提交 8eb1f262 编写于 作者: X xiaolil1 提交者: Tao Luo

Enable INT8 pool OP (#15046)

* Enable INT8 pool OP
test=develop

* fix unittest
test=develop

* Clean unittest code.
test=develop
上级 227e0c45
...@@ -12,6 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. ...@@ -12,6 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. */ limitations under the License. */
#include "paddle/fluid/framework/data_layout_transform.h"
#include "paddle/fluid/operators/pool_op.h" #include "paddle/fluid/operators/pool_op.h"
#include "paddle/fluid/platform/mkldnn_helper.h" #include "paddle/fluid/platform/mkldnn_helper.h"
...@@ -71,7 +72,6 @@ class PoolMKLDNNOpKernel : public paddle::framework::OpKernel<T> { ...@@ -71,7 +72,6 @@ class PoolMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
void Compute(const paddle::framework::ExecutionContext& ctx) const override { void Compute(const paddle::framework::ExecutionContext& ctx) const override {
PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()), PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()),
"It must use CPUPlace."); "It must use CPUPlace.");
auto& dev_ctx = auto& dev_ctx =
ctx.template device_context<platform::MKLDNNDeviceContext>(); ctx.template device_context<platform::MKLDNNDeviceContext>();
const auto& mkldnn_engine = dev_ctx.GetEngine(); const auto& mkldnn_engine = dev_ctx.GetEngine();
...@@ -130,20 +130,25 @@ class PoolMKLDNNOpKernel : public paddle::framework::OpKernel<T> { ...@@ -130,20 +130,25 @@ class PoolMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
CorrectOutputSize(src_tz, dst_tz, ksize, paddings, strides, CorrectOutputSize(src_tz, dst_tz, ksize, paddings, strides,
padding_right_bottom); padding_right_bottom);
} }
auto src_md = platform::MKLDNNMemDesc(
src_tz, platform::MKLDNNGetDataType<T>(), input_format); mkldnn::memory::data_type dt =
paddle::framework::ToMKLDNNDataType(input->type());
auto src_md = platform::MKLDNNMemDesc(src_tz, dt, input_format);
/* create memory descriptor for pooling without specified format /* create memory descriptor for pooling without specified format
* ('any') which lets a primitive (pooling in this case) choose * ('any') which lets a primitive (pooling in this case) choose
* the memory format preferred for best performance * the memory format preferred for best performance
*/ */
auto dst_md = platform::MKLDNNMemDesc(dst_tz, mkldnn::memory::f32, auto dst_md =
mkldnn::memory::format::any); platform::MKLDNNMemDesc(dst_tz, dt, mkldnn::memory::format::any);
auto propagation = src_md.data.data_type == mkldnn_f32
? mkldnn::prop_kind::forward_training
: mkldnn::prop_kind::forward_scoring;
std::shared_ptr<mkldnn::pooling_forward::primitive_desc> pool_pd = std::shared_ptr<mkldnn::pooling_forward::primitive_desc> pool_pd =
CreatePrimitiveDesc(src_md, dst_md, strides, padding_left_top, CreatePrimitiveDesc(src_md, dst_md, propagation, strides,
padding_right_bottom, ksize, pooling_type, padding_left_top, padding_right_bottom, ksize,
mkldnn_engine, ceil_mode, is_test); pooling_type, mkldnn_engine, ceil_mode, is_test);
// save pool_pd into global device context to be referred in backward path // save pool_pd into global device context to be referred in backward path
if (!is_test) dev_ctx.SetBlob(key_pool_pd, pool_pd); if (!is_test) dev_ctx.SetBlob(key_pool_pd, pool_pd);
...@@ -203,7 +208,8 @@ class PoolMKLDNNOpKernel : public paddle::framework::OpKernel<T> { ...@@ -203,7 +208,8 @@ class PoolMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
private: private:
std::unique_ptr<mkldnn::pooling_forward::primitive_desc> CreatePrimitiveDesc( std::unique_ptr<mkldnn::pooling_forward::primitive_desc> CreatePrimitiveDesc(
const mkldnn::memory::desc& src, const mkldnn::memory::desc& dst, const mkldnn::memory::desc& src, const mkldnn::memory::desc& dst,
const std::vector<int>& stride, const std::vector<int>& padding_left_top, const mkldnn::prop_kind& propagation, const std::vector<int>& stride,
const std::vector<int>& padding_left_top,
const std::vector<int>& padding_right_bot, const std::vector<int>& kernel, const std::vector<int>& padding_right_bot, const std::vector<int>& kernel,
const std::string& pooling_type, const mkldnn::engine& engine, const std::string& pooling_type, const mkldnn::engine& engine,
bool ceil_mode, bool is_test) const { bool ceil_mode, bool is_test) const {
...@@ -411,6 +417,9 @@ class PoolMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> { ...@@ -411,6 +417,9 @@ class PoolMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
namespace ops = paddle::operators; namespace ops = paddle::operators;
REGISTER_OP_KERNEL(pool2d, MKLDNN, ::paddle::platform::CPUPlace, REGISTER_OP_KERNEL(pool2d, MKLDNN, ::paddle::platform::CPUPlace,
ops::PoolMKLDNNOpKernel<float>); ops::PoolMKLDNNOpKernel<float>,
ops::PoolMKLDNNOpKernel<int8_t>,
ops::PoolMKLDNNOpKernel<uint8_t>);
REGISTER_OP_KERNEL(pool2d_grad, MKLDNN, ::paddle::platform::CPUPlace, REGISTER_OP_KERNEL(pool2d_grad, MKLDNN, ::paddle::platform::CPUPlace,
ops::PoolMKLDNNGradOpKernel<float>); ops::PoolMKLDNNGradOpKernel<float>);
# 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
from __future__ import division
import unittest
import numpy as np
import paddle.fluid.core as core
from op_test import OpTest
from test_pool2d_op import TestPool2D_Op, avg_pool2D_forward_naive, max_pool2D_forward_naive
class TestPool2dMKLDNNInt8_Op(TestPool2D_Op):
def init_kernel_type(self):
self.use_mkldnn = True
def init_data_type(self):
self.dtype = np.int8
def setUp(self):
TestPool2D_Op.setUp(self)
assert self.dtype in [np.int8, np.uint8
], 'Dtype should be int8 or uint8'
def test_check_output(self):
self.check_output_with_place(core.CPUPlace(), atol=1e-5)
def test_check_grad(self):
pass
class TestCase1Avg(TestPool2dMKLDNNInt8_Op):
def init_test_case(self):
self.shape = [2, 3, 7, 7]
self.ksize = [3, 3]
self.strides = [1, 1]
self.paddings = [0, 0]
def init_global_pool(self):
self.global_pool = False
class TestCase2Avg(TestPool2dMKLDNNInt8_Op):
def init_test_case(self):
self.shape = [2, 3, 7, 7]
self.ksize = [3, 3]
self.strides = [1, 1]
self.paddings = [1, 1]
def init_global_pool(self):
self.global_pool = False
class TestCase0Max(TestPool2dMKLDNNInt8_Op):
def init_pool_type(self):
self.pool_type = "max"
self.pool2D_forward_naive = max_pool2D_forward_naive
class TestCase1Max(TestCase1Avg):
def init_pool_type(self):
self.pool_type = "max"
self.pool2D_forward_naive = max_pool2D_forward_naive
class TestCase2Max(TestCase2Avg):
def init_pool_type(self):
self.pool_type = "max"
self.pool2D_forward_naive = max_pool2D_forward_naive
def create_test_s8_u8_class(parent):
class TestS8Case(parent):
def init_data_type(self):
self.dtype = np.int8
class TestU8Case(parent):
def init_data_type(self):
self.dtype = np.uint8
cls_name_s8 = "{0}_{1}".format(parent.__name__, "mkldnn_s8")
cls_name_u8 = "{0}_{1}".format(parent.__name__, "mkldnn_u8")
TestS8Case.__name__ = cls_name_s8
TestU8Case.__name__ = cls_name_u8
globals()[cls_name_s8] = TestS8Case
globals()[cls_name_u8] = TestU8Case
create_test_s8_u8_class(TestPool2dMKLDNNInt8_Op)
create_test_s8_u8_class(TestCase1Avg)
create_test_s8_u8_class(TestCase2Avg)
create_test_s8_u8_class(TestCase0Max)
create_test_s8_u8_class(TestCase1Max)
create_test_s8_u8_class(TestCase2Max)
if __name__ == '__main__':
unittest.main()
...@@ -18,35 +18,22 @@ import unittest ...@@ -18,35 +18,22 @@ import unittest
from test_pool2d_op import TestPool2D_Op, TestCase1, TestCase2, TestCase3, TestCase4, TestCase5 from test_pool2d_op import TestPool2D_Op, TestCase1, TestCase2, TestCase3, TestCase4, TestCase5
class TestMKLDNNCase1(TestPool2D_Op): def create_test_mkldnn_class(parent):
def init_kernel_type(self): class TestMKLDNNCase(parent):
self.use_mkldnn = True def init_kernel_type(self):
self.use_mkldnn = True
class TestMKLDNNCase2(TestCase1): cls_name = "{0}_{1}".format(parent.__name__, "MKLDNNOp")
def init_kernel_type(self): TestMKLDNNCase.__name__ = cls_name
self.use_mkldnn = True globals()[cls_name] = TestMKLDNNCase
class TestMKLDNNCase3(TestCase2): create_test_mkldnn_class(TestPool2D_Op)
def init_kernel_type(self): create_test_mkldnn_class(TestCase1)
self.use_mkldnn = True create_test_mkldnn_class(TestCase2)
create_test_mkldnn_class(TestCase3)
create_test_mkldnn_class(TestCase4)
class TestMKLDNNCase4(TestCase3): create_test_mkldnn_class(TestCase5)
def init_kernel_type(self):
self.use_mkldnn = True
class TestMKLDNNCase5(TestCase4):
def init_kernel_type(self):
self.use_mkldnn = True
class TestMKLDNNCase6(TestCase5):
def init_kernel_type(self):
self.use_mkldnn = True
if __name__ == '__main__': if __name__ == '__main__':
unittest.main() unittest.main()
...@@ -115,7 +115,7 @@ class TestPool2D_Op(OpTest): ...@@ -115,7 +115,7 @@ class TestPool2D_Op(OpTest):
self.op_type = "pool2d" self.op_type = "pool2d"
self.use_cudnn = False self.use_cudnn = False
self.use_mkldnn = False self.use_mkldnn = False
self.dtype = np.float32 self.init_data_type()
self.init_test_case() self.init_test_case()
self.init_global_pool() self.init_global_pool()
self.init_kernel_type() self.init_kernel_type()
...@@ -177,6 +177,9 @@ class TestPool2D_Op(OpTest): ...@@ -177,6 +177,9 @@ class TestPool2D_Op(OpTest):
def init_kernel_type(self): def init_kernel_type(self):
pass pass
def init_data_type(self):
self.dtype = np.float32
def init_pool_type(self): def init_pool_type(self):
self.pool_type = "avg" self.pool_type = "avg"
self.pool2D_forward_naive = avg_pool2D_forward_naive self.pool2D_forward_naive = avg_pool2D_forward_naive
......
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