wehub-resource-sync
132 lines
4.5 KiB
Python
132 lines
4.5 KiB
Python
# Copyright (c) 2024 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 import convert_float_to_uint16
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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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def sample_output_one_dimension(out, dim):
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# count numbers of different categories
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sample_prob = np.zeros(dim).astype("float32")
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sample_index_prob = np.unique(out, return_counts=True)
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sample_prob[sample_index_prob[0]] = sample_index_prob[1]
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sample_prob /= sample_prob.sum()
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return sample_prob
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def sample_output_two_dimension(out, shape):
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num_dist = shape[0]
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out_list = np.split(out, num_dist, axis=0)
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sample_prob = np.zeros(shape).astype("float32")
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for i in range(num_dist):
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sample_index_prob = np.unique(out_list[i], return_counts=True)
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sample_prob[i][sample_index_prob[0]] = sample_index_prob[1]
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sample_prob /= sample_prob.sum(axis=-1, keepdims=True)
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return sample_prob
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class XPUTestMultinomialOp(XPUOpTestWrapper):
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def __init__(self):
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self.op_name = 'multinomial'
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self.use_dynamic_create_class = False
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class TestMultinomialOp(XPUOpTest):
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def setUp(self):
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self.dtype = self.in_type
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self.place = paddle.XPUPlace(0)
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paddle.enable_static()
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self.op_type = "multinomial"
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self.python_api = paddle.multinomial
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self.init_data()
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if self.in_type == np.uint16:
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self.inputs = {"X": convert_float_to_uint16(self.input_np)}
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else:
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self.inputs = {"X": self.input_np.astype(self.dtype)}
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def init_data(self):
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# input probability is a vector, and replacement is True
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self.input_np = np.random.rand(4).astype(np.float32)
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self.outputs = {"Out": np.zeros(100000).astype("int64")}
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self.attrs = {"num_samples": 100000, "replacement": True}
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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 sample_output(self, out):
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return sample_output_one_dimension(out, 4)
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def verify_output(self, outs):
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# normalize the input to get the probability
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prob = self.input_np / self.input_np.sum(axis=-1, keepdims=True)
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sample_prob = self.sample_output(np.array(outs[0]))
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np.testing.assert_allclose(
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sample_prob,
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prob,
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rtol=0,
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atol=0.01,
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err_msg='sample_prob: '
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+ str(sample_prob)
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+ '\nprob: '
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+ str(prob),
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)
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class TestMultinomialOp2(TestMultinomialOp):
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def init_data(self):
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# input probability is a matrix
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self.input_np = np.random.rand(3, 4).astype(np.float32)
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self.outputs = {"Out": np.zeros((3, 100000)).astype("int64")}
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self.attrs = {"num_samples": 100000, "replacement": True}
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def sample_output(self, out):
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return sample_output_two_dimension(out, [3, 4])
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class TestMultinomialOp3(TestMultinomialOp):
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def init_data(self):
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# replacement is False. number of samples must be less than number of categories.
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self.input_np = np.random.rand(1000).astype(np.float32)
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self.outputs = {"Out": np.zeros(100).astype("int64")}
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self.attrs = {"num_samples": 100, "replacement": False}
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def verify_output(self, outs):
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out = np.array(outs[0])
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unique_out = np.unique(out)
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self.assertEqual(
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len(unique_out),
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100,
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"replacement is False. categories can't be sampled repeatedly",
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)
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support_types = get_xpu_op_support_types('multinomial')
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for stype in support_types:
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create_test_class(globals(), XPUTestMultinomialOp, stype)
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if __name__ == "__main__":
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unittest.main()
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