[ROCm][FEAT] Enable Full Graph Mode in AITER MLA V1 Attn Backend (Decode Phase only) (#20254)

Signed-off-by: vllmellm <vllm.ellm@embeddedllm.com>

Author: vllmellm

vllm/v1/attention/backends/mla/rocm_aiter_mla.py

@@ -2,7 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 from dataclasses import dataclass
-from typing import Any, Optional
+from typing import Any, ClassVar, Optional
 
 import torch
 
@@ -63,63 +63,91 @@ class AiterMLAMetadata(MLACommonMetadata[AiterMLADecodeMetadata]):
 
 
 class AiterMLAMetadataBuilder(MLACommonMetadataBuilder[AiterMLAMetadata]):
+    full_cudagraph_supported: ClassVar[bool] = True  # decode only
 
     def __init__(self, runner, kv_cache_spec: AttentionSpec,
                  block_table: BlockTable):
         super().__init__(runner, kv_cache_spec, block_table, AiterMLAMetadata)
         assert self.kv_cache_spec.block_size == 1, "AITER MLA" \
             "only supports block size 1."
 
-    def _get_paged_kv_tensors(
-            self, block_table: torch.Tensor,
-            seq_lens: torch.Tensor) -> tuple[torch.Tensor, ...]:
+        # Preparing persistent buffers
+        if self.runner.full_cuda_graph:
+            device = self.runner.device
+            max_num_reqs = self.runner.max_num_reqs
+            self.paged_kv_indptr = torch.zeros(max_num_reqs + 1,
+                                               dtype=torch.int32,
+                                               device=device)
+            self.paged_kv_indices = torch.zeros(
+                block_table.get_device_tensor().numel(
+                ),  # max num pages possible
+                dtype=torch.int32,
+                device=device)
+            self.paged_kv_last_page_len = torch.zeros(max_num_reqs,
+                                                      dtype=torch.int32,
+                                                      device=device)
+
+            self.qo_indptr = torch.arange(0,
+                                          max_num_reqs + 1,
+                                          dtype=torch.int32,
+                                          device=device)
+
+    def _build_decode(self, block_table_tensor: torch.Tensor,
+                      seq_lens: torch.Tensor) -> AiterMLADecodeMetadata:
         page_size = self.kv_cache_spec.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,
+        mask = (torch.arange(block_table_tensor.size(1),
+                             dtype=block_table_tensor.dtype,
                              device=device).unsqueeze(0)
                 < block_table_bounds.unsqueeze(1))
-        paged_kv_indices = block_table[mask]
+        paged_kv_indices = block_table_tensor[mask]
+
+        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)
 
         paged_kv_indptr = torch.cat([
             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,
-        )
+        if self.runner.full_cuda_graph:
+            num_reqs = self._num_decodes
 
-    def _build_decode(self, block_table_tensor: torch.Tensor,
-                      seq_lens: torch.Tensor) -> AiterMLADecodeMetadata:
+            num_actual_pages = paged_kv_indices.size(0)
+
+            self.paged_kv_indices[:num_actual_pages].copy_(paged_kv_indices,
+                                                           non_blocking=True)
+            self.paged_kv_indices[num_actual_pages:].fill_(-1)
+            paged_kv_indices = self.paged_kv_indices[:num_actual_pages]
+
+            self.paged_kv_indptr[:1 + num_reqs].copy_(paged_kv_indptr,
+                                                      non_blocking=True)
+            self.paged_kv_indptr[1 + num_reqs:].fill_(paged_kv_indptr[-1])
+            paged_kv_indptr = self.paged_kv_indptr[:1 + num_reqs]
+
+            self.paged_kv_last_page_len[:num_reqs].copy_(
+                paged_kv_last_page_len, non_blocking=True)
+            self.paged_kv_last_page_len[num_reqs:].fill_(1)
+            paged_kv_last_page_len = self.paged_kv_last_page_len[:num_reqs]
+
+            qo_indptr = self.qo_indptr[:1 + num_reqs]
 
-        (
-            paged_kv_indices,
-            paged_kv_indptr,
-            paged_last_page_len,
-            qo_indptr,
-        ) = self._get_paged_kv_tensors(block_table_tensor, seq_lens)
+        else:
+            qo_indptr = torch.arange(0,
+                                     self._num_decodes + 1,
+                                     step=1,
+                                     dtype=torch.int32,
+                                     device=device)
 
         attn_metadata = AiterMLADecodeMetadata(
             block_table=block_table_tensor,
             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_kv_last_page_len,
             qo_indptr=qo_indptr)
 
         return attn_metadata