ee0f50e68f
* [Feature] support deepseek v3/r1/v3.2 * fix gpt_oss * update readme * update readme --------- Co-authored-by: hanhaowen <hanhaowen@baidu.com>
753 lines
30 KiB
Python
753 lines
30 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import math
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from dataclasses import dataclass
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from typing import TYPE_CHECKING, ClassVar, Optional
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import numpy as np
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import torch
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from vllm.attention.backends.abstract import (AttentionBackend, AttentionLayer,
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AttentionMetadata)
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from vllm.attention.backends.utils import get_mla_dims
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from vllm_kunlun.ops.attention.flashmla import (flash_mla_sparse_prefill,
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flash_mla_with_kvcache,
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get_mla_metadata,
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kunlun_flash_mla_with_kvcache)
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from vllm.config import VllmConfig
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from vllm.logger import init_logger
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from vllm.platforms import current_platform
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from vllm.triton_utils import tl, triton
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from vllm.utils import cdiv
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from vllm.v1.attention.backends.mla.common import MLACommonBaseImpl
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from vllm.v1.attention.backends.utils import (AttentionCGSupport,
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AttentionMetadataBuilder,
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CommonAttentionMetadata,
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reshape_attn_output_for_spec_decode,
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reshape_query_for_spec_decode,
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split_decodes_and_prefills)
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from vllm.v1.kv_cache_interface import AttentionSpec
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from vllm.distributed import get_tp_group
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if TYPE_CHECKING:
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from vllm.model_executor.models.deepseek_v2 import Indexer
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logger = init_logger(__name__)
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"""
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NOTE: FlashMLA Sparse uses an fp8 cache with the following format
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In the "FP8 with scale" format, each token's KV cache is 656 Bytes,
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structured as:
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- **First 512 bytes:** The "quantized NoPE" part, containing 512
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`float8_e4m3` values.
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- **Next 16 bytes:** Scale factors, containing 4 `float32` values.
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The first `float32` is the scale for the first 128 `float8_e4m3` values,
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the second for the next 128, and so on.
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- **Last 128 bytes:** The "RoPE" part, containing 64 `bfloat16` values. This
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part is not quantized for accuracy.
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"""
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def _lse2_to_lse(lse_base2: torch.Tensor) -> torch.Tensor:
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# Convert base-2 LSE to natural-log LSE
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# Keep FP32 for numerical stability during the merge.
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return (lse_base2.to(torch.float32) * math.log(2.0))
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class FlashMLASparseBackend(AttentionBackend):
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accept_output_buffer: bool = True
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@staticmethod
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def get_name() -> str:
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return "FLASHMLA_SPARSE_VLLM_V1"
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@staticmethod
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def get_metadata_cls() -> type[AttentionMetadata]:
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return FlashMLASparseMetadata
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@staticmethod
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def get_builder_cls() -> type["FlashMLASparseMetadataBuilder"]:
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return FlashMLASparseMetadataBuilder
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@staticmethod
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def get_impl_cls() -> type["FlashMLASparseImpl"]:
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return FlashMLASparseImpl
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@staticmethod
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def get_kv_cache_shape(
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num_blocks: int,
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block_size: int,
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num_kv_heads: int, # assumed to be 1 for MLA
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head_size: int,
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cache_dtype_str: str = "auto",
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) -> tuple[int, ...]:
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if cache_dtype_str == "fp8_ds_mla":
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# custom storage fromat is 656 bytes
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# see FlashMLA readme.md for details
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return (num_blocks, block_size, 656)
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else:
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return (num_blocks, block_size, head_size)
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@classmethod
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def get_supported_dtypes(cls) -> list[torch.dtype]:
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return [torch.bfloat16]
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@classmethod
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def get_supported_head_sizes(cls) -> list[int]:
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return [576]
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@dataclass
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class MLASparsePrefillMetadata:
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# NOTE(Chen): not call it "FlashMLASparsePrefillMetadata" because
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# the kernel is not from flashmla
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block_table: torch.Tensor = None
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has_context: bool = False
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context_lens: Optional[torch.Tensor] = None
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# Sequence lengths (context + query) for prefill requests
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# Shape: [num_prefill_reqs]
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seq_lens: torch.Tensor = None
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# Request ID for each token: -1 for decode tokens, request index
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# (0, 1, 2, ...) for prefill tokens.
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# Shape: [num_actual_tokens]
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request_ids: torch.Tensor = None
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query_start_loc: torch.Tensor = None
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query_start_loc_cpu: torch.Tensor = None
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@dataclass
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class FlashMLASparseDecodeAndContextMetadata:
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scheduler_metadata: torch.Tensor = None
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num_splits: torch.Tensor = None
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cache_lens: torch.Tensor = None
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prefill_context_lengths: Optional[torch.Tensor] = None
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prefill_new_k_start_locs: Optional[torch.Tensor] = None
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dummy_block_table: torch.Tensor = None
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seq_lens: torch.Tensor = None
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seq_lens_cpu: torch.Tensor = None
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max_seq_len: int = -1 # needed for reshape in spec decode
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def filter_prefill_indices(
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self, indices: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
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assert self.prefill_context_lengths is not None
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prefill_context_lengths = self.prefill_context_lengths.unsqueeze(-1)
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context_indices = torch.where(indices < prefill_context_lengths,
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indices, -1)
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new_token_indices = torch.where(indices >= prefill_context_lengths,
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indices - prefill_context_lengths, -1)
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return context_indices, new_token_indices
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@dataclass
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class FlashMLASparseMetadata:
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num_reqs: int
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max_query_len: int
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max_seq_len: int
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num_actual_tokens: int # Number of tokens excluding padding.
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query_start_loc: torch.Tensor
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slot_mapping: torch.Tensor
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block_table: torch.Tensor
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req_id_per_token: torch.Tensor
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block_size: int = 64
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topk_tokens: int = 2048
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num_prefills: int = 0
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num_decodes: int = 0
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num_prefill_tokens: int = 0
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num_decode_tokens: int = 0
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decode_metadata: Optional[FlashMLASparseDecodeAndContextMetadata] = None
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prefill_metadata: Optional[MLASparsePrefillMetadata] = None
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@dataclass
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class FP8KernelMetadata:
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scheduler_metadata: Optional[torch.Tensor]
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num_splits: torch.Tensor
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dummy_block_table: torch.Tensor
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cache_lens: torch.Tensor
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fp8_extra_metadata: Optional[FP8KernelMetadata] = None
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@triton.jit
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def _convert_req_index_to_global_index_kernel(
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req_id_ptr, # int32 [num_tokens]
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block_table_ptr, # int32 [num_requests, max_num_blocks_per_req]
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token_indices_ptr, # int32 [num_tokens, NUM_TOPK_TOKENS]
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out_ptr, # int32 [num_tokens, NUM_TOPK_TOKENS]
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# shapes (compile-time where possible)
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max_num_blocks_per_req: tl.constexpr,
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BLOCK_SIZE: tl.constexpr,
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BLOCK_N: tl.constexpr, # tile width along columns
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# strides (in elements)
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bt_stride0,
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bt_stride1,
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ti_stride0,
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ti_stride1,
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out_stride0,
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out_stride1,
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):
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# program_id(0) -> token_id (row)
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# program_id(1) -> tile index along columns
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token_id = tl.program_id(0)
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tile_id = tl.program_id(1)
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# Each program covers BLOCK_N consecutive columns
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indice_id = tile_id * BLOCK_N + tl.arange(0, BLOCK_N)
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# Load request id for this token (no mask: grid is exact)
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req = tl.load(req_id_ptr + token_id)
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# Load token indices for this tile
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ti_ptr = token_indices_ptr + token_id * ti_stride0 + indice_id * ti_stride1
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tok = tl.load(ti_ptr) # int32
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# Only token == -1 should propagate as -1
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is_invalid_tok = tok < 0
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# Compute block id and in-block offset
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block_id = tok // BLOCK_SIZE
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inblock_off = tok % BLOCK_SIZE
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# Guard block_table access
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valid_block = block_id < max_num_blocks_per_req
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bt_ptr = block_table_ptr + req * bt_stride0 + block_id * bt_stride1
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base = tl.load(bt_ptr, mask=valid_block, other=0)
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# If token == -1 OR block_id OOB, output -1; else base * BLOCK_SIZE + offset
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out_val = tl.where(is_invalid_tok | (~valid_block), -1,
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base * BLOCK_SIZE + inblock_off)
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# Store results
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out_ptr_ij = out_ptr + token_id * out_stride0 + indice_id * out_stride1
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tl.store(out_ptr_ij, out_val)
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def triton_convert_req_index_to_global_index(
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req_id: torch.Tensor, # int32 [num_tokens]
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block_table: torch.
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Tensor, # int32 [num_requests, max_num_blocks_per_req]
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token_indices: torch.Tensor, # int32 [num_tokens, NUM_TOPK_TOKENS]
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BLOCK_SIZE: int = 64,
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NUM_TOPK_TOKENS: int = 2048,
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BLOCK_N: int = 128, # tile width along columns
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):
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"""
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out[token_id, indice_id] =
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block_table[req_id[token_id],
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token_indices[token_id, indice_id] // BLOCK_SIZE] * BLOCK_SIZE
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+ token_indices[token_id, indice_id] % BLOCK_SIZE
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Only when token_indices[token_id, indice_id] == -1 do we output -1.
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For safety, we also output -1 if the derived block_id would be
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out-of-bounds.
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"""
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assert req_id.dtype == torch.int32
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assert block_table.dtype == torch.int32
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assert token_indices.dtype == torch.int32
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assert token_indices.shape[1] == NUM_TOPK_TOKENS
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assert NUM_TOPK_TOKENS % BLOCK_N == 0, \
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f"NUM_TOPK_TOKENS ({NUM_TOPK_TOKENS}) must be divisible by" \
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f"BLOCK_N ({BLOCK_N})"
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num_tokens = req_id.shape[0]
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num_requests, max_num_blocks_per_req = block_table.shape
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tiles_per_row = NUM_TOPK_TOKENS // BLOCK_N
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# Ensure contiguous tensors on the same device
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req_id_c = req_id.contiguous()
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block_table_c = block_table.contiguous()
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token_indices_c = token_indices.contiguous()
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out = torch.empty_like(token_indices_c)
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# Strides in elements
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bt_stride0, bt_stride1 = block_table_c.stride()
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ti_stride0, ti_stride1 = token_indices_c.stride()
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out_stride0, out_stride1 = out.stride()
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# Exact 2D grid: tokens × column tiles
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grid = (num_tokens, tiles_per_row)
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_convert_req_index_to_global_index_kernel[grid](
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req_id_c,
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block_table_c,
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token_indices_c,
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out,
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# shapes / constexprs
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max_num_blocks_per_req,
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BLOCK_SIZE,
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BLOCK_N,
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# strides
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bt_stride0,
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bt_stride1,
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ti_stride0,
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ti_stride1,
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out_stride0,
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out_stride1,
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)
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return out
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def kunlun_convert_req_index_to_global_index(
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req_id: torch.Tensor, # int32 [num_tokens]
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block_table: torch.Tensor, # int32 [num_requests, max_num_blocks_per_req]
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token_indices: torch.Tensor, # int32 [num_tokens, NUM_TOPK_TOKENS]
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BLOCK_SIZE: int = 64,
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NUM_TOPK_TOKENS: int = 2048,
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):
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assert req_id.dtype == torch.int32
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assert block_table.dtype == torch.int32
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assert token_indices.dtype == torch.int32
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assert token_indices.shape[1] == NUM_TOPK_TOKENS
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num_tokens = req_id.shape[0]
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num_requests, max_num_blocks_per_req = block_table.shape
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out = torch.zeros_like(token_indices)
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# Compute block_id and inblock_off for all tokens at once
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block_id = token_indices // BLOCK_SIZE
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inblock_off = token_indices % BLOCK_SIZE
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# Create mask for invalid tokens (tok < 0)
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invalid_tok_mask = token_indices < 0
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# Create mask for out-of-bounds block_id
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oob_block_mask = block_id >= max_num_blocks_per_req
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# Combine masks - output -1 for either condition
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invalid_mask = invalid_tok_mask | oob_block_mask
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# Get request IDs expanded to match token_indices shape
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req_ids_expanded = req_id.unsqueeze(1).expand(-1, NUM_TOPK_TOKENS)
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# Gather base addresses from block_table
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# Clamp block_id to avoid index errors (we'll mask these out anyway)
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block_id_clamped = torch.clamp(block_id, 0, max_num_blocks_per_req - 1)
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# Use advanced indexing to get base addresses
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base_addrs = block_table[req_ids_expanded, block_id_clamped]
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# Compute the global indices
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global_indices = base_addrs * BLOCK_SIZE + inblock_off
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# Apply mask: set invalid positions to -1
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out = torch.where(invalid_mask, torch.tensor(-1, dtype=torch.int32, device=token_indices.device), global_indices)
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return out
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def kunlun_concat_and_cache_mla(
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kv_c: torch.Tensor, #[num_tokens, kv_lora_rank]
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k_pe: torch.Tensor, #[num_tokens, pe_dim]
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kv_cache: torch.Tensor, #[num_blocks, block_size, (kv_lora_rank + pe_dim)]
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slot_mapping: torch.Tensor, #[num_tokens] or [num_actual_tokens]
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kv_cache_dtype: str,
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scale: torch.Tensor
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):
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num_tokens = slot_mapping.shape[0]
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kv_lora_rank = kv_c.shape[1]
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pe_dim = k_pe.shape[1]
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block_size = kv_cache.shape[1]
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def kunlun_fp8_ds_mla():
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for token_idx in range(num_tokens):
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slot = slot_mapping[token_idx].item()
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if slot < 0: continue
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block_idx = slot // block_size
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block_offset = slot % block_size
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kv_c_i = kv_c[token_idx].view(4,kv_lora_rank//4).contiguous()
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kv_c_i_int8 = torch.zeros(
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kv_c_i.shape,
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device=kv_c.device,
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dtype=torch.int8,
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)
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kv_c_i_scale = torch.zeros(
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[kv_c_i.shape[0], 1],
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device=kv_c.device,
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dtype=torch.float32,
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)
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torch.ops._C.quant2d(kv_c_i, kv_c_i_int8, kv_c_i_scale, force_sdnn=True)
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kv_c_i_scale /= 127
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kv_cache[block_idx, block_offset, :kv_lora_rank] = kv_c_i_int8.view(-1).view(torch.uint8).contiguous()
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kv_cache[block_idx, block_offset, kv_lora_rank:kv_lora_rank + 16] = kv_c_i_scale.view(-1).view(torch.uint8).contiguous()
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kv_cache[block_idx, block_offset, kv_lora_rank+16:] = k_pe[token_idx, :].view(torch.uint8).contiguous()
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def kunlun_mla():
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for token_idx in range(num_tokens):
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slot = slot_mapping[token_idx].item()
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if slot < 0: continue
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block_idx = slot // block_size
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block_offset = slot % block_size
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kv_cache[block_idx, block_offset, :kv_lora_rank] = kv_c[token_idx, :].contiguous()
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kv_cache[block_idx, block_offset, kv_lora_rank:] = k_pe[token_idx, :].contiguous()
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if (kv_cache_dtype == "fp8_ds_mla"):
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assert kv_lora_rank == 512, "kv_lora_rank must be 512 for fp8_ds_mla"
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assert pe_dim == 64, "pe_dim must be 64 for fp8_ds_mla"
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assert kv_cache.shape[2] == 656 // kv_cache.element_size(), "kv_cache.shape[2] must be 656 bytes for fp8_ds_mla"
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assert kv_c.element_size() == 2, "kv_c.element_size() must be 2 for fp8_ds_mla"
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assert k_pe.element_size() == 2, "k_pe.element_size() must be 2 for fp8_ds_mla"
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kunlun_fp8_ds_mla()
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else:
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assert kv_cache.shape[2] == kv_lora_rank + pe_dim
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kunlun_mla()
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@dataclass
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class FlashMLASparseMetadataBuilder(
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AttentionMetadataBuilder[FlashMLASparseMetadata]):
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cudagraph_support: ClassVar[AttentionCGSupport] = \
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AttentionCGSupport.UNIFORM_BATCH
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def __init__(self, kv_cache_spec: AttentionSpec, layer_names: list[str],
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vllm_config: VllmConfig, device: torch.device):
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self.vllm_config = vllm_config
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self.layer_names = layer_names
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cache_config = vllm_config.cache_config
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self.kv_cache_spec = kv_cache_spec
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self.model_config = vllm_config.model_config
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parallel_config = vllm_config.parallel_config
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self.device = device
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# Treat requests with query length <= 1 as decodes to match the
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# DeepGEMM indexer constraint (fp8_paged_mqa_logits only supports next_n <= 2)
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# 从最新版本vllm中引入的
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self._init_reorder_batch_threshold(1, supports_spec_as_decode=True)
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props = torch.cuda.get_device_properties(device)
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sm_count = props.multi_processor_count
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self.num_heads = self.model_config.get_num_attention_heads(
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parallel_config)
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self.mla_dims = get_mla_dims(self.model_config)
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self.topk_tokens = vllm_config.model_config.hf_config.index_topk
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self.use_fp8_kv_cache = cache_config.cache_dtype == "fp8_ds_mla"
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self.topk_tokens_tensor = torch.tensor([self.topk_tokens],
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device=device,
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dtype=torch.int32)
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# self.max_model_len_tensor = torch.tensor(
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# [self.model_config.max_model_len],
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# device=device,
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# dtype=torch.int32)
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# this is ignored by `flash_mla_with_kvcache` if indices not None
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self.dummy_block_table = torch.empty((1, 1),
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dtype=torch.int32,
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device=self.device)
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# 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
|