wehub-resource-sync
130 lines
4.4 KiB
Plaintext
130 lines
4.4 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/logspace_kernel.h"
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#include "paddle/common/enforce.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/core/kernel_registry.h"
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#include "paddle/phi/core/tensor_utils.h"
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#include "paddle/phi/kernels/funcs/data_type_transform.h"
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#include "paddle/phi/kernels/funcs/math_function.h"
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namespace phi {
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template <typename T>
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__global__ void LogspaceKernelInner(
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T start, T stop, double step, T base, int64_t size, T* out) {
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using MT = typename MPTypeTrait<T>::Type;
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MT mt_start = static_cast<MT>(start);
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MT mt_stop = static_cast<MT>(stop);
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MT mt_base = static_cast<MT>(base);
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int64_t index =
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static_cast<int64_t>(blockIdx.x) * static_cast<int64_t>(blockDim.x) +
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static_cast<int64_t>(threadIdx.x);
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for (; index < size; index += blockDim.x * gridDim.x) {
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if (index < size / 2) {
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out[index] =
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static_cast<T>(pow(static_cast<double>(mt_base),
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static_cast<double>(mt_start + step * index)));
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} else {
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out[index] = static_cast<T>(
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pow(static_cast<double>(mt_base),
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static_cast<double>(mt_stop - step * (size - index - 1))));
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}
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}
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}
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template <typename T>
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__global__ void LogspaceSpecialKernel(T start, T base, T* out) {
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using MT = typename MPTypeTrait<T>::Type;
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MT mt_start = static_cast<MT>(start);
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MT mt_base = static_cast<MT>(base);
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out[0] = static_cast<T>(
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pow(static_cast<double>(mt_base), static_cast<double>(mt_start)));
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}
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template <typename T, typename Context>
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void LogspaceKernel(const Context& dev_ctx,
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const DenseTensor& start,
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const DenseTensor& stop,
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const DenseTensor& number,
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const DenseTensor& base,
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DataType dtype,
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DenseTensor* out) {
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using MT = typename MPTypeTrait<T>::Type;
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auto start_t = funcs::TransDataType(dev_ctx, start, dtype);
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auto stop_t = funcs::TransDataType(dev_ctx, stop, dtype);
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auto base_t = funcs::TransDataType(dev_ctx, base, dtype);
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DenseTensor n_start;
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DenseTensor n_stop;
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DenseTensor n_num;
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DenseTensor n_base;
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Copy(dev_ctx, start_t, CPUPlace(), false, &n_start);
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T start_data = n_start.data<T>()[0];
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Copy(dev_ctx, stop_t, CPUPlace(), false, &n_stop);
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T stop_data = n_stop.data<T>()[0];
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Copy(dev_ctx, number, CPUPlace(), false, &n_num);
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int64_t num = static_cast<int64_t>(n_num.data<int32_t>()[0]);
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Copy(dev_ctx, base_t, CPUPlace(), false, &n_base);
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T base_data = n_base.data<T>()[0];
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MT mt_start_data = static_cast<MT>(start_data);
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MT mt_stop_data = static_cast<MT>(stop_data);
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PADDLE_ENFORCE_GT(
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num,
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0,
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common::errors::InvalidArgument("The num of logspace op should be larger "
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"than 0, but received num is %d",
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num));
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out->Resize({num});
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T* out_data = dev_ctx.template Alloc<T>(out);
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double step = 0;
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auto stream = dev_ctx.stream();
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int block = 512;
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int64_t grid_64 = (num + block - 1) / block;
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PADDLE_ENFORCE_LE_UINT32_MAX(grid_64, "grid");
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uint32_t grid = static_cast<uint32_t>(grid_64);
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if (num != 1) {
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step = (static_cast<double>(mt_stop_data - mt_start_data)) / (num - 1);
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LogspaceKernelInner<T><<<grid, block, 0, stream>>>(
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start_data, stop_data, step, base_data, num, out_data);
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} else {
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LogspaceSpecialKernel<T>
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<<<grid, block, 0, stream>>>(start_data, base_data, out_data);
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(logspace,
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GPU,
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ALL_LAYOUT,
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phi::LogspaceKernel,
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float,
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int32_t,
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int64_t,
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double,
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phi::float16,
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phi::bfloat16) {}
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