wehub-resource-sync
565 lines
21 KiB
Python
565 lines
21 KiB
Python
# Copyright (c) 2025 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 get_device_place, is_custom_device
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import paddle
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from paddle.base import core
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class TestCompatMinMaxBase(unittest.TestCase):
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"""The default base class is for testing min-related ops"""
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def __init__(
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self,
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*args,
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test_op=paddle.compat.min,
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origin_op=paddle.min,
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index_op=paddle.argmin,
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test_op_name="paddle.compat.min",
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origin_op_name="paddle.min",
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**kwargs,
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):
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super().__init__(*args, **kwargs)
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paddle.disable_static()
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self.test_op = test_op
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self.origin_op = origin_op
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self.index_op = index_op
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self.test_op_name = test_op_name
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self.origin_op_name = origin_op_name
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np.random.seed(1)
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def test_case1_simple_reduce_all(self):
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data = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0]], dtype='float32')
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val = self.test_op(data)
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if self.test_op_name.endswith("min"):
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self.assertAlmostEqual(val.item(), 1.0)
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else:
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self.assertAlmostEqual(val.item(), 4.0)
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def test_case2_reduce_dim(self):
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"""Test dim/keepdim"""
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data = paddle.to_tensor(
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[[[5, 8], [2, 1]], [[7, 3], [9, 6]]], dtype='float32'
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)
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if self.test_op_name.endswith("min"):
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in_dim = 1
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result = self.test_op(data, dim=in_dim)
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expected_res = np.array([[[5, 3], [2, 1]]])
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self.assertEqual(result.values.shape, [2, 2])
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np.testing.assert_array_equal(
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result.values.numpy(), np.array([[2, 1], [7, 3]])
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)
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np.testing.assert_array_equal(
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result.indices.numpy(), np.array([[1, 1], [0, 0]])
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)
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else:
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in_dim = 2
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result = self.test_op(data, dim=in_dim)
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expected_res = np.array([[[7, 8], [9, 6]]])
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self.assertEqual(result.values.shape, [2, 2])
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np.testing.assert_array_equal(
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result.values.numpy(), np.array([[8, 2], [7, 9]])
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)
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np.testing.assert_array_equal(
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result.indices.numpy(), np.array([[1, 0], [0, 0]])
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)
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result_keep = self.test_op(data, dim=0, keepdim=True)
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self.assertEqual(result_keep.values.shape, [1, 2, 2])
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np.testing.assert_array_equal(result_keep.values.numpy(), expected_res)
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result_keep = self.test_op(data, 0, keepdim=True)
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np.testing.assert_array_equal(result_keep.values.numpy(), expected_res)
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result_neg = self.test_op(data, dim=in_dim - 3)
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np.testing.assert_array_equal(
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result_neg.values.numpy(), result.values.numpy()
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)
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def test_case2_grad(self):
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data = paddle.to_tensor(
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[[[1.0, 2.0], [1.0, 3.0]], [[4.0, 1.0], [5.0, 1.0]]],
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dtype='float32',
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stop_gradient=False,
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)
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y = data * 2
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result = self.test_op(y, dim=2)
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result.values.backward()
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if self.test_op_name.endswith("min"):
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expected_grad = np.array(
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[[[2.0, 0.0], [2.0, 0.0]], [[0.0, 2.0], [0.0, 2.0]]]
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)
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expected_grad2 = np.array(
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[[[2.0, 4.0], [0.0, 0.0]], [[8.0, 2.0], [0.0, 0.0]]]
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)
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else:
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expected_grad = np.array(
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[[[0.0, 2.0], [0.0, 2.0]], [[2.0, 0.0], [2.0, 0.0]]]
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)
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expected_grad2 = np.array(
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[[[2.0, 0.0], [0.0, 6.0]], [[0.0, 2.0], [10.0, 0.0]]]
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)
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np.testing.assert_allclose(data.grad.numpy(), expected_grad, atol=1e-6)
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data.clear_grad()
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y = data * data
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result = self.test_op(y, dim=1)
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result[0].backward()
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np.testing.assert_allclose(data.grad.numpy(), expected_grad2, atol=1e-6)
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def test_case3_elementwise(self):
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x = paddle.to_tensor([[1, 5], [4, 2]], dtype='float32')
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y = paddle.to_tensor([[3, 2], [1, 6]], dtype='float32')
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z = paddle.to_tensor([3, 4], dtype='float32')
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broadcast_res = self.test_op(x, z)
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result = self.test_op(x, y)
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if self.test_op_name.endswith("min"):
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np.testing.assert_array_equal(
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result.numpy(), np.array([[1, 2], [1, 2]])
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)
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np.testing.assert_array_equal(
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broadcast_res.numpy(), np.array([[1, 4], [3, 2]])
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)
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else:
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np.testing.assert_array_equal(
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result.numpy(), np.array([[3, 5], [4, 6]])
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)
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np.testing.assert_array_equal(
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broadcast_res.numpy(), np.array([[3, 5], [4, 4]])
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)
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def test_case3_grad(self):
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x = paddle.to_tensor(
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[[1.0, 2.0], [3.0, 4.0]], dtype=paddle.float32, stop_gradient=False
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)
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y = paddle.to_tensor(
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[[0.5, 2.5], [2.0, 3.5]], dtype=paddle.float32, stop_gradient=False
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)
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val = self.test_op(x, y)
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val.backward()
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expected_x_grad = np.array([[0.0, 1.0], [0.0, 0.0]])
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expected_y_grad = np.array([[1.0, 0.0], [1.0, 1.0]])
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if self.test_op_name.endswith("max"):
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expected_x_grad = 1 - expected_x_grad
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expected_y_grad = 1 - expected_y_grad
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np.testing.assert_allclose(x.grad.numpy(), expected_x_grad)
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np.testing.assert_allclose(y.grad.numpy(), expected_y_grad)
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def test_edge_cases(self):
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"""Edge cases test"""
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# uniform distributed gradient
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uniform_data = paddle.ones([2, 3], dtype='float64')
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uniform_data.stop_gradient = False
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val = self.test_op(uniform_data)
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val.sum().backward()
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# uniformly distributed
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expected_grad = np.full((2, 3), 1.0 / 6.0)
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np.testing.assert_allclose(uniform_data.grad.numpy(), expected_grad)
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uniform_data.clear_grad()
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val = self.test_op(uniform_data, 0)
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val.values.sum().backward()
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# take_along_axis like gradient behavior
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expected_grad = np.array([[1.0, 1.0, 1.0], [0.0, 0.0, 0.0]])
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np.testing.assert_allclose(uniform_data.grad.numpy(), expected_grad)
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# 0-dim tensor
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dim0_tensor = paddle.to_tensor(2, dtype='float32')
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val = self.test_op(dim0_tensor)
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np.testing.assert_allclose(val.numpy(), np.array(2.0, dtype=np.float32))
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# 1-dim tensor
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dim1_tensor = paddle.to_tensor([1], dtype='uint8')
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val = self.test_op(dim1_tensor, dim=-1, keepdim=True)
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np.testing.assert_array_equal(
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val[0].numpy(), np.array([1], dtype=np.uint8)
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)
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np.testing.assert_array_equal(
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val[1].numpy(), np.array([0], dtype=np.int64)
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)
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def test_compare_with_index_ops_to_origin(self):
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dtypes = ['float32', 'float64', 'int32', 'int64', 'uint8']
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for i, dtype in enumerate(dtypes):
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data = paddle.to_tensor([[1, 2, 3], [4, 5, 6]], dtype=dtype)
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# `bfloat16`, `uint8` and `float16` are rejected for min/argmin
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vals_inds = self.test_op(data, dim=0)
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self.assertEqual(vals_inds.values.dtype, data.dtype)
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self.assertEqual(vals_inds.indices.dtype, paddle.int64)
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origin_indices = self.index_op(data, axis=0, dtype="int64")
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if dtype != 'uint8':
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origin_values = self.origin_op(data, axis=0)
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else:
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origin_values = paddle.take_along_axis(
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data, origin_indices.unsqueeze(0), axis=0
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)
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origin_values.squeeze_(axis=0)
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if i < 4: # floating point
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np.testing.assert_allclose(
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vals_inds.values.numpy(), origin_values.numpy()
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)
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else:
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np.testing.assert_array_equal(
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vals_inds.values.numpy(), origin_values.numpy()
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)
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np.testing.assert_array_equal(
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vals_inds[1].numpy(), origin_indices.numpy()
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)
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def test_case1_out(self):
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data = np.random.randn(4, 5, 6).astype(np.float32)
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x = paddle.to_tensor(data, stop_gradient=False)
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y = paddle.to_tensor(data, stop_gradient=False)
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out = paddle.to_tensor(0)
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self.test_op(x, out=out)
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gt_out = self.origin_op(y)
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gt_out.backward()
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out.backward()
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np.testing.assert_allclose(out.numpy(), gt_out.numpy())
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np.testing.assert_allclose(x.grad.numpy(), y.grad.numpy())
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def test_case2_out(self):
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for type_to_use in (list, tuple):
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data = np.random.randn(3, 17, 5).astype(np.float32)
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x = paddle.to_tensor(data, stop_gradient=False)
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y = paddle.to_tensor(data, stop_gradient=False)
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out = type_to_use((paddle.to_tensor(0), paddle.to_tensor(0)))
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self.test_op(x, dim=1, out=out)
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gt_vals = self.origin_op(y, axis=1)
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gt_inds = self.index_op(y, axis=1)
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gt_vals.backward()
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out[0].backward()
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np.testing.assert_allclose(out[0].numpy(), gt_vals.numpy())
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np.testing.assert_array_equal(out[1].numpy(), gt_inds.numpy())
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np.testing.assert_allclose(x.grad.numpy(), y.grad.numpy())
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def test_case3_out(self):
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data = np.random.randn(3, 4, 5).astype(np.float32)
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x = paddle.to_tensor(data)
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y = paddle.to_tensor(data)
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out = paddle.to_tensor(0)
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self.test_op(x, paddle.ones_like(x), out=out)
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if self.test_op_name.endswith("min"):
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gt_vals = paddle.minimum(x, paddle.ones_like(x))
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else:
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gt_vals = paddle.maximum(x, paddle.ones_like(x))
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np.testing.assert_allclose(out.numpy(), gt_vals.numpy())
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def test_error_handling(self):
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"""Test whether correct exception will be thrown. Skip error messages (some of them are long)"""
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err_msg1 = (
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"Tensors with integral type: 'paddle.int32' should stop gradient."
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)
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err_msg2 = (
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f"{self.origin_op_name}() received unexpected keyword arguments 'dim', 'input'. "
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f"\nDid you mean to use {self.test_op_name}() instead?"
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)
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err_msg3 = (
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f"{self.test_op_name}() received unexpected keyword argument 'axis'. "
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f"\nDid you mean to use {self.origin_op_name}() instead?"
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)
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err_msg4 = (
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"Non-CUDA GPU placed Tensor does not have 'paddle.float16' op registered.\n"
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"Paddle support following DataTypes: int32, int64, float64, float32, uint8"
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)
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err_msg5 = (
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"input should be a tensor, but got an instance with type 'list'"
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)
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# empty tensor
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empty_tensor = paddle.to_tensor([], dtype='float32')
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with self.assertRaises(ValueError):
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self.test_op(empty_tensor)
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# mixed parameters case 1
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input_ts = paddle.to_tensor([1, 2, 3], dtype='float32')
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other_ts = paddle.to_tensor([1])
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with self.assertRaises(TypeError):
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self.test_op(input_ts, other=other_ts, dim=0)
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# mixed parameters case 2
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with self.assertRaises(TypeError):
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self.test_op(input_ts, 0, other=other_ts)
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# trying to perform grad ops for integral types
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with self.assertRaises(TypeError) as cm:
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tensor = paddle.ones([2, 2], dtype=paddle.int32)
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tensor.stop_gradient = False
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tensors = self.test_op(tensor, dim=0)
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self.assertEqual(str(cm.exception), err_msg1)
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# explicit None case 1
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with self.assertRaises(TypeError) as cm:
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self.test_op(input_ts, dim=None)
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# explicit None case 2
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with self.assertRaises(TypeError) as cm:
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self.test_op(input_ts, None, keepdim=True)
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# keepdim specified without specifying dim
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with self.assertRaises(TypeError) as cm:
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self.test_op(input_ts, keepdim=True)
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# Wrong *args specification case 1
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with self.assertRaises(TypeError) as cm:
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self.test_op(input_ts, False)
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# Wrong *args specification case 2
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with self.assertRaises(TypeError) as cm:
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self.test_op(input_ts, other_ts, True)
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# Tensor input for dim case 1
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with self.assertRaises(TypeError) as cm:
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self.test_op(input_ts, dim=paddle.to_tensor([0]))
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# Tensor input for dim case 2
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with self.assertRaises(TypeError) as cm:
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self.test_op(input_ts, dim=paddle.to_tensor(0))
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# Tensor input for dim case 3
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with self.assertRaises(TypeError) as cm:
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self.test_op(input_ts, paddle.to_tensor([0]), keepdim=True)
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# Tensor input for dim case 4
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with self.assertRaises(TypeError) as cm:
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self.test_op(input_ts, paddle.to_tensor([0]), True)
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# Duplicate Arguments case 1
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with self.assertRaises(TypeError) as cm:
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self.test_op(input_ts, 0, dim=0)
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# Duplicate Arguments case 2
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with self.assertRaises(TypeError) as cm:
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self.test_op(input_ts, other_ts, other=0)
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# Duplicate Arguments case 3
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with self.assertRaises(TypeError) as cm:
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self.test_op(input_ts, dim=0, other=0, keepdim=True)
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# Wrong API used case 1
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with self.assertRaises(TypeError) as cm:
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self.origin_op(input=input_ts, dim=0)
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self.assertEqual(str(cm.exception), err_msg2)
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# Wrong API used case 2
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with self.assertRaises(TypeError) as cm:
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self.test_op(input_ts, axis=0)
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self.assertEqual(str(cm.exception), err_msg3)
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# Rejected on CPU types
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with self.assertRaises(TypeError) as cm:
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tensor = paddle.to_tensor([1, 2, 3], dtype="float16")
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cpu_tensor = tensor.to("cpu")
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self.test_op(cpu_tensor, dim=0)
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self.assertEqual(str(cm.exception), err_msg4)
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# Wrong input type
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with self.assertRaises(TypeError) as cm:
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self.test_op([1, 2])
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self.assertEqual(str(cm.exception), err_msg5)
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# Wrong second parameter type
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with self.assertRaises(TypeError):
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self.test_op(input_ts, "first_dim")
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paddle.enable_static()
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with (
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self.assertRaises(RuntimeError) as cm,
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paddle.static.program_guard(paddle.static.Program()),
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):
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x = paddle.static.data(name='x', shape=[None, 6], dtype='float32')
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result0, result1 = self.test_op(
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paddle.zeros([3, 4]),
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dim=1,
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out=(
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paddle.zeros([3, 4]),
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paddle.zeros([3, 4], dtype=paddle.int64),
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),
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)
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place = (
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get_device_place()
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if (paddle.is_compiled_with_cuda() or is_custom_device())
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else paddle.CPUPlace()
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)
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paddle.static.Executor(place).run()
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self.assertEqual(
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str(cm.exception),
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"Using `out` static graph CINN backend is currently not supported. Directly return the tensor tuple instead.\n",
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)
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paddle.disable_static()
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def test_wrong_out_input(dim, out_input):
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with self.assertRaises(TypeError) as cm:
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if dim is None:
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self.test_op(input_ts, out=out_input)
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else:
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self.test_op(input_ts, dim=dim, out=out_input)
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test_wrong_out_input(0, [0, paddle.to_tensor(0)])
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test_wrong_out_input(0, paddle.to_tensor(0))
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test_wrong_out_input(None, 0)
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test_wrong_out_input(None, (paddle.to_tensor(0),))
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def _compare_with_origin_static(
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self, input_shape, axis_or_other=0, keepdim=False, use_out=False
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):
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"""Test Case 2 and Case 3 for return output or param output in static graph mode
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TODO(heqianyue): DO NOT set use_out for now!
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Currently, static graph + CINN backend will result in unresolved dependency bug for assign op
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This test is disabled for now, but will be useful when dy2st bug is fixed.
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"""
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numel = 1
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for v in input_shape:
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numel *= v
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paddle.enable_static()
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with paddle.static.program_guard(paddle.static.Program()):
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input_tensor = paddle.arange(numel, dtype=paddle.float32).reshape(
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input_shape
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)
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|
|
y = input_tensor**2
|
|
if isinstance(axis_or_other, int):
|
|
if use_out:
|
|
out = [paddle.to_tensor(0), paddle.to_tensor([0])]
|
|
self.test_op(y, dim=axis_or_other, keepdim=keepdim, out=out)
|
|
values, indices = out
|
|
else:
|
|
values, indices = self.test_op(
|
|
y, dim=axis_or_other, keepdim=keepdim
|
|
)
|
|
gt_values = self.origin_op(
|
|
y, axis=axis_or_other, keepdim=keepdim
|
|
)
|
|
gt_indices = self.index_op(
|
|
y, axis=axis_or_other, keepdim=keepdim
|
|
)
|
|
else:
|
|
if use_out:
|
|
out = paddle.to_tensor(0)
|
|
self.test_op(y, axis_or_other, out=out)
|
|
values, indices = out, paddle.to_tensor(0)
|
|
else:
|
|
values, indices = self.test_op(y, axis_or_other)
|
|
if self.test_op_name.endswith("min"):
|
|
gt_values = paddle.minimum(y, axis=axis_or_other, out=None)
|
|
else:
|
|
gt_values = paddle.maximum(y, axis=axis_or_other)
|
|
gt_indices = paddle.to_tensor(0)
|
|
|
|
place = get_device_place()
|
|
exe = paddle.static.Executor(place)
|
|
values_np, indices_np, gt_values_np, gt_indices_np = exe.run(
|
|
fetch_list=[values, indices, gt_values, gt_indices]
|
|
)
|
|
np.testing.assert_allclose(values_np, gt_values_np)
|
|
np.testing.assert_equal(indices_np, gt_indices_np)
|
|
paddle.disable_static()
|
|
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device()),
|
|
"core is not compiled with CUDA, skipping",
|
|
)
|
|
def test_static_graph(self):
|
|
self._compare_with_origin_static([3, 10, 2], 1)
|
|
self._compare_with_origin_static([3, 10, 2], 0, keepdim=True)
|
|
self._compare_with_origin_static([17], 0)
|
|
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device()),
|
|
"core is not compiled with CUDA, skipping",
|
|
)
|
|
def test_static_unary_shape_infer_1(self):
|
|
# min/max with index is a GPU only op, no need for testing if there is no GPU
|
|
|
|
@paddle.jit.to_static(full_graph=True)
|
|
def static_func1(x):
|
|
y = paddle.zeros([2, 3, 4])
|
|
return paddle._C_ops.min_with_index(y, x.shape[0], False, False)
|
|
|
|
@paddle.jit.to_static(full_graph=True)
|
|
def static_func2(x):
|
|
y = paddle.zeros([2, 3, 4])
|
|
return paddle._C_ops.min_with_index(y, x.shape[0], True, False)
|
|
|
|
input_ts1 = paddle.to_tensor([1])
|
|
input_ts2 = paddle.to_tensor([1, 2])
|
|
val1, ind1 = static_func1(input_ts1)
|
|
val2, ind2 = static_func2(input_ts2)
|
|
|
|
self.assertEqual(val1.shape, [2, 4])
|
|
self.assertEqual(ind1.shape, [2, 4])
|
|
self.assertEqual(val2.shape, [2, 3, 1])
|
|
self.assertEqual(ind2.shape, [2, 3, 1])
|
|
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device()),
|
|
"core is not compiled with CUDA, skipping",
|
|
)
|
|
def test_static_unary_shape_infer_2(self):
|
|
# min/max with index is a GPU only op, no need for testing if there is no GPU
|
|
|
|
@paddle.jit.to_static(full_graph=True)
|
|
def static_func1(x):
|
|
dim = paddle.arange(0, 1).shape[0]
|
|
y = paddle.zeros([2, 3, 4])
|
|
return paddle._C_ops.max_with_index(y, dim, False, True)
|
|
|
|
@paddle.jit.to_static(full_graph=True)
|
|
def static_func2(x):
|
|
dim = paddle.arange(0, 2).shape[0]
|
|
y = paddle.zeros([2, 3, 4])
|
|
return paddle._C_ops.max_with_index(y, dim, True, True)
|
|
|
|
x1 = paddle.to_tensor([1])
|
|
x2 = paddle.to_tensor([1, 2])
|
|
val1, ind1 = static_func1(x1)
|
|
val2, ind2 = static_func2(x2)
|
|
|
|
self.assertEqual(val1.shape, [])
|
|
self.assertEqual(ind1.shape, [])
|
|
self.assertEqual(val2.shape, [1, 1, 1])
|
|
self.assertEqual(ind2.shape, [1, 1, 1])
|
|
|
|
|
|
class TestCompatMax(TestCompatMinMaxBase):
|
|
def __init__(self, *args, **kwargs):
|
|
super().__init__(
|
|
*args,
|
|
test_op=paddle.compat.max,
|
|
origin_op=paddle.max,
|
|
index_op=paddle.argmax,
|
|
test_op_name="paddle.compat.max",
|
|
origin_op_name="paddle.max",
|
|
**kwargs,
|
|
)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|