[Perf] Update causal conv1d fn for better perf

[SunnyLee151064]

What this PR does / why we need it?

Update the causal_conv1d_fn for better perf. For details, get seqlens from input x.shape rather than query_start_loc to remove tolist operation for less duration time.

Does this PR introduce any user-facing change?

No

How was this patch tested?

• vLLM version: v0.12.0
• vLLM main: vllm-project/vllm@ad32e3e

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[gemini-code-assist]

Code Review

This pull request aims to improve the performance of causal_conv1d_fn by removing a loop and a .tolist() operation. While the performance improvement is valid for a batch size of one, the changes introduce a critical bug that breaks support for batch processing. The updated implementation only handles the first sequence in a batch and ignores the rest, which will lead to incorrect outputs for batches larger than one. I've provided a suggestion to fix the batching logic while retaining a bugfix for tensor shapes that was also part of this change.

[gemini-code-assist]

The refactoring of this function to improve performance has unfortunately broken the batch processing capability. The original implementation iterated over sequences in a batch, which is necessary for handling variable-length sequences packed together. The new implementation removes this loop and only processes the first element of the batch (as evidenced by the use of cache_indices[0] and has_initial_state[0]). This will result in incorrect behavior for any input with a batch size greater than one.

While this change fixes a shape mismatch for initial_states by adding .unsqueeze(0), removing the loop is incorrect. The correct approach is to restore the loop for batch processing and apply the unsqueeze(0) fix within the loop. Here is a suggested implementation:

    seqlens = query_start_loc[1:] - query_start_loc[:-1]
    seqlens = seqlens.tolist()
    splits = torch.split(x, seqlens, dim=-1)

    for i in range(len(seqlens)):
        x_s = splits[i]
        if cache_indices[i] == PAD_SLOT_ID:
            continue
        out_ref_b.append(
            causal_conv1d_fn_native(
                x_s,
                weight,
                bias,
                activation=activation,
                return_final_states=True,
                final_states_out=conv_states[cache_indices[i]].unsqueeze(0),
                initial_states=(
                    conv_states[cache_indices[i]].unsqueeze(0)
                    if has_initial_state[i]
                    else None
                ),
            )
        )