wehub-resource-sync
572 lines
18 KiB
Python
572 lines
18 KiB
Python
# Copyright (c) 2018 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_uint16_to_float,
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get_device,
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get_device_place,
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is_custom_device,
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paddle_static_guard,
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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.tensor import random
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class TestGaussianRandomOp(OpTest):
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def setUp(self):
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self.op_type = "gaussian_random"
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self.python_api = paddle.tensor.random.gaussian
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self.set_attrs()
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self.inputs = {}
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self.use_onednn = False
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self.attrs = {
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"shape": [123, 92],
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"mean": self.mean,
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"std": self.std,
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"seed": 10,
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"use_onednn": self.use_onednn,
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}
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paddle.seed(10)
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self.outputs = {'Out': np.zeros((123, 92), dtype='float32')}
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def set_attrs(self):
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self.mean = 1.0
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self.std = 2.0
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def test_check_output(self):
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self.check_output_customized(self.verify_output, check_pir=True)
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def verify_output(self, outs):
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self.assertEqual(outs[0].shape, (123, 92))
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hist, _ = np.histogram(outs[0], range=(-3, 5))
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hist = hist.astype("float32")
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hist /= float(outs[0].size)
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data = np.random.normal(size=(123, 92), loc=1, scale=2)
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hist2, _ = np.histogram(data, range=(-3, 5))
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hist2 = hist2.astype("float32")
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hist2 /= float(outs[0].size)
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np.testing.assert_allclose(hist, hist2, rtol=0, atol=0.01)
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device()),
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"core is not compiled with CUDA",
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)
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class TestGaussianRandomFP16Op(OpTest):
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def setUp(self):
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self.op_type = "gaussian_random"
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self.python_api = paddle.tensor.random.gaussian
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self.set_attrs()
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self.inputs = {}
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self.use_onednn = False
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self.attrs = {
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"shape": [123, 92],
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"mean": self.mean,
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"std": self.std,
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"seed": 10,
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"dtype": paddle.float16,
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"use_onednn": self.use_onednn,
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}
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paddle.seed(10)
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self.outputs = {'Out': np.zeros((123, 92), dtype='float16')}
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def set_attrs(self):
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self.mean = 1.0
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self.std = 2.0
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def test_check_output(self):
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self.check_output_customized(self.verify_output, check_pir=True)
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def verify_output(self, outs):
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self.assertEqual(outs[0].shape, (123, 92))
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hist, _ = np.histogram(outs[0], range=(-3, 5))
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hist = hist.astype("float16")
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hist /= float(outs[0].size)
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data = np.random.normal(size=(123, 92), loc=1, scale=2)
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hist2, _ = np.histogram(data, range=(-3, 5))
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hist2 = hist2.astype("float16")
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hist2 /= float(outs[0].size)
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np.testing.assert_allclose(hist, hist2, rtol=0, atol=0.015)
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def gaussian_wrapper(dtype_=np.uint16):
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def gauss_wrapper(shape, mean, std, seed, dtype=np.uint16, name=None):
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return paddle.tensor.random.gaussian(
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shape, mean, std, seed, dtype, name
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)
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return gauss_wrapper
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device()),
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"core is not compiled with CUDA",
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)
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class TestGaussianRandomBF16Op(OpTest):
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def setUp(self):
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self.op_type = "gaussian_random"
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self.python_api = gaussian_wrapper(dtype_=np.uint16)
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self.__class__.op_type = self.op_type
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self.set_attrs()
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self.inputs = {}
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self.use_onednn = False
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self.attrs = {
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"shape": [123, 92],
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"mean": self.mean,
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"std": self.std,
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"seed": 10,
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"dtype": paddle.bfloat16,
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"use_onednn": self.use_onednn,
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}
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paddle.seed(10)
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self.outputs = {'Out': np.zeros((123, 92), dtype='float32')}
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def set_attrs(self):
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self.mean = 1.0
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self.std = 2.0
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def test_check_output(self):
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self.check_output_customized(self.verify_output, check_pir=True)
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def verify_output(self, outs):
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outs = convert_uint16_to_float(outs)
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self.assertEqual(outs[0].shape, (123, 92))
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hist, _ = np.histogram(outs[0], range=(-3, 5))
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hist = hist.astype("float32")
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hist /= float(outs[0].size)
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data = np.random.normal(size=(123, 92), loc=1, scale=2)
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hist2, _ = np.histogram(data, range=(-3, 5))
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hist2 = hist2.astype("float32")
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hist2 /= float(outs[0].size)
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np.testing.assert_allclose(hist, hist2, rtol=0, atol=0.05)
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class TestMeanStdAreInt(TestGaussianRandomOp):
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def set_attrs(self):
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self.mean = 1
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self.std = 2
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# Situation 2: Attr(shape) is a list(with tensor)
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class TestGaussianRandomOp_ShapeTensorList(TestGaussianRandomOp):
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def setUp(self):
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'''Test gaussian_random op with specified value'''
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self.op_type = "gaussian_random"
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self.python_api = paddle.tensor.random.gaussian
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self.init_data()
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shape_tensor_list = []
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for index, ele in enumerate(self.shape):
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shape_tensor_list.append(
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("x" + str(index), np.ones(1).astype('int32') * ele)
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)
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self.attrs = {
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'shape': self.infer_shape,
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'mean': self.mean,
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'std': self.std,
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'seed': self.seed,
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'use_onednn': self.use_onednn,
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}
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self.inputs = {"ShapeTensorList": shape_tensor_list}
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self.outputs = {'Out': np.zeros((123, 92), dtype='float32')}
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def init_data(self):
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self.shape = [123, 92]
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self.infer_shape = [-1, 92]
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self.use_onednn = False
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self.mean = 1.0
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self.std = 2.0
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self.seed = 10
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def test_check_output(self):
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self.check_output_customized(self.verify_output, check_pir=True)
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class TestGaussianRandomOp2_ShapeTensorList(
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TestGaussianRandomOp_ShapeTensorList
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):
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def init_data(self):
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self.shape = [123, 92]
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self.infer_shape = [-1, -1]
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self.use_onednn = False
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self.mean = 1.0
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self.std = 2.0
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self.seed = 10
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class TestGaussianRandomOp3_ShapeTensorList(
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TestGaussianRandomOp_ShapeTensorList
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):
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def init_data(self):
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self.shape = [123, 92]
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self.infer_shape = [123, -1]
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self.use_onednn = True
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self.mean = 1.0
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self.std = 2.0
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self.seed = 10
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class TestGaussianRandomOp4_ShapeTensorList(
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TestGaussianRandomOp_ShapeTensorList
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):
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def init_data(self):
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self.shape = [123, 92]
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self.infer_shape = [123, -1]
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self.use_onednn = False
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self.mean = 1.0
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self.std = 2.0
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self.seed = 10
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# Situation 3: shape is a tensor
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class TestGaussianRandomOp1_ShapeTensor(TestGaussianRandomOp):
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def setUp(self):
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'''Test gaussian_random op with specified value'''
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self.op_type = "gaussian_random"
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self.init_data()
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self.use_onednn = False
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self.python_api = paddle.tensor.random.gaussian
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self.inputs = {"ShapeTensor": np.array(self.shape).astype("int32")}
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self.attrs = {
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'mean': self.mean,
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'std': self.std,
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'seed': self.seed,
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'use_onednn': self.use_onednn,
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}
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self.outputs = {'Out': np.zeros((123, 92), dtype='float32')}
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def init_data(self):
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self.shape = [123, 92]
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self.use_onednn = False
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self.mean = 1.0
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self.std = 2.0
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self.seed = 10
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# Test python API
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class TestGaussianRandomAPI(unittest.TestCase):
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def test_api(self):
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with paddle_static_guard():
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positive_2_int32 = paddle.tensor.fill_constant([1], "int32", 2000)
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positive_2_int64 = paddle.tensor.fill_constant([1], "int64", 500)
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shape_tensor_int32 = paddle.static.data(
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name="shape_tensor_int32", shape=[2], dtype="int32"
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)
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shape_tensor_int64 = paddle.static.data(
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name="shape_tensor_int64", shape=[2], dtype="int64"
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)
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out_1 = random.gaussian(
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shape=[2000, 500], dtype="float32", mean=0.0, std=1.0, seed=10
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)
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out_2 = random.gaussian(
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shape=[2000, positive_2_int32],
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dtype="float32",
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mean=0.0,
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std=1.0,
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seed=10,
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)
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out_3 = random.gaussian(
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shape=[2000, positive_2_int64],
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dtype="float32",
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mean=0.0,
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std=1.0,
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seed=10,
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)
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out_4 = random.gaussian(
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shape=shape_tensor_int32,
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dtype="float32",
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mean=0.0,
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std=1.0,
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seed=10,
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)
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out_5 = random.gaussian(
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shape=shape_tensor_int64,
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dtype="float32",
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mean=0.0,
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std=1.0,
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seed=10,
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)
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out_6 = random.gaussian(
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shape=shape_tensor_int64,
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dtype=np.float32,
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mean=0.0,
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std=1.0,
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seed=10,
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)
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exe = base.Executor(place=base.CPUPlace())
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res_1, res_2, res_3, res_4, res_5, res_6 = exe.run(
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base.default_main_program(),
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feed={
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"shape_tensor_int32": np.array([2000, 500]).astype("int32"),
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"shape_tensor_int64": np.array([2000, 500]).astype("int64"),
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},
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fetch_list=[out_1, out_2, out_3, out_4, out_5, out_6],
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)
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self.assertAlmostEqual(np.mean(res_1), 0.0, delta=0.1)
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self.assertAlmostEqual(np.std(res_1), 1.0, delta=0.1)
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self.assertAlmostEqual(np.mean(res_2), 0.0, delta=0.1)
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self.assertAlmostEqual(np.std(res_2), 1.0, delta=0.1)
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self.assertAlmostEqual(np.mean(res_3), 0.0, delta=0.1)
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self.assertAlmostEqual(np.std(res_3), 1.0, delta=0.1)
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self.assertAlmostEqual(np.mean(res_4), 0.0, delta=0.1)
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self.assertAlmostEqual(np.std(res_5), 1.0, delta=0.1)
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self.assertAlmostEqual(np.mean(res_5), 0.0, delta=0.1)
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self.assertAlmostEqual(np.std(res_5), 1.0, delta=0.1)
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self.assertAlmostEqual(np.mean(res_6), 0.0, delta=0.1)
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self.assertAlmostEqual(np.std(res_6), 1.0, delta=0.1)
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def test_default_dtype(self):
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def test_default_fp16():
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paddle.framework.set_default_dtype('float16')
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out = paddle.tensor.random.gaussian([2, 3])
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self.assertEqual(out.dtype, paddle.float16)
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def test_default_fp32():
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paddle.framework.set_default_dtype('float32')
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out = paddle.tensor.random.gaussian([2, 3])
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self.assertEqual(out.dtype, paddle.float32)
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def test_default_fp64():
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paddle.framework.set_default_dtype('float64')
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out = paddle.tensor.random.gaussian([2, 3])
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self.assertEqual(out.dtype, paddle.float64)
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if paddle.is_compiled_with_cuda() or is_custom_device():
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paddle.set_device(get_device())
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test_default_fp16()
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test_default_fp64()
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test_default_fp32()
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class TestStandardNormalDtype(unittest.TestCase):
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def test_default_dtype(self):
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def test_default_fp16():
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paddle.framework.set_default_dtype('float16')
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out = paddle.tensor.random.standard_normal([2, 3])
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self.assertEqual(out.dtype, paddle.float16)
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def test_default_fp32():
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paddle.framework.set_default_dtype('float32')
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out = paddle.tensor.random.standard_normal([2, 3])
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self.assertEqual(out.dtype, paddle.float32)
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def test_default_fp64():
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paddle.framework.set_default_dtype('float64')
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out = paddle.tensor.random.standard_normal([2, 3])
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self.assertEqual(out.dtype, paddle.float64)
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if paddle.is_compiled_with_cuda() or is_custom_device():
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paddle.set_device(get_device())
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test_default_fp16()
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test_default_fp64()
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test_default_fp32()
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def test_complex_dtype(self):
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def test_complex64():
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out = paddle.tensor.random.standard_normal(
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[2, 3], dtype='complex64'
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)
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self.assertEqual(out.dtype, paddle.complex64)
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def test_complex128():
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out = paddle.tensor.random.standard_normal(
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[2, 3], dtype='complex128'
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)
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self.assertEqual(out.dtype, paddle.complex128)
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test_complex64()
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test_complex128()
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class TestComplexRandnAPI(unittest.TestCase):
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def test_dygraph(self):
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place = (
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get_device_place()
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if core.is_compiled_with_cuda()
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else paddle.CPUPlace()
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)
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with base.dygraph.guard(place):
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for dtype in ['complex64', 'complex128']:
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out = paddle.randn([5000, 2], dtype=dtype)
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mean = out.numpy().mean()
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np.testing.assert_allclose(
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0.0 + 0.0j, mean, rtol=0.02, atol=0.02
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)
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var = out.numpy().var()
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var_real = out.numpy().real.var()
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var_imag = out.numpy().imag.var()
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np.testing.assert_allclose(var, 1.0, rtol=0.02, atol=0.02)
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np.testing.assert_allclose(var_real, 0.5, rtol=0.02, atol=0.02)
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np.testing.assert_allclose(var_imag, 0.5, rtol=0.02, atol=0.02)
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def test_static(self):
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place = (
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get_device_place()
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if core.is_compiled_with_cuda()
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else paddle.CPUPlace()
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)
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with paddle_static_guard():
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for dtype in ['complex64', 'complex128']:
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main_program = paddle.static.Program()
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with paddle.static.program_guard(main_program):
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out = paddle.randn([5000, 2], dtype=dtype)
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exe = paddle.static.Executor(place)
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ret = exe.run(fetch_list=[out])
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mean = ret[0].mean()
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np.testing.assert_allclose(
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0.0 + 0.0j, mean, rtol=0.02, atol=0.02
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)
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var = ret[0].var()
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var_real = ret[0].real.var()
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var_imag = ret[0].imag.var()
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np.testing.assert_allclose(var, 1.0, rtol=0.02, atol=0.02)
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np.testing.assert_allclose(var_real, 0.5, rtol=0.02, atol=0.02)
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np.testing.assert_allclose(var_imag, 0.5, rtol=0.02, atol=0.02)
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class TestRandomValue(unittest.TestCase):
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def test_fixed_random_number(self):
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# Test GPU Fixed random number, which is generated by 'curandStatePhilox4_32_10_t'
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if not paddle.is_compiled_with_cuda():
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return
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# Different GPU generatte different random value. Only test V100 here.
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if "V100" not in paddle.device.cuda.get_device_name():
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return
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def _check_random_value(shape, dtype, expect, expect_mean, expect_std):
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x = paddle.randn(shape, dtype=dtype)
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actual = x.numpy()
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np.testing.assert_allclose(
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actual[2, 1, 512, 1000:1010], expect, rtol=1e-05
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)
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self.assertTrue(np.mean(actual), expect_mean)
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self.assertTrue(np.std(actual), expect_std)
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print("Test Fixed Random number on V100 GPU------>")
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paddle.disable_static()
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paddle.set_device(get_device())
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paddle.seed(2021)
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expect = [
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-0.79037829,
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-0.54411126,
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-0.32266671,
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0.35791815,
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1.44169267,
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-0.87785644,
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-1.23909874,
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-2.18194139,
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0.49489656,
|
|
0.40703062,
|
|
]
|
|
expect_mean = (
|
|
-0.0000053026194133403266873214888799115129813799285329878330230713
|
|
)
|
|
expect_std = 0.99999191058126390974081232343451119959354400634765625
|
|
_check_random_value(
|
|
[32, 3, 1024, 1024], paddle.float64, expect, expect_mean, expect_std
|
|
)
|
|
|
|
expect = [
|
|
-0.7988942,
|
|
1.8644791,
|
|
0.02782744,
|
|
1.3692524,
|
|
0.6419724,
|
|
0.12436751,
|
|
0.12058455,
|
|
-1.9984808,
|
|
1.5635862,
|
|
0.18506318,
|
|
]
|
|
expect_mean = -0.00004762359094456769526004791259765625
|
|
expect_std = 0.999975681304931640625
|
|
_check_random_value(
|
|
[32, 3, 1024, 1024], paddle.float32, expect, expect_mean, expect_std
|
|
)
|
|
|
|
# test randn in large shape
|
|
expect = [
|
|
-1.4770278,
|
|
-0.637431,
|
|
-0.41728288,
|
|
0.31339037,
|
|
-1.7627009,
|
|
0.4061812,
|
|
1.0679497,
|
|
0.03405872,
|
|
-0.7271235,
|
|
-0.42642546,
|
|
]
|
|
|
|
expect_mean = 0.0000010386128224126878194510936737060547
|
|
expect_std = 1.00000822544097900390625
|
|
_check_random_value(
|
|
[4, 2, 60000, 12000],
|
|
paddle.float32,
|
|
expect,
|
|
expect_mean,
|
|
expect_std,
|
|
)
|
|
|
|
# test randn with seed 0 in large shape
|
|
paddle.seed(0)
|
|
expect = [
|
|
-1.7653463,
|
|
0.5957617,
|
|
0.45865676,
|
|
-0.3061651,
|
|
0.17204928,
|
|
-1.7802757,
|
|
-0.10731091,
|
|
1.042362,
|
|
0.70476884,
|
|
0.2720365,
|
|
]
|
|
expect_mean = -0.0000002320642948916429304517805576324463
|
|
expect_std = 1.00001156330108642578125
|
|
_check_random_value(
|
|
[4, 2, 60000, 12000],
|
|
paddle.float32,
|
|
expect,
|
|
expect_mean,
|
|
expect_std,
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|