paddlepaddle--paddle
wehub-resource-sync
306 行
9.7 KiB
Python
306 行
9.7 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 sys
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import unittest
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import numpy as np
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from op_test import get_places
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import paddle
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from paddle.base import core
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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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else:
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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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class VecDotTestCase(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_expected_output()
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self.places = get_places()
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def generate_input(self):
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np.random.seed(123)
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self.x = np.random.random(self.input_shape).astype(self.dtype)
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self.y = np.random.random(self.input_shape).astype(self.dtype)
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def generate_expected_output(self):
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self.expected_output = np.sum(self.x * self.y, axis=self.axis)
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def init_config(self):
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self.dtype = 'float64'
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self.input_shape = (3, 4)
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self.axis = -1
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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_tensor = paddle.to_tensor(self.x, dtype=self.dtype, place=place)
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y_tensor = paddle.to_tensor(self.y, dtype=self.dtype, place=place)
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result = paddle.vecdot(x_tensor, y_tensor, axis=self.axis)
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np.testing.assert_allclose(
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result.numpy(),
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self.expected_output,
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rtol=RTOL[self.dtype],
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atol=ATOL[self.dtype],
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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 self.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="x", shape=self.input_shape, dtype=self.dtype
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)
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y = paddle.static.data(
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name="y", shape=self.input_shape, dtype=self.dtype
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)
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result = paddle.vecdot(x, y, axis=self.axis)
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exe = paddle.static.Executor(place)
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output = exe.run(
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feed={"x": self.x, "y": self.y},
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fetch_list=[result],
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)[0]
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np.testing.assert_allclose(
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output,
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self.expected_output,
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rtol=RTOL[self.dtype],
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atol=ATOL[self.dtype],
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)
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class VecDotTestCaseFloat32(VecDotTestCase):
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def init_config(self):
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self.dtype = 'float32'
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self.input_shape = (3, 4)
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self.axis = -1
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class VecDotTestCaseHigherDim(VecDotTestCase):
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def init_config(self):
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self.dtype = 'float64'
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self.input_shape = (2, 3, 4)
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self.axis = -1
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class VecDotTestCaseAxis(VecDotTestCase):
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def init_config(self):
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self.dtype = 'float64'
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self.input_shape = (3, 4, 5)
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self.axis = 1
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class VecDotTestCaseZeroSize2D(VecDotTestCase):
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def init_config(self):
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self.dtype = 'float32'
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self.input_shape = (0, 4)
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self.axis = -1
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class VecDotTestCaseZeroSize3D(VecDotTestCase):
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def init_config(self):
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self.dtype = 'float32'
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self.input_shape = (2, 0, 4)
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self.axis = -1
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class VecDotTestCaseZeroSize3DAxis1(VecDotTestCase):
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def init_config(self):
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self.dtype = 'float32'
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self.input_shape = (2, 0, 0)
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self.axis = 0
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class VecDotTestCaseZeroSize3DAxis2(VecDotTestCase):
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def init_config(self):
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self.dtype = 'float32'
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self.input_shape = (2, 0, 0)
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self.axis = 1
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class VecDotTestCaseError(unittest.TestCase):
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def test_axis_mismatch(self):
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with self.assertRaises(ValueError):
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x = paddle.rand([3, 4], dtype="float32")
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y = paddle.rand([3, 5], dtype="float32")
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paddle.vecdot(x, y, axis=-1)
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@unittest.skipIf(
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core.is_compiled_with_xpu(),
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"Skip XPU for not support uniform(dtype=int)",
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)
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def test_dtype_mismatch(self):
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with self.assertRaises(TypeError):
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x = paddle.rand([3, 4], dtype="float32")
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y = paddle.rand([3, 4], dtype="int32")
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paddle.vecdot(x, y, axis=-1)
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@unittest.skipIf(
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core.is_compiled_with_xpu(),
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"Skip XPU for not support uniform(dtype=int)",
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)
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class VecDotTestCaseComplex(unittest.TestCase):
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def run_test_dynamic(self):
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paddle.disable_static()
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x = paddle.to_tensor(
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[[1 + 2j, 3 + 4j], [5 + 6j, 7 + 8j]], dtype="complex64"
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)
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y = paddle.to_tensor(
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[[9 + 1j, 8 + 2j], [7 + 3j, 6 + 4j]], dtype="complex64"
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)
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result = paddle.vecdot(x, y, axis=-1)
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expected = np.sum((x.numpy().conj() * y.numpy()), axis=-1)
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np.testing.assert_allclose(
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result.numpy(), expected, rtol=1e-5, atol=1e-5
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)
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def run_test_static(self):
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paddle.enable_static()
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place = paddle.CPUPlace()
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with paddle.static.program_guard(paddle.static.Program()):
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x = paddle.static.data(name="x", shape=[2, 2], dtype="complex64")
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y = paddle.static.data(name="y", shape=[2, 2], dtype="complex64")
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result = paddle.vecdot(x, y, axis=-1)
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exe = paddle.static.Executor(place)
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output = exe.run(
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feed={
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"x": np.array([[1 + 2j, 3 + 4j], [5 + 6j, 7 + 8j]]).astype(
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"complex64"
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),
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"y": np.array([[9 + 1j, 8 + 2j], [7 + 3j, 6 + 4j]]).astype(
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"complex64"
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),
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},
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fetch_list=[result],
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)[0]
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expected = np.sum(
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np.conj(np.array([[1 + 2j, 3 + 4j], [5 + 6j, 7 + 8j]])).astype(
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"complex64"
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)
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* np.array([[9 + 1j, 8 + 2j], [7 + 3j, 6 + 4j]]).astype(
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"complex64"
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),
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axis=-1,
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)
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np.testing.assert_allclose(output, expected, rtol=1e-5, atol=1e-5)
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def test_complex_conjugate(self):
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self.run_test_dynamic()
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self.run_test_static()
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@unittest.skipIf(
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core.is_compiled_with_xpu(),
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"Skip XPU for not support uniform(dtype=int)",
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)
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class VecDotTestCaseTypePromotion1(unittest.TestCase):
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def test_float32_float64_promotion(self):
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paddle.disable_static()
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x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0]], dtype="float32")
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y = paddle.to_tensor([[5.0, 6.0], [7.0, 8.0]], dtype="float64")
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result = paddle.vecdot(x, y, axis=-1)
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expected = np.sum(x.numpy().astype("float64") * y.numpy(), axis=-1)
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np.testing.assert_allclose(
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result.numpy(), expected, rtol=1e-6, atol=1e-6
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)
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@unittest.skipIf(
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core.is_compiled_with_xpu(),
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"Skip XPU for not support uniform(dtype=int)",
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)
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class VecDotTestCaseTypePromotion2(unittest.TestCase):
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def test_float64_complex64_promotion(self):
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paddle.disable_static()
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x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0]], dtype="float64")
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y = paddle.to_tensor(
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[[5 + 6j, 7 + 8j], [9 + 1j, 2 + 3j]], dtype="complex64"
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)
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result = paddle.vecdot(x, y, axis=-1)
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expected = np.sum(x.numpy().astype("complex64") * y.numpy(), axis=-1)
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np.testing.assert_allclose(
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result.numpy(), expected, rtol=1e-5, atol=1e-5
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)
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class VecDotTestCaseBroadcast0DTensor(unittest.TestCase):
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def test_0d_tensor_broadcast(self):
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paddle.disable_static()
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x = paddle.to_tensor(2.0, dtype="float32")
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y = paddle.to_tensor(3.0, dtype="float32")
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result = paddle.vecdot(x, y)
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expected = x.numpy() * y.numpy()
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np.testing.assert_allclose(
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result.numpy(), expected, rtol=1e-6, atol=1e-6
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)
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class VecDotTestCaseBroadcast1DTensor(unittest.TestCase):
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def test_1d_tensor_broadcast(self):
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paddle.disable_static()
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x = paddle.to_tensor([1.0, 2.0, 3.0], dtype="float32")
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y = paddle.to_tensor([4.0, 5.0, 6.0], dtype="float32")
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result = paddle.vecdot(x, y)
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expected = np.dot(x.numpy(), y.numpy())
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np.testing.assert_allclose(
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result.numpy(), expected, rtol=1e-6, atol=1e-6
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)
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class VecDotTestCaseBroadcast1DNDTensor(unittest.TestCase):
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def test_1d_nd_tensor_broadcast(self):
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paddle.disable_static()
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x = paddle.to_tensor([1.0, 2.0], dtype="float32")
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y = paddle.to_tensor([[3.0, 4.0], [5.0, 6.0]], dtype="float32")
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result = paddle.vecdot(x, y, axis=-1)
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expected = np.sum(x.numpy() * y.numpy(), axis=-1)
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np.testing.assert_allclose(
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result.numpy(), expected, rtol=1e-6, atol=1e-6
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)
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class VecDotTestCaseBroadcastNDTensor(unittest.TestCase):
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def test_nd_nd_tensor_broadcast(self):
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paddle.disable_static()
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x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0]], dtype="float32")
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y = paddle.to_tensor([5.0, 6.0], dtype="float32")
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result = paddle.vecdot(x, y, axis=-1)
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expected = np.sum(x.numpy() * y.numpy(), axis=-1)
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np.testing.assert_allclose(
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result.numpy(), expected, rtol=1e-6, atol=1e-6
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)
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if __name__ == '__main__':
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unittest.main()
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