wehub-resource-sync
108 lines
3.2 KiB
Python
108 lines
3.2 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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import paddle.nn.functional as F
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paddle.enable_static()
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np.random.seed(10)
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def ref_log_softmax(x):
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shiftx = x - np.max(x)
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out = shiftx - np.log(np.exp(shiftx).sum())
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return out
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def ref_log_softmax_grad(x, axis):
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if axis < 0:
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axis += len(x.shape)
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out = np.apply_along_axis(ref_log_softmax, axis, x)
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axis_dim = x.shape[axis]
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dout = np.full_like(x, fill_value=1.0 / x.size)
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dx = dout - np.exp(out) * dout.copy().sum(axis=axis, keepdims=True).repeat(
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axis_dim, axis=axis
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)
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return dx
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class XPUTestLogSoftmaxOp(XPUOpTestWrapper):
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def __init__(self):
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self.op_name = 'log_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.TestXPULogSoftmaxOp
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classes = []
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axis_arr = [-1, 1]
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shape_arr = [[2, 3, 4, 5], [12, 10], [2, 5], [7, 7], [3, 5, 7]]
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for axis in axis_arr:
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for shape in shape_arr:
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class_name = 'XPUTestLogSoftmax_' + str(axis) + "_" + str(shape)
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attr_dict = {'axis': axis, 'shape': shape}
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classes.append([class_name, attr_dict])
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return base_class, classes
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class TestXPULogSoftmaxOp(XPUOpTest):
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def setUp(self):
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self.op_type = 'log_softmax'
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self.python_api = F.log_softmax
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self.dtype = 'float32'
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self.set_attrs()
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self.use_xpu = True
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if not hasattr(self, 'axis'):
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self.shape = [2, 3, 4, 5]
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self.axis = -1
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x = np.random.uniform(0.1, 1.0, self.shape).astype(self.dtype)
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out = np.apply_along_axis(ref_log_softmax, self.axis, x)
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self.x_grad = ref_log_softmax_grad(x, self.axis)
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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}
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def set_attrs(self):
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pass
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def test_check_output(self):
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self.check_output(check_dygraph=True)
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def test_check_grad(self):
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self.check_grad(
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['X'],
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['Out'],
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user_defined_grads=[self.x_grad],
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check_dygraph=True,
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)
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support_types = get_xpu_op_support_types('log_softmax')
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for stype in support_types:
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create_test_class(globals(), XPUTestLogSoftmaxOp, stype)
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if __name__ == "__main__":
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paddle.enable_static()
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unittest.main()
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