[Paddle Tensor No.12][BUPT] 新增 Tensor.__dlpack_device__ #69632
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你的PR提交成功，感谢你对开源项目的贡献! |
test/legacy_test/test_dlpack.py
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| 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, 1) |
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device_type不要用hard code 数字，应该使用DLDeviceType.xxx
test/legacy_test/test_dlpack.py
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| 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, 1) | ||
| self.assertEqual(device_id, 0) | ||
| if paddle.is_compiled_with_cuda(): | ||
| tensor_cuda = paddle.to_tensor( | ||
| [1, 2, 3], place=base.CUDAPlace(0) | ||
| ) | ||
| device_type, device_id = tensor_cuda.__dlpack_device__() | ||
| self.assertEqual(device_type, 2) | ||
| self.assertEqual(device_id, 0) | ||
| if paddle.is_compiled_with_cuda(): | ||
| tensor_pinned = paddle.to_tensor( | ||
| [1, 2, 3], place=base.CUDAPinnedPlace() | ||
| ) | ||
| device_type, device_id = tensor_pinned.__dlpack_device__() | ||
| self.assertEqual(device_type, 1) | ||
| self.assertEqual(device_id, 0) | ||
| 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, 14) | ||
| self.assertEqual(device_id, 0) |
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写代码的时候，不同逻辑的代码块建议添加适当的换行，否则全部挤在一块阅读难度会比较高
Suggested change
| 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, 1) | |
| self.assertEqual(device_id, 0) | |
| if paddle.is_compiled_with_cuda(): | |
| tensor_cuda = paddle.to_tensor( | |
| [1, 2, 3], place=base.CUDAPlace(0) | |
| ) | |
| device_type, device_id = tensor_cuda.__dlpack_device__() | |
| self.assertEqual(device_type, 2) | |
| self.assertEqual(device_id, 0) | |
| if paddle.is_compiled_with_cuda(): | |
| tensor_pinned = paddle.to_tensor( | |
| [1, 2, 3], place=base.CUDAPinnedPlace() | |
| ) | |
| device_type, device_id = tensor_pinned.__dlpack_device__() | |
| self.assertEqual(device_type, 1) | |
| self.assertEqual(device_id, 0) | |
| 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, 14) | |
| self.assertEqual(device_id, 0) | |
| 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, 1) | |
| self.assertEqual(device_id, 0) | |
| if paddle.is_compiled_with_cuda(): | |
| tensor_cuda = paddle.to_tensor( | |
| [1, 2, 3], place=base.CUDAPlace(0) | |
| ) | |
| device_type, device_id = tensor_cuda.__dlpack_device__() | |
| self.assertEqual(device_type, 2) | |
| self.assertEqual(device_id, 0) | |
| if paddle.is_compiled_with_cuda(): | |
| tensor_pinned = paddle.to_tensor( | |
| [1, 2, 3], place=base.CUDAPinnedPlace() | |
| ) | |
| device_type, device_id = tensor_pinned.__dlpack_device__() | |
| self.assertEqual(device_type, 1) | |
| self.assertEqual(device_id, 0) | |
| 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, 14) | |
| self.assertEqual(device_id, 0) |
import paddle
import torch
def test():
#cpu
tensor_cpu = paddle.to_tensor([1, 2, 3], place=base.CPUPlace())
paddle_device_type, paddle_device_id = tensor_cpu.__dlpack_device__()
torch_cpu= torch.randn(100, 10000, device='cpu')
device_type_torch, device_id_torch = (DLDeviceType.kDLCPU if torch_cpu.device.type == "cpu" else -1), torch_cpu.device.index
print("paddle_cpu_device:",paddle_device_type, paddle_device_id)
print("torch_cpu_device:",device_type_torch, device_id_torch)
#gpu
tensor_gpu = paddle.to_tensor([1, 2, 3], place=base.CUDAPlace(0))
torch_gpu= torch.randn(100, 10000, device='gpu')
paddle_device_type, paddle_device_id = tensor_gpu.__dlpack_device__()
device_type_torch, device_id_torch = (DLDeviceType.kDLCUDA if torch_cpu.device.type == "gpu" else -1), torch_gpu.device.index
print("paddle_gpu_device:",paddle_device_type, paddle_device_id)
print("torch_gpu_device:",device_type_torch, device_id_torch)输出： |
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| self.assertEqual(device_type, DLDeviceType.kDLCPU) | ||
| self.assertEqual(device_id, None) | ||
|
|
||
| # 测试 CUDA |
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代码中尽量不要出现中文，"测试" --> test
2 tasks
| elif place.is_cpu_place(): | ||
| return DLDeviceType.kDLCPU, None | ||
| elif place.is_cuda_pinned_place(): | ||
| return DLDeviceType.kDLCUDAHost, 0 |
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为什么cpu_place是None，cuda_pinned_place的第二个返回值是0？
74 tasks
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PR Category
User Experience
PR Types
New features
Description
Paddle Tensor 规范化：新增 Tensor.dlpack_device