wehub-resource-sync
525 lines
17 KiB
Python
525 lines
17 KiB
Python
# Copyright (c) 2019 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_float_to_uint16,
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get_device_place,
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is_custom_device,
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)
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import paddle
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from paddle import base, static
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from paddle.base import core
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class TestDiagV2Op(OpTest):
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def setUp(self):
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self.op_type = "diag_v2"
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self.python_api = paddle.diag
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self.prim_op_type = "comp"
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self.public_python_api = paddle.diag
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self.init_dtype()
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self.init_attrs()
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self.init_input_output()
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self.set_input_output()
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def init_dtype(self):
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self.dtype = np.float64
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def init_attrs(self):
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self.offset = 0
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self.padding_value = 0.0
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def init_input_output(self):
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self.x = np.random.rand(10, 10).astype(self.dtype)
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self.out = np.diag(self.x, self.offset)
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def set_input_output(self):
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self.attrs = {
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'offset': self.offset,
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'padding_value': self.padding_value,
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}
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self.inputs = {'X': self.x}
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self.outputs = {'Out': self.out}
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def test_check_output(self):
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paddle.enable_static()
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if self.dtype == np.complex64 or self.dtype == np.complex128:
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self.check_output(check_pir=True)
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else:
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self.check_output(check_pir=True, check_prim_pir=True)
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def test_check_grad(self):
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paddle.enable_static()
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if self.dtype == np.complex64 or self.dtype == np.complex128:
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self.check_grad(['X'], 'Out', check_pir=True)
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else:
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self.check_grad(['X'], 'Out', check_pir=True, check_prim_pir=True)
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class TestDiagV2OpCase1(TestDiagV2Op):
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def init_attrs(self):
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super().init_attrs()
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self.offset = 1
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class TestDiagV2OpCase2(TestDiagV2Op):
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def init_attrs(self):
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super().init_attrs()
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self.offset = -1
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class TestDiagV2OpCase3(TestDiagV2Op):
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def init_input_output(self):
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self.x = np.random.randint(-10, 10, size=(10, 10)).astype(self.dtype)
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self.out = np.diag(self.x, self.offset)
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class TestDiagV2OpCase4(TestDiagV2Op):
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def init_dtype(self):
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self.dtype = np.float32
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def init_attrs(self):
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super().init_attrs()
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self.padding_value = 2
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def init_input_output(self):
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self.x = np.random.rand(100).astype(self.dtype)
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n = self.x.size
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self.out = (
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self.padding_value * np.ones((n, n))
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+ np.diag(self.x, self.offset)
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- np.diag(self.padding_value * np.ones(n))
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)
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class TestDiagV2Error(unittest.TestCase):
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def test_errors(self):
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paddle.enable_static()
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main = static.Program()
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startup = static.Program()
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with static.program_guard(main, startup):
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def test_diag_v2_type():
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x = [1, 2, 3]
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output = paddle.diag(x)
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self.assertRaises(TypeError, test_diag_v2_type)
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x = paddle.static.data('data', [3, 3])
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self.assertRaises(TypeError, paddle.diag, x, offset=2.5)
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self.assertRaises(TypeError, paddle.diag, x, padding_value=[9])
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x = paddle.static.data('data2', [3, 3, 3])
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self.assertRaises(ValueError, paddle.diag, x)
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class TestDiagV2API(unittest.TestCase):
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def setUp(self):
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self.input_np = np.random.random(size=(10, 10)).astype(np.float32)
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self.expected0 = np.diag(self.input_np)
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self.expected1 = np.diag(self.input_np, k=1)
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self.expected2 = np.diag(self.input_np, k=-1)
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self.input_np2 = np.random.rand(100)
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self.offset = 0
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self.padding_value = 8
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n = self.input_np2.size
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self.expected3 = (
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self.padding_value * np.ones((n, n))
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+ np.diag(self.input_np2, self.offset)
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- np.diag(self.padding_value * np.ones(n))
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)
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self.input_np3 = np.random.randint(-10, 10, size=(100)).astype(np.int64)
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self.padding_value = 8.0
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n = self.input_np3.size
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self.expected4 = (
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self.padding_value * np.ones((n, n))
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+ np.diag(self.input_np3, self.offset)
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- np.diag(self.padding_value * np.ones(n))
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)
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self.padding_value = -8
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self.expected5 = (
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self.padding_value * np.ones((n, n))
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+ np.diag(self.input_np3, self.offset)
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- np.diag(self.padding_value * np.ones(n))
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)
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self.input_np4 = np.random.random(size=(2000, 2000)).astype(np.float32)
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self.expected6 = np.diag(self.input_np4)
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self.expected7 = np.diag(self.input_np4, k=1)
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self.expected8 = np.diag(self.input_np4, k=-1)
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self.input_np5 = np.random.random(size=(2000)).astype(np.float32)
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self.expected9 = np.diag(self.input_np5)
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self.expected10 = np.diag(self.input_np5, k=1)
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self.expected11 = np.diag(self.input_np5, k=-1)
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self.input_np6 = np.random.random(size=(2000, 1500)).astype(np.float32)
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self.expected12 = np.diag(self.input_np6, k=-1)
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def run_imperative(self):
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x = paddle.to_tensor(self.input_np)
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y = paddle.diag(x)
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np.testing.assert_allclose(y.numpy(), self.expected0, rtol=1e-05)
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y = paddle.diag(x, offset=1)
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np.testing.assert_allclose(y.numpy(), self.expected1, rtol=1e-05)
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y = paddle.diag(x, offset=-1)
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np.testing.assert_allclose(y.numpy(), self.expected2, rtol=1e-05)
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x = paddle.to_tensor(self.input_np2)
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y = paddle.diag(x, padding_value=8)
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np.testing.assert_allclose(y.numpy(), self.expected3, rtol=1e-05)
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x = paddle.to_tensor(self.input_np3)
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y = paddle.diag(x, padding_value=8.0)
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np.testing.assert_allclose(y.numpy(), self.expected4, rtol=1e-05)
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y = paddle.diag(x, padding_value=-8)
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np.testing.assert_allclose(y.numpy(), self.expected5, rtol=1e-05)
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x = paddle.to_tensor(self.input_np4)
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y = paddle.diag(x)
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np.testing.assert_allclose(y.numpy(), self.expected6, rtol=1e-05)
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y = paddle.diag(x, offset=1)
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np.testing.assert_allclose(y.numpy(), self.expected7, rtol=1e-05)
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y = paddle.diag(x, offset=-1)
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np.testing.assert_allclose(y.numpy(), self.expected8, rtol=1e-05)
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x = paddle.to_tensor(self.input_np5)
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y = paddle.diag(x)
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np.testing.assert_allclose(y.numpy(), self.expected9, rtol=1e-05)
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y = paddle.diag(x, offset=1)
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np.testing.assert_allclose(y.numpy(), self.expected10, rtol=1e-05)
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y = paddle.diag(x, offset=-1)
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np.testing.assert_allclose(y.numpy(), self.expected11, rtol=1e-05)
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x = paddle.to_tensor(self.input_np6)
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y = paddle.diag(x, offset=-1)
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np.testing.assert_allclose(y.numpy(), self.expected12, rtol=1e-05)
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def run_static(self, use_gpu=False):
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mp, sp = static.Program(), static.Program()
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with static.program_guard(mp, sp):
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x = paddle.static.data(
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name='input', shape=[10, 10], dtype='float32'
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)
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x2 = paddle.static.data(name='input2', shape=[100], dtype='float64')
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x3 = paddle.static.data(name='input3', shape=[100], dtype='int64')
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x4 = paddle.static.data(
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name='input4', shape=[2000, 2000], dtype='float32'
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)
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x5 = paddle.static.data(
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name='input5', shape=[2000], dtype='float32'
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)
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x6 = paddle.static.data(
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name='input6', shape=[2000, 1500], dtype='float32'
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)
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result0 = paddle.diag(x)
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result1 = paddle.diag(x, offset=1)
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result2 = paddle.diag(x, offset=-1)
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result4 = paddle.diag(x2, padding_value=8)
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result5 = paddle.diag(x3, padding_value=8.0)
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result6 = paddle.diag(x3, padding_value=-8)
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result7 = paddle.diag(x4)
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result8 = paddle.diag(x4, offset=1)
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result9 = paddle.diag(x4, offset=-1)
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result10 = paddle.diag(x5)
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result11 = paddle.diag(x5, offset=1)
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result12 = paddle.diag(x5, offset=-1)
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result13 = paddle.diag(x6, offset=-1)
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place = get_device_place() if use_gpu else base.CPUPlace()
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exe = static.Executor(place)
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exe.run(sp)
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[
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res0,
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res1,
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res2,
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res4,
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res5,
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res6,
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res7,
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res8,
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res9,
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res10,
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res11,
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res12,
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res13,
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] = exe.run(
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mp,
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feed={
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"input": self.input_np,
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"input2": self.input_np2,
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'input3': self.input_np3,
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'input4': self.input_np4,
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'input5': self.input_np5,
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'input6': self.input_np6,
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},
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fetch_list=[
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result0,
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result1,
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result2,
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result4,
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result5,
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result6,
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result7,
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result8,
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result9,
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result10,
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result11,
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result12,
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result13,
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],
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)
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np.testing.assert_allclose(res0, self.expected0, rtol=1e-05)
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np.testing.assert_allclose(res1, self.expected1, rtol=1e-05)
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np.testing.assert_allclose(res2, self.expected2, rtol=1e-05)
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np.testing.assert_allclose(res4, self.expected3, rtol=1e-05)
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np.testing.assert_allclose(res5, self.expected4, rtol=1e-05)
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np.testing.assert_allclose(res6, self.expected5, rtol=1e-05)
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np.testing.assert_allclose(res7, self.expected6, rtol=1e-05)
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np.testing.assert_allclose(res8, self.expected7, rtol=1e-05)
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np.testing.assert_allclose(res9, self.expected8, rtol=1e-05)
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np.testing.assert_allclose(res10, self.expected9, rtol=1e-05)
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np.testing.assert_allclose(res11, self.expected10, rtol=1e-05)
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np.testing.assert_allclose(res12, self.expected11, rtol=1e-05)
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np.testing.assert_allclose(res13, self.expected12, rtol=1e-05)
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def test_cpu(self):
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paddle.disable_static(place=paddle.base.CPUPlace())
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self.run_imperative()
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paddle.enable_static()
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self.run_static()
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def test_gpu(self):
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if not (base.core.is_compiled_with_cuda() or is_custom_device()):
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return
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paddle.disable_static(place=get_device_place())
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self.run_imperative()
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paddle.enable_static()
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self.run_static(use_gpu=True)
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class TestDiagV2Compatibility(unittest.TestCase):
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def setUp(self):
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# input arg
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self.input_np1 = np.random.random(size=(10, 10)).astype(np.float32)
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self.expected1 = np.diag(self.input_np1)
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# diagonal arg
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self.input_np2 = np.random.random(size=(10, 10)).astype(np.float32)
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self.expected2 = np.diag(self.input_np2, k=1)
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# out arg
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self.input_np3 = np.random.random(size=(10, 10)).astype(np.float32)
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self.expected3 = np.diag(self.input_np3)
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self.input_np4 = np.random.random(size=(10, 10)).astype(np.float32)
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self.expected4 = np.diag(self.input_np4)
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def run_dygraph(self):
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# input arg
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x = paddle.to_tensor(self.input_np1)
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y = paddle.diag(input=x)
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np.testing.assert_allclose(y.numpy(), self.expected1, rtol=1e-05)
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# diagonal arg
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x = paddle.to_tensor(self.input_np2)
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y = paddle.diag(x, diagonal=1)
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np.testing.assert_allclose(y.numpy(), self.expected2, rtol=1e-05)
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# out arg
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x = paddle.to_tensor(self.input_np3)
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out = paddle.empty([])
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y = paddle.diag(x, out=out)
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np.testing.assert_allclose(out.numpy(), self.expected3, rtol=1e-05)
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np.testing.assert_allclose(y.numpy(), self.expected3, rtol=1e-05)
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x = paddle.to_tensor(self.input_np4)
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out = paddle.empty([])
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paddle.diag(x, out=out)
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np.testing.assert_allclose(out.numpy(), self.expected4, rtol=1e-05)
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def run_static(self, use_gpu=False):
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mp, sp = static.Program(), static.Program()
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with static.program_guard(mp, sp):
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x1 = paddle.static.data(
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name='input1', shape=[10, 10], dtype='float32'
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)
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x2 = paddle.static.data(
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name='input2', shape=[10, 10], dtype='float32'
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)
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# input arg
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result1 = paddle.diag(input=x1)
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# diagonal arg
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result2 = paddle.diag(x2, diagonal=1)
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place = get_device_place() if use_gpu else base.CPUPlace()
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exe = static.Executor(place)
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exe.run(sp)
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[res1, res2] = exe.run(
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mp,
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feed={
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"input1": self.input_np1,
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"input2": self.input_np2,
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},
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fetch_list=[result1, result2],
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)
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# input arg
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np.testing.assert_allclose(res1, self.expected1, rtol=1e-05)
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# diagonal arg
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np.testing.assert_allclose(res2, self.expected2, rtol=1e-05)
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def test_compatibility(self):
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paddle.disable_static(place=paddle.base.CPUPlace())
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self.run_dygraph()
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paddle.enable_static()
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self.run_static()
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class TestDiagV2FP16OP(TestDiagV2Op):
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def init_dtype(self):
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self.dtype = np.float16
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device())
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or not core.is_bfloat16_supported(get_device_place()),
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"core is not compiled with CUDA and not support the bfloat16",
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)
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class TestDiagV2BF16OP(OpTest):
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def setUp(self):
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self.op_type = "diag_v2"
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self.python_api = paddle.diag
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self.prim_op_type = "comp"
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self.public_python_api = paddle.diag
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self.dtype = np.uint16
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x = np.random.rand(10, 10).astype(np.float32)
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offset = 0
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padding_value = 0.0
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out = np.diag(x, offset)
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self.inputs = {'X': convert_float_to_uint16(x)}
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self.attrs = {
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'offset': offset,
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'padding_value': padding_value,
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}
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self.outputs = {'Out': convert_float_to_uint16(out)}
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def test_check_output(self):
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paddle.enable_static()
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place = get_device_place()
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self.check_output_with_place(place, check_pir=True, check_prim_pir=True)
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def test_check_grad(self):
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paddle.enable_static()
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place = get_device_place()
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self.check_grad_with_place(
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place, ['X'], 'Out', check_pir=True, check_prim_pir=True
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)
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@unittest.skipIf(
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core.is_compiled_with_xpu(),
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"xpu does not support complex64",
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)
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class TestDiagV2Complex64OP(TestDiagV2Op):
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def init_dtype(self):
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self.dtype = np.complex64
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def init_input_output(self):
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self.x = (
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np.random.randint(-10, 10, size=(10, 10))
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+ 1j * np.random.randint(-10, 10, size=(10, 10))
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).astype(self.dtype)
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self.out = np.diag(self.x, self.offset)
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@unittest.skipIf(
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core.is_compiled_with_xpu(),
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"xpu does not support complex128",
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)
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class TestDiagV2Complex128OP(TestDiagV2Op):
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def init_dtype(self):
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self.dtype = np.complex128
|
|
|
|
def init_config(self):
|
|
self.x = (
|
|
np.random.randint(-10, 10, size=(10, 10))
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|
+ 1j * np.random.randint(-10, 10, size=(10, 10))
|
|
).astype(self.dtype)
|
|
self.out = np.diag(self.x, self.offset)
|
|
|
|
|
|
class TestDiagV2Op_ZeroSize(OpTest):
|
|
def setUp(self):
|
|
self.op_type = "diag_v2"
|
|
self.python_api = paddle.diag
|
|
self.public_python_api = paddle.diag
|
|
|
|
self.init_dtype()
|
|
self.init_attrs()
|
|
self.init_input_output()
|
|
self.set_input_output()
|
|
|
|
def init_dtype(self):
|
|
self.dtype = np.float64
|
|
|
|
def init_attrs(self):
|
|
self.offset = 1
|
|
self.padding_value = 0.0
|
|
|
|
def init_input_output(self):
|
|
self.x = np.random.rand(10, 0).astype(self.dtype)
|
|
self.out = np.diag(self.x, self.offset)
|
|
|
|
def set_input_output(self):
|
|
self.attrs = {
|
|
'offset': self.offset,
|
|
'padding_value': self.padding_value,
|
|
}
|
|
|
|
self.inputs = {'X': self.x}
|
|
self.outputs = {'Out': self.out}
|
|
|
|
def test_check_output(self):
|
|
self.check_output(check_pir=True)
|
|
|
|
def test_check_grad(self):
|
|
self.check_grad(['X'], 'Out', check_pir=True)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
paddle.enable_static()
|
|
unittest.main()
|