Squash commit

Signed-off-by: hjjq <[email protected]>

Author: hjjq

tests/kernels/test_flashinfer_mla_decode.py

@@ -0,0 +1,123 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import pytest
+import torch
+import torch.nn.functional as F
+from flashinfer.decode import trtllm_batch_decode_with_kv_cache_mla
+from torch import Tensor
+
+from vllm.platforms import current_platform
+
+FLASHINFER_WORKSPACE_BUFFER_SIZE = 128 * 1024 * 1024
+
+if not current_platform.has_device_capability(100):
+    pytest.skip(
+        reason="FlashInfer MLA Requires compute capability of 10 or above.",
+        allow_module_level=True)
+
+
+def ref_mla(
+        out: Tensor,  # (bs, num_heads, v_head_dim)
+        query: Tensor,  # (bs, num_heads, head_dim)
+        kv_cache: Tensor,  # (num_blocks, block_size, head_dim)
+        scale: float,
+        block_tables: Tensor,  # (bs, max_num_blocks)
+        seq_lens: Tensor,  # (bs,)
+):
+    bs, num_heads, v_head_dim = out.shape
+    head_dim = query.shape[2]
+
+    for i in range(bs):
+        # gather and flatten KV-cache
+        kv = kv_cache[
+            block_tables[i]]  # (max_num_blocks, block_size, head_dim)
+        kv = kv.view(1, -1,
+                     head_dim)[:, :seq_lens[i]]  # (1, seq_len, head_dim)
+        v = kv[:, :, :v_head_dim]
+
+        q = query[i].view(num_heads, 1, head_dim)
+        o = F.scaled_dot_product_attention(q,
+                                           kv,
+                                           v,
+                                           scale=scale,
+                                           enable_gqa=True)
+        out[i] = o.view(num_heads, v_head_dim)
+
+    return out
+
+
+@pytest.mark.parametrize("dtype", [torch.bfloat16])
+@pytest.mark.parametrize("bs", [1, 2, 4, 16])
+@pytest.mark.parametrize("block_size", [32, 64])
+def test_flashinfer_mla_decode(dtype: torch.dtype, bs: int, block_size: int):
+    torch.set_default_device('cuda')
+    torch.manual_seed(42)
+
+    # Deepseek R1 config
+    num_heads = 128
+    kv_lora_rank = 512
+    qk_nope_head_dim = 128
+    qk_rope_head_dim = 64
+    qk_head_dim = kv_lora_rank + qk_rope_head_dim
+    scale = (qk_nope_head_dim + qk_rope_head_dim)**-0.5
+
+    MAX_SEQ_LEN = 1024
+
+    seq_lens = [torch.randint(2, MAX_SEQ_LEN, (1, )).item() for _ in range(bs)]
+    seq_lens[-1] = MAX_SEQ_LEN
+    max_seq_len = max(seq_lens)
+    seq_lens_tensor = torch.tensor(seq_lens, dtype=torch.int32)
+
+    # Generate block tables with random but unique block IDs
+    # From https://github.com/flashinfer-ai/flashinfer/pull/1222
+    blocks_per_seq = (seq_lens_tensor + block_size - 1) // block_size
+    max_num_blocks_per_seq = max(blocks_per_seq.max().item(), 4)
+    total_blocks_needed = sum(blocks_per_seq)
+    # Get random unique IDs for all blocks
+    all_block_ids = torch.randperm(total_blocks_needed)
+
+    block_id = 0
+    block_tables = torch.zeros(
+        (bs, max_num_blocks_per_seq),
+        dtype=torch.int32,
+    )
+
+    # Populate block tables and track block assignments
+    block_id = 0
+    for i in range(bs):
+        num_blocks_needed = blocks_per_seq[i]
+        block_tables[i, :num_blocks_needed] = all_block_ids[block_id:block_id +
+                                                            num_blocks_needed]
+        block_id += num_blocks_needed
+
+    kv_cache = torch.randn(block_tables.numel(), block_size,
+                           qk_head_dim).to(dtype)
+    q = torch.randn(bs, num_heads, qk_head_dim).to(dtype)
+
+    out_ref = q.new_zeros(bs, num_heads, kv_lora_rank)
+    ref_mla(out_ref, q, kv_cache, scale, block_tables, seq_lens_tensor)
+
+    workspace_buffer = torch.empty(
+        FLASHINFER_WORKSPACE_BUFFER_SIZE,
+        dtype=torch.uint8,
+        device=q.device,
+    )
+    # Flashinfer MLA expects the query to be of shape
+    # (bs, q_len_per_request, num_heads, qk_head_dim),
+    # where q_len_per_request is the MTP query length (=1 without MTP)
+    q = q.unsqueeze(1)
+
+    out_ans = trtllm_batch_decode_with_kv_cache_mla(
+        query=q,
+        kv_cache=kv_cache.unsqueeze(1),
+        workspace_buffer=workspace_buffer,
+        qk_nope_head_dim=qk_nope_head_dim,
+        kv_lora_rank=kv_lora_rank,
+        qk_rope_head_dim=qk_rope_head_dim,
+        block_tables=block_tables,
+        seq_lens=seq_lens_tensor,
+        max_seq_len=max_seq_len,
+        bmm1_scale=scale,
+    )
+    out_ans = out_ans.squeeze(1)
+    torch.testing.assert_close(out_ans, out_ref, atol=1e-2, rtol=1e-2)

vllm/engine/arg_utils.py

@@ -1465,6 +1465,7 @@ def _is_v1_supported_oracle(self, model_config: ModelConfig) -> bool:
             "FLASHMLA",
             "FLASHINFER",
             "FLASHINFER_VLLM_V1",
+            "FLASHINFER_MLA",
             "ROCM_AITER_MLA",
             "TORCH_SDPA_VLLM_V1",
             "FLEX_ATTENTION",

vllm/platforms/cuda.py

@@ -227,6 +227,19 @@ def get_attn_backend_cls(cls, selected_backend, head_size, dtype,
         if use_mla:
             # TODO(lucas): refactor to be more concise
             #  we should probably consider factoring out V1 here
+            if selected_backend == _Backend.FLASHINFER_MLA:
+                if use_v1 and cls.has_device_capability(100):
+                    from vllm.v1.attention.backends.utils import (
+                        set_kv_cache_layout)
+                    set_kv_cache_layout("HND")
+                    logger.info_once(
+                        "Using FlashInfer MLA backend on V1 engine.")
+                    return ("vllm.v1.attention.backends.mla."
+                            "flashinfer_mla.FlashInferMLABackend")
+                else:
+                    logger.warning(
+                        "FlashInfer MLA backend is only supported on V1 engine"
+                        " and requires compute capability 10.0")
             if selected_backend == _Backend.CUTLASS_MLA:
                 if use_v1:
                     logger.info_once("Using Cutlass MLA backend on V1 engine.")

vllm/platforms/interface.py

@@ -50,6 +50,7 @@ class _Backend(enum.Enum):
     TORCH_SDPA = enum.auto()
     FLASHINFER = enum.auto()
     FLASHINFER_VLLM_V1 = enum.auto()
+    FLASHINFER_MLA = enum.auto()
     TRITON_MLA = enum.auto()  # Supported by V1
     TRITON_MLA_VLLM_V1 = enum.auto()
     FLASHMLA_VLLM_V1 = enum.auto()

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

@@ -349,6 +349,7 @@ class MLACommonMetadata(Generic[D]):
 
     num_reqs: int
     max_query_len: int
+    max_seq_len: int
 
     num_actual_tokens: int  # Number of tokens excluding padding.
     query_start_loc: torch.Tensor
@@ -586,6 +587,7 @@ def build(self,
         num_reqs = common_attn_metadata.num_reqs
         num_tokens = common_attn_metadata.num_actual_tokens
         max_query_len = common_attn_metadata.max_query_len
+        max_seq_len = common_attn_metadata.seq_lens_cpu.max().item()
 
         # Note(simon): be careful about the CPU <> GPU memory movement in this
         # function. We should avoid GPU -> CPU sync as much as possible because
@@ -710,6 +712,7 @@ def build(self,
         attn_metadata = self.metadata_cls(
             num_reqs=common_attn_metadata.num_reqs,
             max_query_len=common_attn_metadata.max_query_len,
+            max_seq_len=max_seq_len,
             num_actual_tokens=num_tokens,
             query_start_loc=query_start_loc,
             slot_mapping=slot_mapping,

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

@@ -0,0 +1,105 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from typing import Optional
+
+import torch
+from flashinfer.decode import trtllm_batch_decode_with_kv_cache_mla
+
+from vllm.attention.backends.abstract import (AttentionType,
+                                              is_quantized_kv_cache)
+from vllm.logger import init_logger
+from vllm.v1.attention.backends.mla.common import (MLACommonBackend,
+                                                   MLACommonImpl,
+                                                   MLACommonMetadata)
+
+logger = init_logger(__name__)
+
+FLASHINFER_MLA_WORKSPACE_BUFFER_SIZE = 128 * 1024 * 1024
+
+
+class FlashInferMLABackend(MLACommonBackend):
+
+    @staticmethod
+    def get_name() -> str:
+        return "FLASHINFER_MLA"
+
+    @staticmethod
+    def get_impl_cls() -> type["FlashInferMLAImpl"]:
+        return FlashInferMLAImpl
+
+
+g_fi_workspace = torch.empty(
+    FLASHINFER_MLA_WORKSPACE_BUFFER_SIZE,
+    dtype=torch.uint8,
+    device="cuda",
+)
+
+
+class FlashInferMLAImpl(MLACommonImpl[MLACommonMetadata]):
+
+    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,
+            logits_soft_cap: Optional[float],
+            attn_type: str,
+            kv_sharing_target_layer_name: Optional[str],
+            # MLA Specific Arguments
+            **mla_args) -> None:
+        super().__init__(num_heads, head_size, scale, num_kv_heads,
+                         alibi_slopes, sliding_window, kv_cache_dtype,
+                         logits_soft_cap, attn_type,
+                         kv_sharing_target_layer_name, **mla_args)
+
+        unsupported_features = [alibi_slopes, sliding_window, logits_soft_cap]
+        if any(unsupported_features):
+            raise NotImplementedError(
+                "FlashInferMLAImpl does not support one of the following: "
+                "alibi_slopes, sliding_window, logits_soft_cap")
+
+        if attn_type != AttentionType.DECODER:
+            raise NotImplementedError("Encoder self-attention and "
+                                      "encoder/decoder cross-attention "
+                                      "are not implemented for "
+                                      "FlashInferMLAImpl")
+
+        if is_quantized_kv_cache(self.kv_cache_dtype):
+            raise NotImplementedError(
+                "FlashInferMLA V1 with FP8 KV cache not yet supported")
+
+        self._workspace_buffer = g_fi_workspace
+
+    def _forward_decode(
+        self,
+        q_nope: torch.Tensor,
+        q_pe: torch.Tensor,
+        kv_c_and_k_pe_cache: torch.Tensor,
+        attn_metadata: MLACommonMetadata,
+    ) -> torch.Tensor:
+        assert kv_c_and_k_pe_cache.numel() > 0
+        assert attn_metadata.decode is not None
+
+        q = torch.cat([q_nope, q_pe], dim=-1)
+        # trtllm API requires extra dimension q_len_per_request for MTP
+        q = q.unsqueeze(1)
+
+        o = trtllm_batch_decode_with_kv_cache_mla(
+            query=q,
+            kv_cache=kv_c_and_k_pe_cache.unsqueeze(1),
+            workspace_buffer=self._workspace_buffer,
+            qk_nope_head_dim=self.qk_nope_head_dim,
+            kv_lora_rank=self.kv_lora_rank,
+            qk_rope_head_dim=self.qk_rope_head_dim,
+            block_tables=attn_metadata.decode.block_table,
+            seq_lens=attn_metadata.decode.seq_lens,
+            max_seq_len=attn_metadata.max_seq_len,
+            bmm1_scale=self.scale,
+        )
+
+        return self._v_up_proj(o)