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d38aa4560c
* add poros to fastdeploy * update readme * update readme & add license for all files * update benchmark * update copyright for some files Co-authored-by: tianjinjin <tianjinjin@baidu.com>
441 lines
21 KiB
C++
441 lines
21 KiB
C++
// Copyright (c) 2022 Baidu, Inc. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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/**
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* @file add_test.cpp
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* @author tianshaoqing@baidu.com
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* @date Wed Sep 27 11:24:21 CST 2021
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* @brief
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**/
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#include <gflags/gflags.h>
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#include <gtest/gtest.h>
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#include "poros/converter/gpu/add.h"
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#include "poros/util/test_util.h"
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static void add_test_helper(const std::string& graph_IR,
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baidu::mirana::poros::IConverter* converter,
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bool singleInput,
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std::vector<int64_t> shape1 = {5},
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std::vector<int64_t> shape2 = {5}){
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std::vector<at::Tensor> input_data;
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input_data.push_back(at::randn(shape1, {at::kCUDA}));
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if (!singleInput){
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input_data.push_back(at::randn(shape2, {at::kCUDA}));
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}
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baidu::mirana::poros::PorosOptions poros_option; // default device GPU
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// 运行原图与engine获取结果
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std::vector<at::Tensor> graph_output;
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std::vector<at::Tensor> poros_output;
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ASSERT_TRUE(baidu::mirana::poros::testutil::run_graph_and_poros(graph_IR, poros_option, converter,
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input_data, graph_output, poros_output));
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ASSERT_EQ(1, graph_output.size());
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ASSERT_EQ(1, poros_output.size());
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ASSERT_TRUE(baidu::mirana::poros::testutil::almost_equal(graph_output[0], poros_output[0], 2e-6));
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}
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static std::string gen_add_sub_tensor_graph(const std::string& op,
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const std::string& alpha) {
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return R"IR(
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graph(%0 : Tensor, %1 : Tensor):
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%2 : float = prim::Constant[value=)IR" + alpha + R"IR(]()
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%3 : Tensor = aten::)IR" + op + R"IR((%0, %1, %2)
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return (%3))IR";
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}
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static std::string gen_add_sub_scalar_graph(const std::string& op,
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const std::string& scalar,
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const std::string& alpha) {
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return R"IR(
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graph(%0 : Tensor):
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%1 : float = prim::Constant[value=)IR" + scalar + R"IR(]()
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%2 : float = prim::Constant[value=)IR" + alpha + R"IR(]()
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%3 : Tensor = aten::)IR" + op + R"IR((%0, %1, %2)
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return (%3))IR";
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}
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TEST(Converters, ATenAddTensorConvertsCorrectly) {
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// aten::add.Tensor(Tensor self, Tensor other, Scalar alpha=1) -> Tensor
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const auto graph_IR = gen_add_sub_tensor_graph("add", "1.0");
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baidu::mirana::poros::AddConverter addconverter;
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add_test_helper(graph_IR, &addconverter, false);
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add_test_helper(graph_IR, &addconverter, false, {3, 4}, {4});
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add_test_helper(graph_IR, &addconverter, false, {4}, {3, 4});
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add_test_helper(graph_IR, &addconverter, false, {4, 1}, {1, 4});
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add_test_helper(graph_IR, &addconverter, false, {3, 4, 3}, {4, 3});
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add_test_helper(graph_IR, &addconverter, false, {4, 3}, {3, 4, 3});
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}
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TEST(Converters, ATenAddScalarConvertsCorrectly) {
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// aten::add.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor
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const auto graph_IR = gen_add_sub_scalar_graph("add", "2.2", "1.0");
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baidu::mirana::poros::AddConverter addconverter;
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add_test_helper(graph_IR, &addconverter, true);
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add_test_helper(graph_IR, &addconverter, true, {3, 4, 3});
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}
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TEST(Converters, ATenAdd_TensorConvertsCorrectly) {
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// aten::add_.Tensor(Tensor(a!) self, Tensor other, *, Scalar alpha=1) -> Tensor(a!)
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const auto graph_IR = gen_add_sub_tensor_graph("add_", "1.0");
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baidu::mirana::poros::AddConverter addconverter;
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add_test_helper(graph_IR, &addconverter, false);
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add_test_helper(graph_IR, &addconverter, false, {3, 4}, {4});
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add_test_helper(graph_IR, &addconverter, false, {3, 4, 3}, {4, 3});
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}
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TEST(Converters, ATenAdd_ScalarConvertsCorrectly) {
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// aten::add_.Scalar(Tensor(a!) self, Scalar other, Scalar alpha=1) -> Tensor(a!)
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const auto graph_IR = gen_add_sub_scalar_graph("add_", "2.2", "1.0");
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baidu::mirana::poros::AddConverter addconverter;
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add_test_helper(graph_IR, &addconverter, true);
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add_test_helper(graph_IR, &addconverter, true, {3, 4, 3});
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}
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TEST(Converters, ATenAddTensorAlphaConvertsCorrectly) {
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// aten::add.Tensor(Tensor self, Tensor other, Scalar alpha=1) -> Tensor
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const auto graph_IR = gen_add_sub_tensor_graph("add", "2.5");
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baidu::mirana::poros::AddConverter addconverter;
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add_test_helper(graph_IR, &addconverter, false);
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add_test_helper(graph_IR, &addconverter, false, {3, 4}, {4});
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add_test_helper(graph_IR, &addconverter, false, {4}, {3, 4});
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add_test_helper(graph_IR, &addconverter, false, {4, 1}, {1, 4});
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add_test_helper(graph_IR, &addconverter, false, {3, 4, 3}, {4, 3});
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add_test_helper(graph_IR, &addconverter, false, {4, 3}, {3, 4, 3});
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}
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TEST(Converters, ATenAddScalarAlphaConvertsCorrectly) {
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// aten::add.Scalar(Tensor(a!) self, Scalar other, Scalar alpha=1) -> Tensor(a!)
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const auto graph_IR = gen_add_sub_scalar_graph("add", "2.2", "2.5");
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baidu::mirana::poros::AddConverter addconverter;
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add_test_helper(graph_IR, &addconverter, true);
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add_test_helper(graph_IR, &addconverter, true, {3, 4, 3});
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}
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TEST(Converters, ATenAdd_TensorAlphaConvertsCorrectly) {
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// aten::add_.Tensor(Tensor self, Tensor other, Scalar alpha=1) -> Tensor
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const auto graph_IR = gen_add_sub_tensor_graph("add_", "2.5");
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baidu::mirana::poros::AddConverter addconverter;
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add_test_helper(graph_IR, &addconverter, false);
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add_test_helper(graph_IR, &addconverter, false, {3, 4}, {4});
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add_test_helper(graph_IR, &addconverter, false, {3, 4, 3}, {4, 3});
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}
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TEST(Converters, ATenAdd_ScalarAlphaConvertsCorrectly) {
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// aten::add_.Scalar(Tensor(a!) self, Scalar other, Scalar alpha=1) -> Tensor(a!)
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const auto graph_IR = gen_add_sub_scalar_graph("add_", "2.2", "2.5");
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baidu::mirana::poros::AddConverter addconverter;
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add_test_helper(graph_IR, &addconverter, true);
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add_test_helper(graph_IR, &addconverter, true, {3, 4, 3});
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}
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TEST(Converters, ATenSubTensorConvertsCorrectly) {
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// aten::sub.Tensor(Tensor self, Tensor other, Scalar alpha=1) -> Tensor
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const auto graph_IR = gen_add_sub_tensor_graph("sub", "1.0");
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baidu::mirana::poros::SubConverter subconverter;
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add_test_helper(graph_IR, &subconverter, false);
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add_test_helper(graph_IR, &subconverter, false, {3, 4}, {4});
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add_test_helper(graph_IR, &subconverter, false, {4}, {3, 4});
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add_test_helper(graph_IR, &subconverter, false, {4, 1}, {1, 4});
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add_test_helper(graph_IR, &subconverter, false, {3, 4, 3}, {4, 3});
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add_test_helper(graph_IR, &subconverter, false, {4, 3}, {3, 4, 3});
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}
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TEST(Converters, ATenSubScalarConvertsCorrectly) {
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// aten::sub.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor
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const auto graph_IR = gen_add_sub_scalar_graph("sub", "2.2", "1.0");
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baidu::mirana::poros::SubConverter subconverter;
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add_test_helper(graph_IR, &subconverter, true);
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add_test_helper(graph_IR, &subconverter, true, {3, 4, 3});
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}
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TEST(Converters, ATenSub_TensorConvertsCorrectly) {
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// aten::sub_.Tensor(Tensor(a!) self, Tensor other, *, Scalar alpha=1) -> Tensor(a!)
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const auto graph_IR = gen_add_sub_tensor_graph("sub_", "1.0");
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baidu::mirana::poros::SubConverter subconverter;
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add_test_helper(graph_IR, &subconverter, false);
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add_test_helper(graph_IR, &subconverter, false, {3, 4}, {4});
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add_test_helper(graph_IR, &subconverter, false, {3, 4, 3}, {4, 3});
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}
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TEST(Converters, ATenSub_ScalarConvertsCorrectly) {
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// aten::sub_.Scalar(Tensor(a!) self, Scalar other, Scalar alpha=1) -> Tensor(a!)
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const auto graph_IR = gen_add_sub_scalar_graph("sub_", "2.2", "1.0");
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baidu::mirana::poros::SubConverter subconverter;
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add_test_helper(graph_IR, &subconverter, true);
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add_test_helper(graph_IR, &subconverter, true, {3, 4, 3});
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}
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TEST(Converters, ATenSubTensorAlphaConvertsCorrectly) {
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// aten::sub.Tensor(Tensor self, Tensor other, Scalar alpha=1) -> Tensor
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const auto graph_IR = gen_add_sub_tensor_graph("sub", "2.5");
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baidu::mirana::poros::SubConverter subconverter;
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add_test_helper(graph_IR, &subconverter, false);
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add_test_helper(graph_IR, &subconverter, false, {3, 4}, {4});
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add_test_helper(graph_IR, &subconverter, false, {4}, {3, 4});
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add_test_helper(graph_IR, &subconverter, false, {4, 1}, {1, 4});
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add_test_helper(graph_IR, &subconverter, false, {3, 4, 3}, {4, 3});
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add_test_helper(graph_IR, &subconverter, false, {4, 3}, {3, 4, 3});
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}
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TEST(Converters, ATenSubScalarAlphaConvertsCorrectly) {
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// aten::sub.Scalar(Tensor(a!) self, Scalar other, Scalar alpha=1) -> Tensor(a!)
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const auto graph_IR = gen_add_sub_scalar_graph("sub", "2.2", "2.5");
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baidu::mirana::poros::SubConverter subconverter;
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add_test_helper(graph_IR, &subconverter, true);
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add_test_helper(graph_IR, &subconverter, true, {3, 4, 3});
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}
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TEST(Converters, ATenSub_TensorAlphaConvertsCorrectly) {
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// aten::sub_.Tensor(Tensor self, Tensor other, Scalar alpha=1) -> Tensor
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const auto graph_IR = gen_add_sub_tensor_graph("sub_", "2.5");
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baidu::mirana::poros::SubConverter subconverter;
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add_test_helper(graph_IR, &subconverter, false);
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add_test_helper(graph_IR, &subconverter, false, {3, 4}, {4});
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add_test_helper(graph_IR, &subconverter, false, {3, 4, 3}, {4, 3});
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}
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TEST(Converters, ATenSub_ScalarAlphaConvertsCorrectly) {
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// aten::sub_.Scalar(Tensor(a!) self, Scalar other, Scalar alpha=1) -> Tensor(a!)
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const auto graph_IR = gen_add_sub_scalar_graph("sub_", "2.2", "2.5");
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baidu::mirana::poros::SubConverter subconverter;
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add_test_helper(graph_IR, &subconverter, true);
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add_test_helper(graph_IR, &subconverter, true, {3, 4, 3});
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}
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TEST(Converters, ATenRsubTensorConvertsCorrectly) {
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// aten::rsub.Tensor(Tensor self, Tensor other, Scalar alpha=1) -> (Tensor)
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const auto graph_IR = gen_add_sub_tensor_graph("rsub", "1.0");
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baidu::mirana::poros::RsubConverter rsubconverter;
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add_test_helper(graph_IR, &rsubconverter, false);
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add_test_helper(graph_IR, &rsubconverter, false, {3, 4}, {4});
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add_test_helper(graph_IR, &rsubconverter, false, {4}, {3, 4});
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add_test_helper(graph_IR, &rsubconverter, false, {4, 1}, {1, 4});
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add_test_helper(graph_IR, &rsubconverter, false, {3, 4, 3}, {4, 3});
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add_test_helper(graph_IR, &rsubconverter, false, {4, 3}, {3, 4, 3});
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}
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TEST(Converters, ATenRsubScalarConvertsCorrectly) {
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// aten::rsub.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> (Tensor)
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const auto graph_IR = gen_add_sub_scalar_graph("rsub", "2.2", "1.0");
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baidu::mirana::poros::RsubConverter rsubconverter;
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add_test_helper(graph_IR, &rsubconverter, true);
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add_test_helper(graph_IR, &rsubconverter, true, {3, 4, 3});
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}
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TEST(Converters, ATenRsubTensorAlphaConvertsCorrectly) {
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// aten::rsub.Tensor(Tensor self, Tensor other, Scalar alpha=1) -> (Tensor)
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const auto graph_IR = gen_add_sub_tensor_graph("rsub", "3.33");
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baidu::mirana::poros::RsubConverter rsubconverter;
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add_test_helper(graph_IR, &rsubconverter, false);
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add_test_helper(graph_IR, &rsubconverter, false, {3, 4}, {4});
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add_test_helper(graph_IR, &rsubconverter, false, {4}, {3, 4});
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add_test_helper(graph_IR, &rsubconverter, false, {4, 1}, {1, 4});
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add_test_helper(graph_IR, &rsubconverter, false, {3, 4, 3}, {4, 3});
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add_test_helper(graph_IR, &rsubconverter, false, {4, 3}, {3, 4, 3});
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}
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TEST(Converters, ATenRsubScalarAlphaConvertsCorrectly) {
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// aten::rsub.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> (Tensor)
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const auto graph_IR = gen_add_sub_scalar_graph("rsub", "2.2", "4.44");
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baidu::mirana::poros::RsubConverter rsubconverter;
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add_test_helper(graph_IR, &rsubconverter, true);
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add_test_helper(graph_IR, &rsubconverter, true, {3, 4, 3});
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}
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TEST(Converters, ATenRsubTensorTypePromotionConvertsCorrectly) {
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// aten::rsub.Tensor(Tensor self, Tensor other, Scalar alpha=1) -> (Tensor)
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const auto graph_IR = R"IR(
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graph(%0 : Tensor, %1 : Tensor):
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%2 : float = prim::Constant[value=3.33]()
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%3 : Tensor = aten::rsub(%0, %1, %2)
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return (%3))IR";
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baidu::mirana::poros::RsubConverter rsubconverter;
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std::vector<at::Tensor> input_data;
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input_data.push_back(at::randn({3,4,3}, {at::kCUDA}));
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input_data.push_back(at::ones({3,4,3}, {at::kCUDA}).to(at::ScalarType::Int));
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baidu::mirana::poros::PorosOptions poros_option; // default device GPU
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// 运行原图与engine获取结果
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std::vector<at::Tensor> graph_output;
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std::vector<at::Tensor> poros_output;
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ASSERT_TRUE(baidu::mirana::poros::testutil::run_graph_and_poros(graph_IR, poros_option, &rsubconverter,
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input_data, graph_output, poros_output));
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ASSERT_EQ(1, graph_output.size());
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ASSERT_EQ(1, poros_output.size());
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ASSERT_TRUE(baidu::mirana::poros::testutil::almost_equal(graph_output[0], poros_output[0], 2e-6));
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}
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TEST(Converters, ATenRsubScalarTypePromotionConvertsCorrectly) {
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// aten::rsub.Tensor(Tensor self, Tensor other, Scalar alpha=1) -> (Tensor)
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const auto graph_IR = R"IR(
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graph(%0 : Tensor):
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%1 : int = prim::Constant[value=5]()
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%2 : float = prim::Constant[value=3.33]()
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%3 : Tensor = aten::rsub(%0, %1, %2)
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return (%3))IR";
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baidu::mirana::poros::RsubConverter rsubconverter;
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add_test_helper(graph_IR, &rsubconverter, true);
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}
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static void add_sub_dynamic_test_helper(const std::string& graph_IR,
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baidu::mirana::poros::IConverter* converter,
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const std::vector<at::Tensor>& input_data,
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bool is_dynamic = false,
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std::vector<std::vector<at::Tensor>>* prewarm_data = nullptr) {
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baidu::mirana::poros::PorosOptions poros_option; // default device GPU
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poros_option.is_dynamic = is_dynamic;
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// 运行原图与engine获取结果
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std::vector<at::Tensor> graph_output;
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std::vector<at::Tensor> poros_output;
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ASSERT_TRUE(baidu::mirana::poros::testutil::run_graph_and_poros(graph_IR, poros_option, converter,
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input_data, graph_output, poros_output, prewarm_data));
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ASSERT_EQ(1, graph_output.size());
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ASSERT_EQ(1, poros_output.size());
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ASSERT_TRUE(graph_output[0].equal(poros_output[0]));
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}
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TEST(Converters, ATenAddIntdynamicConvertsCorrectly) {
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// aten::add.int(int a, int b) -> (int)
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const auto graph_IR = R"IR(
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graph(%0 : Tensor):
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%1 : int = prim::Constant[value=0]()
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%2 : int = prim::Constant[value=1]()
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%3 : int = aten::size(%0, %1)
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%4 : int = aten::size(%0, %2)
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%5 : int = aten::add(%3, %4)
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%6 : Tensor = aten::add(%0, %5, %2)
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return (%6))IR";
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baidu::mirana::poros::AddConverter addconverter;
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std::vector<at::Tensor> input_data;
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input_data.push_back(at::zeros({2, 3}, {at::kCUDA}).to(at::ScalarType::Int));
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std::vector<std::vector<at::Tensor>> prewarm_data = {{}, {}, {}};
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prewarm_data[0].push_back(at::zeros({4, 5}, {at::kCUDA}).to(at::ScalarType::Int));
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prewarm_data[1].push_back(at::zeros({2, 3}, {at::kCUDA}).to(at::ScalarType::Int));
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prewarm_data[2].push_back(at::zeros({2, 3}, {at::kCUDA}).to(at::ScalarType::Int));
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add_sub_dynamic_test_helper(graph_IR, &addconverter, input_data, true, &prewarm_data);
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}
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TEST(Converters, ATenSubIntdynamicConvertsCorrectly) {
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// aten::sub.int(int a, int b) -> (int)
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const auto graph_IR = R"IR(
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graph(%0 : Tensor):
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%1 : int = prim::Constant[value=0]()
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%2 : int = prim::Constant[value=1]()
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%3 : int = aten::size(%0, %1)
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%4 : int = aten::size(%0, %2)
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%5 : int = aten::sub(%3, %4)
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%6 : Tensor = aten::add(%0, %5, %2)
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return (%6))IR";
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baidu::mirana::poros::SubConverter subconverter;
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std::vector<at::Tensor> input_data;
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input_data.push_back(at::zeros({2, 3}, {at::kCUDA}).to(at::ScalarType::Int));
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|
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std::vector<std::vector<at::Tensor>> prewarm_data = {{}, {}, {}};
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prewarm_data[0].push_back(at::zeros({4, 5}, {at::kCUDA}).to(at::ScalarType::Int));
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prewarm_data[1].push_back(at::zeros({2, 3}, {at::kCUDA}).to(at::ScalarType::Int));
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prewarm_data[2].push_back(at::zeros({2, 3}, {at::kCUDA}).to(at::ScalarType::Int));
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|
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add_sub_dynamic_test_helper(graph_IR, &subconverter, input_data, true, &prewarm_data);
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}
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TEST(Converters, ATenAddTdynamicConvertsCorrectly) {
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// aten::add.t(t[] a, t[] b) -> (t[])
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const auto graph_IR = R"IR(
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graph(%0 : Tensor, %1 : Tensor):
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%2 : int[] = aten::size(%0)
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%3 : int[] = aten::size(%1)
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%4 : int[] = aten::add(%2, %3)
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%5 : int = prim::Constant[value=2]()
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|
%6 : int = aten::__getitem__(%4, %5)
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%7 : int = prim::Constant[value=1]()
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%8 : Tensor = aten::add(%0, %6, %7)
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|
return (%8))IR";
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baidu::mirana::poros::AddConverter addconverter;
|
|
std::vector<at::Tensor> input_data;
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input_data.push_back(at::zeros({2, 3}, {at::kCUDA}).to(at::ScalarType::Int));
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input_data.push_back(at::zeros({4, 5}, {at::kCUDA}).to(at::ScalarType::Int));
|
|
|
|
std::vector<std::vector<at::Tensor>> prewarm_data = {{}, {}, {}};
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|
prewarm_data[0].push_back(at::zeros({4, 5}, {at::kCUDA}).to(at::ScalarType::Int));
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|
prewarm_data[0].push_back(at::zeros({6, 7}, {at::kCUDA}).to(at::ScalarType::Int));
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|
prewarm_data[1].push_back(at::zeros({2, 3}, {at::kCUDA}).to(at::ScalarType::Int));
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|
prewarm_data[1].push_back(at::zeros({4, 5}, {at::kCUDA}).to(at::ScalarType::Int));
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|
prewarm_data[2].push_back(at::zeros({2, 3}, {at::kCUDA}).to(at::ScalarType::Int));
|
|
prewarm_data[2].push_back(at::zeros({4, 5}, {at::kCUDA}).to(at::ScalarType::Int));
|
|
|
|
add_sub_dynamic_test_helper(graph_IR, &addconverter, input_data, true, &prewarm_data);
|
|
}
|
|
|
|
TEST(Converters, ATenAddTensordynamicConvertsCorrectly) {
|
|
//dynamic tensor
|
|
const auto graph_IR = gen_add_sub_tensor_graph("add", "1.0");
|
|
baidu::mirana::poros::AddConverter addconverter;
|
|
std::vector<at::Tensor> input_data;
|
|
input_data.push_back(at::randn({15, 1}, {at::kCUDA}));
|
|
input_data.push_back(at::randn({300}, {at::kCUDA}));
|
|
|
|
std::vector<std::vector<at::Tensor>> prewarm_data = {{}, {}, {}};
|
|
prewarm_data[0].push_back(at::randn({40, 1}, {at::kCUDA}));
|
|
prewarm_data[0].push_back(at::randn({300}, {at::kCUDA}));
|
|
prewarm_data[1].push_back(at::randn({8, 1}, {at::kCUDA}));
|
|
prewarm_data[1].push_back(at::randn({300}, {at::kCUDA}));
|
|
prewarm_data[2].push_back(at::randn({20, 1}, {at::kCUDA}));
|
|
prewarm_data[2].push_back(at::randn({300}, {at::kCUDA}));
|
|
|
|
add_sub_dynamic_test_helper(graph_IR, &addconverter, input_data, true, &prewarm_data);
|
|
}
|
|
|
|
TEST(Converters, ATenAddTensordynamicMoreConvertsCorrectly) {
|
|
//dynamic tensor
|
|
const auto graph_IR = gen_add_sub_tensor_graph("add", "1.0");
|
|
baidu::mirana::poros::AddConverter addconverter;
|
|
std::vector<at::Tensor> input_data;
|
|
input_data.push_back(at::randn({4, 1}, {at::kCUDA}));
|
|
input_data.push_back(at::randn({300}, {at::kCUDA}));
|
|
|
|
std::vector<std::vector<at::Tensor>> prewarm_data = {{}, {}, {}};
|
|
prewarm_data[0].push_back(at::randn({4, 1}, {at::kCUDA}));
|
|
prewarm_data[0].push_back(at::randn({400}, {at::kCUDA}));
|
|
prewarm_data[1].push_back(at::randn({4, 1}, {at::kCUDA}));
|
|
prewarm_data[1].push_back(at::randn({100}, {at::kCUDA}));
|
|
prewarm_data[2].push_back(at::randn({4, 1}, {at::kCUDA}));
|
|
prewarm_data[2].push_back(at::randn({200}, {at::kCUDA}));
|
|
|
|
add_sub_dynamic_test_helper(graph_IR, &addconverter, input_data, true, &prewarm_data);
|
|
}
|
|
|
|
TEST(Converters, ATenAddTensordynamicMore2ConvertsCorrectly) {
|
|
//dynamic tensor
|
|
const auto graph_IR = gen_add_sub_tensor_graph("add", "1.0");
|
|
baidu::mirana::poros::AddConverter addconverter;
|
|
std::vector<at::Tensor> input_data;
|
|
input_data.push_back(at::randn({4, 1, 45}, {at::kCUDA}));
|
|
input_data.push_back(at::randn({300, 1}, {at::kCUDA}));
|
|
|
|
std::vector<std::vector<at::Tensor>> prewarm_data = {{}, {}, {}};
|
|
prewarm_data[0].push_back(at::randn({400, 1, 45}, {at::kCUDA}));
|
|
prewarm_data[0].push_back(at::randn({400, 1}, {at::kCUDA}));
|
|
prewarm_data[1].push_back(at::randn({4, 1, 45}, {at::kCUDA}));
|
|
prewarm_data[1].push_back(at::randn({100, 1}, {at::kCUDA}));
|
|
prewarm_data[2].push_back(at::randn({100, 1, 45}, {at::kCUDA}));
|
|
prewarm_data[2].push_back(at::randn({200, 1}, {at::kCUDA}));
|
|
|
|
add_sub_dynamic_test_helper(graph_IR, &addconverter, input_data, true, &prewarm_data);
|
|
} |