class BaseMambaAttentionMetadataBuilder(AttentionMetadataBuilder[M], abc.ABC):
metadata_cls: type[M]
reorder_batch_threshold: int = 1
_cudagraph_support: ClassVar[AttentionCGSupport] = AttentionCGSupport.UNIFORM_BATCH
# Will be disabled if speculative decoding is used
supports_update_block_table: bool = True
def __init__(
self,
kv_cache_spec: AttentionSpec,
layer_names: list[str],
vllm_config: VllmConfig,
device: torch.device,
):
super().__init__(kv_cache_spec, layer_names, vllm_config, device)
# Enable speculative decoding support
self.speculative_config = vllm_config.speculative_config
self.compilation_config = vllm_config.compilation_config
self.num_spec_tokens: int = vllm_config.num_speculative_tokens
self.use_spec_decode = self.num_spec_tokens > 0
assert isinstance(kv_cache_spec, MambaSpec)
self.compilation_config = vllm_config.compilation_config
self.decode_cudagraph_max_bs = self.vllm_config.scheduler_config.max_num_seqs
if self.compilation_config.max_cudagraph_capture_size is not None:
self.decode_cudagraph_max_bs = min(
self.decode_cudagraph_max_bs,
self.compilation_config.max_cudagraph_capture_size,
)
if self.vllm_config.cache_config.mamba_cache_mode == "all":
max_num_blocks = cdiv(
self.vllm_config.model_config.max_model_len,
self.kv_cache_spec.block_size,
)
# Speculative decoding not supported with prefix caching,
# so keep shape consistent with prefill buffer
# TODO: reduce this size as needed for decode-only cudagraph capture
self.state_indices_tensor_d = torch.empty(
(
self.decode_cudagraph_max_bs,
max_num_blocks,
),
dtype=torch.int32,
device=device,
)
self.block_idx_last_scheduled_token = torch.empty(
(self.decode_cudagraph_max_bs,),
dtype=torch.int32,
device=device,
)
self.block_idx_last_computed_token = torch.empty(
(self.decode_cudagraph_max_bs,),
dtype=torch.int32,
device=device,
)
else:
self.state_indices_tensor_d = torch.empty(
(self.decode_cudagraph_max_bs, 1 + self.num_spec_tokens),
dtype=torch.int32,
device=device,
)
# For speculative decoding, we need to store the following buffers
# for CUDA graph capture during decode
if self.num_spec_tokens > 0:
self.decode_num_accepted_tokens = torch.empty(
(self.decode_cudagraph_max_bs,),
dtype=torch.int32,
device=device,
)
self._init_reorder_batch_threshold(1, self.use_spec_decode)
if self.use_spec_decode:
self.supports_update_block_table = False
def build_for_cudagraph_capture(
self, common_attn_metadata: CommonAttentionMetadata
) -> M:
"""
This method builds the metadata for full cudagraph capture.
Currently, only decode is supported for full cudagraphs with Mamba.
"""
m = common_attn_metadata
assert (
m.max_query_len <= 1 + self.num_spec_tokens
and m.num_reqs <= self.decode_cudagraph_max_bs
), (
"Mamba only supports decode-only full CUDAGraph capture. "
"Make sure all cudagraph capture sizes <= max_num_seq."
)
assert m.max_query_len == 1 + self.num_spec_tokens # decode-only
num_accepted_tokens = None
if self.num_spec_tokens > 0:
num_accepted_tokens = torch.diff(m.query_start_loc)
return self.build(0, m, num_accepted_tokens=num_accepted_tokens)
def build(
self,
common_prefix_len: int,
common_attn_metadata: CommonAttentionMetadata,
fast_build: bool = False,
*,
num_accepted_tokens: torch.Tensor | None = None,
**kwargs: Any,
) -> M:
"""
Default build implementation for Mamba-like attention backends.
Subclasses (e.g., Mamba2) can override to add additional metadata.
"""
return self._compute_common_metadata(
common_attn_metadata, num_accepted_tokens=num_accepted_tokens
)
def _compute_prefix_caching_block_indices(
self,
common_attn_metadata: CommonAttentionMetadata,
mamba_block_size: int,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
num_computed_tokens = common_attn_metadata.compute_num_computed_tokens()
# Block index of the last computed token
block_idx_last_computed_token = cdiv(num_computed_tokens, mamba_block_size) - 1
# which is <= block index for the first scheduled token
block_idx_first_scheduled_token = (
cdiv(num_computed_tokens + 1, mamba_block_size) - 1
)
# which is <= block index of the last scheduled token
block_idx_last_scheduled_token = (
cdiv(common_attn_metadata.seq_lens, mamba_block_size) - 1
)
# -1 in case it's non-computed and causes later issues with indexing
block_idx_last_computed_token = torch.clamp(
block_idx_last_computed_token, min=0
)
# -1 in the case we have a padded request (0 seq-len)
block_idx_last_scheduled_token = torch.clamp(
block_idx_last_scheduled_token, min=0
)
return (
block_idx_last_computed_token,
block_idx_first_scheduled_token,
block_idx_last_scheduled_token,
)
def _compute_common_metadata(
self,
common_attn_metadata: CommonAttentionMetadata,
*,
num_accepted_tokens: torch.Tensor | None = None,
) -> M:
"""
Compute metadata common to both Mamba1 and Mamba2.
"""
num_reqs = common_attn_metadata.num_reqs
# Treat multi-token queries as decode requests when
# speculative decoding is enabled. Otherwise, use the
# default decode threshold to prevent misclassification
# of prefill queries as decode requests.
decode_threshold = (
self.reorder_batch_threshold if num_accepted_tokens is not None else 1
)
num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = (
split_decodes_and_prefills(
common_attn_metadata, decode_threshold=decode_threshold
)
)
# Need flags to indicate if there are initial states
has_initial_states_p = None
query_start_loc_p = None
query_start_loc_d = None
num_computed_tokens = None
num_computed_tokens_p = None
# for prefix caching
block_idx_first_scheduled_token = None
block_idx_first_scheduled_token_p = None
block_idx_last_computed_token = None
block_idx_last_scheduled_token = None
# for causal_conv1d
nums_dict, batch_ptr, token_chunk_offset_ptr = None, None, None
if self.vllm_config.cache_config.mamba_cache_mode == "all":
num_computed_tokens = common_attn_metadata.compute_num_computed_tokens()
# Return a tensor of shape (#requests, #max blocks)
state_indices_tensor = common_attn_metadata.block_table_tensor
# Additional cache-related varaiables:
mamba_block_size = self.kv_cache_spec.block_size
(
block_idx_last_computed_token,
block_idx_first_scheduled_token,
block_idx_last_scheduled_token,
) = self._compute_prefix_caching_block_indices(
common_attn_metadata, mamba_block_size
)
else:
state_indices_tensor = mamba_get_block_table_tensor(
common_attn_metadata.block_table_tensor,
common_attn_metadata.seq_lens,
self.kv_cache_spec,
self.vllm_config.cache_config.mamba_cache_mode,
)
if state_indices_tensor.dim() == 1:
state_indices_tensor = state_indices_tensor.unsqueeze(-1)
state_indices_tensor_d, state_indices_tensor_p = torch.split(
state_indices_tensor,
[num_decodes, num_prefills],
dim=0,
)
if self.vllm_config.cache_config.mamba_cache_mode != "all":
state_indices_tensor_d = state_indices_tensor_d[
:, : 1 + self.num_spec_tokens
]
state_indices_tensor_p = state_indices_tensor_p[:, 0]
if num_decodes > 0 and self.use_spec_decode:
assert num_accepted_tokens is not None
query_start_loc_d = common_attn_metadata.query_start_loc[: num_decodes + 1]
num_accepted_tokens = num_accepted_tokens[:num_decodes]
if num_prefills > 0:
if num_computed_tokens is None:
num_computed_tokens = common_attn_metadata.compute_num_computed_tokens()
query_start_loc_p_cpu = (
common_attn_metadata.query_start_loc_cpu[-num_prefills - 1 :]
- num_decode_tokens
)
query_start_loc_p = (
common_attn_metadata.query_start_loc[-num_prefills - 1 :]
- num_decode_tokens
)
has_initial_states_p = (
num_computed_tokens[num_reqs - num_prefills : num_reqs] > 0
)
nums_dict, batch_ptr, token_chunk_offset_ptr = (
compute_causal_conv1d_metadata(
query_start_loc_p_cpu,
device=common_attn_metadata.query_start_loc.device,
)
)
if self.vllm_config.cache_config.mamba_cache_mode == "all":
assert num_computed_tokens is not None
num_computed_tokens_p = num_computed_tokens[
num_reqs - num_prefills : num_reqs
]
assert block_idx_first_scheduled_token is not None
block_idx_first_scheduled_token_p = block_idx_first_scheduled_token[
num_reqs - num_prefills : num_reqs
]
metadata = self.metadata_cls(
num_prefills=num_prefills,
num_prefill_tokens=num_prefill_tokens,
num_decodes=num_decodes,
num_decode_tokens=num_decode_tokens,
query_start_loc_p=query_start_loc_p,
has_initial_states_p=has_initial_states_p,
state_indices_tensor_p=state_indices_tensor_p,
state_indices_tensor_d=state_indices_tensor_d,
num_accepted_tokens=num_accepted_tokens,
query_start_loc_d=query_start_loc_d,
block_idx_last_scheduled_token=block_idx_last_scheduled_token,
block_idx_first_scheduled_token_p=block_idx_first_scheduled_token_p,
block_idx_last_computed_token=block_idx_last_computed_token,
num_computed_tokens_p=num_computed_tokens_p,
num_reqs=num_reqs,
seq_lens=common_attn_metadata.seq_lens,
nums_dict=nums_dict,
batch_ptr=batch_ptr,
token_chunk_offset_ptr=token_chunk_offset_ptr,
)
return self._update_metadata_for_cudagraph_capture(metadata)
def _update_metadata_for_cudagraph_capture(
self,
metadata: M,
) -> M:
"""
Update the metadata for cudagraph capture.
Currently, only decode is supported for full cudagraphs with Mamba.
"""
state_indices_tensor_d = metadata.state_indices_tensor_d
query_start_loc_d = metadata.query_start_loc_d
num_accepted_tokens = metadata.num_accepted_tokens
block_idx_last_scheduled_token = metadata.block_idx_last_scheduled_token
block_idx_last_computed_token = metadata.block_idx_last_computed_token
if (
metadata.num_prefills == 0
and metadata.num_decodes <= self.decode_cudagraph_max_bs
and self.compilation_config.cudagraph_mode.has_full_cudagraphs()
):
padded_bs = metadata.num_reqs
self.state_indices_tensor_d[: metadata.num_decodes].copy_(
state_indices_tensor_d, non_blocking=True
)
state_indices_tensor_d = self.state_indices_tensor_d[:padded_bs]
state_indices_tensor_d[metadata.num_decodes :] = PAD_SLOT_ID
if self.use_spec_decode:
assert query_start_loc_d is not None
assert num_accepted_tokens is not None
query_start_loc_d = query_start_loc_d[: padded_bs + 1]
self.decode_num_accepted_tokens[: metadata.num_decodes].copy_(
num_accepted_tokens, non_blocking=True
)
num_accepted_tokens = self.decode_num_accepted_tokens[:padded_bs]
num_accepted_tokens[metadata.num_decodes :] = (
1 # pad with 1st slot index
)
if self.vllm_config.cache_config.mamba_cache_mode == "all":
assert block_idx_last_scheduled_token is not None
assert block_idx_last_computed_token is not None
self.block_idx_last_scheduled_token[: metadata.num_decodes].copy_(
block_idx_last_scheduled_token[: metadata.num_decodes],
non_blocking=True,
)
block_idx_last_scheduled_token = self.block_idx_last_scheduled_token[
: metadata.num_decode_tokens
]
self.block_idx_last_computed_token[: metadata.num_decodes].copy_(
block_idx_last_computed_token[: metadata.num_decodes],
non_blocking=True,
)
block_idx_last_computed_token = self.block_idx_last_computed_token[
: metadata.num_decode_tokens
]
return replace(
metadata,
state_indices_tensor_d=state_indices_tensor_d,
query_start_loc_d=query_start_loc_d,
num_accepted_tokens=num_accepted_tokens,
block_idx_last_scheduled_token=block_idx_last_scheduled_token,
block_idx_last_computed_token=block_idx_last_computed_token,
)
def update_block_table(
self,
metadata: M,
blk_table: torch.Tensor,
slot_mapping: torch.Tensor,
) -> M:
state_indices_tensor = mamba_get_block_table_tensor(
blk_table,
metadata.seq_lens,
self.kv_cache_spec,
self.vllm_config.cache_config.mamba_cache_mode,
)
if state_indices_tensor.dim() == 1:
state_indices_tensor = state_indices_tensor.unsqueeze(-1)
assert (
metadata.num_prefills + metadata.num_decodes
== state_indices_tensor.shape[0]
), (
"Mismatch in number of requests when updating block table."
f" Expected {metadata.num_prefills + metadata.num_decodes}, "
f"got {state_indices_tensor.shape[0]}."
)
state_indices_tensor_d, state_indices_tensor_p = torch.split(
state_indices_tensor,
[metadata.num_decodes, metadata.num_prefills],
dim=0,
)
if self.vllm_config.cache_config.mamba_cache_mode != "all":
state_indices_tensor_d = state_indices_tensor_d[
:, : 1 + self.num_spec_tokens
]
state_indices_tensor_p = state_indices_tensor_p[:, 0]
new_metadata = replace(
metadata,
state_indices_tensor_d=state_indices_tensor_d,
state_indices_tensor_p=state_indices_tensor_p,
)
return self._update_metadata_for_cudagraph_capture(new_metadata)