[FEAT][ROCm]: Support AITER MLA on V1 Engine

docker/Dockerfile.rocm_base

@@ -12,7 +12,7 @@ ARG PYTORCH_REPO="https://github.com/pytorch/pytorch.git"
 ARG PYTORCH_VISION_REPO="https://github.com/pytorch/vision.git"
 ARG FA_BRANCH="1a7f4dfa"
 ARG FA_REPO="https://github.com/Dao-AILab/flash-attention.git"
-ARG AITER_BRANCH="7e1ed08"
+ARG AITER_BRANCH="5a77249"
 ARG AITER_REPO="https://github.com/ROCm/aiter.git"
 
 FROM ${BASE_IMAGE} AS base

tests/kernels/attention/test_attention_selector.py

@@ -102,7 +102,10 @@ def test_env(
                                                    block_size,
                                                    False,
                                                    use_mla=use_mla)
-                        assert backend.get_name() == name
+                        if use_v1 and name != "TRITON_MLA":
+                            assert backend.get_name() == f"{name}_VLLM_V1"
+                        else:
+                            assert backend.get_name() == name
                     else:
                         with pytest.raises(ValueError) as exc_info:
                             get_attn_backend(16,

tests/kernels/attention/test_rocm_attention_selector.py

@@ -48,7 +48,8 @@ def test_selector(monkeypatch: pytest.MonkeyPatch):
         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"
+        assert (backend.get_name() == "ROCM_AITER_MLA"
+                or backend.get_name() == "ROCM_AITER_MLA_VLLM_V1")
 
         # If attention backend is None
         # If use_mla is true
@@ -58,4 +59,5 @@ def test_selector(monkeypatch: pytest.MonkeyPatch):
         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"
+        assert (backend.get_name() == "ROCM_AITER_MLA"
+                or backend.get_name() == "ROCM_AITER_MLA_VLLM_V1")

vllm/attention/ops/rocm_aiter_mla.py

@@ -4,6 +4,9 @@
 
 import torch
 
+from vllm.platforms import current_platform
+from vllm.utils import direct_register_custom_op
+
 
 def get_aiter_mla_metadata(max_batch_size: int, block_size: int,
                            max_block_per_batch: int,
@@ -30,6 +33,28 @@ def aiter_mla_decode_fwd(
     kv_last_page_lens: Optional[torch.Tensor] = None,
     logit_cap: float = 0.0,
 ):
+
+    torch.ops.vllm.rocm_aiter_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)
+
+
+def mla_decode_fwd_impl(
+    q: torch.Tensor,
+    kv_buffer: torch.Tensor,
+    o: torch.Tensor,
+    kv_indptr: Optional[torch.Tensor] = None,
+    kv_indices: Optional[torch.Tensor] = None,
+    kv_last_page_lens: Optional[torch.Tensor] = None,
+    sm_scale: float = 1.0,
+    logit_cap: float = 0.0,
+) -> None:
     from aiter.mla import mla_decode_fwd
 
     mla_decode_fwd(q,
@@ -40,3 +65,24 @@ def aiter_mla_decode_fwd(
                    kv_last_page_lens,
                    sm_scale=sm_scale,
                    logit_cap=logit_cap)
+
+
+def mla_decode_fwd_fake(
+    q: torch.Tensor,
+    kv_buffer: torch.Tensor,
+    o: torch.Tensor,
+    kv_indptr: Optional[torch.Tensor] = None,
+    kv_indices: Optional[torch.Tensor] = None,
+    kv_last_page_lens: Optional[torch.Tensor] = None,
+    sm_scale: float = 1.0,
+    logit_cap: float = 0.0,
+) -> None:
+    pass
+
+
+if current_platform.is_rocm():
+    direct_register_custom_op(op_name="rocm_aiter_mla_decode_fwd",
+                              op_func=mla_decode_fwd_impl,
+                              mutates_args=["o"],
+                              fake_impl=mla_decode_fwd_fake,
+                              tags=[torch.Tag.needs_fixed_stride_order])

vllm/engine/arg_utils.py

@@ -1319,6 +1319,7 @@ def _is_v1_supported_oracle(self, model_config: ModelConfig) -> bool:
             "FLASHMLA",
             "FLASHINFER",
             "FLASHINFER_VLLM_V1",
+            "ROCM_AITER_MLA",
         ]
         if (envs.is_set("VLLM_ATTENTION_BACKEND")
                 and envs.VLLM_ATTENTION_BACKEND not in V1_BACKENDS):

vllm/model_executor/layers/fused_moe/rocm_aiter_fused_moe.py

@@ -145,7 +145,7 @@ def rocm_aiter_fmoe_fp8_blockscale_g1u1_fake(
         block_shape: List[int],
         smooth_scale: Optional[torch.Tensor] = None) -> torch.Tensor:
 
-    return torch.empty_like(a1, dtype=torch.bf16)
+    return torch.empty_like(a1, dtype=hidden_states_dtype)
 
 
 def rocm_aiter_asm_moe_impl(hidden_states: torch.Tensor,

vllm/platforms/interface.py

@@ -39,7 +39,8 @@ class _Backend(enum.Enum):
     TRITON_ATTN_VLLM_V1 = enum.auto()
     XFORMERS = enum.auto()
     ROCM_FLASH = enum.auto()
-    ROCM_AITER_MLA = enum.auto()
+    ROCM_AITER_MLA = enum.auto()  # Supported by V1
+    ROCM_AITER_MLA_VLLM_V1 = enum.auto()
     TORCH_SDPA = enum.auto()
     FLASHINFER = enum.auto()
     TRITON_MLA = enum.auto()  # Supported by V1

vllm/platforms/rocm.py

@@ -167,10 +167,15 @@ def get_attn_backend_cls(cls, selected_backend, head_size, dtype,
                     raise ValueError(
                         f" The selected backend, {selected_backend.name},"
                         f"does not support block size {block_size}.")
-            elif selected_backend == _Backend.ROCM_AITER_MLA:
+            elif selected_backend == _Backend.ROCM_AITER_MLA \
+                or selected_backend == _Backend.ROCM_AITER_MLA_VLLM_V1:
                 if block_size == 1:
-                    logger.info("Using AITER MLA backend.")
-                    return "vllm.attention.backends.rocm_aiter_mla.AiterMLABackend"  # noqa: E501
+                    if use_v1:
+                        logger.info("Using AITER MLA backend on V1 engine.")
+                        return "vllm.v1.attention.backends.mla.rocm_aiter_mla.AiterMLABackend"  # noqa: E501
+                    else:
+                        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},"

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

@@ -496,11 +496,12 @@ def build(self, num_reqs: int, num_actual_tokens: int, max_query_len: int,
                 max_context_chunk = (self.chunked_prefill_workspace_size //
                                      num_prefills_with_context_cpu)
 
-                # align max_context_chunk to page_size by rounding down,
-                # currently the `gather_cache` kernel cannot handle
-                # `context_chunk_starts` that are not aligned to page_size
-                max_context_chunk = round_down(max_context_chunk,
-                                               self.page_size)
+                if self.aot_schedule:
+                    # align max_context_chunk to page_size by rounding down,
+                    # currently the `gather_cache` kernel cannot handle
+                    # `context_chunk_starts` that are not aligned to page_size
+                    max_context_chunk = round_down(max_context_chunk,
+                                                   self.page_size)
 
                 assert max_context_chunk > 0
                 num_chunks = cdiv(max_context_len_cpu, max_context_chunk)

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

@@ -0,0 +1,196 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from dataclasses import dataclass
+from typing import Any, Optional
+
+import torch
+
+import vllm.envs as envs
+from vllm.attention.ops.rocm_aiter_mla import aiter_mla_decode_fwd
+# yapf conflicts with isort for this docstring
+# yapf: disable
+from vllm.v1.attention.backends.mla.common import (MLACommonBackend,
+                                                   MLACommonDecodeMetadata,
+                                                   MLACommonImpl,
+                                                   MLACommonMetadata,
+                                                   MLACommonMetadataBuilder)
+
+# yapf: enable
+
+
+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_VLLM_V1"
+
+    @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
+
+
+@dataclass
+class AiterMLADecodeMetadata(MLACommonDecodeMetadata):
+    # 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_len: Optional[torch.Tensor] = None
+
+
+class AiterMLAMetadata(MLACommonMetadata[AiterMLADecodeMetadata]):
+    pass
+
+
+class AiterMLAMetadataBuilder(MLACommonMetadataBuilder[AiterMLAMetadata]):
+
+    def __init__(self, runner):
+        super().__init__(runner)
+        max_model_len = self.runner.model_config.max_model_len
+        assert max_model_len == 32768,\
+            "AITER MLA requires max_model_len=32768"
+        assert self.runner.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, ...]:
+        page_size = self.runner.block_size
+        block_table_bounds = (seq_lens + page_size - 1) // page_size
+
+        mask = (torch.arange(block_table.size(1),
+                             dtype=block_table.dtype,
+                             device=block_table.device).unsqueeze(0)
+                < block_table_bounds.unsqueeze(1))
+        paged_kv_indices = block_table[mask]
+
+        paged_kv_indptr = torch.cat([
+            torch.zeros(1,
+                        dtype=block_table_bounds.dtype,
+                        device=block_table_bounds.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)
+        return (
+            paged_kv_indices,
+            paged_kv_indptr,
+            paged_kv_last_page_len,
+        )
+
+    def _build_decode(self, input_positions: torch.Tensor,
+                      block_table: torch.Tensor,
+                      seq_lens: torch.Tensor) -> AiterMLADecodeMetadata:
+
+        (
+            paged_kv_indices,
+            paged_kv_indptr,
+            paged_last_page_len,
+        ) = self._get_paged_kv_tensors(block_table, seq_lens)
+
+        attn_metadata = AiterMLADecodeMetadata(
+            input_positions=input_positions,
+            block_table=block_table,
+            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)
+
+        return attn_metadata
+
+
+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,
+                                         k,
+                                         v,
+                                         return_softmax_lse=False,
+                                         softmax_scale=None,
+                                         **kwargs):
+        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
+        assert attn_metadata.decode 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.decode.paged_kv_indptr,
+                             attn_metadata.decode.paged_kv_indices,
+                             attn_metadata.decode.paged_kv_last_page_len)
+
+        return self._v_up_proj(o)