Fix TRTLLM MLA Cuda KV Blocks Causing accuracy drop (#9675)

python/sglang/srt/layers/attention/trtllm_mla_backend.py

@@ -51,6 +51,7 @@ class TRTLLMMLADecodeMetadata:
 
     workspace: Optional[torch.Tensor] = None
     block_kv_indices: Optional[torch.Tensor] = None
+    max_seq_len: Optional[int] = None
 
 
 class TRTLLMMLABackend(FlashInferMLAAttnBackend):
@@ -207,8 +208,9 @@ class TRTLLMMLABackend(FlashInferMLAAttnBackend):
             )
 
         # Custom fast-path for decode/idle.
-        max_seqlen_pad = self._calc_padded_blocks(seq_lens.max().item())
+        # Capture with full width so future longer sequences are safe during replay
-        block_kv_indices = self.decode_cuda_graph_kv_indices[:bs, :max_seqlen_pad]
+        max_blocks_per_seq = self._calc_padded_blocks(self.max_context_len)
+        block_kv_indices = self.decode_cuda_graph_kv_indices[:bs, :max_blocks_per_seq]
 
         create_flashmla_kv_indices_triton[(bs,)](
             self.req_to_token,
@@ -217,13 +219,20 @@ class TRTLLMMLABackend(FlashInferMLAAttnBackend):
             None,
             block_kv_indices,
             self.req_to_token.stride(0),
-            max_seqlen_pad,
+            max_blocks_per_seq,
             NUM_PAGE_PER_BLOCK=TRITON_PAD_NUM_PAGE_PER_BLOCK,
             PAGED_SIZE=self.page_size,
         )
 
+        # Record the true maximum sequence length for this capture batch so that
+        # the kernel launch path (which requires an int not a tensor) can reuse
+        # it safely during both capture and replay.
+        max_seq_len_val = int(seq_lens.max().item())
+
         metadata = TRTLLMMLADecodeMetadata(
-            self.decode_cuda_graph_workspace, block_kv_indices
+            self.decode_cuda_graph_workspace,
+            block_kv_indices,
+            max_seq_len_val,
         )
         self.decode_cuda_graph_metadata[bs] = metadata
         self.forward_metadata = metadata
@@ -268,6 +277,13 @@ class TRTLLMMLABackend(FlashInferMLAAttnBackend):
             PAGED_SIZE=self.page_size,
         )
 
+        # Update stored max_seq_len so subsequent kernel calls use the correct value
+        # Prefer CPU tensor to avoid GPU synchronization when available.
+        if seq_lens_cpu is not None:
+            metadata.max_seq_len = int(seq_lens_cpu.max().item())
+        else:
+            metadata.max_seq_len = int(seq_lens.max().item())
+
     def get_cuda_graph_seq_len_fill_value(self) -> int:
         """Get the fill value for sequence lengths in CUDA graph."""
         return 1
@@ -295,8 +311,9 @@ class TRTLLMMLABackend(FlashInferMLAAttnBackend):
             forward_batch.seq_lens.device,
         )
 
+        max_seq_len_val = int(max_seq)
         self.forward_metadata = TRTLLMMLADecodeMetadata(
-            self.workspace_buffer, block_kv_indices
+            self.workspace_buffer, block_kv_indices, max_seq_len_val
         )
         forward_batch.decode_trtllm_mla_metadata = self.forward_metadata
 
@@ -471,14 +488,12 @@ class TRTLLMMLABackend(FlashInferMLAAttnBackend):
             qk_rope_head_dim=self.qk_rope_head_dim,
             block_tables=metadata.block_kv_indices,
             seq_lens=forward_batch.seq_lens.to(torch.int32),
-            max_seq_len=int(metadata.block_kv_indices.shape[1] * self.page_size),
+            max_seq_len=metadata.max_seq_len,
             bmm1_scale=bmm1_scale,
         )
 
-        # Extract value projection part and reshape
+        # Reshape output directly without slicing
-        raw_out_v = raw_out[..., : layer.v_head_dim].contiguous()
+        output = raw_out.view(-1, layer.tp_q_head_num * layer.v_head_dim)
-        output = raw_out_v.view(-1, layer.tp_q_head_num * layer.v_head_dim)
-
         return output
 
 

python/sglang/test/attention/test_trtllm_mla_backend.py

@@ -208,6 +208,15 @@ class MockModelRunner:
         self.kv_cache_dtype = config["kv_cache_dtype"]
         self.page_size = config["page_size"]
 
+        # Server args stub - needed by attention backends
+        self.server_args = type(
+            "ServerArgs",
+            (),
+            {
+                "enable_dp_attention": False,  # Default value for testing
+            },
+        )
+
         # Model-config stub with MLA attributes
         self.model_config = type(
             "ModelConfig",
@@ -833,7 +842,7 @@ class TestTRTLLMMLA(CustomTestCase):
 
                 # Test workspace properties
                 self.assertEqual(metadata.workspace.device.type, "cuda")
-                self.assertEqual(metadata.workspace.dtype, torch.int8)
+                self.assertEqual(metadata.workspace.dtype, torch.uint8)
                 self.assertGreater(
                     metadata.workspace.numel(), 0, "Workspace should have non-zero size"
                 )
@@ -993,8 +1002,8 @@ class TestTRTLLMMLA(CustomTestCase):
         )
 
         # Verify CUDA graph buffers are allocated
-        self.assertIsNotNone(backend.cuda_graph_kv_indices)
+        self.assertIsNotNone(backend.decode_cuda_graph_kv_indices)
-        self.assertIsNotNone(backend.cuda_graph_workspace)
+        self.assertIsNotNone(backend.decode_cuda_graph_workspace)
 
         # Test capture metadata
         seq_lens = torch.full(