wehub-resource-sync
966 lines
39 KiB
Python
966 lines
39 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 inspect
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import unittest
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import numpy as np
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from op_test import convert_float_to_uint16, convert_uint16_to_float
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import paddle
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from paddle import base
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class TestMathOpPatchesVarBase(unittest.TestCase):
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def setUp(self):
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self.shape = [10, 1024]
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self.dtype = np.float32
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def test_add(self):
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a_np = np.random.random(self.shape).astype(self.dtype)
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b_np = np.random.random(self.shape).astype(self.dtype)
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with base.dygraph.guard():
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a = paddle.to_tensor(a_np)
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b = paddle.to_tensor(b_np)
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res = a + b
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np.testing.assert_array_equal(res.numpy(), a_np + b_np)
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def test_type_promotion_add_F2_F4(self):
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a_np = np.random.random(self.shape).astype(np.float16)
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b_np = np.random.random(self.shape).astype(np.float32)
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with base.dygraph.guard():
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a = paddle.to_tensor(a_np)
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b = paddle.to_tensor(b_np)
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res = a + b
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res_t = b + a
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np.testing.assert_equal(paddle.float32, res.dtype)
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np.testing.assert_equal(res_t.dtype, res.dtype)
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np.testing.assert_array_equal(res_t.numpy(), res.numpy())
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np.testing.assert_array_equal(res.numpy(), a_np + b_np)
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def test_type_promotion_add_F2_F8(self):
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a_np = np.random.random(self.shape).astype(np.float16)
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b_np = np.random.random(self.shape).astype(np.float64)
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with base.dygraph.guard():
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a = paddle.to_tensor(a_np)
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b = paddle.to_tensor(b_np)
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res = a + b
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res_t = b + a
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np.testing.assert_equal(paddle.float64, res.dtype)
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np.testing.assert_equal(res_t.dtype, res.dtype)
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np.testing.assert_array_equal(res_t.numpy(), res.numpy())
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np.testing.assert_array_equal(res.numpy(), a_np + b_np)
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def test_type_promotion_add_F4_F8(self):
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a_np = np.random.random(self.shape).astype(np.float32)
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b_np = np.random.random(self.shape).astype(np.float64)
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with base.dygraph.guard():
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a = paddle.to_tensor(a_np)
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b = paddle.to_tensor(b_np)
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res = a + b
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res_t = b + a
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np.testing.assert_equal(paddle.float64, res.dtype)
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np.testing.assert_equal(res_t.dtype, res.dtype)
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np.testing.assert_array_equal(res_t.numpy(), res.numpy())
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np.testing.assert_array_equal(res.numpy(), a_np + b_np)
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def test_type_promotion_add_F2_BF(self):
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a_np = np.random.random(self.shape).astype(np.float16)
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b_np = np.random.random(self.shape).astype(np.float32)
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b_np = convert_uint16_to_float(convert_float_to_uint16(b_np))
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with base.dygraph.guard():
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a = paddle.to_tensor(a_np)
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b = paddle.to_tensor(b_np).astype('bfloat16')
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res = a + b
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res_t = b + a
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np.testing.assert_equal(paddle.float32, res.dtype)
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np.testing.assert_equal(res_t.dtype, res.dtype)
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np.testing.assert_array_equal(res_t.numpy(), res.numpy())
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np.testing.assert_array_equal(res.numpy(), a_np + b_np)
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def test_type_promotion_add_F4_BF(self):
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a_np = np.random.random(self.shape).astype(np.float32)
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b_np = np.random.random(self.shape).astype(np.float32)
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b_np = convert_uint16_to_float(convert_float_to_uint16(b_np))
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with base.dygraph.guard():
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a = paddle.to_tensor(a_np)
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b = paddle.to_tensor(b_np).astype('bfloat16')
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res = a + b
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res_t = b + a
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np.testing.assert_equal(paddle.float32, res.dtype)
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np.testing.assert_equal(res_t.dtype, res.dtype)
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np.testing.assert_array_equal(res_t.numpy(), res.numpy())
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np.testing.assert_array_equal(res.numpy(), a_np + b_np)
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def test_type_promotion_add_F8_BF(self):
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a_np = np.random.random(self.shape).astype(np.float64)
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b_np = np.random.random(self.shape).astype(np.float32)
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b_np = convert_uint16_to_float(convert_float_to_uint16(b_np))
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with base.dygraph.guard():
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a = paddle.to_tensor(a_np)
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b = paddle.to_tensor(b_np).astype('bfloat16')
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res = a + b
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res_t = b + a
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np.testing.assert_equal(paddle.float64, res.dtype)
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np.testing.assert_equal(res_t.dtype, res.dtype)
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np.testing.assert_array_equal(res_t.numpy(), res.numpy())
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np.testing.assert_array_equal(res.numpy(), a_np + b_np)
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def test_sub(self):
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a_np = np.random.random(self.shape).astype(self.dtype)
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b_np = np.random.random(self.shape).astype(self.dtype)
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with base.dygraph.guard():
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a = paddle.to_tensor(a_np)
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b = paddle.to_tensor(b_np)
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res = a - b
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np.testing.assert_array_equal(res.numpy(), a_np - b_np)
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def test_type_promotion_sub_F2_F4(self):
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a_np = np.random.random(self.shape).astype(np.float16)
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b_np = np.random.random(self.shape).astype(np.float32)
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with base.dygraph.guard():
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a = paddle.to_tensor(a_np)
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b = paddle.to_tensor(b_np)
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res = a - b
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res_t = b - a
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np.testing.assert_equal(paddle.float32, res.dtype)
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np.testing.assert_equal(res_t.dtype, res.dtype)
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np.testing.assert_array_equal(res.numpy(), a_np - b_np)
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np.testing.assert_array_equal(res_t.numpy(), b_np - a_np)
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def test_type_promotion_sub_F2_F8(self):
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a_np = np.random.random(self.shape).astype(np.float16)
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b_np = np.random.random(self.shape).astype(np.float64)
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with base.dygraph.guard():
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a = paddle.to_tensor(a_np)
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b = paddle.to_tensor(b_np)
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res = a - b
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res_t = b - a
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np.testing.assert_equal(paddle.float64, res.dtype)
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np.testing.assert_equal(res_t.dtype, res.dtype)
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np.testing.assert_array_equal(res.numpy(), a_np - b_np)
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np.testing.assert_array_equal(res_t.numpy(), b_np - a_np)
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def test_type_promotion_sub_F4_F8(self):
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a_np = np.random.random(self.shape).astype(np.float32)
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b_np = np.random.random(self.shape).astype(np.float64)
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with base.dygraph.guard():
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a = paddle.to_tensor(a_np)
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b = paddle.to_tensor(b_np)
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res = a - b
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res_t = b - a
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np.testing.assert_equal(paddle.float64, res.dtype)
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np.testing.assert_equal(res_t.dtype, res.dtype)
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np.testing.assert_array_equal(res.numpy(), a_np - b_np)
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np.testing.assert_array_equal(res_t.numpy(), b_np - a_np)
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def test_type_promotion_sub_F2_BF(self):
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a_np = np.random.random(self.shape).astype(np.float16)
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b_np = np.random.random(self.shape).astype(np.float32)
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b_np = convert_uint16_to_float(convert_float_to_uint16(b_np))
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with base.dygraph.guard():
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a = paddle.to_tensor(a_np)
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b = paddle.to_tensor(b_np).astype('bfloat16')
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res = a - b
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res_t = b - a
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np.testing.assert_equal(paddle.float32, res.dtype)
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np.testing.assert_equal(res_t.dtype, res.dtype)
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np.testing.assert_array_equal(res.numpy(), a_np - b_np)
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np.testing.assert_array_equal(res_t.numpy(), b_np - a_np)
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def test_type_promotion_sub_F4_BF(self):
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a_np = np.random.random(self.shape).astype(np.float32)
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b_np = np.random.random(self.shape).astype(np.float32)
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b_np = convert_uint16_to_float(convert_float_to_uint16(b_np))
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with base.dygraph.guard():
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a = paddle.to_tensor(a_np)
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b = paddle.to_tensor(b_np).astype('bfloat16')
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res = a - b
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res_t = b - a
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np.testing.assert_equal(paddle.float32, res.dtype)
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np.testing.assert_equal(res_t.dtype, res.dtype)
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np.testing.assert_array_equal(res.numpy(), a_np - b_np)
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np.testing.assert_array_equal(res_t.numpy(), b_np - a_np)
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def test_type_promotion_sub_F8_BF(self):
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a_np = np.random.random(self.shape).astype(np.float64)
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b_np = np.random.random(self.shape).astype(np.float32)
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b_np = convert_uint16_to_float(convert_float_to_uint16(b_np))
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with base.dygraph.guard():
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a = paddle.to_tensor(a_np)
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b = paddle.to_tensor(b_np).astype('bfloat16')
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res = a - b
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res_t = b - a
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np.testing.assert_equal(paddle.float64, res.dtype)
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np.testing.assert_equal(res_t.dtype, res.dtype)
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np.testing.assert_array_equal(res.numpy(), a_np - b_np)
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np.testing.assert_array_equal(res_t.numpy(), b_np - a_np)
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def test_mul(self):
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a_np = np.random.random(self.shape).astype(self.dtype)
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b_np = np.random.random(self.shape).astype(self.dtype)
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with base.dygraph.guard():
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a = paddle.to_tensor(a_np)
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b = paddle.to_tensor(b_np)
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res = a * b
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np.testing.assert_array_equal(res.numpy(), a_np * b_np)
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def test_type_promotion_mul_F2_F4(self):
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a_np = np.random.random(self.shape).astype(np.float16)
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b_np = np.random.random(self.shape).astype(np.float32)
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with base.dygraph.guard():
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a = paddle.to_tensor(a_np)
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b = paddle.to_tensor(b_np)
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res = a * b
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res_t = b * a
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np.testing.assert_equal(paddle.float32, res.dtype)
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np.testing.assert_equal(res_t.dtype, res.dtype)
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np.testing.assert_array_equal(res_t.numpy(), res.numpy())
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np.testing.assert_array_equal(res.numpy(), a_np * b_np)
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def test_type_promotion_mul_F2_F8(self):
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a_np = np.random.random(self.shape).astype(np.float16)
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b_np = np.random.random(self.shape).astype(np.float64)
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with base.dygraph.guard():
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a = paddle.to_tensor(a_np)
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b = paddle.to_tensor(b_np)
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res = a * b
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res_t = b * a
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np.testing.assert_equal(paddle.float64, res.dtype)
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np.testing.assert_equal(res_t.dtype, res.dtype)
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np.testing.assert_array_equal(res_t.numpy(), res.numpy())
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np.testing.assert_array_equal(res.numpy(), a_np * b_np)
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def test_type_promotion_mul_F4_F8(self):
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a_np = np.random.random(self.shape).astype(np.float32)
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b_np = np.random.random(self.shape).astype(np.float64)
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with base.dygraph.guard():
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a = paddle.to_tensor(a_np)
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b = paddle.to_tensor(b_np)
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res = a * b
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res_t = b * a
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np.testing.assert_equal(paddle.float64, res.dtype)
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np.testing.assert_equal(res_t.dtype, res.dtype)
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np.testing.assert_array_equal(res_t.numpy(), res.numpy())
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np.testing.assert_array_equal(res.numpy(), a_np * b_np)
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def test_type_promotion_mul_F2_BF(self):
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a_np = np.random.random(self.shape).astype(np.float16)
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b_np = np.random.random(self.shape).astype(np.float32)
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b_np = convert_uint16_to_float(convert_float_to_uint16(b_np))
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with base.dygraph.guard():
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a = paddle.to_tensor(a_np)
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b = paddle.to_tensor(b_np).astype('bfloat16')
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res = a * b
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res_t = b * a
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np.testing.assert_equal(paddle.float32, res.dtype)
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np.testing.assert_equal(res_t.dtype, res.dtype)
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np.testing.assert_array_equal(res_t.numpy(), res.numpy())
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np.testing.assert_array_equal(res.numpy(), a_np * b_np)
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def test_type_promotion_mul_F4_BF(self):
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a_np = np.random.random(self.shape).astype(np.float32)
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b_np = np.random.random(self.shape).astype(np.float32)
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b_np = convert_uint16_to_float(convert_float_to_uint16(b_np))
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with base.dygraph.guard():
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a = paddle.to_tensor(a_np)
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b = paddle.to_tensor(b_np).astype('bfloat16')
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res = a * b
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res_t = b * a
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np.testing.assert_equal(paddle.float32, res.dtype)
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np.testing.assert_equal(res_t.dtype, res.dtype)
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np.testing.assert_array_equal(res_t.numpy(), res.numpy())
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np.testing.assert_array_equal(res.numpy(), a_np * b_np)
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def test_type_promotion_mul_F8_BF(self):
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a_np = np.random.random(self.shape).astype(np.float64)
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b_np = np.random.random(self.shape).astype(np.float32)
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b_np = convert_uint16_to_float(convert_float_to_uint16(b_np))
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with base.dygraph.guard():
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a = paddle.to_tensor(a_np)
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b = paddle.to_tensor(b_np).astype('bfloat16')
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res = a * b
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res_t = b * a
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np.testing.assert_equal(paddle.float64, res.dtype)
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np.testing.assert_equal(res_t.dtype, res.dtype)
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np.testing.assert_array_equal(res_t.numpy(), res.numpy())
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np.testing.assert_array_equal(res.numpy(), a_np * b_np)
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def test_div(self):
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a_np = np.random.random(self.shape).astype(self.dtype)
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b_np = np.random.random(self.shape).astype(self.dtype)
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with base.dygraph.guard():
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a = paddle.to_tensor(a_np)
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b = paddle.to_tensor(b_np)
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res = a / b
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# NOTE: Not sure why array_equal fails on windows, allclose is acceptable
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np.testing.assert_allclose(res.numpy(), a_np / b_np, rtol=1e-05)
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def test_add_scalar(self):
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a_np = np.random.random(self.shape).astype(self.dtype)
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with base.dygraph.guard():
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a = paddle.to_tensor(a_np)
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b = 0.1
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res = a + b
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np.testing.assert_array_equal(res.numpy(), a_np + b)
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def test_add_scalar_reverse(self):
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a_np = np.random.random(self.shape).astype(self.dtype)
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with base.dygraph.guard():
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a = paddle.to_tensor(a_np)
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b = 0.1
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res = b + a
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np.testing.assert_array_equal(res.numpy(), b + a_np)
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def test_sub_scalar(self):
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a_np = np.random.random(self.shape).astype(self.dtype)
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with base.dygraph.guard():
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a = paddle.to_tensor(a_np)
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b = 0.1
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res = a - b
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np.testing.assert_array_equal(res.numpy(), a_np - b)
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def test_sub_scalar_reverse(self):
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a_np = np.random.random(self.shape).astype(self.dtype)
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with base.dygraph.guard():
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a = paddle.to_tensor(a_np)
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b = 0.1
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res = b - a
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np.testing.assert_array_equal(res.numpy(), b - a_np)
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def test_mul_scalar(self):
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a_np = np.random.random(self.shape).astype(self.dtype)
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with base.dygraph.guard():
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a = paddle.to_tensor(a_np)
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b = 0.1
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res = a * b
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np.testing.assert_array_equal(res.numpy(), a_np * b)
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# div_scalar, not equal
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def test_div_scalar(self):
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a_np = np.random.random(self.shape).astype(self.dtype)
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with base.dygraph.guard():
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a = paddle.to_tensor(a_np)
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b = 0.1
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res = a / b
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np.testing.assert_allclose(res.numpy(), a_np / b, rtol=1e-05)
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# pow of float type, not equal
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def test_pow(self):
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a_np = np.random.random(self.shape).astype(self.dtype)
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b_np = np.random.random(self.shape).astype(self.dtype)
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with base.dygraph.guard():
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a = paddle.to_tensor(a_np)
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b = paddle.to_tensor(b_np)
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res = a**b
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np.testing.assert_allclose(res.numpy(), a_np**b_np, rtol=1e-05)
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def test_floor_div(self):
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a_np = np.random.randint(1, 100, size=self.shape)
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b_np = np.random.randint(1, 100, size=self.shape)
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with base.dygraph.guard():
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a = paddle.to_tensor(a_np)
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b = paddle.to_tensor(b_np)
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res = a // b
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np.testing.assert_array_equal(res.numpy(), a_np // b_np)
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def test_mod(self):
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a_np = np.random.randint(1, 100, size=self.shape)
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b_np = np.random.randint(1, 100, size=self.shape)
|
|
with base.dygraph.guard():
|
|
a = paddle.to_tensor(a_np)
|
|
b = paddle.to_tensor(b_np)
|
|
res = a % b
|
|
np.testing.assert_array_equal(res.numpy(), a_np % b_np)
|
|
|
|
# for bitwise and/or/xor/not
|
|
def test_bitwise(self):
|
|
paddle.disable_static()
|
|
|
|
x_np = np.random.randint(-100, 100, [2, 3, 5])
|
|
y_np = np.random.randint(-100, 100, [2, 3, 5])
|
|
x = paddle.to_tensor(x_np)
|
|
y = paddle.to_tensor(y_np)
|
|
|
|
out_np = x_np & y_np
|
|
out = x & y
|
|
np.testing.assert_array_equal(out.numpy(), out_np)
|
|
|
|
out_np = x_np | y_np
|
|
out = x | y
|
|
np.testing.assert_array_equal(out.numpy(), out_np)
|
|
|
|
out_np = x_np ^ y_np
|
|
out = x ^ y
|
|
np.testing.assert_array_equal(out.numpy(), out_np)
|
|
|
|
out_np = ~x_np
|
|
out = ~x
|
|
np.testing.assert_array_equal(out.numpy(), out_np)
|
|
|
|
# for logical compare
|
|
def test_equal(self):
|
|
a_np = np.asarray([1, 2, 3, 4, 5])
|
|
b_np = np.asarray([1, 2, 3, 4, 5])
|
|
c_np = np.asarray([1, 2, 2, 4, 5])
|
|
with base.dygraph.guard():
|
|
a = paddle.to_tensor(a_np)
|
|
b = paddle.to_tensor(b_np)
|
|
c = paddle.to_tensor(c_np)
|
|
res1 = a == b
|
|
res2 = a == c
|
|
np.testing.assert_array_equal(res1.numpy(), a_np == b_np)
|
|
np.testing.assert_array_equal(res2.numpy(), a_np == c_np)
|
|
|
|
def test_not_equal(self):
|
|
a_np = np.asarray([1, 2, 3, 4, 5])
|
|
b_np = np.asarray([1, 2, 3, 4, 5])
|
|
c_np = np.asarray([1, 2, 2, 4, 5])
|
|
with base.dygraph.guard():
|
|
a = paddle.to_tensor(a_np)
|
|
b = paddle.to_tensor(b_np)
|
|
c = paddle.to_tensor(c_np)
|
|
res1 = a != b
|
|
res2 = a != c
|
|
np.testing.assert_array_equal(res1.numpy(), a_np != b_np)
|
|
np.testing.assert_array_equal(res2.numpy(), a_np != c_np)
|
|
|
|
def test_less_than(self):
|
|
a_np = np.random.random(self.shape).astype(self.dtype)
|
|
b_np = np.random.random(self.shape).astype(self.dtype)
|
|
with base.dygraph.guard():
|
|
a = paddle.to_tensor(a_np)
|
|
b = paddle.to_tensor(b_np)
|
|
res = a < b
|
|
np.testing.assert_array_equal(res.numpy(), a_np < b_np)
|
|
|
|
def test_less(self):
|
|
a_np = np.random.random(self.shape).astype(self.dtype)
|
|
b_np = np.random.random(self.shape).astype(self.dtype)
|
|
with base.dygraph.guard():
|
|
a = paddle.to_tensor(a_np)
|
|
b = paddle.to_tensor(b_np)
|
|
res_tensor = a.less(b)
|
|
res_paddle = paddle.less(a, b)
|
|
np.testing.assert_array_equal(res_tensor.numpy(), a_np < b_np)
|
|
np.testing.assert_array_equal(res_paddle.numpy(), a_np < b_np)
|
|
|
|
def test_less_equal(self):
|
|
a_np = np.random.random(self.shape).astype(self.dtype)
|
|
b_np = np.random.random(self.shape).astype(self.dtype)
|
|
with base.dygraph.guard():
|
|
a = paddle.to_tensor(a_np)
|
|
b = paddle.to_tensor(b_np)
|
|
res = a <= b
|
|
np.testing.assert_array_equal(res.numpy(), a_np <= b_np)
|
|
|
|
def test_greater_than(self):
|
|
a_np = np.random.random(self.shape).astype(self.dtype)
|
|
b_np = np.random.random(self.shape).astype(self.dtype)
|
|
with base.dygraph.guard():
|
|
a = paddle.to_tensor(a_np)
|
|
b = paddle.to_tensor(b_np)
|
|
res = a > b
|
|
np.testing.assert_array_equal(res.numpy(), a_np > b_np)
|
|
|
|
def test_greater_equal(self):
|
|
a_np = np.random.random(self.shape).astype(self.dtype)
|
|
b_np = np.random.random(self.shape).astype(self.dtype)
|
|
with base.dygraph.guard():
|
|
a = paddle.to_tensor(a_np)
|
|
b = paddle.to_tensor(b_np)
|
|
res = a >= b
|
|
np.testing.assert_array_equal(res.numpy(), a_np >= b_np)
|
|
|
|
def test_neg(self):
|
|
a_np = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
|
|
with base.dygraph.guard():
|
|
a = paddle.to_tensor(a_np)
|
|
res = -a
|
|
np.testing.assert_array_equal(res.numpy(), -a_np)
|
|
|
|
def test_abs(self):
|
|
# test for real number
|
|
a_np = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
|
|
with base.dygraph.guard():
|
|
a = paddle.to_tensor(a_np)
|
|
res = abs(a)
|
|
np.testing.assert_array_equal(res.numpy(), np.abs(a_np))
|
|
|
|
def test_abs_complex(self):
|
|
# test for complex number
|
|
a_np = np.random.uniform(-1, 1, self.shape).astype(
|
|
self.dtype
|
|
) + 1j * np.random.uniform(-1, 1, self.shape).astype(self.dtype)
|
|
with base.dygraph.guard():
|
|
a = paddle.to_tensor(a_np)
|
|
res = abs(a)
|
|
np.testing.assert_allclose(
|
|
res.numpy(), np.abs(a_np), rtol=2e-7, atol=0.0
|
|
)
|
|
|
|
def test_float_int_long(self):
|
|
with base.dygraph.guard():
|
|
a = paddle.to_tensor(np.array([100.1]))
|
|
self.assertTrue(float(a) == 100.1)
|
|
self.assertTrue(int(a) == 100)
|
|
self.assertTrue(int(a) == 100)
|
|
|
|
a = paddle.to_tensor(1000000.0, dtype='bfloat16')
|
|
self.assertTrue(float(a) == 999424.0)
|
|
self.assertTrue(int(a) == 999424)
|
|
self.assertTrue(int(a) == 999424)
|
|
|
|
def test_complex(self):
|
|
with base.dygraph.guard():
|
|
a = paddle.to_tensor(np.array([100.1 + 99.9j]))
|
|
self.assertTrue(complex(a) == (100.1 + 99.9j))
|
|
|
|
a = paddle.to_tensor(1000000.0, dtype='bfloat16')
|
|
self.assertTrue(complex(a) == (999424 + 0j))
|
|
|
|
def test_len(self):
|
|
a_np = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
|
|
with base.dygraph.guard():
|
|
a = paddle.to_tensor(a_np)
|
|
self.assertTrue(len(a) == 10)
|
|
|
|
def test_index(self):
|
|
with base.dygraph.guard():
|
|
var1 = paddle.to_tensor(np.array([2]))
|
|
i_tmp = 0
|
|
for i in range(var1):
|
|
self.assertTrue(i == i_tmp)
|
|
i_tmp = i_tmp + 1
|
|
list1 = [1, 2, 3, 4, 5]
|
|
self.assertTrue(list1[var1] == 3)
|
|
str1 = "just test"
|
|
self.assertTrue(str1[var1] == 's')
|
|
|
|
var1 = paddle.to_tensor(2.0, dtype='bfloat16')
|
|
i_tmp = 0
|
|
for i in range(var1):
|
|
self.assertTrue(i == i_tmp)
|
|
i_tmp = i_tmp + 1
|
|
list1 = [1, 2, 3, 4, 5]
|
|
self.assertTrue(list1[var1] == 3)
|
|
str1 = "just test"
|
|
self.assertTrue(str1[var1] == 's')
|
|
|
|
def test_np_left_mul(self):
|
|
with base.dygraph.guard():
|
|
t = np.sqrt(2.0 * np.pi)
|
|
x = paddle.ones((2, 2), dtype="float32")
|
|
y = t * x
|
|
|
|
np.testing.assert_allclose(
|
|
y.numpy(),
|
|
t * np.ones((2, 2), dtype='float32'),
|
|
rtol=1e-05,
|
|
atol=0.0,
|
|
)
|
|
|
|
def test_add_different_dtype(self):
|
|
a_np = np.random.random(self.shape).astype(np.float32)
|
|
b_np = np.random.random(self.shape).astype(np.float16)
|
|
with base.dygraph.guard():
|
|
a = paddle.to_tensor(a_np)
|
|
b = paddle.to_tensor(b_np)
|
|
res = a + b
|
|
np.testing.assert_array_equal(res.numpy(), a_np + b_np)
|
|
|
|
def test_floordiv_different_dtype(self):
|
|
a_np = np.full(self.shape, 10, np.float32)
|
|
b_np = np.full(self.shape, 2, np.float16)
|
|
with base.dygraph.guard():
|
|
a = paddle.to_tensor(a_np)
|
|
b = paddle.to_tensor(b_np)
|
|
res = a // b
|
|
np.testing.assert_array_equal(res.numpy(), a_np // b_np)
|
|
|
|
def test_astype(self):
|
|
a_np = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
|
|
with base.dygraph.guard():
|
|
a = paddle.to_tensor(a_np)
|
|
res1 = a.astype(np.float16)
|
|
res2 = a.astype('float16')
|
|
res3 = a.astype(paddle.float16)
|
|
res4 = a.astype(a.dtype)
|
|
|
|
self.assertEqual(res1.dtype, res2.dtype)
|
|
self.assertEqual(res1.dtype, res3.dtype)
|
|
self.assertEqual(res4.dtype, a.dtype)
|
|
self.assertEqual(
|
|
a.data_ptr(), res4.data_ptr()
|
|
) # zero-copy if same dtype
|
|
|
|
np.testing.assert_array_equal(res1.numpy(), res2.numpy())
|
|
np.testing.assert_array_equal(res1.numpy(), res3.numpy())
|
|
|
|
def test_compare_op_broadcast(self):
|
|
a_np = np.random.uniform(-1, 1, [10, 1, 10]).astype(self.dtype)
|
|
b_np = np.random.uniform(-1, 1, [1, 1, 10]).astype(self.dtype)
|
|
with base.dygraph.guard():
|
|
a = paddle.to_tensor(a_np)
|
|
b = paddle.to_tensor(b_np)
|
|
|
|
self.assertEqual((a != b).dtype, paddle.bool)
|
|
np.testing.assert_array_equal((a != b).numpy(), a_np != b_np)
|
|
|
|
def test_tensor_patch_method(self):
|
|
paddle.disable_static()
|
|
x_np = np.random.uniform(-1, 1, [2, 3]).astype(self.dtype)
|
|
y_np = np.random.uniform(-1, 1, [2, 3]).astype(self.dtype)
|
|
z_np = np.random.uniform(-1, 1, [6, 9]).astype(self.dtype)
|
|
|
|
x = paddle.to_tensor(x_np)
|
|
y = paddle.to_tensor(y_np)
|
|
z = paddle.to_tensor(z_np)
|
|
|
|
a = paddle.to_tensor([[1, 1], [2, 2], [3, 3]])
|
|
b = paddle.to_tensor([[1, 1], [2, 2], [3, 3]])
|
|
|
|
# 1. Unary operation for Tensor
|
|
self.assertEqual(x.dim(), 2)
|
|
self.assertEqual(x.ndimension(), 2)
|
|
self.assertEqual(x.ndim, 2)
|
|
self.assertEqual(x.size, 6)
|
|
self.assertEqual(x.numel(), 6)
|
|
np.testing.assert_array_equal(x.exp().numpy(), paddle.exp(x).numpy())
|
|
np.testing.assert_array_equal(x.tanh().numpy(), paddle.tanh(x).numpy())
|
|
np.testing.assert_array_equal(x.atan().numpy(), paddle.atan(x).numpy())
|
|
np.testing.assert_array_equal(x.abs().numpy(), paddle.abs(x).numpy())
|
|
m = x.abs()
|
|
np.testing.assert_array_equal(m.sqrt().numpy(), paddle.sqrt(m).numpy())
|
|
np.testing.assert_array_equal(
|
|
m.rsqrt().numpy(), paddle.rsqrt(m).numpy()
|
|
)
|
|
np.testing.assert_array_equal(x.ceil().numpy(), paddle.ceil(x).numpy())
|
|
np.testing.assert_array_equal(
|
|
x.floor().numpy(), paddle.floor(x).numpy()
|
|
)
|
|
np.testing.assert_array_equal(x.cos().numpy(), paddle.cos(x).numpy())
|
|
np.testing.assert_array_equal(x.acos().numpy(), paddle.acos(x).numpy())
|
|
np.testing.assert_array_equal(x.asin().numpy(), paddle.asin(x).numpy())
|
|
np.testing.assert_array_equal(x.sin().numpy(), paddle.sin(x).numpy())
|
|
np.testing.assert_array_equal(x.sinh().numpy(), paddle.sinh(x).numpy())
|
|
np.testing.assert_array_equal(x.cosh().numpy(), paddle.cosh(x).numpy())
|
|
np.testing.assert_array_equal(
|
|
x.round().numpy(), paddle.round(x).numpy()
|
|
)
|
|
np.testing.assert_array_equal(
|
|
x.reciprocal().numpy(), paddle.reciprocal(x).numpy()
|
|
)
|
|
np.testing.assert_array_equal(
|
|
x.square().numpy(), paddle.square(x).numpy()
|
|
)
|
|
np.testing.assert_array_equal(x.rank().numpy(), paddle.rank(x).numpy())
|
|
np.testing.assert_array_equal(x[0].t().numpy(), paddle.t(x[0]).numpy())
|
|
np.testing.assert_array_equal(
|
|
x.asinh().numpy(), paddle.asinh(x).numpy()
|
|
)
|
|
# acosh(x) = nan, need to change input
|
|
t_np = np.random.uniform(1, 2, [2, 3]).astype(self.dtype)
|
|
t = paddle.to_tensor(t_np)
|
|
np.testing.assert_array_equal(
|
|
t.acosh().numpy(), paddle.acosh(t).numpy()
|
|
)
|
|
np.testing.assert_array_equal(
|
|
x.atanh().numpy(), paddle.atanh(x).numpy()
|
|
)
|
|
d = paddle.to_tensor(
|
|
[
|
|
[1.2285208, 1.3491015, 1.4899898],
|
|
[1.30058, 1.0688717, 1.4928783],
|
|
[1.0958099, 1.3724753, 1.8926544],
|
|
]
|
|
)
|
|
d = d.matmul(d.t())
|
|
# ROCM not support cholesky
|
|
if not base.core.is_compiled_with_rocm():
|
|
np.testing.assert_array_equal(
|
|
d.cholesky().numpy(), paddle.cholesky(d).numpy()
|
|
)
|
|
|
|
np.testing.assert_array_equal(
|
|
x.is_empty().numpy(), paddle.is_empty(x).numpy()
|
|
)
|
|
np.testing.assert_array_equal(
|
|
x.isfinite().numpy(), paddle.isfinite(x).numpy()
|
|
)
|
|
np.testing.assert_array_equal(
|
|
x.cast('int32').numpy(), paddle.cast(x, 'int32').numpy()
|
|
)
|
|
np.testing.assert_array_equal(
|
|
x.expand([3, 2, 3]).numpy(), paddle.expand(x, [3, 2, 3]).numpy()
|
|
)
|
|
np.testing.assert_array_equal(
|
|
x.tile([2, 2]).numpy(), paddle.tile(x, [2, 2]).numpy()
|
|
)
|
|
np.testing.assert_array_equal(
|
|
x.flatten().numpy(), paddle.flatten(x).numpy()
|
|
)
|
|
index = paddle.to_tensor([0, 1])
|
|
np.testing.assert_array_equal(
|
|
x.gather(index).numpy(), paddle.gather(x, index).numpy()
|
|
)
|
|
index = paddle.to_tensor([[0, 1], [1, 2]])
|
|
np.testing.assert_array_equal(
|
|
x.gather_nd(index).numpy(), paddle.gather_nd(x, index).numpy()
|
|
)
|
|
np.testing.assert_array_equal(
|
|
x.reverse([0, 1]).numpy(), paddle.reverse(x, [0, 1]).numpy()
|
|
)
|
|
np.testing.assert_array_equal(
|
|
a.reshape([3, 2]).numpy(), paddle.reshape(a, [3, 2]).numpy()
|
|
)
|
|
np.testing.assert_array_equal(
|
|
x.slice([0, 1], [0, 0], [1, 2]).numpy(),
|
|
paddle.slice(x, [0, 1], [0, 0], [1, 2]).numpy(),
|
|
)
|
|
np.testing.assert_array_equal(
|
|
x.split(2)[0].numpy(), paddle.split(x, 2)[0].numpy()
|
|
)
|
|
m = paddle.to_tensor(
|
|
np.random.uniform(-1, 1, [1, 6, 1, 1]).astype(self.dtype)
|
|
)
|
|
np.testing.assert_array_equal(
|
|
m.squeeze([]).numpy(), paddle.squeeze(m, []).numpy()
|
|
)
|
|
np.testing.assert_array_equal(
|
|
m.squeeze([1, 2]).numpy(), paddle.squeeze(m, [1, 2]).numpy()
|
|
)
|
|
m = paddle.to_tensor([2, 3, 3, 1, 5, 3], 'float32')
|
|
np.testing.assert_array_equal(
|
|
m.unique()[0].numpy(), paddle.unique(m)[0].numpy()
|
|
)
|
|
np.testing.assert_array_equal(
|
|
m.unique(return_counts=True)[1],
|
|
paddle.unique(m, return_counts=True)[1],
|
|
)
|
|
np.testing.assert_array_equal(x.flip([0]), paddle.flip(x, [0]))
|
|
np.testing.assert_array_equal(x.unbind(0), paddle.unbind(x, 0))
|
|
np.testing.assert_array_equal(x.roll(1), paddle.roll(x, 1))
|
|
np.testing.assert_array_equal(x.cumsum(1), paddle.cumsum(x, 1))
|
|
m = paddle.to_tensor(1)
|
|
np.testing.assert_array_equal(m.increment(), paddle.increment(m))
|
|
m = x.abs()
|
|
np.testing.assert_array_equal(m.log(), paddle.log(m))
|
|
np.testing.assert_array_equal(x.pow(2), paddle.pow(x, 2))
|
|
np.testing.assert_array_equal(x.reciprocal(), paddle.reciprocal(x))
|
|
|
|
# 2. Binary operation
|
|
np.testing.assert_array_equal(
|
|
x.divide(y).numpy(), paddle.divide(x, y).numpy()
|
|
)
|
|
np.testing.assert_array_equal(
|
|
x.matmul(y, True, False).numpy(),
|
|
paddle.matmul(x, y, True, False).numpy(),
|
|
)
|
|
np.testing.assert_array_equal(
|
|
x.norm(p='fro', axis=[0, 1]).numpy(),
|
|
paddle.norm(x, p='fro', axis=[0, 1]).numpy(),
|
|
)
|
|
np.testing.assert_array_equal(
|
|
x.dist(y).numpy(), paddle.dist(x, y).numpy()
|
|
)
|
|
np.testing.assert_array_equal(
|
|
x.cross(y).numpy(), paddle.cross(x, y).numpy()
|
|
)
|
|
m = x.expand([2, 2, 3])
|
|
n = y.expand([2, 2, 3]).transpose([0, 2, 1])
|
|
np.testing.assert_array_equal(
|
|
m.bmm(n).numpy(), paddle.bmm(m, n).numpy()
|
|
)
|
|
np.testing.assert_array_equal(
|
|
x.histogram(5, -1, 1).numpy(), paddle.histogram(x, 5, -1, 1).numpy()
|
|
)
|
|
np.testing.assert_array_equal(
|
|
x.equal(y).numpy(), paddle.equal(x, y).numpy()
|
|
)
|
|
np.testing.assert_array_equal(
|
|
x.greater_equal(y).numpy(), paddle.greater_equal(x, y).numpy()
|
|
)
|
|
np.testing.assert_array_equal(
|
|
x.greater_than(y).numpy(), paddle.greater_than(x, y).numpy()
|
|
)
|
|
np.testing.assert_array_equal(
|
|
x.less_equal(y).numpy(), paddle.less_equal(x, y).numpy()
|
|
)
|
|
np.testing.assert_array_equal(
|
|
x.less_than(y).numpy(), paddle.less_than(x, y).numpy()
|
|
)
|
|
np.testing.assert_array_equal(
|
|
x.not_equal(y).numpy(), paddle.not_equal(x, y).numpy()
|
|
)
|
|
np.testing.assert_array_equal(
|
|
x.equal_all(y).numpy(), paddle.equal_all(x, y).numpy()
|
|
)
|
|
np.testing.assert_array_equal(
|
|
x.allclose(y).numpy(), paddle.allclose(x, y).numpy()
|
|
)
|
|
m = x.expand([2, 2, 3])
|
|
np.testing.assert_array_equal(
|
|
x.expand_as(m).numpy(), paddle.expand_as(x, m).numpy()
|
|
)
|
|
index = paddle.to_tensor([2, 1, 0])
|
|
np.testing.assert_array_equal(
|
|
a.scatter(index, b).numpy(), paddle.scatter(a, index, b).numpy()
|
|
)
|
|
|
|
# 3. Bool tensor operation
|
|
x = paddle.to_tensor([[True, False], [True, False]])
|
|
y = paddle.to_tensor([[False, False], [False, True]])
|
|
np.testing.assert_array_equal(
|
|
x.logical_and(y).numpy(), paddle.logical_and(x, y).numpy()
|
|
)
|
|
np.testing.assert_array_equal(
|
|
x.logical_not(y).numpy(), paddle.logical_not(x, y).numpy()
|
|
)
|
|
np.testing.assert_array_equal(
|
|
x.logical_or(y).numpy(), paddle.logical_or(x, y).numpy()
|
|
)
|
|
np.testing.assert_array_equal(
|
|
x.logical_xor(y).numpy(), paddle.logical_xor(x, y).numpy()
|
|
)
|
|
np.testing.assert_array_equal(
|
|
x.logical_and(y).numpy(), paddle.logical_and(x, y).numpy()
|
|
)
|
|
a = paddle.to_tensor([[1, 2], [3, 4]])
|
|
b = paddle.to_tensor([[4, 3], [2, 1]])
|
|
np.testing.assert_array_equal(
|
|
x.where(a, b).numpy(), paddle.where(x, a, b).numpy()
|
|
)
|
|
|
|
x_np = np.random.randn(3, 6, 9, 7)
|
|
x = paddle.to_tensor(x_np)
|
|
x_T = x.T
|
|
self.assertTrue(x_T.shape, [7, 9, 6, 3])
|
|
np.testing.assert_array_equal(x_T.numpy(), x_np.T)
|
|
|
|
x_np = np.random.randn(3, 6, 9, 7)
|
|
x = paddle.to_tensor(x_np)
|
|
x_mT = x.mT
|
|
self.assertTrue(x_mT.shape, [3, 6, 7, 9])
|
|
np.testing.assert_array_equal(
|
|
x_mT.numpy(), x_np.transpose([0, 1, 3, 2])
|
|
)
|
|
|
|
x_np = np.random.randn(3)
|
|
x = paddle.to_tensor(x_np)
|
|
self.assertRaises(ValueError, getattr, x, "mT")
|
|
|
|
self.assertTrue(inspect.ismethod(a.dot))
|
|
self.assertTrue(inspect.ismethod(a.logsumexp))
|
|
self.assertTrue(inspect.ismethod(a.multiplex))
|
|
self.assertTrue(inspect.ismethod(a.prod))
|
|
self.assertTrue(inspect.ismethod(a.scale))
|
|
self.assertTrue(inspect.ismethod(a.stanh))
|
|
self.assertTrue(inspect.ismethod(a.add_n))
|
|
self.assertTrue(inspect.ismethod(a.max))
|
|
self.assertTrue(inspect.ismethod(a.maximum))
|
|
self.assertTrue(inspect.ismethod(a.min))
|
|
self.assertTrue(inspect.ismethod(a.minimum))
|
|
self.assertTrue(inspect.ismethod(a.floor_divide))
|
|
self.assertTrue(inspect.ismethod(a.remainder))
|
|
self.assertTrue(inspect.ismethod(a.floor_mod))
|
|
self.assertTrue(inspect.ismethod(a.multiply))
|
|
self.assertTrue(inspect.ismethod(a.inverse))
|
|
self.assertTrue(inspect.ismethod(a.log1p))
|
|
self.assertTrue(inspect.ismethod(a.erf))
|
|
self.assertTrue(inspect.ismethod(a.addmm))
|
|
self.assertTrue(inspect.ismethod(a.clip))
|
|
self.assertTrue(inspect.ismethod(a.trace))
|
|
self.assertTrue(inspect.ismethod(a.kron))
|
|
self.assertTrue(inspect.ismethod(a.isinf))
|
|
self.assertTrue(inspect.ismethod(a.isnan))
|
|
self.assertTrue(inspect.ismethod(a.concat))
|
|
self.assertTrue(inspect.ismethod(a.broadcast_to))
|
|
self.assertTrue(inspect.ismethod(a.scatter_nd_add))
|
|
self.assertTrue(inspect.ismethod(a.scatter_nd))
|
|
self.assertTrue(inspect.ismethod(a.shard_index))
|
|
self.assertTrue(inspect.ismethod(a.chunk))
|
|
self.assertTrue(inspect.ismethod(a.stack))
|
|
self.assertTrue(inspect.ismethod(a.strided_slice))
|
|
self.assertTrue(inspect.ismethod(a.unsqueeze))
|
|
self.assertTrue(inspect.ismethod(a.unstack))
|
|
self.assertTrue(inspect.ismethod(a.argmax))
|
|
self.assertTrue(inspect.ismethod(a.argmin))
|
|
self.assertTrue(inspect.ismethod(a.argsort))
|
|
self.assertTrue(inspect.ismethod(a.masked_select))
|
|
self.assertTrue(inspect.ismethod(a.topk))
|
|
self.assertTrue(inspect.ismethod(a.index_select))
|
|
self.assertTrue(inspect.ismethod(a.nonzero))
|
|
self.assertTrue(inspect.ismethod(a.sort))
|
|
self.assertTrue(inspect.ismethod(a.index_sample))
|
|
self.assertTrue(inspect.ismethod(a.mean))
|
|
self.assertTrue(inspect.ismethod(a.std))
|
|
self.assertTrue(inspect.ismethod(a.numel))
|
|
self.assertTrue(inspect.ismethod(x.asin_))
|
|
self.assertTrue(inspect.ismethod(x.atan2))
|
|
self.assertTrue(inspect.ismethod(x.atanh_))
|
|
self.assertTrue(inspect.ismethod(x.coalesce))
|
|
self.assertTrue(inspect.ismethod(x.diagflat))
|
|
self.assertTrue(inspect.ismethod(x.multinomial))
|
|
self.assertTrue(inspect.ismethod(x.pinv))
|
|
self.assertTrue(inspect.ismethod(x.renorm))
|
|
self.assertTrue(inspect.ismethod(x.renorm_))
|
|
self.assertTrue(inspect.ismethod(x.tan))
|
|
self.assertTrue(inspect.ismethod(x.tan_))
|
|
self.assertTrue(inspect.ismethod(x.tril))
|
|
self.assertTrue(inspect.ismethod(x.tril_))
|
|
self.assertTrue(inspect.ismethod(x.triu))
|
|
self.assertTrue(inspect.ismethod(x.triu_))
|
|
self.assertTrue(inspect.ismethod(x.stft))
|
|
self.assertTrue(inspect.ismethod(x.istft))
|
|
self.assertTrue(inspect.ismethod(x.abs_))
|
|
self.assertTrue(inspect.ismethod(x.acos_))
|
|
self.assertTrue(inspect.ismethod(x.atan_))
|
|
self.assertTrue(inspect.ismethod(x.cos_))
|
|
self.assertTrue(inspect.ismethod(x.cosh_))
|
|
self.assertTrue(inspect.ismethod(x.sin_))
|
|
self.assertTrue(inspect.ismethod(x.sinh_))
|
|
self.assertTrue(inspect.ismethod(x.acosh_))
|
|
self.assertTrue(inspect.ismethod(x.asinh_))
|
|
self.assertTrue(inspect.ismethod(x.diag))
|
|
|
|
def test_complex_scalar(self):
|
|
a_np = np.random.random(self.shape).astype(self.dtype)
|
|
with base.dygraph.guard():
|
|
a = paddle.to_tensor(a_np)
|
|
res = 1j * a
|
|
np.testing.assert_array_equal(res.numpy(), 1j * a_np)
|
|
|
|
def test_matmul(self):
|
|
x_np = np.random.uniform(-1, 1, [2, 3]).astype(self.dtype)
|
|
y_np = np.random.uniform(-1, 1, [3, 2]).astype(self.dtype)
|
|
except_out = x_np @ y_np
|
|
|
|
with base.dygraph.guard():
|
|
x = paddle.to_tensor(x_np)
|
|
y = paddle.to_tensor(y_np)
|
|
out = x @ y
|
|
np.testing.assert_allclose(out.numpy(), except_out, atol=1e-03)
|
|
|
|
def test_coalesce(self):
|
|
indices = [[0, 0, 1], [1, 1, 2]]
|
|
values = [1.0, 2.0, 3.0]
|
|
sp_x = paddle.sparse.sparse_coo_tensor(indices, values)
|
|
sp_x = sp_x.coalesce()
|
|
self.assertTrue(isinstance(sp_x, paddle.Tensor))
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|