rocm_flash_attn.py

# SPDX-License-Identifier: Apache-2.0
"""Attention layer ROCm GPUs."""
import itertools
from dataclasses import dataclass
from functools import cache
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Type

import torch

import vllm.envs as envs
from vllm import _custom_ops as ops
from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
                                              AttentionLayer,
                                              AttentionMetadata, AttentionType)
from vllm.attention.backends.utils import (CommonAttentionState,
                                           CommonMetadataBuilder)
from vllm.attention.ops.paged_attn import (PagedAttention,
                                           PagedAttentionMetadata)
from vllm.logger import init_logger
from vllm.platforms import current_platform
from vllm.platforms.rocm import use_rocm_custom_paged_attention

if TYPE_CHECKING:
    from vllm.worker.model_runner import ModelInputForGPUWithSamplingMetadata

logger = init_logger(__name__)
_PARTITION_SIZE_ROCM = 256


@cache
def is_rocm_aiter_paged_attn_enabled() -> bool:
    return envs.VLLM_ROCM_USE_AITER_PAGED_ATTN \
        and envs.VLLM_ROCM_USE_AITER \


@cache
def _get_paged_attn_module() -> PagedAttention:
    """
    Initializes the appropriate PagedAttention module from `attention/ops`, 
    which is used as helper function
    by `ROCmFlashAttentionImpl` and `ROCmFlashAttentionBackend`.

    The choice of attention module depends on whether 
    AITER paged attention is enabled:
    - If enabled, `ROCmFlashAttentionImpl` uses `AITERPagedAttention`.
    - Otherwise, it defaults to using the original `PagedAttention`.
    """
    if is_rocm_aiter_paged_attn_enabled():
        # Import AITERPagedAttention only when the flag is enabled
        from vllm.attention.ops.rocm_aiter_paged_attn import (
            AITERPagedAttention)
        return AITERPagedAttention()
    return PagedAttention()


class ROCmFlashAttentionBackend(AttentionBackend):
    accept_output_buffer: bool = True

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

    @staticmethod
    def get_impl_cls() -> Type["ROCmFlashAttentionImpl"]:
        return ROCmFlashAttentionImpl

    @staticmethod
    def get_metadata_cls() -> Type["AttentionMetadata"]:
        return ROCmFlashAttentionMetadata

    @staticmethod
    def get_builder_cls() -> Type["ROCmFlashAttentionMetadataBuilder"]:
        return ROCmFlashAttentionMetadataBuilder

    @staticmethod
    def get_state_cls() -> Type["CommonAttentionState"]:
        return CommonAttentionState

    @staticmethod
    def get_kv_cache_shape(
        num_blocks: int,
        block_size: int,
        num_kv_heads: int,
        head_size: int,
    ) -> Tuple[int, ...]:
        paged_attn = _get_paged_attn_module()
        return paged_attn.get_kv_cache_shape(num_blocks, block_size,
                                             num_kv_heads, head_size)

    @staticmethod
    def swap_blocks(
        src_kv_cache: torch.Tensor,
        dst_kv_cache: torch.Tensor,
        src_to_dst: torch.Tensor,
    ) -> None:
        paged_attn = _get_paged_attn_module()
        paged_attn.swap_blocks(src_kv_cache, dst_kv_cache, src_to_dst)

    @staticmethod
    def copy_blocks(
        kv_caches: List[torch.Tensor],
        src_to_dists: torch.Tensor,
    ) -> None:
        paged_attn = _get_paged_attn_module()
        paged_attn.copy_blocks(kv_caches, src_to_dists)


@dataclass
class ROCmFlashAttentionMetadata(AttentionMetadata, PagedAttentionMetadata):
    """Metadata for FlashAttentionBackend.

    NOTE: Any python object stored here is not updated when it is
    cuda-graph replayed. If you have values that need to be changed
    dynamically, it should be stored in tensor. The tensor has to be
    updated from `CUDAGraphRunner.forward` API.
    """
    # (batch_size,). The sequence length per sequence. Sequence length means
    # the computed tokens + new tokens None if it is a decoding.
    seq_lens: Optional[List[int]]
    # seq_lens stored as a tensor.
    seq_lens_tensor: Optional[torch.Tensor]
    # Maximum sequence length among prefill batch. 0 if there are decoding
    # requests only.
    max_prefill_seq_len: int
    # Maximum sequence length among decode batch. 0 if there are prefill
    # requests only.
    max_decode_seq_len: int

    # Whether or not if cuda graph is enabled.
    # Cuda-graph is currently enabled for decoding only.
    # TODO(woosuk): Move `use_cuda_graph` out since it's unrelated to attention.
    use_cuda_graph: bool

    # NOTE(sang): Definition of context_len, query_len, and seq_len.
    # |---------- N-1 iteration --------|
    # |---------------- N iteration ---------------------|
    # |- tokenA -|......................|-- newTokens ---|
    # |---------- context_len ----------|
    # |-------------------- seq_len ----------------------|
    #                                   |-- query_len ---|

    # Maximum query length in the batch. None for decoding.
    max_query_len: Optional[int] = None
    # (batch_size + 1,). The cumulative subquery lengths of the sequences in
    # the batch, used to index into subquery. E.g., if the subquery length
    # is [4, 6], it is [0, 4, 10].
    query_start_loc: Optional[torch.Tensor] = None
    # (batch_size + 1,). The cumulative sequence lengths of the sequences in
    # the batch, used to index into sequence. E.g., if the sequence length is
    # [4, 6], it is [0, 4, 10].
    seq_start_loc: Optional[torch.Tensor] = None
    # (batch_size,) A tensor of context lengths (tokens that are computed
    # so far).
    context_lens_tensor: Optional[torch.Tensor] = None

    # Max number of query tokens among request in the batch.
    max_decode_query_len: Optional[int] = None

    _cached_prefill_metadata: Optional["ROCmFlashAttentionMetadata"] = None
    _cached_decode_metadata: Optional["ROCmFlashAttentionMetadata"] = None

    # Begin encoder attn & enc/dec cross-attn fields...

    # Encoder sequence lengths representation
    encoder_seq_lens: Optional[List[int]] = None
    encoder_seq_lens_tensor: Optional[torch.Tensor] = None

    # Maximum sequence length among encoder sequences
    max_encoder_seq_len: Optional[int] = None

    # Number of tokens input to encoder
    num_encoder_tokens: Optional[int] = None

    # Cross-attention memory-mapping data structures: slot mapping
    # and block tables
    cross_slot_mapping: Optional[torch.Tensor] = None
    cross_block_tables: Optional[torch.Tensor] = None

    @property
    def prefill_metadata(self) -> Optional["ROCmFlashAttentionMetadata"]:
        if self.num_prefills == 0:
            return None

        if self._cached_prefill_metadata is not None:
            return self._cached_prefill_metadata

        assert self.seq_lens is not None
        assert self.seq_lens_tensor is not None
        assert self.block_tables is not None

        self._cached_prefill_metadata = ROCmFlashAttentionMetadata(
            num_prefills=self.num_prefills,
            num_prefill_tokens=self.num_prefill_tokens,
            num_decode_tokens=0,
            slot_mapping=self.slot_mapping[:self.num_prefill_tokens],
            multi_modal_placeholder_index_maps=self.
            multi_modal_placeholder_index_maps,
            enable_kv_scales_calculation=self.enable_kv_scales_calculation,
            seq_lens=self.seq_lens[:self.num_prefills],
            seq_lens_tensor=self.seq_lens_tensor[:self.num_prefills],
            max_query_len=self.max_query_len,
            max_prefill_seq_len=self.max_prefill_seq_len,
            max_decode_seq_len=0,
            query_start_loc=None if self.query_start_loc is None else
            self.query_start_loc[:self.num_prefills + 1],
            seq_start_loc=None if self.seq_start_loc is None else
            self.seq_start_loc[:self.num_prefills + 1],
            context_lens_tensor=None if self.context_lens_tensor is None else
            self.context_lens_tensor[:self.num_prefills],
            block_tables=self.block_tables[:self.num_prefills],
            use_cuda_graph=False,
            # Begin encoder & cross attn fields below...
            encoder_seq_lens=self.encoder_seq_lens,
            encoder_seq_lens_tensor=self.encoder_seq_lens_tensor,
            max_encoder_seq_len=self.max_encoder_seq_len,
            cross_slot_mapping=self.cross_slot_mapping,
            cross_block_tables=self.cross_block_tables)
        return self._cached_prefill_metadata

    @property
    def decode_metadata(self) -> Optional["ROCmFlashAttentionMetadata"]:
        if self.num_decode_tokens == 0:
            return None

        if self._cached_decode_metadata is not None:
            return self._cached_decode_metadata
        assert self.block_tables is not None
        assert self.seq_lens_tensor is not None

        self._cached_decode_metadata = ROCmFlashAttentionMetadata(
            num_prefills=0,
            num_prefill_tokens=0,
            num_decode_tokens=self.num_decode_tokens,
            slot_mapping=self.slot_mapping[self.num_prefill_tokens:],
            multi_modal_placeholder_index_maps=None,
            enable_kv_scales_calculation=True,
            seq_lens=None,
            seq_lens_tensor=self.seq_lens_tensor[self.num_prefills:],
            max_query_len=None,
            max_prefill_seq_len=0,
            max_decode_seq_len=self.max_decode_seq_len,
            query_start_loc=None,
            seq_start_loc=None,
            context_lens_tensor=None,
            block_tables=self.block_tables[self.num_prefills:],
            use_cuda_graph=self.use_cuda_graph,
            # Begin encoder & cross attn fields below...
            encoder_seq_lens=self.encoder_seq_lens,
            encoder_seq_lens_tensor=self.encoder_seq_lens_tensor,
            max_encoder_seq_len=self.max_encoder_seq_len,
            cross_slot_mapping=self.cross_slot_mapping,
            cross_block_tables=self.cross_block_tables)
        # Batch may be composed of prefill|decodes, adjust query start indices
        # to refer to the start of decodes when the two are split apart.
        # E.g. in tokens:[3 prefills|6 decodes], query_start_loc=[3,9] => [0,6].
        if self._cached_decode_metadata.query_start_loc is not None:
            qs = self._cached_decode_metadata.query_start_loc
            self._cached_decode_metadata.query_start_loc = qs - qs[0]
        return self._cached_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):
        """
        Update metadata in-place to advance one decode step.
        """

        assert not turn_prefills_into_decodes, \
            ("Chunked prefill is not supported with rocm_flash_attn yet."
             "turn_prefills_into_decodes is a Multi-Step + Chunked-Prefill "
             "specific parameter.")

        # When using cudagraph, the num_seqs is padded to the next captured
        # batch sized, but num_queries tracks the actual number of requests in
        # the batch. For --enforce-eager mode, num_seqs == num_queries
        if num_seqs != num_queries:
            assert num_seqs > num_queries
            assert self.use_cuda_graph

        assert self.num_prefills == 0
        assert self.num_prefill_tokens == 0
        assert self.num_decode_tokens == num_seqs
        assert self.slot_mapping.shape == (num_seqs, )

        assert self.seq_lens is not None
        assert len(self.seq_lens) == num_seqs
        assert self.seq_lens_tensor is not None
        assert self.seq_lens_tensor.shape == (num_seqs, )
        assert self.max_query_len == 1
        assert self.max_prefill_seq_len == 0
        assert self.max_decode_seq_len == max(self.seq_lens)

        assert self.query_start_loc is not None
        assert self.query_start_loc.shape == (num_queries + 1, )
        assert self.seq_start_loc is not None
        assert self.seq_start_loc.shape == (num_seqs + 1, )

        assert self.context_lens_tensor is not None
        assert self.context_lens_tensor.shape == (num_queries, )

        assert self.block_tables is not None
        assert self.block_tables.shape[0] == num_seqs

        # Update query lengths. Note that we update only queries and not seqs,
        # since tensors may be padded due to captured cuda graph batch size
        for i in range(num_queries):
            self.seq_lens[i] += 1
        self.max_decode_seq_len = max(self.seq_lens)

        ops.advance_step_flashattn(num_seqs=num_seqs,
                                   num_queries=num_queries,
                                   block_size=block_size,
                                   input_tokens=model_input.input_tokens,
                                   sampled_token_ids=sampled_token_ids,
                                   input_positions=model_input.input_positions,
                                   seq_lens=self.seq_lens_tensor,
                                   slot_mapping=self.slot_mapping,
                                   block_tables=self.block_tables)


class ROCmFlashAttentionMetadataBuilder(
        CommonMetadataBuilder[ROCmFlashAttentionMetadata]):

    _metadata_cls = ROCmFlashAttentionMetadata


def _make_alibi_bias(alibi_slopes: torch.Tensor,
                     dtype: torch.dtype,
                     seq_lens: Optional[List[int]],
                     make_attn_mask: bool = True) -> List[torch.Tensor]:
    attn_biases = []
    if seq_lens:
        for seq_len in seq_lens:
            bias = torch.arange(seq_len, dtype=dtype)
            # NOTE(zhuohan): HF uses
            #     `bias = bias[None, :].repeat(seq_len, 1)`
            # here. We find that both biases give the same results, but
            # the bias below more accurately follows the original ALiBi
            # paper.
            bias = bias[None, :] - bias[:, None]

            num_heads = alibi_slopes.shape[0]
            bias = bias[None, :].repeat(
                (num_heads, 1, 1)).to(alibi_slopes.device)
            bias.mul_(alibi_slopes[:, None, None])
            if make_attn_mask:
                inf_mask = torch.empty(
                    (1, seq_len, seq_len),
                    dtype=bias.dtype).fill_(-torch.inf).triu_(diagonal=1).to(
                        alibi_slopes.device)
                attn_biases.append((bias + inf_mask).to(dtype))
            else:
                attn_biases.append(bias.to(dtype))

    return attn_biases


def _get_seq_len_block_table_args(
    attn_metadata: ROCmFlashAttentionMetadata,
    attn_type: str,
) -> tuple:
    '''
    The particular choice of sequence-length
    attributes which should be extracted from attn_metadata is dependent
    on the type of attention operation.

    Decoder attn -> select entirely decoder self-attention-related fields
    Encoder/decoder cross-attn -> select encoder sequence lengths
    Encoder attn -> select encoder sequence lengths fields
    Encoder-only attn -> select prefill sequence lengths with 
        bidirectional attention
    
    Arguments:

    * attn_metadata: Attention metadata structure associated with attention op
    * attn_type: encoder attention, decoder self-attention,
                encoder/decoder cross-attention, encoder-only

    Returns:

    * Appropriate sequence-lengths tensors for query and key
    * Appropriate max sequence-length scalar
    * Causal masking flag
    '''

    if attn_type == AttentionType.ENCODER:
        assert attn_metadata.encoder_seq_lens is not None
        assert attn_metadata.encoder_seq_lens_tensor is not None
        query_seq_start_loc = torch.tensor(
            list(itertools.accumulate([0] + attn_metadata.encoder_seq_lens)),
            device=attn_metadata.encoder_seq_lens_tensor.device,
            dtype=attn_metadata.encoder_seq_lens_tensor.dtype)
        causal_mask = False

        # No block tables associated with encoder attention
        return (query_seq_start_loc, attn_metadata.max_encoder_seq_len,
                query_seq_start_loc, attn_metadata.max_encoder_seq_len,
                attn_metadata.encoder_seq_lens, causal_mask)

    elif attn_type == AttentionType.ENCODER_ONLY:
        # For encoder-only models, we use the prefill sequence lengths
        assert attn_metadata.seq_lens is not None
        assert attn_metadata.seq_lens_tensor is not None
        query_seq_start_loc = torch.tensor(
            list(itertools.accumulate([0] + attn_metadata.seq_lens)),
            device=attn_metadata.seq_lens_tensor.device,
            dtype=attn_metadata.seq_lens_tensor.dtype)
        max_seq_len = attn_metadata.max_prefill_seq_len
        # Encoder-only models typically use bidirectional attention
        causal_mask = False

        return (query_seq_start_loc, max_seq_len, query_seq_start_loc,
                max_seq_len, attn_metadata.seq_lens, causal_mask)

    elif attn_type == AttentionType.DECODER:
        # Decoder self-attention
        # Choose max_seq_len based on whether we are in prompt_run
        assert attn_metadata.seq_lens is not None
        assert attn_metadata.seq_lens_tensor is not None
        query_seq_start_loc = torch.tensor(
            list(itertools.accumulate([0] + attn_metadata.seq_lens)),
            device=attn_metadata.seq_lens_tensor.device,
            dtype=attn_metadata.seq_lens_tensor.dtype)
        max_seq_len = attn_metadata.max_prefill_seq_len
        causal_mask = True

        return (query_seq_start_loc, max_seq_len, query_seq_start_loc,
                max_seq_len, attn_metadata.seq_lens, causal_mask)
    elif attn_type == AttentionType.ENCODER_DECODER:
        assert attn_metadata.seq_lens is not None
        assert attn_metadata.encoder_seq_lens_tensor is not None
        query_start_loc = torch.tensor(
            list(itertools.accumulate([0] + attn_metadata.seq_lens)),
            device=attn_metadata.encoder_seq_lens_tensor.device,
            dtype=attn_metadata.encoder_seq_lens_tensor.dtype)

        assert attn_metadata.encoder_seq_lens is not None
        assert attn_metadata.seq_lens_tensor is not None
        key_seq_start_loc = torch.tensor(
            list(itertools.accumulate([0] + attn_metadata.encoder_seq_lens)),
            device=attn_metadata.seq_lens_tensor.device,
            dtype=attn_metadata.seq_lens_tensor.dtype)
        causal_mask = False

        # Enc/dec cross-attention KVs match encoder sequence length;
        # cross-attention utilizes special "cross" block tables
        return (query_start_loc, attn_metadata.max_prefill_seq_len,
                key_seq_start_loc, attn_metadata.max_encoder_seq_len,
                attn_metadata.seq_lens, causal_mask)
    else:
        raise AttributeError(f"Invalid attention type {str(attn_type)}")


class ROCmFlashAttentionImpl(AttentionImpl):
    """
    If the input tensors contain prompt tokens, the layout is as follows:
    |<--------------- num_prompt_tokens -------------->|
    |<--prompt_0-->|<--prompt_1-->|...|<--prompt_N-1-->|

    Otherwise, the layout is as follows:
    |<------------------ num_generation_tokens (M) ----------------->|
    |<--generation_0-->|..........|<--generation_M-1-->|<--padding-->|

    Generation tokens can contain padding when cuda-graph is used.
    Currently, prompt tokens don't contain any padding.

    The prompts might have different lengths, while the generation tokens
    always have length 1.

    If chunked prefill is enabled, prefill tokens and decode tokens can be
    batched together in a flattened 1D query.

    |<----- num_prefill_tokens ---->|<------- num_decode_tokens ----------->|	
    |<-prompt_0->|...|<-prompt_N-1->|<-generation_0->|...|<-generation_M-1->|

    Currently, cuda graph is disabled for chunked prefill, meaning there's no
    padding between prefill and decode tokens.
    """

    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]] = None,
        logits_soft_cap: Optional[float] = None,
        attn_type: str = AttentionType.DECODER,
        use_irope: bool = False,
    ) -> None:
        if use_irope:
            logger.warning_once(
                "Using irope in ROCm Flash Attention is not supported yet, it "
                "will fail back to global attention for long context.")
        if blocksparse_params is not None:
            raise ValueError(
                "ROCmFlashAttention does not support blocksparse attention.")
        if use_irope:
            logger.warning(
                "Using irope in V0 is not supported yet, it will fall back "
                "to global attention for long context.")
        if logits_soft_cap is None:
            # In flash-attn, setting logits_soft_cap as 0 means no soft cap.
            self.logits_soft_cap = 0.0
        else:
            self.logits_soft_cap = logits_soft_cap
        self.attn_type = attn_type
        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
        self.sliding_window = ((sliding_window, sliding_window)
                               if sliding_window is not None else (-1, -1))
        self.kv_cache_dtype = kv_cache_dtype

        assert self.num_heads % self.num_kv_heads == 0
        self.num_queries_per_kv = self.num_heads // self.num_kv_heads

        self.paged_attn_module = _get_paged_attn_module()
        supported_head_sizes = self.paged_attn_module.get_supported_head_sizes(
        )

        if head_size not in supported_head_sizes:
            raise ValueError(
                f"Head size {head_size} is not supported by PagedAttention. "
                f"Supported head sizes are: {supported_head_sizes}.")

        self.use_naive_attn = False
        # NOTE: Allow for switching between Triton and CK. Defaulting to triton.
        self.use_triton_flash_attn = envs.VLLM_USE_TRITON_FLASH_ATTN
        if self.use_triton_flash_attn:
            if logits_soft_cap is not None:
                raise ValueError(
                    "ROCm Triton FlashAttention does not support attention"
                    " logits soft capping."
                    " please try using the ROCm CK "
                    "FA backend instead by setting the env var "
                    "`VLLM_USE_TRITON_FLASH_ATTN=0`")

            from vllm.attention.ops.triton_flash_attention import (  # noqa: F401
                triton_attention)
            self.triton_attn_func = triton_attention
            logger.debug("Using Triton FA in ROCmBackend")
            if self.sliding_window != (-1, -1):
                logger.warning("ROCm Triton FA does not currently support "
                               "sliding window attention. If using half "
                               "precision, please try using the ROCm CK "
                               "FA backend instead by setting the env var "
                               "`VLLM_USE_TRITON_FLASH_ATTN=0`")
        else:
            # if not using triton, navi3x/navi21/navi10 do not use flash-attn
            # either
            if not current_platform.has_device_capability(90):
                self.use_naive_attn = True
            else:
                try:
                    from flash_attn import flash_attn_varlen_func  # noqa: F401
                    self.fa_attn_func = flash_attn_varlen_func
                    logger.debug("Using CK FA in ROCmBackend")
                except ModuleNotFoundError:
                    self.use_naive_attn = True

            if self.use_naive_attn:
                if logits_soft_cap is not None:
                    raise ValueError(
                        "ROCm Naive FlashAttention does not support "
                        "attention logits soft capping.")

                self.sdpa_attn_func = _sdpa_attention
                logger.debug("Using naive (SDPA) attention in ROCmBackend")

        self.aiter_kv_scales_initialized = False

    def repeat_kv(self, x: torch.Tensor, n_rep: int) -> torch.Tensor:
        """torch.repeat_interleave(x, dim=1, repeats=n_rep)"""
        tokens, n_kv_heads, head_dim = x.shape
        return (x[:, :,
                  None, :].expand(tokens, n_kv_heads, n_rep,
                                  head_dim).reshape(tokens, n_kv_heads * n_rep,
                                                    head_dim))

    def forward(
        self,
        layer: AttentionLayer,
        query: torch.Tensor,
        key: torch.Tensor,
        value: torch.Tensor,
        kv_cache: torch.Tensor,
        attn_metadata: ROCmFlashAttentionMetadata,
        output: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        """Forward pass with FlashAttention and PagedAttention.

        For decoder-only models: query, key and value must be non-None.

        For encoder/decoder models:
        * ROCmFlashAttentionImpl.forward() may be invoked for both self- and 
            cross-attention layers.
        * For self-attention: query, key and value must be non-None.
        * For cross-attention:
            * Query must be non-None
            * During prefill, key and value must be non-None; key and value
              get cached for use during decode.
            * During decode, key and value may be None, since:
              (1) key and value tensors were cached during prefill, and
              (2) cross-attention key and value tensors do not grow during
                  decode
        
        A note on how the attn_type (attention type enum) argument impacts
        attention forward() behavior:
    
            * DECODER: normal decoder-only behavior;
                use decoder self-attention block table
            * ENCODER: no KV caching; pass encoder sequence
                attributes (encoder_seq_lens/encoder_seq_lens_tensor/
                max_encoder_seq_len) to kernel, in lieu of decoder
                sequence attributes (seq_lens/seq_lens_tensor/max_seq_len)
            * ENCODER_DECODER: cross-attention behavior;
                use cross-attention block table for caching KVs derived
                from encoder hidden states; since KV sequence lengths
                will match encoder sequence lengths, pass encoder sequence
                attributes to kernel (encoder_seq_lens/encoder_seq_lens_tensor/
                max_encoder_seq_len)
            * ENCODER_ONLY: bidirectional attention with no KV caching;
                use prefill sequence attributes

        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 = [2, num_blocks, block_size * num_kv_heads * head_size]
                NOTE: kv_cache will be an empty tensor with shape [0]
                for profiling run.
            attn_metadata: Metadata for attention.
            attn_type: Select attention type, between encoder attention,
                       decoder self-attention, or encoder/decoder cross-
                       attention. Defaults to decoder self-attention,
                       which is the vLLM default generally
        Returns:
            shape = [num_tokens, num_heads * head_size]
        """
        assert output is not None, "Output tensor must be provided."

        query = query.view(-1, self.num_heads, self.head_size)
        if key is not None:
            assert value is not None
            key = key.view(-1, self.num_kv_heads, self.head_size)
            value = value.view(-1, self.num_kv_heads, self.head_size)
        else:
            assert value is None

        paged_attn = self.paged_attn_module

        # Reshaping kv tensors is required for AITER paged attention kernel
        # because it works on a different tensor shape,
        # when the size of one element is one byte (int8/fp8 dtypes).
        # This reshaping is only required on the first forward call
        # and the kv cache must not be empty.
        if (is_rocm_aiter_paged_attn_enabled() and kv_cache.dtype.itemsize == 1
                and not self.aiter_kv_scales_initialized
                and kv_cache.shape != torch.Size([0])):
            num_blocks = kv_cache.shape[1]
            block_size = kv_cache.shape[2] // (self.num_kv_heads *
                                               self.head_size)
            k_scale = torch.empty((self.num_kv_heads, num_blocks * block_size),
                                  dtype=torch.float32,
                                  device=kv_cache.device)
            v_scale = torch.empty((self.num_kv_heads, num_blocks * block_size),
                                  dtype=torch.float32,
                                  device=kv_cache.device)
            self.aiter_kv_scales_initialized = True
            k_scale.fill_(layer._k_scale.item())
            v_scale.fill_(layer._v_scale.item())
            layer._k_scale = k_scale
            layer._v_scale = v_scale

        # Only update KV cache for decoder self-attention
        # and encoder-decoder cross-attention
        if self.attn_type not in [
                AttentionType.ENCODER, AttentionType.ENCODER_ONLY
        ] and kv_cache.numel() > 0:
            key_cache, value_cache = paged_attn.split_kv_cache(
                kv_cache, self.num_kv_heads, self.head_size)

            if key is not None and value is not None:
                # Reshape the input keys and values and store them in the
                # cache. If kv_cache is not provided, the new key and value
                # tensors are not cached. This happens during the initial
                # memory profiling run.
                paged_attn.write_to_paged_cache(
                    key,
                    value,
                    key_cache,
                    value_cache,
                    attn_metadata.slot_mapping
                    if self.attn_type != AttentionType.ENCODER_DECODER else
                    attn_metadata.cross_slot_mapping,
                    self.kv_cache_dtype,
                    layer._k_scale,
                    layer._v_scale,
                )

        if self.attn_type != AttentionType.ENCODER:
            num_prefill_tokens = attn_metadata.num_prefill_tokens
        elif self.attn_type == AttentionType.ENCODER_ONLY:
            # For encoder-only models, all tokens are processed in one go
            num_prefill_tokens = query.shape[0]
        else:
            assert attn_metadata.num_encoder_tokens is not None
            num_prefill_tokens = attn_metadata.num_encoder_tokens

        # Query for decode. KV is not needed because it is already cached.
        decode_query = query[num_prefill_tokens:]
        # QKV for prefill.
        query = query[:num_prefill_tokens]

        # For encoder-only and encoder models,
        # we process all tokens at once
        # For decoder and encoder-decoder,
        # we may need to limit key/value to prefill tokens
        if key is not None and value is not None \
            and self.attn_type not in [AttentionType.ENCODER_DECODER,
                                       AttentionType.ENCODER_ONLY]:
            key = key[:num_prefill_tokens]
            value = value[:num_prefill_tokens]

        if prefill_meta := attn_metadata.prefill_metadata:
            # Prompt run.
            # normal attention and DECODER
            if self.attn_type == AttentionType.DECODER and (
                    kv_cache.numel() == 0 or prefill_meta.block_tables is None
                    or prefill_meta.block_tables.numel() == 0):
                (query_seq_start_loc, query_max_seq_len, key_seq_start_loc,
                 key_max_seq_len, seq_lens,
                 causal_mask) = (prefill_meta.seq_start_loc,
                                 prefill_meta.max_prefill_seq_len,
                                 prefill_meta.seq_start_loc,
                                 prefill_meta.max_prefill_seq_len,
                                 attn_metadata.seq_lens, True)
            # prefix-enabled attention and ENCODER/ENCODER_DECODER
            else:
                (query_seq_start_loc, query_max_seq_len, key_seq_start_loc,
                 key_max_seq_len, seq_lens,
                 causal_mask) = _get_seq_len_block_table_args(
                     prefill_meta, self.attn_type)
            # Prompt run.
            if kv_cache.numel() == 0 or prefill_meta.block_tables.numel() == 0:
                # triton attention
                # When block_tables are not filled, it means q and k are the
                # prompt, and they have the same length.
                attn_masks = None
                if self.use_triton_flash_attn:
                    if self.alibi_slopes is not None:
                        attn_masks = _make_alibi_bias(
                            self.alibi_slopes,
                            query.dtype,
                            seq_lens,
                            make_attn_mask=causal_mask)  # type: ignore
                    self.triton_attn_func(
                        query,
                        key,
                        value,
                        output[:num_prefill_tokens],
                        query_seq_start_loc,
                        key_seq_start_loc,
                        query_max_seq_len,
                        key_max_seq_len,
                        causal_mask,
                        self.scale,
                        attn_masks[0][None]
                        if attn_masks is not None else None,
                    )
                elif self.use_naive_attn:
                    if self.num_kv_heads != self.num_heads:
                        # Interleave for MQA workaround.
                        key = self.repeat_kv(key, self.num_queries_per_kv)
                        value = self.repeat_kv(value, self.num_queries_per_kv)
                    if self.alibi_slopes is not None:
                        attn_masks = _make_alibi_bias(
                            self.alibi_slopes,
                            query.dtype,
                            attn_metadata.seq_lens,
                            make_attn_mask=causal_mask)  # type: ignore
                    query = query.movedim(0, query.dim() - 2)
                    key = key.movedim(0, key.dim() - 2)
                    value = value.movedim(0, value.dim() - 2)
                    # sdpa math backend attention
                    self.sdpa_attn_func(
                        query,
                        key,
                        value,
                        output[:num_prefill_tokens],
                        query_seq_start_loc,
                        num_prefill_tokens,
                        self.num_heads,
                        self.head_size,
                        self.scale,
                        attn_masks,
                    )
                else:
                    # upstream FA does not support an output arg, copy
                    output[:num_prefill_tokens] = self.fa_attn_func(
                        q=query,
                        k=key,
                        v=value,
                        cu_seqlens_q=query_seq_start_loc,
                        cu_seqlens_k=key_seq_start_loc,
                        max_seqlen_q=prefill_meta.max_prefill_seq_len,
                        max_seqlen_k=key_max_seq_len,
                        softmax_scale=self.scale,
                        causal=causal_mask,
                        window_size=self.sliding_window,
                        alibi_slopes=self.alibi_slopes,
                        softcap=self.logits_soft_cap,
                    )

            else:
                # prefix-enabled attention -
                # not applicable for encoder-only models
                if self.attn_type != AttentionType.ENCODER_ONLY:
                    output[:num_prefill_tokens] = paged_attn.forward_prefix(
                        query,
                        key,
                        value,
                        self.kv_cache_dtype,
                        key_cache,
                        value_cache,
                        prefill_meta.block_tables,
                        prefill_meta.query_start_loc,
                        prefill_meta.seq_lens_tensor,
                        prefill_meta.max_query_len,
                        self.alibi_slopes,
                        self.sliding_window[0],
                        layer._k_scale,
                        layer._v_scale,
                    )
        # Skip decode phase for encoder-only models
        if (decode_meta := attn_metadata.decode_metadata) and (
                self.attn_type != AttentionType.ENCODER_ONLY):
            # Decoding run.
            # Whether to use rocm custom paged attention or not
            num_seqs, num_heads, head_size = decode_query.shape
            block_size = value_cache.shape[3]
            gqa_ratio = num_heads // self.num_kv_heads
            use_custom = use_rocm_custom_paged_attention(
                decode_query.dtype, head_size, block_size, gqa_ratio,
                decode_meta.max_decode_seq_len, self.sliding_window)
            if use_custom:
                max_seq_len = (decode_meta.max_decode_seq_len if self.attn_type
                               != AttentionType.ENCODER_DECODER else
                               decode_meta.max_encoder_seq_len)
                assert max_seq_len is not None
                max_num_partitions = (
                    (max_seq_len + _PARTITION_SIZE_ROCM - 1) //
                    _PARTITION_SIZE_ROCM)
                assert _PARTITION_SIZE_ROCM % block_size == 0
                tmp_output = torch.empty(
                    size=(num_seqs, num_heads, max_num_partitions, head_size),
                    dtype=output.dtype,
                    device=output.device,
                )
                exp_sums = torch.empty(
                    size=(num_seqs, num_heads, max_num_partitions),
                    dtype=torch.float32,
                    device=output.device,
                )
                max_logits = torch.empty_like(exp_sums)

                query_start_loc = None
                ops.paged_attention_rocm(
                    output[num_prefill_tokens:],
                    exp_sums,
                    max_logits,
                    tmp_output,
                    decode_query,
                    key_cache,
                    value_cache,
                    self.num_kv_heads,
                    self.scale,
                    decode_meta.block_tables
                    if self.attn_type != AttentionType.ENCODER_DECODER else
                    decode_meta.cross_block_tables,
                    decode_meta.seq_lens_tensor
                    if self.attn_type != AttentionType.ENCODER_DECODER else
                    decode_meta.encoder_seq_lens_tensor,
                    query_start_loc,
                    block_size,
                    max_seq_len,
                    self.alibi_slopes,
                    self.kv_cache_dtype,
                    layer._k_scale,
                    layer._v_scale,
                )
            else:
                output[num_prefill_tokens:] = paged_attn.forward_decode(
                    decode_query,
                    key_cache,
                    value_cache,
                    decode_meta.block_tables
                    if self.attn_type != AttentionType.ENCODER_DECODER else
                    decode_meta.cross_block_tables,
                    decode_meta.seq_lens_tensor
                    if self.attn_type != AttentionType.ENCODER_DECODER else
                    decode_meta.encoder_seq_lens_tensor,
                    decode_meta.max_decode_seq_len
                    if self.attn_type != AttentionType.ENCODER_DECODER else
                    decode_meta.max_encoder_seq_len,
                    self.kv_cache_dtype,
                    self.num_kv_heads,
                    self.scale,
                    self.alibi_slopes,
                    layer._k_scale,
                    layer._v_scale,
                )

        # Reshape the output tensor.
        return output.view(-1, self.num_heads * self.head_size)


def _sdpa_attention(
    query: torch.Tensor,
    key: torch.Tensor,
    value: torch.Tensor,
    output: torch.Tensor,
    seq_lens: torch.Tensor,
    num_tokens: int,
    num_heads: int,
    head_size: int,
    scale: float,
    attn_masks: Optional[List[torch.Tensor]] = None,
) -> torch.Tensor:
    start = 0
    assert output.shape == (num_tokens, num_heads, head_size)
    assert output.dtype == query.dtype
    assert output.device == query.device

    for i, seq_len in enumerate(seq_lens):
        end = start + seq_len
        with torch.nn.attention.sdpa_kernel(
                torch.nn.attention.SDPBackend.MATH):
            sub_out = torch.nn.functional.scaled_dot_product_attention(
                query[:, start:end, :],
                key[:, start:end, :],
                value[:, start:end, :],
                dropout_p=0.0,
                is_causal=attn_masks is None,
                attn_mask=attn_masks[i] if attn_masks else None,
                scale=scale).movedim(query.dim() - 2, 0)
            output[start:end, :, :] = sub_out
            start = end

    return output