wehub-resource-sync
291 lines
11 KiB
Python
291 lines
11 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.compat import sort as compat_sort
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class TestCompatSort(unittest.TestCase):
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def _compare_with_origin(
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self, input_tensor, dtype, dim, descending, stable, use_out=False
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):
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"""DO NOT set use_out to be True in static graph mode."""
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if use_out:
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sort_res = (paddle.to_tensor(0), paddle.to_tensor(0))
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compat_sort(input_tensor, dim, descending, stable, out=sort_res)
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else:
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sort_res = compat_sort(
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input_tensor, dim=dim, descending=descending, stable=stable
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)
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origin_vals = paddle.sort(
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input_tensor, axis=dim, descending=descending, stable=stable
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)
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origin_inds = paddle.argsort(
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input_tensor, axis=dim, descending=descending, stable=stable
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)
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if dtype.find("int"):
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np.testing.assert_array_equal(
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sort_res[0].numpy(), origin_vals.numpy()
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)
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else:
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np.testing.assert_allclose(sort_res[0].numpy(), origin_vals.numpy())
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np.testing.assert_array_equal(sort_res[1].numpy(), origin_inds.numpy())
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def test_with_origin_static(self):
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dtypes = [
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"float16",
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"bfloat16",
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"float32",
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"float64",
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"uint8",
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"int16",
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"int32",
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"int64",
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]
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shapes = [(31, 5), (129,)]
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paddle.seed(1)
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for dtype in dtypes:
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for shape in shapes:
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for dim in range(len(shape)):
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if dtype.find("int") >= 0:
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input_tensor = paddle.randint(0, 255, shape).to(dtype)
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else:
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input_tensor = paddle.randn(shape, dtype=dtype)
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def static_graph_tester(descending, stable):
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with paddle.static.program_guard(
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paddle.static.Program()
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):
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input_data = paddle.static.data(
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name='x', shape=shape, dtype=dtype
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)
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sort_res = compat_sort(
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input_data,
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dim=dim,
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descending=descending,
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stable=stable,
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)
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sort_vals, sort_inds = (
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sort_res.values,
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sort_res.indices,
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)
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origin_vals = paddle.sort(
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input_data,
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axis=dim,
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descending=descending,
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stable=stable,
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)
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origin_inds = paddle.argsort(
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input_data,
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axis=dim,
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descending=descending,
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stable=stable,
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)
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place = (
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get_device_place()
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if (
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paddle.is_compiled_with_cuda()
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or is_custom_device()
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)
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else paddle.CPUPlace()
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)
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exe = paddle.static.Executor(place)
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input_data = np.random.rand(3, 6).astype('float32')
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feed = {'x': input_tensor.numpy()}
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results = exe.run(
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feed=feed,
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fetch_list=[
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sort_vals,
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origin_vals,
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sort_inds,
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origin_inds,
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],
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)
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if dtype.find("int"):
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np.testing.assert_array_equal(
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results[0], results[1]
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)
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else:
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np.testing.assert_allclose(results[0], results[1])
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np.testing.assert_array_equal(results[2], results[3])
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paddle.enable_static()
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static_graph_tester(False, False)
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static_graph_tester(True, False)
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static_graph_tester(False, True)
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static_graph_tester(True, True)
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paddle.disable_static()
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def test_with_origin_dynamic(self, use_static=False):
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dtypes = [
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"float16",
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"bfloat16",
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"float32",
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"float64",
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"uint8",
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"int16",
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"int32",
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"int64",
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]
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shapes = [(31, 5), (129,)]
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paddle.seed(0)
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for dtype in dtypes:
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for shape in shapes:
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if dtype.find("int") >= 0:
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input_tensor = paddle.randint(0, 255, shape).to(dtype)
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else:
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input_tensor = paddle.randn(shape, dtype=dtype)
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for use_out in [False, True]:
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for dim in range(len(shape)):
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self._compare_with_origin(
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input_tensor,
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dtype,
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dim,
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False,
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False,
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use_out=use_out,
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)
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self._compare_with_origin(
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input_tensor,
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dtype,
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dim - len(shape),
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False,
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True,
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use_out=use_out,
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)
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self._compare_with_origin(
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input_tensor,
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dtype,
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dim,
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True,
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False,
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use_out=use_out,
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)
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self._compare_with_origin(
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input_tensor,
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dtype,
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dim - len(shape),
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True,
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True,
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use_out=use_out,
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)
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def test_sort_backward(self):
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"""test the backward behavior for all data types"""
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dtypes = ["float16", "float32", "float64"]
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shapes = [(31, 5), (129,)]
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paddle.seed(2)
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for dtype in dtypes:
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for shape in shapes:
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for dim in range(len(shape)):
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input_tensor = paddle.randn(shape, dtype=dtype)
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input_tensor.stop_gradient = False
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if input_tensor.place.is_gpu_place():
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y = input_tensor * input_tensor
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else:
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y = input_tensor + 1
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sort_vals, sort_inds = compat_sort(y, dim=dim)
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sort_vals.backward()
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if input_tensor.place.is_gpu_place():
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np.testing.assert_allclose(
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input_tensor.grad.numpy(),
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(2 * input_tensor).numpy(),
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)
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else:
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actual_arr = input_tensor.grad.numpy()
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np.testing.assert_allclose(
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actual_arr,
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np.ones_like(actual_arr, dtype=actual_arr.dtype),
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)
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def test_edge_cases(self):
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"""Test edge cases and error handling"""
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x = paddle.to_tensor([])
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sort_res = compat_sort(x, descending=True, stable=True)
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np.testing.assert_array_equal(
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sort_res.values.numpy(), np.array([], dtype=np.float32)
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)
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np.testing.assert_array_equal(
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sort_res.indices.numpy(), np.array([], dtype=np.int64)
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)
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x = paddle.to_tensor(1)
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sort_res = compat_sort(input=x, stable=True)
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np.testing.assert_array_equal(
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sort_res.values.numpy(), np.array(1, dtype=np.float32)
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)
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np.testing.assert_array_equal(
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sort_res.indices.numpy(), np.array(0, dtype=np.int64)
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)
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msg_gt_1 = "paddle.sort() received unexpected keyword arguments 'dim', 'input'. \nDid you mean to use paddle.compat.sort() instead?"
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msg_gt_2 = "paddle.compat.sort() received unexpected keyword arguments 'axis', 'x'. \nDid you mean to use paddle.sort() instead?"
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# invalid split sections
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with self.assertRaises(TypeError) as cm:
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paddle.sort(input=paddle.to_tensor([2, 1, 3]), dim=0)
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self.assertEqual(str(cm.exception), msg_gt_1)
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# invalid split axis
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with self.assertRaises(TypeError) as cm:
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compat_sort(x=paddle.to_tensor([2, 1, 3]), axis=0)
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self.assertEqual(str(cm.exception), msg_gt_2)
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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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compat_sort(paddle.to_tensor([1, 2]), 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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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 = compat_sort(
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paddle.arange(24),
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out=(
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paddle.zeros([24]),
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paddle.zeros([24], 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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if __name__ == "__main__":
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unittest.main()
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