vllm.v1.attention.ops.deepseek_v4_ops ¶

def compute_global_topk_indices_and_lens(
    topk_indices: torch.Tensor,
    token_to_req_indices: torch.Tensor,
    block_table: torch.Tensor,
    block_size: int,
    is_valid_token: torch.Tensor,
) -> tuple[torch.Tensor, torch.Tensor]:
    """Map local topk indices to global KV cache slots and count valid entries.

    Fuses three operations into a single kernel:
    1. Block-table lookup (local index → global slot id)
    2. Valid-entry counting (topk_lens per token)
    3. Masking padding tokens to length 0
    """
    num_tokens = topk_indices.shape[0]
    global_topk_indices = torch.empty_like(topk_indices)
    topk_lens = torch.empty(num_tokens, dtype=torch.int32, device=topk_indices.device)
    _compute_global_topk_indices_and_lens_kernel[(num_tokens,)](
        global_topk_indices,
        global_topk_indices.stride(0),
        topk_lens,
        topk_indices,
        topk_indices.stride(0),
        topk_indices.shape[-1],
        token_to_req_indices,
        block_table,
        block_table.stride(0),
        block_size,
        is_valid_token,
        TRITON_BLOCK_SIZE=1024,
    )
    return global_topk_indices, topk_lens

def fused_indexer_q_rope_quant(
    positions: torch.Tensor,
    index_q: torch.Tensor,
    index_q_cos_sin_cache: torch.Tensor,
    # Index weights
    index_weights: torch.Tensor,
    index_weights_softmax_scale: float,
    index_weights_head_scale: float,
    use_fp4: bool = False,
) -> tuple[
    torch.Tensor | tuple[torch.Tensor, torch.Tensor],
    torch.Tensor,
]:
    """Fused RoPE + quantize Q for the sparse indexer.

    Weight-fold semantics (important — the two paths differ):

    FP8 path (use_fp4=False, default):
        q_fp8      : (T, H, HEAD_DIM) float8_e4m3fn, per-token-per-head
                     scalar scale (NOT stored — folded into weights below)
        weights_out = weights * q_scale * softmax_scale * head_scale
        Rationale: a single per-token q_scale is a scalar the downstream FP8
        logits kernel would otherwise multiply in. Folding it into `weights`
        avoids emitting a separate tensor and is free for the logits kernel.

    MXFP4 path (use_fp4=True):
        q_packed   : (T, H, HEAD_DIM // 2) uint8 (2 E2M1 nibbles per byte)
        q_scale    : (T, H, HEAD_DIM // MXFP4_BLOCK_SIZE) uint8 ue8m0 bytes
        weights_out = weights * softmax_scale * head_scale
        Rationale: MXFP4 has PER-BLOCK (32-element) scales that live with
        the Q values — they cannot be folded into a per-token weight
        scalar, so `weights` carries only the softmax and head scales.

    Returns (q_quant, weights_out) where q_quant is either a Tensor (FP8) or
    a (values, scales) tuple (MXFP4). This matches the union type accepted
    by `SparseAttnIndexer.forward_*`.
    """
    assert positions.ndim == 1
    assert index_q.ndim == 3
    assert index_q_cos_sin_cache.ndim == 2

    num_tokens = positions.shape[0]
    num_index_q_heads = index_q.shape[1]
    index_q_head_dim = index_q.shape[2]

    index_weights_out = torch.empty_like(index_weights, dtype=torch.float32)

    if use_fp4:
        assert index_q_head_dim % MXFP4_BLOCK_SIZE == 0, (
            f"head_dim={index_q_head_dim} must be a multiple of MXFP4 block "
            f"size {MXFP4_BLOCK_SIZE}"
        )
        num_scale_blocks = index_q_head_dim // MXFP4_BLOCK_SIZE
        index_q_packed = torch.empty(
            (num_tokens, num_index_q_heads, index_q_head_dim // 2),
            dtype=torch.uint8,
            device=index_q.device,
        )
        index_q_scale = torch.empty(
            (num_tokens, num_index_q_heads, num_scale_blocks),
            dtype=torch.uint8,
            device=index_q.device,
        )
        _fused_indexer_q_rope_mxfp4_kernel[(num_tokens, num_index_q_heads)](
            positions,
            index_q,
            index_q.stride(0),
            index_q.stride(1),
            index_q_cos_sin_cache,
            index_q_cos_sin_cache.stride(0),
            index_q_cos_sin_cache.shape[-1] // 2,
            index_q_packed,
            index_q_packed.stride(0),
            index_q_packed.stride(1),
            index_q_scale,
            index_q_scale.stride(0),
            index_q_scale.stride(1),
            index_q_head_dim,
            MXFP4_BLOCK_SIZE,
            index_weights,
            index_weights.stride(0),
            index_weights_softmax_scale,
            index_weights_head_scale,
            index_weights_out,
            index_weights_out.stride(0),
            num_warps=1,  # TODO: Tune this
        )
        # Values stay uint8 (2 E2M1 nibbles per byte). Scales are 4 ue8m0
        # bytes per (token, head) reinterpreted as one int32, then squeezed
        # from (T, H, 1) to (T, H) to match DeepGEMM's expected q_sf rank
        # (prefill wants 2-D (seq_len, num_heads); decode reshapes this to
        # 3-D (batch, next_n, num_heads)).
        return (
            index_q_packed,
            index_q_scale.view(torch.int32).squeeze(-1),
        ), index_weights_out

    index_q_fp8 = torch.empty_like(index_q, dtype=torch.float8_e4m3fn)
    _fused_indexer_q_rope_quant_kernel[(num_tokens, num_index_q_heads)](
        positions,
        index_q,
        index_q.stride(0),
        index_q.stride(1),
        index_q_cos_sin_cache,
        index_q_cos_sin_cache.stride(0),
        index_q_cos_sin_cache.shape[-1] // 2,
        index_q_fp8,
        index_q_fp8.stride(0),
        index_q_fp8.stride(1),
        index_q_head_dim,
        index_weights,
        index_weights.stride(0),
        index_weights_softmax_scale,
        index_weights_head_scale,
        index_weights_out,
        index_weights_out.stride(0),
        num_warps=1,  # TODO: Tune this
    )
    return index_q_fp8, index_weights_out

def quantize_and_insert_k_cache(
    k: torch.Tensor,  # [num_tokens, 512] bf16
    k_cache: torch.Tensor,  # [num_blocks, block_bytes] uint8
    slot_mapping: torch.Tensor,  # [num_tokens] int64
    block_size: int = 64,
    is_ue8m0: bool = True,
):
    """
    Quantize K tensor and insert into paged K cache.

    K Cache block layout (block_size=64 tokens):
    - First 64 * 576 = 36864 bytes: Token data
      - Each token: 448 bytes (fp8) + 128 bytes (bf16)
    - Next 64 * 8 = 512 bytes: Scales
      - Each token: 8 bytes (uint8 scales, 7 real + 1 padding)
    - Padded to multiple of 576
    """
    assert k.dim() == 2 and k.shape[1] == 512, (
        f"K must be [num_tokens, 512], got {k.shape}"
    )
    assert k.dtype == torch.bfloat16, f"K must be bf16, got {k.dtype}"
    assert is_ue8m0, "Only support ue8m0 quantization."

    # NOTE: When using DP, slot_mapping.shape[0] can be less than k.shape[0] due to
    # padding. Always use slot_mapping.shape[0] as the token count.
    num_tokens = slot_mapping.shape[0]
    block_stride = k_cache.stride(0)  # bytes per block

    TOKEN_FP8_DIM = 448
    TOKEN_BF16_DIM = 64
    TOKEN_SCALE_DIM = 8
    QUANT_BLOCK_SIZE = 64
    FP8_MAX = 448.0
    TOKEN_DATA_SIZE = TOKEN_FP8_DIM + TOKEN_BF16_DIM * 2

    grid = (num_tokens,)

    quantize_and_insert_k_kernel[grid](
        k,
        slot_mapping,
        k_cache,
        num_tokens,
        input_dim=512,
        fp8_dim=TOKEN_FP8_DIM,
        bf16_dim=TOKEN_BF16_DIM,
        scale_dim=TOKEN_SCALE_DIM,
        quant_block=QUANT_BLOCK_SIZE,
        cache_block_size=block_size,
        token_data_size=TOKEN_DATA_SIZE,
        block_stride=block_stride,
        fp8_max=FP8_MAX,
        n_quant_blocks=8,
    )