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import torch
def chunk_scan(X: torch.Tensor, A: torch.Tensor, B: torch.Tensor, chunk: int = 128, BD: int = 128) -> torch.Tensor:
"""
Baseline Mamba2 chunked scan implementation using PyTorch.
Args:
X: Input tensor of shape (L, D) - input sequence
A: Input tensor of shape (L, D) - decay factors
B: Input tensor of shape (L, D) - input weights
chunk: Chunk size for parallel processing (default 128)
BD: Block dimension for feature dimension tiling (default 128) - unused in baseline
Returns:
Output tensor of shape (L, D) - scan output
"""
# y_t = a_t * y_{t-1} + b_t * x_t
L, D = X.shape
y = torch.zeros(D, device=X.device, dtype=torch.float32)
out = torch.empty(L, D, device=X.device, dtype=torch.float32)
for t in range(L):
y = A[t].float() * y + B[t].float() * X[t].float()
out[t] = y
return out.to(torch.float16)
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