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* 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>
188 lines
8.6 KiB
C++
188 lines
8.6 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 group_norm_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/group_norm.h"
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#include "poros/util/test_util.h"
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static void groupnorm_test_helper(const std::string& graph_IR,
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std::vector<at::Tensor>& input_data) {
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baidu::mirana::poros::PorosOptions poros_option; // default device GPU
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baidu::mirana::poros::GroupNormConverter groupnormconverter;
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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, &groupnormconverter,
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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, ATenGroupNormConvertsCorrectly) {
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// aten::group_norm(Tensor input, int num_groups, Tensor? weight=None, Tensor? bias=None, float eps=1e-05, bool cudnn_enabled=True) -> Tensor
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const auto graph_IR = R"IR(
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graph(%0 : Tensor,
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%gamma : Tensor,
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%beta : Tensor):
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%1: int = prim::Constant[value=2]()
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%7 : bool = prim::Constant[value=0]()
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%8 : float = prim::Constant[value=1.0000000000000001e-05]()
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%9 : Tensor = aten::group_norm(%0, %1, %gamma, %beta, %8, %7)
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return (%9))IR";
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std::vector<at::Tensor> input_data;
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input_data.push_back(at::randn({2, 10, 3, 3}, {at::kCUDA}));
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input_data.push_back(at::randn({10}, {at::kCUDA}));
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input_data.push_back(at::randn({10}, {at::kCUDA}));
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groupnorm_test_helper(graph_IR, input_data);
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}
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TEST(Converters, ATenGroupNormConvertsCorrectly2InputsGamma) {
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// aten::group_norm(Tensor input, int num_groups, Tensor? weight=None, Tensor? bias=None, float eps=1e-05, bool cudnn_enabled=True) -> Tensor
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const auto graph_IR = R"IR(
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graph(%0 : Tensor, %gamma : Tensor):
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%1 : int = prim::Constant[value=20]()
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%2 : None = prim::Constant()
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%7 : bool = prim::Constant[value=0]()
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%8 : float = prim::Constant[value=1.0000000000000001e-05]()
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%9 : Tensor = aten::group_norm(%0, %1, %gamma, %2, %8, %7)
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return (%9))IR";
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std::vector<at::Tensor> input_data;
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input_data.push_back(at::randn({4, 100, 50, 50}, {at::kCUDA}));
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input_data.push_back(at::randn({100}, {at::kCUDA}));
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groupnorm_test_helper(graph_IR, input_data);
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}
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TEST(Converters, ATenGroupNormConvertsCorrectlyOneInput) {
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// aten::group_norm(Tensor input, int num_groups, Tensor? weight=None, Tensor? bias=None, float eps=1e-05, bool cudnn_enabled=True) -> 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=20]()
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%2 : None = prim::Constant()
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%7 : bool = prim::Constant[value=0]()
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%8 : float = prim::Constant[value=1.0000000000000001e-05]()
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%9 : Tensor = aten::group_norm(%0, %1, %2, %2, %8, %7)
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return (%9))IR";
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std::vector<at::Tensor> input_data;
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input_data.push_back(at::randn({4, 100, 50, 50}, {at::kCUDA}));
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groupnorm_test_helper(graph_IR, input_data);
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}
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static void groupnorm_dy_test_helper(const std::string& graph_IR,
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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::GroupNormConverter groupnormconverter;
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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, &groupnormconverter,
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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(baidu::mirana::poros::testutil::almost_equal(graph_output[0], poros_output[0], 2e-6));
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}
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TEST(Converters, ATenGroupNormConvertsDynamicCorrectly) {
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// aten::group_norm(Tensor input, int num_groups, Tensor? weight=None, Tensor? bias=None, float eps=1e-05, bool cudnn_enabled=True) -> Tensor
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const auto graph_IR = R"IR(
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graph(%0 : Tensor,
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%gamma : Tensor,
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%beta : Tensor):
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%1: int = prim::Constant[value=2]()
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%7 : bool = prim::Constant[value=0]()
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%8 : float = prim::Constant[value=1.0000000000000001e-05]()
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%9 : Tensor = aten::group_norm(%0, %1, %gamma, %beta, %8, %7)
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return (%9))IR";
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std::vector<std::vector<at::Tensor>> prewarm_data = {{}, {}, {}};
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prewarm_data[0].push_back(at::randn({5, 10, 3, 3}, {at::kCUDA}));
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prewarm_data[0].push_back(at::ones({10}, {at::kCUDA}));
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prewarm_data[0].push_back(at::ones({10}, {at::kCUDA}));
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prewarm_data[1].push_back(at::randn({2, 10, 3, 3}, {at::kCUDA}));
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prewarm_data[1].push_back(at::ones({10}, {at::kCUDA}));
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prewarm_data[1].push_back(at::ones({10}, {at::kCUDA}));
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prewarm_data[2].push_back(at::randn({2, 10, 3, 3}, {at::kCUDA}));
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prewarm_data[2].push_back(at::ones({10}, {at::kCUDA}));
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prewarm_data[2].push_back(at::ones({10}, {at::kCUDA}));
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std::vector<at::Tensor> input_data;
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input_data.push_back(at::randn({2, 10, 3, 3}, {at::kCUDA}));
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input_data.push_back(at::ones({10}, {at::kCUDA}));
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input_data.push_back(at::ones({10}, {at::kCUDA}));
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groupnorm_dy_test_helper(graph_IR, input_data, true, &prewarm_data);
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}
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TEST(Converters, ATenGroupNormConvertsCorrectlyDynamic2Inputsgamma) {
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// aten::group_norm(Tensor input, int num_groups, Tensor? weight=None, Tensor? bias=None, float eps=1e-05, bool cudnn_enabled=True) -> Tensor
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const auto graph_IR = R"IR(
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graph(%0 : Tensor, %gamma : Tensor):
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%1 : int = prim::Constant[value=2]()
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%2 : None = prim::Constant()
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%7 : bool = prim::Constant[value=0]()
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%8 : float = prim::Constant[value=1.0000000000000001e-05]()
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%9 : Tensor = aten::group_norm(%0, %1, %gamma, %2, %8, %7)
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return (%9))IR";
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std::vector<std::vector<at::Tensor>> prewarm_data = {{}, {}, {}};
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prewarm_data[0].push_back(at::randn({20, 100, 50, 50}, {at::kCUDA}));
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prewarm_data[0].push_back(at::ones({100}, {at::kCUDA}));
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prewarm_data[1].push_back(at::randn({10, 100, 40, 40}, {at::kCUDA}));
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prewarm_data[1].push_back(at::ones({100}, {at::kCUDA}));
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prewarm_data[2].push_back(at::randn({10, 100, 40, 40}, {at::kCUDA}));
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prewarm_data[2].push_back(at::ones({100}, {at::kCUDA}));
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std::vector<at::Tensor> input_data;
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input_data.push_back(at::randn({10, 100, 40, 40}, {at::kCUDA}));
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input_data.push_back(at::ones({100}, {at::kCUDA}));
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groupnorm_dy_test_helper(graph_IR, input_data, true, &prewarm_data);
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}
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TEST(Converters, ATenGroupNormConvertsDynamicOneInputCorrectly) {
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// aten::group_norm(Tensor input, int num_groups, Tensor? weight=None, Tensor? bias=None, float eps=1e-05, bool cudnn_enabled=True) -> 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=2]()
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%2 : None = prim::Constant()
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%7 : bool = prim::Constant[value=0]()
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%8 : float = prim::Constant[value=1.0000000000000001e-05]()
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%9 : Tensor = aten::group_norm(%0, %1, %2, %2, %8, %7)
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return (%9))IR";
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std::vector<std::vector<at::Tensor>> prewarm_data = {{}, {}, {}};
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prewarm_data[0].push_back(at::randn({5, 10, 6, 6}, {at::kCUDA}));
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prewarm_data[1].push_back(at::randn({2, 10, 3, 3}, {at::kCUDA}));
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prewarm_data[2].push_back(at::randn({2, 10, 3, 3}, {at::kCUDA}));
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std::vector<at::Tensor> input_data;
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input_data.push_back(at::randn({2, 10, 3, 3}, {at::kCUDA}));
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groupnorm_dy_test_helper(graph_IR, input_data, true, &prewarm_data);
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} |