init

vllm_vacc/vllm/attention/backends/vacc_mla.py

@@ -0,0 +1,847 @@
+from collections import defaultdict
+from contextlib import contextmanager
+from dataclasses import dataclass
+from itertools import accumulate
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Type
+
+from vllm.multimodal import MultiModalPlaceholderMap
+
+try:
+    from flashinfer import BatchDecodeMlaWithPagedKVCacheWrapper
+    FLASHINFER_WORKSPACE_BUFFER_SIZE = 256 * 1024 * 1024
+except ImportError:
+    BatchDecodeMlaWithPagedKVCacheWrapper = None
+    FLASHINFER_WORKSPACE_BUFFER_SIZE = 0
+
+import torch
+
+from vllm import _custom_ops as ops
+from vllm.attention.backends.abstract import (AttentionBackend,
+                                              AttentionMetadata,
+                                              AttentionMetadataBuilder,
+                                              AttentionState, AttentionType)
+from vllm.attention.backends.mla.common import MLACommonImpl, MLACommonMetadata
+from vllm.attention.backends.utils import (PAD_SLOT_ID, compute_slot_mapping,
+                                           compute_slot_mapping_start_idx,
+                                           is_block_tables_empty)
+
+#from vllm.attention.ops.paged_attn import PagedAttention
+from vllm_vacc.vllm.attention.ops.vacc_paged_attn import VaccPagedAttention as PagedAttention
+# from vllm.attention.ops.triton_decode_attention import decode_attention_fwd
+from vllm.utils import async_tensor_h2d, make_tensor_with_pad
+
+# import time, os
+
+if TYPE_CHECKING:
+    from vllm_vacc.vllm.worker.vacc_model_runner import (ModelInputForVACCBuilder,
+                                          ModelInputForVACCWithSamplingMetadata)
+
+
+class VACCMLABackend(AttentionBackend):
+
+    @staticmethod
+    def get_name() -> str:
+        return "TORCH_VACC"
+
+    @staticmethod
+    def get_impl_cls() -> Type["VACCMLAImpl"]:
+        return VACCMLAImpl
+
+    @staticmethod
+    def get_metadata_cls() -> Type["AttentionMetadata"]:
+        return VACCMLAMetadata
+
+    @staticmethod
+    def get_builder_cls() -> Type["VACCMLAMetadataBuilder"]:
+        return VACCMLAMetadataBuilder
+
+    @staticmethod
+    def get_state_cls() -> Type["VACCMLAState"]:
+        return VACCMLAState
+
+    @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)
+
+    @staticmethod
+    def swap_blocks(
+        src_kv_cache: torch.Tensor,
+        dst_kv_cache: torch.Tensor,
+        src_to_dst: torch.Tensor,
+    ) -> None:
+        PagedAttention.swap_blocks(src_kv_cache, dst_kv_cache, src_to_dst)
+
+    @staticmethod
+    def copy_blocks(
+        kv_caches: List[torch.Tensor],
+        src_to_dists: torch.Tensor,
+    ) -> None:
+        PagedAttention.copy_blocks(kv_caches, src_to_dists)
+
+    @staticmethod
+    def get_supported_head_sizes() -> List[int]:
+        return [576]
+
+
+class VACCMLAState(AttentionState):
+
+    def __init__(self, runner):
+        self.runner = runner
+        self._is_graph_capturing = False
+
+    @contextmanager
+    def graph_capture(self, max_batch_size: int):
+        self._is_graph_capturing = True
+
+        self._graph_slot_mapping = torch.full((max_batch_size, ),
+                                              PAD_SLOT_ID,
+                                              dtype=torch.long,
+                                              device=self.runner.device)
+        self._graph_seq_lens = torch.ones(max_batch_size,
+                                          dtype=torch.int32,
+                                          device=self.runner.device)
+        self._graph_block_tables = torch.from_numpy(
+            self.runner.graph_block_tables).to(device=self.runner.device)
+
+        self._positions = torch.zeros((max_batch_size, ),
+                                      dtype=torch.long,
+                                      device=self.runner.device)
+
+        yield
+
+        self._is_graph_capturing = False
+        del self._graph_slot_mapping
+        del self._graph_seq_lens
+        del self._graph_block_tables
+        del self._positions
+
+    def graph_clone(self, batch_size: int):
+        assert self._is_graph_capturing
+        return self.__class__(self.runner)
+
+    def graph_capture_get_metadata_for_batch(
+            self, batch_size: int, is_encoder_decoder_model: bool = False):
+        assert self._is_graph_capturing
+
+        attn_metadata = self.runner.attn_backend.make_metadata(
+            num_prefills=0,
+            num_prefill_tokens=0,
+            num_decode_tokens=batch_size,
+            slot_mapping=self._graph_slot_mapping[:batch_size],
+            multi_modal_placeholder_index_maps=None,
+            enable_kv_scales_calculation=True,
+            seq_lens=None,
+            seq_lens_tensor=self._graph_seq_lens[:batch_size],
+            # max_query_len=1,
+            # max_decode_query_len=1,
+            max_prefill_seq_len=0,
+            max_decode_seq_len=self.runner.max_seq_len_to_capture,
+            query_start_loc=None,
+            seq_start_loc=None,
+            context_lens_tensor=None,
+            block_tables=self._graph_block_tables[:batch_size],
+            use_cuda_graph=True,
+            input_positions=self._positions[:batch_size],
+            head_dim=self.runner.model_config.get_head_size())
+
+        if is_encoder_decoder_model:
+            raise NotImplementedError(
+                "VACCMLAState does not support encoder/decoder yet")
+
+        return attn_metadata
+
+    def get_graph_input_buffers(self,
+                                attn_metadata,
+                                is_encoder_decoder_model: bool = False):
+        input_buffers = {
+            "slot_mapping": attn_metadata.slot_mapping,
+            "seq_lens_tensor": attn_metadata.decode_metadata.seq_lens_tensor,
+            "block_tables": attn_metadata.decode_metadata.block_tables,
+            "input_positions": attn_metadata.decode_metadata.input_positions,
+        }
+        if is_encoder_decoder_model:
+            raise NotImplementedError(
+                "VACCMLAState does not support encoder/decoder yet")
+
+        return input_buffers
+
+    def prepare_graph_input_buffers(self,
+                                    input_buffers,
+                                    attn_metadata,
+                                    is_encoder_decoder_model: bool = False):
+        input_positions = attn_metadata.input_positions
+        num_positions = input_positions.shape[0]
+        input_buffers["seq_lens_tensor"].copy_(
+            attn_metadata.decode_metadata.seq_lens_tensor, non_blocking=True)
+        input_buffers["block_tables"].copy_(
+            attn_metadata.decode_metadata.block_tables, non_blocking=True)
+        # CUDA graph buffer is padded so only perform a partial copy based on
+        # num_positions
+        input_buffers["input_positions"][:num_positions].copy_(
+            input_positions, non_blocking=True)
+        if is_encoder_decoder_model:
+            raise NotImplementedError(
+                "VACCMLAState does not support encoder/decoder yet")
+
+    def begin_forward(self, model_input):
+        return
+
+
+@dataclass
+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 VACCMLAMetadataBuilder(AttentionMetadataBuilder[VACCMLAMetadata]):
+
+    def __init__(self, input_builder: "ModelInputForVACCBuilder"):
+        self.chunked_prefill = True
+        if hasattr(input_builder, 'chunked_prefill'):
+            self.chunked_prefill = input_builder.chunked_prefill
+        self.input_builder = input_builder
+        self.runner = input_builder.runner
+        self.sliding_window = input_builder.sliding_window
+        self.block_size = input_builder.block_size
+
+    def prepare(self):
+        self.slot_mapping: List[int] = []
+        self.prefill_seq_lens: List[int] = []
+        self.context_lens: List[int] = []
+        self.block_tables: List[List[int]] = []
+        self.curr_seq_lens: List[int] = []
+        self.input_positions: List[int] = []
+        self.multimodal_placeholder_maps: Dict[
+            str,
+            MultiModalPlaceholderMap] = defaultdict(MultiModalPlaceholderMap)
+        self.num_prefills = 0
+        self.num_prefill_tokens = 0
+        self.num_decode_tokens = 0
+        self.has_prefix_cache_hit = False
+
+    def build(self, seq_lens: List[int], query_lens: List[int],
+              cuda_graph_pad_size: int, batch_size: int):
+        """Build attention metadata with on-device tensors.
+
+        Args:
+            seq_lens: The maybe padded sequence lengths of the input sequences.
+            query_lens: The query lengths of the input sequences.
+            cuda_graph_pad_size: The padding size for cuda graph.
+                                 -1 if cuda graph is not used.
+            batch_size: The maybe padded batch size.
+        """
+
+        self.input_data = self.input_builder.input_data
+        
+        self.slot_mapping=self.input_data.slot_mapping
+        self.context_lens= self.input_data.context_lens
+        if self.input_data.num_prefill_tokens !=0:
+            
+            self.block_tables = self.input_data.prefill_block_tables
+        else:
+            self.block_tables= self.input_data.decode_block_tables
+        self.input_positions= self.input_data.input_positions
+        
+        self.prefill_seq_lens = seq_lens[0:self.input_data.num_prefills]
+        
+        self.num_prefills = self.input_data.num_prefills
+        self.num_prefill_tokens = self.input_data.num_prefill_tokens
+        self.num_decode_tokens = self.input_data.num_decode_tokens
+
+        device = self.runner.device
+        use_captured_graph = cuda_graph_pad_size != -1
+
+        # max_query_len = max(query_lens)
+        # decode_query_lens = query_lens[self.num_prefills:]
+        # if len(decode_query_lens) > 0:
+        #     max_decode_query_len = max(decode_query_lens)
+        # else:
+        #     max_decode_query_len = 1
+        # max_prefill_seq_len = max(self.prefill_seq_lens, default=0)
+        # max_decode_seq_len = max(self.curr_seq_lens, default=0)
+        num_decode_tokens = self.num_decode_tokens
+        query_start_loc = list(accumulate(query_lens, initial=0))
+        seq_start_loc = list(accumulate(seq_lens, initial=0))
+
+        num_seqs = len(seq_lens)
+        if use_captured_graph:
+            self.slot_mapping.extend([PAD_SLOT_ID] * cuda_graph_pad_size)
+            self.block_tables.extend([] * cuda_graph_pad_size)
+            num_decode_tokens = batch_size - self.num_prefill_tokens
+            block_tables = self._get_graph_runner_block_tables(
+                num_seqs, self.block_tables)
+        else:
+            block_tables = make_tensor_with_pad(
+                self.block_tables,
+                pad=0,
+                dtype=torch.int,
+                device=device,
+            )
+        # assert max_query_len > 0, ("query_lens: {}".format(query_lens))
+
+        assert device is not None
+        context_lens_tensor = async_tensor_h2d(self.context_lens, torch.int,
+                                               device, self.runner.pin_memory)
+        seq_lens_tensor = async_tensor_h2d(seq_lens, torch.int, device,
+                                           self.runner.pin_memory)
+        input_positions = async_tensor_h2d(self.input_positions, torch.int,
+                                           device, self.runner.pin_memory)
+        slot_mapping_tensor = async_tensor_h2d(self.slot_mapping, torch.int,
+                                               device, self.runner.pin_memory)
+        query_start_loc_tensor = async_tensor_h2d(query_start_loc, torch.int32,
+                                                  device,
+                                                  self.runner.pin_memory)
+        seq_start_loc_tensor = async_tensor_h2d(seq_start_loc, torch.int32,
+                                                device, self.runner.pin_memory)
+        placeholder_index_maps = {
+            modality: placeholder_map.index_map()
+            for modality, placeholder_map in
+            self.multimodal_placeholder_maps.items()
+        }
+        return VACCMLAMetadata(
+            num_prefills=self.num_prefills,
+            slot_mapping=slot_mapping_tensor,
+            num_prefill_tokens=self.num_prefill_tokens,
+            num_decode_tokens=num_decode_tokens,
+            seq_lens=seq_lens,
+            multi_modal_placeholder_index_maps=placeholder_index_maps,
+            enable_kv_scales_calculation=True,
+            input_positions=input_positions,
+            seq_lens_tensor=seq_lens_tensor,
+            # max_query_len=max_query_len,
+            # max_decode_query_len=None,
+            max_prefill_seq_len=None,
+            max_decode_seq_len=None,
+            query_start_loc=query_start_loc_tensor,
+            seq_start_loc=seq_start_loc_tensor,
+            context_lens_tensor=context_lens_tensor,
+            block_tables=block_tables,
+            use_cuda_graph=use_captured_graph,
+            num_kv_splits=4,  # TODO(lucas) add heuristic
+            head_dim=self.runner.model_config.get_head_size(),
+        )
+
+
+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,
+                         blocksparse_params, logits_soft_cap, attn_type,
+                         kv_sharing_target_layer_name, **kwargs)
+        unsupported_features = [
+            alibi_slopes, sliding_window, blocksparse_params, 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, VACCMLAMetadata)
+        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)
+        # print(f'{os.getpid()} paged_atten(seq: {decode_meta.seq_lens}) time: {time.time() - t0}')
+
+        return self._v_up_proj_and_o_proj(o)
+
+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
+
+# MLA single op impl
+def vacc_paged_attention_naive_singleop(
+    query: torch.Tensor,
+    key_cache: torch.Tensor,
+    value_cache: torch.Tensor,
+    seq_lens,
+    block_table = None,
+    out: torch.Tensor = None,
+    sm_scale = -1
+) -> torch.Tensor:
+    k = key_cache.view(-1, key_cache.shape[2], key_cache.shape[3])[:seq_lens]
+    v = value_cache.view(-1, value_cache.shape[2], value_cache.shape[3])[:seq_lens].squeeze(1)
+    pe_cache = k[..., 512:].squeeze(1)
+    print(f'q:{query[..., :512].shape} v:{v.shape} pe_cache:{pe_cache.shape}')
+    q_nope_kv_c = torch.einsum("shc,tc->sht", query[..., :512], v)
+    q_pe_k_pe = torch.einsum("shr,tr->sht", query[..., 512:], pe_cache)
+    scores = (q_nope_kv_c + q_pe_k_pe) * sm_scale
+    scores = scores.softmax(dim=-1, dtype=torch.float32).type_as(query)
+    o = torch.einsum("sht,tc->shc", scores, v)
+    return o