wehub-resource-sync
277 lines
9.3 KiB
C++
277 lines
9.3 KiB
C++
// Copyright (c) 2024 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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#pragma once
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#include <cmath>
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#include <string>
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#include "glog/logging.h"
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#include "paddle/phi/backends/cpu/cpu_context.h"
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#include "paddle/phi/backends/gpu/gpu_context.h"
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#include "paddle/phi/common/amp_type_traits.h"
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#include "paddle/phi/common/data_type.h"
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#include "paddle/phi/common/place.h"
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#include "paddle/phi/core/dense_tensor.h"
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#include "paddle/phi/common/memory_utils.h"
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namespace phi {
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template <typename Context, typename T>
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struct AccuracyCheckFunctor {
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void operator()(const Context& dev_ctx,
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const DenseTensor& in,
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const DenseTensor& other,
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const std::string& fn_name,
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const float rtol,
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const float atol,
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bool equal_nan,
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DenseTensor* output);
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};
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template <typename T>
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struct AccuracyCheckFunctor<CPUContext, T> {
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void operator()(const CPUContext& dev_ctx,
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const DenseTensor& in,
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const DenseTensor& other,
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const std::string& fn_name,
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const double rtol,
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const double atol,
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bool equal_nan,
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DenseTensor* output) {
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auto* in_a = in.data<T>();
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auto* in_b = other.data<T>();
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auto* out_data = dev_ctx.template Alloc<bool>(output);
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auto num = in.numel();
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// *out_data = true;
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for (int i = 0; i < num; i++) {
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out_data[i] = true;
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}
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bool val;
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int res_index = -1;
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for (int i = 0; i < num; i++) {
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const double a = in_a[i], b = in_b[i];
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if (std::isnan(a) || std::isnan(b)) {
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val = equal_nan && std::isnan(a) == std::isnan(b);
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} else {
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double left = (a > b ? a - b : b - a);
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double right = atol + (b > 0 ? rtol * b : (-rtol) * b);
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double diff = (left > right ? left - right : right - left);
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val = a == b || left <= right || diff <= 1e-10;
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}
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// *out_data &= val;
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out_data[i] = val;
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if (!val) {
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VLOG(2) << "Accuracy check failed between" << a << " and " << b
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<< " at index= " << i;
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res_index = i;
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break;
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}
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}
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PADDLE_ENFORCE_EQ(val,
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true,
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common::errors::PreconditionNotMet(
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"Accuracy check failed, kernel name %s, res index %d",
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fn_name,
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res_index));
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}
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};
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template <typename T>
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struct AccuracyCheckFunctor<CPUContext, dtype::complex<T>> {
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void operator()(const CPUContext& dev_ctx,
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const DenseTensor& in,
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const DenseTensor& other,
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const std::string& fn_name,
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const double rtol,
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const double atol,
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bool equal_nan,
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DenseTensor* output) {
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auto* in_a = in.data<dtype::complex<T>>();
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auto* in_b = other.data<dtype::complex<T>>();
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auto* out_data = dev_ctx.template Alloc<bool>(output);
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auto num = in.numel();
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// *out_data = true;
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for (int i = 0; i < num; i++) {
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out_data[i] = true;
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}
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bool val = false;
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int res_index = -1;
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for (int i = 0; i < num; i++) {
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const dtype::complex<T> a = in_a[i], b = in_b[i];
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if (std::isnan(a) || std::isnan(b)) {
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val = equal_nan && std::isnan(a) == std::isnan(b);
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} else {
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T left = abs(a - b);
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T right = atol + rtol * abs(b);
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T diff = abs(left - right);
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val = a == b || left <= right || diff <= 1e-10;
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// *out_data &= val;
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out_data[i] = val;
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if (!val) {
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res_index = i;
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break;
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}
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}
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}
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PADDLE_ENFORCE_EQ(val,
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true,
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common::errors::PreconditionNotMet(
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"Accuracy check failed, kernel name %s, res index %d",
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fn_name,
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res_index));
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}
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};
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#if defined(__NVCC__) || defined(__HIPCC__)
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template <typename T>
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__global__ void AccuracyCheckCUDAKernel(const T* in_data,
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const T* other_data,
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const double rtol,
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const double atol,
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bool equal_nan,
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int64_t num,
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bool* out_data) {
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int64_t idx = static_cast<int64_t>(blockIdx.x) * blockDim.x + threadIdx.x;
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bool val;
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using MPType = typename MPTypeTrait<T>::Type;
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for (int64_t i = idx; i < num; i += blockDim.x * gridDim.x) {
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const double a = static_cast<MPType>(in_data[i]);
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const double b = static_cast<MPType>(other_data[i]);
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if (isnan(a) || isnan(b)) {
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val = equal_nan && isnan(a) == isnan(b);
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} else {
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double left = (a > b ? a - b : b - a);
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double right = atol + (b > 0 ? rtol * b : (-rtol) * b);
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double diff = (left > right ? left - right : right - left);
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val = a == b || left <= right || diff <= 1e-10;
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}
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out_data[i] = val;
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if (!val) {
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*out_data = false;
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break;
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}
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}
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}
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template <>
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__global__ void AccuracyCheckCUDAKernel<complex64>(const complex64* in_data,
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const complex64* other_data,
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const double rtol,
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const double atol,
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bool equal_nan,
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int64_t num,
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bool* out_data) {
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int64_t idx = static_cast<int64_t>(blockIdx.x) * blockDim.x + threadIdx.x;
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bool val;
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for (int64_t i = idx; i < num; i += blockDim.x * gridDim.x) {
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const complex64 a = in_data[i];
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const complex64 b = other_data[i];
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if (isnan(a) || isnan(b)) {
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val = equal_nan && isnan(a) == isnan(b);
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} else {
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float left = abs(a - b);
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float right = atol + rtol * abs(b);
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float diff = abs(left - right);
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val = a == b || left <= right || diff <= 1e-10;
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}
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out_data[i] = val;
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if (!val) {
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*out_data = false;
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break;
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}
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}
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}
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template <>
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__global__ void AccuracyCheckCUDAKernel<complex128>(
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const complex128* in_data,
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const complex128* other_data,
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const double rtol,
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const double atol,
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bool equal_nan,
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int64_t num,
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bool* out_data) {
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int64_t idx = static_cast<int64_t>(blockIdx.x) * blockDim.x + threadIdx.x;
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bool val;
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for (int64_t i = idx; i < num; i += blockDim.x * gridDim.x) {
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const complex128 a = in_data[i];
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const complex128 b = other_data[i];
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if (isnan(a) || isnan(b)) {
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val = equal_nan && isnan(a) == isnan(b);
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} else {
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double left = abs(a - b);
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double right = atol + rtol * abs(b);
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double diff = abs(left - right);
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val = a == b || left <= right || diff <= 1e-10;
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}
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out_data[i] = val;
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if (!val) {
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*out_data = false;
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break;
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}
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}
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}
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template <typename T>
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struct AccuracyCheckFunctor<GPUContext, T> {
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void operator()(const GPUContext& dev_ctx,
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const DenseTensor& in,
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const DenseTensor& other,
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const std::string& fn_name,
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const double rtol,
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const double atol,
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bool equal_nan,
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DenseTensor* output) {
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int64_t num = in.numel();
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const T* in_data = in.data<T>();
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const T* other_data = other.data<T>();
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bool* out_data = dev_ctx.template Alloc<bool>(output);
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int block = 1024;
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int64_t grid = (block - 1 + num) / block;
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grid = (grid > block) ? block : grid;
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#ifdef PADDLE_WITH_HIP
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hipMemset(out_data, true, num * sizeof(bool));
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#else
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cudaMemset(out_data, true, num * sizeof(bool));
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#endif
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AccuracyCheckCUDAKernel<T><<<grid, block, 0, dev_ctx.stream()>>>(
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in_data, other_data, rtol, atol, equal_nan, num, out_data);
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DenseTensor out_cpu;
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Copy(dev_ctx, *output, CPUPlace(), true, &out_cpu);
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auto data_ptr = out_cpu.data<bool>();
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PADDLE_ENFORCE_EQ(*data_ptr,
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true,
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common::errors::PreconditionNotMet(
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"Accuracy check failed, kernel name %s", fn_name));
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}
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};
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#endif
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template <typename T, typename Context>
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void AccuracyCheckKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& y,
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const std::string& fn_name,
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const double rtol,
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const double atol,
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bool equal_nan,
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DenseTensor* out) {
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AccuracyCheckFunctor<Context, T>()(
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dev_ctx, x, y, fn_name, rtol, atol, equal_nan, out);
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}
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} // namespace phi
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