wehub-resource-sync
134 lines
4.6 KiB
C++
134 lines
4.6 KiB
C++
// Copyright (c) 2023 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 <string>
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#include "paddle/phi/kernels/quantize_linear_kernel.h"
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#include "paddle/phi/backends/gpu/gpu_context.h"
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#include "paddle/phi/common/type_traits.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/funcs/eigen/common.h"
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#include "paddle/phi/kernels/impl/quantize_linear_impl.h"
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namespace phi {
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template <typename T>
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struct DequantizeFunctor<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* scale,
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T max_range,
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DenseTensor* out) {
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auto in_e = EigenVector<T>::Flatten(*in);
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const T* scale_factor = scale->data<T>();
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auto out_e = EigenVector<T>::Flatten(*out);
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auto& dev = *dev_ctx.eigen_device();
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out_e.device(dev) = in_e * scale_factor[0] / max_range;
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}
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};
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template <typename T>
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struct ChannelDequantizeFunctorV2<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* scale,
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T max_range,
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const int quant_axis,
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DenseTensor* out) {
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// Dequant op is before quantized op
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// Dequantize the weight of quantized op
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auto in_dims = in->dims();
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const int64_t channel = in_dims[quant_axis];
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const T* scale_factor = scale->data<T>();
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if (quant_axis == 0) {
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for (int64_t i = 0; i < channel; i++) {
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T s = scale_factor[i];
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DenseTensor one_channel_in = in->Slice(i, i + 1);
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DenseTensor one_channel_out = out->Slice(i, i + 1);
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auto in_e = EigenVector<T>::Flatten(one_channel_in);
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auto out_e = EigenVector<T>::Flatten(one_channel_out);
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auto& dev = *dev_ctx.eigen_device();
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out_e.device(dev) = in_e * s / max_range;
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}
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} else if (quant_axis == 1) {
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int64_t out_iter = 1;
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for (int i = 0; i < quant_axis; i++) {
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out_iter *= in_dims[i];
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}
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int64_t step_i = in->numel() / out_iter;
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int64_t step_j = in->numel() / (out_iter * channel);
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auto* in_data = in->data<T>();
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auto* out_data = dev_ctx.Alloc<T>(out, out->numel() * sizeof(T));
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for (int64_t i = 0; i < out_iter; i++) {
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for (int64_t j = 0; j < channel; j++) {
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auto* cur_in = in_data + i * step_i + j * step_j;
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auto* cur_out = out_data + i * step_i + j * step_j;
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T s = scale_factor[j];
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for (int64_t k = 0; k < step_j; k++) {
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*cur_out = (*cur_in) * s / max_range;
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++cur_in;
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++cur_out;
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}
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}
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}
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}
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}
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};
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template struct DequantizeFunctor<CPUContext, float16>;
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template struct DequantizeFunctor<CPUContext, float>;
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template struct DequantizeFunctor<CPUContext, double>;
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template struct ChannelDequantizeFunctorV2<CPUContext, float16>;
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template struct ChannelDequantizeFunctorV2<CPUContext, float>;
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template struct ChannelDequantizeFunctorV2<CPUContext, double>;
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} // namespace phi
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PD_REGISTER_KERNEL(
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quantize_linear, CPU, ALL_LAYOUT, phi::QuantizeLinearKernel, float) {}
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PD_REGISTER_KERNEL(dequantize_linear,
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CPU,
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ALL_LAYOUT,
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phi::DeQuantizeLinearKernel,
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float,
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int8_t,
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double) {
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kernel->OutputAt(0).SetDataType(phi::DataType::UNDEFINED);
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}
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PD_REGISTER_KERNEL(quantize_linear_deprecated_train,
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CPU,
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ALL_LAYOUT,
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phi::QuantizeLinearDeprecatedTrainKernel,
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float) {}
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PD_REGISTER_KERNEL(quantize_linear_deprecated_infer,
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CPU,
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ALL_LAYOUT,
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phi::QuantizeLinearDeprecatedInferKernel,
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float) {}
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PD_REGISTER_KERNEL(dequantize_linear_deprecated,
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CPU,
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ALL_LAYOUT,
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phi::DeQuantizeLinearDeprecatedKernel,
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float,
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int8_t,
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double) {
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kernel->OutputAt(0).SetDataType(phi::DataType::UNDEFINED);
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}
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