// ----------------------------------------------------------------------------
// Copyright 2018 Nervana Systems Inc.
// 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
// ----------------------------------------------------------------------------
#include <algorithm>
#include <cstdio>
#include <iostream>
#include <list>
#include <memory>
#include "gtest/gtest.h"
#include "ngraph/graph_util.hpp"
#include "ngraph/log.hpp"
#include "ngraph/ngraph.hpp"
#include "ngraph/ops/sum.hpp"
#include "ngraph/pass/graph_rewrite.hpp"
#include "ngraph/pass/manager.hpp"
#include "ngraph/pattern/matcher.hpp"
#include "ngraph/pattern/op/any.hpp"
#include "ngraph/pattern/op/label.hpp"
//
#include "ngraph/file_util.hpp"
#include "ngraph/json.hpp"
#include "ngraph/runtime/cpu/ops/matmul_bias.hpp"
#include "ngraph/runtime/cpu/pass/cpu_fusion.hpp"
#include "ngraph/serializer.hpp"
#include "ngraph/util.hpp"
#include "util/matcher.hpp"
#include "util/test_tools.hpp"
using namespace ngraph;
using namespace std;
TEST(cpu_fusion, gemm_pattern)
{
auto shape_w = Shape{2, 4};
auto shape_x = Shape{4, 1};
auto shape_b = Shape{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::Any>(W, reshape_pred);
auto skip_x = std::make_shared<pattern::op::Any>(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, broadcast, W->get_shape(), x->get_shape(), false, false);
}
TEST(cpu_fusion, gemm_cpu)
{
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 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, broadcast, A->get_shape(), B->get_shape(), true, true);
auto f = make_shared<Function>(cg, op::Parameters{A, B});
auto manager = runtime::Manager::get("CPU");
auto external = manager->compile(f);
auto backend = manager->allocate_backend();
auto cf = backend->make_call_frame(external);
shared_ptr<runtime::TensorView> a = backend->make_primary_tensor_view(element::f32, shapeA);
shared_ptr<runtime::TensorView> b = backend->make_primary_tensor_view(element::f32, shapeB);
shared_ptr<runtime::TensorView> result =
backend->make_primary_tensor_view(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);
cf->call({a, b}, {result});
vector<float> expected{10, 28, 37, 109};
ASSERT_TRUE(read_vector<float>(result) == expected);
}
TEST(cpu_fusion, cpu_fusion_pass_basic)
{
auto shape = Shape{};
auto shape_w = Shape{2, 4};
auto shape_x = Shape{4, 1};
auto shape_b = Shape{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<pass::CPUFusion>();
auto func = make_shared<Function>(graph, op::Parameters{A, B, C});
pass_manager.run_passes(func);
ASSERT_NE(std::dynamic_pointer_cast<op::MatmulBias>(graph->get_input_op(0)), nullptr);
}
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<pass::CPUFusion>();
pass_manager.run_passes(func);
size_t ccg = count_ops_of_type<op::MatmulBias>(func);
ASSERT_EQ(ccg, 3);
}