wehub-resource-sync
519 lines
15 KiB
Python
519 lines
15 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import annotations
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import math
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import operator
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import unittest
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import weakref
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from test_case_base import (
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TestCaseBase,
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test_instruction_translator_cache_context,
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)
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import paddle
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from paddle.jit.sot.psdb import check_no_breakgraph
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def dispatch_len(x: paddle.Tensor):
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return len(x.shape)
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def dispatch_tensor_len(x: paddle.Tensor):
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return len(x)
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def dispatch_reversed(x: paddle.Tensor | int, y: paddle.Tensor | int):
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return list(reversed([x + 1, y - 1, x * 10, y + 1000]))
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def dispatch_bool(x: paddle.Tensor):
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return operator.truth(x.shape) and bool(x.shape)
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def dispatch_ceil(x: paddle.Tensor | float):
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return math.ceil(x) + 1
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def dispatch_floor(x: paddle.Tensor | float):
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return math.floor(x) + 1
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def test_sum_tuple(x: paddle.Tensor | int, y: paddle.Tensor | int):
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return sum((x, y))
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def test_sum_tuple2(
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x: paddle.Tensor | int | list[int] | list[paddle.Tensor],
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y: paddle.Tensor | int | list[int] | list[paddle.Tensor],
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):
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return sum((x, y), x)
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def test_sum_tuple3(x):
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return sum((), x)
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def test_sum_list(x: paddle.Tensor | int, y: paddle.Tensor | int):
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return sum([x, y])
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def test_sum_list2(
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x: paddle.Tensor | int | list[int] | list[paddle.Tensor],
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y: paddle.Tensor | int | list[int] | list[paddle.Tensor],
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):
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return sum([x, y], x)
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def test_sum_list3(x):
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return sum([], x)
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def test_tensor_sum(x: paddle.Tensor):
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return sum(x)
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def test_tensor_sum_api(x: paddle.Tensor):
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return x.sum()
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def test_pow(x: paddle.Tensor | int, y: paddle.Tensor | int):
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return pow(x, y)
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def test_pow2(x: paddle.Tensor | int, y: paddle.Tensor | int):
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return pow(x, y, 1)
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def test_tensor_pow_api(x: paddle.Tensor, y: paddle.Tensor | int):
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return x.pow(y)
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def test_math_pow(x: int, y: int):
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return math.pow(x, y)
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def test_chr(x: int | hex | paddle.Tensor):
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return chr(x)
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def test_ord(x: str):
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return ord(x)
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@check_no_breakgraph
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def test_min():
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return min(9, 8, 2, 4, 1, 7, 3, 5, 6)
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@check_no_breakgraph
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def test_max():
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return max(9, 8, 2, 4, 1, 7, 3, 5, 6)
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@check_no_breakgraph
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def test_sqrt(x: int):
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return math.sqrt(x)
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@check_no_breakgraph
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def test_log(x: int):
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return math.log(x)
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@check_no_breakgraph
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def test_any(var):
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return any(var)
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@check_no_breakgraph
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def test_any_iter(var):
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return any(iter(var))
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@check_no_breakgraph
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def test_all(var):
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return all(var)
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@check_no_breakgraph
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def test_all_iter(var):
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return all(iter(var))
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@check_no_breakgraph
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def test_builtin_type_check_eq():
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a = 1
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b = []
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c = ()
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d = {}
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eq_results = (
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a == b, a == c, a == d,
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b == a, b == c, b == d,
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c == a, c == b, c == d,
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) # fmt: skip
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ne_results = (
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a != b, a != c, a != d,
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b != a, b != c, b != d,
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c != a, c != b, c != d,
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) # fmt: skip
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return eq_results, ne_results
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@check_no_breakgraph
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def test_is(x, y):
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return x is y
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class TestBuiltinDispatch(TestCaseBase):
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def test_dispatch_len(self):
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self.assert_results(dispatch_len, paddle.to_tensor([1, 2, 3]))
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def test_dispatch_bool(self):
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self.assert_results(dispatch_bool, paddle.to_tensor([1, 2, 3]))
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def test_dispatch_tensor_len(self):
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with test_instruction_translator_cache_context() as ctx:
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self.assert_results(
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dispatch_tensor_len, paddle.to_tensor([1, 2, 3])
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)
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self.assertEqual(ctx.translate_count, 1)
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self.assert_results(
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dispatch_tensor_len, paddle.to_tensor([4, 5, 6])
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)
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self.assertEqual(ctx.translate_count, 1)
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def test_dispatch_list_reversed(self):
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self.assert_results(dispatch_reversed, paddle.to_tensor(1), 2)
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self.assert_results(dispatch_reversed, 2, paddle.to_tensor(1))
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def test_dispatch_tensor_reversed(self):
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self.assert_results(
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dispatch_reversed,
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paddle.to_tensor([1, 2]),
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paddle.to_tensor([3, 4]),
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)
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def test_not_dispatch_tensor_ceil(self):
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# ceil should break graph, since it returns a int rather than a tensor
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self.assert_results(dispatch_ceil, paddle.to_tensor(1.2))
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def test_dispatch_float_ceil(self):
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self.assert_results(dispatch_ceil, 1.2)
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def test_not_dispatch_tensor_floor(self):
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# floor should break graph, since it returns a int rather than a tensor
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self.assert_results(dispatch_floor, paddle.to_tensor(1.2))
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def test_dispatch_float_floor(self):
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self.assert_results(dispatch_floor, 1.2)
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def test_dispatch_sum(self):
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self.assert_results(test_sum_tuple, 1, 1)
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self.assert_results(test_sum_tuple, paddle.to_tensor(1), 1)
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self.assert_results(
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test_sum_tuple, paddle.to_tensor(1), paddle.to_tensor(1)
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)
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self.assert_results(
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test_sum_tuple, paddle.to_tensor([1, 2]), paddle.to_tensor(1)
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)
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self.assert_results(
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test_sum_tuple, paddle.to_tensor([1, 2]), paddle.to_tensor([1, 3])
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)
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self.assert_results(test_sum_tuple2, 1, 1)
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self.assert_results(test_sum_tuple2, [1, 2], [3, 4])
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self.assert_results(test_sum_tuple2, paddle.to_tensor(1), 1)
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self.assert_results(
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test_sum_tuple2, paddle.to_tensor(1), paddle.to_tensor(1)
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)
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self.assert_results(
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test_sum_tuple2,
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[paddle.to_tensor(1), paddle.to_tensor(2)],
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[paddle.to_tensor(3), paddle.to_tensor(4)],
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)
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self.assert_results(
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test_sum_tuple2, paddle.to_tensor([1, 2]), paddle.to_tensor(1)
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)
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self.assert_results(
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test_sum_tuple2, paddle.to_tensor([1, 2]), paddle.to_tensor([1, 3])
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)
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self.assert_results(test_sum_tuple3, 1)
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self.assert_results(test_sum_tuple3, paddle.to_tensor(1))
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self.assert_results(test_sum_list, 1, 1)
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self.assert_results(test_sum_list, paddle.to_tensor(1), 1)
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self.assert_results(
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test_sum_list, paddle.to_tensor(1), paddle.to_tensor(1)
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)
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self.assert_results(
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test_sum_list, paddle.to_tensor([1, 2]), paddle.to_tensor(1)
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)
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self.assert_results(
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test_sum_list, paddle.to_tensor([1, 2]), paddle.to_tensor([1, 3])
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)
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self.assert_results(test_sum_list2, 1, 1)
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self.assert_results(test_sum_list2, [1, 2], [3, 4])
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self.assert_results(test_sum_list2, paddle.to_tensor(1), 1)
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self.assert_results(
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test_sum_list2, paddle.to_tensor(1), paddle.to_tensor(1)
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)
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self.assert_results(
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test_sum_list2,
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[paddle.to_tensor(1), paddle.to_tensor(2)],
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[paddle.to_tensor(3), paddle.to_tensor(4)],
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)
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self.assert_results(
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test_sum_list2, paddle.to_tensor([1, 2]), paddle.to_tensor(1)
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)
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self.assert_results(
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test_sum_list2, paddle.to_tensor([1, 2]), paddle.to_tensor([1, 3])
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)
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self.assert_results(test_sum_list3, 1)
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self.assert_results(test_sum_list3, paddle.to_tensor(1))
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self.assert_results(test_tensor_sum, paddle.to_tensor([1, 2]))
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self.assert_results(test_tensor_sum, paddle.to_tensor((1, 2)))
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self.assert_results(test_tensor_sum_api, paddle.to_tensor([1, 2]))
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self.assert_results(test_tensor_sum_api, paddle.to_tensor((1, 2)))
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def test_dispatch_pow(self):
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self.assert_results(test_pow, 2, 3)
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self.assert_results(test_pow, paddle.to_tensor(2), 3)
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self.assert_results(test_pow, paddle.to_tensor(2), paddle.to_tensor(3))
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self.assert_results(test_pow2, 2, 3)
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self.assert_results(test_math_pow, 2, 3)
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self.assert_results(test_tensor_pow_api, paddle.to_tensor(2), 3)
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self.assert_results(
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test_tensor_pow_api, paddle.to_tensor(2), paddle.to_tensor(3)
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)
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def test_dispatch_chr(self):
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self.assert_results(test_chr, 65)
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self.assert_results(test_chr, 0x41)
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self.assert_results(test_chr, paddle.to_tensor(65))
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self.assert_results(test_chr, paddle.to_tensor(0x41))
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def test_dispatch_ord(self):
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self.assert_results(test_ord, "a")
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def test_dispatch_sqrt(self):
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self.assert_results(test_sqrt, 9)
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def test_dispatch_log(self):
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self.assert_results(test_log, math.e)
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def test_dispatch_min(self):
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self.assert_results(test_min)
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def test_dispatch_max(self):
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self.assert_results(test_max)
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def test_dispatch_builtin_type_check_eq(self):
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self.assert_results(test_builtin_type_check_eq)
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def test_dispatch_any(self):
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l_pure_true = [1, True, 5, 6]
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l_pure_false = [False, 0, 0]
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l_true_and_false = [1, False, 0, 3]
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d_true = {"a": 1}
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d_false = {}
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self.assert_results(test_any, l_pure_true)
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self.assert_results(test_any, l_pure_false)
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self.assert_results(test_any, l_true_and_false)
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self.assert_results(test_any, d_true)
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self.assert_results(test_any, d_false)
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self.assert_results(test_any_iter, l_true_and_false)
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def test_dispatch_all(self):
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l_pure_true = [1, True, 5, 6]
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l_pure_false = [False, 0, 0]
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l_true_and_false = [1, False, 0, 3]
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d_true = {"a": 1}
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d_false = {}
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self.assert_results(test_all, l_pure_true)
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self.assert_results(test_all, l_pure_false)
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self.assert_results(test_all, l_true_and_false)
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self.assert_results(test_all, d_true)
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self.assert_results(test_all, d_false)
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self.assert_results(test_all_iter, l_true_and_false)
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def test_dispatch_is(self):
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x = paddle.ones(shape=[1, 2])
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y = paddle.ones(shape=[1, 2])
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# TODO(wangmingkai02): support comparison of same tensor object
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# self.assert_results(test_is, x, x)
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# self.assert_results(test_is, [x], [x])
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self.assert_results(test_is, x, y)
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self.assert_results(test_is, x, None)
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self.assert_results(test_is, [x], x)
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self.assert_results(test_is, None, x)
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self.assert_results(test_is, [x], None)
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self.assert_results(test_is, None, [x])
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self.assert_results(test_is, None, None)
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def run_getattr(x: paddle.Tensor):
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attr = 'dtype'
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out = getattr(x, attr)
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return out
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class TestGetattr(TestCaseBase):
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def test_getattr(self):
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x = paddle.to_tensor(4)
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self.assert_results(run_getattr, x)
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def tensor_hasattr(x: paddle.Tensor):
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return (
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hasattr(x, "dtype"),
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hasattr(x, "stop_gradient"),
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hasattr(x, "abs"),
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hasattr(x, "non_tensor_attr"),
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)
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class ObjectHasattr:
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def __init__(self):
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attr1 = 1
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attr2 = "2"
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attr3 = [3]
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def object_hasattr(x: ObjectHasattr):
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return (
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hasattr(x, "attr1"),
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hasattr(x, "attr2"),
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hasattr(x, "attr3"),
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hasattr(x, "non_obj_attr"),
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)
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def layer_hasattr(layer: paddle.nn.Layer):
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return (
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hasattr(layer, "parameters"),
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hasattr(layer, "sublayers"),
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hasattr(layer, "non_layer_attr"),
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)
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class TestHasattr(TestCaseBase):
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def test_tensor_hasattr(self):
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x = paddle.to_tensor(4)
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self.assert_results(tensor_hasattr, x)
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def test_object_hasattr(self):
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x = ObjectHasattr()
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self.assert_results(object_hasattr, x)
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def test_layer_hasattr(self):
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x = paddle.nn.Layer()
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self.assert_results(layer_hasattr, x)
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class WeakrefableObject: ...
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def weakref_breakgraph(obj):
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return weakref.ref(obj)
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class TestWeakref(TestCaseBase):
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def test_weakref_breakgraph(self):
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obj = WeakrefableObject()
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self.assert_results(weakref_breakgraph, obj)
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def test_builtin_type_conversion_breakgraph(x):
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return int(x), bool(x), float(x)
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class TestBuiltinTypeConversion(TestCaseBase):
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def test_builtin_type_conversion_breakgraph(self):
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self.assert_results(
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test_builtin_type_conversion_breakgraph, paddle.to_tensor(1.2)
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)
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self.assert_results(
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test_builtin_type_conversion_breakgraph, paddle.to_tensor(0)
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)
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@check_no_breakgraph
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def test_native_code_function():
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res1 = paddle.base.libpaddle.is_compiled_with_avx()
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res2 = paddle.base.libpaddle.is_compiled_with_cuda()
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res3 = paddle.base.libpaddle.is_compiled_with_cudnn_frontend()
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res4 = paddle.base.libpaddle.is_compiled_with_rocm()
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res5 = paddle.base.libpaddle.is_compiled_with_custom_device("npu")
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res6 = paddle.base.libpaddle.is_compiled_with_ipu()
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res7 = paddle.base.libpaddle.is_compiled_with_xpu()
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res8_deprecated = (
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paddle.base.libpaddle.is_compiled_with_mkldnn()
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) # Paddle 3.3 deprecated
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res8 = paddle.base.libpaddle.is_compiled_with_onednn()
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res9 = paddle.base.libpaddle.is_compiled_with_nccl()
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res10 = paddle.base.libpaddle.is_compiled_with_mpi()
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res11 = paddle.base.libpaddle.is_compiled_with_mpi_aware()
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res12 = paddle.base.libpaddle.is_compiled_with_cinn()
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res13 = paddle.base.libpaddle.is_compiled_with_distribute()
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res14 = paddle.base.libpaddle.is_compiled_with_brpc()
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res15 = paddle.base.libpaddle.is_compiled_with_dist()
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return (
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res1,
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res2,
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res3,
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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_deprecated,
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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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res14,
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res15,
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)
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@check_no_breakgraph
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def test_native_code_function_gpu_only():
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# Directly returning device_properties causes BreakGraph due to FallbackError:
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# "ObjectVariable does not implement '_reconstruct' method"
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# Therefore, we return individual properties as primitive types instead
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device_properties = paddle.device.cuda.get_device_properties()
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return (
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device_properties.name,
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device_properties.major,
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device_properties.minor,
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device_properties.total_memory,
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device_properties.multi_processor_count,
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)
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class TestNativeCodeFunction(TestCaseBase):
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def test_native_code_function(self):
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self.assert_results(test_native_code_function)
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@unittest.skipUnless(paddle.device.is_compiled_with_cuda(), "requires CUDA")
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def test_native_code_function_gpu_only(self):
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self.assert_results(test_native_code_function_gpu_only)
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if __name__ == "__main__":
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unittest.main()
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