Sync from v0.13

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

@@ -0,0 +1,171 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from typing import ClassVar
+
+import torch
+
+from vllm.attention.backends.abstract import (
+    AttentionLayer,
+    AttentionType,
+    is_quantized_kv_cache,
+)
+from vllm.attention.ops.triton_decode_attention import decode_attention_fwd
+from vllm.config.cache import CacheDType
+from vllm.logger import init_logger
+from vllm.model_executor.layers.batch_invariant import (
+    vllm_is_batch_invariant,
+)
+from vllm.platforms.interface import DeviceCapability
+from vllm.v1.attention.backends.mla.common import (
+    MLACommonBackend,
+    MLACommonImpl,
+    MLACommonMetadata,
+)
+
+logger = init_logger(__name__)
+
+
+class TritonMLABackend(MLACommonBackend):
+    supported_dtypes: ClassVar[list[torch.dtype]] = [torch.float16, torch.bfloat16]
+    supported_kv_cache_dtypes: ClassVar[list[CacheDType]] = ["auto"]
+
+    @staticmethod
+    def get_name() -> str:
+        return "TRITON_MLA"
+
+    @staticmethod
+    def get_impl_cls() -> type["TritonMLAImpl"]:
+        return TritonMLAImpl
+
+    @classmethod
+    def supports_compute_capability(cls, capability: DeviceCapability) -> bool:
+        return True
+
+
+class TritonMLAImpl(MLACommonImpl[MLACommonMetadata]):
+    can_return_lse_for_decode: bool = True
+
+    def __init__(
+        self,
+        num_heads: int,
+        head_size: int,
+        scale: float,
+        num_kv_heads: int,
+        alibi_slopes: list[float] | None,
+        sliding_window: int | None,
+        kv_cache_dtype: str,
+        logits_soft_cap: float | None,
+        attn_type: str,
+        kv_sharing_target_layer_name: 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,
+            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(
+                "TritonMLAImpl 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 "
+                "TritonMLAImpl"
+            )
+
+        if is_quantized_kv_cache(self.kv_cache_dtype):
+            raise NotImplementedError(
+                "TritonMLA V1 with FP8 KV cache not yet supported"
+            )
+
+    def _flash_attn_varlen_diff_headdims(
+        self, q, k, v, return_softmax_lse=False, softmax_scale=None, **kwargs
+    ):
+        return super()._flash_attn_varlen_diff_headdims(
+            q,
+            k,
+            v,
+            return_softmax_lse=return_softmax_lse,
+            softmax_scale=softmax_scale,
+            **kwargs,
+        )
+
+    def _forward_decode(
+        self,
+        q: torch.Tensor | tuple[torch.Tensor, torch.Tensor],
+        kv_c_and_k_pe_cache: torch.Tensor,
+        attn_metadata: MLACommonMetadata,
+        layer: AttentionLayer,
+    ) -> tuple[torch.Tensor, torch.Tensor | None]:
+        assert kv_c_and_k_pe_cache.numel() > 0
+        assert attn_metadata.decode is not None
+
+        if self.kv_cache_dtype.startswith("fp8"):
+            raise NotImplementedError("FP8 Triton MLA not yet supported")
+
+        if type(q) is tuple:
+            q = torch.cat(q, dim=-1)
+
+        assert isinstance(q, torch.Tensor)
+        B = q.shape[0]
+        q_num_heads = q.shape[1]
+        o = torch.zeros(
+            B, q_num_heads, self.kv_lora_rank, dtype=q.dtype, device=q.device
+        )
+        lse = torch.zeros(B, q_num_heads, dtype=q.dtype, device=q.device)
+
+        # For batch invariance, use only 1 split to ensure deterministic reduction
+        num_kv_splits = 1 if vllm_is_batch_invariant() else 4
+
+        # TODO(lucas) Allocate ahead of time
+        attn_logits = torch.empty(
+            (
+                B,
+                q_num_heads,
+                num_kv_splits,
+                # NOTE(lucas) idk why the +1 is here but sglang has it so we
+                # just mirror that
+                self.kv_lora_rank + 1,
+            ),
+            dtype=torch.float32,
+            device=q.device,
+        )
+
+        # Add a head dim of 1
+        kv_c_and_k_pe_cache = kv_c_and_k_pe_cache.unsqueeze(2)
+        kv_c_cache = kv_c_and_k_pe_cache[..., : self.kv_lora_rank]
+        PAGE_SIZE = kv_c_and_k_pe_cache.size(1)
+
+        # Run MQA
+        decode_attention_fwd(
+            q,
+            kv_c_and_k_pe_cache,
+            kv_c_cache,
+            o,
+            lse,
+            attn_metadata.decode.block_table,
+            attn_metadata.decode.seq_lens,
+            attn_logits,
+            num_kv_splits,
+            self.scale,
+            PAGE_SIZE,
+        )
+
+        return o, lse