vllm.v1.attention.backends.utils ¶

def _make_metadata_with_slice(
    ubatch_slice: UBatchSlice, attn_metadata: CommonAttentionMetadata
) -> CommonAttentionMetadata:
    """
    This function creates a new CommonAttentionMetadata that corresponds to
    the requests included in ubatch_slice
    """

    assert not ubatch_slice.is_empty(), f"Ubatch slice {ubatch_slice} is empty"

    request_slice = ubatch_slice.request_slice
    token_slice = ubatch_slice.token_slice

    start_locs = attn_metadata.query_start_loc_cpu
    first_req = request_slice.start
    first_tok = token_slice.start
    last_req = request_slice.stop - 1
    last_tok = token_slice.stop - 1

    assert start_locs[first_req] <= first_tok < start_locs[first_req + 1], (
        "Token slice start outside of first request"
    )
    assert start_locs[last_req] <= last_tok < start_locs[last_req + 1], (
        "Token slice end outside of last request"
    )

    # If the "middle" request has tokens in both ubatches, we have to split it.
    # If ubatch_slice is the first ubatch then we will be splitting the last
    # request. If it's the second microbatch, then we will be splitting the
    # first request
    splits_first_request = first_tok > start_locs[first_req]
    splits_last_request = last_tok < start_locs[last_req + 1] - 1

    query_start_loc_cpu = slice_query_start_locs(start_locs, request_slice)
    query_start_loc = slice_query_start_locs(
        attn_metadata.query_start_loc, request_slice
    )

    assert len(query_start_loc) >= 2, (
        f"query_start_loc must have at least 2 elements, got {len(query_start_loc)}"
    )

    if splits_first_request:
        tokens_skipped = first_tok - start_locs[first_req]
        query_start_loc[1:] -= tokens_skipped
        query_start_loc_cpu[1:] -= tokens_skipped
    seq_lens = attn_metadata.seq_lens[request_slice]
    seq_lens_cpu = attn_metadata.seq_lens_cpu[request_slice]

    if splits_last_request:
        tokens_skipped = query_start_loc_cpu[-1] - token_slice.stop
        query_start_loc[-1] -= tokens_skipped
        query_start_loc_cpu[-1] -= tokens_skipped

        # Make sure we don't modify the seq_lens tensors
        #  (not cudagraph compatible)
        seq_lens = seq_lens.clone()
        seq_lens_cpu = seq_lens_cpu.clone()
        seq_lens[-1] -= tokens_skipped
        seq_lens_cpu[-1] -= tokens_skipped

    max_seq_len = int(seq_lens_cpu.max())
    num_computed_tokens_cpu = attn_metadata.num_computed_tokens_cpu[request_slice]

    num_requests = request_slice.stop - request_slice.start
    num_actual_tokens = token_slice.stop - token_slice.start
    max_query_len = int(
        torch.max(torch.abs(query_start_loc_cpu[1:] - query_start_loc_cpu[:-1])).item()
    )

    # This is to account for the case where we are in a dummy
    # run and query_start_loc_cpu is full of 0s
    if max_query_len == 0:
        max_query_len = attn_metadata.max_query_len

    block_table_tensor = attn_metadata.block_table_tensor[request_slice]
    slot_mapping = attn_metadata.slot_mapping[token_slice]

    return CommonAttentionMetadata(
        query_start_loc=query_start_loc,
        query_start_loc_cpu=query_start_loc_cpu,
        seq_lens=seq_lens,
        seq_lens_cpu=seq_lens_cpu,
        num_computed_tokens_cpu=num_computed_tokens_cpu,
        num_reqs=num_requests,
        num_actual_tokens=num_actual_tokens,
        max_query_len=max_query_len,
        max_seq_len=max_seq_len,
        block_table_tensor=block_table_tensor,
        slot_mapping=slot_mapping,
    )

def compute_causal_conv1d_metadata(query_start_loc_p: torch.Tensor):
    # Needed for causal_conv1d
    seqlens = query_start_loc_p.diff().to("cpu")
    nums_dict = {}  # type: ignore
    batch_ptr = None
    token_chunk_offset_ptr = None
    device = query_start_loc_p.device
    for BLOCK_M in [8]:  # cover all BLOCK_M values
        nums = -(-seqlens // BLOCK_M)
        nums_dict[BLOCK_M] = {}
        nums_dict[BLOCK_M]["nums"] = nums
        nums_dict[BLOCK_M]["tot"] = nums.sum().item()
        mlist = torch.from_numpy(np.repeat(np.arange(len(nums)), nums))
        nums_dict[BLOCK_M]["mlist"] = mlist
        mlist_len = len(nums_dict[BLOCK_M]["mlist"])
        nums_dict[BLOCK_M]["mlist_len"] = mlist_len
        MAX_NUM_PROGRAMS = max(1024, mlist_len) * 2
        offsetlist = []  # type: ignore
        for idx, num in enumerate(nums):
            offsetlist.extend(range(num))
        offsetlist = torch.tensor(offsetlist, dtype=torch.int32)
        nums_dict[BLOCK_M]["offsetlist"] = offsetlist

        if batch_ptr is None:
            # Update default value after class definition
            batch_ptr = torch.full(
                (MAX_NUM_PROGRAMS,), PAD_SLOT_ID, dtype=torch.int32, device=device
            )
            token_chunk_offset_ptr = torch.full(
                (MAX_NUM_PROGRAMS,), PAD_SLOT_ID, dtype=torch.int32, device=device
            )
        else:
            if batch_ptr.nelement() < MAX_NUM_PROGRAMS:
                batch_ptr.resize_(MAX_NUM_PROGRAMS).fill_(PAD_SLOT_ID)
                token_chunk_offset_ptr.resize_(  # type: ignore
                    MAX_NUM_PROGRAMS
                ).fill_(PAD_SLOT_ID)

        batch_ptr[0:mlist_len].copy_(mlist)
        token_chunk_offset_ptr[  # type: ignore
            0:mlist_len
        ].copy_(offsetlist)
        nums_dict[BLOCK_M]["batch_ptr"] = batch_ptr
        nums_dict[BLOCK_M]["token_chunk_offset_ptr"] = token_chunk_offset_ptr  # type: ignore

    return nums_dict, batch_ptr, token_chunk_offset_ptr

def create_fast_prefill_custom_backend(
    prefix: str,
    underlying_attn_backend: AttentionBackend,
) -> type[AttentionBackend]:
    underlying_builder = underlying_attn_backend.get_builder_cls()

    class FastPrefillAttentionBuilder(underlying_builder):  # type: ignore
        def build(
            self,
            common_prefix_len: int,
            common_attn_metadata: CommonAttentionMetadata,
            fast_build: bool = False,
        ) -> AttentionMetadata:
            new_common_attn_metadata = (
                make_kv_sharing_fast_prefill_common_attn_metadata(common_attn_metadata)
            )
            metadata = super().build(
                common_prefix_len, new_common_attn_metadata, fast_build
            )

            class KVSharingFastPrefillAttentionMetadata(
                metadata.__class__,  #  type: ignore
                KVSharingFastPrefillMetadata,
            ):
                def __init__(self, metadata, common_attn_metadata):
                    # Shallow copy all fields in metadata cls
                    for _field in fields(metadata.__class__):
                        setattr(self, _field.name, getattr(metadata, _field.name))

                    self.logits_indices_padded = (
                        common_attn_metadata.logits_indices_padded
                    )
                    self.num_logits_indices = common_attn_metadata.num_logits_indices

            return KVSharingFastPrefillAttentionMetadata(metadata, common_attn_metadata)

    attn_backend = subclass_attention_backend(
        name_prefix=prefix,
        attention_backend_cls=underlying_attn_backend,
        builder_cls=FastPrefillAttentionBuilder,
    )

    return attn_backend

def get_dcp_local_seq_lens(
    seq_lens: torch.Tensor,
    dcp_size: int = 1,
    dcp_rank: int | None = None,
    cp_kv_cache_interleave_size: int = 1,
) -> torch.Tensor:
    """While using dcp, kv_cache size stored on each rank may be different,
    use this function to calculate split decode seq_lens of each dcp rank.
    Only consider dcp now, we can extend the case of cp based on this.
    """
    num_requests = seq_lens.size(0)
    if dcp_rank is None:
        rank_offsets = (
            torch.arange(dcp_size, dtype=torch.int32, device=seq_lens.device)
            .unsqueeze(0)
            .repeat(num_requests, 1)
        )
    else:
        rank_offsets = torch.tensor(
            [[dcp_rank]], dtype=torch.int32, device=seq_lens.device
        )
    seq_lens_tiled = (
        seq_lens.to(torch.int32).unsqueeze(-1).repeat(1, rank_offsets.shape[1])
    )
    base = (
        seq_lens_tiled
        // cp_kv_cache_interleave_size
        // dcp_size
        * cp_kv_cache_interleave_size
    )
    remainder = seq_lens_tiled - base * dcp_size
    remainder = torch.clip(
        remainder - rank_offsets * cp_kv_cache_interleave_size,
        0,
        cp_kv_cache_interleave_size,
    )
    dcp_local_seq_lens = base + remainder
    return dcp_local_seq_lens.squeeze(1)

@functools.lru_cache
def get_kv_cache_layout():
    # Format specified by the code.
    global _KV_CACHE_LAYOUT_OVERRIDE

    if _KV_CACHE_LAYOUT_OVERRIDE is not None:
        cache_layout = _KV_CACHE_LAYOUT_OVERRIDE
        logger.info_once(
            "`_KV_CACHE_LAYOUT_OVERRIDE` variable detected. "
            "Setting KV cache layout to %s.",
            cache_layout,
        )
        return cache_layout

    # Format specified by the user.
    cache_layout = envs.VLLM_KV_CACHE_LAYOUT
    # When neither the user nor the override specified a layout, get default
    if cache_layout is None:
        cache_layout = get_kv_connector_cache_layout()
    else:
        assert is_valid_kv_cache_layout(cache_layout)
        logger.info_once(
            "`VLLM_KV_CACHE_LAYOUT` environment variable "
            "detected. Setting KV cache layout to %s.",
            cache_layout,
        )
    return cache_layout

def get_per_layer_parameters(
    vllm_config: VllmConfig, layer_names: list[str], cls_: type["AttentionImpl"]
) -> dict[str, PerLayerParameters]:
    """
    Scan layers in `layer_names` and determine some hyperparameters
    to use during `plan`.
    """

    layers = get_layers_from_vllm_config(vllm_config, AttentionLayerBase, layer_names)
    per_layer_params: dict[str, PerLayerParameters] = {}

    for key, layer in layers.items():
        impl = layer.impl
        assert isinstance(impl, cls_)

        # Infer hyperparameters from the attention layer
        window_size = getattr(impl, "sliding_window", None)
        window_left = window_size[0] if window_size is not None else -1
        logits_soft_cap = getattr(impl, "logits_soft_cap", None)
        sm_scale = impl.scale
        has_sinks = getattr(impl, "sinks", None) is not None

        per_layer_params[key] = PerLayerParameters(
            window_left, logits_soft_cap, sm_scale, has_sinks
        )

    return per_layer_params

def infer_global_hyperparameters(
    per_layer_params: dict[str, PerLayerParameters],
) -> PerLayerParameters:
    """
    Currently, FlashInfer backend other than trtllm-gen
    only support models in which all layers share
    the same values for the following hyperparameters:
    - `window_left`
    - `logits_soft_cap`
    - `sm_scale`

    So this function asserts that all layers share the same values for these
    hyperparameters and returns the global values.
    """

    assert len(per_layer_params) > 0, "No attention layers found in the model."

    param_sets = list(per_layer_params.values())
    global_params = param_sets[0]

    global_params.has_same_window_lefts = all(
        params.window_left == global_params.window_left for params in param_sets
    )
    global_params.has_same_all_params = all(
        params == global_params for params in param_sets
    )

    return global_params

def is_valid_kv_cache_layout(value: str) -> bool:
    return value in get_args(KVCacheLayoutType)

def make_kv_sharing_fast_prefill_common_attn_metadata(
    common_attn_metadata: CommonAttentionMetadata,
) -> CommonAttentionMetadata:
    if common_attn_metadata.max_query_len == 1:
        # All requests are decode (assume 1 token for now)
        # Skip computing fast prefill path
        return common_attn_metadata

    assert common_attn_metadata.logits_indices_padded is not None
    assert common_attn_metadata.num_logits_indices is not None

    logits_indices_padded = common_attn_metadata.logits_indices_padded
    num_logits_indices = common_attn_metadata.num_logits_indices
    # Get rid of CUDAGraph padding, if any
    logits_indices = logits_indices_padded[:num_logits_indices]
    num_reqs = common_attn_metadata.num_reqs
    query_start_loc = common_attn_metadata.query_start_loc
    seq_lens = common_attn_metadata.seq_lens
    # Example inputs
    # num_reqs: 3
    # generation_indices:  [14, 18, 19, 27]
    # query_start_loc: [0, 15, 20, 28]
    # seq_lens:        [41, 31, 40]

    # Find how many decode indices belong to each request
    # request_ids: [0, 1, 1, 2]
    request_ids = torch.bucketize(logits_indices, query_start_loc[1:], right=True)

    # Figure out how many tokens are in each request
    # num_decode_tokens: [1, 2, 1]
    num_decode_tokens = torch.bincount(request_ids, minlength=num_reqs)

    # Calculate new query_start_loc with tokens in generation_indices
    # decode_query_start_loc: [0, 1, 3, 4]
    decode_query_start_loc = torch.empty(
        num_reqs + 1, device=query_start_loc.device, dtype=query_start_loc.dtype
    )

    decode_query_start_loc[0] = 0
    decode_query_start_loc[1:] = torch.cumsum(num_decode_tokens, dim=0)
    decode_max_query_len = int(num_decode_tokens.max().item())
    total_num_decode_tokens = int(num_decode_tokens.sum().item())

    common_attn_metadata = CommonAttentionMetadata(
        query_start_loc=decode_query_start_loc,
        query_start_loc_cpu=decode_query_start_loc.to("cpu", non_blocking=True),
        seq_lens=seq_lens,
        seq_lens_cpu=seq_lens.to("cpu", non_blocking=True),
        num_computed_tokens_cpu=common_attn_metadata.num_computed_tokens_cpu,
        num_reqs=num_reqs,
        num_actual_tokens=total_num_decode_tokens,
        max_query_len=decode_max_query_len,
        max_seq_len=common_attn_metadata.max_seq_len,
        block_table_tensor=common_attn_metadata.block_table_tensor,
        slot_mapping=common_attn_metadata.slot_mapping,
        causal=True,
    )
    return common_attn_metadata