wehub-resource-sync
137 lines
4.5 KiB
Plaintext
137 lines
4.5 KiB
Plaintext
// Copyright (c) 2021 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/lerp_kernel.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/core/kernel_registry.h"
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#include "paddle/phi/kernels/expand_kernel.h"
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#include "paddle/phi/kernels/funcs/broadcast_function.h"
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namespace phi {
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template <typename T>
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struct LerpElementWiseDirectCUDAFunctor {
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HOSTDEVICE inline T operator()(const T x, const T y, const T weight) const {
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if (abs(static_cast<float>(weight)) < 0.5f) {
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return x + weight * (y - x);
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} else {
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return y - (y - x) * (static_cast<T>(1) - weight);
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}
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}
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};
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template <typename T, typename WeightT = T>
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struct LerpScalarDirectCUDAFunctor {
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const WeightT* weight_;
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HOSTDEVICE inline LerpScalarDirectCUDAFunctor(const WeightT* weight)
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: weight_(weight) {}
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HOSTDEVICE inline T operator()(const T x, const T y) const {
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T weight_scalar = static_cast<T>(weight_[0]);
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if (abs(static_cast<float>(weight_[0])) < 0.5f) {
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return x + weight_scalar * (y - x);
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} else {
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return y - (y - x) * (static_cast<T>(1) - weight_scalar);
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}
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}
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};
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template <typename T, typename Context>
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void LerpKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& y,
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const DenseTensor& weight,
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DenseTensor* out) {
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if (out && out->numel() == 0) {
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dev_ctx.template Alloc<T>(out);
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return;
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}
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int rank = out->dims().size();
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PADDLE_ENFORCE_GE(
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rank,
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0,
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common::errors::InvalidArgument(
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"The number of dimensions for LerpOp must be "
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"greater than or equal to 0, but the value received is %d.",
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rank));
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dev_ctx.template Alloc<T>(out);
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std::vector<DenseTensor*> outputs = {out};
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std::vector<const DenseTensor*> inputs;
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if (weight.numel() == 1) {
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inputs.reserve(2);
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inputs.emplace_back(&x);
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inputs.emplace_back(&y);
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if (weight.dtype() == DataType::FLOAT64) {
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const double* weight_ptr = weight.data<double>();
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auto functor = LerpScalarDirectCUDAFunctor<T, double>(weight_ptr);
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funcs::BroadcastKernel<T>(dev_ctx, inputs, &outputs, functor);
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} else {
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const T* weight_ptr = weight.data<T>();
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auto functor = LerpScalarDirectCUDAFunctor<T>(weight_ptr);
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funcs::BroadcastKernel<T>(dev_ctx, inputs, &outputs, functor);
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}
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} else {
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inputs.reserve(3);
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auto functor = LerpElementWiseDirectCUDAFunctor<T>();
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DenseTensor b_min = EmptyLike<T>(dev_ctx, *out);
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if (x.dims().size() != y.dims().size() &&
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weight.dims().size() != y.dims().size()) {
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if (x.dims().size() < y.dims().size() &&
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x.dims().size() < weight.dims().size()) {
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// x broadcast to b_min
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ExpandKernel<T, Context>(dev_ctx, x, vectorize(b_min.dims()), &b_min);
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inputs.emplace_back(&b_min);
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inputs.emplace_back(&y);
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inputs.emplace_back(&weight);
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} else if (y.dims().size() < weight.dims().size()) {
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// y broadcast to b_min
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ExpandKernel<T, Context>(dev_ctx, y, vectorize(b_min.dims()), &b_min);
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inputs.emplace_back(&x);
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inputs.emplace_back(&b_min);
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inputs.emplace_back(&weight);
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} else {
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// weight broadcast to b_min
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ExpandKernel<T, Context>(
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dev_ctx, weight, vectorize(b_min.dims()), &b_min);
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inputs.emplace_back(&x);
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inputs.emplace_back(&y);
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inputs.emplace_back(&b_min);
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}
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} else {
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inputs.emplace_back(&x);
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inputs.emplace_back(&y);
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inputs.emplace_back(&weight);
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}
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funcs::BroadcastKernel<T>(dev_ctx, inputs, &outputs, functor);
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(lerp,
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GPU,
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ALL_LAYOUT,
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phi::LerpKernel,
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phi::float16,
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phi::bfloat16,
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float,
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double) {}
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