wehub-resource-sync
135 lines
4.2 KiB
Python
135 lines
4.2 KiB
Python
# 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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import unittest
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import numpy as np
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from op_test import (
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OpTest,
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convert_float_to_uint16,
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get_device_place,
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is_custom_device,
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)
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import paddle
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from paddle import base
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from paddle.base import core
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from paddle.base.framework import Program, program_guard
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from paddle.base.layer_helper import LayerHelper
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def transpose_layout(x, src_layout, dst_layout):
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return x.transpose([0, 2, 3, 1])
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# default kNCHW
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class TestTransferLayoutOpkNCHWTokNHWC(OpTest):
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def setUp(self):
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ipt = np.random.random(size=[2, 3, 10, 10])
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self.inputs = {'X': ipt.astype('float32')}
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self.outputs = {'Out': ipt.transpose([0, 2, 3, 1])}
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self.attrs = {'src_layout': 0, 'dst_layout': 1} # kNHWC
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self.python_api = transpose_layout
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self.op_type = 'transfer_layout'
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def test_check_output(self):
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self.check_output()
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def softmax_with_data_format(x, data_format, axis=-1, dtype=None, name=None):
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helper = LayerHelper("softmax", **locals())
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outs_cast = x
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outs_softmax = helper.create_variable_for_type_inference(outs_cast.dtype)
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helper.append_op(
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type='softmax',
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inputs={'X': outs_cast},
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outputs={'Out': outs_softmax},
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attrs={'axis': axis, 'use_cudnn': True, 'data_format': data_format},
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)
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return outs_softmax
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class TestTransferLayoutOpGpu(unittest.TestCase):
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def test_layout_transfer(self):
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with paddle.pir_utils.OldIrGuard():
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if not core.is_compiled_with_cuda():
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return
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paddle.enable_static()
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main_program = Program()
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startup_program = Program()
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n, c, h, w = 2, 3, 4, 5
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with program_guard(main_program, startup_program):
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x = paddle.static.data(
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shape=[n, c, h, w], dtype='float32', name='x'
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)
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y = softmax_with_data_format(x, data_format='NCHW')
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z = softmax_with_data_format(y, data_format='NHWC')
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place = get_device_place()
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exe = base.Executor(place)
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exe.run(startup_program)
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ret = exe.run(
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main_program,
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feed={'x': np.full((n, c, h, w), 1, np.float32)},
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fetch_list=[z.name],
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)
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assert len(ret) == 1
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assert ret[0].shape == (n, h, w, c)
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class TestTransferLayoutFP16Op(OpTest):
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def setUp(self):
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self.op_type = 'transfer_layout'
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self.dtype = np.float16
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x = np.random.random(size=[2, 5, 10, 10])
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self.inputs = {'X': x.astype(self.dtype)}
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self.outputs = {'Out': x.transpose([0, 2, 3, 1])}
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self.attrs = {'src_layout': 0, 'dst_layout': 1}
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self.python_api = transpose_layout
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def test_check_output(self):
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self.check_output()
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device())
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or not core.is_bfloat16_supported(get_device_place()),
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"core is not compiled with CUDA and not support the bfloat16",
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)
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class TestTransferLayoutBP16Op(OpTest):
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def setUp(self):
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self.op_type = 'transfer_layout'
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self.dtype = np.uint16
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x = np.random.random(size=[2, 5, 10, 10])
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self.inputs = {'X': convert_float_to_uint16(x.astype('float32'))}
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self.outputs = {
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'Out': convert_float_to_uint16(
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x.transpose([0, 2, 3, 1]), data_format="NHWC"
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)
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}
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self.attrs = {'src_layout': 0, 'dst_layout': 1}
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self.python_api = transpose_layout
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def test_check_output(self):
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self.check_output()
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if __name__ == '__main__':
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paddle.enable_static()
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unittest.main()
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