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tool_server/.venv/lib/python3.12/site-packages/vllm/v1/attention/backends/cpu_attn.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from dataclasses import dataclass
+from typing import Optional
+
+import numpy as np
+import torch
+from torch.nn.functional import scaled_dot_product_attention
+
+from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
+                                              AttentionLayer,
+                                              AttentionMetadata, AttentionType,
+                                              is_quantized_kv_cache)
+from vllm.attention.backends.utils import CommonAttentionState
+from vllm.config import VllmConfig
+from vllm.logger import init_logger
+from vllm.v1.attention.backends.utils import (AttentionMetadataBuilder,
+                                              CommonAttentionMetadata)
+from vllm.v1.core.sched.output import SchedulerOutput
+from vllm.v1.kv_cache_interface import AttentionSpec
+from vllm.v1.worker.gpu_input_batch import InputBatch
+
+try:
+    import intel_extension_for_pytorch.llm.modules as ipex_modules
+    _use_ipex = True
+# AttributeError is to handle a bug in ipex
+# https://github.com/intel/intel-extension-for-pytorch/pull/813
+except (ImportError, AttributeError):
+    _use_ipex = False
+
+from vllm import _custom_ops as ops
+
+logger = init_logger(__name__)
+
+
+class TorchSDPABackend(AttentionBackend):
+    accept_output_buffer: bool = False
+
+    @classmethod
+    def get_supported_dtypes(cls) -> list[torch.dtype]:
+        return [torch.float16, torch.bfloat16, torch.float32]
+
+    @classmethod
+    def validate_head_size(cls, head_size: int) -> None:
+        attn_impl = _get_paged_attn_impl()
+        is_valid, supported_head_sizes = attn_impl.validate_head_size(
+            head_size)
+        if not is_valid:
+            attn_type = cls.__name__.removesuffix("Backend")
+            raise ValueError(
+                f"Head size {head_size} is not supported by {attn_type}. "
+                f"Supported head sizes are: {supported_head_sizes}. "
+                "Set VLLM_ATTENTION_BACKEND=FLEX_ATTENTION to use "
+                "FlexAttention backend which supports all head sizes.")
+
+    @staticmethod
+    def get_name() -> str:
+        return "TORCH_SDPA_VLLM_V1"
+
+    @staticmethod
+    def get_impl_cls() -> type["TorchSDPABackendImpl"]:
+        return TorchSDPABackendImpl
+
+    @staticmethod
+    def get_metadata_cls() -> type["AttentionMetadata"]:
+        return TorchSDPAMetadata
+
+    @staticmethod
+    def get_state_cls() -> type["CommonAttentionState"]:
+        return CommonAttentionState
+
+    @staticmethod
+    def get_builder_cls() -> type["TorchSDPAMetadataBuilderV1"]:
+        return TorchSDPAMetadataBuilderV1
+
+    @staticmethod
+    def get_kv_cache_shape(
+        num_blocks: int,
+        block_size: int,
+        num_kv_heads: int,
+        head_size: int,
+    ) -> tuple[int, ...]:
+        return _get_paged_attn_impl().get_kv_cache_shape(
+            num_blocks, block_size, num_kv_heads, head_size)
+
+    @staticmethod
+    def use_cascade_attention(*args, **kwargs) -> bool:
+        return False
+
+
+@dataclass
+class TorchSDPAMetadata(AttentionMetadata):
+    """Metadata for PagedAttention."""
+    # (batch_size,). The length of sequences (entire tokens seen so far) per
+    # sequence.
+    seq_lens_tensor: Optional[torch.Tensor]
+    # Maximum sequence length in the batch. 0 if it is prefill-only batch.
+    max_decode_seq_len: int
+    # (batch_size, max_blocks_per_seq).
+    # Block addresses per sequence. (Seq id -> list of physical block)
+    # E.g., [0, 1, 2] means tokens are stored in 0th, 1st, and 2nd blocks
+    # in the kv cache. Each block can contain up to block_size tokens.
+    # 2nd dimensions are padded up to max_blocks_per_seq if it is cuda-graph
+    # captured.
+    block_tables: Optional[torch.Tensor]
+    """Metadata for TorchSDPABackend.
+    """
+    # Currently, input sequences can only contain all prompts
+    # or all decoding. True if all sequences are prompts.
+    chunked_prefill: bool
+    seq_lens: Optional[list[int]] = None  # For non-chunked prefill
+
+    # For chunked prefill only
+    max_query_len: Optional[int] = None
+    max_kv_len: Optional[int] = None
+    prefill_query_start_loc: Optional[torch.Tensor] = None
+    kv_start_loc: Optional[torch.Tensor] = None
+    prefill_block_tables: Optional[torch.Tensor] = None
+
+    # For V1 logits index only
+    query_start_loc: Optional[torch.Tensor] = 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
+
+    def __post_init__(self):
+        # Set during the execution of the first attention op.
+        # It is a list because it is needed to set per prompt
+        # when alibi slopes is used. It is because of the limitation
+        # from xformer API.
+        # will not appear in the __repr__ and __init__
+        self.attn_bias: Optional[list[torch.Tensor]] = None
+        self.encoder_attn_bias: Optional[list[torch.Tensor]] = None
+        self.cross_attn_bias: Optional[list[torch.Tensor]] = None
+
+    @property
+    def is_all_encoder_attn_metadata_set(self):
+        '''
+        All attention metadata required for encoder attention is set.
+        '''
+        return ((self.encoder_seq_lens is not None)
+                and (self.encoder_seq_lens_tensor is not None)
+                and (self.max_encoder_seq_len is not None))
+
+    @property
+    def is_all_cross_attn_metadata_set(self):
+        '''
+        All attention metadata required for enc/dec cross-attention is set.
+
+        Superset of encoder attention required metadata.
+        '''
+        return (self.is_all_encoder_attn_metadata_set
+                and (self.cross_slot_mapping is not None)
+                and (self.cross_block_tables is not None))
+
+    @property
+    def prefill_metadata(self) -> Optional["TorchSDPAMetadata"]:
+        if self.num_prefill_tokens == 0:
+            return None
+        return self
+
+    @property
+    def decode_metadata(self) -> Optional["TorchSDPAMetadata"]:
+        if self.num_decode_tokens == 0:
+            return None
+        return self
+
+    def get_seq_lens(
+        self,
+        attn_type: str,
+    ):
+        '''
+        Extract appropriate sequence lengths from attention metadata
+        according to attention type.
+
+        Arguments:
+
+        * attn_metadata: Attention metadata structure associated with attention
+        * attn_type: encoder attention, decoder self-attention,
+                    encoder/decoder cross-attention
+
+        Returns:
+        * Appropriate sequence lengths tensor for query
+        * Appropriate sequence lengths tensor for key & value
+        '''
+
+        if (attn_type == AttentionType.DECODER
+                or attn_type == AttentionType.ENCODER_ONLY):
+            seq_lens_q = self.seq_lens
+            seq_lens_kv = self.seq_lens
+        elif attn_type == AttentionType.ENCODER:
+            seq_lens_q = self.encoder_seq_lens
+            seq_lens_kv = self.encoder_seq_lens
+        elif attn_type == AttentionType.ENCODER_DECODER:
+            seq_lens_q = self.seq_lens
+            seq_lens_kv = self.encoder_seq_lens
+        else:
+            raise AttributeError(f"Invalid attention type {str(attn_type)}")
+        return seq_lens_q, seq_lens_kv
+
+    def get_attn_bias(
+        self,
+        attn_type: str,
+    ) -> Optional[list[torch.Tensor]]:
+        '''
+        Extract appropriate attention bias from attention metadata
+        according to attention type.
+
+        Arguments:
+
+        * attn_metadata: Attention metadata structure associated with attention
+        * attn_type: encoder attention, decoder self-attention,
+                    encoder/decoder cross-attention
+
+        Returns:
+        * Appropriate attention bias value given the attention type
+        '''
+
+        if (attn_type == AttentionType.DECODER
+                or attn_type == AttentionType.ENCODER_ONLY):
+            return self.attn_bias
+        elif attn_type == AttentionType.ENCODER:
+            return self.encoder_attn_bias
+        elif attn_type == AttentionType.ENCODER_DECODER:
+            return self.cross_attn_bias
+        else:
+            raise AttributeError(f"Invalid attention type {str(attn_type)}")
+
+    def set_attn_bias(
+        self,
+        attn_bias: list[torch.Tensor],
+        attn_type: str,
+    ) -> None:
+        '''
+        Update appropriate attention bias field of attention metadata,
+        according to attention type.
+
+        Arguments:
+
+        * attn_metadata: Attention metadata structure associated with attention
+        * attn_bias: The desired attention bias value
+        * attn_type: encoder attention, decoder self-attention,
+                    encoder/decoder cross-attention
+        '''
+
+        if (attn_type == AttentionType.DECODER
+                or attn_type == AttentionType.ENCODER_ONLY):
+            self.attn_bias = attn_bias
+        elif attn_type == AttentionType.ENCODER:
+            self.encoder_attn_bias = attn_bias
+        elif attn_type == AttentionType.ENCODER_DECODER:
+            self.cross_attn_bias = attn_bias
+        else:
+            raise AttributeError(f"Invalid attention type {str(attn_type)}")
+
+    def get_seq_len_block_table_args(
+        self,
+        attn_type: str,
+    ) -> tuple:
+        '''
+        The particular choice of sequence-length- and block-table-related
+        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 &
+                                    cross-attn block-tables fields
+        Encoder attn -> select encoder sequence lengths fields & no block tables
+
+        Arguments:
+
+        * attn_metadata: Attention metadata structure associated with attention
+        * is_prompt: True if prefill, False otherwise
+        * attn_type: encoder attention, decoder self-attention,
+                    encoder/decoder cross-attention
+
+        Returns:
+
+        * Appropriate sequence-lengths tensor
+        * Appropriate max sequence-length scalar
+        * Appropriate block tables (or None)
+        '''
+
+        if (attn_type == AttentionType.DECODER
+                or attn_type == AttentionType.ENCODER_ONLY):
+            # Decoder self-attention
+            # Choose max_seq_len based on whether we are in prompt_run
+            return (self.seq_lens_tensor, self.max_decode_seq_len,
+                    self.block_tables)
+        elif attn_type == AttentionType.ENCODER_DECODER:
+            # Enc/dec cross-attention KVs match encoder sequence length;
+            # cross-attention utilizes special "cross" block tables
+            return (self.encoder_seq_lens_tensor, self.max_encoder_seq_len,
+                    self.cross_block_tables)
+        elif attn_type == AttentionType.ENCODER:
+            # No block tables associated with encoder attention
+            return (self.encoder_seq_lens_tensor, self.max_encoder_seq_len,
+                    None)
+        else:
+            raise AttributeError(f"Invalid attention type {str(attn_type)}")
+
+
+class TorchSDPAMetadataBuilderV1(AttentionMetadataBuilder[TorchSDPAMetadata]):
+
+    def __init__(self, kv_cache_spec: AttentionSpec, layer_names: list[str],
+                 vllm_config: VllmConfig, device: torch.device) -> None:
+        self.kv_cache_spec = kv_cache_spec
+        self.vllm_config = vllm_config
+        self.scheduler_config = vllm_config.scheduler_config
+
+        # For reorder
+        self.reorder_prompt_req_index_list = np.empty(
+            vllm_config.scheduler_config.max_num_seqs, dtype=np.int64)
+        self.reorder_decode_req_index_list = np.empty(
+            vllm_config.scheduler_config.max_num_seqs, dtype=np.int64)
+        self.num_prompt_req: int = 0
+
+        self.seq_start_loc_cpu = torch.zeros(
+            vllm_config.scheduler_config.max_num_seqs + 1,
+            dtype=torch.int32,
+            device="cpu",
+        )
+        self.seq_start_loc_np = self.seq_start_loc_cpu.numpy()
+
+    def reorder_batch(self, input_batch: InputBatch,
+                      scheduler_output: SchedulerOutput) -> bool:
+        prompt_list_idx = 0
+        decode_list_idx = 0
+        for req_index in range(input_batch.num_reqs):
+            if input_batch.num_computed_tokens_cpu[
+                    req_index] < input_batch.num_prompt_tokens[req_index]:
+                # prompt stage
+                self.reorder_prompt_req_index_list[prompt_list_idx] = req_index
+                prompt_list_idx += 1
+            else:
+                # decode stage
+                self.reorder_decode_req_index_list[decode_list_idx] = req_index
+                decode_list_idx += 1
+        assert decode_list_idx + prompt_list_idx == input_batch.num_reqs
+
+        # Update prompt requests number
+        self.num_prompt_req = prompt_list_idx
+
+        reorder_req_num = 0
+        for req_index in range(decode_list_idx):
+            if self.reorder_decode_req_index_list[req_index] < prompt_list_idx:
+                reorder_req_num += 1
+            else:
+                break
+
+        if reorder_req_num == 0:
+            return False
+
+        reorder_prompt_list = (
+            self.reorder_prompt_req_index_list[:prompt_list_idx]
+            [-reorder_req_num:])
+        reorder_decode_list = (
+            self.reorder_decode_req_index_list[:decode_list_idx]
+            [:reorder_req_num])
+        assert reorder_decode_list.size == reorder_prompt_list.size
+
+        for idx in range(reorder_req_num):
+            prompt_req_index = reorder_prompt_list[idx].item()
+            decode_req_index = reorder_decode_list[idx].item()
+            input_batch.swap_states(prompt_req_index, decode_req_index)
+
+        return True
+
+    def build(self,
+              common_prefix_len: int,
+              common_attn_metadata: CommonAttentionMetadata,
+              fast_build: bool = False) -> TorchSDPAMetadata:
+        num_reqs = common_attn_metadata.num_reqs
+        max_query_len = common_attn_metadata.max_query_len
+
+        seq_lens_cpu = common_attn_metadata.seq_lens_cpu
+        seq_lens_np = seq_lens_cpu.numpy()
+        num_prompt_req = self.num_prompt_req
+        max_prefill_seq_len = seq_lens_np[:num_prompt_req].max().item(
+        ) if num_prompt_req > 0 else 0
+        max_decode_seq_len = seq_lens_np[num_prompt_req:num_reqs].max().item(
+        ) if num_prompt_req < num_reqs else 0
+        self.seq_start_loc_np[0] = 0
+        np.cumsum(seq_lens_np, out=self.seq_start_loc_np[1:num_reqs + 1])
+
+        query_start_loc_cpu = common_attn_metadata.query_start_loc_cpu
+        num_prefill_tokens = int(query_start_loc_cpu[num_prompt_req].item())
+        num_decode_tokens = int(query_start_loc_cpu[num_reqs].item() -
+                                num_prefill_tokens)
+
+        slot_mapping = common_attn_metadata.slot_mapping.long()
+        block_table_tensor = common_attn_metadata.block_table_tensor
+
+        attn_metadata = TorchSDPAMetadata(
+            num_prefills=num_prompt_req,
+            num_prefill_tokens=num_prefill_tokens,
+            num_decode_tokens=num_decode_tokens,
+            slot_mapping=slot_mapping,
+            # to ensure inference when chunked_prefill is disabled
+            seq_lens=seq_lens_cpu.tolist(),
+            seq_lens_tensor=seq_lens_cpu[num_prompt_req:num_reqs],  # decode
+            max_decode_seq_len=max_decode_seq_len,  # decode
+            block_tables=block_table_tensor[num_prompt_req:num_reqs],  # decode
+            chunked_prefill=self.scheduler_config.chunked_prefill_enabled,
+            max_query_len=max_query_len,
+            max_kv_len=max_prefill_seq_len,
+            prefill_query_start_loc=query_start_loc_cpu[:num_prompt_req +
+                                                        1],  # prefill
+            kv_start_loc=self.seq_start_loc_cpu[:num_prompt_req +
+                                                1],  # prefill
+            prefill_block_tables=block_table_tensor[:
+                                                    num_prompt_req],  # prefill
+            query_start_loc=query_start_loc_cpu[:num_reqs +
+                                                1],  # for logits index
+            multi_modal_placeholder_index_maps=None,
+            enable_kv_scales_calculation=False,
+        )
+
+        return attn_metadata
+
+
+class TorchSDPABackendImpl(AttentionImpl[TorchSDPAMetadata]):
+
+    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] = None,
+        attn_type: str = AttentionType.DECODER,
+        kv_sharing_target_layer_name: Optional[str] = None,
+    ) -> None:
+        if kv_sharing_target_layer_name is not None:
+            raise NotImplementedError("KV sharing is not supported in V0.")
+        if logits_soft_cap is not None:
+            logger.warning_once("Torch SPDA does not support logits soft cap. "
+                                "Outputs may be slightly off.")
+        self.paged_attn_impl = _get_paged_attn_impl()
+        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
+        self.kv_cache_dtype = kv_cache_dtype
+
+        self.num_queries_per_kv = self.num_heads // self.num_kv_heads
+        self.need_mask = (self.alibi_slopes is not None
+                          or self.sliding_window is not None)
+
+        if is_quantized_kv_cache(kv_cache_dtype) and not _use_ipex:
+            raise NotImplementedError(
+                "Torch SDPA backend FP8 KV cache requires "
+                "intel_extension_for_pytorch support.")
+        self.attn_type = attn_type
+
+    def forward(
+        self,
+        layer: AttentionLayer,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        kv_cache: torch.Tensor,
+        attn_metadata: TorchSDPAMetadata,  # type: ignore
+        output: Optional[torch.Tensor] = None,
+        output_scale: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        """Forward pass with torch SDPA and PagedAttention.
+
+        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.
+        Returns:
+            shape = [num_tokens, num_heads * head_size]
+        """
+        if output_scale is not None:
+            raise NotImplementedError(
+                "fused output quantization is not yet supported"
+                " for TorchSDPABackendImpl")
+
+        # For warming-up
+        if attn_metadata is None:
+            return query
+
+        attn_type = self.attn_type
+        if (attn_type == AttentionType.ENCODER
+                and (not attn_metadata.is_all_encoder_attn_metadata_set)):
+            raise AttributeError("Encoder attention requires setting "
+                                 "encoder metadata attributes.")
+        elif (attn_type == AttentionType.ENCODER_DECODER
+              and (not attn_metadata.is_all_cross_attn_metadata_set)):
+            raise AttributeError("Encoder/decoder cross-attention "
+                                 "requires setting cross-attention "
+                                 "metadata attributes.")
+
+        # Reshape the query, key, and value tensors.
+        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
+
+        if (attn_type != AttentionType.ENCODER and kv_cache.numel() > 0):
+            # KV-cache during decoder-self- or
+            # encoder-decoder-cross-attention, but not
+            # during encoder attention.
+            #
+            # Even if there are no new key/value pairs to cache,
+            # we still need to break out key_cache and value_cache
+            # i.e. for later use by paged attention
+            key_cache, value_cache = self.paged_attn_impl.split_kv_cache(
+                kv_cache, self.num_kv_heads, self.head_size)
+
+            if (key is not None) and (value is not None):
+                if attn_type == AttentionType.ENCODER_DECODER:
+                    # Update cross-attention KV cache (prefill-only)
+                    # During cross-attention decode, key & value will be None,
+                    # preventing this IF-statement branch from running
+                    updated_slot_mapping = attn_metadata.cross_slot_mapping
+                else:
+                    # Update self-attention KV cache (prefill/decode)
+                    updated_slot_mapping = attn_metadata.slot_mapping
+
+                self.paged_attn_impl.write_to_paged_cache(
+                    key, value, key_cache, value_cache, updated_slot_mapping,
+                    self.kv_cache_dtype, layer._k_scale, layer._v_scale)
+
+        if attn_type != AttentionType.ENCODER:
+            # Decoder self-attention supports chunked prefill.
+            # Encoder/decoder cross-attention requires no chunked
+            # prefill (100% prefill or 100% decode tokens, no mix)
+            num_prefill_tokens = attn_metadata.num_prefill_tokens
+            num_decode_tokens = attn_metadata.num_decode_tokens
+        else:
+            # Encoder attention - chunked prefill is not applicable;
+            # derive token-count from query shape & and treat them
+            # as 100% prefill tokens
+            assert attn_metadata.num_encoder_tokens is not None
+            num_prefill_tokens = attn_metadata.num_encoder_tokens
+            num_decode_tokens = 0
+
+        if attn_type == AttentionType.DECODER:
+            # Only enforce this shape-constraint for decoder
+            # self-attention
+            assert key.shape[0] == num_prefill_tokens + num_decode_tokens
+            assert value.shape[0] == num_prefill_tokens + num_decode_tokens
+
+        output = torch.empty_like(query)
+        if prefill_meta := attn_metadata.prefill_metadata:
+            if not prefill_meta.prefill_metadata.chunked_prefill:  # type: ignore
+                assert attn_metadata.seq_lens is not None
+                self._run_sdpa_forward(output,
+                                       query,
+                                       key,
+                                       value,
+                                       prefill_meta,
+                                       attn_type=attn_type)
+            else:
+                # prefix-enabled attention
+                assert not self.need_mask
+                import intel_extension_for_pytorch.llm.modules as ipex_modules
+                output = torch.empty_like(query)
+                ipex_modules.PagedAttention.flash_attn_varlen_func(
+                    output[:prefill_meta.num_prefill_tokens, :, :],
+                    query[:prefill_meta.num_prefill_tokens, :, :],
+                    key_cache,
+                    value_cache,
+                    prefill_meta.prefill_query_start_loc,
+                    prefill_meta.kv_start_loc,
+                    prefill_meta.max_query_len,
+                    prefill_meta.max_kv_len,
+                    self.scale,
+                    True,
+                    prefill_meta.prefill_block_tables,
+                    self.alibi_slopes,
+                )
+
+        if decode_meta := attn_metadata.decode_metadata:
+            assert attn_type != AttentionType.ENCODER_ONLY, (
+                "Encoder-only models should not have decode metadata.")
+            # Decoding run.
+            (
+                seq_lens_arg,
+                max_seq_len_arg,
+                block_tables_arg,
+            ) = decode_meta.get_seq_len_block_table_args(attn_type)
+
+            self.paged_attn_impl.forward_decode(
+                output[attn_metadata.num_prefill_tokens:, :, :],
+                query[attn_metadata.num_prefill_tokens:, :, :],
+                key_cache,
+                value_cache,
+                block_tables_arg,
+                seq_lens_arg,
+                max_seq_len_arg,
+                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 _run_sdpa_forward(
+        self,
+        output: torch.Tensor,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        attn_metadata: TorchSDPAMetadata,
+        attn_type: str = AttentionType.DECODER,
+    ) -> None:
+        if self.num_kv_heads != self.num_heads:
+            key = key.repeat_interleave(self.num_queries_per_kv, dim=1)
+            value = value.repeat_interleave(self.num_queries_per_kv, dim=1)
+
+        attn_masks = attn_metadata.get_attn_bias(attn_type)
+        if attn_masks is None:
+            if self.alibi_slopes is not None:
+                attn_masks = _make_alibi_bias(
+                    self.alibi_slopes, query.dtype,
+                    attn_metadata.seq_lens)  # type: ignore
+            elif self.sliding_window is not None:
+                assert attn_metadata.seq_lens is not None
+                attn_masks = _make_sliding_window_bias(
+                    attn_metadata.seq_lens, self.sliding_window,
+                    query.dtype)  # type: ignore
+            else:
+                seq_lens, _ = attn_metadata.get_seq_lens(attn_type)
+                attn_masks = [None] * len(seq_lens)
+            attn_metadata.set_attn_bias(attn_masks, attn_type)
+
+        query = query.movedim(0, query.dim() - 2)
+        key = key.movedim(0, key.dim() - 2)
+        value = value.movedim(0, value.dim() - 2)
+
+        causal_attn = (attn_type == AttentionType.DECODER)
+
+        seq_lens_q, seq_lens_kv = attn_metadata.get_seq_lens(attn_type)
+        start_q, start_kv = 0, 0
+        for seq_len_q, seq_len_kv, mask in zip(seq_lens_q, seq_lens_kv,
+                                               attn_masks):
+            end_q = start_q + seq_len_q
+            end_kv = start_kv + seq_len_kv
+            sub_out = scaled_dot_product_attention(
+                query[None, :, start_q:end_q, :],
+                key[None, :, start_kv:end_kv, :],
+                value[None, :, start_kv:end_kv, :],
+                attn_mask=mask,
+                dropout_p=0.0,
+                is_causal=causal_attn and mask is None,
+                scale=self.scale).squeeze(0).movedim(query.dim() - 2, 0)
+            output[start_q:end_q, :, :] = sub_out
+            start_q, start_kv = end_q, end_kv
+
+
+def _make_alibi_bias(
+    alibi_slopes: torch.Tensor,
+    dtype: torch.dtype,
+    seq_lens: list[int],
+) -> list[torch.Tensor]:
+    attn_biases: list[torch.Tensor] = []
+    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))
+        bias.mul_(alibi_slopes[:, None, None]).unsqueeze_(0)
+        inf_mask = torch.empty(
+            (1, seq_len, seq_len),
+            dtype=bias.dtype).fill_(-torch.inf).triu_(diagonal=1)
+        attn_biases.append((bias + inf_mask).to(dtype))
+
+    return attn_biases
+
+
+def _make_sliding_window_bias(
+    seq_lens: list[int],
+    window_size: Optional[int],
+    dtype: torch.dtype,
+) -> list[torch.Tensor]:
+    attn_biases: list[torch.Tensor] = []
+    for seq_len in seq_lens:
+        tensor = torch.full(
+            (1, seq_len, seq_len),
+            dtype=dtype,
+            fill_value=1,
+        )
+        shift = 0
+        mask = torch.tril(tensor, diagonal=shift).to(dtype)  # type: ignore
+        if window_size is not None:
+            mask = torch.triu(mask, diagonal=shift - window_size + 1)
+        mask = torch.log(mask)
+        attn_biases.append(mask.to(dtype))
+
+    return attn_biases
+
+
+class _PagedAttention:
+
+    @staticmethod
+    def validate_head_size(head_size: int) -> tuple[bool, list[int]]:
+        SUPPORT_HS = [32, 64, 80, 96, 112, 128, 192, 256]
+        return head_size in SUPPORT_HS, SUPPORT_HS
+
+    @staticmethod
+    def get_kv_cache_shape(
+        num_blocks: int,
+        block_size: int,
+        num_kv_heads: int,
+        head_size: int,
+        *args,
+    ) -> tuple[int, ...]:
+        return 2, num_blocks, block_size * num_kv_heads * head_size
+
+    @staticmethod
+    def split_kv_cache(
+        kv_cache: torch.Tensor,
+        num_kv_heads: int,
+        head_size: int,
+        *args,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        x = 16 // kv_cache.element_size()
+        num_blocks = kv_cache.shape[1]
+
+        key_cache = kv_cache[0]
+        key_cache = key_cache.view(num_blocks, num_kv_heads, head_size // x,
+                                   -1, x)
+        value_cache = kv_cache[1]
+        value_cache = value_cache.view(num_blocks, num_kv_heads, head_size, -1)
+        return key_cache, value_cache
+
+    @staticmethod
+    def write_to_paged_cache(
+        key: torch.Tensor,
+        value: torch.Tensor,
+        key_cache: torch.Tensor,
+        value_cache: torch.Tensor,
+        slot_mapping: torch.Tensor,
+        kv_cache_dtype: str,
+        k_scale: torch.Tensor,
+        v_scale: torch.Tensor,
+        *args,
+    ) -> None:
+        ops.reshape_and_cache(
+            key,
+            value,
+            key_cache,
+            value_cache,
+            slot_mapping.flatten(),
+            kv_cache_dtype,
+            k_scale,
+            v_scale,
+        )
+
+    @staticmethod
+    def forward_decode(
+        output: torch.Tensor,
+        query: torch.Tensor,
+        key_cache: torch.Tensor,
+        value_cache: torch.Tensor,
+        block_tables: torch.Tensor,
+        context_lens: torch.Tensor,
+        max_context_len: int,
+        kv_cache_dtype: str,
+        num_kv_heads: int,
+        scale: float,
+        alibi_slopes: Optional[torch.Tensor],
+        k_scale: torch.Tensor,
+        v_scale: torch.Tensor,
+        *args,
+    ) -> None:
+        tp_rank: int = 0
+        blocksparse_local_blocks: int = 0
+        blocksparse_vert_stride: int = 0
+        blocksparse_block_size: int = 64
+        blocksparse_head_sliding_step: int = 0
+        block_size = value_cache.shape[3]
+
+        ops.paged_attention_v1(
+            output,
+            query,
+            key_cache,
+            value_cache,
+            num_kv_heads,
+            scale,
+            block_tables,
+            context_lens,
+            block_size,
+            max_context_len,
+            alibi_slopes,
+            kv_cache_dtype,
+            k_scale,
+            v_scale,
+            tp_rank,
+            blocksparse_local_blocks,
+            blocksparse_vert_stride,
+            blocksparse_block_size,
+            blocksparse_head_sliding_step,
+        )
+
+    @staticmethod
+    def copy_blocks(
+        kv_caches: list[torch.Tensor],
+        src_to_dists: torch.Tensor,
+        *args,
+    ) -> None:
+        key_caches = [kv_cache[0] for kv_cache in kv_caches]
+        value_caches = [kv_cache[1] for kv_cache in kv_caches]
+        ops.copy_blocks(key_caches, value_caches, src_to_dists)
+
+
+class _IPEXPagedAttention(_PagedAttention):
+
+    @staticmethod
+    def validate_head_size(head_size: int) -> tuple[bool, list[int]]:
+        return True, []
+
+    @staticmethod
+    def split_kv_cache(
+        kv_cache: torch.Tensor,
+        num_kv_heads: int,
+        head_size: int,
+        *args,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        num_blocks = kv_cache.shape[1]
+
+        key_cache = kv_cache[0]
+        key_cache = key_cache.view(num_blocks, num_kv_heads, -1, head_size)
+        value_cache = kv_cache[1]
+        value_cache = value_cache.view(num_blocks, num_kv_heads, -1, head_size)
+        return key_cache, value_cache
+
+    @staticmethod
+    def write_to_paged_cache(
+        key: torch.Tensor,
+        value: torch.Tensor,
+        key_cache: torch.Tensor,
+        value_cache: torch.Tensor,
+        slot_mapping: torch.Tensor,
+        kv_cache_dtype: str,
+        k_scale: torch.Tensor,
+        v_scale: torch.Tensor,
+        *args,
+    ) -> None:
+        ipex_modules.PagedAttention.reshape_and_cache(
+            key, value, key_cache, value_cache,
+            slot_mapping.flatten().int())
+
+    @staticmethod
+    def forward_decode(
+        output: torch.Tensor,
+        query: torch.Tensor,
+        key_cache: torch.Tensor,
+        value_cache: torch.Tensor,
+        block_tables: torch.Tensor,
+        context_lens: torch.Tensor,
+        max_context_len: int,
+        kv_cache_dtype: str,
+        num_kv_heads: int,
+        scale: float,
+        alibi_slopes: Optional[torch.Tensor],
+        k_scale: torch.Tensor,
+        v_scale: torch.Tensor,
+        *args,
+    ) -> None:
+        block_size = value_cache.shape[2]
+        head_mapping = torch.arange(
+            0,
+            num_kv_heads,
+            device="cpu",
+            dtype=torch.int32,
+        ).view(num_kv_heads,
+               1).repeat_interleave(query.size(1) // num_kv_heads).flatten()
+        ipex_modules.PagedAttention.single_query_cached_kv_attention(
+            output, query.contiguous(), key_cache, value_cache, head_mapping,
+            scale, block_tables, context_lens, block_size, max_context_len,
+            alibi_slopes)
+
+
+def _get_paged_attn_impl():
+    if _use_ipex:
+        return _IPEXPagedAttention
+    else:
+        return _PagedAttention

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/attention/backends/flash_attn.py

@@ -0,0 +1,802 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Attention layer with FlashAttention."""
+from dataclasses import dataclass
+from typing import Optional
+
+import numpy as np
+import torch
+
+from vllm import _custom_ops as ops
+from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
+                                              AttentionMetadata, AttentionType,
+                                              is_quantized_kv_cache)
+from vllm.attention.layer import Attention
+from vllm.attention.ops.merge_attn_states import merge_attn_states
+from vllm.attention.utils.fa_utils import (flash_attn_supports_fp8,
+                                           get_flash_attn_version,
+                                           is_flash_attn_varlen_func_available)
+
+if is_flash_attn_varlen_func_available():
+    from vllm.attention.utils.fa_utils import (flash_attn_varlen_func,
+                                               get_scheduler_metadata,
+                                               reshape_and_cache_flash)
+
+from vllm.config import VllmConfig, get_layers_from_vllm_config
+from vllm.logger import init_logger
+from vllm.utils import cdiv
+from vllm.v1.attention.backends.utils import (AttentionCGSupport,
+                                              AttentionMetadataBuilder,
+                                              CommonAttentionMetadata,
+                                              get_kv_cache_layout)
+from vllm.v1.kv_cache_interface import AttentionSpec
+
+logger = init_logger(__name__)
+
+# NOTE(woosuk): This is an arbitrary number. Tune it if needed.
+_DEFAULT_MAX_NUM_SPLITS_FOR_CUDA_GRAPH = 16
+
+
+class FlashAttentionBackend(AttentionBackend):
+
+    accept_output_buffer: bool = True
+
+    @classmethod
+    def get_supported_dtypes(cls) -> list[torch.dtype]:
+        return [torch.float16, torch.bfloat16]
+
+    @classmethod
+    def get_supported_head_sizes(cls) -> list[int]:
+        return [32, 64, 96, 128, 160, 192, 224, 256]
+
+    @classmethod
+    def validate_head_size(cls, head_size: int) -> None:
+        supported_head_sizes = cls.get_supported_head_sizes()
+        if head_size not in supported_head_sizes:
+            attn_type = cls.__name__.removesuffix("Backend")
+            raise ValueError(
+                f"Head size {head_size} is not supported by {attn_type}. "
+                f"Supported head sizes are: {supported_head_sizes}. "
+                "Set VLLM_ATTENTION_BACKEND=FLEX_ATTENTION to use "
+                "FlexAttention backend which supports all head sizes.")
+
+    @staticmethod
+    def get_name() -> str:
+        return "FLASH_ATTN_VLLM_V1"
+
+    @staticmethod
+    def get_impl_cls() -> type["FlashAttentionImpl"]:
+        return FlashAttentionImpl
+
+    @staticmethod
+    def get_metadata_cls() -> type["AttentionMetadata"]:
+        return FlashAttentionMetadata
+
+    @staticmethod
+    def get_builder_cls() -> type["FlashAttentionMetadataBuilder"]:
+        return FlashAttentionMetadataBuilder
+
+    @staticmethod
+    def get_kv_cache_shape(
+        num_blocks: int,
+        block_size: int,
+        num_kv_heads: int,
+        head_size: int,
+    ) -> 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 get_kv_cache_stride_order() -> 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":
+            stride_order = (0, 1, 2, 3, 4)
+        elif cache_layout == "HND":
+            stride_order = (0, 1, 3, 2, 4)
+        else:
+            raise ValueError(f"Unknown cache layout format {cache_layout}.")
+        return stride_order
+
+    @staticmethod
+    def get_fp8_dtype_for_flashattn(kv_cache_dtype: str) -> torch.dtype:
+        if kv_cache_dtype in ("fp8", "fp8_e4m3"):
+            return torch.float8_e4m3fn
+        else:
+            raise ValueError(f"Unrecognized FP8 dtype: {kv_cache_dtype}")
+
+
+@dataclass
+class FlashAttentionMetadata:
+    # NOTE(sang): Definition of context_len, query_len, and seq_len.
+    # |---------- N-1 iteration --------|
+    # |---------------- N iteration ---------------------|
+    # |- tokenA -|......................|-- newTokens ---|
+    # |---------- context_len ----------|
+    # |-------------------- seq_len ---------------------|
+    #                                   |-- query_len ---|
+
+    num_actual_tokens: int  # Number of tokens excluding padding.
+    max_query_len: int
+    query_start_loc: torch.Tensor
+    max_seq_len: int
+    seq_lens: torch.Tensor
+    block_table: torch.Tensor
+    slot_mapping: torch.Tensor
+
+    # For cascade attention.
+    use_cascade: bool
+    common_prefix_len: int
+    cu_prefix_query_lens: Optional[torch.Tensor]
+    prefix_kv_lens: Optional[torch.Tensor]
+    suffix_kv_lens: Optional[torch.Tensor]
+
+    # Optional aot scheduling
+    scheduler_metadata: Optional[torch.Tensor] = None
+    prefix_scheduler_metadata: Optional[torch.Tensor] = None
+    max_num_splits: int = 0
+
+    causal: bool = True
+
+
+def _get_sliding_window_configs(
+        vllm_config: VllmConfig) -> set[Optional[tuple[int, int]]]:
+    """Get the set of all sliding window configs used in the model."""
+    sliding_window_configs: set[Optional[tuple[int, int]]] = set()
+    layers = get_layers_from_vllm_config(vllm_config, Attention)
+    for layer in layers.values():
+        assert isinstance(layer.impl, FlashAttentionImpl)
+        sliding_window_configs.add(layer.impl.sliding_window)
+    return sliding_window_configs
+
+
+class FlashAttentionMetadataBuilder(
+        AttentionMetadataBuilder[FlashAttentionMetadata]):
+    # FA3:
+    # Supports full cudagraphs for all cases.
+    #
+    # FA2:
+    # For FA2, a graph is captured with max_query_len=1, (which is what we
+    # capture by default for num_tokens <= max_num_seqs when there is no
+    # spec-decode) then these graphs will not work for mixed prefill-decode
+    # (unlike FA3). This is due to special max_query_len=1 packed-GQA handling
+    # in FA2.
+    # In summary if we are running with spec decodes the graphs would
+    # work for mixed prefill-decode and uniform-decode. But for non-spec decodes
+    # the graphs would not work for mixed prefill-decode; sorta the inverse
+    # of UNIFORM_SINGLE_TOKEN_DECODE.
+    # Theres probably a better way to describe this using `AttentionCGSupport`
+    # but for now just set it to `UNIFORM_BATCH` to get use to drop down
+    # to FULL_AND_PIECEWISE.
+    # TODO(luka, lucas): audit FA2 as part of:
+    #  https://github.com/vllm-project/vllm/issues/22945
+    cudagraph_support = AttentionCGSupport.ALWAYS \
+        if get_flash_attn_version() == 3 else AttentionCGSupport.UNIFORM_BATCH
+
+    def __init__(self, kv_cache_spec: AttentionSpec, layer_names: list[str],
+                 vllm_config: VllmConfig, device: torch.device):
+        self.vllm_config = vllm_config
+        self.model_config = vllm_config.model_config
+        self.parallel_config = vllm_config.parallel_config
+        self.cache_config = vllm_config.cache_config
+        self.compilation_config = vllm_config.compilation_config
+        self.device = device
+
+        self.num_heads_q = self.model_config.get_num_attention_heads(
+            self.parallel_config)
+        self.num_heads_kv = self.model_config.get_num_kv_heads(
+            self.parallel_config)
+        self.kv_cache_dtype = kv_cache_spec.dtype
+        self.headdim = self.model_config.get_head_size()
+        self.block_size = kv_cache_spec.block_size
+
+        self.max_num_splits = 0  # No upper bound on the number of splits.
+        self.aot_schedule = (get_flash_attn_version() == 3)
+
+        self.use_full_cuda_graph = \
+            self.compilation_config.cudagraph_mode.has_full_cudagraphs()
+
+        if self.use_full_cuda_graph and self.aot_schedule:
+            self.max_cudagraph_size = self.compilation_config.max_capture_size
+
+            if self.max_cudagraph_size > 992:
+                # This condition derives from FA3's internal heuristic.
+                # TODO(woosuk): Support larger cudagraph sizes.
+                raise ValueError(
+                    "Capture size larger than 992 is not supported for "
+                    "full cuda graph.")
+
+            self.scheduler_metadata = torch.zeros(
+                vllm_config.scheduler_config.max_num_seqs + 1,
+                dtype=torch.int32,
+                device=self.device,
+            )
+            # When using cuda graph, we need to set the upper bound of the
+            # number of splits so that large enough intermediate buffers are
+            # pre-allocated during capture.
+            self.max_num_splits = _DEFAULT_MAX_NUM_SPLITS_FOR_CUDA_GRAPH
+
+        # Sliding window size to be used with the AOT scheduler will be
+        # populated on first build() call.
+        self.aot_sliding_window: Optional[tuple[int, int]] = None
+
+    def build(self,
+              common_prefix_len: int,
+              common_attn_metadata: CommonAttentionMetadata,
+              fast_build: bool = False) -> FlashAttentionMetadata:
+        """
+        fast_build disables AOT scheduling, used when there will be few 
+        iterations i.e. spec-decode
+        """
+        num_reqs = common_attn_metadata.num_reqs
+        num_actual_tokens = common_attn_metadata.num_actual_tokens
+        max_query_len = common_attn_metadata.max_query_len
+        max_seq_len = int(common_attn_metadata.seq_lens_cpu.max())
+        query_start_loc = common_attn_metadata.query_start_loc
+        seq_lens = common_attn_metadata.seq_lens
+        seq_lens_cpu = common_attn_metadata.seq_lens_cpu
+        block_table_tensor = common_attn_metadata.block_table_tensor
+        slot_mapping = common_attn_metadata.slot_mapping
+        causal = common_attn_metadata.causal
+
+        # the overhead of the aot schedule is not worth it for spec-decode
+        aot_schedule = self.aot_schedule and not fast_build
+
+        if self.aot_sliding_window is None:
+            self.aot_sliding_window = (-1, -1)
+            # For the AOT scheduler we need the sliding window value to be
+            # constant for all layers to. We have to populate this on the first
+            # build() call so the layers are constructed (cannot populate)
+            # in __init__.
+            if aot_schedule:
+                sliding_window_configs = _get_sliding_window_configs(
+                    self.vllm_config)
+                if len(sliding_window_configs) == 1:
+                    sliding_window_config = sliding_window_configs.pop()
+                    if sliding_window_config is not None:
+                        self.aot_sliding_window = sliding_window_config
+                elif len(sliding_window_configs) > 1:
+                    self.aot_schedule = False
+                    aot_schedule = False
+
+        def schedule(batch_size, cu_query_lens, max_query_len, seqlens,
+                     max_seq_len, causal):
+            cache_dtype = self.cache_config.cache_dtype
+            if cache_dtype.startswith("fp8"):
+                qkv_dtype = FlashAttentionBackend.get_fp8_dtype_for_flashattn(
+                    cache_dtype)
+            else:
+                qkv_dtype = self.kv_cache_dtype
+            if aot_schedule:
+                return get_scheduler_metadata(
+                    batch_size=batch_size,
+                    max_seqlen_q=max_query_len,
+                    max_seqlen_k=max_seq_len,
+                    num_heads_q=self.num_heads_q,
+                    num_heads_kv=self.num_heads_kv,
+                    headdim=self.headdim,
+                    cache_seqlens=seqlens,
+                    qkv_dtype=qkv_dtype,
+                    cu_seqlens_q=cu_query_lens,
+                    page_size=self.block_size,
+                    causal=causal,
+                    window_size=self.aot_sliding_window,
+                    num_splits=self.max_num_splits,
+                )
+            return None
+
+        use_cascade = common_prefix_len > 0
+
+        if use_cascade:
+            cu_prefix_query_lens = torch.tensor([0, num_actual_tokens],
+                                                dtype=torch.int32,
+                                                device=self.device)
+            prefix_kv_lens = torch.tensor([common_prefix_len],
+                                          dtype=torch.int32,
+                                          device=self.device)
+            suffix_kv_lens = (seq_lens_cpu[:num_reqs] - common_prefix_len).to(
+                self.device, non_blocking=True)
+            prefix_scheduler_metadata = schedule(
+                batch_size=1,
+                cu_query_lens=cu_prefix_query_lens,
+                max_query_len=num_actual_tokens,
+                seqlens=prefix_kv_lens,
+                max_seq_len=common_prefix_len,
+                causal=False)
+            scheduler_metadata = schedule(batch_size=num_reqs,
+                                          cu_query_lens=query_start_loc,
+                                          max_query_len=max_query_len,
+                                          seqlens=suffix_kv_lens,
+                                          max_seq_len=max_seq_len -
+                                          common_prefix_len,
+                                          causal=True)
+        else:
+            cu_prefix_query_lens = None
+            prefix_kv_lens = None
+            suffix_kv_lens = None
+            prefix_scheduler_metadata = None
+            scheduler_metadata = schedule(batch_size=num_reqs,
+                                          cu_query_lens=query_start_loc,
+                                          max_query_len=max_query_len,
+                                          seqlens=seq_lens,
+                                          max_seq_len=max_seq_len,
+                                          causal=causal)
+        # For FA3 + full cudagraph
+        max_num_splits = 0
+        if self.use_full_cuda_graph and scheduler_metadata is not None:
+            n = scheduler_metadata.shape[0]
+            self.scheduler_metadata[:n] = scheduler_metadata
+            # NOTE(woosuk): We should zero out the rest of the scheduler
+            # metadata to guarantee the correctness. Otherwise, some thread
+            # blocks may use the invalid scheduler metadata and overwrite the
+            # output buffer.
+            self.scheduler_metadata[n:] = 0
+            scheduler_metadata = self.scheduler_metadata[:n]
+
+            if num_actual_tokens <= self.max_cudagraph_size:
+                # NOTE(woosuk): Setting num_splits > 1 may increase the memory
+                # usage, because the intermediate buffers of size [num_splits,
+                # num_heads, num_tokens, head_size] are allocated. Therefore,
+                # we only set num_splits when using cuda graphs.
+                max_num_splits = self.max_num_splits
+
+        attn_metadata = FlashAttentionMetadata(
+            num_actual_tokens=num_actual_tokens,
+            max_query_len=max_query_len,
+            query_start_loc=query_start_loc,
+            max_seq_len=max_seq_len,
+            seq_lens=seq_lens,
+            block_table=block_table_tensor,
+            slot_mapping=slot_mapping,
+            use_cascade=use_cascade,
+            common_prefix_len=common_prefix_len,
+            scheduler_metadata=scheduler_metadata,
+            cu_prefix_query_lens=cu_prefix_query_lens,
+            prefix_kv_lens=prefix_kv_lens,
+            suffix_kv_lens=suffix_kv_lens,
+            prefix_scheduler_metadata=prefix_scheduler_metadata,
+            max_num_splits=max_num_splits,
+            causal=causal)
+        return attn_metadata
+
+    def use_cascade_attention(self, *args, **kwargs) -> bool:
+        return use_cascade_attention(*args, **kwargs)
+
+
+class FlashAttentionImpl(AttentionImpl):
+
+    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] = None,
+        attn_type: AttentionType = AttentionType.DECODER,
+        kv_sharing_target_layer_name: Optional[str] = None,
+        sinks: Optional[torch.Tensor] = None,
+    ) -> 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 == 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
+
+        FlashAttentionBackend.validate_head_size(head_size)
+
+        if attn_type not in [
+                AttentionType.DECODER, AttentionType.ENCODER_ONLY
+        ]:
+            raise NotImplementedError("Encoder/decoder cross-attention "
+                                      "is not implemented for "
+                                      "FlashAttentionImpl")
+
+        self.attn_type = attn_type
+        self.vllm_flash_attn_version = get_flash_attn_version()
+        if is_quantized_kv_cache(self.kv_cache_dtype) \
+            and not flash_attn_supports_fp8():
+            raise NotImplementedError(
+                "FlashAttention does not support fp8 kv-cache on this device.")
+
+        self.sinks = sinks
+        if self.sinks is not None:
+            assert self.vllm_flash_attn_version == 3, (
+                "Sinks are only supported in FlashAttention 3")
+            assert self.sinks.shape[0] == num_heads, (
+                "Sinks must have the same number of heads as the number of "
+                "heads in the layer")
+
+    def forward(
+        self,
+        layer: torch.nn.Module,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        kv_cache: torch.Tensor,
+        attn_metadata: FlashAttentionMetadata,
+        output: Optional[torch.Tensor] = None,
+        output_scale: Optional[torch.Tensor] = 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 = [2, num_blocks, block_size, num_kv_heads, head_size]
+            attn_metadata: Metadata for attention.
+        Returns:
+            shape = [num_tokens, num_heads * head_size]
+        NOTE: FP8 quantization, flash-attn expect the size of
+              {q,k,v}_descale to be (num_sequences, num_kv_heads).
+              We use torch's .expand() to avoid duplicating values
+        """
+        assert output is not None, "Output tensor must be provided."
+
+        if output_scale is not None:
+            raise NotImplementedError(
+                "fused output quantization is not yet supported"
+                " for FlashAttentionImpl")
+
+        if attn_metadata is None:
+            # Profiling run.
+            return output
+
+        attn_type = self.attn_type
+
+        # 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 attn_type in (AttentionType.ENCODER_ONLY, ):
+            # 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)
+
+        # For decoder and cross-attention, use KV cache as before
+        key_cache, value_cache = kv_cache.unbind(0)
+
+        if self.kv_sharing_target_layer_name is None:
+            # Reshape the input keys and values and store them in the cache.
+            # Skip this if sharing KV cache with an earlier attention layer.
+            # NOTE(woosuk): Here, key and value are padded while slot_mapping is
+            # not padded. However, we don't need to do key[:num_actual_tokens]
+            # and value[:num_actual_tokens] because the reshape_and_cache_flash
+            # op uses the slot_mapping's shape to determine the number of
+            # actual tokens.
+            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"):
+            dtype = FlashAttentionBackend.get_fp8_dtype_for_flashattn(
+                self.kv_cache_dtype)
+            key_cache = key_cache.view(dtype)
+            value_cache = value_cache.view(dtype)
+            num_tokens, num_heads, head_size = query.shape
+            query, _ = ops.scaled_fp8_quant(
+                query.reshape(
+                    (num_tokens, num_heads * head_size)).contiguous(),
+                layer._q_scale)
+            query = query.reshape((num_tokens, num_heads, head_size))
+
+        if not attn_metadata.use_cascade:
+            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
+            scheduler_metadata = attn_metadata.scheduler_metadata
+
+            descale_shape = (cu_seqlens_q.shape[0] - 1, key.shape[1])
+
+            flash_attn_varlen_func(
+                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=attn_metadata.causal,
+                alibi_slopes=self.alibi_slopes,
+                window_size=self.sliding_window,
+                block_table=block_table,
+                softcap=self.logits_soft_cap,
+                scheduler_metadata=scheduler_metadata,
+                fa_version=self.vllm_flash_attn_version,
+                q_descale=layer._q_scale.expand(descale_shape),
+                k_descale=layer._k_scale.expand(descale_shape),
+                v_descale=layer._v_scale.expand(descale_shape),
+                num_splits=attn_metadata.max_num_splits,
+                s_aux=self.sinks,
+            )
+            return output
+
+        # Cascade attention (rare case).
+        cascade_attention(
+            output[:num_actual_tokens],
+            query[:num_actual_tokens],
+            key_cache,
+            value_cache,
+            cu_query_lens=attn_metadata.query_start_loc,
+            max_query_len=attn_metadata.max_query_len,
+            cu_prefix_query_lens=attn_metadata.cu_prefix_query_lens,
+            prefix_kv_lens=attn_metadata.prefix_kv_lens,
+            suffix_kv_lens=attn_metadata.suffix_kv_lens,
+            max_kv_len=attn_metadata.max_seq_len,
+            softmax_scale=self.scale,
+            alibi_slopes=self.alibi_slopes,
+            sliding_window=self.sliding_window,
+            logits_soft_cap=self.logits_soft_cap,
+            block_table=attn_metadata.block_table,
+            common_prefix_len=attn_metadata.common_prefix_len,
+            fa_version=self.vllm_flash_attn_version,
+            prefix_scheduler_metadata=attn_metadata.prefix_scheduler_metadata,
+            suffix_scheduler_metadata=attn_metadata.scheduler_metadata,
+            q_descale=layer._q_scale,
+            k_descale=layer._k_scale,
+            v_descale=layer._v_scale,
+        )
+        return output
+
+    def _forward_encoder_attention(
+        self,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        output: torch.Tensor,
+        attn_metadata: FlashAttentionMetadata,
+        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
+        """
+        # For encoder attention, process FP8 quantization if needed
+        if self.kv_cache_dtype.startswith("fp8"):
+            raise NotImplementedError(
+                "quantization is not supported for encoder attention")
+
+        # Use encoder-specific metadata for sequence information
+        cu_seqlens_q = attn_metadata.query_start_loc
+        cu_seqlens_k = attn_metadata.query_start_loc
+        max_seqlen_q = attn_metadata.max_query_len
+        max_seqlen_k = attn_metadata.max_query_len
+
+        descale_shape = (
+            cu_seqlens_q.shape[0] - 1,  # type: ignore[union-attr]
+            self.num_kv_heads)
+
+        # Call flash attention directly on Q, K, V tensors
+        flash_attn_varlen_func(
+            q=query,
+            k=key,
+            v=value,
+            out=output,
+            cu_seqlens_q=cu_seqlens_q,
+            cu_seqlens_k=cu_seqlens_k,
+            max_seqlen_q=max_seqlen_q,
+            max_seqlen_k=max_seqlen_k,
+            softmax_scale=self.scale,
+            causal=False,  # Encoder attention is bidirectional
+            alibi_slopes=self.alibi_slopes,
+            window_size=self.sliding_window,
+            softcap=self.logits_soft_cap,
+            fa_version=self.vllm_flash_attn_version,
+            q_descale=layer._q_scale.expand(descale_shape),
+            k_descale=layer._k_scale.expand(descale_shape),
+            v_descale=layer._v_scale.expand(descale_shape),
+        )
+
+        return output
+
+
+def use_cascade_attention(
+    common_prefix_len: int,
+    query_lens: np.ndarray,
+    num_query_heads: int,
+    num_kv_heads: int,
+    use_alibi: bool,
+    use_sliding_window: bool,
+    use_local_attention: bool,
+    num_sms: int,
+) -> bool:
+    """Decide whether to use cascade attention.
+
+    This function 1) checks whether cascade attention is supported with the
+    given configuration, and 2) heuristically decides whether using cascade
+    attention can improve performance.
+    """
+    # Too short common prefix. Probably not worth using cascade attention.
+    # We use an arbitrary threshold of 256 tokens. TODO: Tune this threshold.
+    # NOTE(woosuk): This is the common case. We should return False as soon as
+    # possible to avoid any unnecessary computation.
+    if common_prefix_len < 256:
+        return False
+    # Cascade attention is currently not supported with these variants.
+    if use_alibi or use_sliding_window or use_local_attention:
+        return False
+    # Too few queries. Probably not worth using cascade attention.
+    # We use an arbitrary threshold of 8 queries. TODO: Tune this threshold.
+    num_reqs = len(query_lens)
+    if num_reqs < 8:
+        return False
+
+    # Heuristics to decide whether using cascade attention is beneficial.
+    # 1. When FlashDecoding is not used for normal attention, cascade attention
+    #    is likely to be faster since it saves memory bandwidth.
+    num_queries_per_kv = num_query_heads // num_kv_heads
+    # The criteria for using FlashDecoding can be found in the following link:
+    # https://github.com/vllm-project/flash-attention/blob/96266b1111111f3d11aabefaf3bacbab6a89d03c/csrc/flash_attn/flash_api.cpp#L535
+    use_flash_decoding = (num_queries_per_kv > 1 and not use_sliding_window
+                          and not use_alibi and np.all(query_lens == 1))
+    if not use_flash_decoding:
+        # Use cascade attention.
+        return True
+
+    # 2. When FlashDecoding is used for normal attention, it is not clear
+    #    whether cascade attention is beneficial, because FlashDecoding can
+    #    launch more CTAs than cascade attention.
+    #    We use a simple performance model to compare the two methods.
+    #    NOTE(woosuk): The performance model is very rough and may not be
+    #    accurate.
+    num_tokens = num_reqs
+    # NOTE(woosuk): These are default tile sizes. flash-attn might use
+    # different tile sizes (e.g., 64 or 256) depending on the configuration.
+    q_tile_size = 128
+    kv_tile_size = 128
+    num_prefix_tiles = cdiv(common_prefix_len, kv_tile_size)
+
+    cascade_ctas = num_query_heads * cdiv(num_tokens, q_tile_size)
+    cascade_waves = cdiv(cascade_ctas, num_sms)
+    cascade_time = cascade_waves * num_prefix_tiles
+
+    flash_decoding_ctas = (num_reqs * num_kv_heads *
+                           cdiv(num_queries_per_kv, q_tile_size))
+    flash_decoding_ctas *= num_prefix_tiles
+    flash_decoding_time = cdiv(flash_decoding_ctas, num_sms)
+
+    # Use cascade attention if it is faster than FlashDecoding.
+    return cascade_time < flash_decoding_time
+
+
+def cascade_attention(
+    output: torch.Tensor,
+    query: torch.Tensor,
+    key_cache: torch.Tensor,
+    value_cache: torch.Tensor,
+    cu_query_lens: torch.Tensor,
+    max_query_len: int,
+    cu_prefix_query_lens: torch.Tensor,
+    prefix_kv_lens: torch.Tensor,
+    suffix_kv_lens: torch.Tensor,
+    max_kv_len: int,
+    softmax_scale: float,
+    alibi_slopes: Optional[torch.Tensor],
+    sliding_window: tuple[int, int],
+    logits_soft_cap: float,
+    block_table: torch.Tensor,
+    common_prefix_len: int,
+    fa_version: int,
+    prefix_scheduler_metadata: Optional[torch.Tensor] = None,
+    suffix_scheduler_metadata: Optional[torch.Tensor] = None,
+    q_descale: Optional[torch.Tensor] = None,
+    k_descale: Optional[torch.Tensor] = None,
+    v_descale: Optional[torch.Tensor] = None,
+) -> torch.Tensor:
+    assert alibi_slopes is None, ("Cascade attention does not support ALiBi.")
+    # TODO: Support sliding window.
+    assert sliding_window == (-1, -1), (
+        "Cascade attention does not support sliding window.")
+
+    num_tokens = query.shape[0]
+    block_size = key_cache.shape[-3]
+    assert common_prefix_len % block_size == 0
+    num_common_kv_blocks = common_prefix_len // block_size
+    assert num_common_kv_blocks > 0
+    descale_shape = (cu_prefix_query_lens.shape[0] - 1, key_cache.shape[-2])
+
+    # Process shared prefix.
+    prefix_output, prefix_lse = flash_attn_varlen_func(
+        q=query,
+        k=key_cache,
+        v=value_cache,
+        cu_seqlens_q=cu_prefix_query_lens,
+        seqused_k=prefix_kv_lens,
+        max_seqlen_q=num_tokens,
+        max_seqlen_k=common_prefix_len,
+        softmax_scale=softmax_scale,
+        causal=False,
+        window_size=sliding_window,
+        block_table=block_table[:1],
+        softcap=logits_soft_cap,
+        return_softmax_lse=True,
+        scheduler_metadata=prefix_scheduler_metadata,
+        fa_version=fa_version,
+        q_descale=q_descale.expand(descale_shape)
+        if q_descale is not None else None,
+        k_descale=k_descale.expand(descale_shape)
+        if k_descale is not None else None,
+        v_descale=v_descale.expand(descale_shape)
+        if v_descale is not None else None,
+    )
+
+    descale_shape = (cu_query_lens.shape[0] - 1, key_cache.shape[-2])
+
+    # Process suffix per query.
+    suffix_output, suffix_lse = flash_attn_varlen_func(
+        q=query,
+        k=key_cache,
+        v=value_cache,
+        cu_seqlens_q=cu_query_lens,
+        seqused_k=suffix_kv_lens,
+        max_seqlen_q=max_query_len,
+        max_seqlen_k=max_kv_len - common_prefix_len,
+        softmax_scale=softmax_scale,
+        causal=True,
+        window_size=sliding_window,
+        block_table=block_table[:, num_common_kv_blocks:],
+        softcap=logits_soft_cap,
+        return_softmax_lse=True,
+        scheduler_metadata=suffix_scheduler_metadata,
+        fa_version=fa_version,
+        q_descale=q_descale.expand(descale_shape)
+        if q_descale is not None else None,
+        k_descale=k_descale.expand(descale_shape)
+        if k_descale is not None else None,
+        v_descale=v_descale.expand(descale_shape)
+        if v_descale is not None else None,
+    )
+
+    # Merge prefix and suffix outputs, and store the result in output.
+    merge_attn_states(output, prefix_output, prefix_lse, suffix_output,
+                      suffix_lse)

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/attention/backends/flashinfer.py

@@ -0,0 +1,1003 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Attention layer with FlashInfer."""
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import ClassVar, Optional, Union
+
+import torch
+from flashinfer import (BatchDecodeWithPagedKVCacheWrapper,
+                        BatchPrefillWithPagedKVCacheWrapper,
+                        MultiLevelCascadeAttentionWrapper)
+from flashinfer.decode import (_get_range_buf, get_seq_lens,
+                               trtllm_batch_decode_with_kv_cache)
+from flashinfer.prefill import trtllm_batch_context_with_kv_cache
+
+import vllm.envs as envs
+from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
+                                              AttentionType)
+from vllm.config import CUDAGraphMode, VllmConfig
+from vllm.logger import init_logger
+from vllm.utils import cdiv, is_pin_memory_available
+from vllm.utils.flashinfer import use_trtllm_attention
+from vllm.v1.attention.backends.flash_attn import use_cascade_attention
+# yapf conflicts with isort for this block
+# yapf: disable
+from vllm.v1.attention.backends.utils import (AttentionCGSupport,
+                                              AttentionMetadataBuilder,
+                                              CommonAttentionMetadata,
+                                              get_kv_cache_layout,
+                                              get_per_layer_parameters,
+                                              infer_global_hyperparameters,
+                                              split_decodes_and_prefills)
+from vllm.v1.kv_cache_interface import AttentionSpec
+
+FLASHINFER_WORKSPACE_BUFFER_SIZE = 256 * 1024 * 1024
+
+logger = init_logger(__name__)
+
+
+class FlashInferBackend(AttentionBackend):
+
+    accept_output_buffer: bool = True
+
+    @classmethod
+    def get_supported_dtypes(cls) -> list[torch.dtype]:
+        return [torch.float16, torch.bfloat16]
+
+    @classmethod
+    def get_supported_head_sizes(cls) -> list[int]:
+        # https://github.com/flashinfer-ai/flashinfer/blob/3d55c71a62052c590c130897d3a3db49b14fcc34/include/flashinfer/utils.cuh#L157
+        return [64, 128, 256]
+
+    @classmethod
+    def validate_head_size(cls, head_size: int) -> None:
+        supported_head_sizes = cls.get_supported_head_sizes()
+        if head_size not in supported_head_sizes:
+            attn_type = cls.__name__.removesuffix("Backend")
+            raise ValueError(
+                f"Head size {head_size} is not supported by {attn_type}. "
+                f"Supported head sizes are: {supported_head_sizes}. "
+                "Set VLLM_ATTENTION_BACKEND=FLEX_ATTENTION to use "
+                "FlexAttention backend which supports all head sizes.")
+
+    @staticmethod
+    def get_name() -> str:
+        return "FLASHINFER_VLLM_V1"
+
+    @staticmethod
+    def get_impl_cls() -> type[FlashInferImpl]:
+        return FlashInferImpl
+
+    @staticmethod
+    def get_metadata_cls() -> type[FlashInferMetadata]:
+        return FlashInferMetadata
+
+    @staticmethod
+    def get_builder_cls() -> type[FlashInferMetadataBuilder]:
+        return FlashInferMetadataBuilder
+
+    @staticmethod
+    def get_kv_cache_shape(
+        num_blocks: int,
+        block_size: int,
+        num_kv_heads: int,
+        head_size: int,
+    ) -> tuple[int, ...]:
+        return (num_blocks, 2, block_size, num_kv_heads, head_size)
+
+    @staticmethod
+    def get_kv_cache_stride_order() -> 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":
+            stride_order = (0, 1, 2, 3, 4)
+        elif cache_layout == "HND":
+            stride_order = (0, 1, 3, 2, 4)
+        else:
+            raise ValueError(f"Unknown cache layout format {cache_layout}.")
+        return stride_order
+
+    @staticmethod
+    def get_fp8_dtype_for_flashinfer(kv_cache_dtype: str) -> torch.dtype:
+        if kv_cache_dtype in ("fp8", "fp8_e4m3"):
+            return torch.float8_e4m3fn
+        elif kv_cache_dtype == "fp8_e5m2":
+            return torch.float8_e5m2
+        else:
+            raise ValueError(f"Unrecognized FP8 dtype: {kv_cache_dtype}")
+
+
+@dataclass
+class FlashInferMetadata:
+
+    num_actual_tokens: int  # Number of tokens excluding padding.
+
+    # (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].
+    qo_indptr_cpu: torch.Tensor
+    # An example for paged_kv_indices, paged_kv_indptr:
+    # request 1, page indices [0, 5, 8]
+    # request 2, page indices [1, 6, 7]
+    # request 3, page indices [3, 4]
+    # paged_kv_indices is a concatenation of page indices of all requests:
+    # [0, 5, 8, 1, 6, 7, 3, 4]
+    # paged_kv_indptr is used to index into paged_kv_indices:
+    # [0, 3, 6, 8]
+    # The indptr of the paged kv cache, shape: [batch_size + 1] (CPU for plan)
+    paged_kv_indptr_cpu: torch.Tensor
+    # The page indices of the paged kv cache (on device for plan)
+    paged_kv_indices: torch.Tensor
+    # The number of entries in the last page of each request in
+    # the paged kv cache, shape: [batch_size] (CPU for plan)
+    paged_kv_last_page_len_cpu: torch.Tensor
+    # The number of query/output heads
+    num_qo_heads: int
+    # The number of key/value heads
+    num_kv_heads: int
+    # The dimension of the attention heads
+    head_dim: int
+    # Block size of vllm
+    page_size: int
+    # The data type of the paged kv cache
+    kv_data_type: torch.dtype
+    # The data type of the query
+    q_data_type: torch.dtype
+
+    slot_mapping: torch.Tensor
+
+    # For flashinfer trtllm batch decode
+    max_q_len: int
+    max_seq_len: int
+    seq_lens: torch.Tensor
+    block_table_tensor: torch.Tensor
+    prefill_use_trtllm: bool
+    decode_use_trtllm: bool
+
+    # For handling prefill decode split
+    num_decodes: int
+    num_decode_tokens: int
+    num_prefills: int
+    num_prefill_tokens: int
+
+    # For cascade attention (CPU for planning).
+    use_cascade: bool
+    shared_qo_indptr_cpu: Optional[torch.Tensor] = None
+    shared_kv_page_indptr_cpu: Optional[torch.Tensor] = None
+    shared_kv_page_indices_cpu: Optional[torch.Tensor] = None
+    shared_kv_last_page_len_cpu: Optional[torch.Tensor] = None
+
+    prefill_wrapper: Optional[BatchPrefillWithPagedKVCacheWrapper] = None
+    decode_wrapper: Optional[BatchDecodeWithPagedKVCacheWrapper] = None
+    cascade_wrapper: Optional[MultiLevelCascadeAttentionWrapper] = None
+
+    qo_indptr_gpu: Optional[torch.Tensor] = None
+    paged_kv_indptr_gpu: Optional[torch.Tensor] = None
+
+    def __post_init__(self):
+        if self.head_dim is not None:
+            FlashInferBackend.validate_head_size(self.head_dim)
+
+
+class FlashInferMetadataBuilder(AttentionMetadataBuilder[FlashInferMetadata]):
+    cudagraph_support: ClassVar[AttentionCGSupport] = \
+        AttentionCGSupport.UNIFORM_SINGLE_TOKEN_DECODE
+
+    reorder_batch_threshold: ClassVar[int] = 1
+
+    def __init__(self, kv_cache_spec: AttentionSpec, layer_names: list[str],
+                 vllm_config: VllmConfig, device: torch.device):
+        self.device = device
+        self.vllm_config = vllm_config
+        self.cache_config = vllm_config.cache_config
+        self.kv_cache_spec = kv_cache_spec
+        self._workspace_buffer = None
+        self._prefill_wrapper = None  # Wrapper for prefill/append
+        self._decode_wrapper = None  # Wrapper for decode (general shape)
+
+        self.compilation_config = vllm_config.compilation_config
+        max_num_pages_per_req = cdiv(vllm_config.model_config.max_model_len,
+                                     self.kv_cache_spec.block_size)
+        max_num_reqs = vllm_config.scheduler_config.max_num_seqs
+        max_num_pages = max_num_reqs * max_num_pages_per_req
+        self.enable_cuda_graph = self.compilation_config.cudagraph_mode.\
+            decode_mode() == CUDAGraphMode.FULL
+        if self.enable_cuda_graph:
+            # For full cudagraph capture, one `decode_wrapper` for each batch
+            # size is needed for FlashInfer.
+            self._decode_wrappers_cudagraph: dict[
+                int, BatchDecodeWithPagedKVCacheWrapper] = {}
+            self._decode_cudagraph_max_bs = min(
+                max_num_reqs, self.compilation_config.max_capture_size)
+
+        self._cascade_wrapper = None  # Wrapper for cascade attention
+
+        # Global hyperparameters shared by all attention layers
+        # TODO: discard this for trtllm-gen backend
+        self.global_hyperparameters = infer_global_hyperparameters(
+            get_per_layer_parameters(vllm_config, layer_names, FlashInferImpl))
+
+        # Preparing persistent buffers (device-side)
+        self.paged_kv_indptr = torch.zeros(max_num_reqs + 1,
+                                           dtype=torch.int32,
+                                           device=self.device)
+        self.paged_kv_indices = torch.zeros(
+            max_num_pages,  # max num pages possible
+            dtype=torch.int32,
+            device=self.device)
+        self.paged_kv_last_page_len = torch.zeros(max_num_reqs,
+                                                  dtype=torch.int32,
+                                                  device=self.device)
+        # host-side buffer
+        pin_memory = is_pin_memory_available()
+        self.paged_kv_indptr_cpu = torch.zeros(max_num_reqs + 1,
+                                               dtype=torch.int32,
+                                               device="cpu",
+                                               pin_memory=pin_memory)
+        self.paged_kv_indices_cpu = torch.zeros(max_num_pages,
+                                                dtype=torch.int32,
+                                                device="cpu",
+                                                pin_memory=pin_memory)
+        self.paged_kv_last_page_len_cpu = torch.zeros(max_num_reqs,
+                                                      dtype=torch.int32,
+                                                      device="cpu",
+                                                      pin_memory=pin_memory)
+
+        self.block_table_arange = torch.arange(max_num_pages_per_req,
+                                               dtype=torch.int32,
+                                               device=self.device)
+
+    def _get_workspace_buffer(self):
+        if self._workspace_buffer is None:
+            self._workspace_buffer = torch.zeros(
+                FLASHINFER_WORKSPACE_BUFFER_SIZE,
+                dtype=torch.uint8,
+                device=self.device)
+        return self._workspace_buffer
+
+    def _get_prefill_wrapper(self):
+        if self._prefill_wrapper is None:
+            self._prefill_wrapper = BatchPrefillWithPagedKVCacheWrapper(
+                self._get_workspace_buffer(), get_kv_cache_layout())
+        return self._prefill_wrapper
+
+    def _get_decode_wrapper(self,
+                            batch_size: int,
+                            use_cudagraph: bool = False):
+        if use_cudagraph:
+            decode_wrapper = self._decode_wrappers_cudagraph.get(
+                batch_size, None)
+        else:
+            decode_wrapper = self._decode_wrapper
+
+        if decode_wrapper is None:
+            num_qo_heads = (
+                self.vllm_config.model_config.get_num_attention_heads(
+                    self.vllm_config.parallel_config))
+            num_kv_heads = self.vllm_config.model_config.get_num_kv_heads(
+                self.vllm_config.parallel_config)
+            use_tensor_cores = envs.VLLM_FLASHINFER_FORCE_TENSOR_CORES or (
+                num_qo_heads // num_kv_heads > 4)
+
+            if use_cudagraph:
+                paged_kv_indptr = self.paged_kv_indptr[:batch_size + 1]
+                paged_kv_indices = self.paged_kv_indices
+                paged_kv_last_page_len = self.paged_kv_last_page_len[:
+                                                                     batch_size]
+            else:
+                paged_kv_indptr = None
+                paged_kv_indices = None
+                paged_kv_last_page_len = None
+            decode_wrapper = BatchDecodeWithPagedKVCacheWrapper(
+                self._get_workspace_buffer(),
+                get_kv_cache_layout(),
+                use_cuda_graph=use_cudagraph,
+                paged_kv_indptr_buffer=paged_kv_indptr,
+                paged_kv_indices_buffer=paged_kv_indices,
+                paged_kv_last_page_len_buffer=paged_kv_last_page_len,
+                use_tensor_cores=use_tensor_cores)
+
+            # save the decode wrapper
+            if use_cudagraph:
+                self._decode_wrappers_cudagraph[batch_size] = decode_wrapper
+            else:
+                self._decode_wrapper = decode_wrapper
+
+        return decode_wrapper
+
+    def _get_cascade_wrapper(self):
+        if self._cascade_wrapper is None:
+            self._cascade_wrapper = MultiLevelCascadeAttentionWrapper(
+                2, self._get_workspace_buffer(), get_kv_cache_layout())
+        return self._cascade_wrapper
+
+    def _plan(self, attn_metadata: FlashInferMetadata):
+        if attn_metadata.use_cascade:
+            attn_metadata.cascade_wrapper = self._get_cascade_wrapper()
+            attn_metadata.cascade_wrapper.plan(
+                [
+                    attn_metadata.shared_qo_indptr_cpu,
+                    attn_metadata.qo_indptr_cpu
+                ],
+                [
+                    attn_metadata.shared_kv_page_indptr_cpu,
+                    attn_metadata.paged_kv_indptr_cpu
+                ],
+                [
+                    attn_metadata.shared_kv_page_indices_cpu,
+                    attn_metadata.paged_kv_indices
+                ],
+                [
+                    attn_metadata.shared_kv_last_page_len_cpu,
+                    attn_metadata.paged_kv_last_page_len_cpu
+                ],
+                attn_metadata.num_qo_heads,
+                attn_metadata.num_kv_heads,
+                attn_metadata.head_dim,
+                attn_metadata.page_size,
+                causal=True,
+                sm_scale=self.global_hyperparameters.sm_scale,
+                window_left=self.global_hyperparameters.window_left,
+                logits_soft_cap=self.global_hyperparameters.logits_soft_cap,
+                q_data_type=attn_metadata.q_data_type,
+                kv_data_type=attn_metadata.kv_data_type,
+            )
+        else:
+            # Regular attention (common case).
+            # Decodes are at the front and prefills are at the back,
+            # according to reorder_batch()
+            num_prefills = attn_metadata.num_prefills
+            num_decodes = attn_metadata.num_decodes
+            if num_prefills > 0:
+                # Decodes are first so prefills start after the last decode
+                prefill_start = num_decodes
+                attn_metadata.prefill_wrapper = self._get_prefill_wrapper()
+                assert attn_metadata.qo_indptr_cpu[prefill_start:].shape[
+                    0] == num_prefills + 1
+                assert attn_metadata.paged_kv_indptr_cpu[prefill_start:].shape[
+                    0] == num_prefills + 1
+                assert attn_metadata.paged_kv_last_page_len_cpu[
+                    prefill_start:].shape[0] == num_prefills
+                # Since prefill_wrapper.run() will be called with
+                # query[num_decode_tokens:] we need to adjust the qo_indptr
+                # to be relative to the start of the prefill queries.
+                qo_indptr_cpu = attn_metadata.qo_indptr_cpu[
+                    prefill_start:] - attn_metadata.qo_indptr_cpu[prefill_start]
+                paged_kv_indptr_cpu = attn_metadata.paged_kv_indptr_cpu[
+                    prefill_start:]
+                if not attn_metadata.prefill_use_trtllm:
+                    attn_metadata.prefill_wrapper.plan(
+                        qo_indptr_cpu,
+                        paged_kv_indptr_cpu,
+                        attn_metadata.paged_kv_indices,
+                        attn_metadata.
+                        paged_kv_last_page_len_cpu[prefill_start:],
+                        attn_metadata.num_qo_heads,
+                        attn_metadata.num_kv_heads,
+                        attn_metadata.head_dim,
+                        attn_metadata.page_size,
+                        causal=True,
+                        sm_scale=self.global_hyperparameters.sm_scale,
+                        window_left=self.global_hyperparameters.window_left,
+                        logits_soft_cap=self.global_hyperparameters.
+                        logits_soft_cap,
+                        q_data_type=attn_metadata.q_data_type,
+                        kv_data_type=attn_metadata.kv_data_type,
+                    )
+                else:
+                    attn_metadata.qo_indptr_gpu = qo_indptr_cpu.to(self.device)
+                    attn_metadata.paged_kv_indptr_gpu = paged_kv_indptr_cpu.to(
+                        self.device)
+
+            if num_decodes > 0:
+                pure_decode = num_prefills == 0
+                # possible required padding for cudagraph replay
+                use_cudagraph = (self.enable_cuda_graph and pure_decode and
+                                 num_decodes <= self._decode_cudagraph_max_bs)
+                if use_cudagraph:
+                    num_input_tokens = (
+                        self.vllm_config.pad_for_cudagraph(num_decodes))
+                    # Carefully fulfill the padding region with reasonable value
+                    # on cpu.
+                    # Make sure paged_kv_indptr_cpu is not decreasing
+                    self.paged_kv_indptr_cpu[1 + num_decodes:1 +
+                                             num_input_tokens].fill_(
+                                                 attn_metadata.
+                                                 paged_kv_indptr_cpu[-1])
+                    # Fill the remaining paged_kv_last_page_len_cpu with 1.
+                    # This is because flashinfer treats 0 as a full page
+                    # instead of empty.
+                    self.paged_kv_last_page_len_cpu[
+                        num_decodes:num_input_tokens].fill_(1)
+
+                else:
+                    num_input_tokens = num_decodes
+
+                attn_metadata.decode_wrapper = self._get_decode_wrapper(
+                    num_input_tokens, use_cudagraph)
+                if not attn_metadata.decode_use_trtllm:
+                    # Use the persistent buffer with padding length,
+                    # instead of the same address but chunked version
+                    # in atten_metadata when using cudagraph.
+                    fast_plan_decode(
+                        attn_metadata.decode_wrapper,
+                        self.paged_kv_indptr_cpu[:num_input_tokens + 1],
+                        attn_metadata.paged_kv_indices,
+                        self.paged_kv_last_page_len_cpu[:num_input_tokens],
+                        attn_metadata.num_qo_heads,
+                        attn_metadata.num_kv_heads,
+                        attn_metadata.head_dim,
+                        attn_metadata.page_size,
+                        # Disable flashinfer's pos encoding and use vllm's rope.
+                        pos_encoding_mode="NONE",
+                        sm_scale=self.global_hyperparameters.sm_scale,
+                        window_left=self.global_hyperparameters.window_left,
+                        logits_soft_cap=self.global_hyperparameters.
+                        logits_soft_cap,
+                        q_data_type=attn_metadata.q_data_type,
+                        kv_data_type=attn_metadata.kv_data_type,
+                    )
+
+    def build(self,
+              common_prefix_len: int,
+              common_attn_metadata: CommonAttentionMetadata,
+              fast_build: bool = False) -> FlashInferMetadata:
+        num_reqs = common_attn_metadata.num_reqs
+        num_actual_tokens = common_attn_metadata.num_actual_tokens
+        num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens =\
+            split_decodes_and_prefills(common_attn_metadata)
+
+        page_size = self.kv_cache_spec.block_size
+        max_q_len = common_attn_metadata.max_query_len
+        max_seq_len = common_attn_metadata.seq_lens_cpu.max()
+        seq_lens = common_attn_metadata.seq_lens
+        seq_lens_cpu = common_attn_metadata.seq_lens_cpu
+        block_table_tensor = common_attn_metadata.block_table_tensor
+
+        block_table_bounds_cpu = (seq_lens_cpu + page_size - 1) // page_size
+
+        use_cascade = common_prefix_len > 0
+        if use_cascade:
+            # Grab the blocks of the shared prefix from the first request.
+            assert common_prefix_len % page_size == 0
+            num_common_kv_blocks = common_prefix_len // page_size
+
+            # Create CPU versions directly for cascade (no GPU versions needed)
+            shared_qo_indptr_cpu = torch.tensor([0, num_actual_tokens],
+                                                dtype=torch.int32,
+                                                device='cpu')
+            shared_kv_page_indptr_cpu = torch.tensor([0, num_common_kv_blocks],
+                                                     dtype=torch.int32,
+                                                     device='cpu')
+            shared_kv_page_indices_cpu = block_table_tensor[
+                0, :num_common_kv_blocks]
+            shared_kv_last_page_len_cpu = torch.tensor([page_size],
+                                                       dtype=torch.int32,
+                                                       device='cpu')
+
+            # Remove the blocks of the shared prefix from all requests.
+            block_table_tensor = block_table_tensor[:, num_common_kv_blocks:]
+            block_table_bounds_cpu -= num_common_kv_blocks
+        else:
+            shared_qo_indptr_cpu = None
+            shared_kv_page_indptr_cpu = None
+            shared_kv_page_indices_cpu = None
+            shared_kv_last_page_len_cpu = None
+
+        max_num_blocks = block_table_bounds_cpu.max()
+        block_table_bounds = block_table_bounds_cpu.to(self.device,
+                                                       non_blocking=True)
+        mask = (self.block_table_arange[:max_num_blocks].unsqueeze(0)
+                < block_table_bounds.unsqueeze(1))
+        # write self.paged_kv_indices inplace
+        num_actual_pages = torch.sum(mask)
+        paged_kv_indices = self.paged_kv_indices[:num_actual_pages]
+        torch.masked_select(block_table_tensor[:, :max_num_blocks],
+                            mask,
+                            out=paged_kv_indices)
+
+        # write self.paged_kv_indptr_cpu inplace (0-index is always 0)
+        torch.cumsum(block_table_bounds_cpu,
+                     dim=0,
+                     dtype=torch.int32,
+                     out=self.paged_kv_indptr_cpu[1:1 + num_reqs])
+
+        paged_kv_last_page_len_cpu = seq_lens_cpu % page_size
+        # write self.paged_kv_last_page_len_cpu inplace
+        torch.where(paged_kv_last_page_len_cpu == 0,
+                    torch.tensor(page_size),
+                    paged_kv_last_page_len_cpu,
+                    out=self.paged_kv_last_page_len_cpu[:num_reqs])
+
+        cache_dtype = self.cache_config.cache_dtype
+        if cache_dtype.startswith("fp8"):
+            kv_cache_dtype = FlashInferBackend.get_fp8_dtype_for_flashinfer(
+                cache_dtype)
+        else:
+            kv_cache_dtype = self.kv_cache_spec.dtype
+
+        num_qo_heads = self.vllm_config.model_config.get_num_attention_heads(
+            self.vllm_config.parallel_config)
+        num_kv_heads = self.kv_cache_spec.num_kv_heads
+        head_dim = self.kv_cache_spec.head_size
+
+        # Check if any layer uses sinks (requires TRTLLM attention)
+        has_sinks = self.global_hyperparameters.has_sinks
+
+        # currently prefill trtllm attention does not support fp8 kv cache
+        prefill_use_trtllm = not cache_dtype.startswith("fp8") \
+                                and use_trtllm_attention(
+                                num_prefill_tokens, max_seq_len, cache_dtype,
+                                num_qo_heads, num_kv_heads, head_dim, has_sinks)
+        decode_use_trtllm = use_trtllm_attention(
+                                num_decode_tokens, max_seq_len, cache_dtype,
+                                num_qo_heads, num_kv_heads, head_dim, has_sinks)
+
+        attn_metadata = FlashInferMetadata(
+            num_actual_tokens=num_actual_tokens,
+            qo_indptr_cpu=common_attn_metadata.query_start_loc_cpu,
+            paged_kv_indptr_cpu=self.paged_kv_indptr_cpu[:1 + num_reqs],
+            paged_kv_indices=paged_kv_indices,
+            paged_kv_last_page_len_cpu=self.
+            paged_kv_last_page_len_cpu[:num_reqs],
+            num_qo_heads=num_qo_heads,
+            num_kv_heads=num_kv_heads,
+            head_dim=head_dim,
+            page_size=page_size,
+            kv_data_type=kv_cache_dtype,
+            q_data_type=self.vllm_config.model_config.dtype,
+            slot_mapping=common_attn_metadata.slot_mapping,
+            max_q_len=max_q_len,
+            max_seq_len=max_seq_len,
+            seq_lens=seq_lens,
+            block_table_tensor=block_table_tensor,
+            prefill_use_trtllm=prefill_use_trtllm,
+            decode_use_trtllm=decode_use_trtllm,
+            num_decodes=num_decodes,
+            num_decode_tokens=num_decode_tokens,
+            num_prefills=num_prefills,
+            num_prefill_tokens=num_prefill_tokens,
+            use_cascade=use_cascade,
+            shared_qo_indptr_cpu=shared_qo_indptr_cpu,
+            shared_kv_page_indptr_cpu=shared_kv_page_indptr_cpu,
+            shared_kv_page_indices_cpu=shared_kv_page_indices_cpu,
+            shared_kv_last_page_len_cpu=shared_kv_last_page_len_cpu,
+        )
+
+        self._plan(attn_metadata)
+
+        return attn_metadata
+
+    def build_for_cudagraph_capture(
+            self, common_attn_metadata: CommonAttentionMetadata):
+        """
+        This method builds the metadata for full cudagraph capture.
+        Currently, only decode is supported for full cudagraphs with FlashInfer.
+        """
+        m = common_attn_metadata
+
+        assert m.num_reqs == m.num_actual_tokens, \
+            "FlashInfer only supports decode-only full CUDAGraph capture. " \
+            "Make sure all cudagraph capture sizes <= max_num_seq."
+
+        m.max_query_len = 1  # decode-only
+
+        return self.build(0, m)
+
+    def use_cascade_attention(self, *args, **kwargs) -> bool:
+        if self.kv_cache_spec.dtype != self.vllm_config.model_config.dtype:
+            # TODO: The cascade wrapper currently does not support setting
+            # kv cache dtype to something different from query dtype.
+            return False
+        return use_cascade_attention(*args, **kwargs)
+
+
+class FlashInferImpl(AttentionImpl):
+
+    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] = None,
+        attn_type: AttentionType = AttentionType.DECODER,
+        kv_sharing_target_layer_name: Optional[int] = None,
+        sinks: Optional[torch.Tensor] = None,
+    ) -> 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)
+        else:
+            self.sliding_window = (sliding_window - 1, 0)
+        self.kv_cache_dtype = kv_cache_dtype
+        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
+
+        if attn_type != AttentionType.DECODER:
+            raise NotImplementedError("Encoder self-attention and "
+                                      "encoder/decoder cross-attention "
+                                      "are not implemented for "
+                                      "FlashInferImpl")
+
+        self.sinks: Optional[torch.Tensor] = None
+        if sinks is not None:
+            if sinks.shape[0] != num_heads:
+                raise ValueError(
+                    "Sinks must have the same number of heads as the number of "
+                    f"heads in the layer. Expected {num_heads}, but got "
+                    f"{sinks.shape[0]}."
+                )
+            self.sinks = sinks
+
+    def forward(
+        self,
+        layer: torch.nn.Module,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        kv_cache: torch.Tensor,
+        attn_metadata: FlashInferMetadata,
+        output: Optional[torch.Tensor] = None,
+        output_scale: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        """Forward pass with FlashInfer.
+
+        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 -
+            # NHD: [num_blocks, 2, block_size, num_kv_heads, head_size]
+            # HND: [num_blocks, 2,  num_kv_heads, block_size, 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_scale is not None:
+            raise NotImplementedError(
+                "fused output quantization is not yet supported"
+                " for FlashInferImpl")
+
+        if attn_metadata is None:
+            # Profiling run.
+            return output
+
+        # 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
+
+        if self.kv_sharing_target_layer_name is None:
+            # Reshape the input keys and values and store them in the cache.
+            # Skip this if sharing KV cache with an earlier attention layer.
+            # NOTE(woosuk): Here, key and value are padded while slot_mapping is
+            # not padded. However, we don't need to do key[:num_actual_tokens]
+            # and value[:num_actual_tokens] because the reshape_and_cache_flash
+            # op uses the slot_mapping's shape to determine the number of
+            # actual tokens.
+            torch.ops._C_cache_ops.reshape_and_cache_flash(
+                key,
+                value,
+                kv_cache[:, 0],
+                kv_cache[:, 1],
+                attn_metadata.slot_mapping,
+                self.kv_cache_dtype,
+                layer._k_scale,
+                layer._v_scale,
+            )
+
+            # The FlashInfer api requires data to be in fp8_e4m3 or fp8_e5m2
+            # to process the cache when the kv_cache_dtype is fp8
+            if self.kv_cache_dtype.startswith("fp8"):
+                torch_dtype = FlashInferBackend.get_fp8_dtype_for_flashinfer(
+                    self.kv_cache_dtype)
+                kv_cache = kv_cache.view(torch_dtype)
+
+        window_left = (self.sliding_window[0]
+                       if self.sliding_window is not None else -1)
+
+        # Inputs and outputs may be padded for CUDA graphs
+        query = query[:num_actual_tokens]
+        output_padded = output
+        output = output[:num_actual_tokens]
+
+        if attn_metadata.use_cascade:
+            # Cascade attention (rare case).
+            assert attn_metadata.cascade_wrapper is not None
+            output.copy_(attn_metadata.cascade_wrapper.run(query, kv_cache))
+            return output
+
+        num_decode_tokens = attn_metadata.num_decode_tokens
+        num_prefill_tokens = attn_metadata.num_prefill_tokens
+
+        stride_order = FlashInferBackend.get_kv_cache_stride_order()
+        kv_cache_permute = kv_cache.permute(*stride_order)
+        # Regular attention (common case).
+        # Decodes are at the front and prefills are at the back,
+        # according to reorder_batch()
+        if num_prefill_tokens > 0:
+            prefill_wrapper = attn_metadata.prefill_wrapper
+            prefill_query = query[num_decode_tokens:]
+            assert prefill_query.shape[0] == num_prefill_tokens
+            assert prefill_wrapper is not None
+
+            if not attn_metadata.prefill_use_trtllm:
+                assert prefill_wrapper._causal
+                assert prefill_wrapper._window_left == window_left
+                assert prefill_wrapper._logits_soft_cap == (
+                    self.logits_soft_cap or 0.0)
+                assert prefill_wrapper._sm_scale == self.scale
+                prefill_wrapper.run(
+                    prefill_query,
+                    kv_cache_permute,
+                    k_scale=layer._k_scale_float,
+                    v_scale=layer._v_scale_float,
+                    out=output[num_decode_tokens:],
+                )
+            else:
+                # prefill_query may be non-contiguous
+                prefill_query = prefill_query.contiguous()
+                workspace_buffer = prefill_wrapper._float_workspace_buffer
+                block_tables_prefill = attn_metadata.block_table_tensor[
+                    num_decode_tokens:]
+                seq_lens_prefill = attn_metadata.seq_lens[num_decode_tokens:]
+
+                # This path needs to be enabled with VLLM_KV_CACHE_LAYOUT = HND
+                assert get_kv_cache_layout() == "HND"
+                assert prefill_query.is_contiguous()
+                assert kv_cache_permute.is_contiguous()
+                assert workspace_buffer.is_contiguous()
+                assert block_tables_prefill.is_contiguous()
+                assert seq_lens_prefill.is_contiguous()
+
+                trtllm_batch_context_with_kv_cache(
+                    query=prefill_query,
+                    kv_cache=kv_cache_permute,
+                    workspace_buffer=workspace_buffer,
+                    block_tables=block_tables_prefill,
+                    seq_lens=seq_lens_prefill,
+                    max_q_len=attn_metadata.max_q_len,
+                    max_kv_len=attn_metadata.max_seq_len,
+                    bmm1_scale=layer._k_scale_float * self.scale,
+                    bmm2_scale=layer._v_scale_float,
+                    batch_size=attn_metadata.num_prefills,
+                    cum_seq_lens_q=attn_metadata.qo_indptr_gpu,
+                    cum_seq_lens_kv=attn_metadata.paged_kv_indptr_gpu,
+                    window_left=window_left,
+                    sinks=self.sinks,
+                    out=output[num_decode_tokens:],
+                )
+
+        if num_decode_tokens > 0:
+            decode_wrapper = attn_metadata.decode_wrapper
+            decode_query = query[:num_decode_tokens]
+            assert decode_query.shape[0] == num_decode_tokens
+            assert decode_wrapper is not None
+
+            if not attn_metadata.decode_use_trtllm:
+                assert decode_wrapper._window_left == window_left
+                assert decode_wrapper._logits_soft_cap == (self.logits_soft_cap
+                                                           or 0.0)
+                assert decode_wrapper._sm_scale == self.scale
+                decode_wrapper.run(
+                    decode_query,
+                    kv_cache_permute,
+                    k_scale=layer._k_scale_float,
+                    v_scale=layer._v_scale_float,
+                    out=output[:num_decode_tokens],
+                )
+            else:
+                # decode_query may be non-contiguous
+                decode_query = decode_query.contiguous()
+                workspace_buffer = decode_wrapper._float_workspace_buffer
+                block_tables_decode = attn_metadata.block_table_tensor[:
+                                                                       num_decode_tokens]
+                seq_lens_decode = attn_metadata.seq_lens[:num_decode_tokens]
+
+                # This path needs to be enabled with VLLM_KV_CACHE_LAYOUT = HND
+                assert get_kv_cache_layout() == "HND"
+                assert decode_query.is_contiguous()
+                assert kv_cache_permute.is_contiguous()
+                assert workspace_buffer.is_contiguous()
+                assert block_tables_decode.is_contiguous()
+                assert seq_lens_decode.is_contiguous()
+
+                trtllm_batch_decode_with_kv_cache(
+                    query=decode_query,
+                    kv_cache=kv_cache_permute,
+                    workspace_buffer=workspace_buffer,
+                    block_tables=block_tables_decode,
+                    seq_lens=seq_lens_decode,
+                    max_seq_len=attn_metadata.max_seq_len,
+                    bmm1_scale=layer._k_scale_float * self.scale,
+                    bmm2_scale=layer._v_scale_float,
+                    window_left=window_left,
+                    sinks=self.sinks,
+                    out=output[:num_decode_tokens],
+                )
+        return output_padded
+
+
+def fast_plan_decode(
+    self,  # decode wrapper
+    indptr_cpu: torch.Tensor,
+    indices: torch.Tensor,
+    last_page_len_cpu: torch.Tensor,
+    num_qo_heads: int,
+    num_kv_heads: int,
+    head_dim: int,
+    page_size: int,
+    pos_encoding_mode: str = "NONE",
+    window_left: int = -1,
+    logits_soft_cap: Optional[float] = None,
+    q_data_type: Optional[Union[str, torch.dtype]] = "float16",
+    kv_data_type: Optional[Union[str, torch.dtype]] = None,
+    data_type: Optional[Union[str, torch.dtype]] = None,
+    sm_scale: Optional[float] = None,
+    rope_scale: Optional[float] = None,
+    rope_theta: Optional[float] = None,
+    non_blocking: bool = True,
+) -> None:
+    """
+    A faster version of BatchDecodeWithPagedKVCacheWrapper::plan used for
+    cudagraph capture/replay, while the no cudagraph version turns back
+    to the original plan.
+    using original plan after passing host-side buffers:
+    - only host-to-device copy of indptr and last_page_len buffers
+    Modifications for cudagraph:
+    - only host-to-device copy of indptr and last_page_len buffers.
+    - avoid device-to-device copy of indices buffer.
+
+    Part of the code get inspiration from the original plan from FlashInfer repo
+    and the implementation of fast_decode_plan for FlashInfer in SGlang repo.
+    """
+    # Warm up with the original plan if it is first call, and always run the
+    # original plan if we run for dynamic shape. For fixed shape (cudagraph),
+    # this warm up is to generate the _cached_module for the decode wrapper.
+    if not self.is_cuda_graph_enabled or \
+        getattr(self, "vllm_first_call", True):
+        self.plan(
+            indptr_cpu,
+            indices,
+            last_page_len_cpu,
+            num_qo_heads,
+            num_kv_heads,
+            head_dim,
+            page_size,
+            pos_encoding_mode,
+            window_left,
+            logits_soft_cap,
+            q_data_type,
+            kv_data_type,
+            data_type,
+            sm_scale,
+            rope_scale,
+            rope_theta,
+            non_blocking,
+        )
+        self.vllm_first_call = False
+        return
+
+    assert self.is_cuda_graph_enabled, "Should be cudagraph only here"
+
+    batch_size = len(last_page_len_cpu)
+    if logits_soft_cap is None:
+        logits_soft_cap = 0.0
+
+    # Handle data types consistently
+    if data_type is not None:
+        if q_data_type is None:
+            q_data_type = data_type
+        if kv_data_type is None:
+            kv_data_type = data_type
+    elif q_data_type is None:
+        q_data_type = "float16"
+
+    if kv_data_type is None:
+        kv_data_type = q_data_type
+    q_data_type = getattr(torch, q_data_type) if isinstance(
+        q_data_type, str) else q_data_type
+    kv_data_type = getattr(torch, kv_data_type) if isinstance(
+        kv_data_type, str) else kv_data_type
+
+    if self.use_tensor_cores:
+        qo_indptr_host = _get_range_buf(batch_size + 1, "cpu")
+
+    if batch_size != self._fixed_batch_size:
+        raise ValueError(
+            "The batch size should be fixed in cudagraph mode, the runtime "
+            "batch size {} mismatches the batch size set during "
+            "initialization {}".format(batch_size, self._fixed_batch_size))
+    if len(indices) > len(self._paged_kv_indices_buf):
+        raise ValueError(
+            "The size of indices should be less than or equal to the "
+            "allocated buffer")
+
+    # host-to-device copy for the indptr buffer
+    self._paged_kv_indptr_buf.copy_(indptr_cpu, non_blocking=True)
+    # host-to-device copy for the last_page_len buffer
+    self._paged_kv_last_page_len_buf.copy_(last_page_len_cpu,
+                                           non_blocking=True)
+
+    indptr_host = indptr_cpu
+    last_page_len_host = last_page_len_cpu
+
+    if self.use_tensor_cores:
+        kv_lens_arr_host = get_seq_lens(indptr_host, last_page_len_host,
+                                        page_size)
+
+        try:
+            # Make sure we pass exactly 15 arguments for tensor core version
+            self._plan_info = self._cached_module.plan(
+                self._float_workspace_buffer,
+                self._int_workspace_buffer,
+                self._pin_memory_int_workspace_buffer,
+                qo_indptr_host,
+                indptr_host,
+                kv_lens_arr_host,
+                batch_size,  # total_num_rows
+                batch_size,
+                num_qo_heads,
+                num_kv_heads,
+                page_size,
+                self.is_cuda_graph_enabled,
+                head_dim,
+                head_dim,
+                False,  # causal
+            )
+        except Exception as e:
+            raise RuntimeError(f"Error in tensor core plan: {e}") from e
+    else:
+        try:
+            # Make sure we pass exactly 15 arguments for standard version
+            self._plan_info = self._cached_module.plan(
+                self._float_workspace_buffer,
+                self._int_workspace_buffer,
+                self._pin_memory_int_workspace_buffer,
+                indptr_host,
+                batch_size,
+                num_qo_heads,
+                num_kv_heads,
+                page_size,
+                self.is_cuda_graph_enabled,
+                window_left,
+                logits_soft_cap,
+                head_dim,
+                head_dim,
+                torch.empty(0, dtype=q_data_type),
+                torch.empty(0, dtype=kv_data_type),
+            )
+        except Exception as e:
+            raise RuntimeError(f"Error in standard plan: {e}") from e
+
+    self._pos_encoding_mode = pos_encoding_mode
+    self._window_left = window_left
+    self._logits_soft_cap = logits_soft_cap
+    self._sm_scale = sm_scale
+    self._rope_scale = rope_scale
+    self._rope_theta = rope_theta

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/attention/backends/flex_attention.py

@@ -0,0 +1,526 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Attention layer with FlashAttention."""
+from collections import defaultdict
+from dataclasses import dataclass
+from typing import Optional
+
+import torch
+from torch.nn.attention.flex_attention import (BlockMask, _mask_mod_signature,
+                                               _score_mod_signature,
+                                               create_block_mask,
+                                               flex_attention)
+
+from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
+                                              AttentionMetadata, AttentionType,
+                                              is_quantized_kv_cache)
+from vllm.config import VllmConfig
+from vllm.logger import init_logger
+from vllm.platforms import current_platform
+from vllm.v1.attention.backends.utils import (AttentionMetadataBuilder,
+                                              CommonAttentionMetadata)
+from vllm.v1.kv_cache_interface import AttentionSpec
+
+logger = init_logger(__name__)
+
+create_block_mask_compiled = torch.compile(create_block_mask,
+                                           fullgraph=True,
+                                           mode="reduce-overhead")
+flex_attention_compiled = torch.compile(flex_attention, fullgraph=True)
+
+
+def _offsets_to_doc_ids_tensor(offsets: torch.Tensor) -> torch.Tensor:
+    device = offsets.device
+    counts = offsets[1:] - offsets[:-1]
+    return torch.repeat_interleave(
+        torch.arange(len(counts), device=device, dtype=torch.int32), counts)
+
+
+class FlexAttentionBackend(AttentionBackend):
+    accept_output_buffer: bool = True
+
+    @classmethod
+    def get_supported_dtypes(cls) -> list[torch.dtype]:
+        return [torch.float16, torch.bfloat16, torch.float32]
+
+    @classmethod
+    def validate_head_size(cls, head_size: int) -> None:
+        return  # FlexAttention supports any head size
+
+    @staticmethod
+    def get_name() -> str:
+        return "FLEX_ATTENTION"
+
+    @staticmethod
+    def get_impl_cls() -> type["FlexAttentionImpl"]:
+        return FlexAttentionImpl
+
+    @staticmethod
+    def get_metadata_cls() -> type["AttentionMetadata"]:
+        return FlexAttentionMetadata
+
+    @staticmethod
+    def get_kv_cache_shape(
+        num_blocks: int,
+        block_size: int,
+        num_kv_heads: int,
+        head_size: int,
+    ) -> tuple[int, ...]:
+        return (2, num_blocks, block_size, num_kv_heads, head_size)
+
+    @staticmethod
+    def get_builder_cls() -> type["FlexAttentionMetadataBuilder"]:
+        return FlexAttentionMetadataBuilder
+
+    @staticmethod
+    def use_cascade_attention(*args, **kwargs) -> bool:
+        return False
+
+
+# @torch.compile(fullgraph=True, mode="reduce-overhead")
+def physical_to_logical_mapping(
+        block_table: torch.Tensor,
+        total_blocks: Optional[int] = None) -> torch.Tensor:
+    """
+    Creates an inverse mapping from physical block locations to logical indices.
+
+    The original block_table maps from logical blocks to physical locations:
+
+    Logical to Physical (Original block_table):
+    ┌───────────────────────────────────────────┐
+    │ Request 0:                                │
+    │                                           │
+    │ Logical Blocks:  0  1  2  3  4  5  6  7   │
+    │                  │  │  │  │  │  │  │  │   │
+    │                  v  v  v  v  v  v  v  v   │
+    │ Physical Blocks: 3  5  1  7  4  2  0  6   │
+    └───────────────────────────────────────────┘
+
+    This function creates the inverse mapping:
+
+    Physical to Logical (Inverse mapping):
+    ┌───────────────────────────────────────────┐
+    │ Request 0:                                │
+    │                                           │
+    │ Physical Blocks: 0  1  2  3  4  5  6  7   │
+    │                  │  │  │  │  │  │  │  │   │
+    │                  v  v  v  v  v  v  v  v   │
+    │ Logical Blocks:  6  2  5  0  4  1  7  3   │
+    └───────────────────────────────────────────┘
+
+    If multiple logical blocks map to the same physical block,
+    this function returns the first (minimum) logical block index.
+
+    If a physical block is not mapped to by any logical block,
+    its value in the result will be -1.
+
+
+    Args:
+        block_table: Tensor of shape [max_reqs, max_num_blocks]
+            mapping logical blocks to physical locations
+
+    Returns:
+        A tensor of shape [max_reqs, max_physical_block]
+    """
+    max_reqs, max_num_blocks = block_table.shape
+    device = block_table.device
+
+    physical_to_logical = torch.full((max_reqs, total_blocks),
+                                     -1,
+                                     dtype=torch.long,
+                                     device=device)
+
+    logical_indices = (torch.arange(max_num_blocks,
+                                    device=device).unsqueeze(0).expand(
+                                        max_reqs, -1))
+
+    physical_to_logical.scatter_(-1, block_table.to(torch.int64),
+                                 logical_indices)
+    # TODO Confirm - Seems like block 0 is always empty so we reset it manually
+    physical_to_logical[:, 0] = -1
+    return physical_to_logical
+
+
+def causal_mask_mod(b: torch.Tensor, h: torch.Tensor, q_idx: torch.Tensor,
+                    kv_idx: torch.Tensor):
+    return q_idx >= kv_idx
+
+
+@dataclass
+class FlexAttentionMetadata:
+    causal: bool
+    num_actual_tokens: int  # Number of tokens excluding padding.
+    max_query_len: int
+    query_start_loc: torch.Tensor
+    max_seq_len: int
+    seq_lens: torch.Tensor
+    block_table: torch.Tensor
+    slot_mapping: torch.Tensor
+
+    use_cascade: bool
+    common_prefix_len: int
+    cu_prefix_query_lens: Optional[torch.Tensor]
+    prefix_kv_lens: Optional[torch.Tensor]
+    suffix_kv_lens: Optional[torch.Tensor]
+
+    # Block info
+    total_cache_tokens: int
+    block_size: int
+    max_possible_sequence_length: int
+    num_reqs: int
+    physical_to_logical: torch.Tensor
+    decode_offset: torch.Tensor
+
+    # For logging.
+    num_input_tokens: int = 0  # Number of tokens including padding.
+
+    # Flex Metadata
+    num_blocks = 0
+    block_mask: Optional[BlockMask] = None
+    score_mod: Optional[_score_mod_signature] = None
+    logical_mask_mod: _mask_mod_signature = causal_mask_mod
+
+    def get_causal_mask_mod(self) -> _mask_mod_signature:
+        """Creates the mask_mod function for FlexAttention.
+
+        This function creates the combined mask mod function that handles:
+            1. The paged attention block mapping
+            2. The mapping from packed query sequences to logical query entries
+
+        It also by defaults adds the decoding offset to the query indices.
+        With this info we create the "logical" indices that are passed to
+        mask_mod functions. This allows mask mod functions to be agnostic to
+        layout of the query and key/value tensors.
+
+        TODO is_within_lower_bound: do sequences start on block_boundaries?
+        """
+        # Create a lookup mapping from query indices -> request number
+        request_lookup = _offsets_to_doc_ids_tensor(self.query_start_loc)
+
+        def final_mask_mod(
+            b: torch.Tensor,
+            h: torch.Tensor,
+            q_idx: torch.Tensor,
+            physical_kv_idx: torch.Tensor,
+        ) -> torch.Tensor:
+            # Map query indices to corresponding request indices
+            q_req = request_lookup[q_idx]
+
+            # Convert physical KV indices to logical indices
+            physical_kv_block = physical_kv_idx // self.block_size
+            physical_kv_offset = physical_kv_idx % self.block_size
+            logical_block_idx = self.physical_to_logical[q_req,
+                                                         physical_kv_block]
+            logical_kv_idx = logical_block_idx * self.block_size + physical_kv_offset  # noqa: E501
+
+            # Determine valid kv indices
+            live_block = logical_block_idx >= 0
+            within_upper_bound = logical_kv_idx < self.seq_lens[q_req]
+            within_lower_bound = logical_kv_idx >= 0
+
+            is_valid = live_block & within_upper_bound & within_lower_bound
+
+            # Convert physical query indices to logical indices
+            local_q_idx = q_idx - self.query_start_loc[q_req]
+            logical_q_idx = local_q_idx + self.decode_offset[q_req]
+
+            # Apply mask modification only for valid indices
+            return torch.where(
+                is_valid,
+                self.logical_mask_mod(b, h, logical_q_idx, logical_kv_idx),
+                False,
+            )
+
+        return final_mask_mod
+
+    def get_bidirectional_mask_mod(self) -> _mask_mod_signature:
+        """Creates the encoder mask_mod function for FlexAttention.
+
+        Since the encoder bidirectional attention doesn't run with 
+        KV cache, this function creates a mask based on the
+        packed query sequences.
+        """
+        # Create a lookup mapping from query indices -> request number
+        request_lookup = _offsets_to_doc_ids_tensor(self.query_start_loc)
+
+        def final_mask_mod(
+            b: torch.Tensor,
+            h: torch.Tensor,
+            q_idx: torch.Tensor,
+            kv_idx: torch.Tensor,
+        ) -> torch.Tensor:
+            return request_lookup[q_idx] == request_lookup[kv_idx]
+
+        return final_mask_mod
+
+    def build_block_mask(self) -> BlockMask:
+        if self.causal:
+            mask_mod = self.get_causal_mask_mod()
+            kv_len = self.total_cache_tokens
+        else:
+            mask_mod = self.get_bidirectional_mask_mod()
+            kv_len = self.num_actual_tokens
+        return create_block_mask_compiled(
+            mask_mod,
+            None,
+            None,
+            self.num_actual_tokens,
+            kv_len,
+            device=self.block_table.device,
+        )
+
+    def __post_init__(self):
+        assert self.use_cascade is False, "Not implemented yet."
+        assert self.common_prefix_len == 0, "Not implemented yet."
+        assert self.cu_prefix_query_lens is None, "Not implemented yet."
+        assert self.prefix_kv_lens is None, "Not implemented yet."
+        assert self.suffix_kv_lens is None, "Not implemented yet."
+        self.num_blocks = self.total_cache_tokens // self.block_size
+        self.block_mask = self.build_block_mask()
+
+
+class FlexAttentionMetadataBuilder(
+        AttentionMetadataBuilder[FlexAttentionMetadata]):
+
+    def __init__(self, kv_cache_spec: AttentionSpec, layer_names: list[str],
+                 vllm_config: VllmConfig, device: torch.device):
+        self.model_config = vllm_config.model_config
+        self.parallel_config = vllm_config.parallel_config
+        self.cache_config = vllm_config.cache_config
+
+        self.num_heads_q = self.model_config.get_num_attention_heads(
+            vllm_config.parallel_config)
+        self.num_heads_kv = self.model_config.get_num_kv_heads(
+            vllm_config.parallel_config)
+        self.headdim = self.model_config.get_head_size()
+        self.block_size = kv_cache_spec.block_size
+        self.kv_cache_spec = kv_cache_spec
+        self.device = device
+
+    def build(self,
+              common_prefix_len: int,
+              common_attn_metadata: CommonAttentionMetadata,
+              fast_build: bool = False) -> FlexAttentionMetadata:
+        num_reqs = common_attn_metadata.num_reqs
+        num_actual_tokens = common_attn_metadata.num_actual_tokens
+        max_query_len = common_attn_metadata.max_query_len
+
+        max_seq_len = int(common_attn_metadata.seq_lens_cpu.max())
+        query_start_loc = common_attn_metadata.query_start_loc
+        seq_lens = common_attn_metadata.seq_lens
+        block_table_tensor = common_attn_metadata.block_table_tensor
+        slot_mapping = common_attn_metadata.slot_mapping
+
+        use_cascade = common_prefix_len > 0
+        cu_prefix_query_lens = None
+        prefix_kv_lens = None
+        suffix_kv_lens = None
+        if use_cascade:
+            raise NotImplementedError("Not yet my friend")
+
+        block_size = self.kv_cache_spec.block_size
+        max_possible_seq_len = self.model_config.max_model_len
+        total_cache_tokens = self.cache_config.num_gpu_blocks * block_size
+
+        inverse_block_table = physical_to_logical_mapping(
+            block_table_tensor, self.cache_config.num_gpu_blocks)
+
+        # Get the original offset tensor
+        offset_tensor = common_attn_metadata.num_computed_tokens_cpu.to(
+            self.device, non_blocking=True)
+
+        out = FlexAttentionMetadata(
+            causal=common_attn_metadata.causal,
+            num_actual_tokens=num_actual_tokens,
+            max_query_len=max_query_len,
+            query_start_loc=query_start_loc,
+            max_seq_len=max_seq_len,
+            seq_lens=seq_lens,
+            block_table=block_table_tensor,
+            slot_mapping=slot_mapping,
+            use_cascade=use_cascade,
+            common_prefix_len=common_prefix_len,
+            cu_prefix_query_lens=cu_prefix_query_lens,
+            prefix_kv_lens=prefix_kv_lens,
+            suffix_kv_lens=suffix_kv_lens,
+            block_size=block_size,
+            max_possible_sequence_length=max_possible_seq_len,
+            num_reqs=num_reqs,
+            physical_to_logical=inverse_block_table,
+            total_cache_tokens=total_cache_tokens,
+            decode_offset=offset_tensor,
+        )
+        return out
+
+
+class FlexAttentionImpl(AttentionImpl):
+    sliding_window: Optional[tuple[int, int]]
+    alibi_slopes: Optional[torch.Tensor]
+    logits_soft_cap: Optional[float]
+
+    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] = None,
+        attn_type: AttentionType = AttentionType.DECODER,
+        kv_sharing_target_layer_name: Optional[str] = None,
+    ) -> None:
+        self.num_heads = num_heads
+        self.head_size = head_size
+        self.scale = float(scale)
+        self.num_kv_heads = num_kv_heads
+        self.attn_type = attn_type
+
+        if attn_type not in (AttentionType.ENCODER_ONLY,
+                             AttentionType.DECODER):
+            raise NotImplementedError(
+                f"FlexAttention does not support {attn_type} attention")
+
+        if alibi_slopes is not None:
+            raise NotImplementedError(
+                "FlexAttention does not support alibi slopes yet.")
+        else:
+            self.alibi_slopes = None
+        if sliding_window is not None:
+            raise NotImplementedError(
+                "FlexAttention does not support sliding window yet.")
+        else:
+            self.sliding_window = (-1, -1)
+        self.kv_cache_dtype = kv_cache_dtype
+        self.logits_soft_cap = logits_soft_cap
+        if self.logits_soft_cap is not None:
+            raise NotImplementedError(
+                "FlexAttention does not support logits soft cap yet.")
+
+        self.num_queries_per_kv = self.num_heads // self.num_kv_heads
+
+        if kv_sharing_target_layer_name is not None:
+            raise NotImplementedError(
+                "FlexAttention does not support kv sharing yet.")
+
+        FlexAttentionBackend.validate_head_size(head_size)
+
+        if is_quantized_kv_cache(self.kv_cache_dtype):
+            raise NotImplementedError(
+                "FlexAttention does not support quantized kv-cache. Yet")
+
+    @staticmethod
+    def view_as_4d(tensor: torch.Tensor) -> torch.Tensor:
+        """View a 3d tensor as 4D."""
+        if tensor.ndim == 4:
+            return tensor
+        assert tensor.ndim == 3
+        return tensor[None, :, :, :]
+
+    def forward(
+        self,
+        layer: torch.nn.Module,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        kv_cache: torch.Tensor,
+        attn_metadata: FlexAttentionMetadata,
+        output: Optional[torch.Tensor] = None,
+        output_scale: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        """Forward pass with FLexAttention.
+
+        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]
+            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_scale is not None:
+            raise NotImplementedError(
+                "fused output quantization is not yet supported"
+                " for FlexAttentionImpl")
+
+        enable_gqa = self.num_kv_heads != self.num_heads
+
+        if attn_metadata is None:
+            # Profiling run.
+            return output
+            # query = self.view_as_4d(query).permute(0, 2, 1, 3)
+            # return torch.empty_like(query)
+
+        num_actual_tokens = attn_metadata.num_actual_tokens
+
+        if not attn_metadata.causal:
+            assert self.attn_type == AttentionType.ENCODER_ONLY
+
+            query, key_tensor, value_tensor = map(
+                lambda x: self.view_as_4d(x).permute(0, 2, 1, 3),
+                (query, key, value),
+            )
+
+        else:
+            assert self.attn_type == AttentionType.DECODER
+            key_cache, value_cache = kv_cache.unbind(0)
+
+            torch.ops._C_cache_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,
+            )
+
+            # View out the block_size dim
+            key_cache = key_cache.view(-1, self.num_kv_heads, self.head_size)
+            value_cache = value_cache.view(-1, self.num_kv_heads,
+                                           self.head_size)
+            query, key_tensor, value_tensor = map(
+                lambda x: self.view_as_4d(x).permute(0, 2, 1, 3),
+                (query, key_cache, value_cache),
+            )
+
+        query = query[:, :, :num_actual_tokens, :]
+        # Doesn't work for now -> constraint violation
+        # torch._dynamo.try_mark_dynamic(query, 2)
+
+        # default M=64, N=64 may run out of shared memory on some GPUs
+        # TODO: Explicit configs for each GPU?
+        # Not sure how to calculate the shared memory requirement
+        extra_kernel_options = defaultdict[str, int](lambda: 64)
+        if query.dtype == torch.float32:
+            extra_kernel_options["BLOCK_M"] //= 2
+            extra_kernel_options["BLOCK_N"] //= 2
+        if current_platform.is_cuda():
+            device_props = torch.cuda.get_device_properties()
+            max_shared_memory = device_props.shared_memory_per_block_optin
+            if max_shared_memory < 144 * 1024:
+                extra_kernel_options["BLOCK_M"] //= 2
+                extra_kernel_options["BLOCK_N"] //= 2
+
+        out = flex_attention_compiled(
+            query,
+            key_tensor,
+            value_tensor,
+            attn_metadata.score_mod,
+            attn_metadata.block_mask,
+            self.scale,
+            enable_gqa=enable_gqa,
+            kernel_options={
+                "FORCE_USE_FLEX_ATTENTION": True,
+                **extra_kernel_options
+            },
+        )
+
+        # Flex doesn't have an out variant today, rely on epilogue fusion
+        out = out.permute(0, 2, 1, 3).squeeze(0)
+        output[:num_actual_tokens, :, :].copy_(out)
+        return output

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/attention/backends/linear_attn.py

@@ -0,0 +1,67 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from dataclasses import dataclass
+from typing import ClassVar
+
+import torch
+
+from vllm.attention.backends.abstract import AttentionBackend
+from vllm.config import VllmConfig
+from vllm.v1.attention.backends.utils import (AttentionMetadataBuilder,
+                                              CommonAttentionMetadata,
+                                              split_decodes_and_prefills)
+from vllm.v1.kv_cache_interface import AttentionSpec, MambaSpec
+
+
+class LinearAttentionBackend(AttentionBackend):
+
+    @staticmethod
+    def get_builder_cls() -> type["LinearAttentionMetadataBuilder"]:
+        return LinearAttentionMetadataBuilder
+
+
+@dataclass
+class LinearAttentionMetadata:
+    num_prefills: int
+    num_prefill_tokens: int
+    num_decodes: int
+    num_decode_tokens: int
+    query_start_loc: torch.Tensor
+    seq_lens: torch.Tensor
+
+    state_indices_tensor: torch.Tensor  # shape: [batch,]
+
+
+class LinearAttentionMetadataBuilder(
+        AttentionMetadataBuilder[LinearAttentionMetadata]):
+
+    reorder_batch_threshold: ClassVar[int] = 1
+
+    def __init__(self, kv_cache_spec: AttentionSpec, layer_names: list[str],
+                 vllm_config: VllmConfig, device: torch.device):
+        assert isinstance(kv_cache_spec, MambaSpec)
+        self.kv_cache_spec = kv_cache_spec
+
+    def build(self,
+              common_prefix_len: int,
+              common_attn_metadata: CommonAttentionMetadata,
+              fast_build: bool = False) -> LinearAttentionMetadata:
+        query_start_loc = common_attn_metadata.query_start_loc
+        seq_lens = common_attn_metadata.seq_lens
+
+        state_indices_tensor = common_attn_metadata.block_table_tensor[:, 0]
+
+        num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = (
+            split_decodes_and_prefills(common_attn_metadata,
+                                       decode_threshold=1))
+
+        attn_metadata = LinearAttentionMetadata(
+            num_prefills=num_prefills,
+            num_prefill_tokens=num_prefill_tokens,
+            num_decodes=num_decodes,
+            num_decode_tokens=num_decode_tokens,
+            query_start_loc=query_start_loc,
+            seq_lens=seq_lens,
+            state_indices_tensor=state_indices_tensor,
+        )
+        return attn_metadata

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/attention/backends/mamba1_attn.py

@@ -0,0 +1,83 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from dataclasses import dataclass
+from typing import ClassVar, Optional
+
+import torch
+
+from vllm.attention.backends.abstract import AttentionBackend
+from vllm.config import VllmConfig
+from vllm.v1.attention.backends.utils import (AttentionMetadataBuilder,
+                                              CommonAttentionMetadata,
+                                              split_decodes_and_prefills)
+from vllm.v1.kv_cache_interface import AttentionSpec, MambaSpec
+
+
+class Mamba1AttentionBackend(AttentionBackend):
+
+    @staticmethod
+    def get_builder_cls() -> type["Mamba1AttentionMetadataBuilder"]:
+        return Mamba1AttentionMetadataBuilder
+
+
+@dataclass
+class Mamba1AttentionMetadata:
+    query_start_loc: torch.Tensor
+    context_lens_tensor: torch.Tensor
+    state_indices_tensor: torch.Tensor
+    has_initial_states: Optional[torch.Tensor]
+    num_prefills: int
+    num_prefill_tokens: int
+    num_decodes: int
+    num_decode_tokens: int
+
+
+class Mamba1AttentionMetadataBuilder(
+        AttentionMetadataBuilder[Mamba1AttentionMetadata]):
+    reorder_batch_threshold: ClassVar[int] = 1
+
+    def __init__(
+        self,
+        kv_cache_spec: AttentionSpec,
+        vllm_config: VllmConfig,
+        device: torch.device,
+        layer_names: list[str],
+    ):
+        assert isinstance(kv_cache_spec, MambaSpec)
+        self.kv_cache_spec = kv_cache_spec
+        self.device = device
+        self.vllm_config = vllm_config
+        self.layer_names = layer_names
+
+    def build(
+        self,
+        common_prefix_len: int,
+        common_attn_metadata: CommonAttentionMetadata,
+        fast_build: bool = False,
+    ) -> Mamba1AttentionMetadata:
+        query_start_loc = common_attn_metadata.query_start_loc
+
+        state_indices_tensor = common_attn_metadata.block_table_tensor[:, 0]
+        context_lens_tensor = common_attn_metadata.num_computed_tokens_cpu.to(
+            query_start_loc.device)
+
+        num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = (
+            split_decodes_and_prefills(common_attn_metadata,
+                                       decode_threshold=1))
+
+        has_initial_states = None
+
+        if num_prefills > 0:
+            has_initial_states = context_lens_tensor > 0
+
+        return Mamba1AttentionMetadata(
+            query_start_loc=query_start_loc,
+            context_lens_tensor=context_lens_tensor,
+            has_initial_states=has_initial_states,
+            state_indices_tensor=state_indices_tensor,
+            num_prefills=num_prefills,
+            num_prefill_tokens=num_prefill_tokens,
+            num_decodes=num_decodes,
+            num_decode_tokens=num_decode_tokens,
+        )

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/attention/backends/mamba2_attn.py

@@ -0,0 +1,205 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import math
+from dataclasses import dataclass
+from typing import ClassVar, Optional
+
+import torch
+
+from vllm.attention.backends.abstract import AttentionBackend
+from vllm.attention.backends.utils import PAD_SLOT_ID
+from vllm.config import VllmConfig
+from vllm.v1.attention.backends.utils import (AttentionCGSupport,
+                                              AttentionMetadataBuilder,
+                                              CommonAttentionMetadata,
+                                              split_decodes_and_prefills)
+from vllm.v1.kv_cache_interface import AttentionSpec, MambaSpec
+
+
+def _query_start_loc_to_chunk_indices_offsets(query_start_loc: torch.Tensor,
+                                              chunk_size: int,
+                                              total_seqlens: int):
+
+    cu_seqlens = query_start_loc[1:]  # remove prepended 0
+
+    # outputs will have length expansion of chunks that do not divide
+    # chunk_size
+    N = math.ceil(total_seqlens / chunk_size) + (cu_seqlens[:-1] % chunk_size
+                                                 > 0).sum()
+    chunk_indices = torch.arange(N,
+                                 dtype=torch.int,
+                                 device=query_start_loc.device)
+    chunk_offsets = torch.zeros((N, ),
+                                dtype=torch.int,
+                                device=query_start_loc.device)
+
+    p = 0  # num of insertions
+    for s, e in zip(cu_seqlens[:-1], cu_seqlens[1:]):
+
+        # if does not divide chunk_size, then there is one chunk insertion
+        p += (s % chunk_size > 0)
+
+        # get the dimensions
+        # - the + 1 for _e is to shift the boundary by one chunk
+        # - this shifting is not needed if chunk_size divides e
+        _s, _e = s // chunk_size + p, e // chunk_size + p + (e % chunk_size
+                                                             > 0)
+
+        # adjust indices and offsets
+        chunk_indices[_s:_e] -= p
+        chunk_offsets[_s] = s % chunk_size
+
+    return chunk_indices, chunk_offsets
+
+
+class Mamba2AttentionBackend(AttentionBackend):
+
+    @staticmethod
+    def get_builder_cls() -> type["Mamba2AttentionMetadataBuilder"]:
+        return Mamba2AttentionMetadataBuilder
+
+
+@dataclass
+class Mamba2AttentionMetadata:
+    num_prefills: int
+    num_prefill_tokens: int
+    num_decodes: int
+    num_decode_tokens: int
+    query_start_loc: torch.Tensor
+    seq_lens: torch.Tensor
+
+    prep_initial_states: bool
+    chunk_size: int
+
+    # The following tensors only contain prefill requests and will be None if
+    # the batch has no prefill request.
+    has_initial_states_p: Optional[torch.Tensor]
+    seq_idx_p: Optional[torch.Tensor]
+    chunk_indices_p: Optional[torch.Tensor]
+    chunk_offsets_p: Optional[torch.Tensor]
+
+    state_indices_tensor: torch.Tensor  # shape: [batch,]
+
+    # The following attributes are for triton implementation of causal_conv1d
+    nums_dict: Optional[dict] = None
+    cu_seqlen: Optional[int] = None
+    batch_ptr: Optional[torch.tensor] = None
+    token_chunk_offset_ptr: Optional[torch.tensor] = None
+
+
+class Mamba2AttentionMetadataBuilder(
+        AttentionMetadataBuilder[Mamba2AttentionMetadata]):
+    cudagraph_support: ClassVar[AttentionCGSupport] = \
+        AttentionCGSupport.UNIFORM_SINGLE_TOKEN_DECODE
+
+    reorder_batch_threshold: ClassVar[int] = 1
+
+    def __init__(self, kv_cache_spec: AttentionSpec, layer_names: list[str],
+                 vllm_config: VllmConfig, device: torch.device):
+        assert isinstance(kv_cache_spec, MambaSpec)
+        self.kv_cache_spec = kv_cache_spec
+        self.chunk_size = vllm_config.model_config.get_mamba_chunk_size()
+        self.vllm_config = vllm_config
+        self.compilation_config = vllm_config.compilation_config
+        assert self.chunk_size is not None, (
+            "chunk_size needs to be set in the model config for Mamba2 models")
+        self.decode_cudagraph_max_bs = min(
+            self.vllm_config.scheduler_config.max_num_seqs,
+            self.compilation_config.max_capture_size)
+        self.state_indices_tensor = torch.empty(
+            (self.decode_cudagraph_max_bs, ),
+            dtype=torch.int32,
+            device=device,
+        )
+
+    def build(self,
+              common_prefix_len: int,
+              common_attn_metadata: CommonAttentionMetadata,
+              fast_build: bool = False) -> Mamba2AttentionMetadata:
+        num_reqs = common_attn_metadata.num_reqs
+        query_start_loc = common_attn_metadata.query_start_loc
+        seq_lens = common_attn_metadata.seq_lens
+
+        seq_idx_p = None
+        chunk_indices_p, chunk_offsets_p = None, None
+        # Need flags to indicate if there are initial states
+        # currently we really only support the FlashAttention backend
+        has_initial_states_p = None
+        prep_initial_states = False
+
+        state_indices_tensor = common_attn_metadata.block_table_tensor[:, 0]
+
+        num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = (
+            split_decodes_and_prefills(common_attn_metadata,
+                                       decode_threshold=1))
+
+        # Compute seq_idx, chunk_indices and chunk_offsets for prefill only
+        if num_prefills > 0:
+            #[batch,]
+            has_initial_states_cpu = (
+                common_attn_metadata.
+                num_computed_tokens_cpu[num_reqs - num_prefills:num_reqs] > 0)
+            prep_initial_states = torch.any(has_initial_states_cpu).item()
+            has_initial_states_p = has_initial_states_cpu.to(
+                query_start_loc.device)
+
+            query_start_loc_p = common_attn_metadata.query_start_loc[
+                -num_prefills - 1:] - num_decode_tokens
+
+            seq_idx_p = torch.repeat_interleave(torch.arange(
+                num_prefills,
+                dtype=torch.int32,
+                device=query_start_loc_p.device),
+                                                query_start_loc_p.diff(),
+                                                output_size=num_prefill_tokens)
+            seq_idx_p.unsqueeze_(0)
+
+            # We compute metadata for chunked prefill once at the top level
+            # model forward and reuse them in mamba layers. If not needed,
+            # they will be ignored inside mamba kernels.
+            if prep_initial_states:
+                chunk_indices_p, chunk_offsets_p = (
+                    _query_start_loc_to_chunk_indices_offsets(
+                        query_start_loc_p, self.chunk_size,
+                        num_prefill_tokens))
+
+        elif num_decodes <= self.decode_cudagraph_max_bs:
+            # Pad state tensor for CUDA graph
+            num_input_tokens = self.vllm_config.pad_for_cudagraph(num_decodes)
+            self.state_indices_tensor[:num_decodes].copy_(state_indices_tensor,
+                                                          non_blocking=True)
+            state_indices_tensor = self.state_indices_tensor[:num_input_tokens]
+            state_indices_tensor[num_decodes:] = PAD_SLOT_ID
+
+        attn_metadata = Mamba2AttentionMetadata(
+            num_prefills=num_prefills,
+            num_prefill_tokens=num_prefill_tokens,
+            num_decodes=num_decodes,
+            num_decode_tokens=num_decode_tokens,
+            query_start_loc=query_start_loc,
+            seq_lens=seq_lens,
+            prep_initial_states=prep_initial_states,
+            chunk_size=self.chunk_size,
+            has_initial_states_p=has_initial_states_p,
+            seq_idx_p=seq_idx_p,
+            chunk_indices_p=chunk_indices_p,
+            chunk_offsets_p=chunk_offsets_p,
+            state_indices_tensor=state_indices_tensor,
+        )
+        return attn_metadata
+
+    def build_for_cudagraph_capture(
+            self, common_attn_metadata: CommonAttentionMetadata):
+        """
+        This method builds the metadata for full cudagraph capture.
+        Currently, only decode is supported for full cudagraphs with Mamba.
+        """
+        m = common_attn_metadata
+
+        assert m.num_reqs == m.num_actual_tokens, \
+            "Mamba only supports decode-only full CUDAGraph capture. " \
+            "Make sure all cudagraph capture sizes <= max_num_seq."
+
+        m.max_query_len = 1  # decode-only
+
+        return self.build(0, m)

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/attention/backends/mamba_selectors.py

@@ -0,0 +1,18 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from vllm.attention.backends.abstract import AttentionBackend
+from vllm.v1.attention.backends.linear_attn import LinearAttentionBackend
+from vllm.v1.attention.backends.mamba1_attn import Mamba1AttentionBackend
+from vllm.v1.attention.backends.mamba2_attn import Mamba2AttentionBackend
+
+
+def get_mamba_attn_backend(mamba_type: str) -> type[AttentionBackend]:
+    if mamba_type == "mamba1":
+        return Mamba1AttentionBackend
+    if mamba_type == "mamba2":
+        return Mamba2AttentionBackend
+    if mamba_type == "linear_attention":
+        return LinearAttentionBackend
+
+    raise NotImplementedError(f"Mamba Attention type {mamba_type} is not "
+                              "supported yet.")

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/attention/backends/mla/common.py

@@ -0,0 +1,1206 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+# MLA Common Components
+
+This file implements common components for MLA implementations.
+
+First we define:
+
+Sq      as Q sequence length
+Skv     as KV sequence length
+
+MLA has two possible ways of computing, a data-movement friendly approach and a
+compute friendly approach, we generally want to use the compute friendly
+approach for "prefill" (i.e. the ratio Sq / Skv is "small", is near 1)
+and the data-movement friendly approach for "decode" (i.e. the ratio
+Sq / Skv is "large").
+
+NOTE what we deem small and large is currently determined by if its labelled
+prefill or decode by the scheduler, but this is something we should probably
+tune.
+
+Main reference: DeepseekV2 paper, and FlashInfer Implementation
+(https://arxiv.org/abs/2405.04434 and https://github.com/flashinfer-ai/flashinfer/pull/551).
+
+Deepseek's MLA attention works the following way:
+* Use a single latent vector to represent the per-token entry of the KV cache. 
+* For decode (i.e. the memory friendly approach) the attention "simulates" a
+multi-head attention, while the compute is similar to multi-query attention.
+
+Below is example of both paths assuming batchsize = 1
+
+## More Extent Definitions:
+
+C           Context length, `Skv - Sq`
+H           hidden size
+N           number of attention heads
+Lq          latent dimension for Q              1536 in DSV3
+Lkv         latent dimension for K/V            512 in DSV3
+P           nope dimension, no rope.            128 in DSV3
+R           rope dimension, goes through rope.  64 in DSV3
+V           V head dim.                         128 in DSV3
+
+## Vector/Matrix Definitions
+
+h_t         hidden states (input to attention)  shape [Sq, H]
+q_c         latent/compressed Q                 shape [Sq, Lq]
+q_nope      uncompressed Q (no-rope)            shape [Sq, N, P]
+q_pe        uncompressed Q (rope)               shape [Sq, N, R]
+kv_c        latent/compressed KV                shape [Skv, Lkv]
+k_pe        decoupled k position embeddings     shape [Skv, R]
+new_kv_c    new kv_c from current iter          shape [Sq, Lkv]
+new_k_pe    new k_pe from current iter          shape [Sq, R]
+cache_kv_c  cached k_c from previous iters      shape [C, Lkv]
+cache_k_pe  cached k_pe from previous iters     shape [C, R]
+W_DQ        project h_t to q_c                  shape [H, Lq]
+W_UQ        project q_c to q_nope               shape [Lq, N * P]
+W_QR        project q_c to q_pe                 shape [Lq, N * R]
+W_DKV       project h_t to kv_c                 shape [H, Lkv]
+W_UK        project kv_c to k_nope              shape [Lkv, N, P]
+W_KR        project h_t to k_pe                 shape [H, R]
+W_UV        project kv_c to v                   shape [Lkv, N, V]
+W_O         project v to h_t                    shape [N * V, H]
+
+
+## Compute Friendly Approach (i.e. "_forward_prefill"):
+
+q_c      = h_t @ W_DQ
+q_nope   = (q_c @ W_UQ).view(Sq, N, P)
+q_pe     = RoPE(q_c @ W_QR).view(Sq, N, R)
+new_kv_c = h_t @ W_DKV
+new_k_pe = RoPE(h_t @ W_KR)
+kv_c     = torch.cat([new_kv_c, cache_kv_c], dim=0)
+k_pe     = torch.cat([new_k_pe, cache_k_pe], dim=0)
+k_nope   = (kv_c @ W_UK.view(Lkv, N * P)).view(Skv, N, P)
+v        = (kv_c @ W_UV.view(Lkv, N * V)).view(Skv, N, V)
+
+// MHA with QK headdim = P + R
+//           V headdim = V
+//      spda_o shape [Sq, N, V]
+spda_o = scaled_dot_product_attention(
+    torch.cat([q_nope, q_pe], dim=-1),
+    torch.cat([k_nope, k_pe.unsqueeze(1).expand(-1, N, -1)], dim=-1),
+    v
+) 
+return spda_o @ W_O
+
+NOTE: in the actual code,
+    `kv_b_proj` is [W_UK; W_UV] concatenated per head
+    `q_b_proj` is [W_UQ; W_QR] concatenated per head
+    `out_proj` is W_O
+
+
+## Data-Movement Friendly Approach (i.e. "_forward_decode"):
+
+Runtime
+q_c      = h_t @ W_DQ
+q_nope   = (q_c @ W_UQ).view(-1, N, P)
+ql_nope  = einsum("snh,lnh->snl", q, W_UK)
+q_pe     = RoPE(q_c @ W_QR).view(Sq, N, R)
+new_kv_c = h_t @ W_DKV
+new_k_pe = RoPE(h_t @ W_KR)
+kv_c     = torch.cat([new_kv_c, cache_kv_c], dim=0)
+k_pe     = torch.cat([new_k_pe, cache_k_pe], dim=0)
+
+// MQA with QK headdim = Lkv + R
+//           V headdim = Lkv
+//      spda_o shape [Sq, N, Lkv]
+// NOTE: this is less compute-friendly since Lkv > P
+//       but is more data-movement friendly since its MQA vs MHA
+spda_o = scaled_dot_product_attention(
+    torch.cat([ql_nope, q_pe], dim=-1),
+    torch.cat([kv_c, k_pe], dim=-1),
+    kv_c
+)
+
+o = einsum("snl,lnv->snv", spda_o.reshape(-1, N, Lkv), W_UV)
+return o.view(-1, N * V) @ self.num_heads @ W_O
+
+
+## Chunked Prefill
+
+For chunked prefill we want to use the compute friendly algorithm. We are 
+assuming sufficiently large Sq / Skv ratio, in the future may want to switch to 
+the data-movement friendly approach if the chunk (i.e. `Sq`) is small.
+
+However, the compute-friendly approach can potentially run out of memory if Skv
+is large due to: `k_nope = (kv_c @ W_UK).view(Skv, N, P)`
+
+To mitigate this, we chunk the computation of attention with respect to the 
+current context (i.e. `cache_kv_c` and `cache_k_pe`) so that we can used a 
+fixed workspace size.
+
+The chunked prefill approach is as follows:
+
+MCC        Max chunk of context to process per iter, computed dynamically, 
+           used to bound the memory usage
+
+q_c        = h_t @ W_DQ
+q_nope     = (q_c @ W_UQ).view(Sq, N, P)
+q_pe       = RoPE(q_c @ W_QR).view(Sq, N, R)
+new_kv_c   = h_t @ W_DKV
+new_k_pe   = RoPE(h_t @ W_KR)
+new_k_nope = (new_kv_c @ W_UK.view(Lkv, N * P)).view(Sq, N, P)
+new_v      = (new_kv_c @ W_UV.view(Lkv, N * V)).view(Sq, N, V)
+
+// MHA between queries and new KV
+//     with QK headdim = P + R
+//           V headdim = V
+//    curr_o   shape [Sq, N, V]
+//    curr_lse shape [N, Sq], this is just order FA returns
+curr_o, curr_lse = scaled_dot_product_attention(
+    torch.cat([q_nope, q_pe], dim=-1),
+    torch.cat([new_k_nope, new_k_pe.unsqueeze(1).expand(-1, N, -1)], dim=-1),
+    new_v,
+    casual=True,
+    return_softmax_lse=True
+) 
+
+// Compute attention with the already existing context
+for chunk_idx in range(cdiv(C, MCC)):
+    chunk_start  = chunk_idx * MCC
+    chunk_end    = min(chunk_start + MCC, C)
+    Sc           = chunk_end - chunk_start
+    cache_kv_c_chunk   = cache_kv_c[chunk_start:chunk_end]
+    cache_k_pe_chunk   = cache_k_pe[chunk_start:chunk_end]
+    cache_k_nope_chunk = (cache_kv_c_chunk @ W_UK).view(-1, N, P)
+    cache_v_chunk      = (cache_kv_c_chunk @ W_UV).view(-1, N, V)
+
+    chunk_o, chunk_lse = scaled_dot_product_attention(
+        torch.cat([q_nope, q_pe], dim=-1),
+        torch.cat([cache_k_nope_chunk,
+                   cache_k_pe_chunk.unsqueeze(1).expand(-1, N, -1)],
+                   dim=-1),
+        cache_v_chunk,
+        casual=False,
+        return_softmax_lse=True
+    )
+
+    curr_o, curr_lse = merge_attn_states(
+        suffix_output=curr_o,
+        suffix_lse=curr_lse,
+        prefix_output=chunk_o,
+        prefix_lse=chunk_lse,
+    )
+
+return curr_o @ W_O
+"""
+
+import functools
+from abc import abstractmethod
+from dataclasses import dataclass, field
+from typing import ClassVar, Generic, Optional, TypeVar, Union
+
+import torch
+
+import vllm.envs as envs
+from vllm import _custom_ops as ops
+from vllm.attention.backends.abstract import (AttentionBackend, AttentionLayer,
+                                              AttentionMetadata,
+                                              MLAAttentionImpl)
+from vllm.attention.backends.utils import get_mla_dims
+from vllm.attention.ops.merge_attn_states import merge_attn_states
+from vllm.attention.utils.fa_utils import get_flash_attn_version
+from vllm.config import VllmConfig
+from vllm.logger import init_logger
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               LinearBase,
+                                               UnquantizedLinearMethod)
+from vllm.platforms import current_platform
+from vllm.utils import cdiv, round_down
+from vllm.utils.flashinfer import has_nvidia_artifactory
+from vllm.v1.attention.backends.utils import (AttentionMetadataBuilder,
+                                              CommonAttentionMetadata,
+                                              get_per_layer_parameters,
+                                              infer_global_hyperparameters,
+                                              split_decodes_and_prefills)
+from vllm.v1.kv_cache_interface import AttentionSpec
+
+try:
+    from vllm.vllm_flash_attn import flash_attn_varlen_func
+    is_vllm_fa = True
+except ImportError:
+    # For rocm use upstream flash attention
+    if current_platform.is_rocm():
+        from flash_attn import flash_attn_varlen_func
+    is_vllm_fa = False
+
+try:
+    from flashinfer import BatchPrefillWithRaggedKVCacheWrapper
+    from flashinfer.prefill import (  # noqa: F401
+        cudnn_batch_prefill_with_kv_cache)
+    flashinfer_available = True
+except ImportError:
+    flashinfer_available = False
+
+logger = init_logger(__name__)
+
+CUDNN_WORKSPACE_SIZE = 12800
+
+
+class MLACommonBackend(AttentionBackend):
+
+    accept_output_buffer: bool = True
+
+    @staticmethod
+    def get_name() -> str:
+        return "TRITON_MLA_VLLM_V1"
+
+    @staticmethod
+    def get_metadata_cls() -> type["AttentionMetadata"]:
+        return MLACommonMetadata
+
+    @staticmethod
+    def get_builder_cls() -> type["MLACommonMetadataBuilder"]:
+        return MLACommonMetadataBuilder
+
+    @staticmethod
+    def get_kv_cache_shape(
+        num_blocks: int,
+        block_size: int,
+        num_kv_heads: int,  # assumed to be 1 for MLA
+        head_size: int,
+    ) -> tuple[int, ...]:
+        return (num_blocks, block_size, head_size)
+
+    @classmethod
+    def get_supported_dtypes(cls) -> list[torch.dtype]:
+        return [torch.float16, torch.bfloat16]
+
+    @classmethod
+    def get_supported_head_sizes(cls) -> list[int]:
+        return [576]
+
+    @classmethod
+    def validate_head_size(cls, head_size: int) -> None:
+        supported_head_sizes = cls.get_supported_head_sizes()
+        if head_size not in supported_head_sizes:
+            attn_type = cls.__name__.removesuffix("Backend")
+            raise ValueError(
+                f"Head size {head_size} is not supported by {attn_type}. "
+                f"Supported head sizes are: {supported_head_sizes}. "
+                "Set VLLM_ATTENTION_BACKEND=FLEX_ATTENTION to use "
+                "FlexAttention backend which supports all head sizes.")
+
+
+@dataclass
+class MLACommonPrefillMetadata:
+    """ Prefill Specific Metadata """
+
+    @dataclass
+    class ChunkedContextMetadata:
+        # New for MLA (compared to FlashAttention)
+        # For handling chunked prefill
+        cu_seq_lens: torch.Tensor
+        starts: torch.Tensor
+        seq_tot: list[int]
+        max_seq_lens: list[int]
+        seq_lens: torch.Tensor
+        workspace: torch.Tensor
+
+    block_table: torch.Tensor
+    query_start_loc: torch.Tensor
+    max_query_len: int
+    chunked_context: Optional[ChunkedContextMetadata] = None
+
+
+@dataclass
+class FlashInferPrefillMetadata(MLACommonPrefillMetadata):
+    prefill_main: Optional['BatchPrefillWithRaggedKVCacheWrapper'] = None
+    prefill_chunks: list['BatchPrefillWithRaggedKVCacheWrapper'] = field(
+        default_factory=list)
+
+
+@dataclass
+class CudnnPrefillMetadata(MLACommonPrefillMetadata):
+
+    class ChunkedContextMetadata(
+            MLACommonPrefillMetadata.ChunkedContextMetadata):
+        seq_lens: torch.Tensor
+
+    query_seq_lens: Optional[torch.Tensor] = None
+    cudnn_workspace: Optional[torch.Tensor] = None
+
+
+@dataclass
+class MLACommonDecodeMetadata:
+    block_table: torch.Tensor
+    seq_lens: torch.Tensor
+
+
+D = TypeVar("D", bound=MLACommonDecodeMetadata)
+
+
+@dataclass
+class MLACommonMetadata(Generic[D]):
+    """Metadata for MLACommon.
+
+    NOTE: Please read the comment at the top of the file before trying to
+    understand this class
+    """
+    # NOTE(sang): Definition of context_len, query_len, and seq_len.
+    # |---------- N-1 iteration --------|
+    # |---------------- N iteration ---------------------|
+    # |- tokenA -|......................|-- newTokens ---|
+    # |---------- context_len ----------|
+    # |-------------------- seq_len ---------------------|
+    #                                   |-- query_len ---|
+
+    num_reqs: int
+    max_query_len: int
+
+    num_actual_tokens: int  # Number of tokens excluding padding.
+    query_start_loc: torch.Tensor
+    slot_mapping: torch.Tensor
+
+    # New for MLA (compared to FlashAttention)
+    # For handling prefill decode split
+    num_decodes: int
+    num_decode_tokens: int
+    num_prefills: int
+
+    # The dimension of the attention heads
+    head_dim: Optional[int] = None
+
+    decode: Optional[D] = None
+    prefill: Optional[Union[MLACommonPrefillMetadata,
+                            FlashInferPrefillMetadata,
+                            CudnnPrefillMetadata]] = None
+
+    def __post_init__(self):
+        if self.head_dim is not None:
+            MLACommonBackend.validate_head_size(self.head_dim)
+
+
+M = TypeVar("M", bound=MLACommonMetadata)
+
+
+def use_flashinfer_prefill() -> bool:
+    # For blackwell default to flashinfer prefill if its available since
+    # it is faster than FA2.
+    return (flashinfer_available and not envs.VLLM_USE_CUDNN_PREFILL
+            and current_platform.is_device_capability(100))
+
+
+def use_cudnn_prefill() -> bool:
+    return (flashinfer_available and envs.VLLM_USE_CUDNN_PREFILL
+            and current_platform.is_device_capability(100)
+            and has_nvidia_artifactory())
+
+
+# Currently 394MB, this can be tuned based on GEMM sizes used.
+# Chosen to be the same as sglang:
+#  https://github.com/sgl-project/sglang/blob/766392c6bda2558b61ce6d1c1bfd8081a549e1f1/python/sglang/global_config.py#L37
+FLASHINFER_WORKSPACE_BUFFER_SIZE = 394 * 1024 * 1024
+
+
+class MLACommonMetadataBuilder(AttentionMetadataBuilder[M]):
+    """
+    NOTE: Please read the comment at the top of the file before trying to
+    understand this class
+    """
+    reorder_batch_threshold: ClassVar[int] = 1
+
+    def __init__(self,
+                 kv_cache_spec: AttentionSpec,
+                 layer_names: list[str],
+                 vllm_config: VllmConfig,
+                 device: torch.device,
+                 metadata_cls: Optional[type[M]] = None):
+        self.metadata_cls = metadata_cls \
+            if metadata_cls is not None else MLACommonMetadata
+        self.kv_cache_spec = kv_cache_spec
+        self.device = device
+        scheduler_config = vllm_config.scheduler_config
+        self.model_config = vllm_config.model_config
+        cache_config = vllm_config.cache_config
+        parallel_config = vllm_config.parallel_config
+        self.chunked_prefill_enabled = scheduler_config.chunked_prefill_enabled
+        self.num_heads = self.model_config.get_num_attention_heads(
+            parallel_config)
+        self.mla_dims = get_mla_dims(self.model_config)
+        self.aot_schedule = current_platform.is_cuda()
+
+        # Dont try to access the runner on AMD
+        if self.aot_schedule:
+            self.page_size = self.kv_cache_spec.block_size
+
+        if self.chunked_prefill_enabled:
+            self.chunked_prefill_workspace_size = min(
+                # Max sure there is enough for 8 full length request or at least
+                # 4 pages of cache per request
+                max(
+                    8 * self.model_config.max_model_len, 4 *
+                    scheduler_config.max_num_seqs * cache_config.block_size),
+                # For long-context models try not to over-allocate limiting
+                # kv-cache space, limiting it to 64k tokens,
+                # which would result in the workspace being:
+                #   2*(576)*(64*1024) = 144mb
+                # (assuming 576 MLA head dim, and fp16)
+                # which would result in up-projected context being
+                #   2*(192*128)*(64*1024) = 3gb
+                # (assuming 192 QK head dim, 128 heads, and fp16)
+                128 * 1024)
+            assert self.chunked_prefill_workspace_size >= \
+                scheduler_config.max_num_seqs * cache_config.block_size
+            self.chunked_prefill_workspace = torch.empty(
+                (self.chunked_prefill_workspace_size,
+                 self.model_config.get_head_size()),
+                dtype=self.model_config.dtype,
+                device=device,
+            )
+
+        self._use_cudnn_prefill = use_cudnn_prefill()
+        self._use_fi_prefill = use_flashinfer_prefill()
+        self.prefill_metadata_cls = (
+            FlashInferPrefillMetadata
+            if self._use_fi_prefill else CudnnPrefillMetadata
+            if self._use_cudnn_prefill else MLACommonPrefillMetadata)
+
+        if self._use_fi_prefill:
+            self._workspace_buffer = torch.empty(
+                FLASHINFER_WORKSPACE_BUFFER_SIZE,
+                dtype=torch.uint8,
+                device=device)
+
+            self._fi_prefill_main: Optional[
+                BatchPrefillWithRaggedKVCacheWrapper] = None
+            self._fi_prefill_chunks: list[
+                BatchPrefillWithRaggedKVCacheWrapper] = []
+
+            self._global_hyperparameters = infer_global_hyperparameters(
+                get_per_layer_parameters(vllm_config, layer_names,
+                                         MLACommonImpl))
+
+        if self._use_cudnn_prefill:
+            self.cudnn_workspace = torch.empty(
+                CUDNN_WORKSPACE_SIZE * scheduler_config.max_num_seqs,
+                dtype=torch.int8,
+                device=device,
+            )
+
+    def _build_fi_prefill_wrappers(self, prefill: FlashInferPrefillMetadata):
+        qo_indptr = prefill.query_start_loc
+
+        has_context = False
+        if prefill.chunked_context is not None:
+            chunked_context = prefill.chunked_context
+            has_context = True
+
+        if self._fi_prefill_main is None:
+            self._fi_prefill_main = BatchPrefillWithRaggedKVCacheWrapper(
+                self._workspace_buffer, "NHD", backend="cutlass")
+
+        if has_context:
+            num_chunks = chunked_context.cu_seq_lens.shape[0]
+            # Allocate more prefill chunk wrappers if needed
+            if len(self._fi_prefill_chunks) < num_chunks:
+                for _ in range(len(self._fi_prefill_chunks), num_chunks):
+                    self._fi_prefill_chunks.append(
+                        BatchPrefillWithRaggedKVCacheWrapper(
+                            self._workspace_buffer, "NHD", backend="cutlass"))
+            assert num_chunks <= len(self._fi_prefill_chunks)
+
+        # In MLA, the non-latent num_qo_heads == num_kv_heads
+        num_qo_heads = self.num_heads
+        num_kv_heads = num_qo_heads
+
+        # Sanity: Verify that num_kv_heads == 1 since it is latent space
+        assert self.kv_cache_spec.num_kv_heads == 1
+
+        # Get non-latent head_dim_qk and head_dim_vo
+        head_dim_qk = (self.mla_dims.qk_nope_head_dim +
+                       self.mla_dims.qk_rope_head_dim)
+        head_dim_vo = self.mla_dims.v_head_dim
+
+        # For main run, qo_indptr == kv_indptr
+        kv_indptr = qo_indptr.clone()
+
+        # Prepare main prefill
+        self._fi_prefill_main.plan(
+            qo_indptr=qo_indptr,
+            kv_indptr=kv_indptr,
+            num_qo_heads=num_qo_heads,
+            num_kv_heads=num_kv_heads,
+            head_dim_qk=head_dim_qk,
+            head_dim_vo=head_dim_vo,
+            causal=True,  # This is main run
+            sm_scale=self._global_hyperparameters.sm_scale,
+            window_left=self._global_hyperparameters.window_left,
+            logits_soft_cap=self._global_hyperparameters.logits_soft_cap,
+            q_data_type=self.model_config.dtype,
+            kv_data_type=self.kv_cache_spec.dtype,
+        )
+
+        # Prepare context prefills
+        if has_context:
+            for i in range(num_chunks):
+                kv_indptr_chunk = chunked_context.cu_seq_lens[i]
+
+                self._fi_prefill_chunks[i].plan(
+                    qo_indptr=qo_indptr,
+                    kv_indptr=kv_indptr_chunk,
+                    num_qo_heads=num_qo_heads,
+                    num_kv_heads=num_kv_heads,
+                    head_dim_qk=head_dim_qk,
+                    head_dim_vo=head_dim_vo,
+                    causal=False,  # This is context run
+                    sm_scale=self._global_hyperparameters.sm_scale,
+                    window_left=self._global_hyperparameters.window_left,
+                    logits_soft_cap=self._global_hyperparameters.
+                    logits_soft_cap,
+                    q_data_type=self.model_config.dtype,
+                    kv_data_type=self.kv_cache_spec.dtype,
+                )
+
+        prefill.prefill_main = self._fi_prefill_main
+        prefill.prefill_chunks = self._fi_prefill_chunks
+
+    def _build_decode(self, block_table_tensor: torch.Tensor,
+                      seq_lens: torch.Tensor):
+        return MLACommonDecodeMetadata(
+            block_table=block_table_tensor,
+            seq_lens=seq_lens,
+        )
+
+    def build_for_cudagraph_capture(
+            self, common_attn_metadata: CommonAttentionMetadata) -> M:
+        """
+        This method builds the metadata for full cudagraph capture.
+        Currently, only decode is supported for full cudagraphs with MLA.
+        """
+        m = common_attn_metadata
+        assert m.num_reqs == m.num_actual_tokens, \
+            "MLA only supports decode-only full CUDAGraph capture. " \
+            "Make sure all cudagraph capture sizes <= max_num_seq."
+
+        assert m.max_query_len == 1  # decode-only
+
+        return self.build(0, m)
+
+    def build(self,
+              common_prefix_len: int,
+              common_attn_metadata: CommonAttentionMetadata,
+              fast_build: bool = False) -> M:
+        num_reqs = common_attn_metadata.num_reqs
+        num_tokens = common_attn_metadata.num_actual_tokens
+        max_query_len = common_attn_metadata.max_query_len
+
+        # Note(simon): be careful about the CPU <> GPU memory movement in this
+        # function. We should avoid GPU -> CPU sync as much as possible because
+        # it blocks on all previous kernels.
+        device = self.device
+        block_table_tensor = common_attn_metadata.block_table_tensor
+        slot_mapping = common_attn_metadata.slot_mapping
+
+        query_start_loc = common_attn_metadata.query_start_loc
+        query_start_loc_cpu = common_attn_metadata.query_start_loc_cpu
+        seq_lens = common_attn_metadata.seq_lens
+
+        query_seq_lens_cpu = query_start_loc_cpu[1:] - query_start_loc_cpu[:-1]
+
+        num_computed_tokens_cpu = (common_attn_metadata.seq_lens_cpu -
+                                   query_seq_lens_cpu)
+
+        num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = \
+            split_decodes_and_prefills(common_attn_metadata)
+
+        assert num_decodes + num_prefills == num_reqs
+        assert num_decode_tokens + num_prefill_tokens == num_tokens
+
+        prefill_metadata = None
+        if num_prefills > 0:
+            reqs_start = num_decodes  # prefill_start
+
+            context_lens_cpu = num_computed_tokens_cpu[reqs_start:num_reqs]
+            max_context_len_cpu = context_lens_cpu.max().item()
+            num_prefills_with_context_cpu = (context_lens_cpu > 0).sum().item()
+            prefill_query_start_loc = query_start_loc[
+                reqs_start:] - query_start_loc[reqs_start]
+
+            chunked_context_metadata = None
+            if self.chunked_prefill_enabled and num_prefills > 0 \
+                and max_context_len_cpu > 0:
+                # NOTE: it is recommend you read the `Chunked Prefill` section
+                # in the comment at the top of the file before trying to
+                # understand the following code
+
+                # currently we allocate an equal amount of workspace for each
+                # prefill in the batch, we could probably use a more advanced
+                # algorithm here and allocate more workspace to prefills with
+                # longer context lengths
+                max_context_chunk = (self.chunked_prefill_workspace_size //
+                                     num_prefills_with_context_cpu)
+
+                if self.aot_schedule:
+                    # align max_context_chunk to page_size by rounding down,
+                    # currently the `gather_cache` kernel cannot handle
+                    # `context_chunk_starts` that are not aligned to page_size
+                    max_context_chunk = round_down(max_context_chunk,
+                                                   self.page_size)
+
+                assert max_context_chunk > 0
+                num_chunks = cdiv(max_context_len_cpu, max_context_chunk)
+
+                # if `max_context_chunk = 256`, `num_chunks = 3`, and
+                #   `num_prefills_with_context = 4`, create a tensor that looks
+                # like
+                #  [[0, 0, 0, 0], [256, 256, 256, 256], [512, 512, 512, 512]]
+                # Note(simon): this is done in CPU because of downstream's
+                # of `to_list`.
+                chunk_starts = \
+                    torch.arange(num_chunks, dtype=torch.int32) \
+                    .unsqueeze(1).expand(-1, num_prefills) \
+                    * max_context_chunk
+                chunk_ends = torch.min(context_lens_cpu.unsqueeze(0),
+                                       chunk_starts + max_context_chunk)
+                chunk_seq_lens = (chunk_ends - chunk_starts).clamp(min=0)
+
+                cu_seq_lens_cpu = torch.zeros(num_chunks,
+                                              num_prefills + 1,
+                                              dtype=torch.int32,
+                                              pin_memory=True)
+                torch.cumsum(chunk_seq_lens,
+                             dim=1,
+                             out=cu_seq_lens_cpu[:, 1:],
+                             dtype=torch.int32)
+
+                chunked_context_metadata_cls = \
+                    CudnnPrefillMetadata.ChunkedContextMetadata \
+                    if self._use_cudnn_prefill else \
+                        MLACommonPrefillMetadata.ChunkedContextMetadata
+
+                chunked_context_metadata = \
+                    chunked_context_metadata_cls(
+                    cu_seq_lens=cu_seq_lens_cpu.to(device, non_blocking=True),
+                    starts=chunk_starts.to(device, non_blocking=True),
+                    seq_tot=chunk_seq_lens.sum(dim=1).tolist(),
+                    max_seq_lens=chunk_seq_lens.max(dim=1).values.tolist(),
+                    seq_lens=chunk_seq_lens,
+                    workspace=self.chunked_prefill_workspace,
+                )
+
+                if self._use_cudnn_prefill:
+                    chunked_context_metadata.seq_lens = chunk_seq_lens
+
+                assert max(chunked_context_metadata.max_seq_lens) <= \
+                    self.chunked_prefill_workspace_size
+
+            prefill_metadata = self.prefill_metadata_cls(
+                block_table=block_table_tensor[reqs_start:, ...],
+                query_start_loc=prefill_query_start_loc,
+                max_query_len=max_query_len,
+                chunked_context=chunked_context_metadata,
+            )
+
+            if self._use_cudnn_prefill:
+                assert isinstance(prefill_metadata, CudnnPrefillMetadata)
+                prefill_metadata.query_seq_lens = prefill_query_start_loc[1:] \
+                    - prefill_query_start_loc[:-1]
+                prefill_metadata.cudnn_workspace = self.cudnn_workspace
+
+        decode_metadata = None
+        if num_decodes > 0:
+            decode_metadata = self._build_decode(
+                block_table_tensor=block_table_tensor[:num_decodes, ...],
+                seq_lens=seq_lens[:num_decodes],
+            )
+
+        attn_metadata = self.metadata_cls(
+            num_reqs=common_attn_metadata.num_reqs,
+            max_query_len=common_attn_metadata.max_query_len,
+            num_actual_tokens=num_tokens,
+            query_start_loc=query_start_loc,
+            slot_mapping=slot_mapping,
+            head_dim=self.model_config.get_head_size(),
+            # MLACommonMetadata Chunk prefill specific
+            num_decodes=num_decodes,
+            num_decode_tokens=num_decode_tokens,
+            num_prefills=num_prefills,
+            prefill=prefill_metadata,
+            decode=decode_metadata,
+        )
+
+        if self._use_fi_prefill and num_prefills > 0:
+            assert isinstance(attn_metadata.prefill, FlashInferPrefillMetadata)
+            self._build_fi_prefill_wrappers(attn_metadata.prefill)
+
+        return attn_metadata
+
+
+class MLACommonImpl(MLAAttentionImpl[M], Generic[M]):
+    """
+    NOTE: Please read the comment at the top of the file before trying to
+    understand this class
+    """
+
+    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
+        q_lora_rank: Optional[int],
+        kv_lora_rank: int,
+        qk_nope_head_dim: int,
+        qk_rope_head_dim: int,
+        qk_head_dim: int,
+        v_head_dim: int,
+        kv_b_proj: ColumnParallelLinear,
+    ) -> None:
+        if kv_sharing_target_layer_name is not None:
+            raise NotImplementedError("KV sharing is not supported for MLA")
+
+        self.num_heads = num_heads
+        self.head_size = head_size
+        self.scale = float(scale)
+        self.num_kv_heads = num_kv_heads
+        self.kv_cache_dtype = kv_cache_dtype
+
+        self.q_lora_rank = q_lora_rank
+        self.kv_lora_rank = kv_lora_rank
+        self.qk_nope_head_dim = qk_nope_head_dim
+        self.qk_rope_head_dim = qk_rope_head_dim
+        self.qk_head_dim = qk_head_dim
+        self.v_head_dim = v_head_dim
+        self.kv_b_proj = kv_b_proj
+
+        if use_flashinfer_prefill():
+            logger.debug_once("Using FlashInfer prefill for MLA")
+            self._run_prefill_context_chunk = self._run_prefill_context_chunk_fi
+            self._run_prefill_new_tokens = self._run_prefill_new_tokens_fi
+            self._pad_v = False
+        elif use_cudnn_prefill():
+            logger.debug_once("Using CUDNN prefill for MLA")
+            self._run_prefill_context_chunk = \
+                self._run_prefill_context_chunk_cudnn
+            self._run_prefill_new_tokens = self._run_prefill_new_tokens_cudnn
+            self._pad_v = False
+        else:  # Use FlashAttention
+            logger.debug_once("Using FlashAttention prefill for MLA")
+            self._run_prefill_context_chunk = self._run_prefill_context_chunk_fa
+            self._run_prefill_new_tokens = self._run_prefill_new_tokens_fa
+
+            # Handle the differences between the flash_attn_varlen from
+            # flash_attn and the one from vllm_flash_attn. The former is used on
+            # RoCM and the latter has an additional parameter to control
+            # FA2 vs FA3
+            self.flash_attn_varlen_func = flash_attn_varlen_func
+            self.vllm_flash_attn_version = get_flash_attn_version()
+            if self.vllm_flash_attn_version is not None:
+                self.flash_attn_varlen_func = \
+                    functools.partial(flash_attn_varlen_func,
+                                    fa_version=self.vllm_flash_attn_version)
+
+            # For MLA the v head dim is smaller than qk head dim so we pad out
+            # v with 0s to match the qk head dim for attention backends that do
+            # not support different headdims
+            # We don't need to pad V if we are on a hopper system with FA3
+            self._pad_v = self.vllm_flash_attn_version is None or not (
+                self.vllm_flash_attn_version == 3
+                and current_platform.get_device_capability()[0] == 9)
+
+    def _flash_attn_varlen_diff_headdims(self,
+                                         q,
+                                         k,
+                                         v,
+                                         return_softmax_lse=False,
+                                         softmax_scale=None,
+                                         **kwargs):
+        maybe_padded_v = v
+        if self._pad_v:
+            maybe_padded_v = torch.nn.functional.pad(
+                v, [0, q.shape[-1] - v.shape[-1]], value=0)
+
+        if is_vllm_fa:
+            kwargs["return_softmax_lse"] = return_softmax_lse
+        else:
+            # ROCm leverages the upstream flash_attn, which takes a parameter
+            # called "return_attn_probs" instead of return_softmax_lse
+            kwargs["return_attn_probs"] = return_softmax_lse
+
+        attn_out = self.flash_attn_varlen_func(
+            q=q,
+            k=k,
+            v=maybe_padded_v,
+            softmax_scale=softmax_scale,
+            **kwargs,
+        )
+
+        # Unpack the output if there is multiple results
+        lse = None
+        if isinstance(attn_out, tuple):
+            attn_out, lse = attn_out[0], attn_out[1]
+
+        # Remain consistent with old `flash_attn_varlen_func` where there
+        # is only one output tensor if `return_softmax_lse` is False.
+        if return_softmax_lse:
+            return attn_out, lse
+        return attn_out
+
+    def _run_prefill_new_tokens_fa(self, prefill: MLACommonPrefillMetadata, q,
+                                   k, v, return_softmax_lse):
+        return self._flash_attn_varlen_diff_headdims(
+            q=q,
+            k=k,
+            v=v,
+            cu_seqlens_q=prefill.query_start_loc,
+            cu_seqlens_k=prefill.query_start_loc,
+            max_seqlen_q=prefill.max_query_len,
+            max_seqlen_k=prefill.max_query_len,
+            softmax_scale=self.scale,
+            causal=True,
+            return_softmax_lse=return_softmax_lse,
+        )
+
+    def _run_prefill_new_tokens_fi(self, prefill: MLACommonPrefillMetadata, q,
+                                   k, v, return_softmax_lse):
+        assert isinstance(prefill, FlashInferPrefillMetadata)
+        assert prefill.prefill_main is not None
+        return prefill.prefill_main.run(
+            q=q,
+            k=k,
+            v=v,
+            return_lse=return_softmax_lse,
+        )
+
+    def _run_prefill_new_tokens_cudnn(self, prefill: MLACommonPrefillMetadata,
+                                      q, k, v, return_softmax_lse):
+        assert isinstance(prefill, CudnnPrefillMetadata)
+        assert prefill.query_seq_lens is not None
+        output, lse = cudnn_batch_prefill_with_kv_cache(
+            q=q,
+            k_cache=k,
+            v_cache=v,
+            scale=self.scale,
+            workspace_buffer=prefill.cudnn_workspace,
+            max_token_per_sequence=prefill.max_query_len,
+            max_sequence_kv=prefill.max_query_len,
+            actual_seq_lens_q=prefill.query_seq_lens.view(-1, 1, 1, 1),
+            actual_seq_lens_kv=prefill.query_seq_lens.view(-1, 1, 1, 1),
+            causal=True,
+            return_lse=True,  # do not support False for now
+            is_cuda_graph_compatible=
+            True,  #Indicates actual_seq_lens are on GPU or CPU.
+        )
+        if return_softmax_lse:
+            return output, lse
+        return output
+
+    def _run_prefill_context_chunk_fa(self, prefill: MLACommonPrefillMetadata,
+                                      chunk_idx: int, q, k, v):
+        assert prefill.chunked_context is not None
+        return self._flash_attn_varlen_diff_headdims(
+            q=q,
+            k=k,
+            v=v,
+            cu_seqlens_q=prefill.query_start_loc,
+            cu_seqlens_k=prefill.chunked_context.cu_seq_lens[chunk_idx],
+            max_seqlen_q=prefill.max_query_len,
+            max_seqlen_k=prefill.chunked_context.max_seq_lens[chunk_idx],
+            softmax_scale=self.scale,
+            causal=False,  # Context is unmasked
+            return_softmax_lse=True,
+        )
+
+    def _run_prefill_context_chunk_fi(self, prefill: MLACommonPrefillMetadata,
+                                      chunk_idx: int, q, k, v):
+        assert isinstance(prefill, FlashInferPrefillMetadata)
+        return prefill.prefill_chunks[chunk_idx].run(
+            q=q,
+            k=k,
+            v=v,
+            return_lse=True,
+        )
+
+    def _run_prefill_context_chunk_cudnn(self,
+                                         prefill: MLACommonPrefillMetadata,
+                                         chunk_idx: int, q, k, v):
+        assert isinstance(prefill, CudnnPrefillMetadata)
+        assert prefill.chunked_context is not None
+        assert prefill.chunked_context.seq_lens[chunk_idx] is not None
+        assert prefill.query_seq_lens is not None
+        return cudnn_batch_prefill_with_kv_cache(
+            q=q,
+            k_cache=k,
+            v_cache=v,
+            scale=self.scale,
+            workspace_buffer=prefill.cudnn_workspace,
+            max_token_per_sequence=prefill.max_query_len,
+            max_sequence_kv=prefill.chunked_context.max_seq_lens[chunk_idx],
+            actual_seq_lens_q=prefill.query_seq_lens.view(-1, 1, 1, 1),
+            actual_seq_lens_kv=prefill.chunked_context.seq_lens[chunk_idx].
+            view(-1, 1, 1, 1),
+            causal=False,
+            return_lse=True,
+            is_cuda_graph_compatible=
+            True,  #Indicates actual_seq_lens are on GPU or CPU.
+        )
+
+    def _v_up_proj(self, x):
+        # Convert from (B, N, L) to (N, B, L)
+        x = x.view(-1, self.num_heads, self.kv_lora_rank).transpose(0, 1)
+        # Multiply (N, B, L) x (N, L, V) -> (N, B, V)
+        x = torch.bmm(x, self.W_UV)
+        # Convert from (N, B, V) to (B, N * V)
+        return x.transpose(0, 1).reshape(-1, self.num_heads * self.v_head_dim)
+
+    def process_weights_after_loading(self, act_dtype: torch.dtype):
+
+        def get_layer_weight(layer):
+            WEIGHT_NAMES = ("weight", "qweight", "weight_packed")
+            for attr in WEIGHT_NAMES:
+                if hasattr(layer, attr):
+                    return getattr(layer, attr)
+            raise AttributeError(
+                f"Layer '{layer}' has no recognized weight attribute:"
+                f" {WEIGHT_NAMES}.")
+
+        def get_and_maybe_dequant_weights(layer: LinearBase):
+            if not isinstance(layer.quant_method, UnquantizedLinearMethod):
+                # NOTE: This should only be used offline, since it's O(N^3)
+                eye = torch.eye(layer.input_size_per_partition,
+                                dtype=act_dtype,
+                                device=get_layer_weight(layer).device)
+                dequant_weights = layer.quant_method.apply(layer,
+                                                           eye,
+                                                           bias=None)
+                del eye
+                # standardize to (output, input)
+                return dequant_weights.T
+            return layer.weight
+
+        # we currently do not have quantized bmm's which are needed for
+        # `W_UV` and `W_UK_T`, we we just store fp16/bf16 copies and perform
+        # the bmm's in 16-bit, the extra memory overhead of this is fairly low
+        kv_b_proj_weight = get_and_maybe_dequant_weights(self.kv_b_proj).T
+        assert kv_b_proj_weight.shape == (
+            self.kv_lora_rank,
+            self.num_heads * (self.qk_nope_head_dim + self.v_head_dim)), (
+                f"{kv_b_proj_weight.shape=}, "
+                f"{self.kv_lora_rank=}, "
+                f"{self.num_heads=}, "
+                f"{self.qk_nope_head_dim=}, "
+                f"{self.v_head_dim=}")
+        kv_b_proj_weight = kv_b_proj_weight.view(
+            self.kv_lora_rank,
+            self.num_heads,
+            self.qk_nope_head_dim + self.v_head_dim,
+        )
+
+        W_UK, W_UV = kv_b_proj_weight.split(
+            [self.qk_nope_head_dim, self.v_head_dim], dim=-1)
+
+        # Convert from (L, N, V) to (N, L, V)
+        self.W_UV = W_UV.transpose(0, 1)
+        # Convert from (L, N, P) to (N, P, L)
+        self.W_UK_T = W_UK.permute(1, 2, 0)
+
+    def _compute_prefill_context(
+        self,
+        q: torch.Tensor,
+        kv_c_and_k_pe_cache: torch.Tensor,
+        attn_metadata: MLACommonMetadata,
+    ):
+        assert attn_metadata.prefill is not None
+        prefill_metadata = attn_metadata.prefill
+        assert prefill_metadata.chunked_context is not None
+
+        output = None
+        iters = len(prefill_metadata.chunked_context.seq_tot)
+        workspace = prefill_metadata.chunked_context.workspace
+
+        for i in range(iters):
+            toks = prefill_metadata.chunked_context.seq_tot[i]
+
+            ops.gather_cache(
+                src_cache=kv_c_and_k_pe_cache,
+                dst=workspace,
+                block_table=prefill_metadata.block_table,
+                cu_seq_lens=prefill_metadata.chunked_context.cu_seq_lens[i],
+                batch_size=attn_metadata.num_prefills,
+                seq_starts=prefill_metadata.chunked_context.starts[i],
+            )
+
+            kv_c_normed = workspace[:toks]\
+                [..., :self.kv_lora_rank]
+            k_pe = workspace[:toks]\
+                [..., self.kv_lora_rank:].unsqueeze(1)
+
+            kv_nope = self.kv_b_proj(kv_c_normed)[0].view( \
+                -1, self.num_heads, self.qk_nope_head_dim + self.v_head_dim)
+            k_nope, v = kv_nope\
+                .split([self.qk_nope_head_dim, self.v_head_dim], dim=-1)
+
+            k = torch.cat((k_nope, k_pe.expand((*k_nope.shape[:-1], -1))),
+                          dim=-1)
+
+            attn_output, attn_softmax_lse = self._run_prefill_context_chunk(
+                prefill=prefill_metadata,
+                chunk_idx=i,
+                q=q,
+                k=k,
+                v=v,
+            )
+
+            if output is None:
+                output = attn_output
+                output_lse = attn_softmax_lse
+            else:
+                output_tmp = torch.empty_like(output)
+                output_lse_tmp = torch.empty_like(output_lse)
+                merge_attn_states(
+                    output=output_tmp,
+                    output_lse=output_lse_tmp,
+                    prefix_output=output,
+                    prefix_lse=output_lse,
+                    suffix_output=attn_output,
+                    suffix_lse=attn_softmax_lse,
+                )
+                output = output_tmp
+                output_lse = output_lse_tmp
+
+        return output, output_lse
+
+    def _forward_prefill(
+        self,
+        q: torch.Tensor,
+        kv_c_normed: torch.Tensor,
+        k_pe: torch.Tensor,
+        kv_c_and_k_pe_cache: torch.Tensor,
+        attn_metadata: MLACommonMetadata,
+    ) -> torch.Tensor:
+        assert attn_metadata.prefill is not None
+
+        has_context = attn_metadata.prefill.chunked_context is not None
+        kv_nope = self.kv_b_proj(kv_c_normed)[0].view(\
+            -1, self.num_heads, self.qk_nope_head_dim + self.v_head_dim)
+        k_nope, v = kv_nope\
+            .split([self.qk_nope_head_dim, self.v_head_dim], dim=-1)
+
+        k = torch.cat((k_nope, k_pe.expand((*k_nope.shape[:-1], -1))), dim=-1)
+
+        output = self._run_prefill_new_tokens(
+            prefill=attn_metadata.prefill,
+            q=q,
+            k=k,
+            v=v,
+            return_softmax_lse=has_context,
+        )
+
+        if has_context:
+            suffix_output, suffix_lse = output
+            context_output, context_lse = self._compute_prefill_context( \
+                q, kv_c_and_k_pe_cache, attn_metadata)
+
+            output = torch.empty_like(suffix_output)
+            merge_attn_states(
+                output=output,
+                prefix_output=context_output,
+                prefix_lse=context_lse,
+                suffix_output=suffix_output,
+                suffix_lse=suffix_lse,
+            )
+
+        # unpad if necessary
+        if self._pad_v:
+            output = output[..., :v.shape[-1]]
+
+        return output.flatten(start_dim=-2)
+
+    @abstractmethod
+    def _forward_decode(
+        self,
+        ql_nope: torch.Tensor,
+        q_pe: torch.Tensor,
+        kv_c_and_k_pe_cache: torch.Tensor,
+        attn_metadata: M,
+    ) -> torch.Tensor:
+        raise NotImplementedError
+
+    def forward(
+        self,
+        layer: AttentionLayer,
+        q: torch.Tensor,
+        k_c_normed: torch.Tensor,  # key in unified attn
+        k_pe: torch.Tensor,  # value in unified attn
+        kv_cache: torch.Tensor,
+        attn_metadata: M,
+        output: Optional[torch.Tensor] = None,
+        output_scale: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        assert output is not None, "Output tensor must be provided."
+
+        if output_scale is not None:
+            raise NotImplementedError(
+                "fused output quantization is not yet supported"
+                " for MLACommonImpl")
+
+        if attn_metadata is None:
+            # The zero fill is required when used with DP + EP
+            # to ensure all ranks within a DP group compute the
+            # same expert outputs.
+            return output.fill_(0)
+
+        num_actual_toks = attn_metadata.num_actual_tokens
+
+        # Inputs and outputs may be padded for CUDA graphs
+        output_padded = output
+        output = output[:num_actual_toks, ...]
+        q = q[:num_actual_toks, ...]
+        k_c_normed = k_c_normed[:num_actual_toks, ...]
+        k_pe = k_pe[:num_actual_toks, ...]
+
+        assert attn_metadata.num_decodes is not None and \
+            attn_metadata.num_prefills is not None and \
+            attn_metadata.num_decode_tokens is not None
+
+        has_decode = attn_metadata.num_decodes > 0
+        has_prefill = attn_metadata.num_prefills > 0
+        num_decode_tokens = attn_metadata.num_decode_tokens
+
+        decode_q = q[:num_decode_tokens]
+
+        prefill_q = q[num_decode_tokens:]
+        prefill_k_pe = k_pe[num_decode_tokens:]
+        prefill_k_c_normed = k_c_normed[num_decode_tokens:]
+
+        # write the latent and rope to kv cache
+        if kv_cache.numel() > 0:
+            ops.concat_and_cache_mla(
+                k_c_normed,
+                k_pe.squeeze(1),
+                kv_cache,
+                attn_metadata.slot_mapping.flatten(),
+                kv_cache_dtype=self.kv_cache_dtype,
+                scale=layer._k_scale,
+            )
+
+        if has_prefill:
+            output[num_decode_tokens:] = self._forward_prefill(
+                prefill_q, prefill_k_c_normed, prefill_k_pe, kv_cache,
+                attn_metadata)
+
+        if has_decode:
+            assert attn_metadata.decode is not None
+            decode_q_nope, decode_q_pe = decode_q.split(
+                [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)
+            # Convert from (B, N, P) to (N, B, P)
+            decode_q_nope = decode_q_nope.transpose(0, 1)
+            # Multiply (N, B, P) x (N, P, L) -> (N, B, L)
+            decode_ql_nope = torch.bmm(decode_q_nope, self.W_UK_T)
+            # Convert from (N, B, L) to (B, N, L)
+            decode_ql_nope = decode_ql_nope.transpose(0, 1)
+
+            output[:num_decode_tokens] = self._forward_decode(
+                decode_ql_nope, decode_q_pe, kv_cache, attn_metadata)
+
+        return output_padded

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/attention/backends/mla/cutlass_mla.py

@@ -0,0 +1,289 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import os
+from typing import ClassVar, Optional
+
+import torch
+
+import vllm._custom_ops as ops
+from vllm.attention.backends.abstract import (AttentionType,
+                                              is_quantized_kv_cache)
+from vllm.logger import init_logger
+from vllm.v1.attention.backends.mla.common import (MLACommonBackend,
+                                                   MLACommonImpl,
+                                                   MLACommonMetadata,
+                                                   MLACommonMetadataBuilder)
+from vllm.v1.attention.backends.utils import AttentionCGSupport
+
+logger = init_logger(__name__)
+
+
+class CutlassMLAMetadataBuilder(MLACommonMetadataBuilder[MLACommonMetadata]):
+    # enable full CUDA Graph support for decode-only capture
+    cudagraph_support: ClassVar[
+        AttentionCGSupport] = AttentionCGSupport.UNIFORM_SINGLE_TOKEN_DECODE
+
+
+class CutlassMLABackend(MLACommonBackend):
+
+    @staticmethod
+    def get_name() -> str:
+        return "CUTLASS_MLA"
+
+    @staticmethod
+    def get_impl_cls() -> type["CutlassMLAImpl"]:
+        return CutlassMLAImpl
+
+    @staticmethod
+    def get_builder_cls() -> type["CutlassMLAMetadataBuilder"]:
+        return CutlassMLAMetadataBuilder
+
+
+class SM100Workspace:
+
+    def __init__(self, initial_workspace_size):
+        self._workspace_buf = torch.empty(initial_workspace_size,
+                                          device="cuda",
+                                          dtype=torch.uint8)
+
+        self._block_size = 128  # Forced to 128
+
+        # Pre-compute sm_count to avoid recomputing it. Use device 0 as a proxy
+        # (assumes all devices are similar)
+        properties = torch.cuda.get_device_properties(torch.device("cuda:0"))
+        self._sm_count = properties.multi_processor_count
+
+    def get_buf(self):
+        return self._workspace_buf
+
+    def ensure_size(self, attn_metadata: MLACommonMetadata,
+                    num_kv_splits: int):
+        batch_size = attn_metadata.num_reqs
+        max_seq_len = attn_metadata.max_query_len
+
+        workspace_size = ops.sm100_cutlass_mla_get_workspace_size(
+            max_seq_len * self._block_size,
+            batch_size,
+            self._sm_count,
+            num_kv_splits=num_kv_splits)
+
+        if self._workspace_buf.shape[0] < workspace_size:
+            self._workspace_buf.resize_(workspace_size)
+
+
+g_sm100_workspace = SM100Workspace(128 * 1024 * 1024)  # 128MB
+
+
+class CutlassMLAImpl(MLACommonImpl[MLACommonMetadata]):
+
+    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(
+                "CutlassMLAImpl 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 "
+                                      "CutlassMLAImpl")
+
+        if is_quantized_kv_cache(self.kv_cache_dtype):
+            raise NotImplementedError(
+                "CutlassMLA V1 with FP8 KV cache not yet supported")
+
+        self._use_old_cutlass_mla = False
+        force_old_cutlass = os.environ.get("FORCE_OLD_CUTLASS_MLA", None)
+        if force_old_cutlass:
+            logger.warning("Forcing old cutlass mla kernel")
+            self._use_old_cutlass_mla = True
+
+        # TODO: Currently, num_kv_splits is limited to 16 to avoid hanging
+        #       issues. In case the code hangs, use:
+        #       FORCE_NUM_KV_SPLITS=1
+        force_num_kv_splits = os.environ.get("FORCE_NUM_KV_SPLITS", None)
+        if force_num_kv_splits:
+            logger.warning("Forcing num_kv_splits to %d",
+                           int(force_num_kv_splits))
+            self._num_kv_splits = int(force_num_kv_splits)
+        else:
+            self._num_kv_splits = -1  # => Auto-detect
+
+        # Share workspace buffer across all executions
+        self._workspace = g_sm100_workspace
+
+    def _sm100_cutlass_mla_decode(
+        self,
+        q_nope: torch.Tensor,
+        q_pe: torch.Tensor,
+        kv_c_and_k_pe_cache: torch.Tensor,
+        seq_lens: torch.Tensor,
+        page_table: torch.Tensor,
+        workspace: torch.Tensor,
+        sm_scale: float,
+        num_kv_splits: int,
+    ) -> torch.Tensor:
+        assert (q_nope.ndim == 3
+                ), f"q_nope must be a 3D tensor, but got {q_nope.ndim}"
+        assert (
+            q_pe.ndim == 3), f"q_pe must be a 3D tensor, but got {q_pe.ndim}"
+        assert (
+            kv_c_and_k_pe_cache.ndim == 3
+        ), "kv_c_and_k_pe_cache must be a 3D tensor, but got {}".format(
+            kv_c_and_k_pe_cache.ndim)
+
+        B_q, H, D_q_nope = q_nope.shape
+        B_q_2, H_2, D_q_pe = q_pe.shape
+        assert (B_q == B_q_2) and (H == H_2)
+
+        _, PAGE_SIZE, D_ckv = kv_c_and_k_pe_cache.shape
+
+        D_latent = 512
+        D_rope = 64
+        assert D_q_nope == D_latent
+        assert D_q_pe == D_rope
+        assert D_ckv == D_latent + D_rope
+
+        MAX_HEADS = 128
+        assert H <= MAX_HEADS, f"H must be <= {MAX_HEADS}, but got {H}"
+        if H < MAX_HEADS:
+            q_nope_padded = q_nope.new_empty((B_q, MAX_HEADS, D_q_nope))
+            q_nope_padded[:, :H] = q_nope
+            q_nope = q_nope_padded
+
+            q_pe_padded = q_pe.new_empty((B_q, MAX_HEADS, D_q_pe))
+            q_pe_padded[:, :H] = q_pe
+            q_pe = q_pe_padded
+
+        assert len(page_table.shape) == 2
+        B_block_table, block_num = page_table.shape
+        assert B_block_table == B_q
+        assert (block_num
+                > 0), f"block num must be greater than 0, got {block_num}"
+        assert block_num % (128 / PAGE_SIZE) == 0
+
+        # TODO(kaixih@nvidia): support fp8
+        assert q_nope.dtype in (
+            torch.float16,
+            torch.bfloat16,
+        ), f"q_nope.dtype needs to be fp16 or bf16 but got {q_nope.dtype}."
+        assert q_nope.dtype == q_pe.dtype == kv_c_and_k_pe_cache.dtype
+        assert (
+            seq_lens.dtype == torch.int32
+        ), f"seq_lens.dtype needs to be int32 but got {seq_lens.dtype}."
+        assert (
+            page_table.dtype == torch.int32
+        ), f"page_table.dtype needs to be int32 but got {page_table.dtype}."
+
+        out = q_nope.new_empty((B_q, MAX_HEADS, D_latent))
+
+        ops.sm100_cutlass_mla_decode(
+            out,
+            q_nope,
+            q_pe,
+            kv_c_and_k_pe_cache,
+            seq_lens,
+            page_table,
+            workspace,
+            sm_scale,
+            num_kv_splits,
+        )
+        return out[:, :H].contiguous()
+
+    def _sm100_forward_decode(
+        self,
+        q_nope: torch.Tensor,
+        q_pe: torch.Tensor,
+        kv_c_and_k_pe_cache: torch.Tensor,
+        attn_metadata: MLACommonMetadata,
+    ) -> 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 Cutlass MLA not yet supported")
+
+        # Adjust workspace size (if necessary)
+        self._workspace.ensure_size(attn_metadata, self._num_kv_splits)
+
+        # Run MLA
+        # Clone q_nope and q_pe to make sure strides computation is correct.
+        # TODO: Check if we really need it
+        q_nope = q_nope.clone()
+        q_pe = q_pe.clone()
+
+        o = self._sm100_cutlass_mla_decode(q_nope, q_pe, kv_c_and_k_pe_cache,
+                                           attn_metadata.decode.seq_lens,
+                                           attn_metadata.decode.block_table,
+                                           self._workspace.get_buf(),
+                                           self.scale, self._num_kv_splits)
+
+        return self._v_up_proj(o)
+
+    # TODO: Currently we leave it here only for backup in case something is
+    #       wrong with the new SM100 CUTLASS MLA kernel
+    def _old_forward_decode(
+        self,
+        q_nope: torch.Tensor,
+        q_pe: torch.Tensor,
+        kv_c_and_k_pe_cache: torch.Tensor,
+        attn_metadata: MLACommonMetadata,
+    ) -> 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 Cutlass MLA not yet supported")
+
+        B = q_nope.shape[0]
+
+        o = torch.empty((B, self.num_heads, self.kv_lora_rank),
+                        dtype=q_nope.dtype,
+                        device=q_nope.device)
+
+        # Run MLA
+        # Clone q_nope and q_pe to make sure strides computation is correct.
+        q_nope = q_nope.clone()
+        q_pe = q_pe.clone()
+
+        ops.cutlass_mla_decode(o, q_nope, q_pe, kv_c_and_k_pe_cache,
+                               attn_metadata.decode.seq_lens,
+                               attn_metadata.decode.block_table, self.scale)
+
+        return self._v_up_proj(o)
+
+    def _forward_decode(
+        self,
+        q_nope: torch.Tensor,
+        q_pe: torch.Tensor,
+        kv_c_and_k_pe_cache: torch.Tensor,
+        attn_metadata: MLACommonMetadata,
+    ) -> torch.Tensor:
+        if self._use_old_cutlass_mla:
+            # TODO: Remove the old cutlass MLA kernel after more extensive
+            #       testing
+            return self._old_forward_decode(q_nope, q_pe, kv_c_and_k_pe_cache,
+                                            attn_metadata)
+
+        return self._sm100_forward_decode(q_nope, q_pe, kv_c_and_k_pe_cache,
+                                          attn_metadata)

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/attention/backends/mla/flashmla.py

@@ -0,0 +1,199 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from dataclasses import dataclass
+from typing import ClassVar, Optional
+
+import torch
+
+from vllm.attention.backends.abstract import (AttentionType,
+                                              is_quantized_kv_cache)
+from vllm.attention.ops.flashmla import (flash_mla_with_kvcache,
+                                         get_mla_metadata,
+                                         is_flashmla_supported)
+from vllm.config import VllmConfig
+from vllm.logger import init_logger
+from vllm.v1.attention.backends.mla.common import (MLACommonBackend,
+                                                   MLACommonDecodeMetadata,
+                                                   MLACommonImpl,
+                                                   MLACommonMetadata,
+                                                   MLACommonMetadataBuilder)
+from vllm.v1.attention.backends.utils import AttentionCGSupport
+from vllm.v1.kv_cache_interface import AttentionSpec
+
+logger = init_logger(__name__)
+
+
+class FlashMLABackend(MLACommonBackend):
+
+    @staticmethod
+    def get_name() -> str:
+        return "FLASHMLA_VLLM_V1"
+
+    @staticmethod
+    def get_metadata_cls() -> type["FlashMLAMetadata"]:
+        return FlashMLAMetadata
+
+    @staticmethod
+    def get_builder_cls() -> type["FlashMLAMetadataBuilder"]:
+        return FlashMLAMetadataBuilder
+
+    @staticmethod
+    def get_impl_cls() -> type["FlashMLAImpl"]:
+        return FlashMLAImpl
+
+
+@dataclass
+class FlashMLADecodeMetadata(MLACommonDecodeMetadata):
+    tile_scheduler_metadata: torch.Tensor
+    num_splits: torch.Tensor
+
+
+@dataclass
+class FlashMLAMetadata(MLACommonMetadata[FlashMLADecodeMetadata]):
+    pass
+
+
+class FlashMLAMetadataBuilder(MLACommonMetadataBuilder[FlashMLAMetadata]):
+    cudagraph_support: ClassVar[AttentionCGSupport] = \
+        AttentionCGSupport.UNIFORM_BATCH
+
+    def __init__(self, kv_cache_spec: AttentionSpec, layer_names: list[str],
+                 vllm_config: VllmConfig, device: torch.device):
+        super().__init__(kv_cache_spec, layer_names, vllm_config, device,
+                         FlashMLAMetadata)
+
+        self.compilation_config = vllm_config.compilation_config
+        self.num_q_heads = vllm_config.model_config.get_num_attention_heads(
+            vllm_config.parallel_config)
+
+        self.cg_buf_tile_scheduler_metadata = None
+        self.cg_buf_num_splits = None
+
+        device_properties = torch.cuda.get_device_properties(self.device)
+        num_sms = device_properties.multi_processor_count
+
+        if self.compilation_config.cudagraph_mode.has_full_cudagraphs():
+            self.cg_buf_tile_scheduler_metadata = torch.zeros(
+                # Upper bound on size (<= #SMs, TileSchedulerMetaDataSize)
+                # TileSchedulerMetaDataSize = 8
+                (num_sms, 8),
+                device=self.device,
+                dtype=torch.int32,
+            )
+            self.cg_buf_num_splits = torch.empty(
+                (vllm_config.scheduler_config.max_num_seqs + 1),
+                device=self.device,
+                dtype=torch.int32)
+
+    def _build_decode(self, block_table_tensor: torch.Tensor,
+                      seq_lens: torch.Tensor) -> FlashMLADecodeMetadata:
+        tile_scheduler_metadata, num_splits = \
+            get_mla_metadata(
+            seq_lens,
+            self.num_q_heads,
+            1, # MQA for the decode path
+        )
+
+        # TODO: we can disambiguate between decode and mixed-prefill decode here
+        # so we can only use the persistent buffer if a cudagraph is actually
+        # being used.
+        if self.compilation_config.cudagraph_mode.has_full_cudagraphs():
+            assert self.cg_buf_tile_scheduler_metadata is not None
+            assert self.cg_buf_num_splits is not None
+
+            sm_parts = tile_scheduler_metadata.size(0)
+            # Metadata per-SM, upper bound on size (<= #SMs, TileMetadataSize)
+            assert sm_parts <= self.cg_buf_tile_scheduler_metadata.size(0)
+            tile_scheduler_metadata_view = \
+                self.cg_buf_tile_scheduler_metadata[:sm_parts]
+            tile_scheduler_metadata_view.copy_(tile_scheduler_metadata)
+            tile_scheduler_metadata = tile_scheduler_metadata_view
+
+            # Num splits is per-batch, varying size (batch_size,)
+            n = num_splits.size(0)
+            # make sure static buffer is large enough
+            assert n <= self.cg_buf_num_splits.size(0)
+            num_splits_view = self.cg_buf_num_splits[:n]
+            num_splits_view.copy_(num_splits)
+            # Num splits needs to monotonically increasing
+            # (with: https://github.com/vllm-project/FlashMLA/pull/3, otherwise
+            #  it needs to monotonically increasing by 1)
+            self.cg_buf_num_splits[n:].fill_(num_splits[-1])
+            num_splits = num_splits_view
+
+        return FlashMLADecodeMetadata(
+            block_table=block_table_tensor,
+            seq_lens=seq_lens,
+            tile_scheduler_metadata=tile_scheduler_metadata,
+            num_splits=num_splits,
+        )
+
+
+class FlashMLAImpl(MLACommonImpl[FlashMLAMetadata]):
+
+    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)
+
+        assert is_flashmla_supported(), \
+            "FlashMLA is not supported on this device"
+
+        unsupported_features = [alibi_slopes, sliding_window, logits_soft_cap]
+        if any(unsupported_features):
+            raise NotImplementedError(
+                "FlashMLAImpl 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 "
+                                      "FlashMLAImpl")
+
+        if is_quantized_kv_cache(self.kv_cache_dtype):
+            raise NotImplementedError(
+                "FlashMLA V1 with FP8 KV cache not yet supported")
+
+    def _forward_decode(
+        self,
+        q_nope: torch.Tensor,
+        q_pe: torch.Tensor,
+        kv_c_and_k_pe_cache: torch.Tensor,
+        attn_metadata: FlashMLAMetadata,
+    ) -> torch.Tensor:
+        assert kv_c_and_k_pe_cache.numel() > 0
+        assert attn_metadata.decode is not None
+
+        q = torch.cat([q_nope, q_pe], dim=-1)\
+            .unsqueeze(1) # Add seqlen dim of 1 (decode)
+
+        o, _ = flash_mla_with_kvcache(
+            q=q,
+            k_cache=kv_c_and_k_pe_cache.unsqueeze(-2),  # Add head dim of 1
+            block_table=attn_metadata.decode.block_table,
+            cache_seqlens=attn_metadata.decode.seq_lens,
+            head_dim_v=self.kv_lora_rank,
+            tile_scheduler_metadata=attn_metadata.decode.
+            tile_scheduler_metadata,
+            num_splits=attn_metadata.decode.num_splits,
+            softmax_scale=self.scale,
+            causal=True,
+        )
+
+        return self._v_up_proj(o)

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/attention/backends/pallas.py

@@ -0,0 +1,406 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from dataclasses import dataclass
+from typing import Optional
+
+import torch
+import torch_xla.core.xla_builder as xb
+import torch_xla.experimental.custom_kernel  # noqa: F401
+# Required to register custom ops.
+from torch.library import impl
+from torch_xla._internal.jax_workarounds import requires_jax
+from torch_xla.experimental.custom_kernel import XLA_LIB
+
+from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
+                                              AttentionLayer, AttentionType)
+from vllm.attention.backends.utils import CommonAttentionState
+from vllm.config import VllmConfig
+from vllm.logger import init_logger
+from vllm.utils import cdiv, next_power_of_2
+
+logger = init_logger(__name__)
+
+# TPU requires the head size to be a multiple of 128.
+TPU_HEAD_SIZE_ALIGNMENT = 128
+
+# Note: TPU can fp8 as storage dtype but doesn't support converting from uint8
+# from to fp32 directly. That's why it has a dtype mapping different from GPU
+TPU_STR_DTYPE_TO_TORCH_DTYPE = {
+    "half": torch.half,
+    "bfloat16": torch.bfloat16,
+    "float": torch.float,
+    "fp8": torch.float8_e4m3fn,
+    "fp8_e4m3": torch.float8_e4m3fn,
+    "fp8_e5m2": torch.float8_e5m2,
+    "int8": torch.int8,
+    "uint8": torch.uint8,
+}
+
+
+class PallasAttentionBackend(AttentionBackend):
+
+    @staticmethod
+    def get_name() -> str:
+        return "PALLAS_VLLM_V1"
+
+    @staticmethod
+    def get_impl_cls() -> type["PallasAttentionBackendImpl"]:
+        return PallasAttentionBackendImpl
+
+    @staticmethod
+    def get_metadata_cls() -> type["PallasMetadata"]:
+        return PallasMetadata
+
+    @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, ...]:
+        padded_head_size = cdiv(
+            head_size, TPU_HEAD_SIZE_ALIGNMENT) * TPU_HEAD_SIZE_ALIGNMENT
+        return (num_blocks, block_size, num_kv_heads * 2, padded_head_size)
+
+    @staticmethod
+    def swap_blocks(
+        src_kv_cache: torch.Tensor,
+        dst_kv_cache: torch.Tensor,
+        src_to_dst: torch.Tensor,
+    ) -> None:
+        raise RuntimeError("swap_blocks is not used for the TPU backend.")
+
+    # In recent TPU generations, up to v6e, the SMEM size is 1MB. The
+    # block_tables within the PallasMetadata constitute almost the entire SMEM
+    # requirement. Its size is max_num_seqs * num_page_per_seq * 4 (Int). Here
+    # we simply make sure that the size is smaller than half of SMEM capacity.
+    @staticmethod
+    def get_min_page_size(vllm_config: VllmConfig) -> int:
+        max_num_page_per_req = (1024 * 1024 // 2 //
+                                vllm_config.scheduler_config.max_num_seqs // 4)
+        min_page_size = cdiv(vllm_config.model_config.max_model_len,
+                             max_num_page_per_req)
+        min_page_size = 1 << (min_page_size - 1).bit_length()
+        return min_page_size
+
+    @staticmethod
+    def get_max_num_seqs(model_len: int, page_size: int) -> int:
+        num_page_per_req = cdiv(model_len, page_size)
+        return 1024 * 1024 // 2 // num_page_per_req // 4
+
+    # TPU has limited SREGs (scalar registers), if page_size is too small, we
+    # can spill SREGs easily which leads to bad performance. The strategy we
+    # apply here is trying to split max-model-len to 16 pages which make the
+    # spill less likely. Meanwhile we make sure the page size is in [16, 256].
+    @staticmethod
+    def get_page_size(vllm_config: VllmConfig) -> int:
+        # TODO: This is a temporary fix for vmem OOM.
+        # For long model length, we use 16 page-size to avoid too much
+        # VMEM spill. A more robust solution should be implemented to
+        # handle VREG spills.
+        if vllm_config.model_config.max_model_len > 8192:
+            return 16
+        page_size = next_power_of_2(
+            vllm_config.model_config.max_model_len) // 16
+        if page_size <= 16:
+            return 16
+        if page_size >= 256:
+            return 256
+        return page_size
+
+
+@dataclass
+class PallasMetadata:
+    # NOTE(sang): Definition of context_len, query_len, and seq_len.
+    # |---------- N-1 iteration --------|
+    # |---------------- N iteration ---------------------|
+    # |- tokenA -|......................|-- newTokens ---|
+    # |---------- context_len ----------|
+    # |-------------------- seq_len ---------------------|
+    #                                   |-- query_len ---|
+
+    # Used in the PallasAttentionBackendImpl
+    slot_mapping: torch.Tensor
+    block_tables: torch.Tensor
+    context_lens: torch.Tensor
+    query_start_loc: torch.Tensor
+    num_seqs: torch.Tensor
+    num_kv_update_slices: torch.Tensor
+    num_slices_per_kv_cache_update_block: int
+
+
+class PallasAttentionBackendImpl(AttentionImpl):
+
+    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] = None,
+        attn_type: str = AttentionType.DECODER,
+        kv_sharing_target_layer_name: Optional[int] = None,
+    ) -> None:
+        self.num_heads = num_heads
+        self.head_size = head_size
+        self.scale = float(scale)
+        self.num_kv_heads = num_kv_heads
+        self.sliding_window = sliding_window
+        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
+        if alibi_slopes is not None:
+            raise NotImplementedError("Alibi slopes is not supported.")
+
+        if attn_type != AttentionType.DECODER:
+            raise NotImplementedError("Encoder self-attention and "
+                                      "encoder/decoder cross-attention "
+                                      "are not implemented for "
+                                      "PallasAttentionBackendImpl")
+
+        self.kv_cache_quantized_dtype = None
+        if kv_cache_dtype != "auto":
+            self.kv_cache_quantized_dtype = TPU_STR_DTYPE_TO_TORCH_DTYPE.get(
+                kv_cache_dtype.lower().strip())
+
+    def forward(
+        self,
+        layer: AttentionLayer,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        kv_cache: torch.Tensor,
+        attn_metadata: PallasMetadata,
+        output: Optional[torch.Tensor] = None,
+        output_scale: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        """Forward pass with Pallas attention.
+
+        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 = [num_blocks, block_size, num_kv_heads * 2, head_size]
+            attn_metadata: Metadata for attention.
+        Returns:
+            shape = [num_tokens, num_heads * head_size]
+        """
+        if output_scale is not None:
+            raise NotImplementedError(
+                "fused output quantization is not yet supported"
+                " for PallasAttentionBackendImpl")
+
+        # For determine_available_memory case.
+        if kv_cache.numel() == 0:
+            if output is None:
+                output = torch.ones_like(query)
+            return output
+
+        num_tokens, hidden_size = query.shape
+        query = query.view(num_tokens, self.num_heads, self.head_size)
+        key = key.view(-1, self.num_kv_heads, self.head_size)
+        value = value.view(-1, self.num_kv_heads, self.head_size)
+        if self.head_size % TPU_HEAD_SIZE_ALIGNMENT != 0:
+            padded_head_size = cdiv(
+                self.head_size,
+                TPU_HEAD_SIZE_ALIGNMENT) * TPU_HEAD_SIZE_ALIGNMENT
+            query = torch.nn.functional.pad(
+                query, (0, padded_head_size - self.head_size), value=0.0)
+            key = torch.nn.functional.pad(
+                key, (0, padded_head_size - self.head_size), value=0.0)
+            value = torch.nn.functional.pad(
+                value, (0, padded_head_size - self.head_size), value=0.0)
+
+        if self.kv_sharing_target_layer_name is None and kv_cache.numel() > 0:
+            # Write input keys and values to the KV cache.
+            # Skip this if sharing KV cache with an earlier attention layer.
+            slot_mapping = attn_metadata.slot_mapping
+            write_to_kv_cache(
+                key,
+                value,
+                kv_cache,
+                slot_mapping,
+                attn_metadata.num_slices_per_kv_cache_update_block,
+                attn_metadata.num_kv_update_slices,
+                self.kv_cache_quantized_dtype,
+                layer._k_scale_float,
+                layer._v_scale_float,
+            )
+
+        if self.kv_cache_quantized_dtype is not None and (
+                layer._k_scale_float == 0.0 or layer._v_scale_float == 0.0):
+            raise ValueError(
+                "k_scale_float and v_scale_float must be non-zero")
+        output = torch.ops.xla.ragged_paged_attention(
+            query,
+            kv_cache,
+            attn_metadata.context_lens,
+            attn_metadata.block_tables,
+            attn_metadata.query_start_loc,
+            attn_metadata.num_seqs,
+            # By default, the system utilizes optimized block size and
+            # vmem_limit_bytes parameters from the kernel repository. However,
+            # these can be manually adjusted for debugging if necessary.
+            num_kv_pages_per_block=None,
+            num_queries_per_block=None,
+            vmem_limit_bytes=None,
+            use_kernel=True,
+            sm_scale=self.scale,
+            sliding_window=self.sliding_window,
+            soft_cap=self.logits_soft_cap,
+            k_scale=layer._k_scale_float,
+            v_scale=layer._v_scale_float,
+        )
+
+        if self.head_size % TPU_HEAD_SIZE_ALIGNMENT != 0:
+            output = output[:, :, :self.head_size]
+
+        return output.reshape(num_tokens, hidden_size)
+
+
+def write_to_kv_cache(
+    key: torch.Tensor,
+    value: torch.Tensor,
+    kv_cache: torch.Tensor,
+    slot_mapping: torch.Tensor,
+    num_slices_per_kv_cache_update_block: int,
+    num_kv_update_slices: torch.Tensor,
+    kv_cache_quantized_dtype: Optional[torch.dtype] = None,
+    k_scale: float = 1.0,
+    v_scale: float = 1.0,
+) -> None:
+    """ Write the key and values to the KV cache.
+
+    Args:
+        key: shape = [num_tokens, num_kv_heads, head_size]
+        value: shape = [num_tokens, num_kv_heads, head_size]
+        kv_cache = [num_blocks, block_size, num_kv_heads * 2, head_size]
+        num_slices_per_kv_cache_update_block: int
+    """
+    _, page_size, num_combined_kv_heads, head_size = kv_cache.shape
+    head_size = cdiv(head_size,
+                     TPU_HEAD_SIZE_ALIGNMENT) * TPU_HEAD_SIZE_ALIGNMENT
+
+    if kv_cache_quantized_dtype is not None:
+        dtype_info = torch.finfo(kv_cache_quantized_dtype)
+        key = key.to(torch.float32) / k_scale
+        # NOTE: clamp is added here to avoid out of range of quantized dtype
+        key = torch.clamp(key, dtype_info.min, dtype_info.max)
+        key = key.to(kv_cache_quantized_dtype)
+        value = value.to(torch.float32) / v_scale
+        value = torch.clamp(value, dtype_info.min, dtype_info.max)
+        value = value.to(kv_cache_quantized_dtype)
+
+    kv = torch.cat([key, value], axis=-1).reshape(-1, num_combined_kv_heads,
+                                                  head_size)
+
+    torch.ops.xla.dynamo_set_buffer_donor_(kv_cache, True)
+
+    kv_cache = kv_cache.flatten(0, 1)
+    new_kv_cache = torch.ops.xla.kv_cache_update_op(
+        kv, slot_mapping, kv_cache, num_kv_update_slices, page_size,
+        num_slices_per_kv_cache_update_block)
+    # NOTE: the in-place copy will be optimized away by XLA compiler.
+    kv_cache.copy_(new_kv_cache)
+
+
+@requires_jax
+def kv_cache_update_op_impl(kv: torch.Tensor, slot_mapping: torch.Tensor,
+                            kv_cache: torch.Tensor,
+                            num_kv_update_slices: torch.Tensor, page_size: int,
+                            num_slices_per_block: int):
+    from vllm.attention.ops.pallas_kv_cache_update import kv_cache_update
+    new_kv_cache = xb.call_jax(
+        kv_cache_update, (kv, slot_mapping, kv_cache, num_kv_update_slices), {
+            "page_size": page_size,
+            "num_slices_per_block": num_slices_per_block
+        })
+    return new_kv_cache
+
+
+XLA_LIB.define(
+    "kv_cache_update_op(Tensor kv, Tensor slot_mapping, Tensor kv_cache," \
+    "Tensor num_kv_update_slices, int page_size, int num_slices_per_block)" \
+    "-> Tensor", )
+
+
+@impl(XLA_LIB, "kv_cache_update_op", "XLA")
+def kv_cache_update_op_xla(kv: torch.Tensor, slot_mapping: torch.Tensor,
+                           kv_cache: torch.Tensor,
+                           num_kv_update_slices: torch.Tensor, page_size: int,
+                           num_slices_per_block: int) -> torch.Tensor:
+    new_kv_cache = kv_cache_update_op_impl(kv, slot_mapping, kv_cache,
+                                           num_kv_update_slices, page_size,
+                                           num_slices_per_block)
+    return new_kv_cache
+
+
+@impl(XLA_LIB, "kv_cache_update_op", "CompositeExplicitAutograd")
+def kv_cache_update_op_non_xla(kv: torch.Tensor, slot_mapping: torch.Tensor,
+                               kv_cache: torch.Tensor,
+                               num_kv_update_slices: torch.Tensor,
+                               page_size: int,
+                               num_slices_per_block: int) -> torch.Tensor:
+    return kv_cache
+
+
+# We can move this function to a common utils file if it's also useful for other
+# hardware.
+def dtype_bits(dtype: torch.dtype):
+    if dtype.is_floating_point:
+        try:
+            return torch.finfo(dtype).bits
+        except TypeError:
+            pass
+    elif dtype.is_complex:
+        if dtype is torch.complex32:
+            return 32
+        elif dtype is torch.complex64:
+            return 64
+        elif dtype is torch.complex128:
+            return 128
+    else:
+        try:
+            return torch.iinfo(dtype).bits
+        # torch.iinfo cannot support int4, int2, bits8...
+        except TypeError:
+            pass
+    str_dtype = str(dtype)
+    # support torch.int4, torch.int5, torch.uint5...
+    if str_dtype.startswith("torch.int") or str_dtype.startswith("torch.uint"):
+        return int(str_dtype[-1])
+    raise TypeError(f"Getting the bit width of {dtype} is not supported")
+
+
+def get_dtype_packing(dtype):
+    bits = dtype_bits(dtype)
+    if 32 % bits != 0:
+        raise ValueError(
+            f"The bit width must be divisible by 32, but got bits={bits}, "
+            "dtype={dtype}")
+    return 32 // bits
+
+
+def get_page_size_bytes(block_size: int, num_kv_heads: int, head_size: int,
+                        kv_cache_dtype: torch.dtype) -> int:
+    """Returns the size in bytes of one page of the KV cache."""
+    padded_head_size = cdiv(head_size,
+                            TPU_HEAD_SIZE_ALIGNMENT) * TPU_HEAD_SIZE_ALIGNMENT
+    num_combined_kv_heads = num_kv_heads * 2
+
+    # NOTE: for the implicit padding in XLA
+    packing = get_dtype_packing(kv_cache_dtype)
+    num_combined_kv_heads = cdiv(num_combined_kv_heads, packing) * packing
+
+    kv_cache_dtype_bits = dtype_bits(kv_cache_dtype)
+    return (block_size * num_combined_kv_heads * padded_head_size *
+            kv_cache_dtype_bits // 8)

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/attention/backends/rocm_aiter_fa.py

@@ -0,0 +1,546 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Attention layer with AiterFlashAttention."""
+from dataclasses import dataclass
+from typing import ClassVar, Optional
+
+import torch
+
+from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
+                                              AttentionMetadata, AttentionType)
+from vllm.config import VllmConfig
+from vllm.logger import init_logger
+from vllm.platforms import current_platform
+from vllm.v1.attention.backends.utils import (AttentionMetadataBuilder,
+                                              CommonAttentionMetadata)
+from vllm.v1.kv_cache_interface import AttentionSpec
+
+_PARTITION_SIZE_ROCM = 256
+
+if current_platform.is_rocm():
+    import aiter
+
+    from vllm.triton_utils import tl, triton
+    from vllm.utils import direct_register_custom_op
+
+    @triton.jit
+    def _vllm_layout_trans_kernel(
+        k_buffer_ptr,
+        v_buffer_ptr,
+        k_values_ptr,
+        v_values_ptr,
+        b_query_lens_loc,
+        b_seq_lens_loc,
+        block_table,
+        block_table_stride_0,
+        k_scale,
+        v_scale,
+        output_dtype: tl.constexpr,
+        E_DIM: tl.constexpr,
+        BLOCK_SIZE: tl.constexpr,
+    ):
+        batch_idx = tl.program_id(0)
+        block_idx = tl.program_id(1)
+
+        batch_query_indexes = tl.load(b_query_lens_loc + batch_idx +
+                                      tl.arange(0, 2))
+        batch_query_start, batch_query_end = tl.split(batch_query_indexes)
+        query_len = batch_query_end - batch_query_start
+
+        if query_len <= 1:
+            return
+
+        batch_token_indexes = tl.load(b_seq_lens_loc + batch_idx +
+                                      tl.arange(0, 2))
+        batch_token_start, batch_token_end = tl.split(batch_token_indexes)
+        seq_len = batch_token_end - batch_token_start
+
+        if block_idx * BLOCK_SIZE < seq_len:
+            block_mask = (block_idx * BLOCK_SIZE +
+                          tl.arange(0, BLOCK_SIZE)[:, None]) < seq_len
+
+            kv_idx = tl.load(block_table + batch_idx * block_table_stride_0 +
+                             block_idx).to(tl.int64)
+
+            kv_buffer_off = kv_idx * BLOCK_SIZE * E_DIM + tl.arange(
+                0, BLOCK_SIZE)[:, None] * E_DIM + tl.arange(0, E_DIM)[None, :]
+            k_vals = tl.load(k_buffer_ptr + kv_buffer_off,
+                             mask=block_mask,
+                             other=0.0)
+            if k_vals.dtype.is_fp8():
+                k_vals = (k_vals.to(tl.float32) *
+                          tl.load(k_scale)).to(output_dtype)
+            else:
+                k_vals = k_vals.to(output_dtype)
+
+            v_vals = tl.load(v_buffer_ptr + kv_buffer_off,
+                             mask=block_mask,
+                             other=0.0)
+            if v_vals.dtype.is_fp8():
+                v_vals = (v_vals.to(tl.float32) *
+                          tl.load(v_scale)).to(output_dtype)
+            else:
+                v_vals = v_vals.to(output_dtype)
+            kv_values_off = batch_token_start * E_DIM + \
+                block_idx * BLOCK_SIZE * E_DIM + \
+                tl.arange(0, BLOCK_SIZE)[:, None] * E_DIM + \
+                tl.arange(0, E_DIM)[None, :]
+            tl.store(k_values_ptr + kv_values_off, k_vals, mask=block_mask)
+            tl.store(v_values_ptr + kv_values_off, v_vals, mask=block_mask)
+
+    def vllm_layout_trans(b_query_lens_loc, b_seq_lens_loc, block_table,
+                          k_cache, v_cache, max_seq_len, k_scale, v_scale,
+                          output_dtype, total_tokens):
+        H_KV = v_cache.shape[2]
+        D = v_cache.shape[3]
+        BLOCK_SIZE = v_cache.shape[1]
+
+        k_values = torch.empty(
+            (total_tokens, H_KV, D),
+            dtype=output_dtype,
+            device=k_cache.device,
+        )
+        v_values = torch.empty(
+            (total_tokens, H_KV, D),
+            dtype=output_dtype,
+            device=v_cache.device,
+        )
+
+        grid = (block_table.shape[0],
+                (max_seq_len + BLOCK_SIZE - 1) // BLOCK_SIZE)
+
+        if output_dtype == torch.float16:
+            output_dtype = tl.float16
+        elif output_dtype == torch.bfloat16:
+            output_dtype = tl.bfloat16
+        else:
+            raise ValueError(f"Unsupported output dtype: {output_dtype}")
+
+        _vllm_layout_trans_kernel[grid](k_cache,
+                                        v_cache,
+                                        k_values,
+                                        v_values,
+                                        b_query_lens_loc,
+                                        b_seq_lens_loc,
+                                        block_table,
+                                        block_table.stride(0),
+                                        k_scale,
+                                        v_scale,
+                                        output_dtype=output_dtype,
+                                        E_DIM=H_KV * D,
+                                        BLOCK_SIZE=BLOCK_SIZE)
+
+        return k_values, v_values
+
+    def flash_attn_varlen_func_impl(
+        q: torch.Tensor,
+        k_cache: torch.Tensor,
+        v_cache: torch.Tensor,
+        out: torch.Tensor,
+        cu_seqlens_q: torch.Tensor,
+        cu_seqlens_k: torch.Tensor,
+        max_seqlen_q: int,
+        max_seqlen_k: int,
+        softmax_scale: float,
+        window_size: Optional[list[int]],  # -1 means infinite context window
+        alibi_slopes: Optional[list[float]],
+        block_table: torch.Tensor,
+        k_scale: torch.Tensor,
+        v_scale: torch.Tensor,
+        total_tokens: int = 0,
+    ) -> torch.Tensor:
+        if total_tokens == 0:
+            total_tokens = int(cu_seqlens_k[-1].item())
+        k, v = vllm_layout_trans(cu_seqlens_q, cu_seqlens_k, block_table,
+                                 k_cache, v_cache, max_seqlen_k, k_scale,
+                                 v_scale, q.dtype, total_tokens)
+
+        output = aiter.flash_attn_varlen_func(
+            q=q,
+            k=k,
+            v=v,
+            cu_seqlens_q=cu_seqlens_q,
+            max_seqlen_q=max_seqlen_q,
+            min_seqlen_q=1,
+            cu_seqlens_k=cu_seqlens_k,
+            max_seqlen_k=max_seqlen_k,
+            softmax_scale=softmax_scale,
+            causal=True,
+            alibi_slopes=alibi_slopes,
+            window_size=window_size,
+            out=out,
+        )
+        return output
+
+    def flash_attn_varlen_func_fake(
+        q: torch.Tensor,
+        k_cache: torch.Tensor,
+        v_cache: torch.Tensor,
+        out: torch.Tensor,
+        cu_seqlens_q: torch.Tensor,
+        cu_seqlens_k: torch.Tensor,
+        max_seqlen_q: int,
+        max_seqlen_k: int,
+        softmax_scale: float,
+        window_size: Optional[list[int]],  # -1 means infinite context window
+        alibi_slopes: Optional[list[float]],
+        block_table: torch.Tensor,
+        k_scale: torch.Tensor,
+        v_scale: torch.Tensor,
+        total_tokens: int = 0,
+    ) -> torch.Tensor:
+        return torch.empty(q.shape[0],
+                           q.shape[1],
+                           v_cache.shape[-2],
+                           dtype=q.dtype,
+                           device=q.device)
+
+    direct_register_custom_op("flash_attn_varlen_func",
+                              flash_attn_varlen_func_impl, ["out"],
+                              flash_attn_varlen_func_fake,
+                              dispatch_key=current_platform.dispatch_key)
+
+logger = init_logger(__name__)
+
+
+@dataclass
+class AiterFlashAttentionMetadata:
+    # NOTE(sang): Definition of context_len, query_len, and seq_len.
+    # |---------- N-1 iteration --------|
+    # |---------------- N iteration ---------------------|
+    # |- tokenA -|......................|-- newTokens ---|
+    # |---------- context_len ----------|
+    # |-------------------- seq_len ---------------------|
+    #                                   |-- query_len ---|
+
+    num_actual_tokens: int  # Number of tokens excluding padding.
+    num_actual_kv_tokens: int
+    max_query_len: int
+    query_start_loc: torch.Tensor
+    max_seq_len: int
+    seq_lens: torch.Tensor
+    slot_mapping: torch.Tensor
+    block_table: torch.Tensor
+    cu_seq_lens: Optional[torch.Tensor]
+
+    # For cascade attention.
+    use_cascade: bool
+    common_prefix_len: int
+    total_tokens: int
+
+
+class AiterFlashAttentionMetadataBuilder(
+        AttentionMetadataBuilder[AiterFlashAttentionMetadata]):
+    full_cudagraph_supported: ClassVar[bool] = True
+
+    def __init__(self, kv_cache_spec: AttentionSpec, layer_names: list[str],
+                 vllm_config: VllmConfig, device: torch.device):
+        self.vllm_config = vllm_config
+        self.model_config = vllm_config.model_config
+        self.parallel_config = vllm_config.parallel_config
+        self.cache_config = vllm_config.cache_config
+        self.device = device
+
+        self.num_heads_q = self.model_config.get_num_attention_heads(
+            self.parallel_config)
+        self.num_heads_kv = self.model_config.get_num_kv_heads(
+            self.parallel_config)
+        self.headdim = self.model_config.get_head_size()
+        self.block_size = kv_cache_spec.block_size
+        self.kv_cache_spec = kv_cache_spec
+        # Sliding window size to be used with the AOT scheduler will be
+        # populated on first build() call.
+        self.aot_sliding_window: Optional[tuple[int, int]] = None
+        self.total_tokens: int = 0
+
+    def build_for_cudagraph_capture(
+            self, common_attn_metadata: CommonAttentionMetadata):
+        self.total_tokens = self.model_config.max_model_len \
+            * self.vllm_config.scheduler_config.max_num_partial_prefills
+        res = self.build(common_prefix_len=0,
+                         common_attn_metadata=common_attn_metadata)
+        self.total_tokens = 0
+        return res
+
+    def build(self,
+              common_prefix_len: int,
+              common_attn_metadata: CommonAttentionMetadata,
+              fast_build: bool = False) -> 'AiterFlashAttentionMetadata':
+
+        num_actual_tokens = common_attn_metadata.num_actual_tokens
+        max_query_len = common_attn_metadata.max_query_len
+        max_seq_len = int(common_attn_metadata.seq_lens_cpu.max())
+        query_start_loc = common_attn_metadata.query_start_loc
+        seq_lens = common_attn_metadata.seq_lens
+        block_table_tensor = common_attn_metadata.block_table_tensor
+        slot_mapping = common_attn_metadata.slot_mapping
+        if max_query_len > 1:
+            # We pre-compute cumulative seq len needed for prefill attention
+            # here to avoid recomputing it for every layer
+            cu_seq_lens = torch.zeros(seq_lens.shape[0] + 1,
+                                      dtype=torch.int32,
+                                      device=seq_lens.device)
+            torch.cumsum(seq_lens,
+                         dim=0,
+                         dtype=cu_seq_lens.dtype,
+                         out=cu_seq_lens[1:])
+            num_actual_kv_tokens = int(cu_seq_lens[-1].item())
+        else:
+            cu_seq_lens = None
+            num_actual_kv_tokens = 0
+
+        def schedule(batch_size, cu_query_lens, max_query_len, seqlens,
+                     max_seq_len, causal):
+            return None
+
+        use_cascade = common_prefix_len > 0
+
+        attn_metadata = AiterFlashAttentionMetadata(
+            num_actual_tokens=num_actual_tokens,
+            num_actual_kv_tokens=num_actual_kv_tokens,
+            max_query_len=max_query_len,
+            query_start_loc=query_start_loc,
+            max_seq_len=max_seq_len,
+            seq_lens=seq_lens,
+            block_table=block_table_tensor,
+            slot_mapping=slot_mapping,
+            cu_seq_lens=cu_seq_lens,
+            use_cascade=use_cascade,
+            common_prefix_len=common_prefix_len,
+            total_tokens=self.total_tokens,
+        )
+        return attn_metadata
+
+    def use_cascade_attention(self, *args, **kwargs) -> bool:
+        return False
+
+
+class AiterFlashAttentionBackend(AttentionBackend):
+
+    accept_output_buffer: bool = True
+
+    @classmethod
+    def get_supported_dtypes(cls) -> list[torch.dtype]:
+        return [torch.float16, torch.bfloat16]
+
+    @classmethod
+    def get_supported_head_sizes(cls) -> list[int]:
+        return [64, 128, 256]
+
+    @classmethod
+    def validate_head_size(cls, head_size: int) -> None:
+        supported_head_sizes = cls.get_supported_head_sizes()
+        if head_size not in supported_head_sizes:
+            attn_type = cls.__name__.removesuffix("Backend")
+            raise ValueError(
+                f"Head size {head_size} is not supported by {attn_type}. "
+                f"Supported head sizes are: {supported_head_sizes}. "
+                "Set VLLM_ATTENTION_BACKEND=FLEX_ATTENTION to use "
+                "FlexAttention backend which supports all head sizes.")
+
+    @staticmethod
+    def get_name() -> str:
+        return "FLASH_ATTN_VLLM_V1"
+
+    @staticmethod
+    def get_impl_cls() -> type["AiterFlashAttentionImpl"]:
+        return AiterFlashAttentionImpl
+
+    @staticmethod
+    def get_metadata_cls() -> type["AttentionMetadata"]:
+        return AiterFlashAttentionMetadata
+
+    @staticmethod
+    def get_builder_cls() -> type["AiterFlashAttentionMetadataBuilder"]:
+        return AiterFlashAttentionMetadataBuilder
+
+    @staticmethod
+    def get_kv_cache_shape(
+        num_blocks: int,
+        block_size: int,
+        num_kv_heads: int,
+        head_size: int,
+    ) -> 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)
+
+
+class AiterFlashAttentionImpl(AttentionImpl):
+
+    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] = None,
+        attn_type: AttentionType = AttentionType.DECODER,
+        kv_sharing_target_layer_name: Optional[int] = None,
+    ) -> 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]
+        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
+
+        assert self.num_heads % self.num_kv_heads == 0
+        self.num_queries_per_kv = self.num_heads // self.num_kv_heads
+
+        AiterFlashAttentionBackend.validate_head_size(head_size)
+
+        if attn_type != AttentionType.DECODER:
+            raise NotImplementedError("Encoder self-attention and "
+                                      "encoder/decoder cross-attention "
+                                      "are not implemented for "
+                                      "FlashAttentionImpl")
+
+    def forward(
+        self,
+        layer: torch.nn.Module,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        kv_cache: torch.Tensor,
+        attn_metadata: AiterFlashAttentionMetadata,
+        output: Optional[torch.Tensor] = None,
+        output_scale: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        """Forward pass with AiterFlashAttention.
+
+        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]
+            attn_metadata: Metadata for attention.
+        Returns:
+            shape = [num_tokens, num_heads * head_size]
+        NOTE: FP8 quantization, flash-attn expect the size of
+              {q,k,v}_descale to be (num_sequences, num_kv_heads).
+              We use torch's .expand() to avoid duplicating values
+        """
+        assert output is not None, "Output tensor must be provided."
+
+        if output_scale is not None:
+            raise NotImplementedError(
+                "fused output quantization is not yet supported"
+                " for FlashAttentionImpl")
+
+        if attn_metadata is None:
+            # Profiling run.
+            return output
+
+        # 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)
+        if self.kv_sharing_target_layer_name is None:
+            # Reshape the input keys and values and store them in the cache.
+            # Skip this if sharing KV cache with an earlier attention layer.
+            # NOTE(woosuk): Here, key and value are padded while slot_mapping is
+            # not padded. However, we don't need to do key[:num_actual_tokens]
+            # and value[:num_actual_tokens] because the reshape_and_cache_flash
+            # op uses the slot_mapping's shape to determine the number of
+            # actual tokens.
+            torch.ops._C_cache_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(torch.float8_e4m3fnuz)
+            value_cache = value_cache.view(torch.float8_e4m3fnuz)
+
+        if not attn_metadata.use_cascade:
+            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
+
+            if max_seqlen_q > 1:
+                torch.ops.vllm.flash_attn_varlen_func(
+                    query[:num_actual_tokens],
+                    key_cache,
+                    value_cache,
+                    out=output[:num_actual_tokens],
+                    cu_seqlens_q=cu_seqlens_q,
+                    max_seqlen_q=max_seqlen_q,
+                    max_seqlen_k=max_seqlen_k,
+                    softmax_scale=self.scale,
+                    alibi_slopes=self.alibi_slopes,
+                    window_size=self.sliding_window,
+                    block_table=block_table,
+                    cu_seqlens_k=attn_metadata.cu_seq_lens,
+                    k_scale=layer._k_scale,
+                    v_scale=layer._v_scale,
+                    total_tokens=attn_metadata.num_actual_kv_tokens,
+                )
+
+            _, num_heads, head_size = query.shape
+            nbytes_per_qo_elem = torch.finfo(query.dtype).bits // 8
+            num_seqs = seqused_k.shape[0]
+            max_num_partitions = (max_seqlen_k + _PARTITION_SIZE_ROCM -
+                                  1) // _PARTITION_SIZE_ROCM
+
+            workspace_buffer = torch.empty(
+                (num_seqs * num_heads * max_num_partitions * head_size) *
+                nbytes_per_qo_elem + 2 *
+                (num_seqs * num_heads * max_num_partitions) * 4,
+                dtype=torch.uint8,
+                device=output.device,
+            )
+
+            torch.ops.aiter.paged_attention_v1(
+                output[:num_actual_tokens],
+                workspace_buffer,
+                query[:num_actual_tokens],
+                key_cache,
+                value_cache,
+                self.scale,
+                block_table,
+                cu_seqlens_q,
+                seqused_k,
+                max_seqlen_k,
+                self.alibi_slopes,
+                self.kv_cache_dtype,
+                "NHD",
+                self.logits_soft_cap,
+                layer._k_scale,
+                layer._v_scale,
+                None,
+                _PARTITION_SIZE_ROCM,
+            )
+            return output
+        else:
+            raise NotImplementedError(
+                "Cascade attention is not implemented for ROCM AITER")

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/attention/backends/tree_attn.py

@@ -0,0 +1,447 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Attention layer with TreeAttention."""
+
+import ast
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Optional
+
+import torch
+
+from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
+                                              AttentionMetadata, AttentionType)
+from vllm.attention.ops.triton_unified_attention import unified_attention
+from vllm.config import VllmConfig
+from vllm.logger import init_logger
+from vllm.v1.attention.backends.utils import (
+    AttentionMetadataBuilder, CommonAttentionMetadata,
+    reorder_batch_to_split_decodes_and_prefills, split_decodes_and_prefills)
+from vllm.v1.kv_cache_interface import AttentionSpec
+
+if TYPE_CHECKING:
+    from vllm.v1.core.sched.output import SchedulerOutput
+    from vllm.v1.worker.gpu_input_batch import InputBatch
+
+from vllm import _custom_ops as ops
+
+logger = init_logger(__name__)
+
+
+class TreeAttentionBackend(AttentionBackend):
+
+    accept_output_buffer: bool = True
+
+    @classmethod
+    def get_supported_dtypes(cls) -> list[torch.dtype]:
+        return [torch.float16, torch.bfloat16]
+
+    @classmethod
+    def get_supported_head_sizes(cls) -> list[int]:
+        return [32, 64, 96, 128, 160, 192, 224, 256]
+
+    @classmethod
+    def validate_head_size(cls, head_size: int) -> None:
+        supported_head_sizes = cls.get_supported_head_sizes()
+        if head_size not in supported_head_sizes:
+            attn_type = cls.__name__.removesuffix("Backend")
+            raise ValueError(
+                f"Head size {head_size} is not supported by {attn_type}. "
+                f"Supported head sizes are: {supported_head_sizes}. "
+                "Set VLLM_ATTENTION_BACKEND=FLEX_ATTENTION to use "
+                "FlexAttention backend which supports all head sizes.")
+
+    @staticmethod
+    def get_name() -> str:
+        return "TREE_ATTN_VLLM_V1"
+
+    @staticmethod
+    def get_impl_cls() -> type["TreeAttentionImpl"]:
+        return TreeAttentionImpl
+
+    @staticmethod
+    def get_metadata_cls() -> type["AttentionMetadata"]:
+        return TreeAttentionMetadata
+
+    @staticmethod
+    def get_kv_cache_shape(
+        num_blocks: int,
+        block_size: int,
+        num_kv_heads: int,
+        head_size: int,
+    ) -> 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 get_builder_cls() -> type["TreeAttentionMetadataBuilder"]:
+        return TreeAttentionMetadataBuilder
+
+    @staticmethod
+    def use_cascade_attention(*args, **kwargs) -> bool:
+        return False
+
+
+@dataclass
+class TreeAttentionMetadata:
+    num_actual_tokens: int  # Number of tokens excluding padding.
+    max_query_len: int
+    query_start_loc: torch.Tensor
+    max_seq_len: int
+    seq_lens: torch.Tensor
+    block_table: torch.Tensor
+    slot_mapping: torch.Tensor
+
+    num_prefill_tokens: int = 0
+    num_decode_tokens: int = 0
+    num_prefills: int = 0
+    num_decodes: int = 0
+
+    tree_attn_bias: Optional[torch.Tensor] = None
+
+    # Cached Prefill/decode metadata.
+    _cached_prefill_metadata: Optional["TreeAttentionMetadata"] = None
+    _cached_decode_metadata: Optional["TreeAttentionMetadata"] = None
+
+    @property
+    def prefill_metadata(self) -> Optional["TreeAttentionMetadata"]:
+        if self.num_prefills == 0:
+            return None
+
+        if self._cached_prefill_metadata is not None:
+            # Recover cached prefill-phase attention
+            # metadata structure
+            return self._cached_prefill_metadata
+
+        q_start_loc = self.query_start_loc[self.num_decodes:]
+        q_seqlens = torch.diff(q_start_loc)
+        kv_seqlens = self.seq_lens[self.num_decodes:]
+        # Construct & cache prefill-phase attention metadata structure
+        self._cached_prefill_metadata = TreeAttentionMetadata(
+            num_actual_tokens=self.num_prefill_tokens,
+            max_query_len=int(q_seqlens.max().item()),
+            query_start_loc=q_start_loc - q_start_loc[0],
+            max_seq_len=int(kv_seqlens.max().item()),
+            seq_lens=kv_seqlens,
+            block_table=self.block_table[self.num_decodes:],
+            slot_mapping=self.slot_mapping[self.num_decode_tokens:],
+        )
+        return self._cached_prefill_metadata
+
+    @property
+    def decode_metadata(self) -> Optional["TreeAttentionMetadata"]:
+        if self.num_decode_tokens == 0:
+            return None
+
+        if self._cached_decode_metadata is not None:
+            # Recover cached decode-phase attention
+            # metadata structure
+            return self._cached_decode_metadata
+
+        q_start_loc = self.query_start_loc[:self.num_decodes + 1]
+        q_seqlens = torch.diff(q_start_loc)
+        kv_seqlens = self.seq_lens[:self.num_decodes]
+        # Construct & cache decode-phase attention metadata structure
+        self._cached_decode_metadata = TreeAttentionMetadata(
+            num_actual_tokens=self.num_decode_tokens,
+            max_query_len=int(q_seqlens.max().item()),
+            query_start_loc=q_start_loc,
+            max_seq_len=int(kv_seqlens.max().item()),
+            seq_lens=kv_seqlens,
+            block_table=self.block_table[:self.num_decodes],
+            slot_mapping=self.slot_mapping[:self.num_decode_tokens],
+            tree_attn_bias=self.tree_attn_bias,
+        )
+        return self._cached_decode_metadata
+
+
+class TreeAttentionMetadataBuilder(
+        AttentionMetadataBuilder[TreeAttentionMetadata]):
+
+    def __init__(
+        self,
+        kv_cache_spec: AttentionSpec,
+        layer_names: list[str],
+        vllm_config: VllmConfig,
+        device: torch.device,
+    ):
+        self.kv_cache_spec = kv_cache_spec
+        self.block_size = kv_cache_spec.block_size
+
+        spec_config = vllm_config.speculative_config
+        spec_token_tree = (spec := spec_config) and spec.speculative_token_tree
+        tree_choices: list[tuple[int,
+                                 ...]] = (ast.literal_eval(spec_token_tree)
+                                          if spec_token_tree is not None else
+                                          [(0, )])
+        # Construct the tree attention bias.
+        depth_counts = _get_depth_counts(tree_choices)
+        self.tree_attn_bias = _prepare_tree_attn_bias(
+            tree_choices,
+            depth_counts,
+            dtype=torch.float32,
+            device=device,
+        )
+
+    def reorder_batch(self, input_batch: "InputBatch",
+                      scheduler_output: "SchedulerOutput") -> bool:
+        return reorder_batch_to_split_decodes_and_prefills(
+            input_batch,
+            scheduler_output,
+            decode_threshold=self.tree_attn_bias.shape[0])
+
+    def build(
+        self,
+        common_prefix_len: int,
+        common_attn_metadata: CommonAttentionMetadata,
+        fast_build: bool = False,
+    ) -> TreeAttentionMetadata:
+        decode_threshold = self.tree_attn_bias.shape[0]
+        num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = (
+            split_decodes_and_prefills(common_attn_metadata,
+                                       decode_threshold=decode_threshold))
+
+        num_actual_tokens = common_attn_metadata.num_actual_tokens
+        q_start_loc = common_attn_metadata.query_start_loc
+        max_query_len = common_attn_metadata.max_query_len
+        kv_seqlens = common_attn_metadata.seq_lens
+        max_seq_len = int(common_attn_metadata.seq_lens_cpu.max())
+        block_table = common_attn_metadata.block_table_tensor
+        slot_mapping = common_attn_metadata.slot_mapping
+
+        return TreeAttentionMetadata(
+            num_actual_tokens=num_actual_tokens,
+            num_prefill_tokens=num_prefill_tokens,
+            num_decode_tokens=num_decode_tokens,
+            num_prefills=num_prefills,
+            num_decodes=num_decodes,
+            max_query_len=max_query_len,
+            query_start_loc=q_start_loc,
+            max_seq_len=max_seq_len,
+            seq_lens=kv_seqlens,
+            block_table=block_table,
+            slot_mapping=slot_mapping,
+            tree_attn_bias=self.tree_attn_bias,
+        )
+
+    def build_for_drafting(
+        self,
+        common_attn_metadata: CommonAttentionMetadata,
+        draft_index: int,
+    ) -> TreeAttentionMetadata:
+        # Cache the original tree attention bias.
+        orig_tree_attn_bias = self.tree_attn_bias
+
+        if draft_index == 0:
+            # Use prefill for drafting at the root level.
+            self.tree_attn_bias = torch.empty(0)
+        else:
+            # Slice the tree attention bias for drafting. Exclude
+            # the root level.
+            start, end = 1, 1 + common_attn_metadata.max_query_len
+            self.tree_attn_bias = self.tree_attn_bias[start:end,
+                                                      start:end].contiguous()
+
+        # Build attention bias.
+        attn_metadata = self.build(0, common_attn_metadata, fast_build=True)
+
+        # Reset the tree attention bias to the original value.
+        self.tree_attn_bias = orig_tree_attn_bias
+        return attn_metadata
+
+
+def _get_depth_counts(sorted_tree_choices: list[tuple[int, ...]]) -> list[int]:
+    # Count the number of choices at each depth of the tree.
+    depth_counts = []
+    prev_depth = 0
+    for path in sorted_tree_choices:
+        depth = len(path)
+        if depth != prev_depth:
+            depth_counts.append(0)
+        depth_counts[depth - 1] += 1
+        prev_depth = depth
+    return depth_counts
+
+
+def _prepare_tree_attn_bias(
+    sorted_tree_choices: list[tuple[int, ...]],
+    depth_counts: list[int],
+    dtype: Optional[torch.dtype],
+    device: Optional[torch.device],
+) -> torch.Tensor:
+    # +1 comes from the additional root node.
+    tree_len = len(sorted_tree_choices) + 1
+    tree_attn_mask = torch.full((tree_len, tree_len),
+                                -torch.inf,
+                                device=device,
+                                dtype=dtype)
+
+    # Set diagonal to all zeros. Each token should
+    # attend to itself.
+    mask_val = 0
+    for i in range(tree_len):
+        tree_attn_mask[i, i] = mask_val
+
+    # Set root to all zeros. All tokens attend to it.
+    tree_attn_mask[:, 0] = mask_val
+
+    # Set all ancestors to zeros.
+    start = 0
+    for i in range(len(depth_counts)):
+        for j in range(depth_counts[i]):
+            cur_tree_choice = sorted_tree_choices[start + j]
+            # Retrieve ancestor position.
+            if len(cur_tree_choice) == 1:
+                continue
+            ancestor_idx = []
+            for c in range(len(cur_tree_choice) - 1):
+                ancestor_idx.append(
+                    sorted_tree_choices.index(cur_tree_choice[:c + 1]) + 1)
+            tree_attn_mask[j + start + 1, ancestor_idx] = mask_val
+        start += depth_counts[i]
+    return tree_attn_mask
+
+
+class TreeAttentionImpl(AttentionImpl):
+
+    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] = None,
+        attn_type: AttentionType = AttentionType.DECODER,
+        kv_sharing_target_layer_name: Optional[str] = None,
+    ) -> None:
+        self.num_heads = num_heads
+        self.head_size = head_size
+        self.scale = float(scale)
+        self.num_kv_heads = num_kv_heads
+        self.num_queries_per_kv = self.num_heads // self.num_kv_heads
+        self.kv_cache_dtype = kv_cache_dtype
+        self.kv_sharing_target_layer_name = kv_sharing_target_layer_name
+        if alibi_slopes is not None:
+            alibi_slopes = torch.tensor(alibi_slopes, dtype=torch.float32)
+        self.alibi_slopes = alibi_slopes
+        if logits_soft_cap is None:
+            # Setting logits_soft_cap to 0 means no soft cap.
+            logits_soft_cap = 0
+        self.logits_soft_cap = logits_soft_cap
+        if sliding_window is None:
+            self.sliding_window = (-1, -1)
+        else:
+            self.sliding_window = (sliding_window - 1, 0)
+
+        TreeAttentionBackend.validate_head_size(head_size)
+
+        if attn_type != AttentionType.DECODER:
+            raise NotImplementedError("Encoder self-attention and "
+                                      "encoder/decoder cross-attention "
+                                      "are not implemented for "
+                                      "TreeAttentionImpl.")
+
+    def forward(
+        self,
+        layer: torch.nn.Module,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        kv_cache: torch.Tensor,
+        attn_metadata: TreeAttentionMetadata,
+        output: Optional[torch.Tensor] = None,
+        output_scale: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        """Forward pass with TreeAttention.
+
+        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]
+            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_scale is not None:
+            raise NotImplementedError(
+                "fused output quantization is not yet supported"
+                " for TreeAttentionImpl")
+
+        if attn_metadata is None:
+            # Profiling run.
+            return output
+
+        # Cache the input KVs.
+        key_cache, value_cache = kv_cache.unbind(0)
+        if self.kv_sharing_target_layer_name is None:
+            # Reshape the input keys and values and store them in the cache.
+            # Skip this if sharing KV cache with an earlier attention layer.
+            # NOTE(woosuk): Here, key and value are padded while slot_mapping is
+            # not padded. However, we don't need to do key[:num_actual_tokens]
+            # and value[:num_actual_tokens] because the reshape_and_cache_flash
+            # op uses the slot_mapping's shape to determine the number of
+            # actual tokens.
+            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,
+            )
+
+        num_actual_tokens = attn_metadata.num_actual_tokens
+        num_decode_tokens = attn_metadata.num_decode_tokens
+        descale_shape = (attn_metadata.query_start_loc.shape[0] - 1,
+                         key.shape[1])
+        if prefill_meta := attn_metadata.prefill_metadata:
+            unified_attention(
+                q=query[num_decode_tokens:num_actual_tokens],
+                k=key_cache,
+                v=value_cache,
+                out=output[num_decode_tokens:num_actual_tokens],
+                cu_seqlens_q=prefill_meta.query_start_loc,
+                max_seqlen_q=prefill_meta.max_query_len,
+                seqused_k=prefill_meta.seq_lens,
+                max_seqlen_k=prefill_meta.max_seq_len,
+                softmax_scale=self.scale,
+                causal=True,
+                alibi_slopes=self.alibi_slopes,
+                window_size=self.sliding_window,
+                block_table=prefill_meta.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),
+            )
+
+        if decode_meta := attn_metadata.decode_metadata:
+            unified_attention(
+                q=query[:num_decode_tokens],
+                k=key_cache,
+                v=value_cache,
+                out=output[:num_decode_tokens],
+                cu_seqlens_q=decode_meta.query_start_loc,
+                max_seqlen_q=decode_meta.max_query_len,
+                seqused_k=decode_meta.seq_lens,
+                max_seqlen_k=decode_meta.max_seq_len,
+                softmax_scale=self.scale,
+                causal=True,
+                alibi_slopes=self.alibi_slopes,
+                qq_bias=decode_meta.tree_attn_bias,
+                window_size=self.sliding_window,
+                block_table=decode_meta.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),
+            )
+        return output

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/attention/backends/triton_attn.py

@@ -0,0 +1,417 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Attention layer with PagedAttention and Triton prefix prefill."""
+from dataclasses import dataclass
+from functools import cache
+from typing import ClassVar, Optional
+
+import torch
+
+from vllm import _custom_ops as ops
+from vllm import envs
+from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
+                                              AttentionMetadata, AttentionType)
+from vllm.attention.ops.chunked_prefill_paged_decode import (
+    chunked_prefill_paged_decode)
+from vllm.attention.ops.paged_attn import PagedAttention
+from vllm.config import VllmConfig
+from vllm.logger import init_logger
+from vllm.platforms import current_platform
+from vllm.v1.attention.backends.flash_attn import FlashAttentionMetadata
+from vllm.v1.attention.backends.utils import (AttentionCGSupport,
+                                              AttentionMetadataBuilder,
+                                              CommonAttentionMetadata)
+from vllm.v1.kv_cache_interface import AttentionSpec
+
+logger = init_logger(__name__)
+
+
+@dataclass
+class TritonAttentionMetadata:
+    # NOTE(sang): Definition of context_len, query_len, and seq_len.
+    # |---------- N-1 iteration --------|
+    # |---------------- N iteration ---------------------|
+    # |- tokenA -|......................|-- newTokens ---|
+    # |---------- context_len ----------|
+    # |-------------------- seq_len ---------------------|
+    #                                   |-- query_len ---|
+
+    num_actual_tokens: int  # Number of tokens excluding padding.
+    max_query_len: int
+    query_start_loc: torch.Tensor
+    max_seq_len: int
+    seq_lens: torch.Tensor
+    block_table: torch.Tensor
+    slot_mapping: torch.Tensor
+
+    # For cascade attention.
+    use_cascade: bool
+    common_prefix_len: int
+    cu_prefix_query_lens: Optional[torch.Tensor]
+    prefix_kv_lens: Optional[torch.Tensor]
+    suffix_kv_lens: Optional[torch.Tensor]
+
+    # Optional aot scheduling
+    scheduler_metadata: Optional[torch.Tensor] = None
+    prefix_scheduler_metadata: Optional[torch.Tensor] = None
+
+
+class TritonAttentionMetadataBuilder(
+        AttentionMetadataBuilder[TritonAttentionMetadata]):
+    cudagraph_support: ClassVar[AttentionCGSupport] = AttentionCGSupport.ALWAYS
+
+    def __init__(self, kv_cache_spec: AttentionSpec, layer_names: list[str],
+                 vllm_config: VllmConfig, device: torch.device):
+        self.device = device
+        self.block_size = kv_cache_spec.block_size
+        self.kv_cache_spec = kv_cache_spec
+
+        model_config = vllm_config.model_config
+        self.num_heads_q = model_config.get_num_attention_heads(
+            vllm_config.parallel_config)
+        self.num_heads_kv = model_config.get_num_kv_heads(
+            vllm_config.parallel_config)
+        self.headdim = model_config.get_head_size()
+
+    def build_for_cudagraph_capture(
+        self, common_attn_metadata: CommonAttentionMetadata
+    ) -> TritonAttentionMetadata:
+        attn_metadata = self.build(0, common_attn_metadata)
+        # When doing full graph capture, setting seq_lens to
+        # max_model_len will cause graph capture to be extremely
+        # slow, so here we set it to 1.
+        attn_metadata.seq_lens.fill_(1)
+        return attn_metadata
+
+    def build(self,
+              common_prefix_len: int,
+              common_attn_metadata: CommonAttentionMetadata,
+              fast_build: bool = False) -> TritonAttentionMetadata:
+        num_actual_tokens = common_attn_metadata.num_actual_tokens
+        max_query_len = common_attn_metadata.max_query_len
+
+        max_seq_len = int(common_attn_metadata.seq_lens_cpu.max())
+        query_start_loc = common_attn_metadata.query_start_loc
+        seq_lens = common_attn_metadata.seq_lens
+        block_table_tensor = common_attn_metadata.block_table_tensor
+        slot_mapping = common_attn_metadata.slot_mapping
+
+        use_cascade = common_prefix_len > 0
+
+        if use_cascade:
+            cu_prefix_query_lens = torch.tensor([0, num_actual_tokens],
+                                                dtype=torch.int32,
+                                                device=self.device)
+            prefix_kv_lens = torch.tensor([common_prefix_len],
+                                          dtype=torch.int32,
+                                          device=self.device)
+            suffix_kv_lens = (common_attn_metadata.seq_lens_cpu -
+                              common_prefix_len)
+            suffix_kv_lens = suffix_kv_lens.to(self.device)
+        else:
+            cu_prefix_query_lens = None
+            prefix_kv_lens = None
+            suffix_kv_lens = None
+            prefix_scheduler_metadata = None
+
+        attn_metadata = TritonAttentionMetadata(
+            num_actual_tokens=num_actual_tokens,
+            max_query_len=max_query_len,
+            query_start_loc=query_start_loc,
+            max_seq_len=max_seq_len,
+            seq_lens=seq_lens,
+            block_table=block_table_tensor,
+            slot_mapping=slot_mapping,
+            use_cascade=use_cascade,
+            common_prefix_len=common_prefix_len,
+            cu_prefix_query_lens=cu_prefix_query_lens,
+            prefix_kv_lens=prefix_kv_lens,
+            suffix_kv_lens=suffix_kv_lens,
+            prefix_scheduler_metadata=prefix_scheduler_metadata,
+        )
+        return attn_metadata
+
+
+class TritonAttentionBackend(AttentionBackend):
+
+    accept_output_buffer: bool = True
+
+    @classmethod
+    def get_supported_dtypes(cls) -> list[torch.dtype]:
+        return [torch.float16, torch.bfloat16]
+
+    @classmethod
+    def get_supported_head_sizes(cls) -> list[int]:
+        return [32, 64, 96, 128, 160, 192, 224, 256]
+
+    @classmethod
+    def validate_head_size(cls, head_size: int) -> None:
+        supported_head_sizes = cls.get_supported_head_sizes()
+        if head_size not in supported_head_sizes:
+            attn_type = cls.__name__.removesuffix("Backend")
+            raise ValueError(
+                f"Head size {head_size} is not supported by {attn_type}. "
+                f"Supported head sizes are: {supported_head_sizes}. "
+                "Set VLLM_ATTENTION_BACKEND=FLEX_ATTENTION to use "
+                "FlexAttention backend which supports all head sizes.")
+
+    @staticmethod
+    def get_name() -> str:
+        return "TRITON_ATTN_VLLM_V1"
+
+    @staticmethod
+    def get_impl_cls() -> type["TritonAttentionImpl"]:
+        return TritonAttentionImpl
+
+    @staticmethod
+    def get_metadata_cls() -> type["AttentionMetadata"]:
+        return TritonAttentionMetadata
+
+    @staticmethod
+    def get_kv_cache_shape(
+        num_blocks: int,
+        block_size: int,
+        num_kv_heads: int,
+        head_size: int,
+    ) -> 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["TritonAttentionMetadataBuilder"]:
+        return TritonAttentionMetadataBuilder
+
+
+@cache
+def use_aiter_unified_attention() -> bool:
+    """Check if aiter unified attention should be used."""
+    # VLLM_ROCM_USE_AITER_MHA needs to set to 0 as well as it is set
+    # to 1 as default
+    return envs.VLLM_ROCM_USE_AITER \
+        and envs.VLLM_USE_AITER_UNIFIED_ATTENTION
+
+
+class TritonAttentionImpl(AttentionImpl):
+
+    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] = None,
+        attn_type: AttentionType = AttentionType.DECODER,
+        kv_sharing_target_layer_name: Optional[int] = None,
+        sinks: Optional[torch.Tensor] = None,
+    ) -> 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)
+        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
+
+        TritonAttentionBackend.validate_head_size(head_size)
+
+        if attn_type != AttentionType.DECODER:
+            raise NotImplementedError("Encoder self-attention and "
+                                      "encoder/decoder cross-attention "
+                                      "are not implemented for "
+                                      "TritonAttentionImpl")
+
+        self.fp8_dtype = current_platform.fp8_dtype()
+        self.force_prefill_decode_attn = \
+            envs.VLLM_V1_USE_PREFILL_DECODE_ATTENTION
+
+        if not self.force_prefill_decode_attn:
+            # If not using prefill decode attention, we use the Triton
+            # unified attention implementation.
+            if use_aiter_unified_attention():
+                logger.info_once(
+                    "Using aiter unified attention for TritonAttentionImpl")
+                from aiter.ops.triton.unified_attention import (
+                    unified_attention)
+                self.unified_attention = unified_attention
+            else:
+                logger.info_once(
+                    "Using vllm unified attention for TritonAttentionImpl")
+                from vllm.attention.ops.triton_unified_attention import (
+                    unified_attention)
+                self.unified_attention = unified_attention
+
+        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}.")
+
+    def forward(
+        self,
+        layer: torch.nn.Module,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        kv_cache: torch.Tensor,
+        attn_metadata: FlashAttentionMetadata,
+        output: Optional[torch.Tensor] = None,
+        output_scale: Optional[torch.Tensor] = 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 = [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_scale is not None:
+            raise NotImplementedError(
+                "fused output quantization is not yet supported"
+                " for TritonAttentionImpl")
+
+        if attn_metadata is None:
+            # Profiling run.
+            return output
+
+        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.
+
+        use_prefill_decode_attn = self.force_prefill_decode_attn
+        num_actual_tokens = attn_metadata.num_actual_tokens
+
+        if use_prefill_decode_attn:
+            key_cache, value_cache = PagedAttention.split_kv_cache(
+                kv_cache, self.num_kv_heads, self.head_size)
+        else:
+            key_cache, value_cache = kv_cache.unbind(0)
+
+        if self.kv_sharing_target_layer_name is None:
+            # Reshape the input keys and values and store them in the cache.
+            # Skip this if sharing KV cache with an earlier attention layer.
+            if use_prefill_decode_attn:
+                PagedAttention.write_to_paged_cache(
+                    key,
+                    value,
+                    key_cache,
+                    value_cache,
+                    attn_metadata.slot_mapping,
+                    self.kv_cache_dtype,
+                    layer._k_scale,
+                    layer._v_scale,
+                )
+            else:
+                torch.ops._C_cache_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)
+            num_tokens, num_heads, head_size = query.shape
+            assert layer._q_scale == 1.0, \
+                "A non 1.0 q_scale is not currently supported."
+            if not current_platform.is_rocm():
+                # Skip Q quantization on ROCm, since dequantizing back to
+                # f32 in the attention kernel is not supported.
+                query, _ = ops.scaled_fp8_quant(
+                    query.reshape(
+                        (num_tokens, num_heads * head_size)).contiguous(),
+                    layer._q_scale)
+                query = query.reshape((num_tokens, num_heads, head_size))
+
+        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
+
+        if use_prefill_decode_attn:
+            # Compute attention and update output up to `num_actual_tokens`.
+            chunked_prefill_paged_decode(
+                query=query[:num_actual_tokens],
+                key=key[:num_actual_tokens],
+                value=value[:num_actual_tokens],
+                output=output[:num_actual_tokens],
+                kv_cache_dtype=self.kv_cache_dtype,
+                key_cache=key_cache,
+                value_cache=value_cache,
+                block_table=block_table,
+                query_start_loc=cu_seqlens_q,
+                seq_lens=seqused_k,
+                max_seq_len=max_seqlen_k,
+                max_query_len=max_seqlen_q,
+                k_scale=layer._k_scale,
+                v_scale=layer._v_scale,
+                alibi_slopes=self.alibi_slopes,
+                sliding_window=self.sliding_window[0],
+                sm_scale=self.scale,
+                sinks=self.sinks,
+            )
+
+        else:
+            descale_shape = (cu_seqlens_q.shape[0] - 1, key.shape[1])
+
+            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,
+            )
+
+        return output

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/attention/backends/utils.py

@@ -0,0 +1,715 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import abc
+import enum
+import functools
+from abc import abstractmethod
+from dataclasses import dataclass, make_dataclass
+from typing import (TYPE_CHECKING, Any, Callable, ClassVar, Generic, Optional,
+                    TypeVar)
+
+import numpy as np
+import torch
+
+from vllm.config import VllmConfig, get_layers_from_vllm_config
+from vllm.utils import cdiv
+
+if TYPE_CHECKING:
+    from vllm.attention.backends.abstract import AttentionImpl
+    from vllm.v1.core.sched.output import SchedulerOutput
+    from vllm.v1.worker.gpu_input_batch import InputBatch
+
+import vllm.envs as envs
+from vllm.attention.backends.abstract import AttentionBackend
+from vllm.attention.layer import Attention
+from vllm.distributed.kv_transfer.kv_connector.utils import (
+    get_kv_connector_cache_layout)
+from vllm.logger import init_logger
+from vllm.v1.kv_cache_interface import AttentionSpec
+
+logger = init_logger(__name__)
+_KV_CACHE_LAYOUT_OVERRIDE = None
+
+
+@dataclass
+class CommonAttentionMetadata:
+    """
+    Per-batch attention metadata, shared across layers and backends.
+    AttentionMetadataBuilder instances use it to construct per-layer metadata.
+    
+    For many of the tensors we keep both GPU and CPU versions.
+    """
+
+    query_start_loc: torch.Tensor
+    query_start_loc_cpu: torch.Tensor
+    """(batch_size + 1,), the start location of each request in query Tensor"""
+
+    seq_lens: torch.Tensor
+    seq_lens_cpu: torch.Tensor
+    """(batch_size,), the length of each request including both computed tokens
+    and newly scheduled tokens"""
+
+    num_computed_tokens_cpu: torch.Tensor
+    """(batch_size,), the number of computed tokens for each request"""
+
+    num_reqs: int
+    """Number of requests"""
+    num_actual_tokens: int
+    """Total number of tokens in batch"""
+    max_query_len: int
+    """Longest query in batch"""
+
+    block_table_tensor: torch.Tensor
+    slot_mapping: torch.Tensor
+
+    causal: bool = True
+
+
+@dataclass
+class UbatchSlice:
+    request_slice: slice
+    token_slice: slice
+
+
+def slice_query_start_locs(
+    query_start_loc: torch.Tensor,
+    request_slice: slice,
+) -> torch.Tensor:
+    """
+    Creates a new query_start_loc that corresponds to the requests in 
+    request_slice.
+
+    Note: This function creates a new tensor to hold the new query_start_locs.
+    This will break cudagraph compatibility.
+    """
+    return query_start_loc[request_slice.start: request_slice.stop + 1] -\
+        query_start_loc[request_slice.start]
+
+
+def _make_metadata_with_slice(
+        ubatch_slice: UbatchSlice,
+        attn_metadata: CommonAttentionMetadata) -> CommonAttentionMetadata:
+    """
+    This function creates a new CommonAttentionMetadata that corresponds to 
+    the requests included in ubatch_slice
+    """
+
+    request_slice = ubatch_slice.request_slice
+    token_slice = ubatch_slice.token_slice
+
+    query_start_loc = slice_query_start_locs(attn_metadata.query_start_loc,
+                                             request_slice)
+    assert len(query_start_loc) >= 2, (
+        f"query_start_loc must have at least 2 elements, "
+        f"got {len(query_start_loc)}")
+    query_start_loc_cpu = slice_query_start_locs(
+        attn_metadata.query_start_loc_cpu, request_slice)
+
+    seq_lens = attn_metadata.seq_lens[request_slice]
+    seq_lens_cpu = attn_metadata.seq_lens_cpu[request_slice]
+    num_computed_tokens_cpu = attn_metadata.num_computed_tokens_cpu[
+        request_slice]
+
+    num_requests = request_slice.stop - request_slice.start
+    num_actual_tokens = token_slice.stop - token_slice.start
+    max_query_len = int(
+        torch.max(torch.abs(query_start_loc_cpu[1:] -
+                            query_start_loc_cpu[:-1])).item())
+
+    block_table_tensor = attn_metadata.block_table_tensor[request_slice]
+    slot_mapping = attn_metadata.slot_mapping[token_slice]
+
+    return CommonAttentionMetadata(
+        query_start_loc=query_start_loc,
+        query_start_loc_cpu=query_start_loc_cpu,
+        seq_lens=seq_lens,
+        seq_lens_cpu=seq_lens_cpu,
+        num_computed_tokens_cpu=num_computed_tokens_cpu,
+        num_reqs=num_requests,
+        num_actual_tokens=num_actual_tokens,
+        max_query_len=max_query_len,
+        block_table_tensor=block_table_tensor,
+        slot_mapping=slot_mapping,
+    )
+
+
+def split_attn_metadata(
+    ubatch_slices: list[UbatchSlice],
+    common_attn_metadata: CommonAttentionMetadata,
+) -> list[CommonAttentionMetadata]:
+    """
+    Creates a new CommonAttentionMetadata instance that corresponds to the 
+    requests for each UbatchSlice in ubatch_slices.
+
+    Note: This function does not modify common_attn_metadata
+    """
+    results = []
+    for ubatch_slice in ubatch_slices:
+        results.append(
+            _make_metadata_with_slice(ubatch_slice, common_attn_metadata))
+    return results
+
+
+M = TypeVar("M")
+
+
+class AttentionCGSupport(enum.Enum):
+    """ Constants for the cudagraph support of the attention backend
+    Here we do not consider the cascade attention, as currently
+    it is never cudagraph supported."""
+
+    ALWAYS = 3
+    """Cudagraph always supported; supports mixed-prefill-decode"""
+    UNIFORM_BATCH = 2
+    """Cudagraph supported for batches the only contain query lengths that are
+    the same, this can be used for spec-decode 
+        i.e. "decodes" are 1 + num_speculative_tokens"""
+    UNIFORM_SINGLE_TOKEN_DECODE = 1
+    """Cudagraph supported for batches the only contain query_len==1 decodes"""
+    NEVER = 0
+    """NO cudagraph support"""
+
+
+class AttentionMetadataBuilder(abc.ABC, Generic[M]):
+    # Does this backend/builder support CUDA Graphs for attention (default: no).
+    cudagraph_support: ClassVar[AttentionCGSupport] = \
+        AttentionCGSupport.NEVER
+    # Does this backend/builder reorder the batch?
+    # If not, set this to None. Otherwise set it to the query
+    # length that will be pulled into the front of the batch.
+    reorder_batch_threshold: ClassVar[Optional[int]] = None
+
+    @abstractmethod
+    def __init__(self, kv_cache_spec: AttentionSpec, layer_names: list[str],
+                 vllm_config: VllmConfig, device: torch.device):
+        self.kv_cache_spec = kv_cache_spec
+
+    @abstractmethod
+    def build(self,
+              common_prefix_len: int,
+              common_attn_metadata: CommonAttentionMetadata,
+              fast_build: bool = False) -> M:
+        """
+        Central method that builds attention metadata.
+        Some builders (MLA) require reorder_batch to be called prior to build.
+        
+        Args:
+            common_prefix_len: The length of the common prefix of the batch.
+            common_attn_metadata: The common attention metadata.
+            fast_build: The meta-data will prioritize speed of building over
+                then speed at execution. Can be used for spec-decode where the
+                result of a build call may only be used for few layers/iters.
+        """
+        raise NotImplementedError
+
+    def build_for_cudagraph_capture(
+            self, common_attn_metadata: CommonAttentionMetadata) -> M:
+        """
+        Build attention metadata for CUDA graph capture. Uses build by default.
+        Subclasses that override this method should call self.build or
+        super().build_for_cudagraph_capture.
+        """
+        return self.build(common_prefix_len=0,
+                          common_attn_metadata=common_attn_metadata)
+
+    def build_for_drafting(
+        self,
+        common_attn_metadata: CommonAttentionMetadata,
+        draft_index: int,
+    ) -> M:
+        """
+        Build attention metadata for draft model. Uses build by default.
+        
+        Args:
+            common_attn_metadata: The common attention metadata.
+            draft_index: The index of the current draft operation.
+                When speculating a chain of tokens, this index refers to the
+                draft attempt for the i-th token.
+                For tree-based attention, this index instead refers to the
+                draft attempt for the i-th level in the tree of tokens.
+        """
+        return self.build(common_prefix_len=0,
+                          common_attn_metadata=common_attn_metadata,
+                          fast_build=True)
+
+    def use_cascade_attention(
+        self,
+        common_prefix_len: int,
+        query_lens: np.ndarray,
+        num_query_heads: int,
+        num_kv_heads: int,
+        use_alibi: bool,
+        use_sliding_window: bool,
+        use_local_attention: bool,
+        num_sms: int,
+    ) -> bool:
+        return False
+
+
+@functools.lru_cache
+def get_kv_cache_layout():
+    # Format specified by the code.
+    global _KV_CACHE_LAYOUT_OVERRIDE
+
+    if _KV_CACHE_LAYOUT_OVERRIDE is not None:
+        cache_layout = _KV_CACHE_LAYOUT_OVERRIDE
+        logger.info_once("`_KV_CACHE_LAYOUT_OVERRIDE` variable detected. " \
+                         "Setting KV cache layout to %s.", cache_layout)
+        return cache_layout
+
+    # Format specified by the user.
+    cache_layout = envs.VLLM_KV_CACHE_LAYOUT
+    # When neither the user nor the override specified a layout, get default
+    if cache_layout is None:
+        cache_layout = get_kv_connector_cache_layout()
+    else:
+        logger.info_once("`VLLM_KV_CACHE_LAYOUT` environment variable " \
+        "detected. Setting KV cache layout to %s.", cache_layout)
+    return cache_layout
+
+
+def set_kv_cache_layout(cache_layout: str):
+    global _KV_CACHE_LAYOUT_OVERRIDE
+    _KV_CACHE_LAYOUT_OVERRIDE = cache_layout
+
+
+@dataclass
+class PerLayerParameters:
+    """
+    Currently, FlashInfer backend only support models in which all layers share
+    the same values for the following hyperparameters. Should not be used for
+    trtllm-gen backend since it supports different values for the following
+    hyperparameters.
+    """
+
+    window_left: int
+    logits_soft_cap: Optional[float]
+    sm_scale: float
+    has_sinks: bool = False
+
+
+def get_per_layer_parameters(
+        vllm_config: VllmConfig, layer_names: list[str],
+        cls_: type['AttentionImpl']) -> dict[str, PerLayerParameters]:
+    """
+    Scan layers in `layer_names` and determine some hyperparameters
+    to use during `plan`.
+    """
+
+    layers = get_layers_from_vllm_config(vllm_config, Attention, layer_names)
+    per_layer_params: dict[str, PerLayerParameters] = {}
+
+    for key, layer in layers.items():
+        impl = layer.impl
+        assert isinstance(impl, cls_)
+
+        # Infer hyperparameters from the attention layer
+        window_size = getattr(impl, "sliding_window", None)
+        window_left = window_size[0] if window_size is not None else -1
+        logits_soft_cap = getattr(impl, "logits_soft_cap", None)
+        sm_scale = impl.scale
+        has_sinks = getattr(impl, "sinks", None) is not None
+
+        per_layer_params[key] = PerLayerParameters(window_left,
+                                                   logits_soft_cap, sm_scale,
+                                                   has_sinks)
+
+    return per_layer_params
+
+
+def infer_global_hyperparameters(
+        per_layer_params: dict[str, PerLayerParameters]) -> PerLayerParameters:
+    """
+    Currently, FlashInfer backend other than trtllm-gen 
+    only support models in which all layers share
+    the same values for the following hyperparameters:
+    - `window_left`
+    - `logits_soft_cap`
+    - `sm_scale`
+
+    So this function asserts that all layers share the same values for these
+    hyperparameters and returns the global values.
+    """
+
+    assert len(per_layer_params) > 0, "No attention layers found in the model."
+
+    param_sets = list(per_layer_params.values())
+    global_params = param_sets[0]
+
+    # trtllm attention doesn't need global hyper params so disable the check
+    if not envs.VLLM_USE_TRTLLM_ATTENTION:
+        for params in param_sets:
+            if params.window_left != global_params.window_left:
+                raise ValueError(
+                    "Window left is not the same for all layers. " \
+                    "One potential fix is to set disable_sliding_window=True")
+            assert params == global_params, (
+                "FlashInfer backend currently only supports models in which all"
+                "layers share the same values "
+                "for the following hyperparameters:"
+                "`window_left`, `logits_soft_cap`, `sm_scale`.")
+
+    return global_params
+
+
+#
+# Take in `query_start_loc_np` and `seq_lens_np` and break the sequences into
+# local attention blocks, where each block is passed to the attention kernel
+# as an independent local ("virtual") batch item.
+#
+# For example, if are performing a chunked prefill a batch of 3 sequences:
+#   q_seqlens  = [4, 10, 5]
+#   kv_seqlens = [6, 17, 9]
+# Then normally for regular attention we would compute with an attention mask
+#  for batch idx 0 (q_seqlens = 4, kv_seqlens = 6) like:
+#   batch idx: 0 (q_seqlens = 4, kv_seqlens = 6)
+#        k_toks >   0 1 2 3 4 5
+#        q_toks v  _____________
+#               0 | 1 1 1
+#               1 | 1 1 1 1
+#               2 | 1 1 1 1 1
+#               3 | 1 1 1 1 1 1
+#
+# for local attention (with attn_chunk_size = 4) we would compute with an
+#  attention mask like:
+#   batch idx: 0  (q_seqlens = 4, kv_seqlens = 6, attn_chunk_size = 4)
+#        k_toks >   0 1 2 3 4 5
+#        q_toks v  _____________
+#               0 | 1 1 1
+#               1 | 1 1 1 1
+#               2 |         1
+#               3 |         1 1
+#
+# We can simulate this mask using standard flash-attention by breaking the
+#  sequences into local ("virtual") batches, where each local batch item is a
+#  local attention block, so in this case batch idx 0 would be broken up into:
+#
+#   local-batch idx: 0 (q_seqlens = 2, kv_seqlens = 4)  (batch 0)
+#        k_toks >   0 1 2 3
+#        q_toks v  _____________
+#               0 | 1 1 1
+#               1 | 1 1 1 1
+#   local-batch idx: 1 (q_seqlens = 2, kv_seqlens = 2) (batch 0)
+#        k_toks >   4 5
+#        q_toks v  _____________
+#               2 | 1
+#               3 | 1 1
+#
+# e.g. if we have:
+#   attn_chunk_size = 4
+#   query_start_loc_np = [0, 4, 14, 19] (q_seqlens = [4, 10, 5])
+# Then this function would return:
+#                           __b0__  ______b1______  __b2__ < orig batch indices
+#   q_seqlens_local    = [   2,  2,  1,  4,  4,  1,  4,  1]
+#   cu_seqlens_q_local = [0, 4,  6, 10, 14, 18, 19, 23, 24]
+#   seqlens_k_local    = [   4,  2,  4,  4,  4,  1,  4,  1]
+#   block_table_local  : shape[local_virtual_batches, pages_per_local_batch]
+def make_local_attention_virtual_batches(
+    attn_chunk_size: int,
+    common_attn_metadata: CommonAttentionMetadata,
+    block_size: int = 0,
+) -> CommonAttentionMetadata:
+    query_start_loc_np = common_attn_metadata.query_start_loc_cpu.numpy()
+    seq_lens_np = common_attn_metadata.seq_lens_cpu.numpy()
+    block_table = common_attn_metadata.block_table_tensor
+    device = common_attn_metadata.query_start_loc.device
+
+    q_seqlens = query_start_loc_np[1:] - query_start_loc_np[:-1]
+    actual_batch_size = seq_lens_np.shape[0]
+
+    # Handle if we are starting in the middle of a local attention block,
+    #  we assume q_seqlens > 0 (for all elements), for each batch idx we compute
+    #  the number of tokens that are not in the first local attention block and
+    #  then we can simply use a cdiv for the rest.
+    # For example if we have:
+    #   attn_chunk_size = 4
+    #   q_seqlens = [4, 10, 5]
+    #   k_seqlens = [6, 17, 9]
+    # Then we would get:
+    #   new_tokens_in_first_block = [2, 1, 4]
+    #   local_blocks = [2, 4, 2]
+    q_tokens_in_first_block = np.minimum(
+        attn_chunk_size - ((seq_lens_np - q_seqlens) % attn_chunk_size),
+        q_seqlens).astype(np.int32)
+    tokens_in_last_block = attn_chunk_size + (seq_lens_np % -attn_chunk_size)
+    local_blocks = 1 + cdiv(q_seqlens - q_tokens_in_first_block,
+                            attn_chunk_size)
+
+    # Once we know the number of local blocks we can compute the request spans
+    #  for each batch idx, we can figure out the number of "virtual" requests we
+    #  have to make,
+    # For the above example we would get:
+    #   seqlens_q_local = [2, 2, 1, 4, 4, 1, 4, 1]
+    #
+    # First Get batched arange. (E.g., [2, 4, 2] -> [0, 1, 0, 1, 2, 3, 0, 1])
+    #   (TODO: max a utility to share this code with _prepare_inputs)
+    # arange step 1. [2, 4, 2] -> [2, 6, 8]
+    cu_num_blocks = np.cumsum(local_blocks)
+    virtual_batches = cu_num_blocks[-1]
+    # arange step 2. [2, 6, 8] -> [0, 0, 2, 2, 2, 2, 6, 6]
+    block_offsets = np.repeat(cu_num_blocks - local_blocks, local_blocks)
+    # arange step 3. [0, 1, 0, 1, 2, 3, 0, 1]
+    arange = np.arange(virtual_batches, dtype=np.int32) - block_offsets
+    # also compute reverse arange (i.e. [1, 0, 3, 2, 1, 0, 1, 0])
+    rarange = np.repeat(local_blocks, local_blocks) - arange - 1
+    # Then we can compute the seqlens_q_local, handling the fact that the
+    #  first and last blocks could be partial
+    seqlens_q_local = \
+        np.repeat(q_seqlens - q_tokens_in_first_block, local_blocks)
+    # set the first block since this may be a partial block
+    seqlens_q_local[arange == 0] = q_tokens_in_first_block
+    # set the remaining blocks
+    seqlens_q_local[arange > 0] = np.minimum(
+        seqlens_q_local - attn_chunk_size * (arange - 1),
+        attn_chunk_size)[arange > 0]
+
+    # convert from q_seqlens to cu_seqlens_q
+    cu_seqlens_q_local = np.pad(np.cumsum(seqlens_q_local), (1, 0))\
+        .astype(np.int32)
+
+    # compute the seqlens_k_local,
+    #  basically a full local attention block for all but the last block in each
+    #  batch
+    # For our example this will be:
+    #   seqlens_k_local = [4, 2, 4, 4, 4, 1, 4, 1]
+    seqlens_k_local = np.full(cu_num_blocks[-1],
+                              attn_chunk_size,
+                              dtype=np.int32)
+    seqlens_k_local[cu_num_blocks - 1] = tokens_in_last_block
+    num_computed_tokens_local = seqlens_k_local - seqlens_q_local
+
+    k_seqstarts_absolute = np.repeat(seq_lens_np, local_blocks) - \
+        (rarange * attn_chunk_size + \
+            np.repeat(tokens_in_last_block, local_blocks))
+    # For the example the local attention blocks start at:
+    #                           _b0_  _____b1_____  _b2_
+    #   k_seqstarts_absolute = [0, 4, 4, 8, 12, 16, 4, 8]
+    block_starts = k_seqstarts_absolute // block_size
+    assert attn_chunk_size % block_size == 0, \
+        f"attn_chunk_size {attn_chunk_size} is not " \
+        f"divisible by block_size {block_size}"
+    pages_per_local_batch = attn_chunk_size // block_size
+
+    # Create a block_table for the local attention blocks
+    # For out example if we have a block-table like (assuming block_size=2):
+    #   block_table = [
+    #     [ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9],  < batch 0
+    #     [10, 11, 12, 13, 14, 15, 16, 17, 18, 19],  < batch 1
+    #     [20, 21, 22, 23, 24, 25, 26, 27, 28, 29],  < batch 2
+    #   ]
+    # Then for the local batches we would want a block-table like
+    #   block_table_local = [
+    #     [  0,  1 ], < local-batch 0, (batch 0, starting from k[0])
+    #     [  2,  3 ], < local-batch 1, (batch 0, starting from k[4])
+    #     [ 12, 13 ], < local-batch 2, (batch 1, starting from k[4])
+    #     [ 14, 15 ], < local-batch 3, (batch 1, starting from k[8])
+    #     [ 16, 17 ], < local-batch 4, (batch 1, starting from k[12])
+    #     [ 18, 19 ], < local-batch 5, (batch 1, starting from k[16])
+    #     [ 22, 23 ], < local-batch 6, (batch 2, starting from k[4])
+    #     [ 24, 25 ], < local-batch 7, (batch 2, starting from k[8])
+    #   ]
+    block_indices= np.broadcast_to(
+        np.arange(pages_per_local_batch, dtype=np.int32),
+        (virtual_batches, pages_per_local_batch)) \
+            + np.expand_dims(block_starts, axis=1)
+    block_indices = block_indices.flatten().clip(max=block_table.shape[1] - 1)
+    batch_indices = np.repeat(np.arange(actual_batch_size, dtype=np.int32),
+                              local_blocks * pages_per_local_batch)
+    block_table_local = block_table[batch_indices, block_indices]\
+        .view(virtual_batches, -1)
+
+    query_start_loc_cpu = torch.from_numpy(cu_seqlens_q_local)
+    seq_lens_cpu = torch.from_numpy(seqlens_k_local)
+
+    return CommonAttentionMetadata(
+        query_start_loc_cpu=query_start_loc_cpu,
+        query_start_loc=query_start_loc_cpu.to(device=device,
+                                               non_blocking=True),
+        seq_lens_cpu=seq_lens_cpu,
+        seq_lens=seq_lens_cpu.to(device=device, non_blocking=True),
+        num_computed_tokens_cpu=torch.from_numpy(num_computed_tokens_local),
+        num_reqs=len(seq_lens_cpu),
+        num_actual_tokens=common_attn_metadata.num_actual_tokens,
+        max_query_len=seqlens_q_local.max(),
+        block_table_tensor=block_table_local,
+        slot_mapping=common_attn_metadata.slot_mapping,
+        causal=True,
+    )
+
+
+def subclass_attention_metadata_builder(
+    name_prefix: str,
+    builder_cls: type[AttentionMetadataBuilder[M]],
+    build_preprocess_fn: Callable[[CommonAttentionMetadata],
+                                  CommonAttentionMetadata],
+) -> type[AttentionMetadataBuilder[M]]:
+    """
+    Return a new subclass of `builder_cls` whose .build(...) method
+    first calls build_preprocess_fn(common_attn_metadata) on the metadata.
+    """
+    name: str = name_prefix + builder_cls.__name__  # type: ignore
+
+    def build(self,
+              common_prefix_len: int,
+              common_attn_metadata: CommonAttentionMetadata,
+              fast_build: bool = False):
+        return builder_cls.build(self, common_prefix_len,
+                                 build_preprocess_fn(common_attn_metadata),
+                                 fast_build)
+
+    Wrapped = type(
+        name,
+        (builder_cls, ),  # inherit from the original
+        {
+            "build": build,
+        })
+    return Wrapped  # type: ignore
+
+
+def subclass_attention_backend(
+        name_prefix: str, attention_backend_cls: type[AttentionBackend],
+        builder_cls: type[AttentionMetadataBuilder[M]]
+) -> type[AttentionBackend]:
+    """
+    Return a new subclass where `get_builder_cls` returns `builder_cls`.
+    """
+    name: str = name_prefix + attention_backend_cls.__name__  # type: ignore
+
+    return type(name, (attention_backend_cls, ),
+                {"get_builder_cls": lambda: builder_cls})
+
+
+def split_decodes_and_prefills(
+    common_attn_metadata: CommonAttentionMetadata,
+    decode_threshold: int = 1,
+) -> tuple[int, int, int, int]:
+    """
+    Assuming a reordered batch, finds the boundary between prefill and decode
+    requests.
+
+    Args:
+        common_attn_metadata: CommonAttentionMetadata object containing the
+            batch metadata.
+        decode_threshold: The maximum query length to be considered a decode.
+
+    Returns:
+        num_decodes: The number of decode requests.
+        num_prefills: The number of prefill requests.
+        num_decode_tokens: The number of tokens in the decode requests.
+        num_prefill_tokens: The number of tokens in the prefill requests.
+    """
+    max_query_len = common_attn_metadata.max_query_len
+    num_reqs = common_attn_metadata.num_reqs
+    num_tokens = common_attn_metadata.num_actual_tokens
+    query_start_loc = common_attn_metadata.query_start_loc_cpu
+
+    if max_query_len <= decode_threshold:
+        return num_reqs, 0, num_tokens, 0
+
+    query_lens = query_start_loc[1:] - query_start_loc[:-1]
+    is_prefill = query_lens > decode_threshold
+    if not torch.any(is_prefill):
+        return num_reqs, 0, num_tokens, 0
+
+    first_prefill = is_prefill.int().argmax(dim=-1).item()
+    assert torch.all(query_lens[first_prefill:] > decode_threshold)
+    assert torch.all(query_lens[:first_prefill] <= decode_threshold)
+    num_decodes = first_prefill
+    num_prefills = num_reqs - num_decodes
+    num_decode_tokens = query_start_loc[first_prefill].item()
+    num_prefill_tokens = num_tokens - num_decode_tokens
+    return (num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens)
+
+
+def reorder_batch_to_split_decodes_and_prefills(
+    input_batch: "InputBatch",
+    scheduler_output: "SchedulerOutput",
+    decode_threshold: int = 1,
+) -> bool:
+    """
+    Reorders the batch to split into prefill and decode requests; places all
+    requests with <= decode_threshold tokens at the front of the batch.
+    
+    Returns:
+        True if the batch was modified, False otherwise.
+    """
+    # We now want to reorder the batch so that the "decode" requests are at
+    # the front and the "prefill" requests are at the back using the least
+    # amount of swaps possible. (NOTE for now we loosely use "decode" to mean
+    # requests where attention is likely memory-bound and "prefill" to mean
+    # requests where attention is likely compute-bound, TODO(lucas): figure out
+    # a better naming here)
+    decodes = []
+    prefills = []
+    num_decode_tokens = 0
+    num_prefill_tokens = 0
+
+    for i, req_id in enumerate(input_batch.req_ids):
+        num_tokens = scheduler_output.num_scheduled_tokens[req_id]
+        # for now treat 1 scheduled token as "decode" even if its not,
+        # we should update this to something like < 8 in the future but
+        # currently the TritonMLA._forward_decode only supports
+        # num_tokens = 1
+        if num_tokens <= decode_threshold:
+            decodes.append(i)
+            num_decode_tokens += num_tokens
+        else:
+            prefills.append(i)
+            num_prefill_tokens += num_tokens
+
+    # We hope that this is fairly minimal since decodes
+    # should be around for a number of iterations so hopefully they are
+    # relatively stationary (and new request are generally appended to the
+    # persistent batch so already should be at the back)
+    # To achieve this we loop over the decodes in descending order and
+    # the prefills in ascending order. We swap decodes from the  "back"
+    # i.e. past where the last decode should be in the reodorered with
+    # prefills from the front of the batch.
+    # `decodes` and `prefills` are already in ascending order just based on
+    # the above loop
+    num_decodes = len(decodes)
+    num_prefills = len(prefills)
+    modified_batch = False
+
+    for i in range(1, min(num_decodes, num_prefills) + 1):
+        # If the decode is at the "back" of the batch, i, we can swap it
+        # with the prefill closest to the front of the batch
+        decode_idx = decodes[num_decodes - i]
+        if decode_idx < num_decodes:
+            break
+
+        input_batch.swap_states(prefills[i - 1], decode_idx)
+        modified_batch = True
+
+    return modified_batch
+
+
+KV_SHARING_FAST_PREFILL_METADATA_FIELDS = [
+    ('logits_indices_padded', Optional[torch.Tensor], None),
+    ('num_logits_indices', int, 0),
+]
+
+
+def subclass_attention_metadata(
+    name_prefix: str,
+    metadata_cls: Any,
+    fields: list[tuple[str, Any, Any]],
+) -> Any:
+    """
+    Return a new subclass of `metadata_cls` with additional fields
+    """
+    name: str = name_prefix + metadata_cls.__name__  # type: ignore
+    Wrapped = make_dataclass(name, fields, bases=(metadata_cls, ))
+    return Wrapped
+
+
+def make_kv_sharing_fast_prefill_attention_metadata(
+    metadata_cls: Any, ) -> Any:
+    """
+    Return a new subclass of `metadata_cls` for fast prefill
+    """
+    return subclass_attention_metadata(
+        name_prefix="KVSharingFastPrefill",
+        metadata_cls=metadata_cls,
+        fields=KV_SHARING_FAST_PREFILL_METADATA_FIELDS,
+    )

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/attention/backends/xformers.py

@@ -0,0 +1,430 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Attention layer with XFormersAttention."""
+
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Optional
+
+import torch
+
+from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
+                                              AttentionMetadata, AttentionType)
+from vllm.attention.ops.triton_unified_attention import unified_attention
+from vllm.config import VllmConfig
+from vllm.logger import init_logger
+from vllm.v1.attention.backends.utils import (
+    AttentionMetadataBuilder, CommonAttentionMetadata,
+    reorder_batch_to_split_decodes_and_prefills, split_decodes_and_prefills)
+from vllm.v1.kv_cache_interface import AttentionSpec
+
+try:
+    from xformers import ops as xops
+    from xformers.ops.fmha.attn_bias import (
+        AttentionBias, PagedBlockDiagonalCausalWithOffsetPaddedKeysMask)
+
+    XFORMERS_AVAILABLE = True
+except ImportError:
+    XFORMERS_AVAILABLE = False
+
+if TYPE_CHECKING:
+    from vllm.v1.core.sched.output import SchedulerOutput
+    from vllm.v1.worker.gpu_input_batch import InputBatch
+
+from vllm import _custom_ops as ops
+
+logger = init_logger(__name__)
+
+
+class XFormersAttentionBackend(AttentionBackend):
+
+    accept_output_buffer: bool = True
+
+    @classmethod
+    def get_supported_dtypes(cls) -> list[torch.dtype]:
+        return [torch.float16, torch.bfloat16]
+
+    @classmethod
+    def get_supported_head_sizes(cls) -> list[int]:
+        return [
+            32,
+            40,
+            48,
+            56,
+            64,
+            72,
+            80,
+            88,
+            96,
+            104,
+            112,
+            120,
+            128,
+            136,
+            144,
+            152,
+            160,
+            168,
+            176,
+            184,
+            192,
+            200,
+            208,
+            216,
+            224,
+            232,
+            240,
+            248,
+            256,
+        ]
+
+    @classmethod
+    def validate_head_size(cls, head_size: int) -> None:
+        supported_head_sizes = cls.get_supported_head_sizes()
+        if head_size not in supported_head_sizes:
+            attn_type = cls.__name__.removesuffix("Backend")
+            raise ValueError(
+                f"Head size {head_size} is not supported by {attn_type}. "
+                f"Supported head sizes are: {supported_head_sizes}. "
+                "Set VLLM_ATTENTION_BACKEND=FLEX_ATTENTION to use "
+                "FlexAttention backend which supports all head sizes.")
+
+    @staticmethod
+    def get_name() -> str:
+        return "XFORMERS_VLLM_V1"
+
+    @staticmethod
+    def get_impl_cls() -> type["XFormersAttentionImpl"]:
+        return XFormersAttentionImpl
+
+    @staticmethod
+    def get_metadata_cls() -> type["AttentionMetadata"]:
+        return XFormersAttentionMetadata
+
+    @staticmethod
+    def get_kv_cache_shape(
+        num_blocks: int,
+        block_size: int,
+        num_kv_heads: int,
+        head_size: int,
+    ) -> 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 get_builder_cls() -> type["XFormersAttentionMetadataBuilder"]:
+        return XFormersAttentionMetadataBuilder
+
+    @staticmethod
+    def use_cascade_attention(*args, **kwargs) -> bool:
+        return False
+
+
+@dataclass
+class XFormersAttentionMetadata:
+    num_actual_tokens: int  # Number of tokens excluding padding.
+    max_query_len: int
+    query_start_loc: torch.Tensor
+    max_seq_len: int
+    seq_lens: torch.Tensor
+    block_table: torch.Tensor
+    slot_mapping: torch.Tensor
+
+    num_prefill_tokens: int = 0
+    num_decode_tokens: int = 0
+    num_prefills: int = 0
+    num_decodes: int = 0
+
+    # Biases for different attention types.
+    attn_bias: Optional["AttentionBias"] = None
+
+    # Self-attention prefill/decode metadata cache
+    _cached_prefill_metadata: Optional["XFormersAttentionMetadata"] = None
+    _cached_decode_metadata: Optional["XFormersAttentionMetadata"] = None
+
+    @property
+    def prefill_metadata(self) -> Optional["XFormersAttentionMetadata"]:
+        if self.num_prefills == 0:
+            return None
+
+        if self._cached_prefill_metadata is not None:
+            # Recover cached prefill-phase attention
+            # metadata structure
+            return self._cached_prefill_metadata
+
+        q_start_loc = self.query_start_loc[self.num_decodes:]
+        q_seqlens = torch.diff(q_start_loc)
+        kv_seqlens = self.seq_lens[self.num_decodes:]
+        # Construct & cache prefill-phase attention metadata structure
+        self._cached_prefill_metadata = XFormersAttentionMetadata(
+            num_actual_tokens=self.num_prefill_tokens,
+            max_query_len=int(q_seqlens.max().item()),
+            query_start_loc=q_start_loc - q_start_loc[0],
+            max_seq_len=int(kv_seqlens.max().item()),
+            seq_lens=kv_seqlens,
+            block_table=self.block_table[self.num_decodes:],
+            slot_mapping=self.slot_mapping[self.num_decode_tokens:],
+        )
+        return self._cached_prefill_metadata
+
+    @property
+    def decode_metadata(self) -> Optional["XFormersAttentionMetadata"]:
+        if self.num_decode_tokens == 0:
+            return None
+
+        if self._cached_decode_metadata is not None:
+            # Recover cached decode-phase attention
+            # metadata structure
+            return self._cached_decode_metadata
+
+        q_start_loc = self.query_start_loc
+        q_seqlens = torch.diff(q_start_loc)
+        decode_kv_seqlens = self.seq_lens[:self.num_decodes]
+        # Construct & cache decode-phase attention metadata structure
+        self._cached_decode_metadata = XFormersAttentionMetadata(
+            num_actual_tokens=self.num_decode_tokens,
+            max_query_len=int(q_seqlens[:self.num_decodes].max().item()),
+            query_start_loc=q_start_loc[:self.num_decodes + 1],
+            max_seq_len=int(decode_kv_seqlens.max().item()),
+            seq_lens=decode_kv_seqlens,
+            block_table=self.block_table[:self.num_decodes],
+            slot_mapping=self.slot_mapping[:self.num_decode_tokens],
+            attn_bias=self.attn_bias,
+        )
+        return self._cached_decode_metadata
+
+
+class XFormersAttentionMetadataBuilder(
+        AttentionMetadataBuilder[XFormersAttentionMetadata]):
+
+    def __init__(
+        self,
+        kv_cache_spec: AttentionSpec,
+        layer_names: list[str],
+        vllm_config: VllmConfig,
+        device: torch.device,
+    ):
+        assert XFORMERS_AVAILABLE
+        self.kv_cache_spec = kv_cache_spec
+        self.block_size = kv_cache_spec.block_size
+        self._num_decodes = 0
+        self._num_decode_tokens = 0
+
+    def reorder_batch(self, input_batch: "InputBatch",
+                      scheduler_output: "SchedulerOutput") -> bool:
+        return reorder_batch_to_split_decodes_and_prefills(input_batch,
+                                                           scheduler_output,
+                                                           decode_threshold=1)
+
+    def build(
+        self,
+        common_prefix_len: int,
+        common_attn_metadata: CommonAttentionMetadata,
+        fast_build: bool = False,
+    ) -> XFormersAttentionMetadata:
+        num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = (
+            split_decodes_and_prefills(common_attn_metadata,
+                                       decode_threshold=1))
+
+        num_actual_tokens = common_attn_metadata.num_actual_tokens
+        q_start_loc = common_attn_metadata.query_start_loc
+        q_seqlens = torch.diff(q_start_loc)
+        max_query_len = common_attn_metadata.max_query_len
+        kv_seqlens = common_attn_metadata.seq_lens
+        max_seq_len = int(common_attn_metadata.seq_lens_cpu.max())
+        block_table = common_attn_metadata.block_table_tensor
+        slot_mapping = common_attn_metadata.slot_mapping
+
+        bias = None
+        if num_decodes > 0:
+            # Construct the decoder bias.
+            decode_q_seqlens = q_seqlens[:num_decodes]
+            decode_kv_seqlens = kv_seqlens[:num_decodes]
+            bias = (
+                PagedBlockDiagonalCausalWithOffsetPaddedKeysMask.from_seqlens(
+                    q_seqlen=decode_q_seqlens.tolist(),
+                    kv_seqlen=decode_kv_seqlens.tolist(),
+                    page_size=self.block_size,
+                    block_tables=block_table[:num_decodes],
+                    device=block_table.device,
+                ))
+
+        return XFormersAttentionMetadata(
+            num_actual_tokens=num_actual_tokens,
+            num_prefill_tokens=num_prefill_tokens,
+            num_decode_tokens=num_decode_tokens,
+            num_prefills=num_prefills,
+            num_decodes=num_decodes,
+            max_query_len=max_query_len,
+            query_start_loc=q_start_loc,
+            max_seq_len=max_seq_len,
+            seq_lens=kv_seqlens,
+            block_table=block_table,
+            slot_mapping=slot_mapping,
+            attn_bias=bias,
+        )
+
+
+class XFormersAttentionImpl(AttentionImpl):
+
+    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] = None,
+        attn_type: AttentionType = AttentionType.DECODER,
+        kv_sharing_target_layer_name: Optional[str] = None,
+    ) -> None:
+        if kv_sharing_target_layer_name is not None:
+            raise NotImplementedError("KV sharing is not supported in V0.")
+        if alibi_slopes is not None:
+            raise NotImplementedError(
+                "XFormers does not support alibi slopes yet.")
+        self.num_heads = num_heads
+        self.head_size = head_size
+        self.scale = float(scale)
+        self.num_kv_heads = num_kv_heads
+        self.num_queries_per_kv = self.num_heads // self.num_kv_heads
+        self.kv_cache_dtype = kv_cache_dtype
+        self.kv_sharing_target_layer_name = kv_sharing_target_layer_name
+        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)
+        else:
+            self.sliding_window = (sliding_window - 1, 0)
+        if logits_soft_cap is None:
+            # Setting logits_soft_cap to 0 means no soft cap.
+            logits_soft_cap = 0
+        self.logits_soft_cap = logits_soft_cap
+
+        XFormersAttentionBackend.validate_head_size(head_size)
+
+        if attn_type != AttentionType.DECODER:
+            raise NotImplementedError("Encoder self-attention and "
+                                      "encoder/decoder cross-attention "
+                                      "are not implemented for "
+                                      "XFormersAttentionImpl.")
+
+    def forward(
+        self,
+        layer: torch.nn.Module,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        kv_cache: torch.Tensor,
+        attn_metadata: XFormersAttentionMetadata,
+        output: Optional[torch.Tensor] = None,
+        output_scale: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        """Forward pass with XFormers.
+
+        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]
+            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_scale is not None:
+            raise NotImplementedError(
+                "fused output quantization is not yet supported"
+                " for XFormersAttentionImpl")
+
+        if attn_metadata is None:
+            # Profiling run.
+            return output
+
+        # Cache the input KVs.
+        key_cache, value_cache = kv_cache.unbind(0)
+        if self.kv_sharing_target_layer_name is None:
+            # Reshape the input keys and values and store them in the cache.
+            # Skip this if sharing KV cache with an earlier attention layer.
+            # NOTE(woosuk): Here, key and value are padded while slot_mapping is
+            # not padded. However, we don't need to do key[:num_actual_tokens]
+            # and value[:num_actual_tokens] because the reshape_and_cache_flash
+            # op uses the slot_mapping's shape to determine the number of
+            # actual tokens.
+            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,
+            )
+
+        num_actual_tokens = attn_metadata.num_actual_tokens
+        num_decode_tokens = attn_metadata.num_decode_tokens
+        if prefill_meta := attn_metadata.prefill_metadata:
+            descale_shape = (prefill_meta.query_start_loc.shape[0] - 1,
+                             key.shape[1])
+            unified_attention(
+                q=query[num_decode_tokens:num_actual_tokens],
+                k=key_cache,
+                v=value_cache,
+                out=output[num_decode_tokens:num_actual_tokens],
+                cu_seqlens_q=prefill_meta.query_start_loc,
+                max_seqlen_q=prefill_meta.max_query_len,
+                seqused_k=prefill_meta.seq_lens,
+                max_seqlen_k=prefill_meta.max_seq_len,
+                softmax_scale=self.scale,
+                causal=True,
+                alibi_slopes=self.alibi_slopes,
+                window_size=self.sliding_window,
+                block_table=prefill_meta.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),
+            )
+
+        if decode_meta := attn_metadata.decode_metadata:
+            # Query for decode. KV is not needed because it is already cached.
+            decode_query = query[:num_decode_tokens]
+            # Reshape query to [1, B_T, G, H, D].
+            q = decode_query.view(1, -1, self.num_kv_heads,
+                                  self.num_queries_per_kv, self.head_size)
+            # Reshape the k and v caches to [1, Bkv_T, G, H, D]
+            cache_k = key_cache.view(1, -1, self.num_kv_heads, 1,
+                                     self.head_size).expand(
+                                         1,
+                                         -1,
+                                         self.num_kv_heads,
+                                         self.num_queries_per_kv,
+                                         self.head_size,
+                                     )
+            cache_v = value_cache.view(1, -1, self.num_kv_heads, 1,
+                                       self.head_size).expand(
+                                           1,
+                                           -1,
+                                           self.num_kv_heads,
+                                           self.num_queries_per_kv,
+                                           self.head_size,
+                                       )
+
+            attn_bias = decode_meta.attn_bias
+            output[:
+                   num_decode_tokens] = xops.memory_efficient_attention_forward(
+                       q,
+                       cache_k,
+                       cache_v,
+                       attn_bias=attn_bias,
+                       p=0.0,
+                       scale=self.scale,
+                   ).view(decode_query.shape)
+
+        # Reshape the output tensor.
+        return output