[FEAT][ROCm]: Support AITER MLA (#15893)

Signed-off-by: vllmellm <vllm.ellm@embeddedllm.com>
Co-authored-by: qli88 <qiang.li2@amd.com>

tests/kernels/test_attention_selector.py

@@ -19,45 +19,152 @@ def clear_cache():
     _cached_get_attn_backend.cache_clear()
 
 
-@pytest.mark.parametrize(
-    "name", ["TORCH_SDPA", "ROCM_FLASH", "XFORMERS", "FLASHINFER"])
+# Define MLA and non-MLA backends separately
+DEVICE_MLA_BACKENDS = {
+    "cuda": ["TRITON_MLA", "FLASHMLA"],
+    "hip": ["TRITON_MLA", "ROCM_AITER_MLA"],
+    "cpu": [],
+}
+
+DEVICE_REGULAR_ATTN_BACKENDS = {
+    "cuda": ["XFORMERS", "FLASHINFER"],
+    "hip": ["ROCM_FLASH"],
+    "cpu": ["TORCH_SDPA"],
+}
+
+DEVICE_MLA_BLOCK_SIZES = {
+    "cuda": [16, 64],  # CUDA supports both standard and extended block sizes
+    "hip": [16, 1],  # HIP requires special handling for block_size=1
+    "cpu": [16]  # CPU uses fixed block size from test cases
+}
+
+
+def generate_params():
+    params = []
+    for use_mla in [True, False]:
+        for device in ["cuda", "hip", "cpu"]:
+            backends = DEVICE_MLA_BACKENDS[
+                device] if use_mla else DEVICE_REGULAR_ATTN_BACKENDS[device]
+            for name in backends:
+                block_sizes = DEVICE_MLA_BLOCK_SIZES[device] if use_mla else [
+                    16
+                ]
+                for block_size in block_sizes:
+                    params.append(
+                        pytest.param(
+                            device,
+                            name,
+                            use_mla,
+                            block_size,
+                            id=
+                            f"{device}_{name}_mla_{str(use_mla)[0]}_blks{block_size}"
+                        ))
+    return params
+
+
+@pytest.mark.parametrize("device, name, use_mla, block_size",
+                         generate_params())
 @pytest.mark.parametrize("use_v1", [True, False])
-@pytest.mark.parametrize("device", ["cpu", "hip", "cuda"])
 def test_env(
+    device: str,
     name: str,
+    use_mla: bool,
+    block_size: int,
     use_v1: bool,
-    device: str,
     monkeypatch: pytest.MonkeyPatch,
 ):
-    """Test that the attention selector can be set via environment variable.
-    Note that we do not test FlashAttn because it is the default backend.
-    """
-
+    """Test attention backend selection with valid device-backend pairs."""
     with monkeypatch.context() as m:
         m.setenv("VLLM_USE_V1", "1" if use_v1 else "0")
         m.setenv(STR_BACKEND_ENV_VAR, name)
+        m.setenv("VLLM_MLA_DISABLE", "1" if use_mla else "0")
 
         if device == "cpu":
             with patch("vllm.attention.selector.current_platform",
                        CpuPlatform()):
                 backend = get_attn_backend(16, torch.float16, torch.float16,
-                                           16, False)
+                                           block_size, False)
             assert backend.get_name() == "TORCH_SDPA"
+
         elif device == "hip":
             with patch("vllm.attention.selector.current_platform",
                        RocmPlatform()):
-                backend = get_attn_backend(16, torch.float16, torch.float16,
-                                           16, False)
-            EXPECTED = "TRITON_ATTN_VLLM_V1" if use_v1 else "ROCM_FLASH"
-            assert backend.get_name() == EXPECTED
+                if use_mla:
+                    # Validate HIP MLA backend-block_size combinations
+                    valid_combination = (
+                        (name == "TRITON_MLA" and block_size != 1)
+                        or (name == "ROCM_AITER_MLA" and block_size == 1))
+
+                    if valid_combination:
+                        backend = get_attn_backend(16,
+                                                   torch.float16,
+                                                   torch.float16,
+                                                   block_size,
+                                                   False,
+                                                   use_mla=use_mla)
+                        assert backend.get_name() == name
                     else:
-            if name in ["XFORMERS", "FLASHINFER"]:
+                        with pytest.raises(ValueError) as exc_info:
+                            get_attn_backend(16,
+                                             torch.float16,
+                                             torch.float16,
+                                             block_size,
+                                             False,
+                                             use_mla=use_mla)
+                        assert f"The selected backend, {name}" in str(
+                            exc_info.value)
+                else:
+                    backend = get_attn_backend(16,
+                                               torch.float16,
+                                               torch.float16,
+                                               block_size,
+                                               False,
+                                               use_mla=use_mla)
+                    expected = "TRITON_ATTN_VLLM_V1" if use_v1 else "ROCM_FLASH"
+                    assert backend.get_name() == expected
+
+        elif device == "cuda":
             with patch("vllm.attention.selector.current_platform",
                        CudaPlatform()):
-                    backend = get_attn_backend(16, torch.float16,
-                                               torch.float16, 16, False)
-                EXPECTED = "FLASH_ATTN_VLLM_V1" if use_v1 else name
-                assert backend.get_name() == EXPECTED
+                if use_mla:
+                    if name == "FLASHMLA" and block_size == 64:
+                        from vllm.attention.backends.flashmla import (
+                            is_flashmla_supported)
+
+                        # only on cuda platforms with specific capability.
+                        is_supported, _ = is_flashmla_supported()
+
+                        if not is_supported:
+                            # if platform is not supported then skip this case.
+                            pytest.skip()
+                        else:
+                            backend = get_attn_backend(16,
+                                                       torch.float16,
+                                                       torch.float16,
+                                                       block_size,
+                                                       False,
+                                                       use_mla=use_mla)
+                            expected = f"{name}_VLLM_V1" if use_v1 else name
+                            assert backend.get_name() == expected
+                    else:
+                        backend = get_attn_backend(16,
+                                                   torch.float16,
+                                                   torch.float16,
+                                                   block_size,
+                                                   False,
+                                                   use_mla=use_mla)
+                        expected = ("TRITON_MLA_VLLM_V1"
+                                    if use_v1 else "TRITON_MLA")
+                        assert backend.get_name() == expected
+                else:
+                    backend = get_attn_backend(16,
+                                               torch.float16,
+                                               torch.float16,
+                                               block_size,
+                                               False,
+                                               use_mla=use_mla)
+                    expected = "FLASH_ATTN_VLLM_V1" if use_v1 else name
+                    assert backend.get_name() == expected
 
 
 def test_flash_attn(monkeypatch: pytest.MonkeyPatch):

tests/kernels/test_rocm_attention_selector.py

@@ -28,7 +28,34 @@ def test_selector(monkeypatch: pytest.MonkeyPatch):
         assert (backend.get_name() == "ROCM_FLASH"
                 or backend.get_name() == "TRITON_ATTN_VLLM_V1")
 
-        # mla test for deepseek related
+        # MLA test for deepseek related
+
+        # change the attention backend to triton MLA
+        m.setenv(STR_BACKEND_ENV_VAR, "TRITON_MLA")
+        backend = get_attn_backend(576, torch.bfloat16, "auto", 16, False,
+                                   False, True)
+        assert backend.get_name() == "TRITON_MLA"
+
+        # If attention backend is None
+        # If use_mla is true
+        # The selected backend is triton MLA
+        m.setenv(STR_BACKEND_ENV_VAR, None)
         backend = get_attn_backend(576, torch.bfloat16, "auto", 16, False,
                                    False, True)
         assert backend.get_name() == "TRITON_MLA"
+
+        # change the attention backend to AITER MLA
+        m.setenv(STR_BACKEND_ENV_VAR, "ROCM_AITER_MLA")
+        backend = get_attn_backend(576, torch.bfloat16, "auto", 1, False,
+                                   False, True)
+        assert backend.get_name() == "ROCM_AITER_MLA"
+
+        # If attention backend is None
+        # If use_mla is true
+        # If VLLM_ROCM_USE_AITER is enabled
+        # The selected backend is ROCM_AITER_MLA
+        m.setenv(STR_BACKEND_ENV_VAR, None)
+        m.setenv("VLLM_ROCM_USE_AITER", "1")
+        backend = get_attn_backend(576, torch.bfloat16, "auto", 1, False,
+                                   False, True)
+        assert backend.get_name() == "ROCM_AITER_MLA"

vllm/attention/backends/mla/common.py

@@ -711,12 +711,24 @@ class MLACommonMetadata(AttentionMetadata):
             self.seq_lens[i] += 1
         self.max_decode_seq_len = max(self.seq_lens)
 
-        ops.advance_step_flashattn(num_seqs=num_seqs,
+        self._ops_advance_step(num_seqs=num_seqs,
                                num_queries=num_queries,
                                block_size=block_size,
                                input_tokens=model_input.input_tokens,
                                sampled_token_ids=sampled_token_ids,
-                                   input_positions=model_input.input_positions,
+                               input_positions=model_input.input_positions)
+
+    def _ops_advance_step(self, num_seqs: int, num_queries: int,
+                          block_size: int, input_tokens: torch.Tensor,
+                          sampled_token_ids: torch.Tensor,
+                          input_positions: torch.Tensor) -> None:
+        # here we use advance_step_flashinfo to update the paged_kv_* tensors
+        ops.advance_step_flashattn(num_seqs=num_seqs,
+                                   num_queries=num_queries,
+                                   block_size=block_size,
+                                   input_tokens=input_tokens,
+                                   sampled_token_ids=sampled_token_ids,
+                                   input_positions=input_positions,
                                    seq_lens=self.seq_lens_tensor,
                                    slot_mapping=self.slot_mapping,
                                    block_tables=self.block_tables)
@@ -727,6 +739,7 @@ class MLACommonMetadataBuilder(AttentionMetadataBuilder[T], Generic[T]):
     NOTE: Please read the comment at the top of the file before trying to 
     understand this class
     """
+    BLOCK_TABLE_EXTENDER: list[list[int]] = []
 
     def __init__(self, input_builder: "ModelInputForGPUBuilder"):
         self.input_builder = input_builder
@@ -877,8 +890,10 @@ class MLACommonMetadataBuilder(AttentionMetadataBuilder[T], Generic[T]):
         num_seqs = len(seq_lens)
         if use_captured_graph:
             self.slot_mapping.extend([PAD_SLOT_ID] * cuda_graph_pad_size)
-            self.block_tables.extend([] * cuda_graph_pad_size)
+            self.block_tables.extend(self.__class__.BLOCK_TABLE_EXTENDER *
+                                     cuda_graph_pad_size)
             num_decode_tokens = batch_size - self.num_prefill_tokens
+
             block_tables = self._get_graph_runner_block_tables(
                 num_seqs, self.block_tables)
         else:

vllm/attention/backends/rocm_aiter_mla.py

+# SPDX-License-Identifier: Apache-2.0
+
+from contextlib import contextmanager
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Any, Optional, Type, Union
+
+import torch
+
+import vllm._custom_ops as ops
+import vllm.envs as envs
+from vllm.attention.backends.mla.common import (MLACommonBackend,
+                                                MLACommonImpl,
+                                                MLACommonMetadata,
+                                                MLACommonMetadataBuilder,
+                                                MLACommonState)
+from vllm.attention.backends.utils import (compute_slot_mapping,
+                                           compute_slot_mapping_start_idx,
+                                           is_block_tables_empty)
+from vllm.attention.ops.rocm_aiter_mla import (aiter_mla_decode_fwd,
+                                               get_aiter_mla_metadata)
+
+if TYPE_CHECKING:
+    from vllm.worker.model_runner import ModelInputForGPUBuilder
+
+
+def is_aiter_mla_enabled() -> bool:
+    return envs.VLLM_ROCM_USE_AITER \
+        and envs.VLLM_ROCM_USE_AITER_MLA
+
+
+class AiterMLABackend(MLACommonBackend):
+
+    @staticmethod
+    def get_name() -> str:
+        return "ROCM_AITER_MLA"
+
+    @staticmethod
+    def get_impl_cls() -> Type["AiterMLAImpl"]:
+        return AiterMLAImpl
+
+    @staticmethod
+    def get_metadata_cls() -> Type["AiterMLAMetadata"]:
+        return AiterMLAMetadata
+
+    @staticmethod
+    def get_builder_cls() -> Type["AiterMLAMetadataBuilder"]:
+        return AiterMLAMetadataBuilder
+
+    @staticmethod
+    def get_state_cls() -> Type["AiterMLAState"]:
+        return AiterMLAState
+
+
+@dataclass
+class AiterMLAMetadata(MLACommonMetadata):
+    # 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
+    # The page indices of the paged kv cache
+    paged_kv_indices: Optional[torch.Tensor] = None
+    # The number of entries in the last page of each request in
+    # the paged kv cache, shape: [batch_size]
+    paged_kv_last_page_lens: Optional[torch.Tensor] = None
+
+    @property
+    def prefill_metadata(self):
+        prefill_metadata = super().prefill_metadata
+        self._cached_prefill_metadata = prefill_metadata
+
+        if prefill_metadata is not None:
+            prefill_metadata.paged_kv_indptr = self.paged_kv_indptr
+            prefill_metadata.paged_kv_indices = self.paged_kv_indices
+            prefill_metadata\
+                .paged_kv_last_page_lens = self.paged_kv_last_page_lens
+            prefill_metadata.block_table_bound = self.block_table_bound
+
+            # update the cache
+            self._cached_prefill_metadata = self.__class__(
+                **prefill_metadata.__dict__)
+
+        return self._cached_prefill_metadata
+
+    @property
+    def decode_metadata(self):
+        decode_metadata = super().decode_metadata
+
+        self._cached_decode_metadata = decode_metadata
+
+        if decode_metadata is not None:
+            decode_metadata.paged_kv_indptr = self.paged_kv_indptr
+            decode_metadata.paged_kv_indices = self.paged_kv_indices
+            decode_metadata\
+                .paged_kv_last_page_lens = self.paged_kv_last_page_lens
+            decode_metadata.block_table_bound = self.block_table_bound
+
+            # update the cache
+            self._cached_decode_metadata = self.__class__(
+                **decode_metadata.__dict__)
+
+        return self._cached_decode_metadata
+
+    def _ops_advance_step(self, num_seqs: int, num_queries: int,
+                          block_size: int, input_tokens: torch.Tensor,
+                          sampled_token_ids: torch.Tensor,
+                          input_positions: torch.Tensor) -> None:
+
+        ops.advance_step_flashinfer(
+            num_seqs=num_seqs,
+            num_queries=num_queries,
+            block_size=block_size,
+            input_tokens=input_tokens,
+            sampled_token_ids=sampled_token_ids,
+            input_positions=input_positions,
+            seq_lens=self.seq_lens_tensor,
+            slot_mapping=self.slot_mapping,
+            block_tables=self.block_tables,
+            paged_kv_indices=self.paged_kv_indices,
+            paged_kv_indptr=self.paged_kv_indptr,
+            paged_kv_last_page_lens=self.paged_kv_last_page_lens,
+            block_table_bound=self.block_table_bound)
+
+
+class AiterMLAMetadataBuilder(MLACommonMetadataBuilder[AiterMLAMetadata]):
+    BLOCK_TABLE_EXTENDER: list[list[int]] = [[]]
+
+    def __init__(self, input_builder: "ModelInputForGPUBuilder"):
+        super().__init__(input_builder)
+        assert self.runner.model_config.max_model_len == 32768,\
+                "AITER MLA requires max model len to be set to 32768"
+        assert self.block_size == 1, "AITER MLA requires only block size 1."
+
+    def prepare(self):
+        super().prepare()
+        self.paged_kv_indices: list[int] = []
+        self.paged_kv_indptr: list[int] = [0]
+        self.paged_kv_last_page_lens: list[int] = []
+        self.total_blocks = 0
+
+    def _add_seq_group(self, inter_data, chunked_prefill_enabled: bool,
+                       prefix_cache_hit: bool):
+        """Add a sequence group to the metadata. Specifically update/append
+        1. context length.
+        2. block table.
+        3. slot mapping.
+        """
+        is_prompt = inter_data.is_prompt
+        block_tables = inter_data.block_tables
+
+        for (seq_id, token_len, seq_len, curr_seq_len, query_len, context_len,
+             curr_sliding_window_block, input_positions) in zip(
+                 inter_data.seq_ids, [len(t) for t in inter_data.input_tokens],
+                 inter_data.orig_seq_lens, inter_data.seq_lens,
+                 inter_data.query_lens, inter_data.context_lens,
+                 inter_data.curr_sliding_window_blocks,
+                 inter_data.input_positions):
+            self.input_positions.extend(input_positions)
+            self.context_lens.append(context_len)
+            if is_prompt:
+                self.num_prefills += 1
+                self.num_prefill_tokens += token_len
+                self.prefill_seq_lens.append(seq_len)
+            else:
+                self.num_decode_tokens += query_len
+                self.curr_seq_lens.append(curr_seq_len)
+
+            # Compute block table.
+            # TODO(sang): Combine chunked prefill and prefix caching by
+            # only allowing multiple of block_size chunk size.
+            # NOTE: This only works for oooooooxxx style attention.
+            block_table = []
+            if prefix_cache_hit:
+                # NOTE(woosuk): For flash-attn, the block table should
+                # include the entries for the incoming prefill tokens.
+                block_table = block_tables[seq_id]
+            elif ((chunked_prefill_enabled or not is_prompt)
+                  and block_tables is not None):
+                if curr_sliding_window_block == 0:
+                    block_table = block_tables[seq_id]
+                else:
+                    block_table = block_tables[seq_id][
+                        -curr_sliding_window_block:]
+            self.block_tables.append(block_table)
+
+            # Compute slot mapping.
+            is_profile_run = is_block_tables_empty(block_tables)
+            start_idx = compute_slot_mapping_start_idx(is_prompt, query_len,
+                                                       context_len,
+                                                       self.sliding_window)
+            compute_slot_mapping(is_profile_run, self.slot_mapping, seq_id,
+                                 seq_len, context_len, start_idx,
+                                 self.block_size, inter_data.block_tables)
+            if is_profile_run:
+                return
+
+            # Update paged_kv_* tensors only for non-profile run
+            block_table = block_tables[seq_id]
+            self._update_paged_kv_tensors(block_table, seq_len)
+
+    def _update_paged_kv_tensors(self, block_table: list[int], seq_len: int):
+        # Get the number of valid blocks based on sequence length.
+        # If seq_len = 16, block_size = 16,
+        # block_table_bound is 1 with 1 valid block.
+        # If seq_len = 15, block_size = 16,
+        # block_table_bound is 0 + 1 with 1 valid block.
+        self.total_blocks += len(block_table)
+        block_table_bound = seq_len // self.block_size + 1 \
+            if seq_len % self.block_size != 0 \
+            else seq_len // self.block_size
+        self.paged_kv_indices.extend(block_table[:block_table_bound])
+        self.paged_kv_indptr.append(self.paged_kv_indptr[-1] +
+                                    block_table_bound)
+
+        last_page_len = seq_len % self.block_size
+        if last_page_len == 0:
+            last_page_len = self.block_size
+        self.paged_kv_last_page_lens.append(last_page_len)
+
+    def build(self, seq_lens: list[int], query_lens: list[int],
+              cuda_graph_pad_size: int, batch_size: int) -> AiterMLAMetadata:
+        metadata = super().build(seq_lens, query_lens, cuda_graph_pad_size,
+                                 batch_size)
+        device = self.runner.device
+        use_captured_graph = cuda_graph_pad_size != -1
+
+        if use_captured_graph:
+            last_paged_kv_indptr = self.paged_kv_indptr[-1]
+            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)
+
+        # For current version of AITER MLA
+        if len(self.paged_kv_indptr) > 0:
+            # extend to the maximum number of blocks as returned by the
+            # scheduler
+            self.paged_kv_indices.extend(
+                [0] * (self.total_blocks - len(self.paged_kv_indices)))
+            paged_kv_indices_tensor = torch.tensor(self.paged_kv_indices,
+                                                   device=device,
+                                                   dtype=torch.int)
+            paged_kv_indptr_tensor = torch.tensor(self.paged_kv_indptr,
+                                                  device=device,
+                                                  dtype=torch.int)
+            paged_kv_last_page_lens_tensor = torch.tensor(
+                self.paged_kv_last_page_lens, device=device, dtype=torch.int)
+            block_table_bound_tensor = torch.zeros(len(self.paged_kv_indptr) -
+                                                   1,
+                                                   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
+
+        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
+
+        return metadata
+
+
+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)
+        self._paged_kv_indices_tensor = kv_indices
+        self._paged_kv_indptr_tensor = kv_indptr
+        self._paged_kv_last_page_lens_tensor = last_page_lens
+
+        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
+
+    def graph_capture_get_metadata_for_batch(
+            self,
+            batch_size: int,
+            is_encoder_decoder_model: bool = False) -> AiterMLAMetadata:
+
+        metadata = super().graph_capture_get_metadata_for_batch(
+            batch_size, is_encoder_decoder_model)
+
+        paged_kv_indptr = self._paged_kv_indptr_tensor[:batch_size + 1]
+        paged_kv_indices = self._paged_kv_indices_tensor
+        paged_kv_last_page_lens = self._paged_kv_last_page_lens_tensor[:
+                                                                       batch_size]
+
+        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
+
+        return metadata
+
+    def get_graph_input_buffers(self,
+                                attn_metadata: AiterMLAMetadata,
+                                is_encoder_decoder_model: bool = False):
+        input_buffers = super().get_graph_input_buffers(
+            attn_metadata, is_encoder_decoder_model)
+        input_buffers[
+            'paged_kv_indptr'] = attn_metadata.decode_metadata.paged_kv_indptr
+        input_buffers[
+            "paged_kv_indices"] = attn_metadata.\
+            decode_metadata.paged_kv_indices
+        input_buffers[
+            "paged_kv_last_page_lens"] = attn_metadata.\
+            decode_metadata.paged_kv_last_page_lens
+
+        return input_buffers
+
+    def prepare_graph_input_buffers(self,
+                                    input_buffers,
+                                    attn_metadata: AiterMLAMetadata,
+                                    is_encoder_decoder_model: bool = False):
+        super().prepare_graph_input_buffers(input_buffers, attn_metadata,
+                                            is_encoder_decoder_model)
+
+        num_total_blocks = attn_metadata.decode_metadata.paged_kv_indices.shape[
+            0]
+        input_buffers["paged_kv_indptr"].copy_(
+            attn_metadata.decode_metadata.paged_kv_indptr, non_blocking=True)
+        input_buffers["paged_kv_indices"][:num_total_blocks].copy_(
+            attn_metadata.decode_metadata.paged_kv_indices, non_blocking=True)
+        input_buffers["paged_kv_last_page_lens"].copy_(
+            attn_metadata.decode_metadata.paged_kv_last_page_lens,
+            non_blocking=True)
+
+
+class AiterMLAImpl(MLACommonImpl[AiterMLAMetadata]):
+
+    def __init__(
+            self,
+            num_heads: int,
+            head_size: int,
+            scale: float,
+            num_kv_heads: int,
+            alibi_slopes: Optional[list[float]],
+            sliding_window: Optional[int],
+            kv_cache_dtype: str,
+            blocksparse_params: Optional[dict[str, Any]],
+            logits_soft_cap: Optional[float],
+            attn_type: str,
+            # MLA Specific Arguments
+            **mla_args) -> None:
+        super().__init__(num_heads, head_size, scale, num_kv_heads,
+                         alibi_slopes, sliding_window, kv_cache_dtype,
+                         blocksparse_params, logits_soft_cap, attn_type,
+                         **mla_args)
+
+        unsupported_features = [
+            alibi_slopes, sliding_window, blocksparse_params, logits_soft_cap
+        ]
+        if any(unsupported_features):
+            raise NotImplementedError(
+                "Aiter MLA does not support one of the following: "
+                "alibi_slopes, sliding_window, blocksparse_params, "
+                "logits_soft_cap")
+
+        from aiter import flash_attn_varlen_func
+        self.flash_attn_varlen_func = flash_attn_varlen_func
+
+    def _flash_attn_varlen_diff_headdims(
+            self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor,
+            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,
+            **kwargs,
+        )
+
+        return output
+
+    def _forward_decode(
+        self,
+        q_nope: torch.Tensor,
+        q_pe: torch.Tensor,
+        kv_c_and_k_pe_cache: torch.Tensor,
+        attn_metadata: AiterMLAMetadata,
+    ) -> torch.Tensor:
+        assert kv_c_and_k_pe_cache.numel() > 0
+
+        decode_meta = attn_metadata.decode_metadata
+        assert decode_meta is not None
+        B = q_nope.shape[0]
+
+        q = torch.cat([q_nope, q_pe], dim=-1)
+        o = torch.zeros(B,
+                        self.num_heads,
+                        self.kv_lora_rank,
+                        dtype=q.dtype,
+                        device=q.device)
+
+        kv_buffer = kv_c_and_k_pe_cache.unsqueeze(2)
+
+        aiter_mla_decode_fwd(q, kv_buffer, o, self.scale,
+                             attn_metadata.paged_kv_indptr,
+                             attn_metadata.paged_kv_indices,
+                             attn_metadata.paged_kv_last_page_lens)
+
+        return self._v_up_proj_and_o_proj(o)

vllm/attention/ops/rocm_aiter_mla.py

+# SPDX-License-Identifier: Apache-2.0
+
+from typing import Optional
+
+import torch
+
+
+def get_aiter_mla_metadata(max_batch_size: int, block_size: int,
+                           max_block_per_batch: int,
+                           device: torch.device) -> tuple[torch.Tensor, ...]:
+    paged_kv_indices = torch.zeros(max_batch_size * max_block_per_batch,
+                                   dtype=torch.int32,
+                                   device=device)
+    paged_kv_indptr = torch.zeros(max_batch_size + 1,
+                                  dtype=torch.int32,
+                                  device=device)
+    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
+
+
+def aiter_mla_decode_fwd(
+    q: torch.Tensor,
+    kv_buffer: torch.Tensor,
+    o: torch.Tensor,
+    sm_scale: float,
+    kv_indptr: Optional[torch.Tensor] = None,
+    kv_indices: Optional[torch.Tensor] = None,
+    kv_last_page_lens: Optional[torch.Tensor] = None,
+    logit_cap: float = 0.0,
+):
+    from aiter.mla import mla_decode_fwd
+
+    mla_decode_fwd(q,
+                   kv_buffer.view(-1, 1, 1, q.shape[-1]),
+                   o,
+                   kv_indptr,
+                   kv_indices,
+                   kv_last_page_lens,
+                   sm_scale=sm_scale,
+                   logit_cap=logit_cap)

vllm/config.py

@@ -1248,7 +1248,7 @@ class ModelConfig:
                 or getattr(self.hf_config, "is_matryoshka", False))
 
 
-BlockSize = Literal[8, 16, 32, 64, 128]
+BlockSize = Literal[1, 8, 16, 32, 64, 128]
 CacheDType = Literal["auto", "fp8", "fp8_e4m3", "fp8_e5m2"]
 PrefixCachingHashAlgo = Literal["builtin", "sha256"]
 

vllm/envs.py

@@ -79,6 +79,7 @@ if TYPE_CHECKING:
     VLLM_ROCM_USE_AITER_LINEAR: bool = True
     VLLM_ROCM_USE_AITER_MOE: bool = True
     VLLM_ROCM_USE_AITER_RMSNORM: bool = True
+    VLLM_ROCM_USE_AITER_MLA: bool = True
     VLLM_ROCM_USE_SKINNY_GEMM: bool = True
     VLLM_ROCM_FP8_PADDING: bool = True
     VLLM_ROCM_MOE_PADDING: bool = True
@@ -558,6 +559,11 @@ environment_variables: dict[str, Callable[[], Any]] = {
     lambda: (os.getenv("VLLM_ROCM_USE_AITER_RMSNORM", "True").lower() in
              ("true", "1")),
 
+    # Whether to use aiter mla ops.
+    # By default is enabled.
+    "VLLM_ROCM_USE_AITER_MLA":
+    lambda: (os.getenv("VLLM_ROCM_USE_AITER_MLA", "True").lower() in
+             ("true", "1")),
     # use rocm skinny gemms
     "VLLM_ROCM_USE_SKINNY_GEMM":
     lambda: (os.getenv("VLLM_ROCM_USE_SKINNY_GEMM", "True").lower() in

vllm/platforms/interface.py

@@ -39,6 +39,7 @@ class _Backend(enum.Enum):
     TRITON_ATTN_VLLM_V1 = enum.auto()
     XFORMERS = enum.auto()
     ROCM_FLASH = enum.auto()
+    ROCM_AITER_MLA = enum.auto()
     TORCH_SDPA = enum.auto()
     FLASHINFER = enum.auto()
     TRITON_MLA = enum.auto()  # Supported by V1

vllm/platforms/rocm.py

@@ -141,8 +141,36 @@ class RocmPlatform(Platform):
                              kv_cache_dtype, block_size, use_v1,
                              use_mla) -> str:
         if use_mla:
+            from vllm.attention.backends.rocm_aiter_mla import (
+                is_aiter_mla_enabled)
+
+            if selected_backend is None:
+                selected_backend = (_Backend.ROCM_AITER_MLA if
+                                    is_aiter_mla_enabled() or block_size == 1
+                                    else _Backend.TRITON_MLA)
+
+            if selected_backend == _Backend.TRITON_MLA:
+                if block_size != 1:
                     logger.info("Using Triton MLA backend.")
-            return "vllm.attention.backends.triton_mla.TritonMLABackend"
+                    return "vllm.attention.backends.triton_mla.TritonMLABackend"  # noqa: E501
+                else:
+                    raise ValueError(
+                        f" The selected backend, {selected_backend.name},"
+                        f"does not support block size {block_size}.")
+            elif selected_backend == _Backend.ROCM_AITER_MLA:
+                if block_size == 1:
+                    logger.info("Using AITER MLA backend.")
+                    return "vllm.attention.backends.rocm_aiter_mla.AiterMLABackend"  # noqa: E501
+                else:
+                    raise ValueError(
+                        f" The selected backend, {selected_backend.name},"
+                        f"does not support block size {block_size}."
+                        "(currently only supports block size 1)")
+            else:
+                raise ValueError(
+                    f" The selected backend, {selected_backend.name},"
+                    f"is not MLA type while requested for MLA backend.")
+
         selected_backend = (_Backend.ROCM_FLASH if selected_backend
                             == _Backend.FLASH_ATTN else selected_backend)
         if envs.VLLM_USE_V1: