提交 a177d482 编写于 作者: X xiaolil1 提交者: Tao Luo

Add Requantize OP (#15318)

* Enable INT8 ReQuantize OP
test=develop

* Clean code
test=develop

* Add comments
test=develop

* Revert "Clean code"
test=develop

This reverts commit a7a49b8a.

* Modify requantize op test
test=develop

* fix requantize UT by moving public function to public test file.
test=develop

* Fix test fail due to file address change.
test=develop

* Change file address for requantize op.
test=develop
上级 f5a37518
/* 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. */
#include "mkldnn.hpp"
#include "paddle/fluid/framework/data_layout_transform.h"
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/operators/requantize_op.h"
#include "paddle/fluid/platform/mkldnn_helper.h"
namespace paddle {
namespace operators {
using mkldnn::memory;
using mkldnn::primitive;
using mkldnn::reorder;
using platform::to_void_cast;
using Tensor = framework::Tensor;
using framework::DataLayout;
using mkldnn::stream;
using platform::GetMKLDNNFormat;
template <typename T>
class ReQuantOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* input = ctx.Input<Tensor>("Input");
auto scale_in = ctx.Attr<float>("Scale_in");
auto scale_out = ctx.Attr<float>("Scale_out");
auto* output = ctx.Output<Tensor>("Output");
auto& dev_ctx =
ctx.template device_context<platform::MKLDNNDeviceContext>();
const auto& engine = dev_ctx.GetEngine();
std::vector<primitive> pipeline;
std::vector<int> src_tz = paddle::framework::vectorize2int(input->dims());
std::vector<int> dst_tz = paddle::framework::vectorize2int(output->dims());
mkldnn::memory::data_type src_dt =
paddle::framework::ToMKLDNNDataType(input->type());
mkldnn::memory::data_type dst_dt = src_dt; // TODO(Xiaoli) support
// requantize from different
// data type (e.g., s8 to u8)
mkldnn::memory::format src_fmt = memory::format::nhwc;
mkldnn::memory::format dst_fmt = memory::format::nhwc;
const T* input_data = input->data<T>();
T* output_data = output->mutable_data<T>(ctx.GetPlace());
float scale_shift = scale_out / scale_in;
mkldnn::primitive_attr attri;
int mask = 0;
attri.set_output_scales(mask, {scale_shift});
auto src_md = platform::MKLDNNMemDesc({src_tz}, src_dt, src_fmt);
auto src_pd = mkldnn::memory::primitive_desc(src_md, engine);
auto src_memory =
std::make_shared<mkldnn::memory>(src_pd, to_void_cast<T>(input_data));
std::shared_ptr<primitive::at> src_memory_p =
std::shared_ptr<primitive::at>(new primitive::at(*src_memory));
auto dst_md = platform::MKLDNNMemDesc({dst_tz}, dst_dt, dst_fmt);
auto dst_pd = mkldnn::memory::primitive_desc(dst_md, engine);
auto dst_memory = mkldnn::memory(dst_pd, to_void_cast<T>(output_data));
auto reorder_pd = std::shared_ptr<reorder::primitive_desc>(
new reorder::primitive_desc(src_pd, dst_pd, attri));
auto reorder_p = std::shared_ptr<reorder>(
new reorder(*reorder_pd, *src_memory_p, dst_memory));
pipeline.push_back(*reorder_p);
stream(stream::kind::eager).submit(pipeline).wait();
output->set_layout(DataLayout::kMKLDNN);
output->set_format(GetMKLDNNFormat(dst_memory));
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_KERNEL(requantize, MKLDNN, ::paddle::platform::CPUPlace,
ops::ReQuantOpKernel<int8_t>, ops::ReQuantOpKernel<uint8_t>);
/* 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. */
#include "paddle/fluid/operators/requantize_op.h"
#ifdef PADDLE_WITH_MKLDNN
#include "paddle/fluid/platform/mkldnn_helper.h"
#endif
namespace paddle {
namespace operators {
framework::OpKernelType ReQuantOp::GetExpectedKernelType(
const framework::ExecutionContext& ctx) const {
framework::LibraryType library_ = framework::LibraryType::kMKLDNN;
framework::DataLayout layout_ = framework::DataLayout::kMKLDNN;
return framework::OpKernelType(ctx.Input<Tensor>("Input")->type(),
ctx.GetPlace(), layout_, library_);
}
void ReQuantOpMaker::Make() {
AddInput("Input", "input data");
AddOutput("Output", "output data");
AddAttr<float>("Scale_in", "scale in data").SetDefault({1.0f});
AddAttr<float>("Scale_out", "scale out data").SetDefault({1.0f});
AddComment(
R"DOC(This op will re-quantize data from INT8 with scale_in to INT8 with scale_out)DOC");
}
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(requantize, ops::ReQuantOp, ops::ReQuantOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
/* 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 <vector>
#include "paddle/fluid/framework/op_registry.h"
namespace paddle {
namespace operators {
using framework::OpKernelType;
using framework::Tensor;
class ReQuantOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
ctx->SetOutputDim("Output", ctx->GetInputDim("Input"));
ctx->ShareLoD("Input", /*->*/ "Output");
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override;
};
class ReQuantOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override;
};
} // namespace operators
} // namespace paddle
...@@ -70,3 +70,17 @@ def check_if_mkldnn_primitives_exist_in_bwd(test_case, op_type, x, out, ...@@ -70,3 +70,17 @@ def check_if_mkldnn_primitives_exist_in_bwd(test_case, op_type, x, out,
fetch_list=['x@GRAD', 'out']) fetch_list=['x@GRAD', 'out'])
__assert_close(x_grad, out[0], 'x@GRAD') __assert_close(x_grad, out[0], 'x@GRAD')
def format_reorder(out, size):
in_n = size[0]
out_h = size[2]
out_w = size[3]
out_c = size[1]
out_tmp = np.zeros((in_n, out_h, out_w, out_c))
for n in range(in_n):
for i in range(out_h):
for j in range(out_w):
for m in range(out_c):
out_tmp[n, i, j, m] = out[n, m, i, j]
return out_tmp.reshape(in_n, out_c, out_h, out_w)
...@@ -20,6 +20,7 @@ import numpy as np ...@@ -20,6 +20,7 @@ import numpy as np
import paddle.fluid.core as core import paddle.fluid.core as core
from paddle.fluid.tests.unittests.op_test import OpTest from paddle.fluid.tests.unittests.op_test import OpTest
from paddle.fluid.tests.unittests.test_conv2d_op import conv2d_forward_naive, TestConv2dOp from paddle.fluid.tests.unittests.test_conv2d_op import conv2d_forward_naive, TestConv2dOp
from mkldnn_op_test import format_reorder
def conv2d_forward_refer(input, filter, group, conv_param): def conv2d_forward_refer(input, filter, group, conv_param):
...@@ -29,20 +30,6 @@ def conv2d_forward_refer(input, filter, group, conv_param): ...@@ -29,20 +30,6 @@ def conv2d_forward_refer(input, filter, group, conv_param):
return format_reorder(out, size) return format_reorder(out, size)
def format_reorder(out, size):
in_n = size[0]
out_h = size[2]
out_w = size[3]
out_c = size[1]
out_tmp = np.zeros((in_n, out_h, out_w, out_c))
for n in range(in_n):
for i in range(out_h):
for j in range(out_w):
for m in range(out_c):
out_tmp[n, i, j, m] = out[n, m, i, j]
return out_tmp.reshape(in_n, out_c, out_h, out_w)
class TestConv2dInt8Op(TestConv2dOp): class TestConv2dInt8Op(TestConv2dOp):
def setUp(self): def setUp(self):
self.op_type = "conv2d" self.op_type = "conv2d"
......
# Copyright (c) 2018 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.
from __future__ import print_function
import unittest
import numpy as np
from paddle.fluid.tests.unittests.op_test import OpTest
from mkldnn_op_test import format_reorder
class TestReQuantizeOp(OpTest):
def setUp(self):
self.op_type = 'requantize'
self.scale_in = 2.0
self.scale_out = 1.5
self.input_size = [1, 1, 5, 5]
self.data_type = 'int8'
self.set_scale()
self.set_data_type()
scale_shift = self.scale_out / self.scale_in
if self.data_type == 'int8':
input = (np.random.randint(0, 100, self.input_size) - 50
).astype(self.data_type)
output_tmp = np.round(input.astype('float32') *
scale_shift).astype('int8')
else:
input = (np.random.randint(0, 100,
self.input_size)).astype(self.data_type)
output_tmp = np.round(input.astype('float32') *
scale_shift).astype('uint8')
output = format_reorder(output_tmp, self.input_size)
self.inputs = {'Input': OpTest.np_dtype_to_fluid_dtype(input)}
self.outputs = {'Output': output}
self.attrs = {'Scale_in': self.scale_in, 'Scale_out': self.scale_out}
def test_check_output(self):
self.check_output()
def set_scale(self):
pass
def set_data_type(OpTest):
pass
#--------------------test requantize with s8 input--------------------
class TestReQuantizeOp1(TestReQuantizeOp):
def set_scale(self):
self.scale_in = 1.5
self.scale_out = 1.5
class TestReQuantizeOp2(TestReQuantizeOp):
def set_scale(self):
self.scale_in = 0.1
self.scale_out = 0.2
#--------------------test requantize with u8 input--------------------
class TestReQuantizeOp3(TestReQuantizeOp1):
def set_data_type(self):
self.data_type = 'uint8'
class TestReQuantizeOp4(TestReQuantizeOp2):
def set_data_type(self):
self.data_type = 'uint8'
if __name__ == '__main__':
unittest.main()
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