vllm.v1.attention.backends.triton_attn ¶

class TritonAttentionBackend(AttentionBackend):
    supported_dtypes: ClassVar[list[torch.dtype]] = [
        torch.float16,
        torch.bfloat16,
        torch.float32,
    ]
    supported_kv_cache_dtypes: ClassVar[list[CacheDType]] = [
        "auto",
        "float16",
        "bfloat16",
        "fp8",
        "fp8_e4m3",
        "fp8_e5m2",
        "int8_per_token_head",
        "fp8_per_token_head",
    ]

    @staticmethod
    def get_supported_kernel_block_sizes() -> list[int | MultipleOf]:
        return [MultipleOf(16)]

    @classmethod
    def supports_block_size(cls, block_size: int | None) -> bool:
        if block_size is None:
            return True
        return block_size % 16 == 0

    forward_includes_kv_cache_update: bool = False

    @staticmethod
    def get_name() -> str:
        return "TRITON_ATTN"

    @classmethod
    def supports_batch_invariance(cls) -> bool:
        return True

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

    @staticmethod
    def get_kv_cache_shape(
        num_blocks: int,
        block_size: int,
        num_kv_heads: int,
        head_size: int,
        cache_dtype_str: str = "auto",
    ) -> tuple[int, ...]:
        if block_size % 16 != 0:
            raise ValueError("Block size must be a multiple of 16.")
        if kv_cache_uses_per_token_head_scales(cache_dtype_str):
            # Pad head_size by sizeof(float32)/sizeof(cache_dtype) so
            # the per-head scale fits inline.  The backend extracts
            # data[:head_size] and scale[head_size:] via typed views.
            from vllm.utils.torch_utils import (
                STR_DTYPE_TO_TORCH_DTYPE,
                get_dtype_size,
            )

            cache_dtype = STR_DTYPE_TO_TORCH_DTYPE[cache_dtype_str]
            scale_pad = get_dtype_size(torch.float32) // get_dtype_size(cache_dtype)
            return (num_blocks, 2, block_size, num_kv_heads, head_size + scale_pad)
        return (num_blocks, 2, block_size, num_kv_heads, head_size)

    @staticmethod
    def get_kv_cache_stride_order(
        include_num_layers_dimension: bool = False,
    ) -> tuple[int, ...]:
        # `stride_order` indicates the permutation that gets
        # us from `get_kv_cache_shape` to the actual memory layout we want.
        cache_layout = get_kv_cache_layout()
        if cache_layout == "NHD" and include_num_layers_dimension:
            # (num_blocks, num_layers, 2, block_size, num_kv_heads, head_size)
            return (1, 0, 2, 3, 4, 5)
        elif cache_layout == "NHD":
            stride_order = (0, 1, 2, 3, 4)
        elif cache_layout == "HND" and include_num_layers_dimension:
            # (num_blocks, num_kv_heads, num_layers, 2, block_size, head_size)
            return (1, 4, 0, 2, 3, 5)
        elif cache_layout == "HND":
            stride_order = (0, 1, 3, 2, 4)
        else:
            raise ValueError(f"Unknown cache layout: {cache_layout}")
        return stride_order

    @staticmethod
    def use_cascade_attention(*args, **kwargs) -> bool:
        return False

    @staticmethod
    def get_builder_cls() -> type["TritonAttentionMetadataBuilder"]:
        return TritonAttentionMetadataBuilder

    @classmethod
    def supports_head_size(cls, head_size: int) -> bool:
        return head_size >= 32

    @classmethod
    def supports_mm_prefix(cls) -> bool:
        return True

    @classmethod
    def supports_sink(cls) -> bool:
        return True

    @classmethod
    def supports_attn_type(cls, attn_type: str) -> bool:
        """TritonAttention supports all attention types."""
        return attn_type in (
            AttentionType.DECODER,
            AttentionType.ENCODER,
            AttentionType.ENCODER_ONLY,
            AttentionType.ENCODER_DECODER,
        )

    @classmethod
    def supports_alibi_sqrt(cls) -> bool:
        return True

    @classmethod
    def supports_compute_capability(cls, capability: DeviceCapability) -> bool:
        return True

class TritonAttentionImpl(AttentionImpl):
    # Per-token-head quant: scale views carved from inline head padding.
    _k_scale_cache: torch.Tensor | None = None
    _v_scale_cache: torch.Tensor | None = None

    def _ensure_scale_caches(self, kv_cache: torch.Tensor) -> None:
        """Extract per-head scale views from the padded head dimension.

        The KV cache shape is ``(num_blocks, 2, block_size, nkv, hs+pad)``
        where ``pad = sizeof(float32) / sizeof(cache_dtype)``.  The last
        ``pad`` elements of each head hold one float32 scale.  We create
        strided float32 views over those bytes.

        Scale shape: ``(num_blocks, block_size, num_kv_heads)``
        """
        if self._k_scale_cache is not None:
            return
        from vllm.utils.torch_utils import get_dtype_size

        num_blocks, _, block_size, nkv, padded_hs = kv_cache.shape
        dtype_sz = kv_cache.element_size()
        scale_pad = get_dtype_size(torch.float32) // dtype_sz  # e.g. 4
        hs = padded_hs - scale_pad

        raw = kv_cache.untyped_storage()
        base_f32 = torch.tensor([], dtype=torch.float32, device=kv_cache.device).set_(
            raw
        )

        # In the raw bytes, each (block, kv_half, slot, head) occupies
        # padded_hs * dtype_sz bytes.  The scale float32 sits at byte
        # offset hs * dtype_sz within that region.
        kv_half_bytes = block_size * nkv * padded_hs * dtype_sz
        full_block_f32 = 2 * kv_half_bytes // 4  # stride between blocks
        slot_f32 = nkv * padded_hs * dtype_sz // 4  # stride between slots
        head_f32 = padded_hs * dtype_sz // 4  # stride between heads
        scale_off_f32 = hs * dtype_sz // 4  # offset to scale within head

        # K scales: kv_half=0
        self._k_scale_cache = torch.as_strided(
            base_f32,
            size=(num_blocks, block_size, nkv),
            stride=(full_block_f32, slot_f32, head_f32),
            storage_offset=scale_off_f32,
        )
        self._k_scale_cache.fill_(1.0)

        # V scales: kv_half=1, offset by kv_half_bytes
        v_base_f32 = kv_half_bytes // 4
        self._v_scale_cache = torch.as_strided(
            base_f32,
            size=(num_blocks, block_size, nkv),
            stride=(full_block_f32, slot_f32, head_f32),
            storage_offset=v_base_f32 + scale_off_f32,
        )
        self._v_scale_cache.fill_(1.0)

    def fused_output_quant_supported(self, quant_key: QuantKey):
        return quant_key == kFp8StaticTensorSym

    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 = None,
        attn_type: AttentionType = AttentionType.DECODER,
        kv_sharing_target_layer_name: int | None = None,
        sinks: torch.Tensor | None = None,
        use_alibi_sqrt: bool = False,
        chunk_lookback: int = -1,
    ) -> None:
        self.num_heads = num_heads
        self.head_size = head_size
        self.scale = float(scale)
        self.num_kv_heads = num_kv_heads
        if alibi_slopes is not None:
            alibi_slopes = torch.tensor(alibi_slopes, dtype=torch.float32)
        self.alibi_slopes = alibi_slopes
        if sliding_window is None:
            self.sliding_window = (-1, -1)
        elif attn_type in (AttentionType.ENCODER, AttentionType.ENCODER_ONLY):
            self.sliding_window = (sliding_window - 1, sliding_window - 1)
        else:
            self.sliding_window = (sliding_window - 1, 0)
        self.kv_cache_dtype = kv_cache_dtype
        if logits_soft_cap is None:
            # In flash-attn, setting logits_soft_cap as 0 means no soft cap.
            logits_soft_cap = 0
        self.logits_soft_cap = logits_soft_cap
        self.kv_sharing_target_layer_name = kv_sharing_target_layer_name

        self.num_queries_per_kv = self.num_heads // self.num_kv_heads

        self.attn_type = attn_type
        self.fp8_dtype = current_platform.fp8_dtype()

        self.sinks = sinks
        if sinks is not None:
            assert sinks.shape[0] == num_heads, (
                "Sinks must have the same number of heads as the number of "
                f"heads in the layer. Sinks shape: {sinks.shape}, "
                f"num_heads: {num_heads}."
            )
        self.use_alibi_sqrt = use_alibi_sqrt
        self.chunk_lookback = chunk_lookback
        self.supports_quant_query_input = current_platform.is_cuda()

        self._kv_quant_mode = get_kv_quant_mode(kv_cache_dtype)
        self._is_per_token_head_quant = self._kv_quant_mode.is_per_token_head

        # Enable tensor descriptors for Q/K/V load/store on platforms that
        # benefit from HW 2D block reads (Intel Xe2/Xe3).  The dead branch
        # is eliminated at Triton compile time, so other platforms see
        # zero cost when TD is off.
        #
        # ``VLLM_TRITON_ATTN_USE_TD`` is tri-state:
        #   - unset (None): auto-select (TD on for XPU, off elsewhere),
        #   - ``1``: force TD on regardless of platform,
        #   - ``0``: force TD off regardless of platform (useful for A/B).
        td_override = envs.VLLM_TRITON_ATTN_USE_TD
        if td_override is None:
            self.use_td = current_platform.is_xpu()
        else:
            self.use_td = td_override

    def forward(
        self,
        layer: torch.nn.Module,
        query: torch.Tensor,
        key: torch.Tensor,
        value: torch.Tensor,
        kv_cache: torch.Tensor,
        attn_metadata: TritonAttentionMetadata,
        output: torch.Tensor,
        output_scale: torch.Tensor | None = None,
        output_block_scale: torch.Tensor | None = None,
    ) -> torch.Tensor:
        """Forward pass with Paged Attention impl. in Triton.

        Args:
            query: shape = [num_tokens, num_heads, head_size]
            key: shape = [num_tokens, num_kv_heads, head_size]
            value: shape = [num_tokens, num_kv_heads, head_size]
            kv_cache: shape =
                [num_blocks, 2, block_size, num_kv_heads, head_size]
            attn_metadata: Metadata for attention.
        Returns:
            shape = [num_tokens, num_heads * head_size]
        """
        if output_block_scale is not None:
            raise NotImplementedError(
                "fused block_scale output quantization is not yet supported"
                " for TritonAttentionImpl"
            )

        if attn_metadata is None:
            # Profiling run.
            return output.fill_(0)

        assert attn_metadata.use_cascade is False

        # IMPORTANT!
        # NOTE(woosuk): With piece-wise CUDA graphs, this method is executed in
        # eager-mode PyTorch. Thus, we need to be careful about any CPU overhead
        # in this method. For example, `view` and `slice` (or `[:n]`) operations
        # are surprisingly slow even in the case they do not invoke any GPU ops.
        # Minimize the PyTorch ops in this method as much as possible.
        # Whenever making a change in this method, please benchmark the
        # performance to make sure it does not introduce any overhead.

        num_actual_tokens = attn_metadata.num_actual_tokens

        # Handle encoder attention differently - no KV cache needed
        if self.attn_type in (AttentionType.ENCODER_ONLY, AttentionType.ENCODER):
            # For encoder attention,
            # we use direct Q, K, V tensors without caching
            return self._forward_encoder_attention(
                query[:num_actual_tokens],
                key[:num_actual_tokens],
                value[:num_actual_tokens],
                output[:num_actual_tokens],
                attn_metadata,
                layer,
            )

        # Per-token-head quantized KV cache: use separate scale caches.
        if self._is_per_token_head_quant:
            self._ensure_scale_caches(kv_cache)
            key_cache, value_cache = kv_cache.unbind(1)
            if key_cache.dtype == torch.uint8:
                key_cache = key_cache.view(self.fp8_dtype)
                value_cache = value_cache.view(self.fp8_dtype)
            q_descale = None
            k_descale = None
            v_descale = None
            k_scale_cache = self._k_scale_cache
            v_scale_cache = self._v_scale_cache
        # FP8 per-tensor / auto path (original flow).
        else:
            key_cache, value_cache = kv_cache.unbind(1)
            if (
                is_quantized_kv_cache(self.kv_cache_dtype)
                and key_cache.dtype != self.fp8_dtype
            ):
                key_cache = key_cache.view(self.fp8_dtype)
                value_cache = value_cache.view(self.fp8_dtype)
            descale_shape = (
                attn_metadata.query_start_loc.shape[0] - 1,
                key_cache.shape[2],
            )
            q_descale = (
                layer._q_scale
                if (
                    self._kv_quant_mode == KVQuantMode.FP8_PER_TENSOR
                    and query.dtype == self.fp8_dtype
                )
                else None
            )
            k_descale = layer._k_scale.expand(descale_shape)
            v_descale = layer._v_scale.expand(descale_shape)
            k_scale_cache = None
            v_scale_cache = None

        cu_seqlens_q = attn_metadata.query_start_loc
        seqused_k = attn_metadata.seq_lens
        max_seqlen_q = attn_metadata.max_query_len
        max_seqlen_k = attn_metadata.max_seq_len
        block_table = attn_metadata.block_table

        seq_threshold_3D = attn_metadata.seq_threshold_3D
        num_par_softmax_segments = attn_metadata.num_par_softmax_segments
        softmax_segm_output = attn_metadata.softmax_segm_output
        softmax_segm_max = attn_metadata.softmax_segm_max
        softmax_segm_expsum = attn_metadata.softmax_segm_expsum

        mm_prefix_range_tensor = attn_metadata.mm_prefix_range_tensor

        unified_attention(
            q=query[:num_actual_tokens],
            k=key_cache,
            v=value_cache,
            out=output[:num_actual_tokens],
            cu_seqlens_q=cu_seqlens_q,
            max_seqlen_q=max_seqlen_q,
            seqused_k=seqused_k,
            max_seqlen_k=max_seqlen_k,
            softmax_scale=self.scale,
            causal=True,
            alibi_slopes=self.alibi_slopes,
            use_alibi_sqrt=self.use_alibi_sqrt,
            window_size=self.sliding_window,
            block_table=block_table,
            softcap=self.logits_soft_cap,
            q_descale=q_descale,
            k_descale=k_descale,
            v_descale=v_descale,
            seq_threshold_3D=seq_threshold_3D,
            num_par_softmax_segments=num_par_softmax_segments,
            softmax_segm_output=softmax_segm_output,
            softmax_segm_max=softmax_segm_max,
            softmax_segm_expsum=softmax_segm_expsum,
            sinks=self.sinks,
            output_scale=output_scale,
            mm_prefix_range=mm_prefix_range_tensor,
            kv_quant_mode=self._kv_quant_mode,
            k_scale_cache=k_scale_cache,
            v_scale_cache=v_scale_cache,
            chunk_lookback=self.chunk_lookback,
            use_td=self.use_td,
        )

        return output

    def _forward_encoder_attention(
        self,
        query: torch.Tensor,
        key: torch.Tensor,
        value: torch.Tensor,
        output: torch.Tensor,
        attn_metadata: TritonAttentionMetadata,
        layer: torch.nn.Module,
    ) -> torch.Tensor:
        """Forward pass for encoder attention without KV cache.

        Args:
            query: shape = [num_encoder_tokens, num_heads, head_size]
            key: shape = [num_encoder_tokens, num_kv_heads, head_size]
            value: shape = [num_encoder_tokens, num_kv_heads, head_size]
            output: shape = [num_encoder_tokens, num_heads, head_size]
            attn_metadata: Encoder attention metadata
            layer: The attention layer
        """
        # Quantized KV cache is not supported for encoder attention.
        if is_quantized_kv_cache(self.kv_cache_dtype):
            raise NotImplementedError(
                "quantized KV cache is not supported for encoder attention"
            )

        # Use encoder-specific metadata for sequence information
        query_start_loc = attn_metadata.query_start_loc
        seq_lens = attn_metadata.seq_lens
        max_query_len = attn_metadata.max_query_len

        # Call flash attention directly on Q, K, V tensors
        context_attention_fwd(
            q=query,
            k=key,
            v=value,
            o=output,
            b_start_loc=query_start_loc,
            b_seq_len=seq_lens,
            max_input_len=max_query_len,
            is_causal=False,  # Encoder attention is bidirectional
            softmax_scale=self.scale,
            sliding_window_q=self.sliding_window[0],
            sliding_window_k=self.sliding_window[1],
        )
        return output

    def do_kv_cache_update(
        self,
        layer: AttentionLayer,
        key: torch.Tensor,
        value: torch.Tensor,
        kv_cache: torch.Tensor,
        slot_mapping: torch.Tensor,
    ):
        if self.attn_type in (AttentionType.ENCODER_ONLY, AttentionType.ENCODER):
            # For encoder attention,
            # we use direct Q, K, V tensors without caching
            return
        # Reshape the input keys and values and store them in the cache.
        if self._is_per_token_head_quant:
            self._ensure_scale_caches(kv_cache)
            key_cache, value_cache = kv_cache.unbind(1)
            if key_cache.dtype == torch.uint8:
                key_cache = key_cache.view(self.fp8_dtype)
                value_cache = value_cache.view(self.fp8_dtype)
            triton_reshape_and_cache_flash_per_token_head_quant(
                key,
                value,
                key_cache,
                value_cache,
                self._k_scale_cache,
                self._v_scale_cache,
                slot_mapping,
            )
            return
        # For decoder and cross-attention, use KV cache as before.
        key_cache, value_cache = kv_cache.unbind(1)
        if is_quantized_kv_cache(self.kv_cache_dtype):
            key_cache = key_cache.view(self.fp8_dtype)
            value_cache = value_cache.view(self.fp8_dtype)
        triton_reshape_and_cache_flash(
            key,
            value,
            key_cache,
            value_cache,
            slot_mapping,
            self.kv_cache_dtype,
            layer._k_scale,
            layer._v_scale,
        )

    def fused_rope_kvcache_supported(self):
        if self._is_per_token_head_quant:
            return False
        return rocm_aiter_ops.is_enabled()

    def do_rope_and_kv_cache_update(
        self,
        layer: AttentionLayer,
        query: torch.Tensor,
        key: torch.Tensor,
        value: torch.Tensor,
        positions: torch.Tensor,
        cos_sin_cache: torch.Tensor,
        is_neox: bool,
        kv_cache: torch.Tensor,
        layer_slot_mapping: torch.Tensor,
    ):
        key_cache, value_cache = kv_cache.unbind(1)
        flash_layout = True

        is_fp8_kv_cache = is_quantized_kv_cache(self.kv_cache_dtype)
        if is_fp8_kv_cache:
            key_cache = key_cache.view(self.fp8_dtype)
            value_cache = value_cache.view(self.fp8_dtype)

        rocm_aiter_ops.triton_rope_and_cache(
            query,
            key,
            value,
            positions,
            cos_sin_cache,
            is_neox,
            key_cache,
            value_cache,
            layer_slot_mapping,
            layer._k_scale,
            layer._v_scale,
            flash_layout,
            is_fp8_kv_cache,
        )