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/*
* The MIT License (MIT)
*
* Copyright (c) 2015-2025 Advanced Micro Devices, Inc. All rights reserved.
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE.
*/
#include <migraphx/dead_code_elimination.hpp>
#include <migraphx/normalize_ops.hpp>
#include <migraphx/eliminate_pad.hpp>
#include <migraphx/pass_manager.hpp>
#include <migraphx/instruction.hpp>
#include <basic_ops.hpp>
#include <migraphx/op/common.hpp>
#include <migraphx/make_op.hpp>
#include <test.hpp>
static void run_pass(migraphx::module& m)
{
migraphx::run_passes(
m,
{migraphx::normalize_ops{}, migraphx::eliminate_pad{}, migraphx::dead_code_elimination{}});
}
static migraphx::instruction_ref
create_im2col(migraphx::instruction_ref& l_img, size_t channels, migraphx::module& m)
{
size_t f[2] = {1, 1};
std::vector<int32_t> weights(channels * f[0] * f[1]);
migraphx::shape s_weights{migraphx::shape::int32_type, {1, channels, f[0], f[1]}};
auto l_weights = m.add_literal(migraphx::literal{s_weights, weights});
return m.add_instruction(migraphx::make_op("im2col"), l_img, l_weights);
}
static migraphx::instruction_ref
create_conv(migraphx::instruction_ref& l_img,
size_t channels,
migraphx::module& m,
migraphx::op::padding_mode_t padding_mode = migraphx::op::padding_mode_t::default_)
{
migraphx::shape s_weights{migraphx::shape::int32_type, {4, channels, 3, 3}};
std::vector<int32_t> weights(4 * channels * 3 * 3);
auto l_weights = m.add_literal(migraphx::literal{s_weights, weights});
return m.add_instruction(
migraphx::make_op("convolution", {{"padding_mode", padding_mode}}), l_img, l_weights);
}
TEST_CASE(rewrite_pad)
{
migraphx::module m;
size_t img_dim[2] = {2, 2};
size_t channels = 1;
std::vector<int32_t> input(channels * img_dim[0] * img_dim[1]);
std::iota(input.begin(), input.end(), 0);
migraphx::shape s_img{migraphx::shape::int32_type, {1, channels, img_dim[0], img_dim[1]}};
auto l_img = m.add_literal(migraphx::literal{s_img, input});
auto padded_img =
m.add_instruction(migraphx::make_op("pad", {{"pads", {0, 0, 1, 1, 0, 0, 1, 1}}}), l_img);
auto l0 = create_im2col(padded_img, channels, m);
auto l1 = create_conv(padded_img, channels, m);
auto l2 = m.add_instruction(
migraphx::make_op("pooling", {{"mode", migraphx::op::pooling_mode::max}}), padded_img);
m.add_instruction(migraphx::make_op("identity"), l0, l1, l2);
auto s0 = l0->get_shape();
auto s1 = l1->get_shape();
auto s2 = l2->get_shape();
run_pass(m);
EXPECT(l0->get_shape() == s0);
EXPECT(l1->get_shape() == s1);
EXPECT(l2->get_shape() == s2);
auto op0 = l0->get_operator().to_value();
auto om1 = l1->get_operator().to_value();
auto om2 = l2->get_operator().to_value();
EXPECT(op0["padding"].to_vector<std::size_t>() == std::vector<std::size_t>{1, 1, 1, 1});
EXPECT(om1["padding"].to_vector<std::size_t>() == std::vector<std::size_t>{1, 1, 1, 1});
EXPECT(om2["padding"].to_vector<std::size_t>() == std::vector<std::size_t>{1, 1, 1, 1});
EXPECT(std::none_of(
m.begin(), m.end(), [](const migraphx::instruction& ins) { return ins.name() == "pad"; }));
}
TEST_CASE(rewrite_pad_im2col_asymmetric)
{
migraphx::module m;
size_t img_dim[2] = {2, 2};
size_t channels = 1;
std::vector<int32_t> input(channels * img_dim[0] * img_dim[1]);
std::iota(input.begin(), input.end(), 0);
migraphx::shape s_img{migraphx::shape::int32_type, {1, channels, img_dim[0], img_dim[1]}};
auto l_img = m.add_literal(migraphx::literal{s_img, input});
auto padded_img =
m.add_instruction(migraphx::make_op("pad", {{"pads", {0, 0, 0, 0, 0, 0, 2, 2}}}), l_img);
auto l0 = create_im2col(padded_img, channels, m);
auto s0 = l0->get_shape();
run_pass(m);
EXPECT(l0->get_shape() == s0);
auto op0 = l0->get_operator().to_value();
EXPECT(op0["padding"].to_vector<std::size_t>() == std::vector<std::size_t>{0, 0, 2, 2});
run_pass(m);
EXPECT(std::none_of(
m.begin(), m.end(), [](const migraphx::instruction& ins) { return ins.name() == "pad"; }));
}
int main(int argc, const char* argv[]) { test::run(argc, argv); }
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