expected[0], 1e-2, 1e-2);
else
test::ExpectClose(tensors[0], tensors_expected[0], 1e-6);
}
}
};
TEST_F(RemapperFuseConvWithBiasAndActivation, Conv2D_F32) {
RunTest<2, DT_FLOAT>();
}
TEST_F(RemapperFuseConvWithBiasAndActivation, Conv3D_F32) {
RunTest<3, DT_FLOAT>();
}
TEST_F(RemapperFuseConvWithBiasAndActivation, Conv2D_BF16) {
if (!IsMKLEnabled() || !IsDataTypeSupportedByOneDNNOnThisCPU(DT_BFLOAT16))
GTEST_SKIP() << "Intel oneDNN with bfloat16 is not supported, skipping "
"FuseConv2DWithBiasAndActivation with bfloat16.";
RunTest<2, DT_BFLOAT16>();
}
TEST_F(RemapperFuseConvWithBiasAndActivation, Conv3D_BF16) {
if (!IsMKLEnabled() || !IsDataTypeSupportedByOneDNNOnThisCPU(DT_BFLOAT16))
GTEST_SKIP() << "Intel oneDNN with bfloat16 is not supported, skipping "
"FuseConv3DWithBiasAndActivation with bfloat16.";
RunTest<3, DT_BFLOAT16>();
}
class RemapperFuseConvWithBiasAndAddActivation : public RemapperTest {
public:
template <int dim, DataType DTYPE>
void RunTest() {
if (!IsMKLEnabled()) GTEST_SKIP() << "Test only applicable to oneDNN.";
using ::tensorflow::ops::Placeholder;
for (const string& activation : {"Relu", "Relu6", "Elu", "LeakyRelu"}) {
tensorflow::Scope s = tensorflow::Scope::NewRootScope();
auto input_shape = Placeholder::Shape({8, 32, 32, 3});
auto filter_shape = Placeholder::Shape({1, 1, 3, 128});
auto bias_shape = Placeholder::Shape({128});
auto add_shape = ops::Placeholder::Shape({8, 32, 32, 128});
auto input_t = GenerateRandomTensor<DT_FLOAT>({8, 32, 32, 3});
auto filter_t = GenerateRandomTensor<DT_FLOAT>({1, 1, 3, 128});
auto bias_t = GenerateRandomTensor<DT_FLOAT>({128});
auto add_t = GenerateRandomTensor<DT_FLOAT>({8, 32, 32, 128});
float leakyrelu_alpha = 0.5;
std::vector<int> strides = {1, 1, 1, 1};
if (dim == 3) {
input_shape = Placeholder::Shape({8, 4, 32, 32, 3});
filter_shape = Placeholder::Shape({1, 1, 1, 3, 128});
bias_shape = Placeholder::Shape({128});
add_shape = ops::Placeholder::Shape({8, 4, 32, 32, 128});
strides = {1, 1, 1, 1, 1};
input_t = GenerateRandomTensor<DT_FLOAT>({8, 4, 32, 32, 3});
filter_t = GenerateRandomTensor<DT_FLOAT>({1, 1, 1, 3, 128});
bias_t = GenerateRandomTensor<DT_FLOAT>({128});
add_t = GenerateRandomTensor<DT_FLOAT>({8, 4, 32, 32, 128});
}
auto input = Placeholder(s.WithOpName("input"), DT_FLOAT, input_shape);
auto filter = Placeholder(s.WithOpName("filter"), DT_FLOAT, filter_shape);
auto bias = Placeholder(s.WithOpName("bias"), DT_FLOAT, bias_shape);
auto input_add =
Placeholder(s.WithOpName("input_add"), DT_FLOAT, add_shape);
if (dim == 2) {
auto conv =
ops::Conv2D(s.WithOpName("conv"), input, filter, strides, "SAME");
auto bias_add = ops::BiasAdd(s.WithOpName("bias_add"), conv, bias);
auto add = ops::Add(s.WithOpName("add_op"), input_add, bias_add);
ops::Identity fetch = [&]() -> ops::Identity {
auto activate = s.WithOpName("activation");
auto fetch = s.WithOpName("fetch");
if (activation == "Relu") {
return ops::Identity(fetch, ops::Relu(activate, add));
} else if (activation == "Relu6") {
return ops::Identity(fetch, ops::Relu6(activate, add));
} else if (activation == "Elu") {
return ops::Identity(fetch, ops::Elu(activate, add));
} else if (activation == "LeakyRelu") {
auto attr = ops::internal::LeakyRelu::Alpha(leakyrelu_alpha);
return ops::Identity(fetch,
ops::internal::LeakyRelu(activate, add, attr));
}
return ops::Identity(fetch, bias);
}();
} else if (dim == 3) {
auto conv =
ops::Conv3D(s.WithOpName("conv"), input, filter, strides, "SAME");
auto bias_add = ops::BiasAdd(s.WithOpName("bias_add"), conv, bias);
auto add = ops::Add(s.WithOpName("add_op"), input_add, bias_add);
ops::Identity fetch = [&]() -> ops::Identity {
auto activate = s.WithOpName("activation");
auto fetch = s.WithOpName("fetch");
if (activation == "Relu") {
return ops::Identity(fetch, ops::Relu(activate, add));
} else if (activation == "Relu6") {
return ops::Identity(fetch, ops::Relu6(activate, add));
} else if (activation == "Elu") {
return ops::Identity(fetch, ops::Elu(activate, add));
} else if (activation == "LeakyRelu") {
auto attr = ops::internal::LeakyRelu::Alpha(leakyrelu_alpha);
return ops::Identity(fetch,
ops::internal::LeakyRelu(activate, add, attr));
}
return ops::Identity(fetch, bias);
}();
}
GrapplerItem item;
item.fetch = {"fetch"};
item.feed = {{"input", input_t},
{"filter", filter_t},
{"bias", bias_t},
{"input_add", add_t}};
TF_ASSERT_OK(s.ToGraphDef(&item.graph));
// Place all nodes on CPU.
for (int i = 0; i < item.graph.node_size(); ++i) {
item.graph.mutable_node(i)->set_device("/device:CPU:0");
}
Remapper optimizer(RewriterConfig::AGGRESSIVE);