wehub-resource-sync
137 lines
4.1 KiB
Plaintext
137 lines
4.1 KiB
Plaintext
/* Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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/scale_kernel.h"
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#include "paddle/phi/backends/gpu/gpu_context.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/funcs/elementwise_base.h"
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namespace phi {
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template <typename DataT, typename ParamT>
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struct ScaleFunctor {
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ParamT bias;
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ParamT scale;
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bool bias_after_scale;
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ScaleFunctor(ParamT scale_data, ParamT bias_data, bool is_bias_after_scale)
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: bias(bias_data),
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scale(scale_data),
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bias_after_scale(is_bias_after_scale) {}
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__device__ __forceinline__ DataT operator()(const DataT x) const {
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if (bias_after_scale) {
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return static_cast<DataT>(scale * static_cast<ParamT>(x) + bias);
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} else {
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return static_cast<DataT>(scale * (static_cast<ParamT>(x) + bias));
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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 ScaleKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const Scalar& scale,
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const Scalar& bias,
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bool bias_after_scale,
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DenseTensor* out) {
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using MT = typename MPTypeTrait<T>::Type;
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std::vector<const DenseTensor*> inputs;
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std::vector<DenseTensor*> outputs;
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inputs.emplace_back(&x);
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outputs.emplace_back(out);
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dev_ctx.template Alloc<T>(out);
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if (x.numel() <= 0 || (!x.IsInitialized())) {
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return;
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}
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funcs::ElementwiseKernel<T>(
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dev_ctx,
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inputs,
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&outputs,
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ScaleFunctor<T, MT>(scale.to<MT>(), bias.to<MT>(), bias_after_scale));
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}
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template <typename T, typename Context>
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void DivScaleKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const Scalar& scale,
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DenseTensor* out) {
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using MT = typename MPTypeTrait<T>::Type;
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std::vector<const DenseTensor*> inputs;
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std::vector<DenseTensor*> outputs;
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inputs.emplace_back(&x);
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outputs.emplace_back(out);
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dev_ctx.template Alloc<T>(out);
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if (x.numel() <= 0 || (!x.IsInitialized())) {
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return;
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}
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funcs::ElementwiseKernel<T>(
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dev_ctx,
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inputs,
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&outputs,
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ScaleFunctor<T, MT>(
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static_cast<MT>(1.0) / scale.to<MT>(), static_cast<MT>(0), true));
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}
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#ifdef _WIN32
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INSTANCE_SCALAR_KERNEL(int, GPUContext)
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INSTANCE_SCALAR_KERNEL(int64_t, GPUContext)
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INSTANCE_SCALAR_KERNEL(float, GPUContext)
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INSTANCE_SCALAR_KERNEL(double, GPUContext)
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INSTANCE_SCALAR_KERNEL(float16, GPUContext)
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INSTANCE_SCALAR_KERNEL(int16_t, GPUContext)
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INSTANCE_SCALAR_KERNEL(uint8_t, GPUContext)
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INSTANCE_SCALAR_KERNEL(int8_t, GPUContext)
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#endif
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} // namespace phi
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PD_REGISTER_KERNEL(scale,
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GPU,
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ALL_LAYOUT,
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phi::ScaleKernel,
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bool,
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float,
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double,
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phi::float16,
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phi::bfloat16,
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phi::float8_e4m3fn,
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phi::float8_e5m2,
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uint8_t,
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int8_t,
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int16_t,
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int,
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int64_t,
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phi::complex64,
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phi::complex128) {}
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PD_REGISTER_KERNEL(div_scale,
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GPU,
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ALL_LAYOUT,
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phi::DivScaleKernel,
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bool,
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float,
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double,
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phi::float16,
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phi::bfloat16,
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phi::float8_e4m3fn,
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phi::float8_e5m2,
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uint8_t,
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int8_t,
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int16_t,
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int,
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int64_t,
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phi::complex64,
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phi::complex128) {}
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