[BugFix][AMD] Compatible patch for latest AITER(05/07/2025) (#17864)

Signed-off-by: Qiang Li <[email protected]>

Author: qli88

vllm/attention/backends/mla/common.py

@@ -1213,9 +1213,9 @@ def _compute_prefill_context(
 
             attn_output, attn_softmax_lse = \
                 self._flash_attn_varlen_diff_headdims(
-                q=q,
-                k=k,
-                v=v,
+                q,
+                k,
+                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 @@ def _forward_prefill(
         k = torch.cat((k_nope, k_pe.expand((*k_nope.shape[:-1], -1))), dim=-1)
 
         output = self._flash_attn_varlen_diff_headdims(
-            q=q,
-            k=k,
-            v=v,
+            q,
+            k,
+            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,

vllm/attention/backends/rocm_aiter_mla.py

@@ -53,7 +53,7 @@ def get_state_cls() -> Type["AiterMLAState"]:
 
 @dataclass
 class AiterMLAMetadata(MLACommonMetadata):
-    # The following 4 tensors are for current version of AITER MLA
+    # The following 5 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
@@ -63,6 +63,10 @@ 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
@@ -74,6 +78,7 @@ def prefill_metadata(self):
             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__(
@@ -93,6 +98,7 @@ def decode_metadata(self):
             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__(
@@ -136,6 +142,7 @@ def prepare(self):
         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):
@@ -208,6 +215,7 @@ def _update_paged_kv_tensors(self, block_table: list[int], seq_len: int):
         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:
@@ -226,6 +234,8 @@ def build(self, seq_lens: list[int], query_lens: list[int],
             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:
@@ -245,16 +255,22 @@ def build(self, seq_lens: list[int], query_lens: list[int],
                                                    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
 
@@ -263,21 +279,25 @@ class AiterMLAState(MLACommonState[AiterMLAMetadata]):
 
     @contextmanager
     def graph_capture(self, max_batch_size: int):
-        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)
+        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)
         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
 
         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,
@@ -291,10 +311,12 @@ def graph_capture_get_metadata_for_batch(
         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
 
@@ -311,6 +333,7 @@ def get_graph_input_buffers(self,
         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
 
@@ -330,6 +353,8 @@ def prepare_graph_input_buffers(self,
         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]):
@@ -370,11 +395,9 @@ def _flash_attn_varlen_diff_headdims(
             softmax_scale: float, return_softmax_lse: bool,
             **kwargs) -> Union[tuple[torch.Tensor, ...], torch.Tensor]:
         output = self.flash_attn_varlen_func(
-            q=q,
-            k=k,
-            v=v,
-            softmax_scale=softmax_scale,
-            return_lse=return_softmax_lse,
+            q,
+            k,
+            v,
             **kwargs,
         )
 
@@ -394,7 +417,7 @@ def _forward_decode(
         B = q_nope.shape[0]
 
         q = torch.cat([q_nope, q_pe], dim=-1)
-        o = torch.zeros(B,
+        o = torch.empty(B,
                         self.num_heads,
                         self.kv_lora_rank,
                         dtype=q.dtype,
@@ -403,6 +426,8 @@ def _forward_decode(
         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)

vllm/attention/ops/rocm_aiter_mla.py

@@ -20,14 +20,17 @@ 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)
-    return paged_kv_indices, paged_kv_indptr, paged_kv_last_page_lens
+    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
 
 
 def aiter_mla_decode_fwd(
     q: torch.Tensor,
     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,
@@ -60,9 +63,11 @@ 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)
 

vllm/model_executor/layers/fused_moe/rocm_aiter_fused_moe.py

@@ -123,10 +123,11 @@ 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, w1_scale.view(local_E, -1),
-                             w2_scale.view(local_E, -1),
-                             a1_scale.t().contiguous(), *block_shape,
-                             smooth_scale)
+                             num_valid_ids, topk,
+                             a1_scale.t().contiguous(),
+                             w1_scale.view(local_E, -1),
+                             w2_scale.view(local_E,
+                                           -1), *block_shape, smooth_scale)
 
     return out_asm