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When working with PyTorch tensors, prioritize operations that avoid unnecessary memory allocations and copies to improve performance. Choose tensor operations carefully based on whether data will be immediately overwritten or needs preservation.

Key guidelines:

• Use tensor.view(dtype) instead of tensor.to(dtype) when possible to avoid copies
• Use torch.empty() instead of torch.zeros() when the tensor will be fully populated immediately after creation
• Understand memory allocation contexts (like symmetric memory pools) and their implications for tensor operations

Example:

# Avoid unnecessary copy
if tensor.dtype != target_dtype:
    tensor = tensor.view(target_dtype)  # No copy if compatible
    # instead of: tensor = tensor.to(target_dtype)  # May cause copy

# Avoid unnecessary initialization
kv_indices = torch.empty(size, dtype=torch.int32)  # Will be filled next
# instead of: kv_indices = torch.zeros(size, dtype=torch.int32)  # Extra kernel launch

This approach reduces memory overhead and kernel launches, leading to better performance in tensor-heavy operations.