提交 8f051b36 编写于 作者: X xiaoli.liu@intel.com

Enable INT8 pool OP

test=develop
上级 aba1f9b0
......@@ -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
limitations under the License. */
#include "paddle/fluid/framework/data_layout_transform.h"
#include "paddle/fluid/operators/pool_op.h"
#include "paddle/fluid/platform/mkldnn_helper.h"
......@@ -71,7 +72,6 @@ class PoolMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
void Compute(const paddle::framework::ExecutionContext& ctx) const override {
PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()),
"It must use CPUPlace.");
auto& dev_ctx =
ctx.template device_context<platform::MKLDNNDeviceContext>();
const auto& mkldnn_engine = dev_ctx.GetEngine();
......@@ -130,20 +130,25 @@ class PoolMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
CorrectOutputSize(src_tz, dst_tz, ksize, paddings, strides,
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
* ('any') which lets a primitive (pooling in this case) choose
* the memory format preferred for best performance
*/
auto dst_md = platform::MKLDNNMemDesc(dst_tz, mkldnn::memory::f32,
mkldnn::memory::format::any);
auto dst_md =
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 =
CreatePrimitiveDesc(src_md, dst_md, strides, padding_left_top,
padding_right_bottom, ksize, pooling_type,
mkldnn_engine, ceil_mode, is_test);
CreatePrimitiveDesc(src_md, dst_md, propagation, strides,
padding_left_top, padding_right_bottom, ksize,
pooling_type, mkldnn_engine, ceil_mode, is_test);
// save pool_pd into global device context to be referred in backward path
if (!is_test) dev_ctx.SetBlob(key_pool_pd, pool_pd);
......@@ -203,7 +208,8 @@ class PoolMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
private:
std::unique_ptr<mkldnn::pooling_forward::primitive_desc> CreatePrimitiveDesc(
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::string& pooling_type, const mkldnn::engine& engine,
bool ceil_mode, bool is_test) const {
......@@ -411,6 +417,9 @@ class PoolMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
namespace ops = paddle::operators;
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,
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
def adaptive_start_index(index, input_size, output_size):
return int(np.floor(index * input_size / output_size))
def adaptive_end_index(index, input_size, output_size):
return int(np.ceil((index + 1) * input_size / output_size))
def max_pool2D_forward_naive(x,
ksize,
strides,
paddings,
global_pool=0,
ceil_mode=False,
exclusive=True,
adaptive=False):
N, C, H, W = x.shape
if global_pool == 1:
ksize = [H, W]
if adaptive:
H_out, W_out = ksize
else:
H_out = (H - ksize[0] + 2 * paddings[0] + strides[0] - 1
) // strides[0] + 1 if ceil_mode else (
H - ksize[0] + 2 * paddings[0]) // strides[0] + 1
W_out = (W - ksize[1] + 2 * paddings[1] + strides[1] - 1
) // strides[1] + 1 if ceil_mode else (
W - ksize[1] + 2 * paddings[1]) // strides[1] + 1
out = np.zeros((N, C, H_out, W_out))
for i in range(H_out):
for j in range(W_out):
if adaptive:
r_start = adaptive_start_index(i, H, ksize[0])
r_end = adaptive_end_index(i, H, ksize[0])
c_start = adaptive_start_index(j, W, ksize[1])
c_end = adaptive_end_index(j, W, ksize[1])
else:
r_start = np.max((i * strides[0] - paddings[0], 0))
r_end = np.min((i * strides[0] + ksize[0] - paddings[0], H))
c_start = np.max((j * strides[1] - paddings[1], 0))
c_end = np.min((j * strides[1] + ksize[1] - paddings[1], W))
x_masked = x[:, :, r_start:r_end, c_start:c_end]
out[:, :, i, j] = np.max(x_masked, axis=(2, 3))
return out
def avg_pool2D_forward_naive(x,
ksize,
strides,
paddings,
global_pool=0,
ceil_mode=False,
exclusive=True,
adaptive=False):
N, C, H, W = x.shape
if global_pool == 1:
ksize = [H, W]
if adaptive:
H_out, W_out = ksize
else:
H_out = (H - ksize[0] + 2 * paddings[0] + strides[0] - 1
) // strides[0] + 1 if ceil_mode else (
H - ksize[0] + 2 * paddings[0]) // strides[0] + 1
W_out = (W - ksize[1] + 2 * paddings[1] + strides[1] - 1
) // strides[1] + 1 if ceil_mode else (
W - ksize[1] + 2 * paddings[1]) // strides[1] + 1
out = np.zeros((N, C, H_out, W_out))
for i in range(H_out):
for j in range(W_out):
if adaptive:
r_start = adaptive_start_index(i, H, ksize[0])
r_end = adaptive_end_index(i, H, ksize[0])
c_start = adaptive_start_index(j, W, ksize[1])
c_end = adaptive_end_index(j, W, ksize[1])
else:
r_start = np.max((i * strides[0] - paddings[0], 0))
r_end = np.min((i * strides[0] + ksize[0] - paddings[0], H))
c_start = np.max((j * strides[1] - paddings[1], 0))
c_end = np.min((j * strides[1] + ksize[1] - paddings[1], W))
x_masked = x[:, :, r_start:r_end, c_start:c_end]
field_size = ((r_end - r_start) * (c_end - c_start)) \
if (exclusive or adaptive) else (ksize[0] * ksize[1])
out[:, :, i, j] = np.sum(x_masked, axis=(2, 3)) / field_size
return out
class TestPool2D_Op(OpTest):
def setUp(self):
self.op_type = "pool2d"
self.use_cudnn = False
self.use_mkldnn = True
self.dtype = np.int8
self.init_test_case()
self.init_global_pool()
self.init_pool_type()
self.init_ceil_mode()
self.init_exclusive()
self.init_adaptive()
if self.global_pool:
self.paddings = [0 for _ in range(len(self.paddings))]
input = np.random.random(self.shape).astype(self.dtype)
output = self.pool2D_forward_naive(
input, self.ksize, self.strides, self.paddings, self.global_pool,
self.ceil_mode, self.exclusive, self.adaptive).astype(self.dtype)
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(input)}
self.attrs = {
'strides': self.strides,
'paddings': self.paddings,
'ksize': self.ksize,
'pooling_type': self.pool_type,
'global_pooling': self.global_pool,
'use_cudnn': self.use_cudnn,
'use_mkldnn': self.use_mkldnn,
'ceil_mode': self.ceil_mode,
'data_format':
'AnyLayout', # TODO(dzhwinter) : should be fix latter
'exclusive': self.exclusive,
'adaptive': self.adaptive
}
self.outputs = {'Out': output}
def test_check_output(self):
self.check_output()
def init_test_case(self):
self.shape = [2, 3, 5, 5]
self.ksize = [3, 3]
self.strides = [1, 1]
self.paddings = [0, 0]
self.dtype = np.int8
def init_pool_type(self):
self.pool_type = "avg"
self.pool2D_forward_naive = avg_pool2D_forward_naive
def init_global_pool(self):
self.global_pool = True
def init_ceil_mode(self):
self.ceil_mode = False
def init_exclusive(self):
self.exclusive = True
def init_adaptive(self):
self.adaptive = False
class TestCase1(TestPool2D_Op):
def init_test_case(self):
self.shape = [2, 3, 7, 7]
self.ksize = [3, 3]
self.strides = [1, 1]
self.paddings = [0, 0]
self.dtype = np.int8
def init_pool_type(self):
self.pool_type = "avg"
self.pool2D_forward_naive = avg_pool2D_forward_naive
def init_global_pool(self):
self.global_pool = False
class TestCase2(TestPool2D_Op):
def init_test_case(self):
self.shape = [2, 3, 7, 7]
self.ksize = [3, 3]
self.strides = [1, 1]
self.paddings = [1, 1]
self.dtype = np.uint8
def init_pool_type(self):
self.pool_type = "avg"
self.pool2D_forward_naive = avg_pool2D_forward_naive
def init_global_pool(self):
self.global_pool = False
class TestCase3(TestPool2D_Op):
def init_test_case(self):
self.shape = [2, 3, 7, 7]
self.ksize = [3, 3]
self.strides = [1, 1]
self.paddings = [0, 0]
self.dtype = np.int8
def init_pool_type(self):
self.pool_type = "max"
self.pool2D_forward_naive = max_pool2D_forward_naive
class TestCase4(TestCase1):
def init_test_case(self):
self.shape = [2, 3, 7, 7]
self.ksize = [3, 3]
self.strides = [1, 1]
self.paddings = [1, 1]
self.dtype = np.uint8
def init_pool_type(self):
self.pool_type = "max"
self.pool2D_forward_naive = max_pool2D_forward_naive
if __name__ == '__main__':
unittest.main()
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