chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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// Copyright (c) 2024 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 "paddle/phi/kernels/fake_quantize_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 T, typename Context>
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void FakeQuantizeRangeAbsMaxKernel(const Context &dev_ctx,
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const DenseTensor &x,
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const DenseTensor &in_scale,
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const optional<DenseTensor> &iter,
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int window_size,
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int bit_length,
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bool is_test,
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int round_type,
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DenseTensor *out,
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DenseTensor *out_scale,
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DenseTensor *out_scales) {
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dev_ctx.template Alloc<T>(out);
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int bin_cnt = std::pow(2, bit_length - 1) - 1;
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// testing
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if (is_test) {
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funcs::ClipAndFakeQuantFunctor<Context, T>()(
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dev_ctx, x, in_scale, bin_cnt, round_type, out);
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return;
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}
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// training
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dev_ctx.template Alloc<T>(out_scale);
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DenseTensor cur_scale;
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cur_scale.Resize({1});
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T *cur_scale_data = dev_ctx.template Alloc<T>(&cur_scale);
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funcs::FindAbsMaxFunctor<Context, T>()(
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dev_ctx, x.data<T>(), x.numel(), cur_scale_data);
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funcs::FindRangeAbsMaxFunctor<Context, T>()(dev_ctx,
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cur_scale,
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in_scale,
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iter.get(),
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window_size,
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out_scales,
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out_scale);
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funcs::ClipAndFakeQuantFunctor<Context, T>()(
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dev_ctx, x, *out_scale, bin_cnt, round_type, out);
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}
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template <typename T, typename Context>
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void FakeQuantizeAbsMaxKernel(const Context &dev_ctx,
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const DenseTensor &x,
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int bit_length,
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int round_type,
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DenseTensor *out,
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DenseTensor *out_scale) {
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T *out_s = dev_ctx.template Alloc<T>(out_scale);
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int bin_cnt = std::pow(2, bit_length - 1) - 1;
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const T *in_data = x.data<T>();
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funcs::FindAbsMaxFunctor<Context, T> find_abs_max_functor;
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find_abs_max_functor(dev_ctx, in_data, x.numel(), out_s);
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funcs::ClipAndFakeQuantFunctor<Context, T> clip_and_fake_quant_functor;
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clip_and_fake_quant_functor(dev_ctx, x, *out_scale, bin_cnt, round_type, out);
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}
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template <typename T, typename Context>
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void FakeQuantOrWithDequantMovingAverageAbsMaxKernel(
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const Context &dev_ctx,
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const DenseTensor &x,
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const DenseTensor &in_scale,
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const optional<DenseTensor> &in_accum,
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const optional<DenseTensor> &in_state,
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float moving_rate,
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int bit_length,
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bool is_test,
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int round_type,
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DenseTensor *out,
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DenseTensor *out_scale,
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DenseTensor *out_state,
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DenseTensor *out_accum) {
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int bin_cnt = std::pow(2, bit_length - 1) - 1;
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// testing
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if (is_test) {
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funcs::ClipAndFakeQuantFunctor<Context, T>()(
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dev_ctx, x, in_scale, bin_cnt, round_type, out);
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return;
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}
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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, x.data<T>(), x.numel(), cur_scale_data);
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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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moving_rate,
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out_state,
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out_accum,
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out_scale);
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funcs::ClipAndFakeQuantFunctor<Context, T>()(
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dev_ctx, x, *out_scale, bin_cnt, round_type, out);
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}
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template <typename T, typename Context>
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void FakeChannelWiseQuantizeAbsMaxKernel(const Context &dev_ctx,
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const DenseTensor &x,
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int bit_length,
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int round_type,
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int quant_axis,
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bool is_test,
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DenseTensor *out,
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DenseTensor *out_scale) {
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dev_ctx.template Alloc<T>(out);
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int bin_cnt = std::pow(2, bit_length - 1) - 1;
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if (!is_test) {
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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, x, quant_axis, out_scale_data);
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}
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funcs::ChannelClipAndFakeQuantFunctor<Context, T>()(
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dev_ctx, x, *out_scale, bin_cnt, round_type, quant_axis, out);
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}
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template <typename T, typename Context>
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void FakeChannelWiseQuantizeDequantizeAbsMaxKernel(const Context &dev_ctx,
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const DenseTensor &x,
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int bit_length,
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int round_type,
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int quant_axis,
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DenseTensor *out,
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DenseTensor *out_scale) {
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T *out_scale_data = dev_ctx.template Alloc<T>(out_scale);
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dev_ctx.template Alloc<T>(out);
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int bin_cnt = std::pow(2, bit_length - 1) - 1;
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funcs::FindChannelAbsMaxFunctor<Context, T>()(
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dev_ctx, x, quant_axis, out_scale_data);
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funcs::ChannelClipFakeQuantDequantFunctor<Context, T>()(
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dev_ctx, x, *out_scale, bin_cnt, round_type, quant_axis, out);
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}
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template <typename T, typename Context>
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void FakeQuantizeDequantizeMovingAverageAbsMaxKernel(
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const Context &dev_ctx,
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const DenseTensor &x,
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const DenseTensor &in_scale,
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const optional<DenseTensor> &in_accum,
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const optional<DenseTensor> &in_state,
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float moving_rate,
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int bit_length,
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bool is_test,
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int round_type,
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DenseTensor *out,
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DenseTensor *out_scale,
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DenseTensor *out_state,
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DenseTensor *out_accum) {
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dev_ctx.template Alloc<T>(out);
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int bin_cnt = std::pow(2, bit_length - 1) - 1;
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// testing
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if (is_test) {
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funcs::ClipAndFakeQuantDequantFunctor<Context, T>()(
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dev_ctx, x, in_scale, bin_cnt, round_type, out);
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return;
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}
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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, x.data<T>(), x.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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moving_rate,
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out_state,
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out_accum,
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out_scale);
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funcs::ClipAndFakeQuantDequantFunctor<Context, T>()(
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dev_ctx, x, *out_scale, bin_cnt, round_type, out);
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}
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template <typename T, typename Context>
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void FakeQuantizeDequantizeAbsMaxKernel(const Context &dev_ctx,
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const DenseTensor &x,
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int bit_length,
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int round_type,
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DenseTensor *out,
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DenseTensor *out_scale) {
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T *out_s = dev_ctx.template Alloc<T>(out_scale);
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int bin_cnt = std::pow(2, bit_length - 1) - 1;
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const T *in_data = x.data<T>();
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funcs::FindAbsMaxFunctor<Context, T>()(dev_ctx, in_data, x.numel(), out_s);
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funcs::ClipAndFakeQuantDequantFunctor<Context, T>()(
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dev_ctx, x, *out_scale, bin_cnt, round_type, out);
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}
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} // namespace phi
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