wehub-resource-sync
114 lines
3.3 KiB
Python
114 lines
3.3 KiB
Python
# Copyright (c) 2023 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 get_test_cover_info import (
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XPUOpTestWrapper,
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create_test_class,
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get_xpu_op_support_types,
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)
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from op_test_xpu import XPUOpTest
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import paddle
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paddle.enable_static()
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from paddle.base import core
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typeid_dict = {
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'int32': int(core.VarDesc.VarType.INT32),
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'int64': int(core.VarDesc.VarType.INT64),
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'float32': int(core.VarDesc.VarType.FP32),
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'float16': int(core.VarDesc.VarType.FP16),
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'bfloat16': int(core.VarDesc.VarType.BF16),
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'bool': int(core.VarDesc.VarType.BOOL),
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'int8': int(core.VarDesc.VarType.INT8),
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'uint8': int(core.VarDesc.VarType.UINT8),
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'float64': int(core.VarDesc.VarType.FP64),
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'complex64': int(core.VarDesc.VarType.COMPLEX64),
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}
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def output_hist(out):
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if out.dtype == np.uint16:
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out = convert_uint16_to_float(out)
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hist, _ = np.histogram(out, range=(-5, 10))
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hist = hist.astype("float32")
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hist /= float(out.size)
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prob = 0.1 * np.ones(10)
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return hist, prob
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from op_test import convert_uint16_to_float
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class XPUTestUniformRandomOp(XPUOpTestWrapper):
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def __init__(self):
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self.op_name = 'uniform_random'
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self.use_dynamic_create_class = False
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class TestUniformRandomOp(XPUOpTest):
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def init(self):
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self.dtype = self.in_type
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self.place = paddle.XPUPlace(0)
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self.op_type = "uniform_random"
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self.python_api = paddle.uniform
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def setUp(self):
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self.init()
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self.inputs = {}
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self.use_onednn = False
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self.set_attrs()
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paddle.seed(10)
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self.outputs = {"Out": np.zeros((1000, 784), dtype=self.dtype)}
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def set_attrs(self):
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self.attrs = {
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"shape": [1000, 784],
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"min": -5.0,
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"max": 10.0,
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"dtype": typeid_dict[self.in_type_str],
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}
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self.output_hist = output_hist
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def test_check_output(self):
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self.check_output_with_place_customized(
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self.verify_output, self.place
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)
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def verify_output(self, outs):
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hist, prob = self.output_hist(np.array(outs[0]))
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np.testing.assert_allclose(hist, prob, rtol=0, atol=0.01)
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class TestMaxMinAreInt(TestUniformRandomOp):
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def set_attrs(self):
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self.attrs = {
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"shape": [1000, 784],
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"min": -5,
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"max": 10,
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"dtype": typeid_dict[self.in_type_str],
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}
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self.output_hist = output_hist
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support_types = get_xpu_op_support_types('uniform_random')
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for stype in support_types:
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create_test_class(globals(), XPUTestUniformRandomOp, stype)
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if __name__ == "__main__":
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unittest.main()
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