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[FEAT][ROCm] Upgrade AITER MLA v1 backend (#18338)

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Signed-off-by: vllmellm <vllm.ellm@embeddedllm.com>
Co-authored-by: Luka Govedič <ProExpertProg@users.noreply.github.com>
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vllmellm 2025-05-22 01:34:28 +08:00 committed by GitHub
parent bb0a311213
commit 94d8ec8d2b
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2 changed files with 31 additions and 7 deletions
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2
docker/Dockerfile.rocm_base
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@ -12,7 +12,7 @@ ARG PYTORCH_REPO="https://github.com/pytorch/pytorch.git"
ARG PYTORCH_VISION_REPO="https://github.com/pytorch/vision.git"
ARG FA_BRANCH="1a7f4dfa"
ARG FA_REPO="https://github.com/Dao-AILab/flash-attention.git"
ARG AITER_BRANCH="5a77249"
ARG AITER_BRANCH="c1debd8"
ARG AITER_REPO="https://github.com/ROCm/aiter.git"
FROM ${BASE_IMAGE} AS base

36
vllm/v1/attention/backends/mla/rocm_aiter_mla.py
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@ -53,6 +53,8 @@ class AiterMLADecodeMetadata(MLACommonDecodeMetadata):
# The number of entries in the last page of each request in
# the paged kv cache, shape: [batch_size]
paged_kv_last_page_len: Optional[torch.Tensor] = None
# The query indptr, shape : [num_decode + 1]
qo_indptr: Optional[torch.Tensor] = None
class AiterMLAMetadata(MLACommonMetadata[AiterMLADecodeMetadata]):
@ -75,27 +77,33 @@ class AiterMLAMetadataBuilder(MLACommonMetadataBuilder[AiterMLAMetadata]):
seq_lens: torch.Tensor) -> tuple[torch.Tensor, ...]:
page_size = self.runner.block_size
block_table_bounds = (seq_lens + page_size - 1) // page_size
device = self.runner.device
mask = (torch.arange(block_table.size(1),
dtype=block_table.dtype,
device=block_table.device).unsqueeze(0)
device=device).unsqueeze(0)
< block_table_bounds.unsqueeze(1))
paged_kv_indices = block_table[mask]
paged_kv_indptr = torch.cat([
torch.zeros(1,
dtype=block_table_bounds.dtype,
device=block_table_bounds.device),
torch.zeros(1, dtype=block_table_bounds.dtype, device=device),
block_table_bounds.cumsum(dim=0, dtype=torch.int32)
])
paged_kv_last_page_len = seq_lens % page_size
paged_kv_last_page_len = torch.where(paged_kv_last_page_len == 0,
page_size, paged_kv_last_page_len)
qo_indptr = torch.arange(0,
self._num_decodes + 1,
step=1,
dtype=torch.int32,
device=device)
return (
paged_kv_indices,
paged_kv_indptr,
paged_kv_last_page_len,
qo_indptr,
)
def _build_decode(self, block_table_tensor: torch.Tensor,
@ -105,6 +113,7 @@ class AiterMLAMetadataBuilder(MLACommonMetadataBuilder[AiterMLAMetadata]):
paged_kv_indices,
paged_kv_indptr,
paged_last_page_len,
qo_indptr,
) = self._get_paged_kv_tensors(block_table_tensor, seq_lens)
attn_metadata = AiterMLADecodeMetadata(
@ -112,7 +121,8 @@ class AiterMLAMetadataBuilder(MLACommonMetadataBuilder[AiterMLAMetadata]):
seq_lens=seq_lens,
paged_kv_indptr=paged_kv_indptr,
paged_kv_indices=paged_kv_indices,
paged_kv_last_page_len=paged_last_page_len)
paged_kv_last_page_len=paged_last_page_len,
qo_indptr=qo_indptr)
return attn_metadata
@ -137,7 +147,10 @@ class AiterMLAImpl(MLACommonImpl[AiterMLAMetadata]):
alibi_slopes, sliding_window, kv_cache_dtype,
blocksparse_params, logits_soft_cap, attn_type,
**mla_args)
assert (num_heads == 16 or num_heads == 128), (
f"Aiter MLA only supports 16 or 128 number of heads.\n"
f"Provided {num_heads} number of heads.\n"
"Try adjusting tensor_parallel_size value.")
unsupported_features = [
alibi_slopes, sliding_window, blocksparse_params, logits_soft_cap
]
@ -189,7 +202,18 @@ class AiterMLAImpl(MLACommonImpl[AiterMLAMetadata]):
kv_buffer = kv_c_and_k_pe_cache.unsqueeze(2)
if self.num_heads == 16:
# AITER MLA decode kernel only supports
# max_seqlen_q=1 when using 16 heads.
max_seqlen_qo = 1
else:
# AITER MLA decode Kernel handles arbitrary
# max_seqlen_q values when using 128 heads.
assert attn_metadata.prefill is not None
max_seqlen_qo = attn_metadata.prefill.max_query_len
aiter_mla_decode_fwd(q, kv_buffer, o, self.scale,
attn_metadata.decode.qo_indptr, max_seqlen_qo,
attn_metadata.decode.paged_kv_indptr,
attn_metadata.decode.paged_kv_indices,
attn_metadata.decode.paged_kv_last_page_len)