chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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// 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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#pragma once
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#include <string>
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#include "paddle/phi/kernels/quantize_linear_kernel.h"
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#include "paddle/common/hostdevice.h"
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#include "paddle/phi/backends/gpu/gpu_launch_config.h"
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#include "paddle/phi/common/place.h"
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#include "paddle/phi/core/tensor_utils.h"
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#include "paddle/phi/kernels/cast_kernel.h"
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#include "paddle/phi/kernels/funcs/fake_quantize_functor.h"
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namespace phi {
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template <typename Context, typename T>
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struct DequantizeFunctor {
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void operator()(const Context& 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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};
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template <typename Context, typename T>
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struct ChannelDequantizeFunctorV2 {
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void operator()(const Context& dev_ctx,
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const DenseTensor* in,
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const DenseTensor** scales,
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const int scale_num,
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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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};
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template <typename T, typename Context, typename D>
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void DeQuantizeLinearImpl(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& scale,
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int quant_axis,
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int qmax,
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bool only_observer,
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DenseTensor* out) {
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auto* in = &x;
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auto in_tmp = Cast<T>(dev_ctx, *in, CppTypeToDataType<D>::Type());
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dev_ctx.template Alloc<D>(out, out->numel() * sizeof(D));
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if (only_observer) {
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Copy(dev_ctx, *in, dev_ctx.GetPlace(), false, out);
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return;
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}
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if (quant_axis < 0) {
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float max_range = qmax;
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DequantizeFunctor<Context, D>()(
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dev_ctx, &in_tmp, &scale, static_cast<D>(max_range), out);
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} else {
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PADDLE_ENFORCE_EQ(
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scale.numel(),
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in_tmp.dims()[quant_axis],
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common::errors::PreconditionNotMet(
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"The number of first scale values must be the same with "
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"quant_axis dimension value of Input(X) when the `scale` has "
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"only one element, but %ld != %ld here.",
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scale.numel(),
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in_tmp.dims()[quant_axis]));
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int max_range = qmax;
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ChannelDequantizeFunctorV2<Context, D>()(
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dev_ctx, &in_tmp, &scale, static_cast<D>(max_range), quant_axis, out);
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}
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}
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template <typename T, typename Context>
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void DeQuantizeLinearKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const optional<DenseTensor>& in_scale,
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const DenseTensor& zero_point,
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const optional<DenseTensor>& in_accum,
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const optional<DenseTensor>& in_state,
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int quant_axis,
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int bit_length,
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int qmin,
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int qmax,
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int round_type,
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bool is_test,
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bool only_observer,
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DenseTensor* out,
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DenseTensor* out_state,
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DenseTensor* out_accum,
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DenseTensor* out_scale) {
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PADDLE_ENFORCE_NE(in_scale.get_ptr(),
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nullptr,
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common::errors::PreconditionNotMet(
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"in_scale can't be nullptr in DeQuantizeLinearKernel"));
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auto scale = in_scale.get();
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switch (scale.dtype()) {
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case DataType::FLOAT64:
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DeQuantizeLinearImpl<T, Context, double>(
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dev_ctx, x, scale, quant_axis, qmax, only_observer, out);
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break;
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case DataType::FLOAT32:
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DeQuantizeLinearImpl<T, Context, float>(
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dev_ctx, x, scale, quant_axis, qmax, only_observer, out);
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break;
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case DataType::FLOAT16:
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DeQuantizeLinearImpl<T, Context, float16>(
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dev_ctx, x, scale, quant_axis, qmax, only_observer, out);
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break;
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default:
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PADDLE_THROW(common::errors::Unimplemented(
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"In DeQuantizeLinearKernel, "
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"data type %d for scale/output is not supported ",
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scale.dtype()));
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break;
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}
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}
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template <typename T, typename Context>
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void QuantizeLinearTrainKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const optional<DenseTensor>& scale,
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const DenseTensor& zero_point,
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const optional<DenseTensor>& in_accum,
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const optional<DenseTensor>& in_state,
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int quant_axis,
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int bit_length,
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int qmin,
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int qmax,
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int round_type,
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bool only_observer,
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DenseTensor* out,
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DenseTensor* out_state,
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DenseTensor* out_accum,
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DenseTensor* out_scale) {
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PADDLE_ENFORCE_NE(scale.get_ptr(),
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nullptr,
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common::errors::PreconditionNotMet(
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"in_scale can't be nullptr in DeQuantizeLinearKernel"));
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auto* in = &x;
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dev_ctx.template Alloc<float>(out);
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if (quant_axis < 0) {
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// training
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DenseTensor tmp_scale;
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tmp_scale.Resize(common::make_dim(1));
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T* cur_scale_data = dev_ctx.template Alloc<T>(&tmp_scale);
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funcs::FindAbsMaxFunctor<Context, T>()(
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dev_ctx, in->data<T>(), in->numel(), cur_scale_data);
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dev_ctx.template Alloc<T>(out_state);
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dev_ctx.template Alloc<T>(out_accum);
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dev_ctx.template Alloc<T>(out_scale);
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funcs::FindMovingAverageAbsMaxFunctor<Context, T>()(dev_ctx,
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in_accum.get(),
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in_state.get(),
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cur_scale_data,
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0.9,
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out_state,
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out_accum,
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out_scale);
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if (only_observer) {
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Copy<Context>(dev_ctx, *in, dev_ctx.GetPlace(), false, out);
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} else {
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funcs::ClipAndFakeQuantFunctor<Context, T>()(
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dev_ctx, *in, *out_scale, qmax, round_type, out);
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}
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} else {
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T* out_scale_data = dev_ctx.template Alloc<T>(out_scale);
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funcs::FindChannelAbsMaxFunctor<Context, T>()(
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dev_ctx, *in, quant_axis, out_scale_data);
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if (only_observer) {
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Copy<Context>(dev_ctx, *in, dev_ctx.GetPlace(), false, out);
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} else {
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funcs::ChannelClipAndFakeQuantFunctor<Context, T>()(
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dev_ctx, *in, *out_scale, qmax, round_type, quant_axis, out);
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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 QuantizeLinearInferKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const optional<DenseTensor>& scale,
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const DenseTensor& zero_point,
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int quant_axis,
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int bit_length,
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int qmin,
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int qmax,
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int round_type,
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bool only_observer,
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DenseTensor* out) {
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PADDLE_ENFORCE_NE(scale.get_ptr(),
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nullptr,
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common::errors::PreconditionNotMet(
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"in_scale can't be nullptr in DeQuantizeLinearKernel"));
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auto* in = &x;
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auto* in_scale = scale.get_ptr();
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dev_ctx.template Alloc<float>(out);
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if (quant_axis < 0) {
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if (only_observer) {
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Copy<Context>(dev_ctx, *in, dev_ctx.GetPlace(), false, out);
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} else {
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funcs::ClipAndFakeQuantFunctor<Context, T>()(
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dev_ctx, *in, *in_scale, qmax, round_type, out);
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}
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} else {
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if (only_observer) {
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Copy<Context>(dev_ctx, *in, dev_ctx.GetPlace(), false, out);
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} else {
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funcs::ChannelClipAndFakeQuantFunctor<Context, T>()(
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dev_ctx, *in, *in_scale, qmax, round_type, quant_axis, out);
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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 QuantizeLinearKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const optional<DenseTensor>& scale,
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const DenseTensor& zero_point,
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const optional<DenseTensor>& in_accum,
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const optional<DenseTensor>& in_state,
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int quant_axis,
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int bit_length,
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int qmin,
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int qmax,
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int round_type,
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bool is_test,
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bool only_observer,
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DenseTensor* out,
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DenseTensor* out_state,
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DenseTensor* out_accum,
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DenseTensor* out_scale) {
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if (!is_test) {
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QuantizeLinearTrainKernel<T, Context>(dev_ctx,
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x,
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scale,
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zero_point,
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in_accum,
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in_state,
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quant_axis,
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bit_length,
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qmin,
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qmax,
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round_type,
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only_observer,
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out,
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out_state,
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out_accum,
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out_scale);
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} else {
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QuantizeLinearInferKernel<T, Context>(dev_ctx,
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x,
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scale,
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zero_point,
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quant_axis,
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bit_length,
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qmin,
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qmax,
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round_type,
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only_observer,
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out);
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}
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}
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template <typename T, typename Context>
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void QuantizeLinearDeprecatedTrainKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& in_scale,
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const DenseTensor& zero_point,
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const optional<DenseTensor>& in_accum,
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const optional<DenseTensor>& in_state,
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int quant_axis,
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int bit_length,
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int qmin,
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int qmax,
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int round_type,
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bool only_observer,
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DenseTensor* out,
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DenseTensor* out_state,
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DenseTensor* out_accum,
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DenseTensor* out_scale) {
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optional<DenseTensor> scale = paddle::make_optional<DenseTensor>(in_scale);
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QuantizeLinearTrainKernel<T, Context>(dev_ctx,
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x,
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scale,
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zero_point,
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in_accum,
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in_state,
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quant_axis,
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bit_length,
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qmin,
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qmax,
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round_type,
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only_observer,
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out,
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out_state,
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out_accum,
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out_scale);
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}
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template <typename T, typename Context>
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void QuantizeLinearDeprecatedInferKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& in_scale,
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const DenseTensor& zero_point,
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int quant_axis,
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int bit_length,
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int qmin,
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int qmax,
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int round_type,
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bool only_observer,
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DenseTensor* out) {
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optional<DenseTensor> scale = paddle::make_optional<DenseTensor>(in_scale);
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QuantizeLinearInferKernel<T, Context>(dev_ctx,
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x,
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scale,
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zero_point,
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quant_axis,
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bit_length,
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qmin,
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qmax,
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round_type,
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only_observer,
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out);
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}
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template <typename T, typename Context>
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void DeQuantizeLinearDeprecatedKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& in_scale,
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const DenseTensor& zero_point,
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int quant_axis,
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int bit_length,
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int qmin,
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int qmax,
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int round_type,
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bool only_observer,
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DenseTensor* out) {
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optional<DenseTensor> scale = paddle::make_optional<DenseTensor>(in_scale);
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DeQuantizeLinearKernel<T, Context>(dev_ctx,
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x,
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scale,
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zero_point,
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nullptr,
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nullptr,
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quant_axis,
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bit_length,
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qmin,
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qmax,
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round_type,
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true,
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only_observer,
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out,
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nullptr,
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nullptr,
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nullptr);
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}
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} // namespace phi
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