[Feature] support deepseek v3/r1/v3.2 (#78)

* [Feature] support deepseek v3/r1/v3.2

* fix gpt_oss

* update readme

* update readme

---------

Co-authored-by: hanhaowen <hanhaowen@baidu.com>

vllm_kunlun/v1/attention/backends/mla/flashmla.py

@@ -0,0 +1,202 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from dataclasses import dataclass
+from typing import ClassVar, Optional, Union
+
+import torch
+
+from vllm.attention.backends.abstract import AttentionLayer, AttentionType
+from vllm_kunlun.ops.attention.flashmla import (flash_mla_with_kvcache,
+                                         get_mla_metadata,
+                                         is_flashmla_supported)
+from vllm.config import VllmConfig
+from vllm.logger import init_logger
+from vllm_kunlun.v1.attention.backends.mla.common import (MLACommonBackend,
+                                                   MLACommonDecodeMetadata,
+                                                   MLACommonImpl,
+                                                   MLACommonMetadata,
+                                                   MLACommonMetadataBuilder)
+from vllm.v1.attention.backends.utils import AttentionCGSupport
+from vllm.v1.kv_cache_interface import AttentionSpec
+
+logger = init_logger(__name__)
+
+class FlashMLABackend(MLACommonBackend):
+
+    @staticmethod
+    def get_name() -> str:
+        return "FLASHMLA"
+
+    @staticmethod
+    def get_metadata_cls() -> type["FlashMLAMetadata"]:
+        return FlashMLAMetadata
+
+    @staticmethod
+    def get_builder_cls() -> type["FlashMLAMetadataBuilder"]:
+        return FlashMLAMetadataBuilder
+
+    @staticmethod
+    def get_impl_cls() -> type["FlashMLAImpl"]:
+        return FlashMLAImpl
+
+
+@dataclass
+class FlashMLADecodeMetadata(MLACommonDecodeMetadata):
+    tile_scheduler_metadata: torch.Tensor
+    num_splits: torch.Tensor
+
+
+@dataclass
+class FlashMLAMetadata(MLACommonMetadata[FlashMLADecodeMetadata]):
+    pass
+
+
+class FlashMLAMetadataBuilder(MLACommonMetadataBuilder[FlashMLAMetadata]):
+    cudagraph_support: ClassVar[AttentionCGSupport] = \
+        AttentionCGSupport.UNIFORM_BATCH
+
+    def __init__(self, kv_cache_spec: AttentionSpec, layer_names: list[str],
+                 vllm_config: VllmConfig, device: torch.device):
+        super().__init__(kv_cache_spec, layer_names, vllm_config, device,
+                         FlashMLAMetadata)
+
+        self.num_q_heads = vllm_config.model_config.get_num_attention_heads(
+            vllm_config.parallel_config)
+
+        self.cg_buf_tile_scheduler_metadata = None
+        self.cg_buf_num_splits = None
+
+        device_properties = torch.cuda.get_device_properties(self.device)
+        num_sms = device_properties.multi_processor_count
+
+        if self.compilation_config.cudagraph_mode.has_full_cudagraphs():
+            self.cg_buf_tile_scheduler_metadata = torch.zeros(
+                # Upper bound on size (<= #SMs, TileSchedulerMetaDataSize)
+                # TileSchedulerMetaDataSize = 8
+                (num_sms, 8),
+                device=self.device,
+                dtype=torch.int32,
+            )
+            self.cg_buf_num_splits = torch.empty(
+                (vllm_config.scheduler_config.max_num_seqs + 1),
+                device=self.device,
+                dtype=torch.int32)
+
+    def _build_decode(self, block_table_tensor: torch.Tensor,
+                      seq_lens_cpu: torch.Tensor,
+                      seq_lens_device: torch.Tensor,
+                      query_start_loc_cpu: torch.Tensor,
+                      query_start_loc_device: torch.Tensor,
+                      num_decode_tokens: int) -> FlashMLADecodeMetadata:
+        tile_scheduler_metadata, num_splits = \
+            get_mla_metadata(
+            seq_lens_device,
+            self.num_q_heads,
+            1, # MQA for the decode path
+        )
+
+        # TODO: we can disambiguate between decode and mixed-prefill decode here
+        # so we can only use the persistent buffer if a cudagraph is actually
+        # being used.
+        if self.compilation_config.cudagraph_mode.has_full_cudagraphs():
+            assert self.cg_buf_tile_scheduler_metadata is not None
+            assert self.cg_buf_num_splits is not None
+
+            sm_parts = tile_scheduler_metadata.size(0)
+            # Metadata per-SM, upper bound on size (<= #SMs, TileMetadataSize)
+            assert sm_parts <= self.cg_buf_tile_scheduler_metadata.size(0)
+            tile_scheduler_metadata_view = \
+                self.cg_buf_tile_scheduler_metadata[:sm_parts]
+            tile_scheduler_metadata_view.copy_(tile_scheduler_metadata)
+            tile_scheduler_metadata = tile_scheduler_metadata_view
+
+            # Num splits is per-batch, varying size (batch_size,)
+            n = num_splits.size(0)
+            # make sure static buffer is large enough
+            assert n <= self.cg_buf_num_splits.size(0)
+            num_splits_view = self.cg_buf_num_splits[:n]
+            num_splits_view.copy_(num_splits)
+            # Num splits needs to monotonically increasing
+            # (with: https://github.com/vllm-project/FlashMLA/pull/3, otherwise
+            #  it needs to monotonically increasing by 1)
+            self.cg_buf_num_splits[n:].fill_(num_splits[-1])
+            num_splits = num_splits_view
+
+        return FlashMLADecodeMetadata(
+            block_table=block_table_tensor,
+            seq_lens=seq_lens_device,
+            tile_scheduler_metadata=tile_scheduler_metadata,
+            num_splits=num_splits,
+        )
+
+
+class FlashMLAImpl(MLACommonImpl[FlashMLAMetadata]):
+
+    can_return_lse_for_decode: bool = True
+
+    def __init__(
+            self,
+            num_heads: int,
+            head_size: int,
+            scale: float,
+            num_kv_heads: int,
+            alibi_slopes: Optional[list[float]],
+            sliding_window: Optional[int],
+            kv_cache_dtype: str,
+            logits_soft_cap: Optional[float],
+            attn_type: str,
+            kv_sharing_target_layer_name: Optional[str],
+            # MLA Specific Arguments
+            **mla_args) -> None:
+        super().__init__(num_heads, head_size, scale, num_kv_heads,
+                         alibi_slopes, sliding_window, kv_cache_dtype,
+                         logits_soft_cap, attn_type,
+                         kv_sharing_target_layer_name, **mla_args)
+
+        is_supported, reason = is_flashmla_supported()
+        assert is_supported, reason
+
+        unsupported_features = [alibi_slopes, sliding_window, logits_soft_cap]
+        if any(unsupported_features):
+            raise NotImplementedError(
+                "FlashMLAImpl does not support one of the following: "
+                "alibi_slopes, sliding_window, logits_soft_cap")
+
+        if attn_type != AttentionType.DECODER:
+            raise NotImplementedError("Encoder self-attention and "
+                                      "encoder/decoder cross-attention "
+                                      "are not implemented for "
+                                      "FlashMLAImpl")
+
+    def _forward_decode(
+        self,
+        q: Union[torch.Tensor, tuple[torch.Tensor, torch.Tensor]],
+        kv_c_and_k_pe_cache: torch.Tensor,
+        attn_metadata: FlashMLAMetadata,
+        layer: AttentionLayer,
+    ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
+        # TODO: (zyongye) decode function for mla here
+        assert kv_c_and_k_pe_cache.numel() > 0
+        assert attn_metadata.decode is not None
+
+        if type(q) is tuple:
+            q = torch.cat(q, dim=-1)
+
+        assert isinstance(q, torch.Tensor)
+        o, lse = flash_mla_with_kvcache(
+            q=q.unsqueeze(1),  # Add seqlen dim of 1 (decode)
+            k_cache=kv_c_and_k_pe_cache.unsqueeze(-2),  # Add head dim of 1
+            block_table=attn_metadata.decode.block_table,
+            cache_seqlens=attn_metadata.decode.seq_lens,
+            head_dim_v=self.kv_lora_rank,
+            tile_scheduler_metadata=attn_metadata.decode.
+            tile_scheduler_metadata,
+            num_splits=attn_metadata.decode.num_splits,
+            softmax_scale=self.scale,
+            causal=True,
+            descale_q=layer._q_scale.reshape(1),
+            descale_k=layer._k_scale.reshape(1),
+        )
+
+        return o, lse

vllm_kunlun/v1/attention/backends/mla/flashmla_sparse.py

@@ -0,0 +1,752 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import math
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, ClassVar, Optional
+
+import numpy as np
+import torch
+
+from vllm.attention.backends.abstract import (AttentionBackend, AttentionLayer,
+                                              AttentionMetadata)
+from vllm.attention.backends.utils import get_mla_dims
+from vllm_kunlun.ops.attention.flashmla import (flash_mla_sparse_prefill,
+                                         flash_mla_with_kvcache,
+                                         get_mla_metadata,
+                                         kunlun_flash_mla_with_kvcache)
+from vllm.config import VllmConfig
+from vllm.logger import init_logger
+from vllm.platforms import current_platform
+from vllm.triton_utils import tl, triton
+from vllm.utils import cdiv
+from vllm.v1.attention.backends.mla.common import MLACommonBaseImpl
+from vllm.v1.attention.backends.utils import (AttentionCGSupport,
+                                              AttentionMetadataBuilder,
+                                              CommonAttentionMetadata,
+                                              reshape_attn_output_for_spec_decode,
+                                              reshape_query_for_spec_decode,
+                                              split_decodes_and_prefills)
+from vllm.v1.kv_cache_interface import AttentionSpec
+from vllm.distributed import get_tp_group
+
+if TYPE_CHECKING:
+    from vllm.model_executor.models.deepseek_v2 import Indexer
+
+logger = init_logger(__name__)
+"""
+NOTE: FlashMLA Sparse uses an fp8 cache with the following format
+
+In the "FP8 with scale" format, each token's KV cache is 656 Bytes, 
+structured as:
+-   **First 512 bytes:** The "quantized NoPE" part, containing 512 
+    `float8_e4m3` values.
+-   **Next 16 bytes:** Scale factors, containing 4 `float32` values. 
+    The first `float32` is the scale for the first 128 `float8_e4m3` values, 
+    the second for the next 128, and so on.
+-   **Last 128 bytes:** The "RoPE" part, containing 64 `bfloat16` values. This 
+    part is not quantized for accuracy.
+"""
+
+
+def _lse2_to_lse(lse_base2: torch.Tensor) -> torch.Tensor:
+    # Convert base-2 LSE to natural-log LSE
+    # Keep FP32 for numerical stability during the merge.
+    return (lse_base2.to(torch.float32) * math.log(2.0))
+
+
+class FlashMLASparseBackend(AttentionBackend):
+
+    accept_output_buffer: bool = True
+
+    @staticmethod
+    def get_name() -> str:
+        return "FLASHMLA_SPARSE_VLLM_V1"
+
+    @staticmethod
+    def get_metadata_cls() -> type[AttentionMetadata]:
+        return FlashMLASparseMetadata
+
+    @staticmethod
+    def get_builder_cls() -> type["FlashMLASparseMetadataBuilder"]:
+        return FlashMLASparseMetadataBuilder
+
+    @staticmethod
+    def get_impl_cls() -> type["FlashMLASparseImpl"]:
+        return FlashMLASparseImpl
+
+    @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, ...]:
+        if cache_dtype_str == "fp8_ds_mla":
+            # custom storage fromat is 656 bytes
+            #  see FlashMLA readme.md for details
+            return (num_blocks, block_size, 656)
+        else:
+            return (num_blocks, block_size, head_size)
+
+    @classmethod
+    def get_supported_dtypes(cls) -> list[torch.dtype]:
+        return [torch.bfloat16]
+
+    @classmethod
+    def get_supported_head_sizes(cls) -> list[int]:
+        return [576]
+
+
+@dataclass
+class MLASparsePrefillMetadata:
+    # NOTE(Chen): not call it "FlashMLASparsePrefillMetadata" because
+    # the kernel is not from flashmla
+    block_table: torch.Tensor = None
+    has_context: bool = False
+    context_lens: Optional[torch.Tensor] = None
+
+    # Sequence lengths (context + query) for prefill requests
+    # Shape: [num_prefill_reqs]
+    seq_lens: torch.Tensor = None
+
+    # Request ID for each token: -1 for decode tokens, request index
+    # (0, 1, 2, ...) for prefill tokens.
+    # Shape: [num_actual_tokens]
+    request_ids: torch.Tensor = None
+    query_start_loc: torch.Tensor = None
+    query_start_loc_cpu: torch.Tensor = None
+
+@dataclass
+class FlashMLASparseDecodeAndContextMetadata:
+    scheduler_metadata: torch.Tensor = None
+    num_splits: torch.Tensor = None
+    cache_lens: torch.Tensor = None
+    prefill_context_lengths: Optional[torch.Tensor] = None
+    prefill_new_k_start_locs: Optional[torch.Tensor] = None
+    dummy_block_table: torch.Tensor = None
+
+    seq_lens: torch.Tensor = None
+    seq_lens_cpu: torch.Tensor = None
+    max_seq_len: int = -1 # needed for reshape in spec decode
+
+    def filter_prefill_indices(
+            self, indices: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+        assert self.prefill_context_lengths is not None
+        prefill_context_lengths = self.prefill_context_lengths.unsqueeze(-1)
+        context_indices = torch.where(indices < prefill_context_lengths,
+                                      indices, -1)
+        new_token_indices = torch.where(indices >= prefill_context_lengths,
+                                        indices - prefill_context_lengths, -1)
+        return context_indices, new_token_indices
+
+
+@dataclass
+class FlashMLASparseMetadata:
+    num_reqs: int
+    max_query_len: int
+    max_seq_len: int
+
+    num_actual_tokens: int  # Number of tokens excluding padding.
+    query_start_loc: torch.Tensor
+    slot_mapping: torch.Tensor
+
+    block_table: torch.Tensor
+    req_id_per_token: torch.Tensor
+    block_size: int = 64
+    topk_tokens: int = 2048
+
+    num_prefills: int = 0
+    num_decodes: int = 0
+    num_prefill_tokens: int = 0
+    num_decode_tokens: int = 0
+
+    decode_metadata: Optional[FlashMLASparseDecodeAndContextMetadata] = None
+    prefill_metadata: Optional[MLASparsePrefillMetadata] = None
+
+    @dataclass
+    class FP8KernelMetadata:
+        scheduler_metadata: Optional[torch.Tensor]
+        num_splits: torch.Tensor
+        dummy_block_table: torch.Tensor
+        cache_lens: torch.Tensor
+
+    fp8_extra_metadata: Optional[FP8KernelMetadata] = None
+
+
+@triton.jit
+def _convert_req_index_to_global_index_kernel(
+    req_id_ptr,  # int32 [num_tokens]
+    block_table_ptr,  # int32 [num_requests, max_num_blocks_per_req]
+    token_indices_ptr,  # int32 [num_tokens, NUM_TOPK_TOKENS]
+    out_ptr,  # int32 [num_tokens, NUM_TOPK_TOKENS]
+    # shapes (compile-time where possible)
+    max_num_blocks_per_req: tl.constexpr,
+    BLOCK_SIZE: tl.constexpr,
+    BLOCK_N: tl.constexpr,  # tile width along columns
+    # strides (in elements)
+    bt_stride0,
+    bt_stride1,
+    ti_stride0,
+    ti_stride1,
+    out_stride0,
+    out_stride1,
+):
+    # program_id(0) -> token_id (row)
+    # program_id(1) -> tile index along columns
+    token_id = tl.program_id(0)
+    tile_id = tl.program_id(1)
+
+    # Each program covers BLOCK_N consecutive columns
+    indice_id = tile_id * BLOCK_N + tl.arange(0, BLOCK_N)
+
+    # Load request id for this token (no mask: grid is exact)
+    req = tl.load(req_id_ptr + token_id)
+
+    # Load token indices for this tile
+    ti_ptr = token_indices_ptr + token_id * ti_stride0 + indice_id * ti_stride1
+    tok = tl.load(ti_ptr)  # int32
+
+    # Only token == -1 should propagate as -1
+    is_invalid_tok = tok < 0
+
+    # Compute block id and in-block offset
+    block_id = tok // BLOCK_SIZE
+    inblock_off = tok % BLOCK_SIZE
+
+    # Guard block_table access
+    valid_block = block_id < max_num_blocks_per_req
+    bt_ptr = block_table_ptr + req * bt_stride0 + block_id * bt_stride1
+    base = tl.load(bt_ptr, mask=valid_block, other=0)
+
+    # If token == -1 OR block_id OOB, output -1; else base * BLOCK_SIZE + offset
+    out_val = tl.where(is_invalid_tok | (~valid_block), -1,
+                       base * BLOCK_SIZE + inblock_off)
+
+    # Store results
+    out_ptr_ij = out_ptr + token_id * out_stride0 + indice_id * out_stride1
+    tl.store(out_ptr_ij, out_val)
+
+
+def triton_convert_req_index_to_global_index(
+        req_id: torch.Tensor,  # int32 [num_tokens]
+        block_table: torch.
+    Tensor,  # int32 [num_requests, max_num_blocks_per_req]
+        token_indices: torch.Tensor,  # int32 [num_tokens, NUM_TOPK_TOKENS]
+        BLOCK_SIZE: int = 64,
+        NUM_TOPK_TOKENS: int = 2048,
+        BLOCK_N: int = 128,  # tile width along columns
+):
+    """
+    out[token_id, indice_id] =
+        block_table[req_id[token_id], 
+            token_indices[token_id, indice_id] // BLOCK_SIZE] * BLOCK_SIZE
+        + token_indices[token_id, indice_id] % BLOCK_SIZE
+
+    Only when token_indices[token_id, indice_id] == -1 do we output -1.
+    For safety, we also output -1 if the derived block_id would be 
+        out-of-bounds.
+    """
+    assert req_id.dtype == torch.int32
+    assert block_table.dtype == torch.int32
+    assert token_indices.dtype == torch.int32
+    assert token_indices.shape[1] == NUM_TOPK_TOKENS
+    assert NUM_TOPK_TOKENS % BLOCK_N == 0, \
+        f"NUM_TOPK_TOKENS ({NUM_TOPK_TOKENS}) must be divisible by" \
+        f"BLOCK_N ({BLOCK_N})"
+
+    num_tokens = req_id.shape[0]
+    num_requests, max_num_blocks_per_req = block_table.shape
+    tiles_per_row = NUM_TOPK_TOKENS // BLOCK_N
+
+    # Ensure contiguous tensors on the same device
+    req_id_c = req_id.contiguous()
+    block_table_c = block_table.contiguous()
+    token_indices_c = token_indices.contiguous()
+    out = torch.empty_like(token_indices_c)
+
+    # Strides in elements
+    bt_stride0, bt_stride1 = block_table_c.stride()
+    ti_stride0, ti_stride1 = token_indices_c.stride()
+    out_stride0, out_stride1 = out.stride()
+
+    # Exact 2D grid: tokens × column tiles
+    grid = (num_tokens, tiles_per_row)
+
+    _convert_req_index_to_global_index_kernel[grid](
+        req_id_c,
+        block_table_c,
+        token_indices_c,
+        out,
+        # shapes / constexprs
+        max_num_blocks_per_req,
+        BLOCK_SIZE,
+        BLOCK_N,
+        # strides
+        bt_stride0,
+        bt_stride1,
+        ti_stride0,
+        ti_stride1,
+        out_stride0,
+        out_stride1,
+    )
+    return out
+
+def kunlun_convert_req_index_to_global_index(
+        req_id: torch.Tensor,  # int32 [num_tokens]
+        block_table: torch.Tensor,  # int32 [num_requests, max_num_blocks_per_req]
+        token_indices: torch.Tensor,  # int32 [num_tokens, NUM_TOPK_TOKENS]
+        BLOCK_SIZE: int = 64,
+        NUM_TOPK_TOKENS: int = 2048,
+):
+    assert req_id.dtype == torch.int32
+    assert block_table.dtype == torch.int32
+    assert token_indices.dtype == torch.int32
+    assert token_indices.shape[1] == NUM_TOPK_TOKENS
+
+    num_tokens = req_id.shape[0]
+    num_requests, max_num_blocks_per_req = block_table.shape
+    
+    out = torch.zeros_like(token_indices)
+    
+    # Compute block_id and inblock_off for all tokens at once
+    block_id = token_indices // BLOCK_SIZE
+    inblock_off = token_indices % BLOCK_SIZE
+    
+    # Create mask for invalid tokens (tok < 0)
+    invalid_tok_mask = token_indices < 0
+    
+    # Create mask for out-of-bounds block_id
+    oob_block_mask = block_id >= max_num_blocks_per_req
+    
+    # Combine masks - output -1 for either condition
+    invalid_mask = invalid_tok_mask | oob_block_mask
+    
+    # Get request IDs expanded to match token_indices shape
+    req_ids_expanded = req_id.unsqueeze(1).expand(-1, NUM_TOPK_TOKENS)
+    
+    # Gather base addresses from block_table
+    # Clamp block_id to avoid index errors (we'll mask these out anyway)
+    block_id_clamped = torch.clamp(block_id, 0, max_num_blocks_per_req - 1)
+    
+    # Use advanced indexing to get base addresses
+    base_addrs = block_table[req_ids_expanded, block_id_clamped]
+    
+    # Compute the global indices
+    global_indices = base_addrs * BLOCK_SIZE + inblock_off
+    
+    # Apply mask: set invalid positions to -1
+    out = torch.where(invalid_mask, torch.tensor(-1, dtype=torch.int32, device=token_indices.device), global_indices)
+    
+    return out
+
+def kunlun_concat_and_cache_mla(
+    kv_c: torch.Tensor, #[num_tokens, kv_lora_rank]
+    k_pe: torch.Tensor, #[num_tokens, pe_dim]
+    kv_cache: torch.Tensor, #[num_blocks, block_size, (kv_lora_rank + pe_dim)]
+    slot_mapping: torch.Tensor, #[num_tokens] or [num_actual_tokens]
+    kv_cache_dtype: str, 
+    scale: torch.Tensor
+):
+    num_tokens = slot_mapping.shape[0]
+    kv_lora_rank = kv_c.shape[1]
+    pe_dim = k_pe.shape[1]
+    block_size = kv_cache.shape[1]
+    
+    def kunlun_fp8_ds_mla():
+        for token_idx in range(num_tokens):
+            slot = slot_mapping[token_idx].item()
+            if slot < 0: continue
+            block_idx = slot // block_size
+            block_offset = slot % block_size
+            kv_c_i = kv_c[token_idx].view(4,kv_lora_rank//4).contiguous()
+            kv_c_i_int8 = torch.zeros(
+            kv_c_i.shape,
+            device=kv_c.device,
+            dtype=torch.int8,
+            )
+            kv_c_i_scale = torch.zeros(
+                [kv_c_i.shape[0], 1],
+                device=kv_c.device,
+                dtype=torch.float32,
+            )
+            torch.ops._C.quant2d(kv_c_i, kv_c_i_int8, kv_c_i_scale, force_sdnn=True)
+            kv_c_i_scale /= 127
+            kv_cache[block_idx, block_offset, :kv_lora_rank] = kv_c_i_int8.view(-1).view(torch.uint8).contiguous()
+            kv_cache[block_idx, block_offset, kv_lora_rank:kv_lora_rank + 16] = kv_c_i_scale.view(-1).view(torch.uint8).contiguous()
+            kv_cache[block_idx, block_offset, kv_lora_rank+16:] = k_pe[token_idx, :].view(torch.uint8).contiguous()
+            
+    def kunlun_mla():
+        for token_idx in range(num_tokens):
+            slot = slot_mapping[token_idx].item()
+            if slot < 0: continue
+            block_idx = slot // block_size
+            block_offset = slot % block_size
+            kv_cache[block_idx, block_offset, :kv_lora_rank] = kv_c[token_idx, :].contiguous()
+            kv_cache[block_idx, block_offset, kv_lora_rank:] = k_pe[token_idx, :].contiguous()
+            
+    if (kv_cache_dtype == "fp8_ds_mla"):
+        assert kv_lora_rank == 512, "kv_lora_rank must be 512 for fp8_ds_mla"
+        assert pe_dim == 64, "pe_dim must be 64 for fp8_ds_mla"
+        assert kv_cache.shape[2] == 656 // kv_cache.element_size(), "kv_cache.shape[2] must be 656 bytes for fp8_ds_mla"
+        assert kv_c.element_size() == 2, "kv_c.element_size() must be 2 for fp8_ds_mla"
+        assert k_pe.element_size() == 2, "k_pe.element_size() must be 2 for fp8_ds_mla"
+        kunlun_fp8_ds_mla()
+    else:
+        assert kv_cache.shape[2] == kv_lora_rank + pe_dim
+        kunlun_mla()
+    
+    
+@dataclass
+class FlashMLASparseMetadataBuilder(
+        AttentionMetadataBuilder[FlashMLASparseMetadata]):
+    cudagraph_support: ClassVar[AttentionCGSupport] = \
+        AttentionCGSupport.UNIFORM_BATCH
+
+    def __init__(self, kv_cache_spec: AttentionSpec, layer_names: list[str],
+                 vllm_config: VllmConfig, device: torch.device):
+        self.vllm_config = vllm_config
+        self.layer_names = layer_names
+        cache_config = vllm_config.cache_config
+        self.kv_cache_spec = kv_cache_spec
+        self.model_config = vllm_config.model_config
+        parallel_config = vllm_config.parallel_config
+        self.device = device
+
+        # Treat requests with query length <= 1 as decodes to match the
+        # DeepGEMM indexer constraint (fp8_paged_mqa_logits only supports next_n <= 2)
+        # 从最新版本vllm中引入的
+        self._init_reorder_batch_threshold(1, supports_spec_as_decode=True)
+
+        props = torch.cuda.get_device_properties(device)
+        sm_count = props.multi_processor_count
+
+        self.num_heads = self.model_config.get_num_attention_heads(
+            parallel_config)
+        self.mla_dims = get_mla_dims(self.model_config)
+        self.topk_tokens = vllm_config.model_config.hf_config.index_topk
+        self.use_fp8_kv_cache = cache_config.cache_dtype == "fp8_ds_mla"
+
+        self.topk_tokens_tensor = torch.tensor([self.topk_tokens],
+                                               device=device,
+                                               dtype=torch.int32)
+        # self.max_model_len_tensor = torch.tensor(
+        #     [self.model_config.max_model_len],
+        #     device=device,
+        #     dtype=torch.int32)
+
+        # this is ignored by `flash_mla_with_kvcache` if indices not None
+        self.dummy_block_table = torch.empty((1, 1),
+                                             dtype=torch.int32,
+                                             device=self.device)
+
+        # Equation taken from FlashMLA/csrc/pybind.cpp
+        h_q, h_k = self.num_heads, 1
+        s_q = 1  # inversely proportional to s_q, so s_q = 1 is the largest
+        max_num_sm_parts = int(
+            max((sm_count // 2) / h_k // (cdiv(h_q // h_k, 2 * 64) * s_q), 1))
+        if current_platform.is_device_capability(100):
+            max_num_sm_parts *= 2
+        self.tile_scheduler_metadata_buffer = torch.zeros(
+            # TileSchedulerMetaDataSize = 8
+            # see: FlashMLA/csrc/params.h
+            (max_num_sm_parts, 8),
+            dtype=torch.int32,
+            device=device)
+        self.num_splits_buffer = torch.zeros(
+            # We pack all the tokens into one batch for sparse attention.
+            # Otherwise, we can exceed the sm of `get_mla_metadata`.
+            (
+                2, ),
+            dtype=torch.int32,
+            device=device)
+        self.req_id_per_token_buffer = torch.zeros(
+            (vllm_config.scheduler_config.max_num_batched_tokens, ),
+            dtype=torch.int32,
+            device=device)
+    def build(self,
+              common_prefix_len: int,
+              common_attn_metadata: CommonAttentionMetadata,
+              fast_build: bool = False) -> FlashMLASparseMetadata:
+
+        num_tokens = common_attn_metadata.num_actual_tokens
+        starts = np.asarray(common_attn_metadata.query_start_loc_cpu,
+                            dtype=np.int32)
+        seg_lengths = np.diff(starts)
+        req_id_per_token = np.repeat(
+            np.arange(seg_lengths.shape[0], dtype=np.int32), seg_lengths)
+        # Zero-fill for cudagraphs
+        self.req_id_per_token_buffer.fill_(0)
+        self.req_id_per_token_buffer[:req_id_per_token.shape[0]]\
+            .copy_(torch.from_numpy(req_id_per_token), non_blocking=True)
+        req_id_per_token = self.req_id_per_token_buffer[:num_tokens]
+
+        fp8_extra_metadata = None
+
+        if self.use_fp8_kv_cache:
+            cache_seqlens_cpu, cache_seqlens = get_mla_metadata(
+                cache_seqlens=self.topk_tokens_tensor,
+            )
+            fp8_extra_metadata = FlashMLASparseMetadata.FP8KernelMetadata(
+                scheduler_metadata=None,
+                num_splits=None,
+                # cache_lens and block_table are basically unused in sparse case
+                # but the decode kernel will treat -1 and indices >= cache_lens
+                # as invalid so we make sure cache_lens is large enough to not
+                # accidentally mark indices invalid, we will use -1 exclusively
+                # to mark invalid indices
+                cache_lens=cache_seqlens_cpu,
+                dummy_block_table=self.dummy_block_table)
+
+        (num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens) = (
+            split_decodes_and_prefills(
+                common_attn_metadata,
+                decode_threshold=self.reorder_batch_threshold or 1,
+                require_uniform=True,
+            )
+        )
+
+        # For pure decode batches, prefill_request_id will be None
+        # For mixed batches, it will have -1 for decode and request_id for prefill
+        prefill_metadata = None
+        if num_prefills > 0:
+            prefill_metadata = MLASparsePrefillMetadata(
+                query_start_loc = common_attn_metadata.query_start_loc[num_decodes:] - common_attn_metadata.query_start_loc[num_decodes], #因为prefiil、decode请求是分离，所以需要对q进行切分，故需调整该值
+                query_start_loc_cpu = common_attn_metadata.query_start_loc_cpu[num_decodes:] - common_attn_metadata.query_start_loc_cpu[num_decodes],
+            )
+
+        decode_metadata = None
+        if num_decodes > 0:
+            max_seq_len = int(common_attn_metadata.seq_lens_cpu[:num_decodes].max())
+
+            decode_metadata = FlashMLASparseDecodeAndContextMetadata(
+                max_seq_len=max_seq_len,
+                seq_lens=common_attn_metadata.seq_lens[:num_decodes],
+                seq_lens_cpu=common_attn_metadata.seq_lens_cpu[:num_decodes],
+            )
+
+
+        metadata = FlashMLASparseMetadata(
+            num_reqs=common_attn_metadata.num_reqs,
+            max_query_len=common_attn_metadata.max_query_len,
+            max_seq_len=common_attn_metadata.max_seq_len,
+            num_actual_tokens=common_attn_metadata.num_actual_tokens,
+            query_start_loc=common_attn_metadata.query_start_loc,
+            slot_mapping=common_attn_metadata.slot_mapping,
+            block_table=common_attn_metadata.block_table_tensor,
+            req_id_per_token=req_id_per_token,
+            block_size=self.kv_cache_spec.block_size,
+            topk_tokens=self.topk_tokens,
+            fp8_extra_metadata=fp8_extra_metadata,
+            num_prefills=num_prefills,
+            num_decodes=num_decodes,
+            num_prefill_tokens=num_prefill_tokens,
+            num_decode_tokens=num_decode_tokens,
+            decode_metadata=decode_metadata,
+            prefill_metadata=prefill_metadata
+        )
+        return metadata
+
+
+class FlashMLASparseImpl(MLACommonBaseImpl[FlashMLASparseMetadata]):
+
+    def __init__(
+            self,
+            num_heads: int,
+            head_size: int,
+            scale: float,
+            num_kv_heads: int,
+            alibi_slopes: Optional[list[float]],
+            sliding_window: Optional[int],
+            kv_cache_dtype: str,
+            logits_soft_cap: Optional[float],
+            attn_type: str,
+            kv_sharing_target_layer_name: Optional[str],
+            # MLA Specific Arguments
+            topk_indice_buffer: Optional[torch.Tensor] = None,
+            indexer: Optional["Indexer"] = None,
+            **mla_args) -> None:
+        super().__init__(num_heads, head_size, scale, num_kv_heads,
+                         alibi_slopes, sliding_window, kv_cache_dtype,
+                         logits_soft_cap, attn_type,
+                         kv_sharing_target_layer_name, **mla_args)
+        self.softmax_scale = scale
+        assert indexer is not None
+        self.topk_indices_buffer = indexer.topk_indices_buffer
+        self.padding = 128 if current_platform.is_device_capability(
+            100) else 64
+
+    def _forward_bf16_kv(
+            self, q: torch.Tensor, kv_c_and_k_pe_cache: torch.Tensor,
+            topk_indices: torch.Tensor,
+            attn_metadata: FlashMLASparseMetadata) -> torch.Tensor:
+        
+        num_tokens = q.shape[0]
+        kv_c_and_k_pe_cache = kv_c_and_k_pe_cache.contiguous().view(
+            -1,  kv_c_and_k_pe_cache.shape[-1])
+    
+        # num_decode_tokens = attn_metadata.num_decode_tokens
+        num_prefill_tokens = attn_metadata.num_prefill_tokens
+        num_decodes = attn_metadata.num_decodes
+
+        has_decode = attn_metadata.num_decodes > 0
+        has_prefill = attn_metadata.num_prefills > 0
+        num_decode_tokens = attn_metadata.num_decode_tokens
+
+        def _bf16_decode(q: torch.Tensor, topk_indices: torch.Tensor) -> torch.Tensor:
+            # Reshape q: (num_decode_tokens, num_heads, head_dim)
+            #         -> (num_decodes, seq_len, num_heads, head_dim)
+            q = reshape_query_for_spec_decode(q, num_decodes)
+            seq_len = q.shape[1]
+            # Reshape topk_indices: (num_decode_tokens, topk)
+            #                    -> (num_decodes, seq_len, topk)
+            topk_indices = topk_indices.view(num_decodes, seq_len, -1)
+            decode_metadata = attn_metadata.decode_metadata
+            _attn_out, _, _ = kunlun_flash_mla_with_kvcache(
+                q=q,
+                k_cache=kv_c_and_k_pe_cache,
+                head_dim_v=512,
+                cache_seqlens=decode_metadata.seq_lens,
+                cache_seqlens_cpu=decode_metadata.seq_lens_cpu,
+                is_fp8_kvcache=False,
+                indices=topk_indices, 
+                softmax_scale=self.softmax_scale,
+                max_seq_kv=decode_metadata.max_seq_len
+            )
+            # Reshape output: (num_decodes, seq_len, num_heads, head_dim_v)
+            #              -> (num_decode_tokens, num_heads, head_dim_v)
+            return reshape_attn_output_for_spec_decode(_attn_out)
+        
+        def _bf16_prefill(q: torch.Tensor, topk_indices: torch.Tensor) -> torch.Tensor:
+            prefill_metadata = attn_metadata.prefill_metadata
+            topk_indices = topk_indices.view(num_prefill_tokens, 1, -1)
+            # NOTE: 只有prefill阶段attn_metadata.query_start_loc是符合klx算子需求的
+            _attn_out = flash_mla_sparse_prefill(
+                q=q,
+                kv=kv_c_and_k_pe_cache, 
+                indices=topk_indices,
+                sm_scale=self.softmax_scale,
+                q_lod_xpu=prefill_metadata.query_start_loc,
+                q_lod_cpu=prefill_metadata.query_start_loc_cpu
+            )[0]
+            return _attn_out
+
+        topk_indices_global = torch.ops.xspeedgate_ops.convert_req_index_to_global_index(
+                req_id=attn_metadata.req_id_per_token,
+                block_table=attn_metadata.block_table,
+                token_indices=topk_indices,
+                block_size=attn_metadata.block_size,
+                num_topk_tokens=attn_metadata.topk_tokens,
+        )
+
+        attn_out = torch.empty(
+            (num_tokens, self.num_heads, self.kv_lora_rank),
+            dtype=q.dtype,
+            device=q.device,
+        )
+        if has_prefill:
+            prefill_q = q[num_decode_tokens:]
+            prefill_topk_indices_global = topk_indices_global[num_decode_tokens:]
+            attn_out[num_decode_tokens:] = _bf16_prefill(prefill_q, prefill_topk_indices_global)
+
+        # 处理decode部分 - 需要正确的block table映射print
+        if has_decode:
+            decode_q = q[:num_decode_tokens]
+            decode_topk_indices_global = topk_indices_global[:num_decode_tokens]
+            attn_out[:num_decode_tokens] = _bf16_decode(decode_q, decode_topk_indices_global)
+
+        return attn_out
+        
+
+    def _forward_fp8_kv(self, q: torch.Tensor,
+                        kv_c_and_k_pe_cache: torch.Tensor,
+                        topk_indices: torch.Tensor,
+                        attn_metadata: FlashMLASparseMetadata) -> torch.Tensor:
+        # TODO: When fwd_kvcache_mla supports uint8 kv cache, execute this function.
+        assert attn_metadata.fp8_extra_metadata is not None
+        extra_metadata = attn_metadata.fp8_extra_metadata
+
+        _attn_out, _ = flash_mla_with_kvcache(
+            q=q.unsqueeze(0),  # unsqueeze to add batch_dim
+            k_cache=kv_c_and_k_pe_cache,
+            block_table=extra_metadata.dummy_block_table,
+            head_dim_v=512,
+            cache_seqlens=extra_metadata.cache_lens,
+            tile_scheduler_metadata=extra_metadata.scheduler_metadata, # None
+            num_splits=extra_metadata.num_splits, # None
+            is_fp8_kvcache=True,
+            indices=topk_indices.unsqueeze(0),  # unsqueeze to add batch_dim
+            softmax_scale=self.softmax_scale,
+            max_seq_kv=attn_metadata.max_seq_len
+        )
+
+        return _attn_out
+
+    def forward(
+        self,
+        layer: AttentionLayer,
+        q: torch.Tensor,
+        k_c_normed: torch.Tensor,  # key in unified attn
+        k_pe: torch.Tensor,  # value in unified attn
+        kv_cache: torch.Tensor,
+        attn_metadata: FlashMLASparseMetadata,
+        output: Optional[torch.Tensor] = None,
+        output_scale: Optional[torch.Tensor] = None,
+        output_block_scale: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        # NOTE(lucas): for the sparse FlashMLA kernels the kernels want to use
+        # MQA 576/512 approach for both prefill and decode
+
+        assert output is not None, "Output tensor must be provided."
+
+        if output_scale is not None or output_block_scale is not None:
+            raise NotImplementedError(
+                "fused output quantization is not yet supported"
+                " for MLACommonImpl")
+
+        if attn_metadata is None:
+            # The zero fill is required when used with DP + EP
+            # to ensure all ranks within a DP group compute the
+            # same expert outputs.
+            return output.fill_(0)
+
+        num_actual_toks = attn_metadata.num_actual_tokens
+
+        # Inputs and outputs may be padded for CUDA graphs
+
+        q = q[:num_actual_toks, ...]
+        k_c_normed = k_c_normed[:num_actual_toks, ...]
+        k_pe = k_pe[:num_actual_toks, ...]
+
+        q_nope, q_pe = q.split([self.qk_nope_head_dim, self.qk_rope_head_dim],
+                               dim=-1)
+        # Convert from (B, N, P) to (N, B, P)
+        q_nope = q_nope.transpose(0, 1)
+        # Multiply (N, B, P) x (N, P, L) -> (N, B, L)
+        ql_nope = torch.bmm(q_nope, self.W_UK_T)
+        # Convert from (N, B, L) to (B, N, L)
+        ql_nope = ql_nope.transpose(0, 1)
+
+        topk_indices = self.topk_indices_buffer[:num_actual_toks]
+        
+        q = torch.cat([ql_nope, q_pe], dim=-1)
+
+        # write the latent and rope to kv cache
+        if kv_cache.numel() > 0:
+            torch.ops._C.concat_and_cache_mla(
+                kv_c=k_c_normed,
+                k_pe=k_pe.squeeze(1),
+                kv_cache=kv_cache,
+                slot_mapping=attn_metadata.slot_mapping.flatten(),
+            )
+
+        if self.kv_cache_dtype != "fp8_ds_mla":
+            attn_out = self._forward_bf16_kv(q, kv_cache, topk_indices,
+                                             attn_metadata)
+        else:
+            # attn_out = self._forward_fp8_kv(q, kv_cache, topk_indices_global,
+            #                                 attn_metadata)
+            raise NotImplementedError
+
+        self._v_up_proj(attn_out, out=output[:num_actual_toks])
+        return output

vllm_kunlun/v1/attention/backends/mla/indexer.py

@@ -0,0 +1,133 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import torch
+from dataclasses import dataclass
+from typing import ClassVar, Optional
+from vllm.logger import init_logger
+from vllm.v1.attention.backends.utils import ( CommonAttentionMetadata,
+                                              split_decodes_and_prefills)
+from vllm.v1.attention.backends.mla.indexer import (split_prefill_chunks,
+                                                    DeepseekV32IndexerMetadataBuilder,
+                                                    DeepseekV32IndexerPrefillMetadata)
+
+logger = init_logger(__name__)
+
+@dataclass
+class DeepSeekV32IndexerDecodeMetadata:
+    block_table: torch.Tensor
+    seq_lens: torch.Tensor
+    seq_lens_cpu: torch.Tensor
+    decode_lens: torch.Tensor
+    requires_padding: bool
+    schedule_metadata: torch.Tensor
+
+
+@dataclass
+class DeepseekV32IndexerMetadata:
+
+    # FIXME (zyongye)
+    # hacky way to access the data now, need to be in chunked meta
+    seq_lens: torch.Tensor
+    seq_lens_cpu: torch.Tensor
+
+    num_reqs: int
+    max_query_len: int
+    max_seq_len: int
+
+    num_actual_tokens: int  # Number of tokens excluding padding.
+    query_start_loc: torch.Tensor
+    slot_mapping: torch.Tensor
+    # The dimension of the attention heads
+    head_dim: int
+
+    # 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
+
+    decode: Optional[DeepSeekV32IndexerDecodeMetadata] = None
+    prefill: Optional[DeepseekV32IndexerPrefillMetadata] = None
+
+def kunlun_build(self,
+              common_prefix_len: int,
+              common_attn_metadata: CommonAttentionMetadata,
+              fast_build: bool = False) -> DeepseekV32IndexerMetadata:
+
+        num_reqs = common_attn_metadata.num_reqs
+        num_tokens = common_attn_metadata.num_actual_tokens
+
+        query_start_loc_cpu = common_attn_metadata.query_start_loc_cpu
+        num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = \
+            split_decodes_and_prefills(
+                common_attn_metadata,
+                decode_threshold=self.reorder_batch_threshold)
+
+        assert num_decodes + num_prefills == num_reqs
+        assert num_decode_tokens + num_prefill_tokens == num_tokens
+
+        prefill_metadata = None
+        if num_prefills > 0:
+            chunk_seq_ids = split_prefill_chunks(
+                common_attn_metadata.seq_lens_cpu,
+                self.max_prefill_buffer_size,
+                num_decodes,
+            )
+            chunks = [
+                self.build_one_prefill_chunk(
+                    reqs_start, reqs_end, query_start_loc_cpu,
+                    common_attn_metadata.seq_lens_cpu,
+                    common_attn_metadata.block_table_tensor)
+                for reqs_start, reqs_end in chunk_seq_ids
+            ]
+            prefill_metadata = DeepseekV32IndexerPrefillMetadata(
+                chunks=chunks, )
+
+        decode_metadata = None
+        if num_decodes > 0:
+            torch.diff(common_attn_metadata.query_start_loc[:num_decodes + 1],
+                       out=self.decode_lens_buffer[:num_decodes])
+            decode_lens = self.decode_lens_buffer[:num_decodes]
+            decode_lens_cpu = torch.diff(
+                common_attn_metadata.query_start_loc_cpu[:num_decodes + 1])
+
+            # Use CPU to avoid GPU sync; breaking async scheduling
+            requires_padding = (decode_lens_cpu.max()
+                                > decode_lens_cpu.min()).item()
+
+            seq_lens = common_attn_metadata.seq_lens[:num_decodes]
+
+            decode_metadata = DeepSeekV32IndexerDecodeMetadata(
+                block_table=common_attn_metadata.
+                block_table_tensor[:num_decodes, ...],
+                seq_lens=common_attn_metadata.seq_lens[:num_decodes],
+                seq_lens_cpu=common_attn_metadata.seq_lens[:num_decodes].cpu(),
+                decode_lens=decode_lens,
+                requires_padding=requires_padding,
+                schedule_metadata=self.scheduler_metadata_buffer,
+            )
+
+        attn_metadata = DeepseekV32IndexerMetadata(
+            seq_lens=common_attn_metadata.seq_lens,
+            seq_lens_cpu=common_attn_metadata.seq_lens.cpu(),
+            num_reqs=common_attn_metadata.num_reqs,
+            max_query_len=common_attn_metadata.max_query_len,
+            max_seq_len=common_attn_metadata.max_seq_len,
+            num_actual_tokens=common_attn_metadata.num_actual_tokens,
+            query_start_loc=common_attn_metadata.query_start_loc,
+            slot_mapping=common_attn_metadata.slot_mapping,
+            head_dim=128,
+            num_decodes=num_decodes,
+            num_decode_tokens=num_decode_tokens,
+            num_prefills=num_prefills,
+            num_prefill_tokens=num_prefill_tokens,
+            prefill=prefill_metadata,
+            decode=decode_metadata,
+        )
+
+        # if get_tensor_model_parallel_rank() == 0:
+        #     logger.info(f"attn_metadata: {attn_metadata}")
+        return attn_metadata
+
+DeepseekV32IndexerMetadataBuilder.build = kunlun_build

vllm_kunlun/v1/sample/ops/topk_topp_sampler.py

@@ -2,7 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 from typing import Optional
-import os
+
 import torch
 import torch.nn as nn
 from packaging import version
@@ -24,6 +24,7 @@ class TopKTopPSampler(nn.Module):
 
     def __init__(self, logprobs_mode):
         super().__init__()
+        self.logprobs_mode = logprobs_mode
         logger.info_once(
             "Using FlashInfer for top-p & top-k sampling.")
         self.forward = self.forward_kunlun
@@ -40,9 +41,14 @@ class TopKTopPSampler(nn.Module):
 
         The logits tensor may be updated in-place.
         """
-        logits = apply_top_k_top_p(logits, k, p)
+        logits = self.apply_top_k_top_p(logits, k, p)
+        logits_to_return = None
+        if self.logprobs_mode == "processed_logits":
+            logits_to_return = logits
+        elif self.logprobs_mode == "processed_logprobs":
+            logits_to_return = logits.log_softmax(dim=-1, dtype=torch.float32)
         probs = logits.softmax(dim=-1, dtype=torch.float32)
-        return random_sample(probs, generators), None
+        return random_sample(probs, generators), logits_to_return
 
     def forward_kunlun(
         self,
@@ -52,16 +58,13 @@ class TopKTopPSampler(nn.Module):
         p: Optional[torch.Tensor],
     ) -> torch.Tensor:
         """More optimized implementation for top-k and top-p sampling."""
-        if k is None and p is None:
-            # We prefer `random_sample` over `flashinfer_sample` when sorting is
-            # not needed. This is because `random_sample` does not require
-            # CPU-GPU synchronization while `flashinfer_sample` does.
-            probs = logits.softmax(dim=-1, dtype=torch.float32)
-            return random_sample(probs, generators), None
-        if generators:
-            logger.warning_once("FlashInfer 0.2.3+ does not support "
-                                "per-request generators. Falling back to "
-                                "PyTorch-native implementation.")
+        if (k is None and p is None) or generators:
+            if generators:
+                logger.debug_once(
+                    "FlashInfer 0.2.3+ does not support "
+                    "per-request generators. Falling back to "
+                    "PyTorch-native implementation."
+                )
             return self.forward_native(logits, generators, k, p)
         # flashinfer sampling functions expect contiguous logits.
         # In flex_attn/triton_attn fp32 inference, logits can be non-contiguous
@@ -196,6 +199,7 @@ def flashinfer_sample(
             probs, top_k=k, deterministic=True)
     else:
         # Both top-k and top-p.
+        k = k.to(torch.int32)
         next_token_ids = xtorch_ops.top_k_top_p_sampling_from_probs(
             probs, top_k=k, top_p=p, deterministic=True)
 

vllm_kunlun/v1/worker/utils.py

@@ -0,0 +1,344 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from collections import defaultdict
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Optional
+
+import torch
+
+from vllm.attention.backends.abstract import AttentionBackend
+from vllm.config import ModelConfig, SchedulerConfig, VllmConfig
+from vllm.model_executor.models.interfaces import MultiModalEmbeddings
+from vllm.model_executor.models.utils import extract_layer_index
+from vllm.multimodal.cache import processor_only_cache_from_config
+from vllm.multimodal.registry import MultiModalRegistry
+from vllm.platforms import current_platform
+from vllm.v1.attention.backends.utils import AttentionMetadataBuilder
+from vllm.v1.core.encoder_cache_manager import compute_mm_encoder_budget
+from vllm.v1.kv_cache_interface import KVCacheGroupSpec, KVCacheSpec
+
+if TYPE_CHECKING:
+    from vllm.attention.layer import Attention
+
+
+class MultiModalBudget:
+    """Helper class to calculate budget information for multi-modal models."""
+
+    def __init__(
+        self,
+        model_config: ModelConfig,
+        scheduler_config: SchedulerConfig,
+        mm_registry: MultiModalRegistry,
+    ) -> None:
+        super().__init__()
+
+        self.model_config = model_config
+        self.scheduler_config = scheduler_config
+        self.mm_registry = mm_registry
+        self.cache = cache = processor_only_cache_from_config(
+            model_config, mm_registry)
+
+        self.max_model_len = model_config.max_model_len
+        self.max_num_reqs = scheduler_config.max_num_seqs
+
+        self.mm_limits = mm_registry.get_mm_limits_per_prompt(model_config,
+                                                              cache=cache)
+
+        max_tokens_by_modality = mm_registry \
+            .get_max_tokens_per_item_by_nonzero_modality(model_config,
+                                                         cache=cache)
+
+        encoder_compute_budget, encoder_cache_size = compute_mm_encoder_budget(
+            scheduler_config,
+            max_tokens_by_modality,
+        )
+
+        self.encoder_compute_budget = encoder_compute_budget
+        self.encoder_cache_size = encoder_cache_size
+
+        max_items_per_prompt_by_modality = dict[str, int]()
+        max_items_per_batch_by_modality = dict[str, int]()
+
+        for modality, max_tokens in max_tokens_by_modality.items():
+            (
+                max_items_per_prompt,
+                max_items_per_batch,
+            ) = self.get_max_items(modality, max_tokens)
+
+            max_items_per_prompt_by_modality[modality] = max_items_per_prompt
+            max_items_per_batch_by_modality[modality] = max_items_per_batch
+
+        self.max_tokens_by_modality = max_tokens_by_modality
+        self.max_items_per_prompt_by_modality = max_items_per_prompt_by_modality
+        self.max_items_per_batch_by_modality = max_items_per_batch_by_modality
+
+    def get_modality_with_max_tokens(self) -> str:
+        max_tokens_by_modality = self.max_tokens_by_modality
+        modality, _ = max(max_tokens_by_modality.items(), key=lambda x: x[1])
+
+        return modality
+
+    def get_encoder_budget(self) -> int:
+        return min(self.encoder_compute_budget, self.encoder_cache_size)
+
+    def get_max_items(
+        self,
+        modality: str,
+        max_tokens_per_item: int,
+    ) -> tuple[int, int]:
+        if max_tokens_per_item == 0:
+            return 0, 0
+
+        # Check how many items of this modality can be supported by
+        # the encoder budget.
+        encoder_budget = self.get_encoder_budget()
+
+        # TODO: handle encoder-decoder models once we support them.
+        if encoder_budget == 0:
+            return 0, 0
+
+        max_encoder_items_per_batch = encoder_budget // max_tokens_per_item
+
+        # Check how many items of this modality can be supported by
+        # the decoder budget.
+        mm_limit = self.mm_limits[modality]
+
+        max_items_per_prompt = max(
+            1,
+            min(mm_limit, self.max_model_len // max_tokens_per_item),
+        )
+
+        scheduler_config = self.scheduler_config
+        max_num_reqs = self.max_num_reqs
+
+        if not scheduler_config.enable_chunked_prefill:
+            max_num_reqs = min(
+                max_num_reqs,
+                scheduler_config.max_num_batched_tokens // max_tokens_per_item,
+            )
+
+        max_decoder_items_per_batch = max_num_reqs * max_items_per_prompt
+
+        max_items_per_batch = max(
+            1,
+            min(max_encoder_items_per_batch, max_decoder_items_per_batch),
+        )
+
+        return max_items_per_prompt, max_items_per_batch
+
+
+@dataclass
+class AttentionGroup:
+    backend: type[AttentionBackend]
+    # When ubatching is enabled we will have a metadata builder for each ubatch
+    # so that if they use internal persistant buffers for cudagraphs, and they
+    # won't have to worry about conflicting with the other ubatches.
+    metadata_builders: list[AttentionMetadataBuilder]
+    layer_names: list[str]
+    kv_cache_spec: KVCacheSpec
+
+    @staticmethod
+    def create_with_metadata_builders(
+        backend: type[AttentionBackend],
+        layer_names: list[str],
+        kv_cache_spec: KVCacheSpec,
+        vllm_config: VllmConfig,
+        device: torch.device,
+        num_metadata_builders: int = 1,
+    ) -> 'AttentionGroup':
+        metadata_builders = [
+            backend.get_builder_cls()(kv_cache_spec, layer_names, vllm_config,
+                                      device)
+            for _ in range(num_metadata_builders)
+        ]
+        return AttentionGroup(backend, metadata_builders, layer_names,
+                              kv_cache_spec)
+
+    def get_metadata_builder(self,
+                             ubatch_id: int = 0) -> AttentionMetadataBuilder:
+        assert len(self.metadata_builders) > ubatch_id
+        return self.metadata_builders[ubatch_id]
+
+
+def sanity_check_mm_encoder_outputs(
+    mm_embeddings: MultiModalEmbeddings,
+    expected_num_items: int,
+) -> None:
+    """
+    Perform sanity checks for the result of
+    [`vllm.model_executor.models.SupportsMultiModal.get_multimodal_embeddings`][].
+    """
+    assert isinstance(mm_embeddings, (list, tuple, torch.Tensor)), (
+        "Expected multimodal embeddings to be a list/tuple of 2D tensors, "
+        f"or a single 3D tensor, but got {type(mm_embeddings)} "
+        "instead. This is most likely due to incorrect implementation "
+        "of the model's `get_multimodal_embeddings` method.")
+
+    assert len(mm_embeddings) == expected_num_items, (
+        "Expected number of multimodal embeddings to match number of "
+        f"input items: {expected_num_items}, but got {len(mm_embeddings)=} "
+        "instead. This is most likely due to incorrect implementation "
+        "of the model's `get_multimodal_embeddings` method.")
+
+    assert all(e.ndim == 2 for e in mm_embeddings), (
+        "Expected multimodal embeddings to be a sequence of 2D tensors, "
+        f"but got tensors with shapes {[e.shape for e in mm_embeddings]} "
+        "instead. This is most likely due to incorrect implementation "
+        "of the model's `get_multimodal_embeddings` method.")
+
+
+def scatter_mm_placeholders(
+    embeds: torch.Tensor,
+    is_embed: Optional[torch.Tensor],
+) -> torch.Tensor:
+    """
+    Scatter the multimodal embeddings into a contiguous tensor that represents
+    the placeholder tokens.
+
+    [`vllm.multimodal.processing.PromptUpdateDetails.is_embed`][].
+
+    Args:
+        embeds: The multimodal embeddings.
+            Shape: `(num_embeds, embed_dim)`
+        is_embed: A boolean mask indicating which positions in the placeholder
+            tokens need to be filled with multimodal embeddings.
+            Shape: `(num_placeholders, num_embeds)`
+    """
+    if is_embed is None:
+        return embeds
+
+    placeholders = embeds.new_full(
+        (is_embed.shape[0], embeds.shape[-1]),
+        fill_value=torch.nan,
+    )
+    placeholders[is_embed] = embeds
+    return placeholders
+
+
+def gather_mm_placeholders(
+    placeholders: torch.Tensor,
+    is_embed: Optional[torch.Tensor],
+) -> torch.Tensor:
+    """
+    Reconstructs the embeddings from the placeholder tokens.
+
+    This is the operation of [`scatter_mm_placeholders`]
+    [vllm.v1.worker.utils.scatter_mm_placeholders].
+    """
+    if is_embed is None:
+        return placeholders
+
+    return placeholders[is_embed]
+
+
+def add_kv_sharing_layers_to_kv_cache_groups(
+    shared_kv_cache_layers: dict[str, str],
+    kv_cache_groups: list[KVCacheGroupSpec],
+    runner_only_attn_layers: Optional[set[str]] = None,
+) -> None:
+    """
+    Sets up KV cache sharing by reusing the allocated KV caches in `kv_caches`
+    for layers that do not allocate its own KV cache, based on the mapping in
+    `shared_kv_cache_layers`. Adds these layers to the corresponding KV cache
+    group, which is needed to ensure that attention metadata is assigned later.
+
+    Args:
+        shared_kv_cache_layers: Layer pairings for cross-layer KV sharing.
+            If an Attention layer `layer_name` is in the keys of this dict, it
+            means this layer will perform attention using the keys and values
+            from the KV cache of `shared_kv_cache_layers[layer_name]`.
+        kv_cache_groups: The KV cache groups of the model.
+    """
+    layer_to_kv_cache_group: dict[str, KVCacheGroupSpec] = {}
+    for kv_cache_group in kv_cache_groups:
+        for layer_name in kv_cache_group.layer_names:
+            layer_to_kv_cache_group[layer_name] = kv_cache_group
+
+    for layer_name, target_layer_name in shared_kv_cache_layers.items():
+        tgt_kv_cache_group = layer_to_kv_cache_group[target_layer_name]
+        tgt_kv_cache_group.layer_names.append(layer_name)
+
+        if runner_only_attn_layers is not None:
+            runner_only_attn_layers.add(layer_name)
+
+
+def bind_kv_cache(
+    kv_caches: dict[str, torch.Tensor],
+    forward_context: dict[str, "Attention"],
+    runner_kv_caches: list[torch.Tensor],
+    num_attn_module: Optional[int] = 1,
+) -> None:
+    """
+    Bind the allocated KV cache to both ModelRunner and forward context so
+    that the KV cache can be used in the forward pass.
+
+    This function:
+      1) Fills the ModelRunner's kv cache list (`runner_kv_caches`) with
+         kv_caches.
+      2) Associates each attention layer in the `forward_context` with its
+         corresponding KV cache in kv_caches.
+
+    Args:
+        kv_caches: The allocated kv_caches with layer names as keys.
+        forward_context: The global forward context containing all Attention
+            layers with layer names as keys.
+        runner_kv_caches: The kv_cache declared by ModelRunner.
+    """
+    # Bind kv_caches to ModelRunner
+    assert len(runner_kv_caches) == 0
+
+    # Convert kv_caches dict to a list of tensors in the order of layer_index.
+    index2name = defaultdict(list)
+    for layer_name in kv_caches:
+        index2name[extract_layer_index(layer_name,
+                                       num_attn_module)].append(layer_name)
+
+    for layer_index in sorted(index2name.keys()):
+        layer_names = index2name[layer_index]
+        if len(layer_names) > 1:
+            # One typical case is encoder-decoder model, e.g., bart.
+            # The cross attention and self attention in the same decoder layer
+            # has different layer_name but the same layer_index.
+
+            # TODO - analyze where runner_kv_caches is used and the right
+            # way to ensure it properly reflects multiple attention layers
+            # in the same decoder block.
+            if current_platform.is_kunlun() or current_platform.is_cuda() or current_platform.is_xpu():
+                # We know that the GPU runner is not impacted by this
+                # case. Some test code depends on runner_kv_caches, but
+                # not in a way that's impacted by ignoring this.
+                pass
+            else:
+                raise NotImplementedError
+        layer_name = layer_names[0]
+        runner_kv_caches.append(kv_caches[layer_name])
+
+    # Bind kv_caches to forward context
+    for layer_name, kv_cache in kv_caches.items():
+        # NOTE: Use list because of v0 PP virtual engine.
+        forward_context[layer_name].kv_cache = [kv_cache]
+
+
+def is_residual_scattered_for_sp(vllm_config: VllmConfig,
+                                 num_input_tokens: int) -> bool:
+    """Check if the residual tensor is scattered for sequence parallelism.
+
+    The residual tensor is scattered across tensor parallel ranks when sequence
+    parallelism and tensor parallelism is enabled, and the number of
+    input tokens is one of the compilation sizes.
+    """
+    if not vllm_config.compilation_config.pass_config.\
+        enable_sequence_parallelism:
+        return False
+
+    tp = vllm_config.parallel_config.tensor_parallel_size
+
+    if tp == 1:
+        return False
+
+    # When sequence parallelism is enabled, we always pad num_input_tokens
+    # to be a multiple of tensor_parallel_size (tp) earlier.
+    assert num_input_tokens % tp == 0
+
+    # Currently, SP is only enabled for static size fx graphs.
+    return (num_input_tokens in vllm_config.compilation_config.compile_sizes)