chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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# 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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from itertools import product
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import numpy as np
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from op_test import get_device, get_device_place, is_custom_device
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from utils import dygraph_guard
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import paddle
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class TestTensorCreation(unittest.TestCase):
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def setUp(self):
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self.devices = [paddle.CPUPlace(), "cpu"]
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if paddle.device.is_compiled_with_cuda() or is_custom_device():
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self.devices.append(get_device_place())
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self.devices.append(get_device())
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self.devices.append(get_device(True))
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if paddle.device.is_compiled_with_xpu():
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self.devices.append(paddle.XPUPlace(0))
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if paddle.device.is_compiled_with_ipu():
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self.devices.append(paddle.device.IPUPlace())
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self.requires_grads = [True, False]
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self.dtypes = [None, paddle.float32]
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self.pin_memories = [False]
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if (
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paddle.device.is_compiled_with_cuda()
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and not paddle.device.is_compiled_with_rocm()
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):
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self.pin_memories.append(True)
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def test_empty(self):
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for device, requires_grad, dtype, pin_memory in product(
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self.devices,
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self.requires_grads,
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self.dtypes,
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self.pin_memories,
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):
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if (
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device
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not in [
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get_device(),
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get_device(True),
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get_device_place()
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if (
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paddle.device.is_compiled_with_cuda()
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or is_custom_device()
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)
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else None,
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paddle.XPUPlace(0)
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if paddle.device.is_compiled_with_xpu()
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else None,
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]
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and pin_memory
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):
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continue # skip
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with dygraph_guard():
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x = paddle.empty(
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[2],
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dtype=dtype,
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requires_grad=requires_grad,
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device=device,
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pin_memory=pin_memory,
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)
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if pin_memory:
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self.assertTrue("pinned" in str(x.place))
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if (
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isinstance(device, paddle.framework.core.Place)
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and not pin_memory
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):
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self.assertEqual(x.place, device)
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self.assertEqual(x.stop_gradient, not requires_grad)
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if isinstance(dtype, paddle.dtype):
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self.assertEqual(x.dtype, dtype)
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def wrapped_empty(
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shape,
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dtype=None,
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name=None,
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*,
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out=None,
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device=None,
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requires_grad=False,
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pin_memory=False,
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):
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return paddle.empty(
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shape,
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dtype,
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name,
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out=out,
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device=device,
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requires_grad=requires_grad,
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pin_memory=pin_memory,
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)
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st_f = paddle.jit.to_static(
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wrapped_empty, full_graph=True, backend=None
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)
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x = st_f(
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[2],
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out=None,
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dtype=dtype,
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requires_grad=requires_grad,
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device=device,
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pin_memory=pin_memory,
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)
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if (
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isinstance(device, paddle.framework.core.Place)
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and not pin_memory
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):
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self.assertEqual(x.place, device)
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self.assertEqual(x.stop_gradient, not requires_grad)
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if isinstance(dtype, paddle.dtype):
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self.assertEqual(x.dtype, dtype)
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def test_empty_like(self):
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for device, requires_grad, dtype, pin_memory in product(
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self.devices, self.requires_grads, self.dtypes, self.pin_memories
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):
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if (
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device
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not in [
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get_device(),
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get_device(True),
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get_device_place()
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if (
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paddle.device.is_compiled_with_cuda()
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or is_custom_device()
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)
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else None,
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paddle.XPUPlace(0)
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if paddle.device.is_compiled_with_xpu()
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else None,
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]
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and pin_memory
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):
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continue # skip
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with dygraph_guard():
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x = paddle.empty_like(
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paddle.randn([2, 2]),
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dtype=dtype,
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requires_grad=requires_grad,
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device=device,
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pin_memory=pin_memory,
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)
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if pin_memory:
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self.assertTrue("pinned" in str(x.place))
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if (
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not paddle.device.is_compiled_with_xpu()
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and isinstance(device, paddle.framework.core.Place)
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and not pin_memory
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):
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self.assertEqual(x.place, device)
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self.assertEqual(x.stop_gradient, not requires_grad)
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if isinstance(dtype, paddle.dtype):
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self.assertEqual(x.dtype, dtype)
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st_f = paddle.jit.to_static(
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paddle.empty_like, full_graph=True, backend=None
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)
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x = st_f(
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paddle.randn([2, 2]),
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dtype=dtype,
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requires_grad=requires_grad,
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device=device,
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)
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if (
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isinstance(device, paddle.framework.core.Place)
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and not pin_memory
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):
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self.assertEqual(x.place, device)
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self.assertEqual(x.stop_gradient, not requires_grad)
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if isinstance(dtype, paddle.dtype):
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self.assertEqual(x.dtype, dtype)
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class TestTensorPatchMethod(unittest.TestCase):
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def setUp(self):
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self.devices = [None, paddle.CPUPlace(), "cpu"]
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if paddle.device.is_compiled_with_cuda() or is_custom_device():
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self.devices.append(get_device_place())
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self.devices.append(get_device())
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self.devices.append(get_device(True))
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if paddle.device.is_compiled_with_xpu():
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self.devices.append(paddle.XPUPlace(0))
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if paddle.device.is_compiled_with_ipu():
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self.devices.append(paddle.device.IPUPlace())
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self.requires_grads = [True, False]
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self.shapes = [
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[4, 4],
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]
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self.dtypes = ["float32", paddle.float32, "int32", paddle.int32]
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self.pin_memories = [False]
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if (
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paddle.device.is_compiled_with_cuda()
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and not paddle.device.is_compiled_with_rocm()
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):
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self.pin_memories.append(True)
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def test_Tensor_new_empty(self):
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for shape, device, requires_grad, dtype, pin_memory in product(
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self.shapes,
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self.devices,
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self.requires_grads,
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self.dtypes,
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self.pin_memories,
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):
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if (
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device
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not in [
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get_device(),
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get_device(True),
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get_device_place()
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if (
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paddle.device.is_compiled_with_cuda()
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or is_custom_device()
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)
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else None,
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paddle.XPUPlace(0)
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if paddle.device.is_compiled_with_xpu()
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else None,
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]
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and pin_memory
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):
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continue # skip
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with dygraph_guard():
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x = paddle.empty(
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[1],
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).new_empty(
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shape,
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dtype=dtype,
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requires_grad=requires_grad,
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device=device,
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pin_memory=pin_memory,
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)
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if pin_memory:
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self.assertTrue("pinned" in str(x.place))
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if (
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isinstance(device, paddle.framework.core.Place)
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and not pin_memory
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):
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self.assertEqual(x.place, device)
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self.assertEqual(x.stop_gradient, not requires_grad)
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if isinstance(dtype, paddle.dtype):
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self.assertEqual(x.dtype, dtype)
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x = paddle.empty(
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[2],
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).new_empty(
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*shape,
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dtype=dtype,
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requires_grad=requires_grad,
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device=device,
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pin_memory=pin_memory,
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)
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self.assertEqual(x.shape, shape)
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def new_empty(
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x, shape, dtype, requires_grad, device, pin_memory
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):
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return x.new_empty(
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shape,
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dtype=dtype,
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requires_grad=requires_grad,
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device=device,
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pin_memory=pin_memory,
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)
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st_f = paddle.jit.to_static(
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new_empty, full_graph=True, backend=None
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)
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x = st_f(
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paddle.randn([1]),
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shape,
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dtype=dtype,
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requires_grad=requires_grad,
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device=device,
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pin_memory=pin_memory,
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)
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if (
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isinstance(device, paddle.framework.core.Place)
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and not pin_memory
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):
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self.assertEqual(x.place, device)
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self.assertEqual(x.stop_gradient, not requires_grad)
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if isinstance(dtype, paddle.dtype):
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self.assertEqual(x.dtype, dtype)
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def new_empty_size_arg(
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x, shape, dtype, requires_grad, device, pin_memory
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):
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return x.new_empty(
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*shape,
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dtype=dtype,
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requires_grad=requires_grad,
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device=device,
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pin_memory=pin_memory,
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)
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st_f = paddle.jit.to_static(
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new_empty_size_arg, full_graph=True, backend=None
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)
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x = st_f(
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paddle.randn([1]),
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shape,
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dtype=dtype,
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requires_grad=requires_grad,
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device=device,
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pin_memory=pin_memory,
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)
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self.assertEqual(x.shape, shape)
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class TestCreationOut(unittest.TestCase):
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def setUp(self):
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self.x_np = np.random.rand(3, 4).astype(np.float32)
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self.constant = 3.14
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def test_empty(self):
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x = paddle.randn([2, 2])
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t = paddle.empty_like(x)
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y = paddle.empty(x.shape, out=t, requires_grad=True)
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self.assertEqual(t.data_ptr(), y.data_ptr())
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self.assertEqual(y.stop_gradient, False)
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self.assertEqual(t.stop_gradient, False)
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if __name__ == '__main__':
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unittest.main()
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