[ROCm][MLA] Support block-size > 1 for AITER MLA backend

tests/kernels/attention/test_attention_selector.py

@@ -104,13 +104,6 @@ def test_env(
                                 16, torch.float16, None, block_size, use_mla=use_mla
                             )
                         assert f"The selected backend, {name}" in str(exc_info.value)
-                    elif name == "ROCM_AITER_MLA" and block_size != 1:
-                        # ROCM_AITER_MLA only supports block_size == 1
-                        with pytest.raises(ValueError) as exc_info:
-                            get_attn_backend(
-                                16, torch.float16, None, block_size, use_mla=use_mla
-                            )
-                        assert f"The selected backend, {name}" in str(exc_info.value)
                     else:
                         # Valid backend-block_size combination
                         backend = get_attn_backend(

vllm/platforms/rocm.py

@@ -261,16 +261,8 @@ def get_attn_backend_cls(
                     f"does not support block size {block_size}."
                 )
             if selected_backend == _Backend.ROCM_AITER_MLA:
-                if block_size == 1:
-                    logger.info("Using AITER MLA backend on V1 engine.")
-                    return (
-                        "vllm.v1.attention.backends.mla.rocm_aiter_mla.AiterMLABackend"  # noqa: E501
-                    )
-                raise ValueError(
-                    f" The selected backend, {selected_backend.name},"
-                    f"does not support block size {block_size}."
-                    "(currently only supports block size 1)"
-                )
+                logger.info("Using AITER MLA backend on V1 engine.")
+                return "vllm.v1.attention.backends.mla.rocm_aiter_mla.AiterMLABackend"  # noqa: E501
             raise ValueError(
                 f" The selected backend, {selected_backend.name},"
                 f"is not MLA type while requested for MLA backend."

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

@@ -78,9 +78,6 @@ def __init__(
         super().__init__(
             kv_cache_spec, layer_names, vllm_config, device, AiterMLAMetadata
         )
-        assert self.kv_cache_spec.block_size == 1, (
-            "AITER MLAonly supports block size 1."
-        )
 
         self.compilation_config = vllm_config.compilation_config
         max_num_pages_per_req = cdiv(
@@ -94,6 +91,11 @@ def __init__(
         # so we can only use the persistent buffer if a cudagraph is actually
         # being used.
         if self.compilation_config.cudagraph_mode.has_full_cudagraphs():
+            self.block_table_remapping = torch.zeros(
+                [max_num_reqs, max_num_pages_per_req * self.kv_cache_spec.block_size],
+                dtype=torch.int32,
+                device=device,
+            )
             self.paged_kv_indptr = torch.zeros(
                 max_num_reqs + 1, dtype=torch.int32, device=device
             )
@@ -119,13 +121,29 @@ def _build_decode(
         dcp_tot_seq_lens_device: torch.Tensor | None,
     ) -> AiterMLADecodeMetadata:
         page_size = self.kv_cache_spec.block_size
-        block_table_bounds = (seq_lens_device + page_size - 1) // page_size
         device = self.device
         num_reqs = seq_lens_device.size(0)
+        bs, _ = block_table_tensor.shape
+        block_table_tensor = (
+            block_table_tensor.unsqueeze(-1).expand(-1, -1, page_size) * page_size
+        )
+        block_table_tensor = (
+            block_table_tensor
+            + torch.arange(
+                0,
+                page_size,
+                device=block_table_tensor.device,
+                dtype=block_table_tensor.dtype,
+            )[None, None, :]
+        )
+        block_table_tensor = block_table_tensor.view(bs, -1)
 
+        # after remapping, we assume the block size already equals to 1
+
+        max_blk_size_per_req = block_table_tensor.shape[-1]
         mask = torch.arange(
             block_table_tensor.size(1), dtype=block_table_tensor.dtype, device=device
-        ).unsqueeze(0) < block_table_bounds.unsqueeze(1)
+        ).unsqueeze(0) < seq_lens_device.unsqueeze(1)
         paged_kv_indices = block_table_tensor[mask]
 
         paged_kv_last_page_len = seq_lens_device % page_size
@@ -135,13 +153,19 @@ def _build_decode(
 
         paged_kv_indptr = torch.cat(
             [
-                torch.zeros(1, dtype=block_table_bounds.dtype, device=device),
-                block_table_bounds.cumsum(dim=0, dtype=torch.int32),
+                torch.zeros(1, dtype=seq_lens_device.dtype, device=device),
+                seq_lens_device.cumsum(dim=0, dtype=torch.int32),
             ]
         )
 
         if self.compilation_config.cudagraph_mode.has_full_cudagraphs():
             num_actual_pages = paged_kv_indices.size(0)
+            self.block_table_remapping[:num_reqs, :max_blk_size_per_req].copy_(
+                block_table_tensor, non_blocking=True
+            )
+            block_table_tensor = self.block_table_remapping[
+                :num_reqs, :max_blk_size_per_req
+            ]
 
             self.paged_kv_indices[:num_actual_pages].copy_(
                 paged_kv_indices, non_blocking=True