# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest import numpy as np from get_test_cover_info import ( XPUOpTestWrapper, create_test_class, get_xpu_op_support_types, ) from op_test_xpu import XPUOpTest import paddle from paddle import base from paddle.base import core paddle.enable_static() from paddle.tensor import random typeid_dict = { 'int32': int(core.VarDesc.VarType.INT32), 'int64': int(core.VarDesc.VarType.INT64), 'float32': int(core.VarDesc.VarType.FP32), 'float16': int(core.VarDesc.VarType.FP16), 'bfloat16': int(core.VarDesc.VarType.BF16), 'bool': int(core.VarDesc.VarType.BOOL), 'int8': int(core.VarDesc.VarType.INT8), 'uint8': int(core.VarDesc.VarType.UINT8), 'float64': int(core.VarDesc.VarType.FP64), } from op_test import convert_uint16_to_float class XPUTestGaussianRandomOp(XPUOpTestWrapper): def __init__(self): self.op_name = 'gaussian_random' self.use_dynamic_create_class = False class TestGaussianRandomOp(XPUOpTest): def init(self): self.dtype = self.in_type self.place = paddle.XPUPlace(0) self.op_type = 'gaussian_random' def setUp(self): self.init() self.python_api = paddle.normal self.set_attrs() self.inputs = {} self.use_onednn = False self.attrs = { "shape": [123, 92], "mean": self.mean, "std": self.std, "seed": 10, "use_onednn": self.use_onednn, "dtype": typeid_dict[self.in_type_str], } paddle.seed(10) self.outputs = {'Out': np.zeros((123, 92), dtype=self.dtype)} def set_attrs(self): self.mean = 1.0 self.std = 2.0 def test_check_output(self): self.check_output_with_place_customized( self.verify_output, self.place ) def verify_output(self, outs): # special for bf16 if self.in_type_str == "bfloat16": outs = convert_uint16_to_float(outs) self.assertEqual(outs[0].shape, (123, 92)) hist, _ = np.histogram(outs[0], range=(-3, 5)) hist = hist.astype("float32") hist /= float(outs[0].size) data = np.random.normal(size=(123, 92), loc=1, scale=2) hist2, _ = np.histogram(data, range=(-3, 5)) hist2 = hist2.astype("float32") hist2 /= float(outs[0].size) np.testing.assert_allclose(hist, hist2, rtol=0, atol=0.01) class TestMeanStdAreInt(TestGaussianRandomOp): def set_attrs(self): self.mean = 1 self.std = 2 # Situation 2: Attr(shape) is a list(with tensor) class TestGaussianRandomOp_ShapeTensorList(TestGaussianRandomOp): def setUp(self): '''Test gaussian_random op with specified value''' self.init() self.init_data() shape_tensor_list = [] for index, ele in enumerate(self.shape): shape_tensor_list.append( ("x" + str(index), np.ones(1).astype('int32') * ele) ) self.attrs = { 'shape': self.infer_shape, 'mean': self.mean, 'std': self.std, 'seed': self.seed, 'use_onednn': self.use_onednn, "dtype": typeid_dict[self.in_type_str], } self.inputs = {"ShapeTensorList": shape_tensor_list} self.outputs = {'Out': np.zeros(self.shape, dtype=self.dtype)} def init_data(self): self.shape = [123, 92] self.infer_shape = [-1, 92] self.use_onednn = False self.mean = 1.0 self.std = 2.0 self.seed = 10 def test_check_output(self): self.check_output_with_place_customized( self.verify_output, self.place ) class TestGaussianRandomOp2_ShapeTensorList( TestGaussianRandomOp_ShapeTensorList ): def init_data(self): self.shape = [123, 92] self.infer_shape = [-1, -1] self.use_onednn = False self.mean = 1.0 self.std = 2.0 self.seed = 10 class TestGaussianRandomOp3_ShapeTensorList( TestGaussianRandomOp_ShapeTensorList ): def init_data(self): self.shape = [123, 92] self.infer_shape = [123, -1] self.use_onednn = True self.mean = 1.0 self.std = 2.0 self.seed = 10 class TestGaussianRandomOp4_ShapeTensorList( TestGaussianRandomOp_ShapeTensorList ): def init_data(self): self.shape = [123, 92] self.infer_shape = [123, -1] self.use_onednn = False self.mean = 1.0 self.std = 2.0 self.seed = 10 # Situation 3: shape is a tensor class TestGaussianRandomOp1_ShapeTensor(TestGaussianRandomOp): def setUp(self): '''Test gaussian_random op with specified value''' self.init() self.init_data() self.use_onednn = False self.inputs = {"ShapeTensor": np.array(self.shape).astype("int32")} self.attrs = { 'mean': self.mean, 'std': self.std, 'seed': self.seed, 'use_onednn': self.use_onednn, "dtype": typeid_dict[self.in_type_str], } self.outputs = {'Out': np.zeros((123, 92), dtype=self.dtype)} def init_data(self): self.shape = [123, 92] self.use_onednn = False self.mean = 1.0 self.std = 2.0 self.seed = 10 # Test python API class TestGaussianRandomAPI(unittest.TestCase): def test_api(self): positive_2_int32 = paddle.tensor.fill_constant([1], "int32", 2000) positive_2_int64 = paddle.tensor.fill_constant([1], "int64", 500) shape_tensor_int32 = paddle.static.data( name="shape_tensor_int32", shape=[2], dtype="int32" ) shape_tensor_int64 = paddle.static.data( name="shape_tensor_int64", shape=[2], dtype="int64" ) out_1 = random.gaussian( shape=[2000, 500], dtype="float32", mean=0.0, std=1.0, seed=10 ) out_2 = random.gaussian( shape=[2000, positive_2_int32], dtype="float32", mean=0.0, std=1.0, seed=10, ) out_3 = random.gaussian( shape=[2000, positive_2_int64], dtype="float32", mean=0.0, std=1.0, seed=10, ) out_4 = random.gaussian( shape=shape_tensor_int32, dtype="float32", mean=0.0, std=1.0, seed=10, ) out_5 = random.gaussian( shape=shape_tensor_int64, dtype="float32", mean=0.0, std=1.0, seed=10, ) out_6 = random.gaussian( shape=shape_tensor_int64, dtype=np.float32, mean=0.0, std=1.0, seed=10, ) exe = base.Executor(place=base.XPUPlace(0)) res_1, res_2, res_3, res_4, res_5, res_6 = exe.run( base.default_main_program(), feed={ "shape_tensor_int32": np.array([2000, 500]).astype("int32"), "shape_tensor_int64": np.array([2000, 500]).astype("int64"), }, fetch_list=[out_1, out_2, out_3, out_4, out_5, out_6], ) self.assertAlmostEqual(np.mean(res_1), 0.0, delta=0.1) self.assertAlmostEqual(np.std(res_1), 1.0, delta=0.1) self.assertAlmostEqual(np.mean(res_2), 0.0, delta=0.1) self.assertAlmostEqual(np.std(res_2), 1.0, delta=0.1) self.assertAlmostEqual(np.mean(res_3), 0.0, delta=0.1) self.assertAlmostEqual(np.std(res_3), 1.0, delta=0.1) self.assertAlmostEqual(np.mean(res_4), 0.0, delta=0.1) self.assertAlmostEqual(np.std(res_5), 1.0, delta=0.1) self.assertAlmostEqual(np.mean(res_5), 0.0, delta=0.1) self.assertAlmostEqual(np.std(res_5), 1.0, delta=0.1) self.assertAlmostEqual(np.mean(res_6), 0.0, delta=0.1) self.assertAlmostEqual(np.std(res_6), 1.0, delta=0.1) def test_default_dtype(self): paddle.disable_static() def test_default_fp16(): paddle.framework.set_default_dtype('float16') out = paddle.tensor.random.gaussian([2, 3]) self.assertEqual(out.dtype, paddle.float16) def test_default_bf16(): paddle.framework.set_default_dtype('bfloat16') out = paddle.tensor.random.gaussian([2, 3]) self.assertEqual(out.dtype, paddle.bfloat16) def test_default_fp32(): paddle.framework.set_default_dtype('float32') out = paddle.tensor.random.gaussian([2, 3]) self.assertEqual(out.dtype, paddle.float32) def test_default_fp64(): paddle.framework.set_default_dtype('float64') out = paddle.tensor.random.gaussian([2, 3]) self.assertEqual(out.dtype, paddle.float64) test_default_fp64() test_default_fp32() test_default_fp16() test_default_bf16() paddle.enable_static() class TestStandardNormalDtype(unittest.TestCase): def test_default_dtype(self): paddle.disable_static() def test_default_fp16(): paddle.framework.set_default_dtype('float16') out = paddle.tensor.random.standard_normal([2, 3]) self.assertEqual(out.dtype, paddle.float16) def test_default_bf16(): paddle.framework.set_default_dtype('bfloat16') out = paddle.tensor.random.standard_normal([2, 3]) self.assertEqual(out.dtype, paddle.bfloat16) def test_default_fp32(): paddle.framework.set_default_dtype('float32') out = paddle.tensor.random.standard_normal([2, 3]) self.assertEqual(out.dtype, paddle.float32) def test_default_fp64(): paddle.framework.set_default_dtype('float64') out = paddle.tensor.random.standard_normal([2, 3]) self.assertEqual(out.dtype, paddle.float64) test_default_fp64() test_default_fp32() test_default_fp16() test_default_bf16() paddle.enable_static() class TestZeroSizeRandN(unittest.TestCase): def test_zero_size_randn(self): paddle.disable_static() x = paddle.randn((0,)) paddle.enable_static() support_types = get_xpu_op_support_types('gaussian_random') for stype in support_types: create_test_class(globals(), XPUTestGaussianRandomOp, stype) if __name__ == "__main__": unittest.main()