When working with PyTorch tensors, use memory-efficient operations that avoid unnecessary copies. Specify memory formats directly during tensor creation instead of applying operations like `.t().contiguous()`. For C++/CUDA kernel interfacing, use `.data_ptr()` instead of `.view(dtype)` to ensure safe memory access and maintain compatibility with future...
When working with PyTorch tensors, use memory-efficient operations that avoid unnecessary copies. Specify memory formats directly during tensor creation instead of applying operations like .t().contiguous(). For C++/CUDA kernel interfacing, use .data_ptr() instead of .view(dtype) to ensure safe memory access and maintain compatibility with future PyTorch versions.
# Inefficient approach with unnecessary copy:
tensor = torch.empty((n, m), device="cuda", dtype=torch.bfloat16).t().contiguous()
# Efficient approach:
tensor = torch.empty((n, m), device="cuda", dtype=torch.bfloat16,
memory_format=torch.contiguous_format).t()
# Unsafe C++ kernel interfacing:
cutlass_function(w1_scale.view(torch.int32), w1.view(torch.long))
# Safe approach with explicit pointer access:
cutlass_function(w1_scale.data_ptr(), w1.data_ptr())
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