Implement a causal depthwise 1D convolution kernel.

This is a core component of Mamba/Mamba-2 architectures. Each channel is
convolved independently (depthwise) with causal (left) zero-padding so that
output[t] depends only on input[t-W+1:t+1].

For each batch b, channel d, and time t:
  out[b, d, t] = bias[d] + sum_{k=0}^{W-1} weight[d, k] * x[b, d, t - W + 1 + k]
where out-of-bounds values are treated as zero.

Input: tuple(x, weight, bias) where:
  - x: torch.Tensor of shape [B, D, S] (float32)
  - weight: torch.Tensor of shape [D, W] (float32)
  - bias: torch.Tensor of shape [D] (float32)

Output: torch.Tensor of shape [B, D, S] (float32)
