enginex-mthreads-vllm/vllm/v1/attention/backends/rocm_aiter_unified_attn.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Attention layer with PagedAttention and Triton prefix prefill."""

import torch

from vllm import _custom_ops as ops
from vllm.attention.backends.abstract import AttentionType
from vllm.logger import init_logger
from vllm.model_executor.layers.quantization.utils.quant_utils import (
    QuantKey,
    kFp8StaticTensorSym,
)
from vllm.v1.attention.backends.flash_attn import FlashAttentionMetadata
from vllm.v1.attention.backends.rocm_attn import (
    RocmAttentionBackend,
    RocmAttentionImpl,
    RocmAttentionMetadataBuilder,
)

logger = init_logger(__name__)


class RocmAiterUnifiedAttentionBackend(RocmAttentionBackend):
    accept_output_buffer: bool = True

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

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

    @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.")
        return (2, num_blocks, block_size, num_kv_heads, head_size)

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

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


class RocmAiterUnifiedAttentionImpl(RocmAttentionImpl):
    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,
    ) -> 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,
            sinks,
        )
        logger.info_once(
            "Using aiter unified attention for RocmAiterUnifiedAttentionImpl"
        )
        from aiter.ops.triton.unified_attention import unified_attention

        self.unified_attention = unified_attention

    def forward(
        self,
        layer: torch.nn.Module,
        query: torch.Tensor,
        key: torch.Tensor,
        value: torch.Tensor,
        kv_cache: torch.Tensor,
        attn_metadata: FlashAttentionMetadata,
        output: torch.Tensor | None = None,
        output_scale: torch.Tensor | None = None,
        output_block_scale: torch.Tensor | None = None,
    ) -> torch.Tensor:
        """Forward pass with FlashAttention.

        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 =
                [2, num_blocks, block_size, num_kv_heads, head_size]
            attn_metadata: Metadata for attention.
        Returns:
            shape = [num_tokens, num_heads * head_size]
        """
        assert output is not None, "Output tensor must be provided."

        if output_block_scale is not None:
            raise NotImplementedError(
                "fused block_scale output quantization is not yet supported"
                " for RocmAttentionImpl"
            )

        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

        key_cache, value_cache = kv_cache.unbind(0)

        # key and value may be None in the case of cross attention. They are
        # calculated once based on the output from the encoder and then cached
        # in KV cache.
        if (
            self.kv_sharing_target_layer_name is None
            and key is not None
            and value is not None
        ):
            # Reshape the input keys and values and store them in the cache.
            # Skip this if sharing KV cache with an earlier attention layer.
            ops.reshape_and_cache_flash(
                key,
                value,
                key_cache,
                value_cache,
                attn_metadata.slot_mapping,
                self.kv_cache_dtype,
                layer._k_scale,
                layer._v_scale,
            )

        if self.kv_cache_dtype.startswith("fp8"):
            key_cache = key_cache.view(self.fp8_dtype)
            value_cache = value_cache.view(self.fp8_dtype)
            assert layer._q_scale_float == 1.0, (
                "A non 1.0 q_scale is not currently supported."
            )

        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

        descale_shape = (
            cu_seqlens_q.shape[0] - 1,
            key.shape[1] if key is not None else self.num_kv_heads,
        )

        self.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,
            window_size=self.sliding_window,
            block_table=block_table,
            softcap=self.logits_soft_cap,
            q_descale=None,  # Not supported
            k_descale=layer._k_scale.expand(descale_shape),
            v_descale=layer._v_scale.expand(descale_shape),
            sinks=self.sinks,
            output_scale=output_scale,
        )

        return output