Add Conv Transpose BF16 (#30877)
* Add conv transpose BF16 * Share function GetWeightsTz * Adjust to review and fix op compatibility * Add bias to unique handler name * Remove errors related to paddle enforce * Add conv2d_transpose to bf16 list and kernel refatorrevert-31068-fix_conv3d_windows
joanna.wozna.intel
4 years ago
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# Copyright (c) 2021 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 paddle.fluid.tests.unittests.op_test import OpTest, convert_float_to_uint16
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from paddle.fluid.tests.unittests.test_conv2d_transpose_op import conv2dtranspose_forward_naive
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from paddle import enable_static
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def conv2d_bias_naive(out, bias):
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_, out_c, _, _ = out.shape
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for l in range(out_c):
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out[:, l, :, :] = out[:, l, :, :] + bias[l]
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return out
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@unittest.skipIf(not core.supports_bfloat16(),
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"place does not support BF16 evaluation")
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class TestConv2DTransposeBF16MKLDNNOp(OpTest):
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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_input(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 init_op_type(self):
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self.data_format = "NCHW"
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self.op_type = 'conv2d_transpose'
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self._cpu_only = True
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def init_test_case(self):
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self.pad = [0, 0]
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self.fuse_bias = False
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self.use_mkldnn = True
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self.is_test = True
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self.bias_size = None
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self.fuse_activation = ""
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self.fuse_alpha = 0.0
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self.fuse_beta = 0.0
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self.stride = [1, 1]
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self.dilations = [1, 1]
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self.input_size = [2, 3, 5, 5] # NCHW
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f_c = self.input_size[1]
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self.filter_size = [f_c, 6, 3, 3]
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self.groups = 1
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self.output_size = None
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self.output_padding = []
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self.data_format = "NCHW"
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self.pad = [0, 0]
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self.padding_algorithm = "EXPLICIT"
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self.force_fp32_output = False
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def setUp(self):
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self.input_type = np.uint16
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self.dtype = np.uint16
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self.mkldnn_data_type = "bfloat16"
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self.init_op_type()
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self.init_test_case()
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input = np.random.random(self.input_size).astype(np.float32)
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filter = np.random.random(self.filter_size).astype(np.float32)
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self.attrs = {
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'strides': self.stride,
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'paddings': self.pad,
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'padding_algorithm': self.padding_algorithm,
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'groups': self.groups,
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'dilations': self.dilations,
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'is_test': self.is_test,
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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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'data_format': self.data_format,
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'fuse_activation': self.fuse_activation,
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'fuse_alpha': self.fuse_alpha,
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'fuse_beta': self.fuse_beta
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}
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if self.output_size is not None:
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self.attrs['output_size'] = self.output_size
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if len(self.output_padding) > 0:
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self.attrs['output_padding'] = self.output_padding
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output = conv2dtranspose_forward_naive(input, filter,
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self.attrs).astype(np.float32)
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if self.input_type is not np.float32:
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input = convert_float_to_uint16(input)
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self.inputs = {
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'Input': input.view(self.input_type),
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'Filter': OpTest.np_dtype_to_fluid_dtype(filter)
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}
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if self.fuse_bias and self.bias_size is not None:
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bias = np.random.random(self.bias_size).astype(np.float32)
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output = conv2d_bias_naive(output, bias)
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output = output.astype(np.float32)
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self.attrs['fuse_bias'] = self.fuse_bias
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self.inputs['Bias'] = OpTest.np_dtype_to_fluid_dtype(bias)
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if self.fuse_activation == "relu":
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output = np.maximum(output, 0).astype(np.float32)
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output = output.astype(np.float32)
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if not self.force_fp32_output:
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output = convert_float_to_uint16(output, self.attrs['data_format'])
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self.outputs['Output'] = output
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class TestMKLDNNFuseBias(TestConv2DTransposeBF16MKLDNNOp):
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def init_test_case(self):
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super(TestMKLDNNFuseBias, self).init_test_case()
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self.pad = [1, 1]
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self.fuse_bias = True
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self.bias_size = [6]
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class TestMKLDNNWithPad(TestConv2DTransposeBF16MKLDNNOp):
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def init_test_case(self):
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super(TestMKLDNNWithPad, self).init_test_case()
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self.pad = [1, 1]
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self.input_size = [2, 3, 10, 10]
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class TestMKLDNNWithStride(TestConv2DTransposeBF16MKLDNNOp):
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def init_test_case(self):
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super(TestMKLDNNWithStride, self).init_test_case()
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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] # NCHW
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class TestMKLDNNWithAsymPad(TestConv2DTransposeBF16MKLDNNOp):
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def init_test_case(self):
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super(TestMKLDNNWithAsymPad, self).init_test_case()
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self.pad = [0, 0, 1, 2]
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self.padding_algorithm = "EXPLICIT"
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class TestMKLDNNWithSamePad(TestConv2DTransposeBF16MKLDNNOp):
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def init_test_case(self):
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super(TestMKLDNNWithSamePad, self).init_test_case()
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self.pad = [0, 0]
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self.padding_algorithm = "SAME"
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class TestMKLDNNWithValidPad(TestConv2DTransposeBF16MKLDNNOp):
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def init_test_case(self):
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super(TestMKLDNNWithValidPad, self).init_test_case()
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self.pad = [1, 1]
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self.padding_algorithm = "VALID"
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class TestMKLDNNWithValidPad_NHWC(TestMKLDNNWithValidPad):
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def init_test_case(self):
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super(TestMKLDNNWithValidPad_NHWC, self).init_test_case()
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self.data_format = 'NHWC'
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N, C, H, W = self.input_size
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self.input_size = [N, H, W, C]
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class TestConv2DTransposeMKLDNNWithDilationsExplicitPad(
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TestConv2DTransposeBF16MKLDNNOp):
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def init_test_case(self):
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super(TestConv2DTransposeMKLDNNWithDilationsExplicitPad,
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self).init_test_case()
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self.stride = [2, 1]
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self.dilations = [1, 2]
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self.groups = 1
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self.input_size = [4, 3, 8, 7] # NCHW
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f_c = self.input_size[1]
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self.filter_size = [f_c, 6, 4, 3]
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self.pad = [1, 3, 2, 1]
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self.padding_algorithm = "EXPLICIT"
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if __name__ == '__main__':
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enable_static()
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unittest.main()
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