init

vllm_vacc/vllm/v1/attention/backends/vacc_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 TYPE_CHECKING, Any, ClassVar, Optional
+import os
+
+import numpy as np
+import torch
+
+from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
+                                              AttentionMetadata, AttentionType)
+# from vllm_vacc.vllm.attention.backends.vacc_attn import (VACCAttentionBackendImpl,
+#                                                 VACCAttentionMetadata)
+# from vllm_vacc.vllm.attention.backends.vacc_attn import VACCAttentionBackendImpl
+# from vllm.attention.backends.utils import CommonAttentionState
+from vllm.config import VllmConfig
+
+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.block_table import BlockTable
+from vllm.v1.worker.cpu_model_runner import CPUModelRunner
+from vllm.v1.worker.gpu_input_batch import InputBatch
+
+
+from vllm_vacc.vllm.model_executor.models.vars import BLOCK_GROUP_SIZE as env_blk_grp_size
+from vllm_vacc.vllm.attention.ops.vacc_paged_attn import VaccPagedAttention as PagedAttention
+
+
+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
+
+@dataclass
+class VACCAttentionMetadata(AttentionMetadata):
+    """Metadata for VACCAttentionMetadata.
+    """
+    # Total number of prefill requests.
+    num_prefills: int
+    # Number of prefill tokens.
+    num_prefill_tokens: int
+    # Number of decode tokens. Note that it is equivalent to the number of
+    # decode requests.
+    num_decode_tokens: int
+    # (num_tokens,). The indices of the token slots that input tokens will be
+    # stored into. E.g., if `slot_mapping` is [35, 2, 17] and the block size
+    # is 16, the three tokens are stored in the 3rd slot in block 2, 2nd slot
+    # in block 0, and 1st slot in block 1, respectively.
+    slot_mapping: torch.Tensor
+    """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["VACCAttentionMetadata"]:
+        # Currently chunked prefill is not supported
+        if self.num_prefill_tokens == 0:
+            return None
+        return self
+
+    @property
+    def decode_metadata(self) -> Optional["VACCAttentionMetadata"]:
+        # Currently chunked prefill is not supported
+        if self.num_decode_tokens == 0:
+            return None
+        return self
+
+    def get_seq_lens(
+        self,
+        attn_type: AttentionType,
+    ):
+        '''
+        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: AttentionType,
+    ) -> 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: AttentionType,
+    ) -> 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 VACCMetadataBuilder(AttentionMetadataBuilder[VACCAttentionMetadata]):
+
+#     def __init__(self, input_builder: ModelInputForVACCBuilder) -> None:
+#         self.chunked_prefill = input_builder.chunked_prefill
+#         self.input_builder = input_builder
+
+#     def prepare(self):
+#         self.input_data = self.input_builder.input_data
+
+#     def build(self, seq_lens: list[int], query_lens: list[int],
+#               cuda_graph_pad_size: int, batch_size: int) -> VACCAttentionMetadata:
+#         input_data = self.input_data
+#         prefill_seq_lens = seq_lens[0:input_data.num_prefills]
+#         prefill_query_lens = query_lens[0:input_data.num_prefills]
+#         slot_mapping = torch.tensor(input_data.slot_mapping,
+#                                     dtype=torch.int32,
+#                                     device=self.input_builder.device)
+
+#         # For chunked-prefill
+#         if self.chunked_prefill and input_data.num_prefill_tokens != 0:
+#             prefill_block_tables = make_tensor_with_pad(
+#                 self.input_data.prefill_block_tables,
+#                 pad=0,
+#                 dtype=torch.int32,
+#                 device=self.input_builder.device,
+#             )
+#             query_lens_tensor = torch.tensor(prefill_query_lens,
+#                                              dtype=torch.int32,
+#                                              device=self.input_builder.device)
+#             kv_lens_tensor = torch.tensor(prefill_seq_lens,
+#                                           dtype=torch.int32,
+#                                           device=self.input_builder.device)
+#             query_start_loc = torch.zeros(input_data.num_prefills + 1,
+#                                           dtype=torch.int32,
+#                                           device=self.input_builder.device)
+#             kv_start_loc = torch.zeros(input_data.num_prefills + 1,
+#                                        dtype=torch.int32,
+#                                        device=self.input_builder.device)
+#             torch.cumsum(query_lens_tensor,
+#                          dim=0,
+#                          dtype=torch.int32,
+#                          out=query_start_loc[1:])
+#             torch.cumsum(kv_lens_tensor,
+#                          dim=0,
+#                          dtype=torch.int32,
+#                          out=kv_start_loc[1:])
+#             max_query_len = max(prefill_query_lens)
+#             max_kv_len = max(prefill_seq_lens)
+#         else:
+#             prefill_block_tables = None
+#             query_start_loc = None
+#             kv_start_loc = None
+#             max_query_len = None
+#             max_kv_len = None
+
+#         # For paged attention
+#         if input_data.num_decode_tokens != 0:
+#             seq_lens_tensor = torch.tensor(
+#                 input_data.seq_lens[input_data.num_prefills:],
+#                 dtype=torch.int32,
+#                 device=self.input_builder.device,
+#             )
+#             block_tables = make_tensor_with_pad(
+#                 self.input_data.decode_block_tables,
+#                 pad=0,
+#                 dtype=torch.int32,
+#                 device=self.input_builder.device,
+#             )
+#             # lowest_dim_size = block_tables.size(-1)
+#             # if lowest_dim_size < 1024:
+#             #     padding_amount = 1024 - lowest_dim_size
+#             #     padding = torch.zeros(*block_tables.size()[:-1], padding_amount, dtype=block_tables.dtype, device=block_tables.device)
+#             #     block_tables = torch.cat((block_tables, padding), dim=-1)
+#         else:
+#             block_tables = torch.tensor([])
+#             seq_lens_tensor = torch.tensor(
+#                 input_data.seq_lens[:input_data.num_prefills],
+#                 dtype=torch.int32,
+#                 device=self.input_builder.device,
+#             )
+
+#         # For multi-modal models
+#         placeholder_index_maps = None
+#         if len(input_data.multi_modal_inputs_list) != 0:
+#             placeholder_index_maps = {
+#                 modality: placeholder_map.index_map()
+#                 for modality, placeholder_map in
+#                 input_data.multi_modal_placeholder_maps.items()
+#             }
+
+#         attn_metadata = VACCAttentionMetadata(
+#             chunked_prefill=self.chunked_prefill,
+#             seq_lens=seq_lens, #prefill_seq_lens,
+#             seq_lens_tensor=seq_lens_tensor,
+#             max_query_len=max_query_len,
+#             max_kv_len=max_kv_len,
+#             query_start_loc=query_start_loc,
+#             kv_start_loc=kv_start_loc,
+#             max_decode_seq_len=None,
+#             num_prefills=input_data.num_prefills,
+#             num_prefill_tokens=input_data.num_prefill_tokens,
+#             num_decode_tokens=input_data.num_decode_tokens,
+#             block_tables=block_tables,
+#             prefill_block_tables=prefill_block_tables,
+#             slot_mapping=slot_mapping,
+#             multi_modal_placeholder_index_maps=placeholder_index_maps,
+#             enable_kv_scales_calculation=False,
+#         )
+#         return attn_metadata
+
+def fp32_attention(
+    query_layer,
+    key_layer,
+    value_layer,
+    mask,
+    norm_factor,
+    out_type=None,
+):
+    ori_type = out_type if out_type is not None else query_layer.dtype
+    query_layer = query_layer.to(torch.float32)
+    key_layer = key_layer.to(torch.float32)
+    value_layer = value_layer.to(torch.float32)
+
+    # GQA
+    if query_layer.size(1) != key_layer.size(1):
+        if query_layer.size(1) % key_layer.size(1) != 0:
+            assert False
+        groups = query_layer.size(1) // key_layer.size(1)
+        key_layer = torch.repeat_interleave(key_layer, groups, dim=1)
+        value_layer = torch.repeat_interleave(value_layer, groups, dim=1)
+
+    matmul_result = torch.bmm(
+        query_layer.transpose(0, 1),  # [b * np, sq, hn]
+        key_layer.transpose(0, 1).transpose(1, 2),  # [b * np, hn, sk]
+    ) * norm_factor
+    mask_output = matmul_result
+    if mask != None:
+        mask = mask if mask.dim() >= 3 else mask.unsqueeze(0)
+        mask_output = matmul_result.masked_fill_(mask, -10000.0)  # [b * np, sq, sk]
+    probs = torch.nn.Softmax(dim=-1)(mask_output)
+    context_layer = torch.bmm(probs, value_layer.transpose(0, 1))
+
+    return context_layer.transpose(0, 1).to(ori_type)
+
+
+class VACCAttentionBackendImpl(AttentionImpl[VACCAttentionMetadata]):
+
+    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 logits_soft_cap is not None:
+        #     logger.warning_once("Torch SPDA does not support logits soft cap. "
+        #                         "Outputs may be slightly off.")
+        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
+
+        assert self.num_heads % self.num_kv_heads == 0
+        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 kv_cache_dtype != "auto":
+            raise NotImplementedError(
+                "Torch SDPA backend does not support FP8 KV cache. "
+                "Please use xFormers backend instead.")
+        self.attn_type = attn_type
+
+    def forward(
+        self,
+        layer: torch.nn.Module,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        kv_cache: torch.Tensor,
+        attn_metadata: VACCAttentionMetadata,  # type: ignore
+        output: 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]
+        """
+        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 = PagedAttention.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
+                PagedAttention.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:
+            assert attn_metadata.seq_lens is not None
+            if (kv_cache.numel() == 0
+                    or prefill_meta.block_tables.numel() == 0):
+                self._run_vacc_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.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)
+
+            # Note:
+            #   decode attention still use SDPA method
+            # reshape k/v_cache to (num_block_grp, block_grp_size, head, hidden_size)
+            k_cache = key_cache.view(-1, env_blk_grp_size, key_cache.shape[2], key_cache.shape[3])
+            v_cache = value_cache.view(-1, env_blk_grp_size, value_cache.shape[2], value_cache.shape[3])
+            block_per_group = env_blk_grp_size // 16
+            # convert block_tables to 8K group index
+            block_tables = (decode_meta.block_tables // block_per_group).to(torch.int32)
+            attn_outs = []
+            for i in range(len(decode_meta.seq_lens_tensor)):
+                seq_len = decode_meta.seq_lens_tensor[i]
+                k_slices = k_cache[block_tables[i], ...]
+                k = \
+                    torch.cat([k_slices[i, ...] for i in range(len(block_tables[i]))], dim=0)[:seq_len]
+                v_slices = v_cache[block_tables[i], ...]
+                v = \
+                    torch.cat([v_slices[i, ...] for i in range(len(block_tables[i]))], dim=0)[:seq_len]
+                q = query[i : i + 1, ...]
+                if q.dtype == torch.bfloat16:
+                    attn_out = fp32_attention(
+                                q.cpu(),
+                                k.cpu(),
+                                v.cpu(),
+                                None,
+                                self.scale
+                                ).to(query.dtype).to(query.device)
+                else:
+                    attn_out = torch.vacc.scaled_dot_product_attention(
+                        query=q,
+                        key=k,
+                        value=v,
+                        attn_mask=None,
+                        dropout_p=0,
+                        is_causal=False,
+                        is_train=False,
+                        recompute=False,
+                        flash_attention=False,
+                        sm_scale=self.scale,
+                    )
+                attn_outs.append(attn_out)
+            output = torch.cat(attn_outs, dim=0)
+            # '''
+
+            # PagedAttention.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_vacc_forward(
+        self,
+        output: torch.Tensor,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        attn_metadata: VACCAttentionMetadata,
+        attn_type: AttentionType = AttentionType.DECODER,
+    ):
+        # 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)
+
+        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=torch.vacc.scaled_dot_product_attention(
+                query[start_q:end_q,:, :].to(torch.float16) * (self.scale),
+                key[start_kv:end_kv,:, :].to(torch.float16),
+                value[start_kv:end_kv,:, :].contiguous().to(torch.float16),
+                attn_mask=None,
+                dropout_p=0.0,
+                is_causal=True if attn_type == AttentionType.DECODER else False, #causal_attn and not self.need_mask,
+                is_train=False,
+                recompute=False,
+                flash_attention=False,
+                sm_scale=1)
+            output[ start_q:end_q,:, :] = sub_out
+            start_q, start_kv = end_q, end_kv
+        return output
+
+
+class VACCAttentionBackend(AttentionBackend):
+    accept_output_buffer: bool = False
+
+    @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 "TORCH_SDPA_VLLM_V1"
+
+    @staticmethod
+    def get_impl_cls() -> type["VACCAttentionBackendImpl"]:
+        return VACCAttentionBackendImpl
+
+    @staticmethod
+    def get_metadata_cls() -> type["AttentionMetadata"]:
+        return VACCAttentionMetadata
+
+    # @staticmethod
+    # def get_state_cls() -> type["CommonAttentionState"]:
+    #     return CommonAttentionState
+    
+    @classmethod
+    def get_supported_head_sizes(cls) -> list[int]:
+        return [32, 64, 96, 128, 160, 192, 224, 256]
+
+    @staticmethod
+    def get_builder_cls() -> type["VACCAttentionMetadataBuilderV1"]:
+        return VACCAttentionMetadataBuilderV1
+
+    @staticmethod
+    def get_kv_cache_shape(
+        num_blocks: int,
+        block_size: int,
+        num_kv_heads: int,
+        head_size: int,
+        cache_dtype_str:str,
+    ) -> tuple[int, ...]:
+        # return PagedAttention.get_kv_cache_shape(num_blocks, block_size,
+                                                #  num_kv_heads, head_size)
+        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
+
+
+class VACCAttentionMetadataBuilderV1(AttentionMetadataBuilder[VACCAttentionMetadata]):
+
+    # def __init__(self, runner: CPUModelRunner, kv_cache_spec: AttentionSpec,
+    #              block_table: BlockTable) -> None:
+    #     self.runner = runner
+    #     self.block_table = block_table
+
+    #     # For reorder
+    #     self.reorder_prompt_req_index_list = np.empty(self.runner.max_num_reqs,
+    #                                                   dtype=np.int64)
+    #     self.reorder_decode_req_index_list = np.empty(self.runner.max_num_reqs,
+    #                                                   dtype=np.int64)
+    #     self.num_prompt_req: int = 0
+
+    #     self.seq_start_loc_cpu = torch.zeros(
+    #         runner.max_num_reqs + 1,
+    #         dtype=torch.int32,
+    #         device="cpu",
+    #     )
+    #     self.seq_start_loc_np = self.seq_start_loc_cpu.numpy()
+    
+    
+    def __init__(self, kv_cache_spec: AttentionSpec, layer_names: list[str],
+                 vllm_config: VllmConfig, device: torch.device) -> None:
+        super().__init__(kv_cache_spec, layer_names, vllm_config, device)
+
+        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 __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)
+
+    #     self.block_size = kv_cache_spec.block_size
+
+    #     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 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):
+        num_reqs = common_attn_metadata.num_reqs
+        num_actual_tokens = common_attn_metadata.num_actual_tokens
+        
+        seq_lens_cpu = common_attn_metadata.seq_lens_cpu
+        seq_lens = common_attn_metadata.seq_lens
+        
+        # seq_lens = common_attn_metadata.seq_lens
+
+        # runner = self.runner
+        # block_table = self.block_table
+        # seq_lens = runner.seq_lens[:num_reqs]
+        num_prompt_req = self.num_prompt_req
+        
+        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)
+
+        # print('query_start_loc_cpu', query_start_loc_cpu)
+        # print('num_prompt_req', num_prompt_req)
+        # print('num_reqs', num_reqs)
+
+        # num_prefill_tokens = runner.query_start_loc_np[num_prompt_req].item()
+        # num_decode_tokens = runner.query_start_loc_np[num_reqs].item(
+        # ) - num_prefill_tokens
+        
+        # block_table.slot_mapping[:num_actual_tokens].copy_(
+        #     block_table.slot_mapping_cpu[:num_actual_tokens],
+        #     non_blocking=True)
+        
+        # slot_mapping = block_table.slot_mapping[:num_actual_tokens] #.long()
+        
+        # block_table_tensor = block_table.get_device_tensor()
+        
+        slot_mapping = common_attn_metadata.slot_mapping
+        block_table_tensor = common_attn_metadata.block_table_tensor
+        
+        block_num_per_group = env_blk_grp_size // 16
+        block_table_tensor_new = block_table_tensor[:num_reqs-num_prompt_req, ::block_num_per_group].contiguous()
+        # [bs, seq//16]  => [bs, seq//16//block_num_per_group, block_num_per_group]  
+        # => [:num_reqs, :, 0]  提取前reqs行，并且把 block_num_per_group 的倍数提取出
+
+        attn_metadata = VACCAttentionMetadata(
+            num_prefills=num_prompt_req,
+            num_prefill_tokens=num_prefill_tokens,
+            num_decode_tokens=num_decode_tokens,
+            seq_lens=seq_lens,
+            slot_mapping=slot_mapping,
+            seq_lens_tensor=seq_lens,  # decode
+            max_decode_seq_len=None,  # decode
+            block_tables=block_table_tensor_new,  # decode
+            chunked_prefill=False,
+            # max_query_len=max_query_len,
+            # max_kv_len=max_prefill_seq_len,
+            # prefill_query_start_loc=runner.
+            # 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

vllm_vacc/vllm/v1/attention/backends/vacc_mla.py

@@ -0,0 +1,953 @@
+
+
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, ClassVar, Generic, List, Optional, Dict, TypeVar, Type
+
+import torch
+
+from vllm.attention.backends.abstract import (AttentionBackend,
+                                              AttentionMetadata, AttentionType,
+                                              )
+from vllm.attention.backends.utils import get_mla_dims
+
+from vllm.logger import init_logger
+
+from vllm.platforms import current_platform
+from vllm.utils import cdiv, round_down
+from vllm.attention.backends.utils import get_mla_dims
+
+from vllm.v1.attention.backends.utils import (AttentionMetadataBuilder,
+                                              CommonAttentionMetadata)
+from vllm.config import VllmConfig
+from vllm.v1.kv_cache_interface import AttentionSpec
+from vllm.v1.worker.block_table import BlockTable
+from vllm.v1.attention.backends.mla.common import (MLACommonBackend,
+                                                   MLACommonMetadata)
+from vllm.v1.attention.backends.utils import (AttentionMetadataBuilder,
+                                              CommonAttentionMetadata,
+                                              get_per_layer_parameters,
+                                              infer_global_hyperparameters,
+                                              split_decodes_and_prefills)
+from vllm.v1.attention.backends.mla.common import MLACommonImpl
+
+if TYPE_CHECKING:
+    from vllm.v1.core.sched.output import SchedulerOutput
+    from vllm.v1.worker.gpu_input_batch import InputBatch
+    from vllm.v1.worker.gpu_model_runner import GPUModelRunner
+
+
+from vllm_vacc.vllm.model_executor.models.vars import BLOCK_GROUP_SIZE as env_blk_grp_size
+
+logger = init_logger(__name__)
+
+
+
+M = TypeVar("M", bound=MLACommonMetadata)
+
+def vacc_paged_attention_naive(
+    query: torch.Tensor,
+    key_cache: torch.Tensor,
+    value_cache: torch.Tensor,
+    block_table: torch.Tensor,
+    # seq_lens: torch.Tensor,
+    seq_lens: int,
+    out: Optional[torch.Tensor] = None,
+    sm_scale = -1
+) -> torch.Tensor:
+
+    # gurantee batch=1 perf
+    if len(seq_lens) == 1:
+        k = key_cache.view(-1, key_cache.shape[2], key_cache.shape[3])[:seq_lens[0]]
+        v = value_cache.view(-1, value_cache.shape[2], value_cache.shape[3])[:seq_lens[0]]
+        attn_out = torch.vacc.scaled_dot_product_attention(
+            query=query,
+            key=k,
+            value=v,
+            attn_mask=None,
+            dropout_p=0,
+            is_causal=False,
+            is_train=False,
+            recompute=False,
+            flash_attention=False,
+            sm_scale=sm_scale
+        )
+    else:
+        # t0 = time.time()
+        attn_outs = []
+        for i in range(len(seq_lens)):
+            k_slices = key_cache[block_table[i], :, :, :]
+            k = torch.cat([k_slices[i, :, :, :].unsqueeze(1) for i in range(len(block_table[i]))], dim=0)
+            k = k.view(-1, key_cache.shape[2], key_cache.shape[3])[:seq_lens[i]]
+            v_slices = value_cache[block_table[i], :, :, :]
+            v = torch.cat([v_slices[i, :, :, :].unsqueeze(1) for i in range(len(block_table[i]))], dim=0)
+            v = v.view(-1, value_cache.shape[2], value_cache.shape[3])[:seq_lens[i]]
+
+            attn_out = torch.vacc.scaled_dot_product_attention(
+                query=query[i:i+1,:,:],
+                key=k,
+                value=v,
+                attn_mask=None,
+                dropout_p=0,
+                is_causal=False,
+                is_train=False,
+                recompute=False,
+                flash_attention=False,
+                sm_scale=sm_scale
+            )
+            attn_outs.append(attn_out)
+
+        attn_out = torch.cat(attn_outs, dim=0)
+    # print(f'{os.getpid()} call spda(seq: {seq_lens}) time: {time.time() - t0}')
+    return attn_out
+
+
+@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]
+        workspace: torch.Tensor
+
+    block_tables: torch.Tensor  #block_table => block_tables 兼容v0
+    query_start_loc: torch.Tensor
+    # max_query_len: int
+    seq_lens: list[int]
+    chunked_context: Optional[ChunkedContextMetadata] = None
+    
+    
+@dataclass
+class MLACommonDecodeMetadata:
+    block_tables: torch.Tensor #block_table => block_tables 兼容v0
+    seq_lens: torch.Tensor
+
+class VACCMLAMetadata(MLACommonMetadata):
+    """Metadata for VACCMLAMetadata.
+
+    NOTE: Any python object stored here is not updated when it is
+    cuda-graph replayed. If you have values that need to be changed
+    dynamically, it should be stored in tensor. The tensor has to be
+    updated from `CUDAGraphRunner.forward` API.
+    """
+    # (batch_size,). The sequence length per sequence. Sequence length means
+    # the computed tokens + new tokens None if it is a decoding.
+    seq_lens: Optional[List[int]]
+    # seq_lens stored as a tensor.
+    seq_lens_tensor: Optional[torch.Tensor]
+
+    # NOTE(sang): Definition of context_len, query_len, and seq_len.
+    # |---------- N-1 iteration --------|
+    # |---------------- N iteration ---------------------|
+    # |- tokenA -|......................|-- newTokens ---|
+    # |---------- context_len ----------|
+    # |-------------------- seq_len ---------------------|
+    #                                   |-- query_len ---|
+
+    # Maximum sequence length among prefill batch. 0 if there are decoding
+    # requests only.
+    max_prefill_seq_len: int
+    # Maximum sequence length among decode batch. 0 if there are prefill
+    # requests only.
+    max_decode_seq_len: int
+    # (batch_size,) A tensor of context lengths (tokens that are computed
+    # so far).
+    context_lens_tensor: Optional[torch.Tensor]
+
+    # (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]
+
+    # Whether or not if cuda graph is enabled.
+    # Cuda-graph is currently enabled for decoding only.
+    # TODO(woosuk): Move `use_cuda_graph` out since it's unrelated to attention.
+
+    use_cuda_graph: bool
+
+    # Maximum query length in the batch.
+    max_query_len: Optional[int] = None
+    input_positions: Optional[torch.Tensor] = None
+    # Max number of query tokens among request in the batch.
+    max_decode_query_len: Optional[int] = None
+
+    # (batch_size + 1,). The cumulative subquery lengths of the sequences in
+    # the batch, used to index into subquery. E.g., if the subquery length
+    # is [4, 6], it is [0, 4, 10].
+    query_start_loc: Optional[torch.Tensor] = None
+    # (batch_size + 1,). The cumulative sequence lengths of the sequences in
+    # the batch, used to index into sequence. E.g., if the sequence length is
+    # [4, 6], it is [0, 4, 10].
+    seq_start_loc: Optional[torch.Tensor] = None
+
+    _cached_prefill_metadata: Optional["VACCMLAMetadata"] = None
+    _cached_decode_metadata: Optional["VACCMLAMetadata"] = None
+
+    num_prefill_tokens: int
+
+    num_kv_splits: int = 4  # TODO(lucas) add heuristic
+    attn_logits: Optional[torch.Tensor] = None
+    req_idx: Optional[torch.Tensor] = None
+
+    # The dimension of the attention heads
+    head_dim: Optional[int] = None
+
+    def __post_init__(self):
+        supported_head_sizes = VACCMLABackend.get_supported_head_sizes()
+        if self.head_dim is not None and self.head_dim \
+                not in supported_head_sizes:
+            raise ValueError(
+                f"Only {supported_head_sizes} are supported for head_dim,",
+                f"received {self.head_dim}.")
+
+    @property
+    def prefill_metadata(self) -> Optional["VACCMLAMetadata"]:
+        if self.num_prefills == 0:
+            return None
+
+        if self._cached_prefill_metadata is not None:
+            return self._cached_prefill_metadata
+
+        assert self.seq_lens is not None
+        assert self.seq_lens_tensor is not None
+
+        # Compute some attn_metadata fields which default to None
+        query_start_loc = (None if self.query_start_loc is None else
+                           self.query_start_loc[:self.num_prefills + 1])
+        slot_mapping = (None if self.slot_mapping is None else
+                        self.slot_mapping[:self.num_prefill_tokens])
+        seq_lens = (None if self.seq_lens is None else
+                    self.seq_lens[:self.num_prefills])
+        seq_lens_tensor = (None if self.seq_lens_tensor is None else
+                           self.seq_lens_tensor[:self.num_prefills])
+        seq_start_loc = (None if self.seq_start_loc is None else
+                         self.seq_start_loc[:self.num_prefills + 1])
+        context_lens_tensor = (None if self.context_lens_tensor is None else
+                               self.context_lens_tensor[:self.num_prefills])
+        block_tables = (None if self.block_tables is None else
+                        self.block_tables[:self.num_prefills])
+        input_positions = (None if self.input_positions is None else
+                           self.input_positions[:self.num_prefill_tokens])
+
+        self._cached_prefill_metadata = VACCMLAMetadata(
+            num_prefills=self.num_prefills,
+            num_prefill_tokens=self.num_prefill_tokens,
+            num_decode_tokens=0,
+            slot_mapping=slot_mapping,
+            multi_modal_placeholder_index_maps=self.
+            multi_modal_placeholder_index_maps,
+            enable_kv_scales_calculation=self.enable_kv_scales_calculation,
+            input_positions=input_positions,
+            seq_lens=seq_lens,
+            seq_lens_tensor=seq_lens_tensor,
+            max_prefill_seq_len=None,
+            max_decode_seq_len=0,
+            query_start_loc=query_start_loc,
+            seq_start_loc=seq_start_loc,
+            context_lens_tensor=context_lens_tensor,
+            block_tables=block_tables,
+            use_cuda_graph=False,
+            head_dim=self.head_dim)
+        return self._cached_prefill_metadata
+
+    @property
+    def decode_metadata(self) -> Optional["VACCMLAMetadata"]:
+        if self.num_decode_tokens == 0:
+            return None
+
+        if self._cached_decode_metadata is not None:
+            return self._cached_decode_metadata
+        assert self.seq_lens_tensor is not None
+
+        # Compute some attn_metadata fields which default to None
+        slot_mapping = (None if self.slot_mapping is None else
+                        self.slot_mapping[self.num_prefill_tokens:])
+        seq_lens_tensor = (None if self.seq_lens_tensor is None else
+                           self.seq_lens_tensor[self.num_prefills:])
+        block_tables = (None if self.block_tables is None else
+                        self.block_tables[self.num_prefills:])
+        input_positions = (None if self.input_positions is None else
+                           self.input_positions[self.num_prefill_tokens:])
+
+        self._cached_decode_metadata = VACCMLAMetadata(
+            num_prefills=0,
+            num_prefill_tokens=0,
+            num_decode_tokens=self.num_decode_tokens,
+            slot_mapping=slot_mapping,
+            multi_modal_placeholder_index_maps=None,
+            enable_kv_scales_calculation=True,
+            seq_lens=self.seq_lens,
+            seq_lens_tensor=seq_lens_tensor,
+            max_decode_query_len=self.max_decode_query_len,
+            max_query_len=self.max_query_len,
+            max_prefill_seq_len=0,
+            max_decode_seq_len=self.max_decode_seq_len,
+            # Batch may be composed of prefill|decodes, adjust query start
+            # indices to refer to the start of decodes. E.g.
+            # in tokens:[3 prefills|6 decodes], query_start_loc=[3,9] => [0,6].
+            query_start_loc=(self.query_start_loc[self.num_prefills:] -
+                             self.query_start_loc[self.num_prefills])
+            if self.query_start_loc is not None else None,
+            seq_start_loc=self.seq_start_loc[self.num_prefills:]
+            if self.seq_start_loc is not None else None,
+            context_lens_tensor=None,
+            block_tables=block_tables,
+            use_cuda_graph=self.use_cuda_graph,
+            input_positions=input_positions,
+            head_dim=self.head_dim)
+        return self._cached_decode_metadata
+
+    def advance_step(self,
+                     model_input: "ModelInputForVACCWithSamplingMetadata",
+                     sampled_token_ids: Optional[torch.Tensor],
+                     block_size: int,
+                     num_seqs: int,
+                     num_queries: int,
+                     turn_prefills_into_decodes: bool = False):
+        """
+        Update metadata in-place to advance one decode step.
+        """
+        # When using cudagraph, the num_seqs is padded to the next captured
+        # batch sized, but num_queries tracks the actual number of requests in
+        # the batch. For --enforce-eager mode, num_seqs == num_queries
+        if num_seqs != num_queries:
+            assert num_seqs > num_queries
+            assert self.use_cuda_graph
+
+        if turn_prefills_into_decodes:
+            # When Mutli-Step is enabled with Chunked-Prefill, prefills and
+            # decodes are scheduled together. In the first step, all the
+            # prefills turn into decodes. This update reflects that
+            # conversion.
+            assert self.num_decode_tokens + self.num_prefills == num_seqs
+            self.num_decode_tokens += self.num_prefills
+            self.num_prefills = 0
+            # self.num_prefill_tokens = 0
+            # self.max_prefill_seq_len = 0
+            self.max_query_len = 1
+
+            self.slot_mapping = self.slot_mapping[:num_seqs]
+        else:
+            assert self.seq_lens is not None
+            assert self.max_decode_seq_len == max(self.seq_lens)
+
+        assert self.num_prefills == 0
+        assert self.num_prefill_tokens == 0
+        assert self.num_decode_tokens == num_seqs
+        assert self.slot_mapping.shape == (num_seqs, )
+
+        assert self.seq_lens is not None
+        assert len(self.seq_lens) == num_seqs
+        assert self.seq_lens_tensor is not None
+        assert self.seq_lens_tensor.shape == (num_seqs, )
+        # assert self.max_query_len == 1
+        # assert self.max_prefill_seq_len == 0
+
+        assert self.query_start_loc is not None
+        assert self.query_start_loc.shape == (num_queries + 1, )
+        assert self.seq_start_loc is not None
+        assert self.seq_start_loc.shape == (num_seqs + 1, )
+
+        assert self.context_lens_tensor is not None
+        assert self.context_lens_tensor.shape == (num_queries, )
+
+        assert self.block_tables is not None
+        assert self.block_tables.shape[0] == num_seqs
+
+        # Update query lengths. Note that we update only queries and not seqs,
+        # since tensors may be padded due to captured cuda graph batch size
+        for i in range(num_queries):
+            self.seq_lens[i] += 1
+        # self.max_decode_seq_len = None
+
+        ops.advance_step_flashattn(num_seqs=num_seqs,
+                                   num_queries=num_queries,
+                                   block_size=block_size,
+                                   input_tokens=model_input.input_tokens,
+                                   sampled_token_ids=sampled_token_ids,
+                                   input_positions=model_input.input_positions,
+                                   seq_lens=self.seq_lens_tensor,
+                                   slot_mapping=self.slot_mapping,
+                                   block_tables=self.block_tables)
+
+
+
+class VACCMLAImpl(MLACommonImpl[VACCMLAMetadata]):
+
+    def __init__(
+            self,
+            num_heads: int,
+            head_size: int,
+            scale: float,
+            num_kv_heads: int,
+            alibi_slopes: Optional[List[float]],
+            sliding_window: Optional[int],
+            kv_cache_dtype: str,
+            # blocksparse_params: Optional[Dict[str, Any]],
+            logits_soft_cap: Optional[float],
+            attn_type: str,
+            kv_sharing_target_layer_name: Optional[str],
+            # MLA Specific Arguments
+            **kwargs) -> 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, **kwargs)
+        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
+
+        # print('kwargs', kwargs) # 手动写老版本的继承
+        self.q_lora_rank = kwargs['q_lora_rank'] if 'q_lora_rank' in kwargs else None
+        self.kv_lora_rank = kwargs['kv_lora_rank'] if 'kv_lora_rank' in kwargs else None
+        self.qk_nope_head_dim = kwargs['qk_nope_head_dim'] if 'qk_nope_head_dim' in kwargs else None
+        self.qk_head_dim = kwargs['qk_head_dim'] if 'qk_head_dim' in kwargs else None
+        self.qk_head_dim = kwargs['qk_head_dim'] if 'qk_head_dim' in kwargs else None
+        self.v_head_dim = kwargs['v_head_dim'] if 'v_head_dim' in kwargs else None
+        self.rotary_emb = kwargs['rotary_emb'] if 'rotary_emb' in kwargs else None
+        self.q_proj = kwargs['q_proj'] if 'q_proj' in kwargs else None
+        self.kv_b_proj = kwargs['kv_b_proj'] if 'kv_b_proj' in kwargs else None
+        self.o_proj = kwargs['o_proj'] if 'o_proj' in kwargs else None
+        
+        unsupported_features = [
+            alibi_slopes, sliding_window, logits_soft_cap
+        ]
+        if any(unsupported_features):
+            raise NotImplementedError(
+                "VACCMLAImpl does not support one of the following: "
+                "alibi_slopes, sliding_window, blocksparse_params, "
+                "logits_soft_cap")
+
+        if attn_type != AttentionType.DECODER:
+            raise NotImplementedError("Encoder self-attention and "
+                                      "encoder/decoder cross-attention "
+                                      "are not implemented for "
+                                      "VACCMLAImpl")
+
+    def extract_weights(self):
+        weights = {}
+        if hasattr(self, 'W_Q'):
+            weights["W_Q"] = self.W_Q
+        if hasattr(self, 'W_Q_scales'):
+            weights["W_Q_scales"] = self.W_Q_scales
+        if hasattr(self, 'W_QR'):
+            weights['W_QR'] = self.W_QR
+        if hasattr(self, 'W_QR_scales'):
+            weights["W_QR_scales"] = self.W_QR_scales
+        if hasattr(self, 'W_Q_QR'):
+            weights["W_Q_QR"] = self.W_Q_QR
+        if hasattr(self, 'W_Q_QR_scales'):
+            weights["W_Q_QR_scales"] = self.W_Q_QR_scales
+        if hasattr(self, 'W_UK'):
+            weights['W_UK'] = self.W_UK
+        if hasattr(self, 'W_UK_scales'):
+            weights['W_UK_scales'] = self.W_UK_scales
+        if hasattr(self, 'W_Q_UK_scales'):
+            weights['W_Q_UK_scales'] = self.W_Q_UK_scales
+        if hasattr(self, 'W_UV'):
+            weights['W_UV'] = self.W_UV
+        if hasattr(self, 'W_UV_scales'):
+            weights['W_UV_scales'] = self.W_UV_scales
+        if hasattr(self, 'W_UV_O'):
+            weights['W_UV_O'] = self.W_UV_O
+        if hasattr(self, 'W_UV_O_scales'):
+            weights['W_UV_O_scales'] = self.W_UV_O_scales
+        return weights
+
+    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: VACCMLAMetadata,
+    ) -> torch.Tensor:
+        
+        assert isinstance(attn_metadata, VACCMLAV1Metadata)
+        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)
+        v = v.contiguous()
+
+        # 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
+        # v_padded = torch.nn.functional.pad(v, [0, q.shape[-1] - v.shape[-1]],
+        #                                    value=0)
+        # attn_output = torch.vacc.scaled_dot_product_attention(
+        #     query=q,
+        #     key=k,
+        #     value=v_padded,
+        #     attn_mask=None,
+        #     dropout_p=0,
+        #     is_causal=True,
+        #     is_train=False,
+        #     recompute=False,
+        #     flash_attention=True,
+        #     sm_scale=self.scale
+        # )
+
+        # attn_output = attn_output\
+        #     .view(-1, self.num_heads, q.shape[-1])[..., :v.shape[-1]]\
+        #         .reshape(-1, self.num_heads * v.shape[-1])
+        seq_lens = attn_metadata.prefill_metadata.seq_lens
+        if len(seq_lens) == 1:
+            # Vacc supports different head dim of v and qk.
+            attn_output = torch.vacc.scaled_dot_product_attention(
+                query=q,
+                key=k,
+                value=v,
+                attn_mask=None,
+                dropout_p=0,
+                is_causal=True,
+                is_train=False,
+                recompute=False,
+                flash_attention=False,
+                sm_scale=self.scale
+            )
+            attn_out = attn_output.view(-1, self.num_heads * v.shape[-1])
+        else:
+            attn_outs = []
+            start = 0
+            for seq in seq_lens:
+                end = start + seq
+                attn_out = torch.vacc.scaled_dot_product_attention(
+                    query=q[start:end, :],
+                    key=k[start:end, :],
+                    value=v[start:end, :],
+                    attn_mask=None,
+                    dropout_p=0,
+                    is_causal=True,
+                    is_train=False,
+                    recompute=False,
+                    flash_attention=False,
+                    sm_scale=self.scale
+                )
+                start = end
+                attn_outs.append(attn_out)
+            attn_out = torch.cat(attn_outs, dim=0).view(-1, self.num_heads * v.shape[-1])
+
+        return self.o_proj(attn_out)[0]
+
+    def _forward_decode(
+        self,
+        q_nope: torch.Tensor,
+        q_pe: torch.Tensor,
+        kv_c_and_k_pe_cache: torch.Tensor,
+        attn_metadata: VACCMLAMetadata,
+    ) -> torch.Tensor:
+        assert kv_c_and_k_pe_cache.numel() > 0
+        if self.kv_cache_dtype.startswith("fp8"):
+            raise NotImplementedError("FP8 Triton MLA not yet supported")
+
+        decode_meta = attn_metadata.decode_metadata
+        assert decode_meta is not None
+        B = q_nope.shape[0]
+
+        q = torch.cat([q_nope, q_pe], dim=-1)
+        o = torch.zeros(B,
+                        self.num_heads,
+                        self.kv_lora_rank,
+                        dtype=q.dtype,
+                        device=q.device)
+
+        # Add a head dim of 1
+        kv_c_and_k_pe_cache = kv_c_and_k_pe_cache.unsqueeze(2)
+        # print(f"kv_c_and_k_pe_cache: {kv_c_and_k_pe_cache.shape} ")
+        kv_c_cache = kv_c_and_k_pe_cache[..., :self.kv_lora_rank]
+
+        # Run MQA using paged_attention
+        # o = torch.vacc.paged_attention(
+        #     query=q,
+        #     key_cache=kv_c_and_k_pe_cache,
+        #     value_cache=kv_c_cache,
+        #     block_table=decode_meta.block_tables,
+        #     seq_len=decode_meta.seq_lens_tensor,
+        #     out=o,
+        #     sm_scale=self.scale
+        # )
+
+        # Run MQA using spda
+        # t0 = time.time()
+        o = vacc_paged_attention_naive(
+            q,
+            kv_c_and_k_pe_cache,
+            kv_c_cache,
+            block_table = decode_meta.block_tables,
+            # seq_lens = decode_meta.seq_lens_tensor,
+            seq_lens=decode_meta.seq_lens,
+            out = o,
+            sm_scale=self.scale)
+
+        return self._v_up_proj_and_o_proj(o)
+
+# patch from MLACommonBackend
+class VACCMLABackend(AttentionBackend):
+
+    accept_output_buffer: bool = False
+
+    @staticmethod
+    def get_name() -> str:
+        return "TRITON_MLA_VLLM_V1"
+
+    @staticmethod
+    def get_metadata_cls() -> type["AttentionMetadata"]:
+        return VACCMLAMetadata
+    
+    @staticmethod
+    def get_impl_cls() -> Type["VACCMLAImpl"]:
+        return VACCMLAImpl
+
+    @staticmethod
+    def get_builder_cls() -> type["MLAVaccMetadataBuilder"]:
+        return MLAVaccMetadataBuilder
+
+    @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,
+        cache_dtype_str: str = "auto",
+    ) -> tuple[int, ...]:
+        return (num_blocks, block_size, head_size)
+
+    @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.")
+
+
+D = TypeVar("D", bound=MLACommonDecodeMetadata)
+# patch from MLACommonMetadata
+@dataclass
+class VACCMLAV1Metadata(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_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
+    
+    num_prefill_tokens: int
+    
+    # The dimension of the attention heads
+    head_dim: Optional[int] = None
+
+    decode_metadata: Optional[D] = None
+    prefill_metadata: Optional[MLACommonPrefillMetadata] = None
+
+    def __post_init__(self):
+        if self.head_dim is not None:
+            MLACommonBackend.validate_head_size(self.head_dim)
+
+
+
+class MLAVaccMetadataBuilder(AttentionMetadataBuilder[M]):
+    """
+    NOTE: Please read the comment at the top of the file before trying to
+    understand this class
+    """
+    reorder_batch_threshold: ClassVar[int] = 2
+    #TODO 区分 prefill decode 阈值
+    def __init__(self,
+                #  runner: "GPUModelRunner",
+                 kv_cache_spec: AttentionSpec,
+                 layer_names: list[str],
+                 vllm_config: VllmConfig,
+                 device: torch.device,
+                 metadata_cls: Optional[type[M]] = None):
+        self._global_hyperparameters = infer_global_hyperparameters(
+                get_per_layer_parameters(vllm_config, layer_names,
+                                         MLACommonImpl))
+        self.metadata_cls = metadata_cls \
+            if metadata_cls is not None else VACCMLAV1Metadata
+        self.kv_cache_spec = kv_cache_spec
+        scheduler_config = vllm_config.scheduler_config
+        self.model_config = vllm_config.model_config
+        parallel_config = vllm_config.parallel_config
+        cache_config = vllm_config.cache_config
+        self.compilation_config = vllm_config.compilation_config
+        self.device = device
+        # self.runner = runner
+        # scheduler_config = runner.scheduler_config
+        # model_config = runner.model_config
+        # cache_config = runner.cache_config
+        self.chunked_prefill_enabled = False
+        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()
+        self.kv_cache_spec = kv_cache_spec
+
+        # 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 * 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,
+        #          model_config.get_head_size()),
+        #         dtype=model_config.dtype,
+        #         device=runner.device,
+        #     )
+        # self.block_table = block_table
+
+    def reorder_batch(self, input_batch: "InputBatch",
+                      scheduler_output: "SchedulerOutput") -> bool:
+        # We now want to reorder the batch so that the "decode" requests are and
+        # the front and the "prefill" requests are at the using the least amount
+        # 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 scheduler_output.scheduled_cached_reqs.num_computed_tokens != []:
+                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
+
+        # Save for next `build` call
+        # TODO(lucas): this is a bit of a hack, we should probably have a
+        # better way of doing this
+        self._num_decodes = num_decodes
+        self._num_prefills = num_prefills
+        self._num_decode_tokens = num_decode_tokens
+        self._num_prefill_tokens = num_prefill_tokens
+
+        return modified_batch
+
+    def _build_decode(self, block_table_tensor: torch.Tensor,
+                      seq_lens: torch.Tensor):
+        return MLACommonDecodeMetadata(
+            block_tables=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."
+
+    #     # m.max_query_len = 1  # decode-only
+
+    #     # Update state usually set in reorder_batch.
+    #     self._num_decodes = m.num_reqs
+    #     self._num_decode_tokens = m.num_actual_tokens
+    #     self._num_prefills = 0
+    #     self._num_prefill_tokens = 0
+    #     return self.build(0, m)
+
+    def append_seqlen(self, seq_len: list[int], all_len: int):
+        # print('append_seqlen seq_len', seq_len)
+        # print('append_seqlen all_len', all_len)
+        if all_len > len(seq_len) and all_len % len(seq_len) == 0:
+            new_seq_len = []
+            mtp_num = all_len // len(seq_len)
+            for start_len in seq_len:
+                for i in range(1,1+mtp_num):
+                    new_seq_len.append(start_len-mtp_num+i)
+            return new_seq_len
+        return seq_len
+    
+    def build(self, common_prefix_len: int,
+              common_attn_metadata: CommonAttentionMetadata,
+              fast_build: bool = False) -> M:
+        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
+
+        # assert self._num_decodes + self._num_prefills == num_reqs
+
+        # 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.runner.device
+        # block_table = self.block_table
+        # block_table_tensor = block_table.get_device_tensor()#[:num_reqs]
+        
+        block_table_tensor = common_attn_metadata.block_table_tensor
+        slot_mapping = common_attn_metadata.slot_mapping
+        #decode common_attn_metadata: CommonAttentionMetadata(query_start_loc=tensor([0, 2], device='vacc:20', dtype=torch.int32), query_start_loc_cpu=tensor([0, 2], dtype=torch.int32), seq_lens=[40], seq_lens_cpu=[40, 0, 0, 0], num_computed_tokens_cpu=tensor([38], dtype=torch.int32), num_reqs=1, num_actual_tokens=2, max_query_len=2, max_seq_len=40, block_table_tensor=tensor([[1536, 1537, 1538,  ...,    0,    0,    0]], device='vacc:20',
+        
+        # block_table.slot_mapping[:num_actual_tokens].copy_(
+        #     block_table.slot_mapping_cpu[:num_actual_tokens],
+        #     non_blocking=True)
+        # # block_table.slot_mapping[num_actual_tokens:].fill_(-1)
+        # slot_mapping = block_table.slot_mapping[:num_actual_tokens]
+        # num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = \
+        #     split_decodes_and_prefills(common_attn_metadata,
+        #                                decode_threshold=self.reorder_batch_threshold)
+        num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = 0, 0, 0, 0
+        token_nums = common_attn_metadata.query_start_loc_cpu[1:] - common_attn_metadata.query_start_loc_cpu[:-1]
+        if token_nums.max().item() > self.reorder_batch_threshold:
+            num_prefills = num_reqs
+            num_prefill_tokens = num_actual_tokens
+        else:
+            num_decodes = num_reqs
+            num_decode_tokens = num_actual_tokens
+
+        query_start_loc = common_attn_metadata.query_start_loc
+        seq_lens = common_attn_metadata.seq_lens
+
+        prefill_metadata = None
+        if num_prefills > 0:
+            reqs_start = num_decodes  # prefill_start
+
+            # context_lens_cpu = self.runner.input_batch.\
+            #     num_computed_tokens_cpu_tensor[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 self._num_prefills > 0 \
+            #     and max_context_len_cpu > 0:
+            # dont support chunked prefill
+                
+
+            prefill_metadata = MLACommonPrefillMetadata(
+                block_tables=block_table_tensor[reqs_start:reqs_start+num_prefills, ...],
+                query_start_loc=prefill_query_start_loc,
+                # max_query_len=None,
+                chunked_context=chunked_context_metadata,
+                seq_lens=seq_lens[:num_prefills],
+            )
+
+        if not isinstance(seq_lens, list):
+            # TODO init set list in init: vllm/v1/spec_decode/eagle.py
+            seq_lens = seq_lens.tolist()
+        decode_metadata = None
+        if num_decodes > 0:
+            block_num_per_group = env_blk_grp_size // 16
+            block_table_tensor_new = block_table_tensor[:num_decodes, ::block_num_per_group].contiguous()
+
+            seq_lens_new = self.append_seqlen(seq_lens[:slot_mapping.shape[-1]], slot_mapping.shape[-1])
+            
+            if slot_mapping.shape[-1] > num_decodes:
+                mtp_numbers = [query_start_loc[i+1]-query_start_loc[i] for i in range(len(query_start_loc)-1)] #query_start_loc[1:] - query_start_loc[:-1]
+
+                block_table_tensor_list = []
+                for bi,mtp_number in enumerate(mtp_numbers):
+                    for _ in range(mtp_number):
+                        block_table_tensor_list.append(block_table_tensor_new[bi:bi+1])
+                block_table_tensor_new = torch.concatenate(block_table_tensor_list, 0)
+                
+            decode_metadata = self._build_decode(
+                block_table_tensor=block_table_tensor_new,
+                seq_lens=seq_lens_new,
+            )
+            
+        return self.metadata_cls(
+            num_actual_tokens=num_actual_tokens,
+            query_start_loc=query_start_loc,
+            slot_mapping=slot_mapping,
+            head_dim=self.model_config.get_head_size(),
+            # prefill_seq_lens=seq_lens[:self._num_prefills].tolist(), # device to host, todo optimiz
+            # MLACommonMetadata Chunk prefill specific
+            num_decodes=num_decodes,
+            num_decode_tokens=num_decode_tokens,
+            num_prefills=num_prefills,
+            num_prefill_tokens=num_prefill_tokens,
+            prefill_metadata=prefill_metadata,
+            decode_metadata=decode_metadata,
+        )
+