init src 0.9.2

2026-01-09 15:09:53 +08:00
parent 0eb2c0a4b3
commit 41d98d4359
1438 changed files with 417605 additions and 683 deletions
--- a/vllm/attention/backends/flashmla.py
+++ b/vllm/attention/backends/flashmla.py
@@ -0,0 +1,249 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from contextlib import contextmanager
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Type
+
+import torch
+
+from vllm.attention.backends.abstract import (AttentionType,
+                                              is_quantized_kv_cache)
+from vllm.attention.backends.mla.common import (MLACommonBackend,
+                                                MLACommonImpl,
+                                                MLACommonMetadata,
+                                                MLACommonMetadataBuilder,
+                                                MLACommonState)
+from vllm.attention.ops.flashmla import (flash_mla_with_kvcache,
+                                         get_mla_metadata,
+                                         is_flashmla_supported)
+
+if TYPE_CHECKING:
+    from vllm.worker.model_runner import ModelInputForGPUWithSamplingMetadata
+
+
+class FlashMLABackend(MLACommonBackend):
+
+    @staticmethod
+    def get_name() -> str:
+        return "FLASHMLA"
+
+    @staticmethod
+    def get_impl_cls() -> Type["FlashMLAImpl"]:
+        return FlashMLAImpl
+
+    @staticmethod
+    def get_metadata_cls() -> Type["FlashMLAMetadata"]:
+        return FlashMLAMetadata
+
+    @staticmethod
+    def get_builder_cls() -> Type["FlashMLAMetadataBuilder"]:
+        return FlashMLAMetadataBuilder
+
+    @staticmethod
+    def get_state_cls() -> Type["FlashMLAState"]:
+        return FlashMLAState
+
+
+@dataclass
+class FlashMLAMetadata(MLACommonMetadata):
+    decode_tile_scheduler_metadata: Optional[Tuple[torch.Tensor,
+                                                   torch.Tensor]] = None
+    decode_num_splits: Optional[torch.Tensor] = None
+
+    @property
+    def decode_metadata(self):
+        decode_metadata = super().decode_metadata
+        # TODO: cache assignment?
+        if decode_metadata is not None:
+            decode_metadata.decode_tile_scheduler_metadata=\
+                self.decode_tile_scheduler_metadata
+            decode_metadata.decode_num_splits=\
+                self.decode_num_splits
+        return decode_metadata
+
+    def advance_step(self,
+                     model_input: "ModelInputForGPUWithSamplingMetadata",
+                     sampled_token_ids: Optional[torch.Tensor],
+                     block_size: int,
+                     num_seqs: int,
+                     num_queries: int,
+                     turn_prefills_into_decodes: bool = False):
+        raise NotImplementedError(
+            "advance_step is not implemented for FlashMLA")
+
+
+class FlashMLAMetadataBuilder(MLACommonMetadataBuilder[FlashMLAMetadata]):
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        self.num_q_heads = self.runner.model_config.get_num_attention_heads(
+            self.runner.parallel_config)
+
+    def build(self, seq_lens: List[int], query_lens: List[int],
+              cuda_graph_pad_size: int, batch_size: int):
+        m = super().build(seq_lens, query_lens, cuda_graph_pad_size,
+                          batch_size)
+
+        if m.num_decode_tokens > 0:
+            m.decode_tile_scheduler_metadata, m.decode_num_splits = \
+                get_mla_metadata(
+                m.seq_lens_tensor[m.num_prefills:],
+                self.num_q_heads,
+                1, # MQA for the decode path
+            )
+
+        return m
+
+
+class FlashMLAState(MLACommonState[FlashMLAMetadata]):
+
+    def __init__(self, *args, **kwds):
+        super().__init__(*args, **kwds)
+
+        self.num_q_heads = self.runner.model_config.get_num_attention_heads(
+            self.runner.parallel_config)
+
+    @contextmanager
+    def graph_capture(self, max_batch_size: int):
+        # Run a dummy `get_mla_metadata` so we can get the right shapes
+        self._graph_decoder_tile_scheduler_metadata, \
+            self._graph_decode_num_splits = get_mla_metadata(
+            torch.ones(
+                max_batch_size, dtype=torch.int32, device=self.runner.device),
+            self.num_q_heads,
+            1, # MQA for the decode path
+        )
+
+        with super().graph_capture(max_batch_size):
+            yield
+
+        del self._graph_decoder_tile_scheduler_metadata
+        del self._graph_decode_num_splits
+
+    def graph_capture_get_metadata_for_batch(
+            self, batch_size: int, is_encoder_decoder_model: bool = False):
+        metadata = super().graph_capture_get_metadata_for_batch(
+            batch_size, is_encoder_decoder_model)
+        assert metadata.num_decode_tokens > 0
+
+        decoder_tile_scheduler_metadata, decode_num_splits = get_mla_metadata(
+            self._graph_seq_lens[:batch_size],
+            self.num_q_heads,
+            1,  # MQA for the decode path
+        )
+
+        self._graph_decoder_tile_scheduler_metadata.copy_(
+            decoder_tile_scheduler_metadata)
+        self._graph_decode_num_splits[:batch_size + 1].copy_(decode_num_splits)
+
+        metadata.decode_tile_scheduler_metadata=\
+            self._graph_decoder_tile_scheduler_metadata
+        metadata.decode_num_splits=\
+            self._graph_decode_num_splits[:batch_size + 1]
+
+        return metadata
+
+    def get_graph_input_buffers(self,
+                                attn_metadata,
+                                is_encoder_decoder_model: bool = False):
+        input_buffers = super().get_graph_input_buffers(
+            attn_metadata, is_encoder_decoder_model)
+        input_buffers["decode_tile_scheduler_metadata"] = \
+                attn_metadata.decode_metadata.decode_tile_scheduler_metadata
+        input_buffers["decode_num_splits"] = \
+                attn_metadata.decode_metadata.decode_num_splits
+
+        return input_buffers
+
+    def prepare_graph_input_buffers(self,
+                                    input_buffers,
+                                    attn_metadata,
+                                    is_encoder_decoder_model: bool = False):
+        super().prepare_graph_input_buffers(input_buffers, attn_metadata,
+                                            is_encoder_decoder_model)
+
+        input_buffers["decode_tile_scheduler_metadata"].copy_(
+            attn_metadata.decode_metadata.decode_tile_scheduler_metadata)
+        input_buffers["decode_num_splits"].copy_(
+            attn_metadata.decode_metadata.decode_num_splits)
+
+
+class FlashMLAImpl(MLACommonImpl[FlashMLAMetadata]):
+
+    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,
+            kv_sharing_target_layer_name: Optional[str] = None,
+            # 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,
+                         kv_sharing_target_layer_name, **mla_args)
+
+        assert is_flashmla_supported(), \
+            "FlashMLA is not supported on this device"
+
+        unsupported_features = [
+            alibi_slopes, sliding_window, blocksparse_params, logits_soft_cap
+        ]
+        if any(unsupported_features):
+            raise NotImplementedError(
+                "FlashMLAImpl does not support one of the following: "
+                "alibi_slopes, sliding_window, blocksparse_params, "
+                "logits_soft_cap")
+
+        if attn_type != AttentionType.DECODER:
+            raise NotImplementedError("Encoder self-attention and "
+                                      "encoder/decoder cross-attention "
+                                      "are not implemented for "
+                                      "FlashMLAImpl")
+
+        if is_quantized_kv_cache(self.kv_cache_dtype):
+            if self.kv_cache_dtype != "fp8":
+                raise NotImplementedError(
+                    "FlashMLA with other KV cache not yet supported")
+
+    def _forward_decode(
+        self,
+        q_nope: torch.Tensor,
+        q_pe: torch.Tensor,
+        kv_c_and_k_pe_cache: torch.Tensor,
+        attn_metadata: FlashMLAMetadata,
+        k_scale = None,
+        kv_cache_dtype = "auto",
+    ) -> torch.Tensor:
+        assert kv_c_and_k_pe_cache.numel() > 0
+
+        decode_meta = attn_metadata.decode_metadata
+        assert decode_meta is not None
+
+        q = torch.cat([q_nope, q_pe], dim=-1)\
+            .unsqueeze(1) # Add seqlen dim of 1 (decode)
+
+        o, _ = flash_mla_with_kvcache(
+            q=q,
+            k_cache=kv_c_and_k_pe_cache.unsqueeze(-2),  # Add head dim of 1
+            block_table=decode_meta.block_tables,
+            cache_seqlens=decode_meta.seq_lens_tensor,
+            head_dim_v=self.kv_lora_rank,
+            tile_scheduler_metadata=decode_meta.decode_tile_scheduler_metadata,
+            num_splits=decode_meta.decode_num_splits,
+            softmax_scale=self.scale,
+            causal=True,
+            k_scale = k_scale,
+            kv_cache_dtype = kv_cache_dtype,
+        )
+
+        return self._v_up_proj(o)