/*******************************************************************************
* Copyright 2017-2018 Intel Corporation
*
* 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 <algorithm>
#include <cstdio>
#include <iostream>
#include <list>
#include <memory>
#include "gtest/gtest.h"
#include "ngraph/autodiff/adjoints.hpp"
#include "ngraph/file_util.hpp"
#include "ngraph/graph_util.hpp"
#include "ngraph/log.hpp"
#include "ngraph/ngraph.hpp"
#include "ngraph/op/batch_norm.hpp"
#include "ngraph/op/get_output_element.hpp"
#include "ngraph/op/max_pool.hpp"
#include "ngraph/op/parameter.hpp"
#include "ngraph/op/relu.hpp"
#include "ngraph/op/sum.hpp"
#include "ngraph/pass/graph_rewrite.hpp"
#include "ngraph/pass/manager.hpp"
#include "ngraph/pass/reshape_elimination.hpp"
#include "ngraph/pass/visualize_tree.hpp"
#include "ngraph/pattern/matcher.hpp"
#include "ngraph/pattern/op/label.hpp"
#include "ngraph/pattern/op/skip.hpp"
#include "ngraph/runtime/cpu/cpu_layout_descriptor.hpp"
#include "ngraph/runtime/cpu/op/batch_norm_relu.hpp"
#include "ngraph/runtime/cpu/op/conv_bias.hpp"
#include "ngraph/runtime/cpu/op/conv_relu.hpp"
#include "ngraph/runtime/cpu/op/convert_layout.hpp"
#include "ngraph/runtime/cpu/op/matmul_bias.hpp"
#include "ngraph/runtime/cpu/op/sigmoid.hpp"
#include "ngraph/runtime/cpu/pass/cpu_fusion.hpp"
#include "ngraph/runtime/cpu/pass/cpu_post_layout_optimizations.hpp"
#include "ngraph/runtime/cpu/pass/cpu_rnn_mat_fusion.hpp"
#include "ngraph/runtime/cpu/pass/cpu_workspace_insertion.hpp"
#include "ngraph/serializer.hpp"
#include "ngraph/util.hpp"
#include "nlohmann/json.hpp"
#include "util/all_close.hpp"
#include "util/autodiff/backprop_function.hpp"
#include "util/autodiff/numeric_compare.hpp"
#include "util/matcher.hpp"
#include "util/random.hpp"
#include "util/test_tools.hpp"
#include "util/random.hpp"
using namespace ngraph;
using namespace std;
TEST(cpu_fusion, gemm_pattern)
{
Shape shape_w{2, 4};
Shape shape_x{4, 1};
Shape shape_b{1};
auto A = make_shared<op::Parameter>(element::f32, shape_w);
auto B = make_shared<op::Parameter>(element::f32, shape_x);
auto C = make_shared<op::Parameter>(element::f32, shape_b);
auto dot = make_shared<op::Dot>(A, B);
auto broadcast = make_shared<op::Broadcast>(C, dot->get_shape(), AxisSet{0});
auto add = dot + broadcast;
auto W = std::make_shared<pattern::op::Label>(A);
auto x = std::make_shared<pattern::op::Label>(B);
auto reshape_pred = [](std::shared_ptr<Node> n) {
return static_cast<bool>(std::dynamic_pointer_cast<op::Reshape>(n));
};
auto skip_w = std::make_shared<pattern::op::Skip>(W, reshape_pred);
auto skip_x = std::make_shared<pattern::op::Skip>(x, reshape_pred);
auto pdot = make_shared<op::Dot>(skip_w, skip_x);
auto b = std::make_shared<pattern::op::Label>(C);
auto pbroadcast = make_shared<op::Broadcast>(b, dot->get_shape(), AxisSet{0});
auto padd = pdot + pbroadcast;
TestMatcher n(nullptr);
ASSERT_TRUE(n.match(padd, add));
ASSERT_EQ(n.get_pattern_map()[W], A);
ASSERT_EQ(n.get_pattern_map()[x], B);
ASSERT_EQ(n.get_pattern_map()[b], C);
auto reshape_w = make_shared<op::Reshape>(A, AxisVector{1, 0}, W->get_shape());
auto reshape_x = make_shared<op::Reshape>(B, AxisVector{1, 0}, x->get_shape());
auto re_dot = make_shared<op::Dot>(reshape_w, reshape_x);
auto re_add = re_dot + broadcast;
ASSERT_TRUE(n.match(padd, re_add));
ASSERT_EQ(n.get_pattern_map()[W], A);
ASSERT_EQ(n.get_pattern_map()[x], B);
ASSERT_EQ(n.get_pattern_map()[b], C);
auto cg = make_shared<op::MatmulBias>(
W, x, C, W->get_shape(), x->get_shape(), false, false, AxisSet{0});
}
TEST(cpu_fusion, gemm_cpu_broadcast_row)
{
Shape shapeA{3, 2};
Shape shapeB{2, 3};
Shape shapeC{2, 2};
auto A = make_shared<op::Parameter>(element::f32, shapeA);
auto B = make_shared<op::Parameter>(element::f32, shapeB);
auto bias = op::Constant::create<float>(element::f32, Shape{2}, std::vector<float>{2.0f, 3.0f});
auto cg = make_shared<op::MatmulBias>(
A, B, bias, A->get_shape(), B->get_shape(), true, true, AxisSet{0});
auto f = make_shared<Function>(cg, op::ParameterVector{A, B});
auto backend = runtime::Backend::create("CPU");
shared_ptr<runtime::TensorView> a = backend->create_tensor(element::f32, shapeA);
shared_ptr<runtime::TensorView> b = backend->create_tensor(element::f32, shapeB);
shared_ptr<runtime::TensorView> result = backend->create_tensor(element::f32, shapeC);
vector<float> dataA{1.0f, 4.0f, 1.0f, 4.0f, 1.0f, 4.0f};
vector<float> dataB{3.0f, 3.0f, 3.0f, 9.0f, 9.0f, 9.0f};
copy_data(a, dataA);
copy_data(b, dataB);
backend->call(f, {result}, {a, b});
vector<float> expected{11, 30, 38, 111};
EXPECT_EQ(read_vector<float>(result), expected);
}
TEST(cpu_fusion, gemm_cpu_broadcast_column)
{
Shape shapeA{3, 2};
Shape shapeB{2, 3};
Shape shapeC{2, 2};
auto A = make_shared<op::Parameter>(element::f32, shapeA);
auto B = make_shared<op::Parameter>(element::f32, shapeB);
auto bias = op::Constant::create<float>(element::f32, Shape{2}, std::vector<float>{2.0f, 3.0f});
auto cg = make_shared<op::MatmulBias>(
A, B, bias, A->get_shape(), B->get_shape(), true, true, AxisSet{1});
auto f = make_shared<Function>(cg, op::ParameterVector{A, B});
auto backend = runtime::Backend::create("CPU");
shared_ptr<runtime::TensorView> a = backend->create_tensor(element::f32, shapeA);
shared_ptr<runtime::TensorView> b = backend->create_tensor(element::f32, shapeB);
shared_ptr<runtime::TensorView> result = backend->create_tensor(element::f32, shapeC);
vector<float> dataA{1.0f, 4.0f, 1.0f, 4.0f, 1.0f, 4.0f};
vector<float> dataB{3.0f, 3.0f, 3.0f, 9.0f, 9.0f, 9.0f};
copy_data(a, dataA);
copy_data(b, dataB);
backend->call(f, {result}, {a, b});
vector<float> expected{11, 29, 39, 111};
EXPECT_EQ(read_vector<float>(result), expected);
}
TEST(cpu_fusion, gemm_cpu_broadcast_matrix)
{
Shape shapeA{3, 2};
Shape shapeB{2, 3};
Shape shapeC{2, 2};
auto A = make_shared<op::Parameter>(element::f32, shapeA);
auto B = make_shared<op::Parameter>(element::f32, shapeB);
auto reshape_w = make_shared<op::Reshape>(A, AxisVector{1, 0}, Shape{2, 3});
auto reshape_x = make_shared<op::Reshape>(B, AxisVector{1, 0}, Shape{3, 2});
auto one = op::Constant::create<float>(element::f32, Shape{}, std::vector<float>{1.0f});
auto broadcast = make_shared<op::Broadcast>(one, shapeC, AxisSet{0, 1});
auto cg = make_shared<op::MatmulBias>(
A, B, one, A->get_shape(), B->get_shape(), true, true, AxisSet{0, 1});
auto f = make_shared<Function>(cg, op::ParameterVector{A, B});
auto backend = runtime::Backend::create("CPU");
shared_ptr<runtime::TensorView> a = backend->create_tensor(element::f32, shapeA);
shared_ptr<runtime::TensorView> b = backend->create_tensor(element::f32, shapeB);
shared_ptr<runtime::TensorView> result = backend->create_tensor(element::f32, shapeC);
vector<float> dataA{1.0f, 4.0f, 1.0f, 4.0f, 1.0f, 4.0f};
vector<float> dataB{3.0f, 3.0f, 3.0f, 9.0f, 9.0f, 9.0f};
copy_data(a, dataA);
copy_data(b, dataB);
backend->call(f, {result}, {a, b});
vector<float> expected{10, 28, 37, 109};
ASSERT_TRUE(read_vector<float>(result) == expected);
}
TEST(cpu_fusion, gemm_cpu_no_bias)
{
auto shapeA = Shape{3, 2};
auto shapeB = Shape{2, 3};
auto shapeC = Shape{2, 2};
auto A = make_shared<op::Parameter>(element::f32, shapeA);
auto B = make_shared<op::Parameter>(element::f32, shapeB);
auto reshape_w = make_shared<op::Reshape>(A, AxisVector{1, 0}, Shape{2, 3});
auto reshape_x = make_shared<op::Reshape>(B, AxisVector{1, 0}, Shape{3, 2});
auto cg =
make_shared<op::MatmulBias>(A, B, nullptr, A->get_shape(), B->get_shape(), true, true);
auto f = make_shared<Function>(cg, op::ParameterVector{A, B});
auto backend = runtime::Backend::create("CPU");
shared_ptr<runtime::TensorView> a = backend->create_tensor(element::f32, shapeA);
shared_ptr<runtime::TensorView> b = backend->create_tensor(element::f32, shapeB);
shared_ptr<runtime::TensorView> result = backend->create_tensor(element::f32, shapeC);
vector<float> dataA{1.0f, 4.0f, 1.0f, 4.0f, 1.0f, 4.0f};
vector<float> dataB{3.0f, 3.0f, 3.0f, 9.0f, 9.0f, 9.0f};
copy_data(a, dataA);
copy_data(b, dataB);
backend->call(f, {result}, {a, b});
vector<float> expected{9, 27, 36, 108};
ASSERT_TRUE(read_vector<float>(result) == expected);
}
TEST(cpu_fusion, cpu_fusion_pass_basic)
{
Shape shape{};
Shape shape_w{2, 4};
Shape shape_x{4, 1};
Shape shape_b{1};
auto A = make_shared<op::Parameter>(element::f32, shape_w);
auto B = make_shared<op::Parameter>(element::f32, shape_x);
auto C = make_shared<op::Parameter>(element::f32, shape_b);
auto dot = make_shared<op::Dot>(A, B);
auto broadcast = make_shared<op::Broadcast>(C, dot->get_shape(), AxisSet{0});
auto add = dot + broadcast;
auto graph = make_shared<op::Abs>(add);
pass::Manager pass_manager;
pass_manager.register_pass<runtime::cpu::pass::CPUFusion>();
auto func = make_shared<Function>(graph, op::ParameterVector{A, B, C});
pass_manager.run_passes(func);
ASSERT_NE(std::dynamic_pointer_cast<op::MatmulBias>(graph->get_argument(0)), nullptr);
}
TEST(cpu_fusion, commutative_matmul_bias)
{
Shape shape{};
Shape shape_w{2, 4};
Shape shape_x{4, 1};
Shape shape_b{1};
auto A = make_shared<op::Parameter>(element::f32, shape_w);
auto B = make_shared<op::Parameter>(element::f32, shape_x);
auto C = make_shared<op::Parameter>(element::f32, shape_b);
auto dot = make_shared<op::Dot>(A, B);
auto broadcast = make_shared<op::Broadcast>(C, dot->get_shape(), AxisSet{0});
auto add = broadcast + dot;
auto graph = make_shared<op::Abs>(add);
pass::Manager pass_manager;
pass_manager.register_pass<runtime::cpu::pass::CPUFusion>();
auto func = make_shared<Function>(graph, op::ParameterVector{A, B, C});
pass_manager.run_passes(func);
ASSERT_NE(std::dynamic_pointer_cast<op::MatmulBias>(graph->get_argument(0)), nullptr);
}
TEST(cpu_fusion, cpu_fusion_pass_matmul_bias)
{
Shape shape_w{2, 4};
Shape shape_x{4, 1};
Shape shape_b{1};
auto W = make_shared<op::Parameter>(element::f32, shape_w);
auto x = make_shared<op::Parameter>(element::f32, shape_x);
auto b = make_shared<op::Parameter>(element::f32, shape_b);
auto mmb = std::make_shared<op::MatmulBias>(
W, x, nullptr, W->get_shape(), x->get_shape(), false, false);
auto broadcast = std::make_shared<op::Broadcast>(b, mmb->get_shape(), AxisSet{0});
auto add = mmb + broadcast;
auto graph = make_shared<op::Abs>(add);
pass::Manager pass_manager;
pass_manager.register_pass<runtime::cpu::pass::CPUFusion>();
auto func = make_shared<Function>(graph, op::ParameterVector{W, x, b});
pass_manager.run_passes(func);
auto gmm = graph->get_argument(0);
ASSERT_TRUE(std::dynamic_pointer_cast<op::MatmulBias>(gmm));
ASSERT_EQ(gmm->get_argument(2), b);
}
TEST(cpu_fusion, cpu_fusion_pass_matmul_no_bias)
{
Shape shape_w{4, 2};
Shape shape_x{1, 4};
auto W = make_shared<op::Parameter>(element::f32, shape_w);
auto x = make_shared<op::Parameter>(element::f32, shape_x);
auto reshape_w = std::make_shared<op::Reshape>(W, AxisVector{1, 0}, Shape{2, 4});
auto reshape_x = std::make_shared<op::Reshape>(x, AxisVector{1, 0}, Shape{4, 1});
auto re_dot = make_shared<op::Dot>(reshape_w, reshape_x);
auto graph = make_shared<op::Abs>(re_dot);
pass::Manager pass_manager;
pass_manager.register_pass<runtime::cpu::pass::CPUFusion>();
auto func = make_shared<Function>(graph, op::ParameterVector{W, x});
pass_manager.run_passes(func);
size_t mmb = count_ops_of_type<op::MatmulBias>(func);
ASSERT_EQ(mmb, 1);
}
TEST(cpu_fusion, gemm_mlp)
{
const string json_path = file_util::path_join(SERIALIZED_ZOO, "mxnet/mnist_mlp_forward.json");
const string json_string = file_util::read_file_to_string(json_path);
stringstream ss(json_string);
shared_ptr<Function> func = ngraph::deserialize(ss);
pass::Manager pass_manager;
pass_manager.register_pass<runtime::cpu::pass::CPUFusion>();
pass_manager.run_passes(func);
auto mmbs = count_ops_of_type<op::MatmulBias>(func);
ASSERT_EQ(mmbs, 3);
}
TEST(cpu_fusion, fuse_fprop_bn)
{
pass::Manager pass_manager;
pass_manager.register_pass<pass::VisualizeTree>("bn_fprop_before_fusion.png");
pass_manager.register_pass<ngraph::pass::ReshapeElimination>();
pass_manager.register_pass<runtime::cpu::pass::CPUFusion>();
pass_manager.register_pass<pass::VisualizeTree>("bn_fprop_after_fusion.png");
const string json_path = file_util::path_join(SERIALIZED_ZOO, "mxnet/bn_fprop_b2c3h2w2.json");
const string json_string = file_util::read_file_to_string(json_path);
stringstream ss(json_string);
shared_ptr<Function> func = ngraph::deserialize(ss);
pass_manager.run_passes(func);
size_t ccg = count_ops_of_type<op::BatchNorm>(func);
ASSERT_EQ(ccg, 1);
}
TEST(cpu_fusion, zero_padded_reshaped_conv)
{
auto X = make_shared<op::Parameter>(element::f32, Shape{1, 2, 2, 1});
auto F = make_shared<op::Parameter>(element::f32, Shape{1, 1, 1, 1});
auto pad_value = op::Constant::create<float>(element::f32, Shape{}, std::vector<float>{0.0f});
auto pad =
make_shared<op::Pad>(X, pad_value, Shape{0, 1, 0, 0}, Shape{0, 0, 1, 0}, Shape{0, 0, 0, 0});
auto reshape = make_shared<op::Reshape>(pad, AxisVector{0, 3, 1, 2}, Shape{1, 1, 3, 3});
auto conv = make_shared<op::Convolution>(reshape,
F,
Strides{1, 1},
Strides{1, 1},
CoordinateDiff{0, 0},
CoordinateDiff{0, 0},
Strides{1, 1});
auto func = make_shared<Function>(conv, op::ParameterVector{X, F});
ASSERT_EQ(count_ops_of_type<op::Pad>(func), 1);
auto backend = runtime::Backend::create("CPU");
backend->compile(func);
ASSERT_EQ(count_ops_of_type<op::Pad>(func), 0);
}
TEST(cpu_fusion, zero_padded_conv)
{
auto X = make_shared<op::Parameter>(element::f32, Shape{1, 1, 2, 2});
auto F = make_shared<op::Parameter>(element::f32, Shape{1, 1, 1, 1});
auto pad_value = op::Constant::create<float>(element::f32, Shape{}, std::vector<float>{0.0f});
auto pad =
make_shared<op::Pad>(X, pad_value, Shape{0, 0, 0, 1}, Shape{0, 0, 1, 0}, Shape{0, 0, 0, 0});
auto conv = make_shared<op::Convolution>(pad,
F,
Strides{1, 1},
Strides{1, 1},
CoordinateDiff{0, 0},
CoordinateDiff{0, 0},
Strides{1, 1});
auto func = make_shared<Function>(conv, op::ParameterVector{X, F});
ASSERT_EQ(count_ops_of_type<op::Pad>(func), 1);
auto backend = runtime::Backend::create("CPU");
backend->compile(func);
ASSERT_EQ(count_ops_of_type<op::Pad>(func), 0);
}
TEST(cpu_fusion, non_zero_padded_conv)
{
auto X = make_shared<op::Parameter>(element::f32, Shape{1, 1, 2, 2});
auto F = make_shared<op::Parameter>(element::f32, Shape{1, 1, 1, 1});
auto pad_value = op::Constant::create<float>(element::f32, Shape{}, std::vector<float>{1.0f});
auto pad =
make_shared<op::Pad>(X, pad_value, Shape{0, 0, 0, 1}, Shape{0, 0, 1, 0}, Shape{0, 0, 0, 0});
auto conv = make_shared<op::Convolution>(pad,
F,
Strides{1, 1},
Strides{1, 1},
CoordinateDiff{0, 0},
CoordinateDiff{0, 0},
Strides{1, 1});
auto func = make_shared<Function>(conv, op::ParameterVector{X, F});
ASSERT_EQ(count_ops_of_type<op::Pad>(func), 1);
auto backend = runtime::Backend::create("CPU");
backend->compile(func);
ASSERT_EQ(count_ops_of_type<op::Pad>(func), 1);
}
TEST(cpu_fusion, zero_padded_conv_backprop_filters)
{
auto X = make_shared<op::Parameter>(element::f32, Shape{1, 1, 2, 2});
auto F = make_shared<op::Parameter>(element::f32, Shape{1, 1, 2, 2});
auto pad_value = op::Constant::create<float>(element::f32, Shape{}, std::vector<float>{0.0f});
auto pad =
make_shared<op::Pad>(X, pad_value, Shape{0, 0, 0, 1}, Shape{0, 0, 1, 0}, Shape{0, 0, 0, 0});
auto conv = make_shared<op::ConvolutionBackpropFilters>(pad,
Shape{1, 1, 2, 2},
F,
Strides{1, 1},
Strides{1, 1},
CoordinateDiff{0, 0},
CoordinateDiff{0, 0},
Strides{1, 1});
auto func = make_shared<Function>(conv, op::ParameterVector{X, F});
ASSERT_EQ(count_ops_of_type<op::Pad>(func), 1);
auto backend = runtime::Backend::create("CPU");
backend->compile(func);
ASSERT_EQ(count_ops_of_type<op::Pad>(func), 0);
}
TEST(cpu_fusion, fuse_conv_bias)
{
pass::Manager pass_manager;
pass_manager.register_pass<ngraph::pass::ReshapeElimination>();
pass_manager.register_pass<runtime::cpu::pass::CPUFusion>();
const string json_path = file_util::path_join(SERIALIZED_ZOO, "conv_bias.json");
const string json_string = file_util::read_file_to_string(json_path);
stringstream ss(json_string);
shared_ptr<Function> func = ngraph::deserialize(ss);
pass_manager.run_passes(func);
size_t cb = count_ops_of_type<op::ConvolutionBias>(func);
ASSERT_GT(cb, 0);
}
struct ConvolutionBiasTestData
{
size_t n{0};
size_t c{0};
size_t filter{0};
size_t kernel_size{0};
size_t w{0};
size_t h{0};
shared_ptr<runtime::TensorView> data_val;
shared_ptr<runtime::TensorView> weights_val;
shared_ptr<runtime::TensorView> bias_val;
shared_ptr<runtime::TensorView> result_val;
shared_ptr<runtime::TensorView> delta_val;
shared_ptr<runtime::TensorView> d_data_val;
shared_ptr<runtime::TensorView> d_weights_val;
shared_ptr<runtime::TensorView> d_bias_val;
vector<float> expected_result_val;
vector<float> expected_d_data_val;
vector<float> expected_d_weights_val;
vector<float> expected_d_bias_val;
Shape data_shape;
Shape weights_shape;
Shape bias_shape;
Shape result_shape;
shared_ptr<op::Parameter> data;
shared_ptr<op::Parameter> weights;
shared_ptr<op::Parameter> bias;
shared_ptr<op::Parameter> delta;
void n1c1h3w3(shared_ptr<runtime::Backend> backend)
{
n = 1;
c = 1;
filter = 1;
kernel_size = 3;
w = 3;
h = w;
data_shape = Shape{n, c, h, w};
data = make_shared<op::Parameter>(element::f32, data_shape);
weights_shape = Shape{filter, c, kernel_size, kernel_size};
weights = make_shared<op::Parameter>(element::f32, weights_shape);
bias_shape = Shape{filter};
bias = make_shared<op::Parameter>(element::f32, bias_shape);
result_shape = Shape{n, filter, 1, 1};
data_val = backend->create_tensor(element::f32, data_shape);
copy_data(data_val,
vector<float>{-0.67765152f,
0.10073948f,
0.57595438f,
-0.3469252f,
-0.22134334f,
-1.80471897f,
-0.80642909f,
1.22033095f,
2.23235631f});
weights_val = backend->create_tensor(element::f32, weights_shape);
copy_data(weights_val,
vector<float>{0.20070229f,
-0.54968649f,
-0.19819015f,
-0.38577855f,
1.37109005f,
-0.23789984f,
0.14867957f,
-0.49851316f,
-0.84815776f});
bias_val = backend->create_tensor(element::f32, bias_shape);
copy_data(bias_val, vector<float>{0.07811152f});
result_val = backend->create_tensor(element::f32, result_shape);
copy_data(result_val, vector<float>{0});
delta = make_shared<op::Parameter>(element::f32, result_shape);
delta_val = backend->create_tensor(element::f32, result_shape);
copy_data(delta_val, vector<float>{-2.58936238f});
d_data_val = backend->create_tensor(element::f32, data_shape);
copy_data(d_data_val, vector<float>{0, 0, 0, 0, 0, 0, 0, 0, 0});
d_weights_val = backend->create_tensor(element::f32, weights_shape);
copy_data(d_weights_val, vector<float>{0, 0, 0, 0, 0, 0, 0, 0, 0});
d_bias_val = backend->create_tensor(element::f32, bias_shape);
copy_data(d_bias_val, vector<float>{0});
expected_result_val = vector<float>{-2.58936238f};
expected_d_data_val = vector<float>{-0.51969099f,
1.42333758f,
0.5131861f,
0.99892044f,
-3.5502491f,
0.61600888f,
-0.3849853f,
1.29083121f,
2.19618773f};
expected_d_weights_val = vector<float>{1.7546854f,
-0.26085103f,
-1.49135458f,
0.89831507f,
0.57313812f,
4.67307138f,
2.08813715f,
-3.15987897f,
-5.7803793f};
expected_d_bias_val = vector<float>{-2.58936238f};
}
};
TEST(cpu_fusion, conv_bias_fprop_n1c1h3w3)
{
auto backend = runtime::Backend::create("CPU");
ConvolutionBiasTestData conv_test;
conv_test.n1c1h3w3(backend);
auto convolution = make_shared<op::Convolution>(conv_test.data, conv_test.weights);
auto convolution_bias = make_shared<op::ConvolutionBias>(convolution, conv_test.bias);
auto f = make_shared<Function>(
convolution_bias, op::ParameterVector{conv_test.data, conv_test.weights, conv_test.bias});
backend->call(
f, {conv_test.result_val}, {conv_test.data_val, conv_test.weights_val, conv_test.bias_val});
auto result_vec = read_vector<float>(conv_test.result_val);
EXPECT_TRUE(
test::all_close(conv_test.expected_result_val, read_vector<float>(conv_test.result_val)));
}
TEST(cpu_fusion, conv_bias_bprop_n1c1h3w3)
{
auto backend = runtime::Backend::create("CPU");
ConvolutionBiasTestData conv_test;
conv_test.n1c1h3w3(backend);
auto convolution = make_shared<op::Convolution>(conv_test.data, conv_test.weights);
auto convolution_bias = make_shared<op::ConvolutionBias>(convolution, conv_test.bias);
auto f = make_shared<Function>(
convolution_bias, op::ParameterVector{conv_test.data, conv_test.weights, conv_test.bias});
ngraph::autodiff::Adjoints adjoints(NodeVector{convolution_bias}, NodeVector{conv_test.delta});
auto d_data = adjoints.backprop_node(conv_test.data);
auto d_weights = adjoints.backprop_node(conv_test.weights);
auto d_bias = adjoints.backprop_node(conv_test.bias);
auto df = make_shared<Function>(
NodeVector{d_data, d_weights, d_bias},
op::ParameterVector{conv_test.data, conv_test.weights, conv_test.bias, conv_test.delta});
backend->call(
df,
{conv_test.d_data_val, conv_test.d_weights_val, conv_test.d_bias_val},
{conv_test.data_val, conv_test.weights_val, conv_test.bias_val, conv_test.delta_val});
EXPECT_TRUE(
test::all_close(conv_test.expected_d_data_val, read_vector<float>(conv_test.d_data_val)));
EXPECT_TRUE(test::all_close(conv_test.expected_d_weights_val,
read_vector<float>(conv_test.d_weights_val)));
EXPECT_TRUE(
test::all_close(conv_test.expected_d_bias_val, read_vector<float>(conv_test.d_bias_val)));
}
TEST(cpu_fusion, sigmoid_fprop_fusion)
{
pass::Manager pass_manager;
pass_manager.register_pass<runtime::cpu::pass::CPUFusion>();
const string json_path = file_util::path_join(SERIALIZED_ZOO, "mxnet/Graph_fprop_sigmoid.json");
const string json_string = file_util::read_file_to_string(json_path);
stringstream ss(json_string);
shared_ptr<Function> func = ngraph::deserialize(ss);
pass_manager.run_passes(func);
size_t ccg = count_ops_of_type<op::Sigmoid>(func);
ASSERT_EQ(ccg, 1);
}
TEST(cpu_fusion, sigmoid_n1c1h2w2)
{
auto input = make_shared<op::Parameter>(element::f32, Shape{1, 1, 2, 2});
auto sigmoid_node = make_shared<op::Sigmoid>(input);
auto func = make_shared<Function>(sigmoid_node, op::ParameterVector{input});
auto backend = runtime::Backend::create("CPU");
shared_ptr<runtime::TensorView> a = backend->create_tensor(element::f32, input->get_shape());
shared_ptr<runtime::TensorView> result =
backend->create_tensor(element::f32, input->get_shape());
vector<float> dataA{1.0f, 4.0f, 1.0f, 4.0f};
copy_data(a, dataA);
backend->call(func, {result}, {a});
vector<float> expected{0.73105858f, 0.98201379f, 0.73105858f, 0.98201379f};
ASSERT_TRUE(read_vector<float>(result) == expected);
}
TEST(cpu_fusion, sigmoid_n1c1h4)
{
auto input = make_shared<op::Parameter>(element::f32, Shape{1, 1, 4});
auto sigmoid_node = make_shared<op::Sigmoid>(input);
auto func = make_shared<Function>(sigmoid_node, op::ParameterVector{input});
auto backend = runtime::Backend::create("CPU");
shared_ptr<runtime::TensorView> a = backend->create_tensor(element::f32, input->get_shape());
shared_ptr<runtime::TensorView> result =
backend->create_tensor(element::f32, input->get_shape());
vector<float> dataA{1.0f, 4.0f, 1.0f, 4.0f};
copy_data(a, dataA);
backend->call(func, {result}, {a});
vector<float> expected{0.73105858f, 0.98201379f, 0.73105858f, 0.98201379f};
ASSERT_TRUE(read_vector<float>(result) == expected);
}
TEST(cpu_fusion, sigmoid_bprop_fusion)
{
const string json_path = file_util::path_join(SERIALIZED_ZOO, "mxnet/Graph_fprop_sigmoid.json");
const string json_string = file_util::read_file_to_string(json_path);
stringstream ss(json_string);
shared_ptr<Function> func = ngraph::deserialize(ss);
auto df = autodiff::backprop_function(func);
auto backend = runtime::Backend::create("CPU");
backend->compile(df);
size_t ccg = count_ops_of_type<op::SigmoidBackprop>(df);
ASSERT_EQ(ccg, 1);
}
TEST(cpu_fusion, sigmoid_bprop_n1c1h4)
{
auto input = make_shared<op::Parameter>(element::f32, Shape{1, 1, 4});
auto delta = make_shared<op::Parameter>(element::f32, Shape{1, 1, 4});
auto sigmoid_node = make_shared<op::SigmoidBackprop>(input, delta);
auto func = make_shared<Function>(sigmoid_node, op::ParameterVector{input, delta});
auto backend = runtime::Backend::create("CPU");
shared_ptr<runtime::TensorView> a = backend->create_tensor(element::f32, input->get_shape());
shared_ptr<runtime::TensorView> b = backend->create_tensor(element::f32, delta->get_shape());
shared_ptr<runtime::TensorView> result =
backend->create_tensor(element::f32, input->get_shape());
vector<float> dataA{1.0f, 4.0f, 1.0f, 4.0f};
vector<float> dataB{1.0f, 1.0f, 1.0f, 1.0f};
copy_data(a, dataA);
copy_data(b, dataB);
backend->call(func, {result}, {a, b});
vector<float> expected{0.196612f, 0.0176627f, 0.196612f, 0.0176627f};
EXPECT_TRUE(test::all_close(expected, read_vector<float>(result)));
}
TEST(cpu_fusion, batchnorm_fprop_relu_b1c2h2w2)
{
auto input_shape = Shape{1, 2, 2, 2};
auto input = make_shared<op::Parameter>(element::f32, input_shape);
auto mean_shape = Shape{2};
auto var_shape = Shape{2};
auto gamma_shape = Shape{2};
auto gamma = make_shared<op::Parameter>(element::f32, gamma_shape);
auto beta_shape = Shape{2};
auto beta = make_shared<op::Parameter>(element::f32, beta_shape);
double eps = 0.001;
auto shape_r = Shape{1, 2, 2, 2};
auto bn = make_shared<op::BatchNorm>(eps, gamma, beta, input);
auto output_rt = std::make_shared<op::GetOutputElement>(bn, 0);
// Note, op::Splice is used to break Relu(BatchNorm) fusion
// otherwise we will be comparing two BatchNormRelus
// Unfortunately, we can't use INTERPRETER for
// verifying the results as it doesn't implement
// BatchNorm op.
auto slice =
std::make_shared<op::Slice>(output_rt, Coordinate{0, 0, 0, 0}, Coordinate{1, 2, 2, 2});
auto output_relu = std::make_shared<op::Relu>(slice);
auto mean_rt = std::make_shared<op::GetOutputElement>(bn, 1);
auto variance_rt = std::make_shared<op::GetOutputElement>(bn, 2);
auto bn_relu = make_shared<op::BatchNormRelu>(eps, gamma, beta, input);
auto output_rt_bnr = std::make_shared<op::GetOutputElement>(bn_relu, 0);
auto mean_rt_bnr = std::make_shared<op::GetOutputElement>(bn_relu, 1);
auto variance_rt_bnr = std::make_shared<op::GetOutputElement>(bn_relu, 2);
auto f = make_shared<Function>(
NodeVector{output_relu, mean_rt, variance_rt, output_rt_bnr, mean_rt_bnr, variance_rt_bnr},
op::ParameterVector{input, gamma, beta});
auto backend = runtime::Backend::create("CPU");
// Create some tensors for input/output
auto input_t = backend->create_tensor(element::f32, Shape{1, 2, 2, 2});
copy_data(input_t,
vector<float>{0.54881352f,
0.71518934f,
0.60276335f,
0.54488319f,
0.42365479f,
0.64589411f,
0.4375872f,
0.89177299f});
auto gamma_t = backend->create_tensor(element::f32, gamma_shape);
copy_data(gamma_t, vector<float>{1.0f, 1.0f});
auto beta_t = backend->create_tensor(element::f32, beta_shape);
copy_data(beta_t, vector<float>{0.0f, 0.0f});
auto bn_output = backend->create_tensor(element::f32, shape_r);
auto result_mean = backend->create_tensor(element::f32, mean_shape);
auto result_variance = backend->create_tensor(element::f32, var_shape);
auto bn_output_bnr = backend->create_tensor(element::f32, shape_r);
auto result_mean_bnr = backend->create_tensor(element::f32, mean_shape);
auto result_variance_bnr = backend->create_tensor(element::f32, var_shape);
backend->call(f,
{bn_output,
result_mean,
result_variance,
bn_output_bnr,
result_mean_bnr,
result_variance_bnr},
{input_t, gamma_t, beta_t});
EXPECT_TRUE(test::all_close(read_vector<float>(bn_output), read_vector<float>(bn_output_bnr)));
EXPECT_TRUE(
test::all_close(read_vector<float>(result_mean), read_vector<float>(result_mean_bnr)));
EXPECT_TRUE(test::all_close(read_vector<float>(result_variance),
read_vector<float>(result_variance_bnr)));
}
TEST(cpu_fusion, fuse_conv_relu)
{
auto A = std::make_shared<op::Parameter>(element::f32, Shape{2, 1, 2, 2});
auto weights = std::make_shared<op::Parameter>(element::f32, Shape{1, 1, 2, 2});
auto convolution = std::make_shared<op::Convolution>(A, weights, Strides{1, 1}, Strides{1, 1});
auto relu = std::make_shared<op::Relu>(convolution);
auto abs_node =
std::make_shared<op::Abs>(std::make_shared<op::Abs>(std::make_shared<op::Abs>(relu)));
auto func = make_shared<Function>(abs_node, op::ParameterVector{A, weights});
pass::Manager pass_manager;
pass_manager.register_pass<runtime::cpu::pass::CPUFusion>();
pass_manager.run_passes(func);
size_t cb = count_ops_of_type<op::ConvolutionRelu>(func);
ASSERT_GT(cb, 0);
}
template <typename T>
static std::vector<std::vector<T>>
execute(std::shared_ptr<Function> f, std::vector<std::vector<T>> args, std::string cbackend)
{
auto backend = runtime::Backend::create(cbackend);
auto parms = f->get_parameters();
if (parms.size() != args.size())
{
throw ngraph_error("number of parameters and arguments don't match");
}
std::vector<std::shared_ptr<ngraph::runtime::TensorView>> arg_tensors(args.size());
for (size_t i = 0; i < args.size(); i++)
{
auto t = backend->create_tensor(parms.at(i)->get_element_type(), parms.at(i)->get_shape());
copy_data(t, args.at(i));
arg_tensors.at(i) = t;
}
auto results = f->get_results();
std::vector<std::shared_ptr<ngraph::runtime::TensorView>> result_tensors(results.size());
for (size_t i = 0; i < results.size(); i++)
{
result_tensors.at(i) =
backend->create_tensor(results.at(i)->get_element_type(), results.at(i)->get_shape());
}
backend->call(f, result_tensors, arg_tensors);
std::vector<std::vector<T>> result_vectors;
for (auto rt : result_tensors)
{
result_vectors.push_back(read_vector<T>(rt));
}
return result_vectors;
}
TEST(cpu_fusion, conv_relu_n2c1h2w2_2)
{
Shape shape_a{2, 1, 6, 6};
Shape shape_weights{1, 1, 2, 2};
auto make_int_function = [shape_a, shape_weights]() {
auto A = std::make_shared<op::Parameter>(element::f32, shape_a);
auto weights = std::make_shared<op::Parameter>(element::f32, shape_weights);
auto conv = std::make_shared<op::Convolution>(A, weights, Strides{2, 2}, Strides{1, 1});
auto relu = std::make_shared<op::Relu>(conv);
auto f = make_shared<Function>(NodeVector{relu}, op::ParameterVector{A, weights});
return f;
};
auto int_f = make_int_function();
auto make_cpu_function = [shape_a, shape_weights]() {
auto A = std::make_shared<op::Parameter>(element::f32, shape_a);
auto weights = std::make_shared<op::Parameter>(element::f32, shape_weights);
auto conv = std::make_shared<op::Convolution>(A, weights, Strides{2, 2}, Strides{1, 1});
auto conv_relu = std::make_shared<op::ConvolutionRelu>(conv);
auto f = make_shared<Function>(NodeVector{conv_relu}, op::ParameterVector{A, weights});
return f;
};
auto cpu_f = make_cpu_function();
vector<vector<float>> args{
{1.25f, 2.25f, 5.25f, 6.25f, -1.25f, -1.25f, 3.25f, -4.25f, 7.25f, 8.25f, -1.25f,
-1.25f, 1.25f, 2.25f, -3.25f, 2.25f, 4.25f, 4.25f, 1.25f, 2.25f, -4.25f, 2.25f,
4.25f, 4.25f, 0.f, 0.f, -1.f, 0.f, 2.f, 2.f, 0.f, 0.f, 0.f,
0.f, 2.f, 2.f, 1.25f, 2.25f, 5.25f, 6.25f, 1.25f, 1.25f, 3.25f, 4.25f,
-7.25f, 8.25f, 1.25f, -1.25f, -1.25f, 2.25f, 3.25f, 2.25f, -4.25f, -4.25f, -1.25f,
-2.25f, 4.25f, 2.25f, 4.25f, 4.25f, 0.f, 0.f, 1.f, 0.f, -2.f, 2.f,
0.f, 0.f, 0.f, 0.f, -2.f, -2.f},
{2., 2., 2., 2.}};
auto int_results = execute(int_f, args, "INTERPRETER");
auto cpu_results = execute(cpu_f, args, "CPU");
EXPECT_TRUE(test::all_close(cpu_results.at(0), int_results.at(0)));
}
TEST(cpu_fusion, conv_bias_relu_n2c1h2w2_2)
{
Shape shape_a{2, 1, 6, 6};
Shape shape_weights{1, 1, 2, 2};
Shape shape_bias{1};
auto make_int_function = [shape_a, shape_weights, shape_bias]() {
auto A = std::make_shared<op::Parameter>(element::f32, shape_a);
auto weights = std::make_shared<op::Parameter>(element::f32, shape_weights);
auto conv = std::make_shared<op::Convolution>(A, weights, Strides{2, 2}, Strides{1, 1});
auto bias = std::make_shared<op::Parameter>(element::f32, shape_bias);
auto conv_bias =
conv + std::make_shared<op::Broadcast>(bias, conv->get_shape(), AxisSet{0, 2, 3});
auto relu = std::make_shared<op::Relu>(conv_bias);
auto f = make_shared<Function>(NodeVector{relu}, op::ParameterVector{A, weights, bias});
return f;
};
auto int_f = make_int_function();
auto make_cpu_function = [shape_a, shape_weights, shape_bias]() {
auto A = std::make_shared<op::Parameter>(element::f32, shape_a);
auto weights = std::make_shared<op::Parameter>(element::f32, shape_weights);
auto bias = std::make_shared<op::Parameter>(element::f32, shape_bias);
auto conv = std::make_shared<op::Convolution>(A, weights, Strides{2, 2}, Strides{1, 1});
auto conv_bias_relu = std::make_shared<op::ConvolutionBiasRelu>(
std::make_shared<op::ConvolutionBias>(conv, bias));
auto f = make_shared<Function>(NodeVector{conv_bias_relu},
op::ParameterVector{A, weights, bias});
return f;
};
auto cpu_f = make_cpu_function();
vector<vector<float>> args{
{1.25f, 2.25f, 5.25f, 6.25f, -1.25f, -1.25f, 3.25f, -4.25f, 7.25f, 8.25f, -1.25f,
-1.25f, 1.25f, 2.25f, -3.25f, 2.25f, 4.25f, 4.25f, 1.25f, 2.25f, -4.25f, 2.25f,
4.25f, 4.25f, 0.f, 0.f, -1.f, 0.f, 2.f, 2.f, 0.f, 0.f, 0.f,
0.f, 2.f, 2.f, 1.25f, 2.25f, 5.25f, 6.25f, 1.25f, 1.25f, 3.25f, 4.25f,
-7.25f, 8.25f, 1.25f, -1.25f, -1.25f, 2.25f, 3.25f, 2.25f, -4.25f, -4.25f, -1.25f,
-2.25f, 4.25f, 2.25f, 4.25f, 4.25f, 0.f, 0.f, 1.f, 0.f, -2.f, 2.f,
0.f, 0.f, 0.f, 0.f, -2.f, -2.f},
{2., 2., 2., 2.},
{0.1f}};
auto int_results = execute(int_f, args, "INTERPRETER");
auto cpu_results = execute(cpu_f, args, "CPU");
EXPECT_TRUE(test::all_close(cpu_results.at(0), int_results.at(0)));
}
std::vector<shared_ptr<runtime::TensorView>>
rnn_matrix_fusion_eval(const size_t time_steps,
const Shape& data_shape,
const Shape& weights_shape,
const Shape& bias_shape,
const vector<float>& data_val,
const vector<float>& weights_val,
const vector<float>& bias_val,
const bool enable_pass)
{
auto data = make_shared<op::Parameter>(element::f32, data_shape);
auto weights = make_shared<op::Parameter>(element::f32, weights_shape);
auto bias = make_shared<op::Parameter>(element::f32, bias_shape);
// results from each time step
NodeVector results;
for (size_t t = 0; t < time_steps; ++t)
{
auto data_slice = make_shared<op::Slice>(
data, Coordinate{0, t, 0}, Coordinate{data_shape[0], t + 1, data_shape[2]});
auto data_reshape = make_shared<op::Reshape>(
data_slice, AxisVector{0, 1, 2}, Shape{data_shape[0], data_shape[2]});
auto weights_reshape = make_shared<op::Reshape>(
weights, AxisVector{1, 0}, Shape{weights_shape[1], weights_shape[0]});
auto dot = make_shared<op::Dot>(data_reshape, weights_reshape);
auto bias_broadcast = make_shared<op::Broadcast>(bias, dot->get_shape(), AxisSet{0});
auto add = make_shared<op::Add>(dot, bias_broadcast);
results.push_back(add);
}
auto func = make_shared<Function>(results, op::ParameterVector{data, weights, bias});
if (enable_pass)
{
pass::Manager pass_manager;
pass_manager.register_pass<runtime::cpu::pass::CPURnnMatFusion>();
pass_manager.register_pass<runtime::cpu::pass::CPUFusion>();
pass_manager.run_passes(func);
// check all of our dot/add are converted to a single MatmulBias op.
size_t count = count_ops_of_type<op::MatmulBias>(func);
EXPECT_EQ(count, 1);
}
auto backend = runtime::Backend::create("CPU");
shared_ptr<runtime::TensorView> data_tensor =
backend->create_tensor(element::f32, data->get_shape());
shared_ptr<runtime::TensorView> weights_tensor =
backend->create_tensor(element::f32, weights->get_shape());
shared_ptr<runtime::TensorView> bias_tensor =
backend->create_tensor(element::f32, bias->get_shape());
std::vector<shared_ptr<runtime::TensorView>> result_tensors;
for (auto r : results)
{
result_tensors.push_back(backend->create_tensor(element::f32, r->get_shape()));
}
copy_data(data_tensor, data_val);
copy_data(weights_tensor, weights_val);
copy_data(bias_tensor, bias_val);
backend->call(func, result_tensors, {data_tensor, weights_tensor, bias_tensor});
return result_tensors;
}
TEST(cpu_fusion, rnn_matrix_fusion_eval_pass)
{
const size_t time_steps = 4;
Shape data_shape{3, time_steps, 5};
Shape weights_shape{6, data_shape[2]};
Shape bias_shape{6};
test::Uniform<float> rng{0, 1, 0};
vector<float> data_val(shape_size(data_shape));
vector<float> weights_val(shape_size(weights_shape));
vector<float> bias_val(shape_size(bias_shape));
rng.initialize(data_val);
rng.initialize(weights_val);
rng.initialize(bias_val);
std::vector<shared_ptr<runtime::TensorView>> result_expected = rnn_matrix_fusion_eval(
time_steps, data_shape, weights_shape, bias_shape, data_val, weights_val, bias_val, false);
std::vector<shared_ptr<runtime::TensorView>> result_fused = rnn_matrix_fusion_eval(
time_steps, data_shape, weights_shape, bias_shape, data_val, weights_val, bias_val, true);
for (size_t i = 0; i < result_expected.size(); ++i)
{
EXPECT_TRUE(test::all_close<float>(result_expected[i], result_fused[i]));
}
}
TEST(cpu_fusion, rnn_fusion_from_json_model)
{
pass::Manager pass_manager;
pass_manager.register_pass<runtime::cpu::pass::CPURnnMatFusion>();
pass_manager.register_pass<runtime::cpu::pass::CPUFusion>();
const string json_path =
file_util::path_join(SERIALIZED_ZOO, "mxnet/rnn-10-step-fusion-test.json");
const string json_string = file_util::read_file_to_string(json_path);
stringstream ss(json_string);
shared_ptr<Function> func = ngraph::deserialize(ss);
pass_manager.run_passes(func);
const size_t NUM_STEPS = 10;
auto mmb_predicate = [](std::shared_ptr<Node> node) {
auto users = node->get_users();
return users.size() == NUM_STEPS &&
std::all_of(begin(users), end(users), [](std::shared_ptr<Node> n) {
return std::dynamic_pointer_cast<op::Slice>(n) != nullptr;
});
};
auto mmbs = get_ops_of_type<op::MatmulBias>(func);
ASSERT_TRUE(std::any_of(begin(mmbs), end(mmbs), mmb_predicate));
}
TEST(cpu_fusion, weight_fusion)
{
auto param = std::make_shared<op::Parameter>(element::f32, Shape{64});
auto reshape_conv =
std::make_shared<ngraph::op::Reshape>(param, AxisVector{0}, Shape{16, 4, 1, 1});
auto data_conv = std::make_shared<op::Parameter>(element::f32, Shape{16, 4, 7, 7});
auto tvt = reshape_conv->get_outputs().at(0).get_tensor_view().get();
auto lt_desc = std::make_shared<runtime::cpu::LayoutDescriptor>(*tvt, AxisVector{0, 1, 2, 3});
auto cvt_lt_conv = std::make_shared<runtime::cpu::op::ConvertLayout>(reshape_conv, lt_desc);
auto conv = std::make_shared<ngraph::op::Convolution>(
data_conv, cvt_lt_conv, Strides{1, 1}, Strides{1, 1});
auto reshape_conv_bprop =
std::make_shared<op::Reshape>(param, AxisVector{0}, Shape{16, 4, 1, 1});
auto dummy_arg_conv_bprop = std::make_shared<op::Parameter>(element::f32, Shape{1, 16, 7, 7});
auto tvt_bprop = reshape_conv_bprop->get_outputs().at(0).get_tensor_view().get();
auto lt_desc_bprop =
std::make_shared<runtime::cpu::LayoutDescriptor>(*tvt_bprop, AxisVector{0, 1, 2, 3});
auto cvt_lt_conv_bprop =
std::make_shared<runtime::cpu::op::ConvertLayout>(reshape_conv_bprop, lt_desc_bprop);
auto conv_bprop = std::make_shared<op::ConvolutionBackpropData>(Shape{1, 4, 7, 7},
cvt_lt_conv_bprop,
dummy_arg_conv_bprop,
Strides{1, 1},
Strides{1, 1},
CoordinateDiff{0, 0},
CoordinateDiff{0, 0},
Strides{1, 1});
auto conv_relu = std::make_shared<op::Relu>(conv);
auto conv_bprop_abs = std::make_shared<op::Abs>(conv_bprop);
auto f = make_shared<Function>(NodeVector{conv_relu, conv_bprop_abs},
op::ParameterVector{param, data_conv, dummy_arg_conv_bprop});
pass::Manager pass_manager;
pass_manager.register_pass<runtime::cpu::pass::CPUPostLayoutOptimizations>();
pass_manager.run_passes(f);
auto new_conv_bprop_data = conv_bprop_abs->get_argument(0);
auto new_convert_layout = new_conv_bprop_data->get_argument(0);
ASSERT_EQ(std::dynamic_pointer_cast<runtime::cpu::op::ConvertLayout>(
new_convert_layout->get_argument(0)),
cvt_lt_conv);
}
TEST(cpu_fusion, max_pool_with_indices)
{
Shape shape_a{10, 3, 28, 28};
auto input = std::make_shared<op::Parameter>(element::f32, shape_a);
Shape window_shape{2, 2};
auto max_pool = std::make_shared<op::MaxPool>(input, window_shape);
auto C = std::make_shared<op::Parameter>(element::f32, max_pool->get_shape());
ngraph::autodiff::Adjoints adjoints(NodeVector{max_pool}, NodeVector{C});
auto dinput = adjoints.backprop_node(input);
auto df = std::make_shared<Function>(NodeVector{dinput}, op::ParameterVector{input, C});
auto f = std::make_shared<Function>(NodeVector{max_pool}, op::ParameterVector{input});
{
pass::Manager pass_manager;
pass_manager.register_pass<pass::VisualizeTree>("max_pool_fprop_before.pdf");
pass_manager.run_passes(f);
}
{
pass::Manager pass_manager;
pass_manager.register_pass<pass::VisualizeTree>("max_pool_bprop_before.pdf");
pass_manager.register_pass<runtime::cpu::pass::CPUWorkspaceInsertion>();
pass_manager.register_pass<pass::VisualizeTree>("max_pool_bprop_after.pdf");
pass_manager.run_passes(df);
}
{
pass::Manager pass_manager;
pass_manager.register_pass<pass::VisualizeTree>("max_pool_fprop_after.pdf");
pass_manager.run_passes(f);
}
auto maxpool_goe_output =
std::dynamic_pointer_cast<op::GetOutputElement>(f->get_results().at(0)->get_argument(0));
ASSERT_TRUE(maxpool_goe_output);
ASSERT_EQ(maxpool_goe_output->get_n(), 0);
auto maxpool_with_indices = df->get_results().at(0)->get_argument(0);
auto maxpool_goe_indices =
std::dynamic_pointer_cast<op::GetOutputElement>(maxpool_with_indices->get_argument(2));
ASSERT_TRUE(maxpool_goe_indices);
ASSERT_EQ(maxpool_goe_indices->get_n(), 1);
}
TEST(cpu_fusion, backwards_maxpool_with_indices_n4_c1_hw4_2x2_max)
{
Shape shape_a{1, 4, 4, 4};
Shape maxpool_shape{1, 4, 3, 3};
auto A = std::make_shared<op::Parameter>(element::f32, shape_a);
Shape window_shape{2, 2};
auto window_movement_strides = Strides{1, 1};
auto maxpool = std::make_shared<op::MaxPool>(A, window_shape, window_movement_strides);
auto f = std::make_shared<Function>(maxpool, op::ParameterVector{A});
auto backend = runtime::Backend::create("CPU");
shared_ptr<runtime::TensorView> ep = backend->create_tensor(element::f32, maxpool_shape);
vector<float> dataEp(shape_size(maxpool_shape), 4);
shared_ptr<runtime::TensorView> input = backend->create_tensor(element::f32, shape_a);
shared_ptr<runtime::TensorView> output = backend->create_tensor(element::f32, shape_a);
vector<float> dataInput{11.f, 31.f, 40.f, 47.f, 13.f, 61.f, 48.f, 59.f, 17.f, 39.f, 64.f,
62.f, 45.f, 55.f, 36.f, 19.f, 65.f, 33.f, 49.f, 30.f, 56.f, 41.f,
53.f, 58.f, 22.f, 35.f, 52.f, 50.f, 63.f, 54.f, 12.f, 26.f, 44.f,
21.f, 69.f, 24.f, 46.f, 25.f, 51.f, 29.f, 72.f, 15.f, 73.f, 10.f,
16.f, 37.f, 70.f, 32.f, 28.f, 66.f, 57.f, 27.f, 60.f, 42.f, 43.f,
71.f, 18.f, 38.f, 67.f, 68.f, 14.f, 20.f, 34.f, 23.f};
vector<float> expected{0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 12.0f, 0.0f, 4.0f, 0.0f, 0.0f, 16.0f,
0.0f, 0.0f, 4.0f, 0.0f, 0.0f, 4.0f, 0.0f, 0.0f, 0.0f, 4.0f, 0.0f,
8.0f, 8.0f, 0.0f, 0.0f, 4.0f, 0.0f, 4.0f, 4.0f, 0.0f, 0.0f, 0.0f,
0.0f, 8.0f, 0.0f, 4.0f, 0.0f, 0.0f, 0.0f, 8.0f, 0.0f, 16.0f, 0.0f,
0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 8.0f, 0.0f, 0.0f, 4.0f, 0.0f, 0.0f,
8.0f, 0.0f, 4.0f, 8.0f, 4.0f, 0.0f, 0.0f, 0.0f, 0.0f};
copy_data(ep, dataEp);
copy_data(input, dataInput);
auto C = std::make_shared<op::Parameter>(element::f32, maxpool_shape);
auto df = autodiff::backprop_function(f);
{
pass::Manager pass_manager;
pass_manager.register_pass<pass::VisualizeTree>("max_pool_bprop_before2.pdf");
pass_manager.register_pass<runtime::cpu::pass::CPUWorkspaceInsertion>();
pass_manager.register_pass<pass::VisualizeTree>("max_pool_bprop_after2.pdf");
pass_manager.run_passes(df);
}
backend->call(df, {output}, {input, ep});
ASSERT_TRUE(read_vector<float>(output) == expected);
}