chore: import upstream snapshot with attribution
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wehub-resource-sync
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// Copyright (c) 2022 PaddlePaddle Authors. 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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#include <sstream>
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#include "glog/logging.h"
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#include "gtest/gtest.h"
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#include "paddle/common/flags.h"
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#include "paddle/fluid/eager/api/generated/eager_generated/forwards/dygraph_functions.h"
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#include "paddle/fluid/eager/api/utils/hook_utils.h"
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#include "paddle/fluid/eager/backward.h"
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#include "paddle/fluid/prim/utils/utils.h"
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#include "paddle/phi/core/dense_tensor.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/core/tensor_meta.h"
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#include "test/cpp/eager/test_utils.h"
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#include "test/cpp/prim/init_env_utils.h"
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COMMON_DECLARE_string(tensor_operants_mode);
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namespace paddle {
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namespace prim {
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TEST(EagerPrim, TanhBackwardTest) {
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// 1. Initialized
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eager_test::InitEnv(phi::CPUPlace());
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FLAGS_tensor_operants_mode = "eager";
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paddle::prim::InitTensorOperants();
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// 2. pre
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phi::DDim ddim = common::make_ddim({4, 16, 16, 32});
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paddle::Tensor tensor0 =
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eager_test::CreateTensorWithValue(ddim,
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phi::CPUPlace(),
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phi::DataType::FLOAT32,
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phi::DataLayout::NCHW,
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5.0 /*value*/,
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true /*is_leaf*/);
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::egr::egr_utils_api::RetainGradForTensor(tensor0);
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paddle::Tensor tensor1 =
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eager_test::CreateTensorWithValue(ddim,
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phi::CPUPlace(),
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phi::DataType::FLOAT32,
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phi::DataLayout::NCHW,
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5.0 /*value*/,
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true /*is_leaf*/);
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::egr::egr_utils_api::RetainGradForTensor(tensor1);
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// 3. Run Forward once
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paddle::Tensor out0 = tanh_ad_func(tensor0);
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std::vector<paddle::Tensor> outs0 = {out0};
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// Disable prim
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PrimCommonUtils::SetBwdPrimEnabled(false);
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ASSERT_FALSE(PrimCommonUtils::IsBwdPrimEnabled());
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// 4. Run Backward
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egr::Backward(outs0, {}, false);
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paddle::Tensor out1 = tanh_ad_func(tensor1);
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std::vector<paddle::Tensor> outs1 = {out1};
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// Enable prim
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PrimCommonUtils::SetBwdPrimEnabled(true);
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ASSERT_TRUE(PrimCommonUtils::IsBwdPrimEnabled());
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// 4. Run Backward
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::egr::Backward(outs1, {}, false);
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VLOG(7)
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<< "Target Grad is: "
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<< std::static_pointer_cast<phi::DenseTensor>(
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::egr::EagerUtils::unsafe_autograd_meta(tensor0)->Grad().impl())
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->data<float>()[0];
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VLOG(7)
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<< "Result Grad is: "
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<< std::static_pointer_cast<phi::DenseTensor>(
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::egr::EagerUtils::unsafe_autograd_meta(tensor1)->Grad().impl())
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->data<float>()[0];
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// Examine Backward Grad
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eager_test::CompareGradTensorWithValue<float>(
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tensor1,
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std::static_pointer_cast<phi::DenseTensor>(
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::egr::EagerUtils::unsafe_autograd_meta(tensor0)->Grad().impl())
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->data<float>()[0]);
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}
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TEST(EagerPrim, LogicalOperantsTest) {
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// 1. Initialized
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eager_test::InitEnv(phi::CPUPlace());
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FLAGS_tensor_operants_mode = "eager";
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paddle::prim::InitTensorOperants();
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// 2. pre
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phi::DDim ddim = common::make_ddim({4, 16, 16, 32});
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paddle::Tensor tensor0 =
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eager_test::CreateTensorWithValue(ddim,
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phi::CPUPlace(),
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phi::DataType::INT32,
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phi::DataLayout::NCHW,
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1 /*value*/,
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true /*is_leaf*/);
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::egr::egr_utils_api::RetainGradForTensor(tensor0);
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paddle::Tensor tensor1 =
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eager_test::CreateTensorWithValue(ddim,
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phi::CPUPlace(),
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phi::DataType::INT32,
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phi::DataLayout::NCHW,
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0 /*value*/,
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true /*is_leaf*/);
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::egr::egr_utils_api::RetainGradForTensor(tensor1);
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// 3. Run Forward once
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paddle::Tensor out0 = tensor0 & tensor1;
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paddle::Tensor out1 = bitwise_and_ad_func(tensor0, tensor1);
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EXPECT_EQ(out0.data<int>()[0], out1.data<int>()[0]);
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out0 = tensor0 | tensor1;
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out1 = bitwise_or_ad_func(tensor0, tensor1);
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EXPECT_EQ(out0.data<int>()[0], out1.data<int>()[0]);
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out0 = tensor0 ^ tensor1;
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out1 = bitwise_xor_ad_func(tensor0, tensor1);
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EXPECT_EQ(out0.data<int>()[0], out1.data<int>()[0]);
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out0 = ~tensor0;
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out1 = bitwise_not_ad_func(tensor0);
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EXPECT_EQ(out0.data<int>()[0], out1.data<int>()[0]);
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}
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TEST(EagerPrim, CompareOperantsTest) {
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// 1. Initialized
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eager_test::InitEnv(phi::CPUPlace());
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FLAGS_tensor_operants_mode = "eager";
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paddle::prim::InitTensorOperants();
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// 2. pre
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phi::DDim ddim = common::make_ddim({4, 16, 16, 32});
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paddle::Tensor tensor0 =
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eager_test::CreateTensorWithValue(ddim,
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phi::CPUPlace(),
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phi::DataType::INT32,
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phi::DataLayout::NCHW,
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1 /*value*/,
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true /*is_leaf*/);
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::egr::egr_utils_api::RetainGradForTensor(tensor0);
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paddle::Tensor tensor1 =
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eager_test::CreateTensorWithValue(ddim,
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phi::CPUPlace(),
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phi::DataType::INT32,
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phi::DataLayout::NCHW,
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0 /*value*/,
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true /*is_leaf*/);
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::egr::egr_utils_api::RetainGradForTensor(tensor1);
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// 3. Run Forward once
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paddle::Tensor out0 = (tensor0 < tensor1);
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paddle::Tensor out1 = less_than_ad_func(tensor0, tensor1);
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EXPECT_EQ(out0.data<bool>()[0], out1.data<bool>()[0]);
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out0 = (tensor0 <= tensor1);
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out1 = less_equal_ad_func(tensor0, tensor1);
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EXPECT_EQ(out0.data<bool>()[0], out1.data<bool>()[0]);
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out0 = (tensor0 == tensor1);
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out1 = equal_ad_func(tensor0, tensor1);
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EXPECT_EQ(out0.data<bool>()[0], out1.data<bool>()[0]);
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out0 = (tensor0 != tensor1);
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out1 = not_equal_ad_func(tensor0, tensor1);
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EXPECT_EQ(out0.data<bool>()[0], out1.data<bool>()[0]);
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out0 = (tensor0 > tensor1);
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out1 = greater_than_ad_func(tensor0, tensor1);
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EXPECT_EQ(out0.data<bool>()[0], out1.data<bool>()[0]);
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out0 = (tensor0 >= tensor1);
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out1 = greater_equal_ad_func(tensor0, tensor1);
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EXPECT_EQ(out0.data<bool>()[0], out1.data<bool>()[0]);
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}
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TEST(EagerPrim, TestFlags) {
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PrimCommonUtils::SetBwdPrimEnabled(true);
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ASSERT_TRUE(PrimCommonUtils::IsBwdPrimEnabled());
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PrimCommonUtils::SetBwdPrimEnabled(false);
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ASSERT_FALSE(PrimCommonUtils::IsBwdPrimEnabled());
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}
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} // namespace prim
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} // namespace paddle
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