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Revert "[BugFix][AMD] Compatible patch for latest AITER(05/07/2025)" (#17910)

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Michael Goin 2025-05-09 09:58:18 -06:00 committed by GitHub
parent 6e5595ca39
commit 85b72cb7b1
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4 changed files with 23 additions and 54 deletions
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12
vllm/attention/backends/mla/common.py
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@ -1213,9 +1213,9 @@ class MLACommonImpl(MLAAttentionImpl[T], Generic[T]):
attn_output, attn_softmax_lse = \
self._flash_attn_varlen_diff_headdims(
q,
k,
v,
q=q,
k=k,
v=v,
cu_seqlens_q=prefill_metadata.query_start_loc,
cu_seqlens_k=prefill_metadata.context_chunk_cu_seq_lens[i],
max_seqlen_q=prefill_metadata.max_query_len,
@ -1267,9 +1267,9 @@ class MLACommonImpl(MLAAttentionImpl[T], Generic[T]):
k = torch.cat((k_nope, k_pe.expand((*k_nope.shape[:-1], -1))), dim=-1)
output = self._flash_attn_varlen_diff_headdims(
q,
k,
v,
q=q,
k=k,
v=v,
cu_seqlens_q=prefill_metadata.query_start_loc,
cu_seqlens_k=prefill_metadata.query_start_loc,
max_seqlen_q=prefill_metadata.max_prefill_seq_len,

49
vllm/attention/backends/rocm_aiter_mla.py
View File

@ -53,7 +53,7 @@ class AiterMLABackend(MLACommonBackend):
@dataclass
class AiterMLAMetadata(MLACommonMetadata):
# The following 5 tensors are for current version of AITER MLA
# The following 4 tensors are for current version of AITER MLA
block_table_bound: Optional[torch.Tensor] = None
# The indptr of the paged kv cache, shape: [batch_size + 1]
paged_kv_indptr: Optional[torch.Tensor] = None
@ -63,10 +63,6 @@ class AiterMLAMetadata(MLACommonMetadata):
# the paged kv cache, shape: [batch_size]
paged_kv_last_page_lens: Optional[torch.Tensor] = None
# This is just to make new AITER MLA API work
# -- MTP support is not added yet.
qo_indptr: Optional[torch.Tensor] = None
@property
def prefill_metadata(self):
prefill_metadata = super().prefill_metadata
@ -78,7 +74,6 @@ class AiterMLAMetadata(MLACommonMetadata):
prefill_metadata\
.paged_kv_last_page_lens = self.paged_kv_last_page_lens
prefill_metadata.block_table_bound = self.block_table_bound
prefill_metadata.qo_indptr = self.qo_indptr
# update the cache
self._cached_prefill_metadata = self.__class__(
@ -98,7 +93,6 @@ class AiterMLAMetadata(MLACommonMetadata):
decode_metadata\
.paged_kv_last_page_lens = self.paged_kv_last_page_lens
decode_metadata.block_table_bound = self.block_table_bound
decode_metadata.qo_indptr = self.qo_indptr
# update the cache
self._cached_decode_metadata = self.__class__(
@ -142,7 +136,6 @@ class AiterMLAMetadataBuilder(MLACommonMetadataBuilder[AiterMLAMetadata]):
self.paged_kv_indptr: list[int] = [0]
self.paged_kv_last_page_lens: list[int] = []
self.total_blocks = 0
self.qo_indptr: list[int] = [0]
def _add_seq_group(self, inter_data, chunked_prefill_enabled: bool,
prefix_cache_hit: bool):
@ -215,7 +208,6 @@ class AiterMLAMetadataBuilder(MLACommonMetadataBuilder[AiterMLAMetadata]):
self.paged_kv_indices.extend(block_table[:block_table_bound])
self.paged_kv_indptr.append(self.paged_kv_indptr[-1] +
block_table_bound)
self.qo_indptr.append(self.qo_indptr[-1] + 1)
last_page_len = seq_len % self.block_size
if last_page_len == 0:
@ -234,8 +226,6 @@ class AiterMLAMetadataBuilder(MLACommonMetadataBuilder[AiterMLAMetadata]):
self.paged_kv_indptr.extend([last_paged_kv_indptr] *
cuda_graph_pad_size)
self.paged_kv_last_page_lens.extend([0] * cuda_graph_pad_size)
last_qo_indptr = self.qo_indptr[-1]
self.qo_indptr.extend([last_qo_indptr] * cuda_graph_pad_size)
# For current version of AITER MLA
if len(self.paged_kv_indptr) > 0:
@ -255,22 +245,16 @@ class AiterMLAMetadataBuilder(MLACommonMetadataBuilder[AiterMLAMetadata]):
1,
device=device,
dtype=torch.int)
qo_indptr = torch.tensor(self.qo_indptr,
device=device,
dtype=torch.int)
else:
paged_kv_indices_tensor = None
paged_kv_indptr_tensor = None
paged_kv_last_page_lens_tensor = None
block_table_bound_tensor = None
qo_indptr = None
metadata.paged_kv_indptr = paged_kv_indptr_tensor
metadata.paged_kv_indices = paged_kv_indices_tensor
metadata.paged_kv_last_page_lens = paged_kv_last_page_lens_tensor
metadata.block_table_bound = block_table_bound_tensor
metadata.qo_indptr = qo_indptr
return metadata
@ -279,17 +263,14 @@ class AiterMLAState(MLACommonState[AiterMLAMetadata]):
@contextmanager
def graph_capture(self, max_batch_size: int):
kv_indices, kv_indptr, last_page_lens, qo_indptr = \
get_aiter_mla_metadata(
max_batch_size=max_batch_size,
block_size=self.runner.block_size,
max_block_per_batch=\
self.runner.get_max_block_per_batch(),
device=self.runner.device)
kv_indices, kv_indptr, last_page_lens = get_aiter_mla_metadata(
max_batch_size=max_batch_size,
block_size=self.runner.block_size,
max_block_per_batch=self.runner.get_max_block_per_batch(),
device=self.runner.device)
self._paged_kv_indices_tensor = kv_indices
self._paged_kv_indptr_tensor = kv_indptr
self._paged_kv_last_page_lens_tensor = last_page_lens
self._qo_indptr_tensor = qo_indptr
with super().graph_capture(max_batch_size):
yield
@ -297,7 +278,6 @@ class AiterMLAState(MLACommonState[AiterMLAMetadata]):
del self._paged_kv_indices_tensor
del self._paged_kv_indptr_tensor
del self._paged_kv_last_page_lens_tensor
del self._qo_indptr_tensor
def graph_capture_get_metadata_for_batch(
self,
@ -311,12 +291,10 @@ class AiterMLAState(MLACommonState[AiterMLAMetadata]):
paged_kv_indices = self._paged_kv_indices_tensor
paged_kv_last_page_lens = self._paged_kv_last_page_lens_tensor[:
batch_size]
qo_indptr = self._qo_indptr_tensor[:batch_size + 1]
metadata.paged_kv_indptr = paged_kv_indptr
metadata.paged_kv_indices = paged_kv_indices
metadata.paged_kv_last_page_lens = paged_kv_last_page_lens
metadata.qo_indptr = qo_indptr
return metadata
@ -333,7 +311,6 @@ class AiterMLAState(MLACommonState[AiterMLAMetadata]):
input_buffers[
"paged_kv_last_page_lens"] = attn_metadata.\
decode_metadata.paged_kv_last_page_lens
input_buffers['qo_indptr'] = attn_metadata.qo_indptr
return input_buffers
@ -353,8 +330,6 @@ class AiterMLAState(MLACommonState[AiterMLAMetadata]):
input_buffers["paged_kv_last_page_lens"].copy_(
attn_metadata.decode_metadata.paged_kv_last_page_lens,
non_blocking=True)
input_buffers["qo_indptr"].copy_(
attn_metadata.decode_metadata.qo_indptr, non_blocking=True)
class AiterMLAImpl(MLACommonImpl[AiterMLAMetadata]):
@ -395,9 +370,11 @@ class AiterMLAImpl(MLACommonImpl[AiterMLAMetadata]):
softmax_scale: float, return_softmax_lse: bool,
**kwargs) -> Union[tuple[torch.Tensor, ...], torch.Tensor]:
output = self.flash_attn_varlen_func(
q,
k,
v,
q=q,
k=k,
v=v,
softmax_scale=softmax_scale,
return_lse=return_softmax_lse,
**kwargs,
)
@ -417,7 +394,7 @@ class AiterMLAImpl(MLACommonImpl[AiterMLAMetadata]):
B = q_nope.shape[0]
q = torch.cat([q_nope, q_pe], dim=-1)
o = torch.empty(B,
o = torch.zeros(B,
self.num_heads,
self.kv_lora_rank,
dtype=q.dtype,
@ -426,8 +403,6 @@ class AiterMLAImpl(MLACommonImpl[AiterMLAMetadata]):
kv_buffer = kv_c_and_k_pe_cache.unsqueeze(2)
aiter_mla_decode_fwd(q, kv_buffer, o, self.scale,
attn_metadata.qo_indptr,
attn_metadata.max_query_len,
attn_metadata.paged_kv_indptr,
attn_metadata.paged_kv_indices,
attn_metadata.paged_kv_last_page_lens)

7
vllm/attention/ops/rocm_aiter_mla.py
View File

@ -20,8 +20,7 @@ def get_aiter_mla_metadata(max_batch_size: int, block_size: int,
paged_kv_last_page_lens = torch.full((max_batch_size, ),
block_size,
dtype=torch.int32)
qo_indptr = torch.zeros(max_batch_size + 1, dtype=torch.int, device=device)
return paged_kv_indices, paged_kv_indptr, paged_kv_last_page_lens, qo_indptr
return paged_kv_indices, paged_kv_indptr, paged_kv_last_page_lens
def aiter_mla_decode_fwd(
@ -29,8 +28,6 @@ def aiter_mla_decode_fwd(
kv_buffer: torch.Tensor,
o: torch.Tensor,
sm_scale: float,
qo_indptr: torch.Tensor,
max_seqlen_qo: int,
kv_indptr: Optional[torch.Tensor] = None,
kv_indices: Optional[torch.Tensor] = None,
kv_last_page_lens: Optional[torch.Tensor] = None,
@ -63,11 +60,9 @@ def mla_decode_fwd_impl(
mla_decode_fwd(q,
kv_buffer.view(-1, 1, 1, q.shape[-1]),
o,
qo_indptr,
kv_indptr,
kv_indices,
kv_last_page_lens,
max_seqlen_qo,
sm_scale=sm_scale,
logit_cap=logit_cap)

9
vllm/model_executor/layers/fused_moe/rocm_aiter_fused_moe.py
View File

@ -123,11 +123,10 @@ def rocm_aiter_fmoe_fp8_blockscale_g1u1_impl(
fmoe_fp8_blockscale_g1u1(out_asm, a1, w1, w2, sorted_token_ids,
sorted_weight_buf, sorted_expert_ids,
num_valid_ids, topk,
a1_scale.t().contiguous(),
w1_scale.view(local_E, -1),
w2_scale.view(local_E,
-1), *block_shape, smooth_scale)
num_valid_ids, topk, w1_scale.view(local_E, -1),
w2_scale.view(local_E, -1),
a1_scale.t().contiguous(), *block_shape,
smooth_scale)
return out_asm