wehub-resource-sync
132 lines
4.6 KiB
Plaintext
132 lines
4.6 KiB
Plaintext
// 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/phi/kernels/allclose_kernel.h"
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#include <type_traits>
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#include "glog/logging.h"
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#include "paddle/phi/backends/gpu/gpu_launch_config.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/core/enforce.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/full_kernel.h"
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namespace phi {
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template <typename T, typename IndexType>
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__global__ void AllcloseCUDAKernel(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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IndexType num,
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bool* out_data) {
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unsigned int idx = threadIdx.x + blockIdx.x * blockDim.x;
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bool val;
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using BaseMT = typename MPTypeTrait<T>::Type;
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using MT = typename std::conditional<std::is_same<T, int32_t>::value ||
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std::is_same<T, int64_t>::value ||
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std::is_same<T, bool>::value,
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double,
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BaseMT>::type;
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for (IndexType i = idx; i < num; i += blockDim.x * gridDim.x) {
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const MT a = static_cast<MT>(in_data[i]);
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const MT b = static_cast<MT>(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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MT left = (a > b ? a - b : b - a);
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MT right = atol + (b > 0 ? rtol * b : (-rtol) * b);
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MT diff = (left > right ? left - right : right - left);
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val = a == b || left <= right || diff <= 1e-15;
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}
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if (!val) *out_data = false;
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}
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}
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template <typename T, typename Context>
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void AllCloseKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& y,
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const Scalar& rtol,
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const Scalar& atol,
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bool equal_nan,
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DenseTensor* out) {
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if (x.numel() == 0 || y.numel() == 0) {
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Full<bool, Context>(dev_ctx, out->dims(), true, out);
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return;
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}
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double rtol_v, atol_v;
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if (rtol.dtype() == DataType::FLOAT64) {
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rtol_v = rtol.to<double>();
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} else if (rtol.dtype() == DataType::FLOAT32) {
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rtol_v = rtol.to<float>();
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} else {
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PADDLE_THROW(common::errors::InvalidArgument(
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"Input (Rtol) type must be double or float, but get %s.",
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rtol.dtype()));
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}
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if (atol.dtype() == DataType::FLOAT64) {
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atol_v = atol.to<double>();
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} else if (atol.dtype() == DataType::FLOAT32) {
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atol_v = atol.to<float>();
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} else {
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PADDLE_THROW(common::errors::InvalidArgument(
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"Input (Atol) type must be double or float, but get %s.",
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atol.dtype()));
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}
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VLOG(3) << "rtol and atol is : " << rtol_v << " " << atol_v;
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const T* in_data = x.data<T>();
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const T* other_data = y.data<T>();
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bool* out_data = dev_ctx.template Alloc<bool>(out);
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int64_t num = x.numel();
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const int vec_size = 4;
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auto config = backends::gpu::GetGpuLaunchConfig1D(dev_ctx, num, vec_size);
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uint32_t grid = config.block_per_grid.x;
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uint32_t block = config.thread_per_block.x;
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#ifdef PADDLE_WITH_HIP
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hipMemset(out_data, true, sizeof(bool));
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#else
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cudaMemset(out_data, true, sizeof(bool));
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#endif
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if (num > std::numeric_limits<int32_t>::max()) {
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AllcloseCUDAKernel<T, int64_t><<<grid, block, 0, dev_ctx.stream()>>>(
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in_data, other_data, rtol_v, atol_v, equal_nan, num, out_data);
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} else {
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AllcloseCUDAKernel<T, int32_t><<<grid, block, 0, dev_ctx.stream()>>>(
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in_data, other_data, rtol_v, atol_v, equal_nan, num, out_data);
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(allclose,
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GPU,
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ALL_LAYOUT,
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phi::AllCloseKernel,
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float,
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double,
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bool,
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int,
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int64_t,
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phi::float16) {
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kernel->OutputAt(0).SetDataType(phi::DataType::BOOL);
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}
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