wehub-resource-sync
94 lines
2.9 KiB
Python
94 lines
2.9 KiB
Python
# 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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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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np.random.seed(10)
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def stable_softmax(x):
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"""Compute the softmax of vector x in a numerically stable way."""
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# clip to shiftx, otherwise, when calc loss with
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# log(exp(shiftx)), may get log(0)=INF
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shiftx = (x - np.max(x)).clip(-64.0)
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exps = np.exp(shiftx)
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return exps / np.sum(exps)
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def ref_softmax(x, axis=None, dtype=None):
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x_t = x.copy()
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if dtype is not None:
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x_t = x_t.astype(dtype)
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if axis is None:
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axis = -1
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return np.apply_along_axis(stable_softmax, axis, x_t)
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class XPUTestSoftmaxOp(XPUOpTestWrapper):
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def __init__(self):
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self.op_name = 'softmax'
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self.use_dynamic_create_class = True
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def dynamic_create_class(self):
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base_class = self.TestSoftmaxOp
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classes = []
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shapes = [[2, 3, 4, 5], [63, 18], [2, 38512], [3, 4095]]
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axis = [-1, 0, 1]
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for shape in shapes:
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for axi in axis:
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class_name = 'XPUTestSoftmax_' + str(shape) + "_" + str(axi)
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attr_dict = {'shape': shape, 'axis': axi}
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classes.append([class_name, attr_dict])
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return base_class, classes
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class TestSoftmaxOp(XPUOpTest):
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def setUp(self):
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self.op_type = "softmax"
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if not hasattr(self, 'shape'):
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self.shape = [2, 3, 4, 5]
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self.axis = -1
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self.dtype = self.in_type
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x = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
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out = np.apply_along_axis(stable_softmax, self.axis, x)
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self.inputs = {'X': x}
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self.outputs = {'Out': out}
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self.attrs = {'axis': self.axis, 'use_xpu': True}
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def test_check_output(self):
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self.check_output_with_place(paddle.XPUPlace(0), atol=1e-4)
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def test_check_grad(self):
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self.check_grad_with_place(paddle.XPUPlace(0), ['X'], 'Out')
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support_types = get_xpu_op_support_types('softmax')
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for stype in support_types:
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create_test_class(globals(), XPUTestSoftmaxOp, stype)
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if __name__ == "__main__":
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unittest.main()
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