# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest import numpy as np from op_test import get_device_place, is_custom_device from utils import dygraph_guard, static_guard import paddle from paddle import base from paddle.base import core from paddle.utils.dlpack import DLDeviceType class TestDLPack(unittest.TestCase): def test_dlpack_dygraph(self): with dygraph_guard(): tensor = paddle.to_tensor(np.array([1, 2, 3, 4]).astype("int")) dlpack_v1 = paddle.to_dlpack(tensor) out_from_dlpack_v1 = paddle.from_dlpack(dlpack_v1) dlpack_v2 = paddle.to_dlpack(tensor) out_from_dlpack_v2 = paddle.from_dlpack(dlpack_v2) self.assertTrue( isinstance(out_from_dlpack_v1, paddle.base.core.eager.Tensor) ) self.assertTrue( isinstance(out_from_dlpack_v2, paddle.base.core.eager.Tensor) ) self.assertEqual(str(tensor.place), str(out_from_dlpack_v1.place)) self.assertEqual(str(tensor.place), str(out_from_dlpack_v2.place)) np.testing.assert_array_equal( out_from_dlpack_v1.numpy(), np.array([1, 2, 3, 4]).astype("int") ) np.testing.assert_array_equal( out_from_dlpack_v2.numpy(), np.array([1, 2, 3, 4]).astype("int") ) def test_dlpack_tensor_larger_than_2dim(self): with dygraph_guard(): numpy_data = np.random.randn(4, 5, 6) t = paddle.to_tensor(numpy_data) dlpack_v1 = paddle.to_dlpack(t) dlpack_v2 = paddle.to_dlpack(t) out_v1 = paddle.from_dlpack(dlpack_v1) out_v2 = paddle.from_dlpack(dlpack_v2) self.assertEqual(str(t.place), str(out_v1.place)) self.assertEqual(str(t.place), str(out_v2.place)) np.testing.assert_allclose(numpy_data, out_v1.numpy(), rtol=1e-05) np.testing.assert_allclose(numpy_data, out_v2.numpy(), rtol=1e-05) def test_dlpack_static(self): with static_guard(): tensor = base.create_lod_tensor( np.array([[1], [2], [3], [4]]).astype("int"), [[1, 3]], base.CPUPlace(), ) dlpack_v1 = paddle.to_dlpack(tensor) out_from_dlpack_v1 = paddle.from_dlpack(dlpack_v1) dlpack_v2 = paddle.to_dlpack(tensor) out_from_dlpack_v2 = paddle.from_dlpack(dlpack_v2) self.assertTrue( isinstance(out_from_dlpack_v1, base.core.DenseTensor) ) self.assertTrue( isinstance(out_from_dlpack_v2, base.core.DenseTensor) ) np.testing.assert_array_equal( np.array(out_from_dlpack_v1), np.array([[1], [2], [3], [4]]).astype("int"), ) np.testing.assert_array_equal( np.array(out_from_dlpack_v2), np.array([[1], [2], [3], [4]]).astype("int"), ) # when build with cuda if core.is_compiled_with_cuda() or is_custom_device(): gtensor = base.create_lod_tensor( np.array([[1], [2], [3], [4]]).astype("int"), [[1, 3]], get_device_place(), ) gdlpack_v1 = paddle.to_dlpack(gtensor) gdlpack_v2 = paddle.to_dlpack(gtensor) gout_from_dlpack_v1 = paddle.from_dlpack(gdlpack_v1) gout_from_dlpack_v2 = paddle.from_dlpack(gdlpack_v2) self.assertTrue( isinstance(gout_from_dlpack_v1, base.core.DenseTensor) ) self.assertTrue( isinstance(gout_from_dlpack_v2, base.core.DenseTensor) ) np.testing.assert_array_equal( np.array(gout_from_dlpack_v1), np.array([[1], [2], [3], [4]]).astype("int"), ) np.testing.assert_array_equal( np.array(gout_from_dlpack_v2), np.array([[1], [2], [3], [4]]).astype("int"), ) def test_dlpack_dtype_and_place_consistency(self): with dygraph_guard(): dtypes = [ "float16", "float32", "float64", "int8", "int16", "int32", "int64", "uint8", "bool", ] places = [base.CPUPlace()] if paddle.is_compiled_with_cuda() or is_custom_device(): places.append(get_device_place()) places.append(base.CUDAPinnedPlace()) dtypes.append("bfloat16") data = np.ones((2, 3, 4)) for place in places: for dtype in dtypes: x = paddle.to_tensor(data, dtype=dtype, place=place) dlpack_v1 = paddle.to_dlpack(x) o_v1 = paddle.from_dlpack(dlpack_v1) dlpack_v2 = paddle.to_dlpack(x) o_v2 = paddle.from_dlpack(dlpack_v2) self.assertEqual(x.dtype, o_v1.dtype) self.assertEqual(x.dtype, o_v2.dtype) np.testing.assert_allclose( x.numpy(), o_v1.numpy(), rtol=1e-05 ) np.testing.assert_allclose( x.numpy(), o_v2.numpy(), rtol=1e-05 ) self.assertEqual(str(x.place), str(o_v1.place)) self.assertEqual(str(x.place), str(o_v2.place)) complex_dtypes = ["complex64", "complex128"] for place in places: for dtype in complex_dtypes: x = paddle.to_tensor( [[1 + 6j, 2 + 5j, 3 + 4j], [4 + 3j, 5 + 2j, 6 + 1j]], dtype=dtype, place=place, ) dlpack_v1 = paddle.to_dlpack(x) o_v1 = paddle.from_dlpack(dlpack_v1) dlpack_v2 = paddle.to_dlpack(x) o_v2 = paddle.from_dlpack(dlpack_v2) self.assertEqual(x.dtype, o_v1.dtype) self.assertEqual(x.dtype, o_v2.dtype) np.testing.assert_allclose( x.numpy(), o_v1.numpy(), rtol=1e-05 ) np.testing.assert_allclose( x.numpy(), o_v2.numpy(), rtol=1e-05 ) self.assertEqual(str(x.place), str(o_v1.place)) self.assertEqual(str(x.place), str(o_v2.place)) def test_dlpack_deletion(self): # See Paddle issue 47171 with dygraph_guard(): places = [base.CPUPlace()] if paddle.is_compiled_with_cuda() or is_custom_device(): places.append(get_device_place()) for place in places: for _ in range(4): a = paddle.rand(shape=[3, 5], dtype="float32").to( device=place ) dlpack_v1 = paddle.to_dlpack(a) dlpack_v2 = paddle.to_dlpack(a) b1 = paddle.from_dlpack(dlpack_v1) b2 = paddle.from_dlpack(dlpack_v2) self.assertEqual(str(a.place), str(b1.place)) self.assertEqual(str(a.place), str(b2.place)) def test_to_dlpack_for_loop(self): # See Paddle issue 50120 with dygraph_guard(): places = [base.CPUPlace()] if paddle.is_compiled_with_cuda() or is_custom_device(): places.append(get_device_place()) for place in places: for _ in range(4): x = paddle.rand([3, 5]).to(device=place) dlpack_v1 = paddle.to_dlpack(x) dlpack_v2 = paddle.to_dlpack(x) def test_to_dlpack_modification(self): # See Paddle issue 50120 with dygraph_guard(): places = [base.CPUPlace()] if paddle.is_compiled_with_cuda() or is_custom_device(): places.append(get_device_place()) for place in places: for _ in range(4): x = paddle.rand([3, 5]).to(device=place) dlpack_v1 = paddle.to_dlpack(x) dlpack_v2 = paddle.to_dlpack(x) y1 = paddle.from_dlpack(dlpack_v1) y2 = paddle.from_dlpack(dlpack_v2) y1[1:2, 2:5] = 2.0 y2[1:2, 2:5] = 2.0 np.testing.assert_allclose(x.numpy(), y1.numpy()) np.testing.assert_allclose(x.numpy(), y2.numpy()) self.assertEqual(str(x.place), str(y1.place)) self.assertEqual(str(x.place), str(y2.place)) def test_to_dlpack_data_ptr_consistency(self): # See Paddle issue 50120 with dygraph_guard(): places = [base.CPUPlace()] if paddle.is_compiled_with_cuda() or is_custom_device(): places.append(get_device_place()) for place in places: for _ in range(4): x = paddle.rand([3, 5]).to(device=place) dlpack_v1 = paddle.to_dlpack(x) dlpack_v2 = paddle.to_dlpack(x) y1 = paddle.from_dlpack(dlpack_v1) y2 = paddle.from_dlpack(dlpack_v2) self.assertEqual(x.data_ptr(), y1.data_ptr()) self.assertEqual(x.data_ptr(), y2.data_ptr()) self.assertEqual(str(x.place), str(y1.place)) self.assertEqual(str(x.place), str(y2.place)) def test_to_dlpack_strides_consistency(self): with dygraph_guard(): places = [base.CPUPlace()] if paddle.is_compiled_with_cuda() or is_custom_device(): places.append(get_device_place()) for place in places: for _ in range(4): x = paddle.rand([10, 10]).to(device=place) x_strided = x[::2, ::2] dlpack_v1 = paddle.to_dlpack(x_strided) dlpack_v2 = paddle.to_dlpack(x_strided) y1 = paddle.from_dlpack(dlpack_v1) y2 = paddle.from_dlpack(dlpack_v2) self.assertEqual(x_strided.strides, y1.strides) self.assertEqual(x_strided.strides, y2.strides) self.assertEqual(str(x_strided.place), str(y1.place)) self.assertEqual(str(x_strided.place), str(y2.place)) np.testing.assert_equal(x_strided.numpy(), y1.numpy()) np.testing.assert_equal(x_strided.numpy(), y2.numpy()) def test_to_dlpack_from_ext_tensor(self): with dygraph_guard(): for _ in range(4): x = np.random.randn(3, 5) y1 = paddle.from_dlpack(x) y2 = paddle.from_dlpack(x) self.assertEqual( x.__array_interface__['data'][0], y1.data_ptr() ) self.assertEqual( x.__array_interface__['data'][0], y2.data_ptr() ) np.testing.assert_allclose(x, y1.numpy()) np.testing.assert_allclose(x, y2.numpy()) def test_to_dlpack_from_zero_dim(self): with dygraph_guard(): places = [base.CPUPlace()] if paddle.is_compiled_with_cuda() or is_custom_device(): places.append(get_device_place()) for place in places: for _ in range(4): x = paddle.to_tensor(1.0, place=place) dlpack_v1 = paddle.to_dlpack(x) dlpack_v2 = paddle.to_dlpack(x) y1 = paddle.from_dlpack(dlpack_v1) y2 = paddle.from_dlpack(dlpack_v2) self.assertEqual(x.data_ptr(), y1.data_ptr()) self.assertEqual(x.data_ptr(), y2.data_ptr()) self.assertEqual(str(x.place), str(y1.place)) self.assertEqual(str(x.place), str(y2.place)) self.assertEqual(y1.shape, []) self.assertEqual(y2.shape, []) self.assertEqual(y1.numel().item(), 1) self.assertEqual(y2.numel().item(), 1) np.testing.assert_array_equal(x.numpy(), y1.numpy()) np.testing.assert_array_equal(x.numpy(), y2.numpy()) def test_to_dlpack_from_zero_size(self): with dygraph_guard(): places = [base.CPUPlace()] if paddle.is_compiled_with_cuda() or is_custom_device(): places.append(get_device_place()) for place in places: for _ in range(4): x = paddle.zeros([0, 10]).to(device=place) dlpack_v1 = paddle.to_dlpack(x) dlpack_v2 = paddle.to_dlpack(x) y1 = paddle.from_dlpack(dlpack_v1) y2 = paddle.from_dlpack(dlpack_v2) self.assertEqual(x.data_ptr(), y1.data_ptr()) self.assertEqual(x.data_ptr(), y2.data_ptr()) self.assertEqual(str(x.place), str(y1.place)) self.assertEqual(str(x.place), str(y2.place)) self.assertEqual(y1.shape, [0, 10]) self.assertEqual(y2.shape, [0, 10]) self.assertEqual(y1.numel().item(), 0) self.assertEqual(y2.numel().item(), 0) np.testing.assert_array_equal(x.numpy(), y1.numpy()) np.testing.assert_array_equal(x.numpy(), y2.numpy()) class TestDLPackDevice(unittest.TestCase): def test_dlpack_device(self): with dygraph_guard(): tensor_cpu = paddle.to_tensor([1, 2, 3], place=base.CPUPlace()) device_type, device_id = tensor_cpu.__dlpack_device__() self.assertEqual(device_type, DLDeviceType.kDLCPU) self.assertEqual(device_id, None) if paddle.is_compiled_with_cuda() or is_custom_device(): tensor_cuda = paddle.to_tensor( [1, 2, 3], place=get_device_place() ) device_type, device_id = tensor_cuda.__dlpack_device__() self.assertEqual(device_type, DLDeviceType.kDLCUDA) self.assertEqual(device_id, 0) if paddle.is_compiled_with_cuda() or is_custom_device(): tensor_pinned = paddle.to_tensor( [1, 2, 3], place=base.CUDAPinnedPlace() ) device_type, device_id = tensor_pinned.__dlpack_device__() self.assertEqual(device_type, DLDeviceType.kDLCUDAHost) self.assertEqual(device_id, None) if paddle.is_compiled_with_xpu(): tensor_xpu = paddle.to_tensor([1, 2, 3], place=base.XPUPlace(0)) device_type, device_id = tensor_xpu.__dlpack_device__() self.assertEqual(device_type, DLDeviceType.kDLOneAPI) self.assertEqual(device_id, 0) def test_dlpack_device_zero_dim(self): with dygraph_guard(): tensor = paddle.to_tensor(5.0, place=base.CPUPlace()) device_type, device_id = tensor.__dlpack_device__() self.assertEqual(device_type, DLDeviceType.kDLCPU) self.assertEqual(device_id, None) if paddle.is_compiled_with_cuda() or is_custom_device(): tensor_cuda = paddle.to_tensor(5.0, place=get_device_place()) device_type, device_id = tensor_cuda.__dlpack_device__() self.assertEqual(device_type, DLDeviceType.kDLCUDA) self.assertEqual(device_id, 0) if paddle.is_compiled_with_xpu(): tensor_xpu = paddle.to_tensor(5.0, place=base.XPUPlace(0)) device_type, device_id = tensor_xpu.__dlpack_device__() self.assertEqual(device_type, DLDeviceType.kDLOneAPI) self.assertEqual(device_id, 0) def test_dlpack_device_zero_size(self): with dygraph_guard(): tensor = paddle.to_tensor( paddle.zeros([0, 10]), place=base.CPUPlace() ) device_type, device_id = tensor.__dlpack_device__() self.assertEqual(device_type, DLDeviceType.kDLCPU) self.assertEqual(device_id, None) if paddle.is_compiled_with_cuda() or is_custom_device(): tensor_cuda = paddle.to_tensor( paddle.zeros([0, 10]), place=get_device_place() ) device_type, device_id = tensor_cuda.__dlpack_device__() self.assertEqual(device_type, DLDeviceType.kDLCUDA) self.assertEqual(device_id, 0) if paddle.is_compiled_with_xpu(): tensor_xpu = paddle.to_tensor( paddle.zeros([0, 10]), place=base.XPUPlace(0) ) device_type, device_id = tensor_xpu.__dlpack_device__() self.assertEqual(device_type, DLDeviceType.kDLOneAPI) self.assertEqual(device_id, 0) class TestRaiseError(unittest.TestCase): def test_to_dlpack_raise_type_error(self): self.assertRaises(TypeError, paddle.to_dlpack, np.zeros(5)) self.assertRaises(TypeError, paddle.to_dlpack, np.zeros(5)) class TestVersioned(unittest.TestCase): CAPSULE = "dltensor" CAPSULE_VERSIONED = "dltensor_versioned" def test_to_dlpack_versioned(self): a = paddle.to_tensor([1, 2, 3]) # version independent DLPack when max_version=None capsule = a.__dlpack__(max_version=None) self.assertIn(f'"{TestVersioned.CAPSULE}"', str(capsule)) # version independent DLPack when max_version=(0, 8) capsule = a.__dlpack__(max_version=(0, 8)) self.assertIn(f'"{TestVersioned.CAPSULE}"', str(capsule)) # versioned DLPack when max_version=(1, 0) capsule = a.__dlpack__(max_version=(1, 0)) self.assertIn(f'"{TestVersioned.CAPSULE_VERSIONED}"', str(capsule)) # 1version DLPack when max_version=(1, 1) capsule = a.__dlpack__(max_version=(1, 1)) self.assertIn(f'"{TestVersioned.CAPSULE_VERSIONED}"', str(capsule)) def test_from_dlpack_versioned(self): a = paddle.to_tensor([1, 2, 3]) versioned_capsule = a.__dlpack__(max_version=(1, 0)) # from versioned DLPack capsule b = paddle.from_dlpack(versioned_capsule) np.testing.assert_array_equal(a.numpy(), b.numpy()) self.assertEqual(a.data_ptr(), b.data_ptr()) class TestDtypesLowPrecision(unittest.TestCase): @dygraph_guard() def test_dlpack_low_precision(self): dtypes = [ paddle.float8_e4m3fn, paddle.float8_e5m2, ] places = [paddle.CPUPlace()] if paddle.is_compiled_with_cuda(): places.append(paddle.CUDAPlace(0)) places.append(paddle.CUDAPinnedPlace()) for dtype in dtypes: for place in places: data = np.random.randn(2, 3, 4) x = paddle.to_tensor(data, place=place).cast(dtype) dlpack_v1 = paddle.to_dlpack(x) o_v1 = paddle.from_dlpack(dlpack_v1) dlpack_v2 = paddle.to_dlpack(x) o_v2 = paddle.from_dlpack(dlpack_v2) self.assertEqual(x.dtype, o_v1.dtype) self.assertEqual(x.dtype, o_v2.dtype) np.testing.assert_allclose(x.numpy(), o_v1.numpy(), rtol=1e-05) np.testing.assert_allclose(x.numpy(), o_v2.numpy(), rtol=1e-05) self.assertEqual(str(x.place), str(o_v1.place)) self.assertEqual(str(x.place), str(o_v2.place)) self.assertEqual(x.data_ptr(), o_v1.data_ptr()) self.assertEqual(x.data_ptr(), o_v2.data_ptr()) class TestDtypesUnsignedInt(unittest.TestCase): @dygraph_guard() def test_dlpack_unsigned_int(self): dtypes = [ paddle.uint8, paddle.uint16, paddle.uint32, paddle.uint64, ] places = [paddle.CPUPlace()] if paddle.is_compiled_with_cuda(): places.append(paddle.CUDAPlace(0)) places.append(paddle.CUDAPinnedPlace()) for dtype in dtypes: for place in places: data = np.random.randint(low=0, high=100, size=(2, 3, 4)) x = paddle.to_tensor(data, place=place).cast(dtype) dlpack_v1 = paddle.to_dlpack(x) o_v1 = paddle.from_dlpack(dlpack_v1) dlpack_v2 = paddle.to_dlpack(x) o_v2 = paddle.from_dlpack(dlpack_v2) self.assertEqual(x.dtype, o_v1.dtype) self.assertEqual(x.dtype, o_v2.dtype) np.testing.assert_allclose(x.numpy(), o_v1.numpy(), rtol=1e-05) np.testing.assert_allclose(x.numpy(), o_v2.numpy(), rtol=1e-05) self.assertEqual(str(x.place), str(o_v1.place)) self.assertEqual(str(x.place), str(o_v2.place)) self.assertEqual(x.data_ptr(), o_v1.data_ptr()) self.assertEqual(x.data_ptr(), o_v2.data_ptr()) class TestCopySemanticDLPackProtocol(unittest.TestCase): @dygraph_guard() def test_dlpack_same_place_cpu(self): cpu_place = paddle.CPUPlace() tensor = paddle.to_tensor([1, 2, 3], place=cpu_place) dlpack_with_cpu_place = tensor.__dlpack__( dl_device=(DLDeviceType.kDLCPU, 0) ) tensor_from_dlpack = paddle.from_dlpack(dlpack_with_cpu_place) self.assertEqual(tensor.data_ptr(), tensor_from_dlpack.data_ptr()) np.testing.assert_array_equal( tensor.numpy(), tensor_from_dlpack.numpy() ) @dygraph_guard() def test_dlpack_same_place_cuda(self): if not paddle.is_compiled_with_cuda(): return cuda_place = paddle.CUDAPlace(0) tensor = paddle.to_tensor([1, 2, 3], place=cuda_place) dlpack_with_cuda_place = tensor.__dlpack__( dl_device=(DLDeviceType.kDLCUDA, 0) ) tensor_from_dlpack = paddle.from_dlpack(dlpack_with_cuda_place) self.assertEqual(tensor.data_ptr(), tensor_from_dlpack.data_ptr()) np.testing.assert_array_equal( tensor.numpy(), tensor_from_dlpack.numpy() ) @dygraph_guard() def test_dlpack_same_place_cpu_force_copy(self): cpu_place = paddle.CPUPlace() tensor = paddle.to_tensor([1, 2, 3], place=cpu_place) dlpack_with_cpu_place = tensor.__dlpack__( dl_device=(DLDeviceType.kDLCPU, 0), copy=True, ) tensor_from_dlpack = paddle.from_dlpack(dlpack_with_cpu_place) self.assertNotEqual(tensor.data_ptr(), tensor_from_dlpack.data_ptr()) np.testing.assert_array_equal( tensor.numpy(), tensor_from_dlpack.numpy() ) @dygraph_guard() def test_dlpack_same_place_cuda_force_copy(self): if not paddle.is_compiled_with_cuda(): return cuda_place = paddle.CUDAPlace(0) tensor = paddle.to_tensor([1, 2, 3], place=cuda_place) dlpack_with_cuda_place = tensor.__dlpack__( dl_device=(DLDeviceType.kDLCUDA, 0), copy=True, ) tensor_from_dlpack = paddle.from_dlpack(dlpack_with_cuda_place) self.assertNotEqual(tensor.data_ptr(), tensor_from_dlpack.data_ptr()) np.testing.assert_array_equal( tensor.numpy(), tensor_from_dlpack.numpy() ) @dygraph_guard() def test_dlpack_same_place_cpu_disallow_copy(self): cpu_place = paddle.CPUPlace() tensor = paddle.to_tensor([1, 2, 3], place=cpu_place) dlpack_with_cpu_place = tensor.__dlpack__( dl_device=(DLDeviceType.kDLCPU, 0), copy=False, ) tensor_from_dlpack = paddle.from_dlpack(dlpack_with_cpu_place) self.assertEqual(tensor.data_ptr(), tensor_from_dlpack.data_ptr()) np.testing.assert_array_equal( tensor.numpy(), tensor_from_dlpack.numpy() ) @dygraph_guard() def test_dlpack_same_place_cuda_disallow_copy(self): if not paddle.is_compiled_with_cuda(): return cuda_place = paddle.CUDAPlace(0) tensor = paddle.to_tensor([1, 2, 3], place=cuda_place) dlpack_with_cuda_place = tensor.__dlpack__( dl_device=(DLDeviceType.kDLCUDA, 0), copy=False, ) tensor_from_dlpack = paddle.from_dlpack(dlpack_with_cuda_place) self.assertEqual(tensor.data_ptr(), tensor_from_dlpack.data_ptr()) np.testing.assert_array_equal( tensor.numpy(), tensor_from_dlpack.numpy() ) @dygraph_guard() def test_dlpack_cross_device_cpu_to_cuda(self): if not paddle.is_compiled_with_cuda(): return cpu_place = paddle.CPUPlace() cuda_place = paddle.CUDAPlace(0) tensor = paddle.to_tensor([1, 2, 3], place=cpu_place) dlpack_with_cuda_place = tensor.__dlpack__( dl_device=(DLDeviceType.kDLCUDA, 0), ) tensor_from_dlpack = paddle.from_dlpack(dlpack_with_cuda_place) self.assertNotEqual(tensor.data_ptr(), tensor_from_dlpack.data_ptr()) self.assertEqual(str(tensor_from_dlpack.place), str(cuda_place)) np.testing.assert_array_equal( tensor.numpy(), tensor_from_dlpack.numpy() ) @dygraph_guard() def test_dlpack_cross_device_cuda_to_cpu(self): if not paddle.is_compiled_with_cuda(): return cpu_place = paddle.CPUPlace() cuda_place = paddle.CUDAPlace(0) tensor = paddle.to_tensor([1, 2, 3], place=cuda_place) dlpack_with_cpu_place = tensor.__dlpack__( dl_device=(DLDeviceType.kDLCPU, 0), ) tensor_from_dlpack = paddle.from_dlpack(dlpack_with_cpu_place) self.assertNotEqual(tensor.data_ptr(), tensor_from_dlpack.data_ptr()) self.assertEqual(str(tensor_from_dlpack.place), str(cpu_place)) np.testing.assert_array_equal( tensor.numpy(), tensor_from_dlpack.numpy() ) @dygraph_guard() def test_dlpack_cross_device_cpu_to_cuda_force_copy(self): if not paddle.is_compiled_with_cuda(): return cpu_place = paddle.CPUPlace() cuda_place = paddle.CUDAPlace(0) tensor = paddle.to_tensor([1, 2, 3], place=cpu_place) dlpack_with_cuda_place = tensor.__dlpack__( dl_device=(DLDeviceType.kDLCUDA, 0), copy=True, ) tensor_from_dlpack = paddle.from_dlpack(dlpack_with_cuda_place) self.assertNotEqual(tensor.data_ptr(), tensor_from_dlpack.data_ptr()) self.assertEqual(str(tensor_from_dlpack.place), str(cuda_place)) np.testing.assert_array_equal( tensor.numpy(), tensor_from_dlpack.numpy() ) @dygraph_guard() def test_dlpack_cross_device_cuda_to_cpu_force_copy(self): if not paddle.is_compiled_with_cuda(): return cpu_place = paddle.CPUPlace() cuda_place = paddle.CUDAPlace(0) tensor = paddle.to_tensor([1, 2, 3], place=cuda_place) dlpack_with_cpu_place = tensor.__dlpack__( dl_device=(DLDeviceType.kDLCPU, 0), copy=True, ) tensor_from_dlpack = paddle.from_dlpack(dlpack_with_cpu_place) self.assertNotEqual(tensor.data_ptr(), tensor_from_dlpack.data_ptr()) self.assertEqual(str(tensor_from_dlpack.place), str(cpu_place)) np.testing.assert_array_equal( tensor.numpy(), tensor_from_dlpack.numpy() ) @dygraph_guard() def test_dlpack_cross_device_cpu_to_cuda_disallow_copy(self): if not paddle.is_compiled_with_cuda(): return cpu_place = paddle.CPUPlace() tensor = paddle.to_tensor([1, 2, 3], place=cpu_place) with self.assertRaises(BufferError): tensor.__dlpack__(dl_device=(DLDeviceType.kDLCUDA, 0), copy=False) @dygraph_guard() def test_dlpack_cross_device_cuda_to_cpu_disallow_copy(self): if not paddle.is_compiled_with_cuda(): return cuda_place = paddle.CUDAPlace(0) tensor = paddle.to_tensor([1, 2, 3], place=cuda_place) with self.assertRaises(BufferError): tensor.__dlpack__(dl_device=(DLDeviceType.kDLCPU, 0), copy=False) class TestCopySemanticFromDLPack(unittest.TestCase): @dygraph_guard() def test_from_dlpack_same_place(self): cpu_place = paddle.CPUPlace() tensor = paddle.to_tensor([1, 2, 3], place=cpu_place) tensor_from_dlpack = paddle.from_dlpack(tensor) self.assertEqual(tensor.data_ptr(), tensor_from_dlpack.data_ptr()) np.testing.assert_array_equal( tensor.numpy(), tensor_from_dlpack.numpy() ) @dygraph_guard() def test_from_dlpack_same_place_cuda(self): if not paddle.is_compiled_with_cuda(): return cuda_place = paddle.CUDAPlace(0) tensor = paddle.to_tensor([1, 2, 3], place=cuda_place) tensor_from_dlpack = paddle.from_dlpack(tensor) self.assertEqual(tensor.data_ptr(), tensor_from_dlpack.data_ptr()) np.testing.assert_array_equal( tensor.numpy(), tensor_from_dlpack.numpy() ) @dygraph_guard() def test_from_dlpack_same_place_force_copy(self): cpu_place = paddle.CPUPlace() tensor = paddle.to_tensor([1, 2, 3], place=cpu_place) tensor_from_dlpack = paddle.from_dlpack(tensor, copy=True) self.assertNotEqual(tensor.data_ptr(), tensor_from_dlpack.data_ptr()) np.testing.assert_array_equal( tensor.numpy(), tensor_from_dlpack.numpy() ) @dygraph_guard() def test_from_dlpack_same_place_disallow_copy(self): cpu_place = paddle.CPUPlace() tensor = paddle.to_tensor([1, 2, 3], place=cpu_place) tensor_from_dlpack = paddle.from_dlpack(tensor, copy=False) self.assertEqual(tensor.data_ptr(), tensor_from_dlpack.data_ptr()) np.testing.assert_array_equal( tensor.numpy(), tensor_from_dlpack.numpy() ) @dygraph_guard() def test_from_dlpack_cross_device(self): if not paddle.is_compiled_with_cuda(): return cpu_place = paddle.CPUPlace() cuda_place = paddle.CUDAPlace(0) tensor = paddle.to_tensor([1, 2, 3], place=cpu_place) tensor_from_dlpack = paddle.from_dlpack(tensor, device=cuda_place) self.assertNotEqual(tensor.data_ptr(), tensor_from_dlpack.data_ptr()) self.assertEqual(str(tensor_from_dlpack.place), str(cuda_place)) np.testing.assert_array_equal( tensor.numpy(), tensor_from_dlpack.numpy() ) @dygraph_guard() def test_from_dlpack_cross_device_force_copy(self): if not paddle.is_compiled_with_cuda(): return cpu_place = paddle.CPUPlace() cuda_place = paddle.CUDAPlace(0) tensor = paddle.to_tensor([1, 2, 3], place=cpu_place) tensor_from_dlpack = paddle.from_dlpack( tensor, device=cuda_place, copy=True ) self.assertNotEqual(tensor.data_ptr(), tensor_from_dlpack.data_ptr()) self.assertEqual(str(tensor_from_dlpack.place), str(cuda_place)) np.testing.assert_array_equal( tensor.numpy(), tensor_from_dlpack.numpy() ) @dygraph_guard() def test_from_dlpack_cross_device_disallow_copy(self): if not paddle.is_compiled_with_cuda(): return cpu_place = paddle.CPUPlace() tensor = paddle.to_tensor([1, 2, 3], place=cpu_place) with self.assertRaises(BufferError): paddle.from_dlpack(tensor, device=paddle.CUDAPlace(0), copy=False) if __name__ == "__main__": unittest.main()