wehub-resource-sync
277 lines
7.9 KiB
Python
277 lines
7.9 KiB
Python
# Copyright (c) 2023 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 os
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import sys
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import unittest
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import numpy as np
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import scipy
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from op_test import get_places
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import paddle
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os.environ['NVIDIA_TF32_OVERRIDE'] = '0'
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if sys.platform == 'win32':
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RTOL = {'float32': 1e-02, 'float64': 1e-04}
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ATOL = {'float32': 1e-02, 'float64': 1e-04}
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elif sys.platform == 'darwin':
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RTOL = {'float32': 1e-06, 'float64': 1e-12}
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ATOL = {'float32': 1e-06, 'float64': 1e-12}
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elif scipy.__version__ < '1.15':
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RTOL = {'float32': 1e-06, 'float64': 1e-15}
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ATOL = {'float32': 1e-06, 'float64': 1e-15}
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else:
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RTOL = {'float32': 1e-06, 'float64': 1e-13}
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ATOL = {'float32': 1e-06, 'float64': 1e-13}
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class MatrixExpTestCase(unittest.TestCase):
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def setUp(self):
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self.init_config()
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self.generate_input()
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self.generate_output()
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self.places = get_places()
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def generate_input(self):
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self._input_shape = (5, 5)
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np.random.seed(123)
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self._input_data = np.random.random(self._input_shape).astype(
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self.dtype
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)
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def generate_output(self):
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self._output_data = scipy.linalg.expm(self._input_data)
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def init_config(self):
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self.dtype = 'float64'
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def test_dygraph(self):
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for place in self.places:
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paddle.disable_static(place)
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x = paddle.to_tensor(self._input_data, place=place)
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out = paddle.linalg.matrix_exp(x).numpy()
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np.testing.assert_allclose(
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out,
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self._output_data,
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rtol=RTOL.get(self.dtype),
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atol=ATOL.get(self.dtype),
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)
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# TODO(megemini): cond/while_loop should be tested in pir
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#
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def test_static(self):
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paddle.enable_static()
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for place in get_places():
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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x = paddle.static.data(
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name="input",
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shape=self._input_shape,
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dtype=self._input_data.dtype,
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)
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out = paddle.linalg.matrix_exp(x)
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exe = paddle.static.Executor(place)
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res = exe.run(
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feed={"input": self._input_data},
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fetch_list=[out],
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)[0]
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np.testing.assert_allclose(
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res,
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self._output_data,
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rtol=RTOL.get(self.dtype),
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atol=ATOL.get(self.dtype),
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)
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def test_grad(self):
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for place in self.places:
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x = paddle.to_tensor(
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self._input_data, place=place, stop_gradient=False
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)
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out = paddle.linalg.matrix_exp(x)
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out.backward()
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x_grad = x.grad
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self.assertEqual(list(x_grad.shape), list(x.shape))
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self.assertEqual(x_grad.dtype, x.dtype)
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class MatrixExpTestCaseFloat32(MatrixExpTestCase):
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def init_config(self):
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self.dtype = 'float32'
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class MatrixExpTestCase3D(MatrixExpTestCase):
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def generate_input(self):
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self._input_shape = (2, 5, 5)
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np.random.seed(123)
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self._input_data = np.random.random(self._input_shape).astype(
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self.dtype
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)
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class MatrixExpTestCase3DFloat32(MatrixExpTestCase3D):
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def init_config(self):
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self.dtype = 'float32'
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class MatrixExpTestCase4D(MatrixExpTestCase):
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def generate_input(self):
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self._input_shape = (2, 3, 5, 5)
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np.random.seed(123)
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self._input_data = np.random.random(self._input_shape).astype(
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self.dtype
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)
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class MatrixExpTestCase4DFloat32(MatrixExpTestCase4D):
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def init_config(self):
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self.dtype = 'float32'
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class MatrixExpTestCaseEmpty(MatrixExpTestCase):
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def generate_input(self):
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self._input_shape = ()
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np.random.seed(123)
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self._input_data = np.random.random(self._input_shape).astype(
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self.dtype
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)
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class MatrixExpTestCaseEmptyFloat32(MatrixExpTestCaseEmpty):
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def init_config(self):
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self.dtype = 'float32'
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class MatrixExpTestCaseScalar(MatrixExpTestCase):
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def generate_input(self):
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self._input_shape = (2, 3, 1, 1)
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np.random.seed(123)
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self._input_data = np.random.random(self._input_shape).astype(
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self.dtype
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)
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class MatrixExpTestCaseScalarFloat32(MatrixExpTestCaseScalar):
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def init_config(self):
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self.dtype = 'float32'
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# test precision for float32 with l1_norm comparing `conds`
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class MatrixExpTestCasePrecisionFloat32L1norm0(MatrixExpTestCase):
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def init_config(self):
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self.dtype = 'float32'
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def generate_input(self):
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self._input_shape = (2, 2)
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self._input_data = np.array([[0, 0.2], [-0.2, 0]]).astype(self.dtype)
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class MatrixExpTestCasePrecisionFloat32L1norm1(MatrixExpTestCase):
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def init_config(self):
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self.dtype = 'float32'
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def generate_input(self):
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self._input_shape = (2, 2)
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self._input_data = np.array([[0, 0.8], [-0.8, 0]]).astype(self.dtype)
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class MatrixExpTestCasePrecisionFloat32L1norm2(MatrixExpTestCase):
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def init_config(self):
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self.dtype = 'float32'
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def generate_input(self):
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self._input_shape = (2, 2)
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self._input_data = np.array([[0, 2.0], [-2.0, 0]]).astype(self.dtype)
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# test precision for float64 with l1_norm comparing `conds`
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class MatrixExpTestCasePrecisionFloat64L1norm0(MatrixExpTestCase):
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def init_config(self):
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self.dtype = 'float64'
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def generate_input(self):
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self._input_shape = (2, 2)
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self._input_data = np.array([[0, 0.01], [-0.01, 0]]).astype(self.dtype)
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class MatrixExpTestCasePrecisionFloat64L1norm1(MatrixExpTestCase):
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def init_config(self):
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self.dtype = 'float64'
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def generate_input(self):
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self._input_shape = (2, 2)
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self._input_data = np.array([[0, 0.1], [-0.1, 0]]).astype(self.dtype)
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class MatrixExpTestCasePrecisionFloat64L1norm2(MatrixExpTestCase):
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def init_config(self):
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self.dtype = 'float64'
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def generate_input(self):
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self._input_shape = (2, 2)
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self._input_data = np.array([[0, 0.5], [-0.5, 0]]).astype(self.dtype)
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class MatrixExpTestCasePrecisionFloat64L1norm3(MatrixExpTestCase):
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def init_config(self):
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self.dtype = 'float64'
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def generate_input(self):
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self._input_shape = (2, 2)
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self._input_data = np.array([[0, 1.5], [-1.5, 0]]).astype(self.dtype)
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class MatrixExpTestCasePrecisionFloat64L1norm4(MatrixExpTestCase):
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def init_config(self):
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self.dtype = 'float64'
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def generate_input(self):
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self._input_shape = (2, 2)
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self._input_data = np.array([[0, 2.5], [-2.5, 0]]).astype(self.dtype)
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# test error cases
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class MatrixExpTestCaseError(unittest.TestCase):
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def test_error_dtype(self):
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with self.assertRaises(ValueError):
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x = np.array(123, dtype=int)
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paddle.linalg.matrix_exp(x)
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def test_error_ndim(self):
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# 1-d
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with self.assertRaises(ValueError):
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x = np.random.rand(1)
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paddle.linalg.matrix_exp(x)
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# not square
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with self.assertRaises(ValueError):
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x = np.random.rand(3, 4)
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paddle.linalg.matrix_exp(x)
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with self.assertRaises(ValueError):
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x = np.random.rand(2, 3, 4)
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paddle.linalg.matrix_exp(x)
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if __name__ == '__main__':
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unittest.main()
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