Add conv2d bfloat16 support (#27325)
joanna.wozna.intel
4 years ago
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GitHub
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# Copyright (c) 2020 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 struct
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import paddle.fluid.core as core
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from paddle.fluid.tests.unittests.op_test import OpTest, skip_check_grad_ci, convert_float_to_uint16
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from paddle.fluid.tests.unittests.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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return out
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def conv2d_residual_naive(out, residual):
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assert out.shape == residual.shape
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out = np.add(out, residual)
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return out
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class TestConv2dBf16Op(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 = True
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self.weight_type = np.float32
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self.input_type = np.float32
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self.use_mkldnn = True
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self.mkldnn_data_type = "bfloat16"
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self.force_fp32_output = False
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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_fuse_relu()
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self.init_fuse_residual()
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self.init_data_type()
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self.init_force_fp32_output()
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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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self.input = np.random.random(self.input_size).astype(np.float32)
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self.filter = np.random.random(self.filter_size).astype(np.float32)
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conv_out, _, _, _, _ = conv2d_forward_naive(self.input, self.filter,
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self.groups, conv2d_param)
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self.conv_output_float = conv_out
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if self.fuse_residual:
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self.input_residual = np.random.random(
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self.input_residual_size).astype(np.float32)
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self.conv_output_float = conv2d_residual_naive(
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self.conv_output_float, self.input_residual)
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self.conv_output = convert_float_to_uint16(self.conv_output_float)
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self.outputs = {'Output': self.conv_output}
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elif self.force_fp32_output:
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self.outputs = {'Output': self.conv_output_float.astype(np.float32)}
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if self.input_type is not np.float32:
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self.input = convert_float_to_uint16(self.input)
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self.inputs = {
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'Input': self.input.view(self.input_type),
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'Filter': OpTest.np_dtype_to_fluid_dtype(
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self.filter.astype(self.weight_type))
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}
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if self.fuse_residual:
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self.inputs['ResidualData'] = OpTest.np_dtype_to_fluid_dtype(
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convert_float_to_uint16(self.input_residual))
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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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'mkldnn_data_type': self.mkldnn_data_type,
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'force_fp32_output': self.force_fp32_output,
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'fuse_residual_connection': self.fuse_residual
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}
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def test_check_output(self):
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self.check_output_with_place(core.CPUPlace())
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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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self.input_size = [1, 1, 5, 5] # NCHW
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f_c = self.input_size[1] // self.groups
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self.input_residual_size = [1, 2, 3, 3]
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self.filter_size = [2, f_c, 3, 3]
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def init_data_type(self):
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self.weight_type = np.float32
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self.input_type = np.float32
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def init_force_fp32_output(self):
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self.force_fp32_output = False
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def init_fuse_relu(self):
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self.fuse_activation = "relu"
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def init_fuse_residual(self):
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self.fuse_residual = True
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class TestConv2d(TestConv2dBf16Op):
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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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self.input_residual_size = [2, 6, 3, 3]
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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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def init_data_type(self):
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self.input_type = np.uint16
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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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self.input_residual_size = [2, 6, 5, 5]
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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(TestConv2dBf16Op):
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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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self.input_residual_size = [2, 6, 3, 3]
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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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def init_data_type(self):
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self.input_type = np.uint16
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class TestWith1x1ForceFP32Output(TestConv2dBf16Op):
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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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def init_force_fp32_output(self):
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self.force_fp32_output = True
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def init_fuse_residual(self):
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self.fuse_residual = False
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class TestWithInput1x1Filter1x1(TestConv2dBf16Op):
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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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self.input_residual_size = [2, 6, 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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def init_group(self):
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self.groups = 3
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if __name__ == '__main__':
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unittest.main()
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