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 "paddle/fluid/eager/nan_inf_utils.h"
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#include <iostream>
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#include <limits>
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#include <ostream>
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#include <string>
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#include <tuple>
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#include "gtest/gtest.h"
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#include "paddle/common/flags.h"
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#include "paddle/fluid/framework/tensor_util.h"
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#include "paddle/fluid/platform/enforce.h"
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#include "paddle/phi/api/include/api.h"
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#include "paddle/phi/api/include/strings_api.h"
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#include "paddle/phi/core/kernel_registry.h"
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PD_DECLARE_KERNEL(full, CPU, ALL_LAYOUT);
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PD_DECLARE_KERNEL(strings_empty, CPU, ALL_LAYOUT);
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COMMON_DECLARE_string(check_nan_inf_blacklist);
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namespace egr {
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using paddle_flags::FLAGS_check_nan_inf_blacklist;
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#define CHECK_NAN_INF(tensors) \
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{ \
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bool caught_exception = false; \
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try { \
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CheckTensorHasNanOrInf("nan_inf_test", tensors); \
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} catch (paddle::platform::EnforceNotMet & error) { \
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caught_exception = true; \
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std::string ex_msg = error.what(); \
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EXPECT_TRUE(ex_msg.find("There are NAN or INF") != std::string::npos); \
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} \
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EXPECT_TRUE(caught_exception); \
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}
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#define CHECK_NO_NAN_INF(tensors) \
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{ \
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bool caught_exception = false; \
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try { \
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CheckTensorHasNanOrInf("nan_inf_test", tensors); \
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} catch (paddle::platform::EnforceNotMet & error) { \
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caught_exception = true; \
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std::string ex_msg = error.what(); \
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EXPECT_TRUE(ex_msg.find("There are NAN or INF") != std::string::npos); \
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} \
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EXPECT_FALSE(caught_exception); \
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}
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#define CHECK_APINAME_SKIP(api_name, tensor) \
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{ \
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bool caught_exception = false; \
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try { \
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CheckTensorHasNanOrInf(api_name, tensor); \
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} catch (paddle::platform::EnforceNotMet & error) { \
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caught_exception = true; \
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} \
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EXPECT_FALSE(caught_exception); \
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}
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#define CHECK_APINAME_NO_SKIP(api_name, tensor) \
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{ \
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bool caught_exception = false; \
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try { \
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CheckTensorHasNanOrInf(api_name, tensor); \
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} catch (paddle::platform::EnforceNotMet & error) { \
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caught_exception = true; \
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} \
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EXPECT_TRUE(caught_exception); \
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}
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TEST(NanInfUtils, BlacklistSkipCheck) {
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auto nan_tensor = paddle::experimental::full(
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{3, 4}, std::numeric_limits<double>::quiet_NaN(), phi::DataType::FLOAT64);
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FLAGS_check_nan_inf_blacklist = "";
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CHECK_APINAME_SKIP("empty", nan_tensor);
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// Test that "empty_like" always skips regardless of blacklist
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FLAGS_check_nan_inf_blacklist = "";
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CHECK_APINAME_SKIP("empty_like", nan_tensor);
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// Test with empty blacklist (default behavior)
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FLAGS_check_nan_inf_blacklist = "";
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CHECK_APINAME_NO_SKIP("some_op", nan_tensor);
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// Test with single op in blacklist
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FLAGS_check_nan_inf_blacklist = "single_op";
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CHECK_APINAME_SKIP("single_op", nan_tensor);
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CHECK_APINAME_NO_SKIP("other_op", nan_tensor);
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// Even when blacklist is set, these should still skip
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CHECK_APINAME_SKIP("empty", nan_tensor);
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CHECK_APINAME_SKIP("empty_like", nan_tensor);
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// blacklist="op1,op2,op3" and op is in blacklist
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FLAGS_check_nan_inf_blacklist = "op1,op2,op3";
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CHECK_APINAME_SKIP("op1", nan_tensor);
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CHECK_APINAME_SKIP("op2", nan_tensor);
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CHECK_APINAME_SKIP("op3", nan_tensor);
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// not in blacklist, should perform nan_or_inf check
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CHECK_APINAME_NO_SKIP("op4", nan_tensor);
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FLAGS_check_nan_inf_blacklist = "";
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}
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TEST(NanInfUtils, Functions) {
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// test all methods
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auto tensor = paddle::experimental::full(
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{3, 4}, std::numeric_limits<double>::quiet_NaN(), phi::DataType::FLOAT64);
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CHECK_NAN_INF(tensor);
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auto tensor1 = paddle::experimental::full(
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{3, 4}, std::numeric_limits<double>::quiet_NaN(), phi::DataType::FLOAT64);
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auto two_tensors = std::make_tuple(tensor, tensor1);
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CHECK_NAN_INF(two_tensors);
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auto tensor2 = paddle::experimental::full(
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{3, 4}, std::numeric_limits<double>::quiet_NaN(), phi::DataType::FLOAT64);
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auto three_tensors = std::make_tuple(tensor, tensor1, tensor2);
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CHECK_NAN_INF(three_tensors);
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auto tensor3 = paddle::experimental::full(
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{3, 4}, std::numeric_limits<double>::quiet_NaN(), phi::DataType::FLOAT64);
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auto four_tensors = std::make_tuple(tensor, tensor1, tensor2, tensor3);
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CHECK_NAN_INF(four_tensors);
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auto tensor4 = paddle::experimental::full(
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{3, 4}, std::numeric_limits<double>::quiet_NaN(), phi::DataType::FLOAT64);
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auto five_tensors =
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std::make_tuple(tensor, tensor1, tensor2, tensor3, tensor4);
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CHECK_NAN_INF(five_tensors);
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auto tensor5 = paddle::experimental::full(
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{3, 4}, std::numeric_limits<double>::quiet_NaN(), phi::DataType::FLOAT64);
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auto six_tensors =
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std::make_tuple(tensor, tensor1, tensor2, tensor3, tensor4, tensor5);
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CHECK_NAN_INF(six_tensors);
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std::vector<paddle::Tensor> tensor_vec;
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tensor_vec.emplace_back(tensor);
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tensor_vec.emplace_back(tensor1);
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CHECK_NAN_INF(tensor_vec);
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paddle::small_vector<std::vector<paddle::Tensor>, egr::kSlotSmallVectorSize>
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small_vec;
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small_vec.emplace_back(tensor_vec);
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CHECK_NAN_INF(small_vec);
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// test selected_rows
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paddle::Tensor tensor_sr;
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auto sr = std::make_shared<phi::SelectedRows>();
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*sr->mutable_value() =
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*(static_cast<const phi::DenseTensor*>(tensor.impl().get()));
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tensor_sr.set_impl(sr);
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CHECK_NAN_INF(tensor_sr);
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// test other tensor
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auto tensor_str = paddle::experimental::strings::empty({3, 4});
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CHECK_NO_NAN_INF(tensor_str);
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}
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} // namespace egr
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