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Dev/perf sync #754

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4c9d1ec
support block-size > 1 for mla by remapping block table
ganyi1996ppo Oct 20, 2025
8d16a52
resolve comments
ganyi1996ppo Oct 20, 2025
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12 changes: 2 additions & 10 deletions vllm/platforms/rocm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -296,16 +296,8 @@ def get_attn_backend_cls(
f"does not support block size {block_size}."
)
if selected_backend == _Backend.ROCM_AITER_MLA:
if block_size == 1:
logger.info("Using AITER MLA backend on V1 engine.")
return (
"vllm.v1.attention.backends.mla.rocm_aiter_mla.AiterMLABackend" # noqa: E501
)
raise ValueError(
f" The selected backend, {selected_backend.name},"
f"does not support block size {block_size}."
"(currently only supports block size 1)"
)
logger.info("Using AITER MLA backend on V1 engine.")
return "vllm.v1.attention.backends.mla.rocm_aiter_mla.AiterMLABackend" # noqa: E501
raise ValueError(
f" The selected backend, {selected_backend.name},"
f"is not MLA type while requested for MLA backend."
Expand Down
38 changes: 31 additions & 7 deletions vllm/v1/attention/backends/mla/rocm_aiter_mla.py
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Original file line number Diff line number Diff line change
Expand Up @@ -78,9 +78,6 @@ def __init__(
super().__init__(
kv_cache_spec, layer_names, vllm_config, device, AiterMLAMetadata
)
assert self.kv_cache_spec.block_size == 1, (
"AITER MLAonly supports block size 1."
)

self.compilation_config = vllm_config.compilation_config
max_num_pages_per_req = cdiv(
Expand All @@ -94,6 +91,11 @@ def __init__(
# so we can only use the persistent buffer if a cudagraph is actually
# being used.
if self.compilation_config.cudagraph_mode.has_full_cudagraphs():
self.block_table_remapping = torch.zeros(
[max_num_reqs, max_num_pages_per_req * self.kv_cache_spec.block_size],
dtype=torch.int32,
device=device,
)
self.paged_kv_indptr = torch.zeros(
max_num_reqs + 1, dtype=torch.int32, device=device
)
Expand All @@ -119,13 +121,29 @@ def _build_decode(
dcp_tot_seq_lens_device: torch.Tensor | None,
) -> AiterMLADecodeMetadata:
page_size = self.kv_cache_spec.block_size
block_table_bounds = (seq_lens_device + page_size - 1) // page_size
device = self.device
num_reqs = seq_lens_device.size(0)
bs, _ = block_table_tensor.shape
block_table_tensor = (
block_table_tensor.unsqueeze(-1).expand(-1, -1, page_size) * page_size
)
block_table_tensor = (
block_table_tensor
+ torch.arange(
0,
page_size,
device=block_table_tensor.device,
dtype=block_table_tensor.dtype,
)[None, None, :]
)
block_table_tensor = block_table_tensor.view(bs, -1)

# after remapping, we assume the block size already equals to 1

max_blk_size_per_req = block_table_tensor.shape[-1]
mask = torch.arange(
block_table_tensor.size(1), dtype=block_table_tensor.dtype, device=device
).unsqueeze(0) < block_table_bounds.unsqueeze(1)
).unsqueeze(0) < seq_lens_device.unsqueeze(1)
paged_kv_indices = block_table_tensor[mask]

paged_kv_last_page_len = seq_lens_device % page_size
Expand All @@ -135,13 +153,19 @@ def _build_decode(

paged_kv_indptr = torch.cat(
[
torch.zeros(1, dtype=block_table_bounds.dtype, device=device),
block_table_bounds.cumsum(dim=0, dtype=torch.int32),
torch.zeros(1, dtype=seq_lens_device.dtype, device=device),
seq_lens_device.cumsum(dim=0, dtype=torch.int32),
]
)

if self.compilation_config.cudagraph_mode.has_full_cudagraphs():
num_actual_pages = paged_kv_indices.size(0)
self.block_table_remapping[:num_reqs, :max_blk_size_per_req].copy_(
block_table_tensor, non_blocking=True
)
block_table_tensor = self.block_table_remapping[
:num_reqs, :max_blk_size_per_req
]

self.paged_kv_indices[:num_actual_pages].copy_(
paged_kv_indices, non_blocking=True
Expand Down