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/*
* Copyright (c) 2019 Samsung Electronics Co., Ltd. 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 "passes/optimizations/SinkTranspose.h"
#include "passes/optimizations/SinkRelu.h"
#include "Util.h"
#include "mir/ops/TransposeOp.h"
#include "mir/ops/ReluOp.h"
#include "mir/ops/TanhOp.h"
#include "mir/ops/ConcatOp.h"
#include "mir/ops/OutputOp.h"
#include "mir/Graph.h"
#include <gtest/gtest.h>
#include <sstream>
#include <vector>
using namespace std;
using namespace nnc;
using namespace mir;
namespace
{
Operation *getPrev(Operation *op)
{
assert(op->getNumInputs() == 1);
return op->getInput(0)->getNode();
}
Operation *getNext(Operation *op)
{
assert(op->getNumOutputs() == 1 && (op->getOutput(0)->getUses().size() == 1));
Operation::Use use = op->getOutput(0)->getUses()[0];
return use.getNode();
}
/* This tests swapping relu and transpose */
TEST(OptPass, sinkTrReLU)
{
mir::Graph g;
/*
* Create graph:
* [input]
* ||
* [Transpose]
* ||
* [relu]
* ||
* [tanh]
*/
mir::TensorType input_type{mir::DataType::FLOAT32, Shape{1, 2, 3}};
Operation *input = g.create<ops::InputOp>(input_type);
Operation *tr1 = g.create<ops::TransposeOp>(input->getOutput(0), vector<size_t>{1, 0, 2});
Operation *relu = g.create<ops::ReluOp>(tr1->getOutput(0));
Operation *tanh = g.create<ops::TanhOp>(relu->getOutput(0));
Operation *out = g.create<ops::OutputOp>(tanh->getOutput(0));
(void)out;
// Check that layout is desired
SinkTranspose pass;
pass.run(&g);
// Assert transposes are removed
ASSERT_EQ(g.getInputs()[0]->getType(), mir::Operation::Type::input);
ASSERT_EQ(getPrev(g.getOutputs()[0])->getType(), mir::Operation::Type::tanh);
ASSERT_EQ(getNext(g.getInputs()[0])->getType(), mir::Operation::Type::ReLU);
ASSERT_EQ(getPrev(tanh)->getType(), mir::Operation::Type::transpose);
}
/* This tests swapping concat and transpose */
TEST(OptPass, sinkTrConcat)
{
mir::Graph g;
/*
* Create graph:
* [input] [input2]
* || ||
* [Transpose 1] [Transpose 2]
* \\ //
* [Concat]
* ||
* [TanH]
*/
mir::TensorType in1_type{mir::DataType::FLOAT32, Shape{1, 1, 2, 3}};
Operation *in1 = g.create<ops::InputOp>(in1_type);
mir::TensorType in2_type{mir::DataType::FLOAT32, Shape{1, 1, 2, 3}};
Operation *in2 = g.create<ops::InputOp>(in2_type);
Operation *tr1 = g.create<ops::TransposeOp>(in1->getOutput(0), vector<size_t>{0, 3, 1, 2});
Operation *tr2 = g.create<ops::TransposeOp>(in2->getOutput(0), vector<size_t>{0, 3, 1, 2});
Operation *conc =
g.create<ops::ConcatOp>(vector<Operation::Output *>{tr1->getOutput(0), tr2->getOutput(0)}, 1);
Operation *tanh = g.create<ops::TanhOp>(conc->getOutput(0));
Operation *out = g.create<ops::OutputOp>(tanh->getOutput(0));
(void)out;
// Check that layout is as desired
SinkTranspose pass;
pass.run(&g);
ASSERT_EQ(getPrev(getPrev(g.getOutputs()[0]))->getType(), Operation::Type::transpose);
ASSERT_TRUE(static_cast<ops::TransposeOp *>(getPrev(tanh))->getAxisOrder() ==
vector<size_t>({0, 3, 1, 2}));
/* Expected Result:
* TanH(Transpose(Concat(inp1,inp2)))
*/
}
/* This tests swapping concat and transpose */
TEST(OptPass, sinkReluConcat)
{
mir::Graph g;
/*
* Create graph:
* [ inp1 ] [ inp2 ]
* || ||
* [ Relu 1] [ Relu 2]
* \\ //
* [ Concat ]
* ||
* [TanH]
*/
mir::TensorType in1_type{mir::DataType::FLOAT32, Shape{1, 1, 2, 3}};
Operation *in1 = g.create<ops::InputOp>(in1_type);
mir::TensorType in2_type{mir::DataType::FLOAT32, Shape{1, 1, 2, 3}};
Operation *in2 = g.create<ops::InputOp>(in2_type);
Operation *relu1 = g.create<ops::ReluOp>(in1->getOutput(0));
Operation *relu2 = g.create<ops::ReluOp>(in2->getOutput(0));
Operation *conc = g.create<ops::ConcatOp>(
vector<Operation::Output *>{relu1->getOutput(0), relu2->getOutput(0)}, 1);
Operation *tanh = g.create<ops::TanhOp>(conc->getOutput(0));
Operation *out = g.create<ops::OutputOp>(tanh->getOutput(0));
(void)out;
// Check that layout is as desired
SinkRelu pass;
pass.run(&g);
ASSERT_EQ(getPrev(getPrev(g.getOutputs()[0]))->getType(), Operation::Type::ReLU);
/* Expected Result:
* TanH(Relu(Concat(inp1,inp2)))
*/
}
/* This tests swapping relu and max_pool */
TEST(OptPass, sinkPoolReLU)
{
mir::Graph g;
/*
* Create graph:
* [input]
* ||
* [relu]
* ||
* [MaxPool]
* ||
* [tanh]
*/
mir::TensorType input_type{mir::DataType::FLOAT32, Shape{1, 4, 4, 3}};
Operation *input = g.create<ops::InputOp>(input_type);
Operation *relu = g.create<ops::ReluOp>(input->getOutput(0));
mir::MaxPool2DOpAttributes attributes;
attributes.window = {2, 2};
attributes.strides = {2, 2};
Operation *mp = g.create<ops::MaxPool2DOp>(relu->getOutput(0), attributes);
Operation *tanh = g.create<ops::TanhOp>(mp->getOutput(0));
Operation *out = g.create<ops::OutputOp>(tanh->getOutput(0));
(void)out;
SinkRelu pass;
pass.run(&g);
stringstream ss;
DumpVisitor d{ss};
g.accept(&d);
// tanh(relu(pool(input)))
ASSERT_EQ(getNext(g.getInputs()[0])->getType(), mir::Operation::Type::maxPool2D);
ASSERT_EQ(getPrev(g.getOutputs()[0])->getType(), mir::Operation::Type::tanh);
ASSERT_EQ("i_0.p_5.r_6.th_3.", ss.str());
}
} // unnamed namespace
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