nvidia-cutlass-dsl-libs-base

import cutlass.cute as cute import torch from cutlass.cute.runtime import from_dlpack @cute.kernel def elementwise_add_kernel( gA: cute.Tensor, gB: cute.Tensor, gC: cute.Tensor, shape: cute.Shape ): # Get thread index within the block tidx, _, _ = cute.arch.thread_idx() # Map thread index to global memory coordinate # Example: Simple 1D mapping for illustration # In real kernels, you'd use more sophisticated layouts and transforms val_layout = cute.make_layout(shape) coords = val_layout(tidx) # Perform element-wise addition if tidx < shape[0] * shape[1]: # Basic bounds check gC[coords] = gA[coords] + gB[coords] M, N = 1024, 512 A_torch = torch.randn(M, N, dtype=torch.float32, device='cuda') B_torch = torch.randn(M, N, dtype=torch.float32, device='cuda') C_torch = torch.zeros(M, N, dtype=torch.float32, device='cuda') # Convert torch tensors to CuTe Tensors mA = from_dlpack(A_torch).mark_layout_dynamic() mB = from_dlpack(B_torch).mark_layout_dynamic() mC = from_dlpack(C_torch).mark_layout_dynamic() # Compile the kernel compiled_kernel = cute.compile(elementwise_add_kernel, mA, mB, mC, (M, N)) # Launch the kernel # A simple block/grid configuration. More complex kernels would use CuTe's layout algebra. block_size = 256 # Example thread block size grid_size = (M * N + block_size - 1) // block_size # Ensure enough blocks compiled_kernel.launch(grid=[grid_size, 1, 1], block=[block_size, 1, 1]) # Verify (optional, requires torch.testing) try: torch.testing.assert_close(C_torch, A_torch + B_torch) print("Kernel executed successfully and results match!") except AssertionError as e: print(f"Verification failed: {e}")