Enable basic MKL-DNN INT8 Conv OP (#15124)
* Enable basic MKL-DNN INT8 Conv OP test=develop * Modify test case test=develop * Clean unittest code test=develop * Fix test test=develop * Modify test test=develop * Modify basic INT8 Conv test=developrevert-15207-remove_op_handle_lock_and_fix_var
xiaolil1
6 years ago
committed by
Tao Luo
parent
a1e60ab19b
commit
bbc9336878
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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import print_function
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import unittest
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import numpy as np
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import paddle.fluid.core as core
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from op_test import OpTest
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from test_conv2d_op import conv2d_forward_naive, TestConv2dOp
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def conv2d_forward_refer(input, filter, group, conv_param):
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out, in_n, out_h, out_w, out_c = conv2d_forward_naive(input, filter, group,
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conv_param)
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out_tmp = np.zeros((in_n, out_h, out_w, out_c))
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for n in range(in_n):
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for i in range(out_h):
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for j in range(out_w):
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for m in range(out_c):
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out_tmp[n, i, j, m] = out[n, m, i, j]
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return out_tmp.reshape(in_n, out_c, out_h, out_w)
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class TestConv2dInt8Op(TestConv2dOp):
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def setUp(self):
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self.op_type = "conv2d"
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self.use_cudnn = False
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self.exhaustive_search = False
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self.use_cuda = False
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self.use_mkldnn = False
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self.data_format = "AnyLayout"
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self.weighttype = np.float32
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self.use_mkldnn = True
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self.init_group()
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self.init_dilation()
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self.init_test_case()
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self.init_dtype()
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conv2d_param = {
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'stride': self.stride,
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'pad': self.pad,
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'dilation': self.dilations
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}
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filter = np.random.random(self.filter_size).astype(self.weighttype)
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if self.srctype == np.uint8:
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input = np.random.randint(0, 10,
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self.input_size).astype(self.srctype)
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else:
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input = np.random.randint(-5, 5,
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self.input_size).astype(self.srctype)
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input_shift = (np.ones(self.input_size) * 128).astype(np.uint8)
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if self.srctype == np.int8:
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filter_int = np.round(filter * self.scale_weights[0] *
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0.5).astype(np.int32)
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scale_output_shift = self.scale_out / (self.scale_in *
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self.scale_weights[0] * 0.5)
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output1 = conv2d_forward_refer(
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np.round((input.astype(np.int32) + input_shift) *
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self.scale_in).astype(np.int32), filter_int,
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self.groups,
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conv2d_param).astype(np.float32) * scale_output_shift
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output2 = conv2d_forward_refer(
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np.round((input_shift) * self.scale_in).astype(np.int32),
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filter_int, self.groups,
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conv2d_param).astype(np.float32) * scale_output_shift
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output = np.round(output1 - output2).astype(self.dsttype)
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else:
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filter_int = np.round(filter *
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self.scale_weights[0]).astype(np.int32)
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scale_output_shift = self.scale_out / (self.scale_in *
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self.scale_weights[0])
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output1 = conv2d_forward_refer(
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input.astype(np.int32), filter_int, self.groups,
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conv2d_param).astype(np.float32)
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output = np.round(output1 * scale_output_shift).astype(self.dsttype)
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self.inputs = {
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'Input':
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OpTest.np_dtype_to_fluid_dtype(input.astype(self.srctype)),
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'Filter': OpTest.np_dtype_to_fluid_dtype(filter)
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}
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self.attrs = {
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'strides': self.stride,
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'paddings': self.pad,
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'groups': self.groups,
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'dilations': self.dilations,
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'use_cudnn': self.use_cudnn,
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'use_mkldnn': self.use_mkldnn,
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'data_format': self.data_format,
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'exhaustive_search': self.exhaustive_search,
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'Scale_in': self.scale_in,
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'Scale_out': self.scale_out,
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'Scale_weights': self.scale_weights,
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}
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self.outputs = {'Output': output}
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def test_check_output(self):
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self.check_output_with_place(core.CPUPlace(), atol=0)
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def test_check_grad(self):
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pass
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def test_check_grad_no_filter(self):
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pass
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def test_check_grad_no_input(self):
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pass
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def init_test_case(self):
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TestConv2dOp.init_test_case(self)
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f_c = self.input_size[1] // self.groups
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self.filter_size = [1, f_c, 3, 3]
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self.scale_in = 1.0
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self.scale_out = 0.5
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self.scale_weights = [10.0]
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def init_dtype(self):
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self.srctype = np.uint8
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self.dsttype = np.int8
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#--------------------test conv2d u8 in and s8 out--------------------
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class TestConv2d(TestConv2dInt8Op):
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def init_test_case(self):
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self.pad = [0, 0]
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self.stride = [1, 1]
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self.input_size = [2, 3, 5, 5] # NCHW
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assert np.mod(self.input_size[1], self.groups) == 0
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f_c = self.input_size[1] // self.groups
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self.filter_size = [6, f_c, 3, 3]
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self.scale_in = 1.0
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self.scale_out = 0.5
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self.scale_weights = [10.0]
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class TestWithPad(TestConv2d):
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def init_test_case(self):
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TestConv2d.init_test_case(self)
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self.pad = [1, 1]
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class TestWithGroup(TestConv2d):
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def init_group(self):
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self.groups = 3
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class TestWithStride(TestConv2dInt8Op):
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def init_test_case(self):
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self.pad = [1, 1]
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self.stride = [2, 2]
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self.input_size = [2, 3, 6, 6]
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assert np.mod(self.input_size[1], self.groups) == 0
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f_c = self.input_size[1] // self.groups
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self.filter_size = [6, f_c, 3, 3]
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self.scale_in = 1.0
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self.scale_out = 0.8
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self.scale_weights = [10.0]
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class TestWith1x1(TestConv2dInt8Op):
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def init_test_case(self):
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self.pad = [0, 0]
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self.stride = [1, 1]
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self.input_size = [1, 3, 5, 5]
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assert np.mod(self.input_size[1], self.groups) == 0
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f_c = self.input_size[1] // self.groups
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self.filter_size = [6, f_c, 1, 1]
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self.scale_in = 1.0
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self.scale_out = 0.5
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self.scale_weights = [12.0]
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class TestWithInput1x1Filter1x1(TestConv2dInt8Op):
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def init_test_case(self):
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self.pad = [0, 0]
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self.stride = [1, 1]
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self.input_size = [2, 3, 1, 1]
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assert np.mod(self.input_size[1], self.groups) == 0
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f_c = self.input_size[1] // self.groups
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self.filter_size = [6, f_c, 1, 1]
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self.scale_in = 1.0
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self.scale_out = 0.5
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self.scale_weights = [10.0]
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def init_group(self):
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self.groups = 3
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#--------------------test conv2d s8 in and s8 out--------------------
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def create_test_int8_class(parent):
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class TestInt8Case(parent):
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def init_dtype(self):
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self.srctype = np.int8
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self.dsttype = np.int8
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cls_name = "{0}_{1}".format(parent.__name__, "s8s8")
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TestInt8Case.__name__ = cls_name
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globals()[cls_name] = TestInt8Case
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create_test_int8_class(TestConv2dInt8Op)
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create_test_int8_class(TestWithPad)
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create_test_int8_class(TestWithStride)
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create_test_int8_class(TestWithGroup)
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create_test_int8_class(TestWith1x1)
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create_test_int8_class(TestWithInput1x1Filter1x1)
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if __name__ == '__main__':
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unittest.main()
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