You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
275 lines
11 KiB
275 lines
11 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
|
|
|
|
Licensed under the Apache License, Version 2.0 (the "License");
|
|
you may not use this file except in compliance with the License.
|
|
You may obtain a copy of the License at
|
|
|
|
http://www.apache.org/licenses/LICENSE-2.0
|
|
|
|
Unless required by applicable law or agreed to in writing, software
|
|
distributed under the License is distributed on an "AS IS" BASIS,
|
|
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
See the License for the specific language governing permissions and
|
|
limitations under the License. */
|
|
|
|
#pragma once
|
|
|
|
#include <string>
|
|
#include "paddle/fluid/framework/eigen.h"
|
|
#include "paddle/fluid/framework/op_registry.h"
|
|
#include "paddle/fluid/framework/tensor_util.h"
|
|
#include "paddle/fluid/memory/malloc.h"
|
|
#include "paddle/fluid/operators/math/blas.h"
|
|
|
|
namespace paddle {
|
|
namespace operators {
|
|
|
|
template <typename DeviceContext, typename T>
|
|
struct FindAbsMaxFunctor {
|
|
void operator()(const DeviceContext& ctx, const T* in, const int num, T* out);
|
|
};
|
|
|
|
template <typename DeviceContext, typename T>
|
|
struct ClipAndFakeQuantFunctor {
|
|
void operator()(const DeviceContext& ctx, const framework::Tensor& in,
|
|
const framework::Tensor& scale, const int bin_cnt,
|
|
framework::Tensor* out);
|
|
};
|
|
|
|
template <typename DeviceContext, typename T>
|
|
struct ClipAndFakeQuantDequantFunctor {
|
|
void operator()(const DeviceContext& ctx, const framework::Tensor& in,
|
|
const framework::Tensor& scale, const int bin_cnt,
|
|
framework::Tensor* out);
|
|
};
|
|
|
|
template <typename DeviceContext, typename T>
|
|
struct FindRangeAbsMaxFunctor {
|
|
void operator()(const DeviceContext& ctx, const framework::Tensor& cur_scale,
|
|
const framework::Tensor& last_scale,
|
|
const framework::Tensor& iter, const int window_size,
|
|
framework::Tensor* scales_arr, framework::Tensor* out_scale);
|
|
};
|
|
|
|
template <typename DeviceContext, typename T>
|
|
struct FindChannelAbsMaxFunctor {
|
|
void operator()(const DeviceContext& ctx, const T* in, const int num,
|
|
const int channel, T* out);
|
|
};
|
|
|
|
template <typename DeviceContext, typename T>
|
|
struct ChannelClipAndFakeQuantFunctor {
|
|
void operator()(const DeviceContext& ctx, const framework::Tensor& in,
|
|
const framework::Tensor& scale, const int bin_cnt,
|
|
const int channel, framework::Tensor* out);
|
|
};
|
|
|
|
template <typename DeviceContext, typename T>
|
|
struct FindMovingAverageAbsMaxFunctor {
|
|
void operator()(const DeviceContext& ctx, const framework::Tensor& in_accum,
|
|
const framework::Tensor& in_state,
|
|
const framework::Tensor& cur_scale,
|
|
framework::Tensor* out_state, framework::Tensor* out_accum,
|
|
framework::Tensor* out_scale);
|
|
};
|
|
|
|
template <typename DeviceContext, typename T>
|
|
class FakeQuantizeAbsMaxKernel : public framework::OpKernel<T> {
|
|
public:
|
|
void Compute(const framework::ExecutionContext& context) const override {
|
|
auto* in = context.Input<framework::Tensor>("X");
|
|
auto* out = context.Output<framework::Tensor>("Out");
|
|
auto* out_scale = context.Output<framework::Tensor>("OutScale");
|
|
T* out_s = out_scale->mutable_data<T>(context.GetPlace());
|
|
|
|
int bit_length = context.Attr<int>("bit_length");
|
|
int bin_cnt = std::pow(2, bit_length - 1) - 1;
|
|
|
|
auto& dev_ctx = context.template device_context<DeviceContext>();
|
|
const T* in_data = in->data<T>();
|
|
FindAbsMaxFunctor<DeviceContext, T>()(dev_ctx, in_data, in->numel(), out_s);
|
|
ClipAndFakeQuantFunctor<DeviceContext, T>()(dev_ctx, *in, *out_scale,
|
|
bin_cnt, out);
|
|
}
|
|
};
|
|
|
|
template <typename DeviceContext, typename T>
|
|
class FakeChannelWiseQuantizeAbsMaxKernel : public framework::OpKernel<T> {
|
|
public:
|
|
void Compute(const framework::ExecutionContext& context) const override {
|
|
auto* in = context.Input<framework::Tensor>("X");
|
|
|
|
auto* out = context.Output<framework::Tensor>("Out");
|
|
auto* out_scale = context.Output<framework::Tensor>("OutScale");
|
|
T* out_scale_data = out_scale->mutable_data<T>(context.GetPlace());
|
|
out->mutable_data<T>(context.GetPlace());
|
|
|
|
int bit_length = context.Attr<int>("bit_length");
|
|
int bin_cnt = std::pow(2, bit_length - 1) - 1;
|
|
|
|
auto& dev_ctx = context.template device_context<DeviceContext>();
|
|
FindChannelAbsMaxFunctor<DeviceContext, T>()(
|
|
dev_ctx, in->data<T>(), in->numel(), in->dims()[0], out_scale_data);
|
|
ChannelClipAndFakeQuantFunctor<DeviceContext, T>()(
|
|
dev_ctx, *in, *out_scale, bin_cnt, in->dims()[0], out);
|
|
}
|
|
};
|
|
|
|
template <typename DeviceContext, typename T>
|
|
class FakeQuantizeRangeAbsMaxKernel : public framework::OpKernel<T> {
|
|
public:
|
|
void Compute(const framework::ExecutionContext& context) const override {
|
|
auto* in = context.Input<framework::Tensor>("X");
|
|
auto* in_scale = context.Input<framework::Tensor>("InScale");
|
|
|
|
auto* out = context.Output<framework::Tensor>("Out");
|
|
out->mutable_data<T>(context.GetPlace());
|
|
|
|
bool is_test = context.Attr<bool>("is_test");
|
|
int bit_length = context.Attr<int>("bit_length");
|
|
int bin_cnt = std::pow(2, bit_length - 1) - 1;
|
|
auto& dev_ctx = context.template device_context<DeviceContext>();
|
|
|
|
// testing
|
|
if (is_test) {
|
|
ClipAndFakeQuantFunctor<DeviceContext, T>()(dev_ctx, *in, *in_scale,
|
|
bin_cnt, out);
|
|
return;
|
|
}
|
|
|
|
// training
|
|
auto* out_scale = context.Output<framework::Tensor>("OutScale");
|
|
auto* out_scales = context.Output<framework::Tensor>("OutScales");
|
|
auto* iter = context.Input<framework::Tensor>("Iter");
|
|
|
|
int window_size = context.Attr<int>("window_size");
|
|
out_scale->mutable_data<T>(context.GetPlace());
|
|
|
|
framework::Tensor cur_scale;
|
|
T* cur_scale_data = cur_scale.mutable_data<T>({1}, context.GetPlace());
|
|
FindAbsMaxFunctor<DeviceContext, T>()(dev_ctx, in->data<T>(), in->numel(),
|
|
cur_scale_data);
|
|
FindRangeAbsMaxFunctor<DeviceContext, T>()(dev_ctx, cur_scale, *in_scale,
|
|
*iter, window_size, out_scales,
|
|
out_scale);
|
|
ClipAndFakeQuantFunctor<DeviceContext, T>()(dev_ctx, *in, *out_scale,
|
|
bin_cnt, out);
|
|
}
|
|
};
|
|
|
|
template <typename DeviceContext, typename T>
|
|
class FakeMovingAverageAbsMaxKernelBase : public framework::OpKernel<T> {
|
|
public:
|
|
~FakeMovingAverageAbsMaxKernelBase() {}
|
|
virtual void RunClipFunctor(const DeviceContext& dev_ctx,
|
|
const framework::Tensor& in,
|
|
const framework::Tensor& in_scale, int bin_cnt,
|
|
framework::Tensor* out) const = 0;
|
|
void Compute(const framework::ExecutionContext& context) const override {
|
|
auto* in = context.Input<framework::Tensor>("X");
|
|
auto* in_scale = context.Input<framework::Tensor>("InScale");
|
|
auto* out = context.Output<framework::Tensor>("Out");
|
|
out->mutable_data<T>(context.GetPlace());
|
|
|
|
bool is_test = context.Attr<bool>("is_test");
|
|
int bit_length = context.Attr<int>("bit_length");
|
|
int bin_cnt = std::pow(2, bit_length - 1) - 1;
|
|
auto& dev_ctx = context.template device_context<DeviceContext>();
|
|
|
|
// testing
|
|
if (is_test) {
|
|
RunClipFunctor(dev_ctx, *in, *in_scale, bin_cnt, out);
|
|
return;
|
|
}
|
|
|
|
// training
|
|
auto* in_accum = context.Input<framework::Tensor>("InAccum");
|
|
auto* in_state = context.Input<framework::Tensor>("InState");
|
|
auto cur_scale = memory::Alloc(dev_ctx, sizeof(T));
|
|
T* cur_scale_data = static_cast<T*>(cur_scale->ptr());
|
|
|
|
FindAbsMaxFunctor<DeviceContext, T>()(dev_ctx, in->data<T>(), in->numel(),
|
|
cur_scale_data);
|
|
|
|
auto* out_state = context.Output<framework::Tensor>("OutState");
|
|
auto* out_accum = context.Output<framework::Tensor>("OutAccum");
|
|
auto* out_scale = context.Output<framework::Tensor>("OutScale");
|
|
out_state->mutable_data<T>(context.GetPlace());
|
|
out_accum->mutable_data<T>(context.GetPlace());
|
|
out_scale->mutable_data<T>(context.GetPlace());
|
|
float moving_rate = context.Attr<float>("moving_rate");
|
|
|
|
FindMovingAverageAbsMaxFunctor<DeviceContext, T>()(
|
|
dev_ctx, *in_accum, *in_state, cur_scale_data, moving_rate, out_state,
|
|
out_accum, out_scale);
|
|
|
|
RunClipFunctor(dev_ctx, *in, *out_scale, bin_cnt, out);
|
|
}
|
|
};
|
|
|
|
template <typename DeviceContext, typename T>
|
|
class FakeQuantizeMovingAverageAbsMaxKernel
|
|
: public FakeMovingAverageAbsMaxKernelBase<DeviceContext, T> {
|
|
public:
|
|
void RunClipFunctor(const DeviceContext& dev_ctx, const framework::Tensor& in,
|
|
const framework::Tensor& in_scale, int bin_cnt,
|
|
framework::Tensor* out) const override {
|
|
ClipAndFakeQuantFunctor<DeviceContext, T>()(dev_ctx, in, in_scale, bin_cnt,
|
|
out);
|
|
}
|
|
};
|
|
|
|
template <typename DeviceContext, typename T>
|
|
class FakeQuantizeDequantizeMovingAverageAbsMaxKernel
|
|
: public FakeMovingAverageAbsMaxKernelBase<DeviceContext, T> {
|
|
public:
|
|
void RunClipFunctor(const DeviceContext& dev_ctx, const framework::Tensor& in,
|
|
const framework::Tensor& in_scale, int bin_cnt,
|
|
framework::Tensor* out) const override {
|
|
ClipAndFakeQuantDequantFunctor<DeviceContext, T>()(dev_ctx, in, in_scale,
|
|
bin_cnt, out);
|
|
}
|
|
};
|
|
|
|
template <typename DeviceContext, typename T>
|
|
class MovingAverageAbsMaxScaleKernel : public framework::OpKernel<T> {
|
|
public:
|
|
void Compute(const framework::ExecutionContext& context) const override {
|
|
auto* in = context.Input<framework::Tensor>("X");
|
|
auto* out = context.Output<framework::Tensor>("Out");
|
|
out->mutable_data<T>(context.GetPlace());
|
|
auto& dev_ctx = context.template device_context<DeviceContext>();
|
|
framework::TensorCopy(*in, context.GetPlace(), dev_ctx, out);
|
|
|
|
bool is_test = context.Attr<bool>("is_test");
|
|
// testing
|
|
if (is_test) {
|
|
return;
|
|
}
|
|
|
|
// training
|
|
auto* in_accum = context.Input<framework::Tensor>("InAccum");
|
|
auto* in_state = context.Input<framework::Tensor>("InState");
|
|
auto cur_scale = memory::Alloc(dev_ctx, sizeof(T));
|
|
T* cur_scale_data = static_cast<T*>(cur_scale->ptr());
|
|
|
|
FindAbsMaxFunctor<DeviceContext, T>()(dev_ctx, in->data<T>(), in->numel(),
|
|
cur_scale_data);
|
|
|
|
auto* out_state = context.Output<framework::Tensor>("OutState");
|
|
auto* out_accum = context.Output<framework::Tensor>("OutAccum");
|
|
auto* out_scale = context.Output<framework::Tensor>("OutScale");
|
|
out_state->mutable_data<T>(context.GetPlace());
|
|
out_accum->mutable_data<T>(context.GetPlace());
|
|
out_scale->mutable_data<T>(context.GetPlace());
|
|
float moving_rate = context.Attr<float>("moving_rate");
|
|
|
|
FindMovingAverageAbsMaxFunctor<DeviceContext, T>()(
|
|
dev_ctx, *in_accum, *in_state, cur_scale_data, moving_rate, out_state,
|
|
out_accum, out_scale);
|
|
}
|
|
};
|
|
|
|
} // namespace operators
|
|
} // namespace paddle
|