Add mkldnn int8 mul-op kernel (#17834)
Physher
6 years ago
committed by
Tao Luo
parent
ac81c81be1
commit
0caa08ea40
File diff suppressed because it is too large
Load Diff
@ -0,0 +1,166 @@
|
||||
# Copyright (c) 2019 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
|
||||
import paddle.fluid.core as core
|
||||
from paddle.fluid.tests.unittests.op_test import OpTest
|
||||
'''
|
||||
test case for s8 * s8
|
||||
'''
|
||||
|
||||
|
||||
class TestMKLDNNMulOpS8S8(OpTest):
|
||||
def setUp(self):
|
||||
self.op_type = "mul"
|
||||
self.init_kernel_type()
|
||||
self.init_data_type()
|
||||
self.init_data()
|
||||
self.attrs = {
|
||||
"use_mkldnn": self.use_mkldnn,
|
||||
"scale_x": self.scale_x,
|
||||
"scale_y": self.scale_y,
|
||||
"scale_out": self.scale_out,
|
||||
"force_fp32_output": self.force_fp32,
|
||||
}
|
||||
|
||||
def init_kernel_type(self):
|
||||
self.use_mkldnn = True
|
||||
self.force_fp32 = True
|
||||
|
||||
def init_data_type(self):
|
||||
self.srctype = np.uint8
|
||||
self.dsttype = np.float32 if self.force_fp32 else np.int8
|
||||
|
||||
def init_data(self):
|
||||
self.scale_x = 0.6
|
||||
self.scale_y = [0.8]
|
||||
self.scale_out = 1.0
|
||||
|
||||
# limit random range inside |-127, 127| to avoid overflow on SKL
|
||||
if self.srctype == np.int8:
|
||||
A_data = np.random.randint(-127, 127, (2, 5)).astype(np.int8)
|
||||
else:
|
||||
A_data = np.random.randint(0, 127, (2, 5)).astype(np.uint8)
|
||||
|
||||
B_data = np.random.uniform(-127, 127, (5, 3)).astype(np.float32)
|
||||
|
||||
quant_B = np.round(B_data * self.scale_y[0]).astype(np.int)
|
||||
output = np.dot(A_data, quant_B)
|
||||
|
||||
scale_output_shift = (self.scale_out) / \
|
||||
(self.scale_x * self.scale_y[0])
|
||||
|
||||
if (self.force_fp32):
|
||||
output = (output * scale_output_shift).astype(self.dsttype)
|
||||
else:
|
||||
output = np.round(output * scale_output_shift).astype(self.dsttype)
|
||||
|
||||
self.inputs = {'X': A_data, 'Y': B_data}
|
||||
self.outputs = {'Out': output}
|
||||
|
||||
def test_check_output(self):
|
||||
self.check_output_with_place(core.CPUPlace(), atol=0)
|
||||
|
||||
def test_check_grad_normal(self):
|
||||
pass
|
||||
|
||||
def test_check_grad_ingore_x(self):
|
||||
pass
|
||||
|
||||
def test_check_grad_ingore_y(self):
|
||||
pass
|
||||
|
||||
|
||||
'''
|
||||
test case for s8 * u8
|
||||
'''
|
||||
|
||||
|
||||
class TestMKLDNNMulOpS8U8(TestMKLDNNMulOpS8S8):
|
||||
def init_data_type(self):
|
||||
self.srctype = np.uint8
|
||||
self.dsttype = np.float32 if self.force_fp32 else np.int8
|
||||
|
||||
|
||||
'''
|
||||
test case for s8 * s8
|
||||
'''
|
||||
|
||||
|
||||
class TestMKLDNNMulOpS8S8WithFlatten(TestMKLDNNMulOpS8S8):
|
||||
def setUp(self):
|
||||
self.op_type = "mul"
|
||||
self.init_kernel_type()
|
||||
self.init_data_type()
|
||||
self.init_data()
|
||||
self.attrs = {
|
||||
"use_mkldnn": self.use_mkldnn,
|
||||
"scale_x": self.scale_x,
|
||||
"scale_y": self.scale_y,
|
||||
"scale_out": self.scale_out,
|
||||
"force_fp32_output": self.force_fp32,
|
||||
"x_num_col_dims": 2,
|
||||
"y_num_col_dims": 2,
|
||||
}
|
||||
|
||||
def init_data(self):
|
||||
self.scale_x = 0.6
|
||||
self.scale_y = [0.8]
|
||||
self.scale_out = 1.0
|
||||
|
||||
# limit random range inside |-127, 127| to avoid overflow on SKL
|
||||
if self.srctype == np.int8:
|
||||
A_data = np.random.randint(-127, 127, (3, 4, 4, 3)).astype(np.int8)
|
||||
else:
|
||||
A_data = np.random.randint(0, 127, (3, 4, 4, 3)).astype(np.uint8)
|
||||
|
||||
B_data = np.random.uniform(-127, 127,
|
||||
(2, 6, 1, 2, 3)).astype(np.float32)
|
||||
|
||||
A_data_reshape = A_data.reshape(3 * 4, 4 * 3)
|
||||
B_data_reshape = B_data.reshape(2 * 6, 1 * 2 * 3)
|
||||
|
||||
quant_B = np.round(B_data_reshape * self.scale_y[0]).astype(np.int)
|
||||
output = np.dot(A_data_reshape, quant_B)
|
||||
|
||||
scale_output_shift = (self.scale_out) / \
|
||||
(self.scale_x * self.scale_y[0])
|
||||
|
||||
if (self.force_fp32):
|
||||
output = (output * scale_output_shift).astype(self.dsttype)
|
||||
else:
|
||||
output = np.round(output * scale_output_shift).astype(self.dsttype)
|
||||
|
||||
output = output.reshape(3, 4, 1, 2, 3)
|
||||
|
||||
self.inputs = {'X': A_data, 'Y': B_data}
|
||||
self.outputs = {'Out': output}
|
||||
|
||||
|
||||
'''
|
||||
test case for s8 * u8
|
||||
'''
|
||||
|
||||
|
||||
class TestMKLDNNMulOpS8U8WithFlatten(TestMKLDNNMulOpS8S8WithFlatten):
|
||||
def init_data_type(self):
|
||||
self.srctype = np.uint8
|
||||
self.dsttype = np.float32 if self.force_fp32 else np.int8
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
Loading…
Reference in new issue