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November 12, 2022 15:37
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convert numba cuda array to pytorch tensor
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| from numba import cuda | |
| import ctypes | |
| import numpy as np | |
| import torch | |
| def devndarray2torch(dev_arr): | |
| t = torch.empty(size=dev_arr.shape, dtype=dtyp).cuda() | |
| ctx = cuda.cudadrv.driver.driver.get_context() | |
| # constant value of #bytes in case of float32 = 4 | |
| mp = cuda.cudadrv.driver.MemoryPointer(ctx, ctypes.c_ulong(t.data_ptr()), t.numel()*4) | |
| tmp_arr = cuda.cudadrv.devicearray.DeviceNDArray(t.size(), [i*4 for i in t.stride()], np.dtype('float32'), | |
| gpu_data=mp, stream=torch.cuda.current_stream().cuda_stream) | |
| # To verify whether the data pointer is same or not. | |
| # print(tmp_arr.__cuda_array_interface__) | |
| # print(dev_arr.__cuda_array_interface__) | |
| tmp_arr.copy_to_device(dev_arr) | |
| return t | |
| d_arr = cuda.to_device(np.array([[10,20,30],[40,50,60.0]], dtype=np.float32)) | |
| tensor = devndarray2tensor(d_arr) | |
| print(tensor) |
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Huh interesting, I'm running on Windows with
PyTorch 1.2.0,numba 0.45.1,Python 3.7.4The output is:
{'shape': (2, 3), 'strides': (12, 4), 'data': (37748736, False), 'typestr': '<f4', 'version': 1} {'shape': (2, 3), 'strides': (12, 4), 'data': (21510488064, False), 'typestr': '<f4', 'version': 1}