vllm.v1.attention.backends.mla.triton_mla ¶

class TritonMLABackend(MLACommonBackend):
    @staticmethod
    def get_name() -> str:
        return "TRITON_MLA"

    @staticmethod
    def get_impl_cls() -> type["TritonMLAImpl"]:
        return TritonMLAImpl

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: 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(
                "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"
            )

        self.use_triton_flash_attn = envs.VLLM_USE_TRITON_FLASH_ATTN
        self.triton_fa_func = triton_attention if HAS_TRITON else None

    def _flash_attn_varlen_diff_headdims_rocm(
        self, q, k, v, softmax_scale=None, **kwargs
    ):
        assert self.triton_fa_func is not None

        # Triton Attention requires a padded V
        padded_v = torch.nn.functional.pad(v, [0, q.shape[-1] - v.shape[-1]], value=0)
        # The output of triton_attention is a tuple of
        # [output_tensor, encoded_softmax] where encoded_softmax is always None
        output_tensor, _ = self.triton_fa_func(
            q,
            k,
            padded_v,
            None,  # output
            kwargs["cu_seqlens_q"],
            kwargs["cu_seqlens_k"],
            kwargs["max_seqlen_q"],
            kwargs["max_seqlen_k"],
            kwargs["causal"],
            softmax_scale,
            None,  # bias
        )

        return output_tensor

    def _flash_attn_varlen_diff_headdims(
        self, q, k, v, return_softmax_lse=False, softmax_scale=None, **kwargs
    ):
        if (
            current_platform.is_rocm()
            and self.use_triton_flash_attn
            and not return_softmax_lse
        ):
            return self._flash_attn_varlen_diff_headdims_rocm(
                q, k, v, softmax_scale=softmax_scale, **kwargs
            )
        else:
            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: Union[torch.Tensor, tuple[torch.Tensor, torch.Tensor]],
        kv_c_and_k_pe_cache: torch.Tensor,
        attn_metadata: MLACommonMetadata,
        layer: AttentionLayer,
    ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
        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)
        num_kv_splits = 4  # TODO: heuristic

        # 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