843 lines
30 KiB
Python
843 lines
30 KiB
Python
from __future__ import annotations
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"""
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Support attention backend for flashinfer MLA.
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The flashinfer_mla_disable_ragged flag controls whether to use ragged prefill wrapper and defaults to be false.
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When it's set to false, all wrappers are BatchMLAPaged wrapper.
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When it's set to true, the backend uses BatchRagged and BatchMLAPaged wrapper for prefilling,
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and uses BatchMLAPaged wrapper for decoding.
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More details can be found in https://docs.flashinfer.ai/api/mla.html
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"""
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from dataclasses import dataclass
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from functools import partial
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from typing import TYPE_CHECKING, Callable, Optional, Union
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import torch
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import triton
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from sglang.global_config import global_config
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from sglang.srt.layers.attention.base_attn_backend import AttentionBackend
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from sglang.srt.layers.attention.flashinfer_backend import (
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create_flashinfer_kv_indices_triton,
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)
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from sglang.srt.layers.dp_attention import get_attention_tp_size
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from sglang.srt.managers.schedule_batch import global_server_args_dict
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from sglang.srt.model_executor.forward_batch_info import ForwardBatch, ForwardMode
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from sglang.srt.speculative.eagle_utils import EagleDraftInput, EagleVerifyInput
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from sglang.srt.utils import is_flashinfer_available
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if TYPE_CHECKING:
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from sglang.srt.layers.radix_attention import RadixAttention
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from sglang.srt.model_executor.model_runner import ModelRunner
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from sglang.srt.speculative.spec_info import SpecInfo
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if is_flashinfer_available():
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from flashinfer import (
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BatchMLAPagedAttentionWrapper,
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BatchPrefillWithRaggedKVCacheWrapper,
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)
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@dataclass
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class DecodeMetadata:
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decode_wrapper: BatchMLAPagedAttentionWrapper
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@dataclass
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class PrefillMetadata:
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prefill_wrapper: BatchMLAPagedAttentionWrapper
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use_ragged: bool
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# Reuse this workspace buffer across all flashinfer wrappers
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global_workspace_buffer = None
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class FlashInferMLAAttnBackend(AttentionBackend):
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"""Flashinfer attention kernels."""
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def __init__(
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self,
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model_runner: ModelRunner,
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skip_prefill: bool = False,
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kv_indptr_buf: Optional[torch.Tensor] = None,
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q_indptr_decode_buf: Optional[torch.Tensor] = None,
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):
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super().__init__()
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# Parse constants
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self.max_context_len = model_runner.model_config.context_len
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self.device = model_runner.device
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self.skip_prefill = skip_prefill
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global_config.enable_flashinfer_mla = True
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# Allocate buffers
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global global_workspace_buffer
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if global_workspace_buffer is None:
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global_workspace_buffer = torch.empty(
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global_config.flashinfer_workspace_size,
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dtype=torch.uint8,
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device=model_runner.device,
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)
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self.workspace_buffer = global_workspace_buffer
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max_bs = model_runner.req_to_token_pool.size
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if kv_indptr_buf is None:
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self.kv_indptr = torch.zeros(
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(max_bs + 1,), dtype=torch.int32, device=model_runner.device
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)
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else:
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self.kv_indptr = kv_indptr_buf
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if not self.skip_prefill:
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self.qo_indptr = torch.zeros(
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(max_bs + 1,), dtype=torch.int32, device=model_runner.device
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)
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if q_indptr_decode_buf is None:
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self.q_indptr_decode = torch.arange(
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0, max_bs + 1, dtype=torch.int32, device=model_runner.device
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)
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else:
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self.q_indptr_decode = q_indptr_decode_buf
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self.prefill_wrapper_ragged = BatchPrefillWithRaggedKVCacheWrapper(
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self.workspace_buffer, "NHD"
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)
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if not self.skip_prefill:
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self.prefill_wrapper_paged = BatchMLAPagedAttentionWrapper(
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self.workspace_buffer,
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backend="auto",
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)
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# FlashinferMLA backend uses mla wrapper for target verify
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self.prefill_wrapper_verify = BatchMLAPagedAttentionWrapper(
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self.workspace_buffer,
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backend="auto",
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)
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self.decode_wrapper = BatchMLAPagedAttentionWrapper(
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self.workspace_buffer, backend="auto"
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)
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# Create indices updater
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if not skip_prefill:
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self.indices_updater_prefill = FlashInferMLAIndicesUpdaterPrefill(
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model_runner, self
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)
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self.indices_updater_decode = FlashInferMLAIndicesUpdaterDecode(
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model_runner, self
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)
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# Other metadata
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self.forward_metadata: Union[PrefillMetadata, DecodeMetadata] = None
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self.decode_cuda_graph_metadata = {}
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self.prefill_cuda_graph_metadata = {} # For verify
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def init_forward_metadata(self, forward_batch: ForwardBatch):
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if forward_batch.forward_mode.is_decode_or_idle():
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self.indices_updater_decode.update(
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forward_batch.req_pool_indices,
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forward_batch.seq_lens,
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forward_batch.seq_lens_sum,
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decode_wrapper=self.decode_wrapper,
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init_metadata_replay=False,
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)
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self.forward_metadata = DecodeMetadata(self.decode_wrapper)
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elif forward_batch.forward_mode.is_draft_extend():
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self.indices_updater_prefill.update(
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forward_batch.req_pool_indices,
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forward_batch.seq_lens,
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forward_batch.seq_lens_sum,
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prefix_lens=None,
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prefill_wrapper_paged=self.prefill_wrapper_paged,
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use_ragged=False,
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spec_info=forward_batch.spec_info,
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)
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self.forward_metadata = PrefillMetadata(self.prefill_wrapper_paged, False)
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elif forward_batch.forward_mode.is_target_verify():
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self.indices_updater_prefill.update(
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forward_batch.req_pool_indices,
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forward_batch.seq_lens,
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forward_batch.seq_lens_sum,
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prefix_lens=None,
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prefill_wrapper_paged=self.prefill_wrapper_verify,
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use_ragged=False,
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spec_info=forward_batch.spec_info,
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)
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self.forward_metadata = PrefillMetadata(self.prefill_wrapper_verify, False)
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else:
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prefix_lens = forward_batch.extend_prefix_lens
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extend_no_prefix = not any(forward_batch.extend_prefix_lens_cpu)
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use_ragged = (
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not global_server_args_dict["flashinfer_mla_disable_ragged"]
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and extend_no_prefix
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)
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self.indices_updater_prefill.update(
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forward_batch.req_pool_indices,
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forward_batch.seq_lens,
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forward_batch.seq_lens_sum,
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prefix_lens,
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prefill_wrapper_paged=self.prefill_wrapper_paged,
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use_ragged=use_ragged,
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)
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self.forward_metadata = PrefillMetadata(
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self.prefill_wrapper_paged, use_ragged
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)
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def init_cuda_graph_state(
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self, max_bs: int, kv_indices_buf: Optional[torch.Tensor] = None
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):
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if kv_indices_buf is None:
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cuda_graph_kv_indices = torch.zeros(
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(max_bs * self.max_context_len,),
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dtype=torch.int32,
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device="cuda",
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)
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else:
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cuda_graph_kv_indices = kv_indices_buf
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self.cuda_graph_kv_indices = cuda_graph_kv_indices
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self.cuda_graph_qo_indptr = self.q_indptr_decode.clone()
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self.cuda_graph_kv_indptr = self.kv_indptr.clone()
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self.cuda_graph_kv_lens = torch.ones(
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(max_bs,), dtype=torch.int32, device=self.device
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)
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# For fast decode plan in graph replaying
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self.cuda_graph_qo_indptr_cpu = self.cuda_graph_qo_indptr.to("cpu")
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self.cuda_graph_kv_indptr_cpu = self.cuda_graph_kv_indptr.to("cpu")
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self.fast_decode_kwargs = {
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"qo_indptr_cpu": self.cuda_graph_qo_indptr_cpu,
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"kv_indptr_cpu": self.cuda_graph_kv_indptr_cpu,
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"kv_indices": self.cuda_graph_kv_indices,
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}
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def init_forward_metadata_capture_cuda_graph(
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self,
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bs: int,
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num_tokens: int,
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req_pool_indices: torch.Tensor,
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seq_lens: torch.Tensor,
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encoder_lens: Optional[torch.Tensor],
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forward_mode: ForwardMode,
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spec_info: Optional[SpecInfo],
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):
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if forward_mode.is_decode_or_idle():
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decode_wrapper = BatchMLAPagedAttentionWrapper(
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self.workspace_buffer,
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use_cuda_graph=True,
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qo_indptr=self.cuda_graph_qo_indptr[: num_tokens + 1],
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kv_indptr=self.cuda_graph_kv_indptr[: num_tokens + 1],
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kv_indices=self.cuda_graph_kv_indices,
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kv_len_arr=self.cuda_graph_kv_lens[:num_tokens],
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backend="auto",
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)
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seq_lens_sum = seq_lens.sum().item()
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self.indices_updater_decode.update(
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req_pool_indices,
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seq_lens,
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seq_lens_sum,
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decode_wrapper=decode_wrapper,
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init_metadata_replay=False,
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spec_info=spec_info,
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)
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self.decode_cuda_graph_metadata[bs] = decode_wrapper
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self.forward_metadata = DecodeMetadata(decode_wrapper)
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decode_wrapper.plan = partial(fast_mla_decode_plan, decode_wrapper)
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elif forward_mode.is_target_verify():
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verify_wrapper = BatchMLAPagedAttentionWrapper(
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self.workspace_buffer,
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use_cuda_graph=True,
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qo_indptr=self.cuda_graph_qo_indptr[: bs + 1],
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kv_indptr=self.cuda_graph_kv_indptr[: bs + 1],
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kv_indices=self.cuda_graph_kv_indices,
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kv_len_arr=self.cuda_graph_kv_lens[:bs],
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backend="auto",
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)
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seq_lens_sum = seq_lens.sum().item()
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self.indices_updater_prefill.update(
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req_pool_indices,
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seq_lens,
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seq_lens_sum,
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prefix_lens=None,
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prefill_wrapper_paged=verify_wrapper,
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use_ragged=False,
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spec_info=spec_info,
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)
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self.prefill_cuda_graph_metadata[bs] = verify_wrapper
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self.forward_metadata = PrefillMetadata(verify_wrapper, False)
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else:
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raise ValueError(f"Invalid mode: {forward_mode=}")
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def init_forward_metadata_replay_cuda_graph(
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self,
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bs: int,
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req_pool_indices: torch.Tensor,
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seq_lens: torch.Tensor,
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seq_lens_sum: int,
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encoder_lens: Optional[torch.Tensor],
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forward_mode: ForwardMode,
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spec_info: Optional[SpecInfo],
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seq_lens_cpu: Optional[torch.Tensor],
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):
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if forward_mode.is_decode_or_idle():
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assert seq_lens_cpu is not None
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kv_len_arr_cpu = seq_lens_cpu[:bs]
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self.cuda_graph_kv_indptr_cpu[1 : bs + 1] = torch.cumsum(
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kv_len_arr_cpu, dim=0
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)
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self.fast_decode_kwargs.update(
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{
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"qo_indptr_cpu": self.cuda_graph_qo_indptr_cpu[: bs + 1],
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"kv_indptr_cpu": self.cuda_graph_kv_indptr_cpu[: bs + 1],
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"kv_len_arr_cpu": kv_len_arr_cpu,
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}
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)
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self.indices_updater_decode.update(
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req_pool_indices[:bs],
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seq_lens[:bs],
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seq_lens_sum,
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decode_wrapper=self.decode_cuda_graph_metadata[bs],
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init_metadata_replay=True,
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spec_info=spec_info,
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**self.fast_decode_kwargs,
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)
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elif forward_mode.is_target_verify():
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self.indices_updater_prefill.update(
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req_pool_indices[:bs],
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seq_lens[:bs],
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seq_lens_sum,
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prefix_lens=None,
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prefill_wrapper_paged=self.prefill_cuda_graph_metadata[bs],
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use_ragged=False,
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spec_info=spec_info,
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)
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else:
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raise ValueError(f"Invalid forward mode: {forward_mode=}")
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def get_cuda_graph_seq_len_fill_value(self):
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return 0
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def forward_extend(
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self,
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q: torch.Tensor,
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k: torch.Tensor,
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v: torch.Tensor,
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layer: RadixAttention,
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forward_batch: ForwardBatch,
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save_kv_cache: bool = True,
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):
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cache_loc = forward_batch.out_cache_loc
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logits_soft_cap = layer.logit_cap
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prefill_wrapper_paged = self.forward_metadata.prefill_wrapper
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qall = q.view(-1, layer.tp_q_head_num, layer.head_dim)
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k_buf = forward_batch.token_to_kv_pool.get_key_buffer(layer.layer_id)
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# Save kv cache
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if save_kv_cache and k is not None:
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assert v is not None
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if save_kv_cache:
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forward_batch.token_to_kv_pool.set_kv_buffer(layer, cache_loc, k, v)
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if self.forward_metadata.use_ragged:
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# ragged prefill
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o, _ = self.prefill_wrapper_ragged.forward_return_lse(
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qall,
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k.view(-1, layer.tp_k_head_num, layer.head_dim),
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v.view(-1, layer.tp_k_head_num, layer.v_head_dim),
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causal=True,
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sm_scale=layer.scaling,
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logits_soft_cap=logits_soft_cap,
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)
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else:
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# mla paged prefill
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o = prefill_wrapper_paged.run(
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qall[:, :, : layer.v_head_dim],
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qall[:, :, layer.v_head_dim :],
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k_buf[:, :, : layer.v_head_dim],
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k_buf[:, :, layer.v_head_dim :],
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)
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return o.view(-1, layer.tp_q_head_num * layer.v_head_dim)
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def forward_decode(
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self,
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q: torch.Tensor,
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k: torch.Tensor,
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v: torch.Tensor,
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layer: RadixAttention,
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forward_batch: ForwardBatch,
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save_kv_cache: bool = True,
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):
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decode_wrapper = self.forward_metadata.decode_wrapper
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cache_loc = forward_batch.out_cache_loc
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if k is not None:
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assert v is not None
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if save_kv_cache:
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forward_batch.token_to_kv_pool.set_kv_buffer(
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layer,
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cache_loc,
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k,
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v,
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)
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reshaped_q = q.view(-1, layer.tp_q_head_num, layer.head_dim)
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k_buffer = forward_batch.token_to_kv_pool.get_key_buffer(layer.layer_id)
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reshaped_k = k_buffer.view(-1, 1, layer.head_dim)
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o = decode_wrapper.run(
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reshaped_q[:, :, : layer.v_head_dim],
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reshaped_q[:, :, layer.v_head_dim :],
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reshaped_k[:, :, : layer.v_head_dim],
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reshaped_k[:, :, layer.v_head_dim :],
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)
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return o.view(-1, layer.tp_q_head_num * layer.v_head_dim)
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class FlashInferMLAIndicesUpdaterDecode:
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def __init__(self, model_runner: ModelRunner, attn_backend: AttentionBackend):
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# Parse Constants
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self.num_local_heads = (
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model_runner.model_config.num_attention_heads // get_attention_tp_size()
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)
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self.kv_lora_rank = model_runner.model_config.kv_lora_rank
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self.qk_nope_head_dim = model_runner.model_config.qk_nope_head_dim
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self.qk_rope_head_dim = model_runner.model_config.qk_rope_head_dim
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self.scaling = model_runner.model_config.scaling
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self.data_type = model_runner.kv_cache_dtype
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self.attn_backend = attn_backend
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# Buffers and wrappers
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self.kv_indptr = attn_backend.kv_indptr
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self.req_to_token = model_runner.req_to_token_pool.req_to_token
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self.q_indptr = attn_backend.q_indptr_decode
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def update(
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self,
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req_pool_indices: torch.Tensor,
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seq_lens: torch.Tensor,
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seq_lens_sum: int,
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decode_wrapper: BatchMLAPagedAttentionWrapper,
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init_metadata_replay: bool = False,
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spec_info: Optional[Union[EagleDraftInput, EagleVerifyInput]] = None,
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**fast_decode_kwargs,
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):
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decode_wrapper = decode_wrapper or self.decode_wrapper
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self.call_begin_forward(
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decode_wrapper,
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req_pool_indices,
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seq_lens,
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seq_lens_sum,
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self.q_indptr,
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self.kv_indptr,
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init_metadata_replay,
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spec_info,
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**fast_decode_kwargs,
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)
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def call_begin_forward(
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self,
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wrapper: BatchMLAPagedAttentionWrapper,
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req_pool_indices: torch.Tensor,
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paged_kernel_lens: torch.Tensor,
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paged_kernel_lens_sum: int,
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q_indptr: torch.Tensor,
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kv_indptr: torch.Tensor,
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init_metadata_replay: bool = False,
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spec_info: Optional[Union[EagleDraftInput, EagleVerifyInput]] = None,
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**fast_decode_kwargs,
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):
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bs = len(req_pool_indices)
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q_indptr = q_indptr[: bs + 1]
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kv_lens = paged_kernel_lens.to(torch.int32)
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sm_scale = self.scaling
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if spec_info is None:
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kv_indptr[1 : bs + 1] = torch.cumsum(paged_kernel_lens, dim=0)
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kv_indptr = kv_indptr[: bs + 1]
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kv_indices = (
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torch.empty(paged_kernel_lens_sum, dtype=torch.int32, device="cuda")
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if not init_metadata_replay
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else fast_decode_kwargs["kv_indices"]
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)
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create_flashinfer_kv_indices_triton[(bs,)](
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self.req_to_token,
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|
req_pool_indices,
|
|
paged_kernel_lens,
|
|
kv_indptr,
|
|
None,
|
|
kv_indices,
|
|
self.req_to_token.shape[1],
|
|
)
|
|
else:
|
|
kv_indptr, kv_indices = spec_info.kv_indptr, spec_info.kv_indices
|
|
|
|
if not init_metadata_replay:
|
|
wrapper.plan(
|
|
q_indptr,
|
|
kv_indptr,
|
|
kv_indices,
|
|
kv_lens,
|
|
self.num_local_heads,
|
|
self.kv_lora_rank,
|
|
self.qk_rope_head_dim,
|
|
1,
|
|
False,
|
|
sm_scale,
|
|
self.data_type,
|
|
self.data_type,
|
|
)
|
|
else:
|
|
wrapper.plan(
|
|
fast_decode_kwargs["qo_indptr_cpu"],
|
|
fast_decode_kwargs["kv_indptr_cpu"],
|
|
kv_indices,
|
|
fast_decode_kwargs["kv_len_arr_cpu"],
|
|
self.num_local_heads,
|
|
self.kv_lora_rank,
|
|
self.qk_rope_head_dim,
|
|
1,
|
|
False,
|
|
sm_scale,
|
|
self.data_type,
|
|
self.data_type,
|
|
)
|
|
|
|
|
|
class FlashInferMLAIndicesUpdaterPrefill:
|
|
def __init__(self, model_runner: ModelRunner, attn_backend: AttentionBackend):
|
|
# Parse Constants
|
|
self.num_local_heads = (
|
|
model_runner.model_config.num_attention_heads // get_attention_tp_size()
|
|
)
|
|
self.kv_lora_rank = model_runner.model_config.kv_lora_rank
|
|
self.qk_nope_head_dim = model_runner.model_config.qk_nope_head_dim
|
|
self.qk_rope_head_dim = model_runner.model_config.qk_rope_head_dim
|
|
self.v_head_dim = model_runner.model_config.v_head_dim
|
|
self.scaling = model_runner.model_config.scaling
|
|
self.data_type = model_runner.kv_cache_dtype
|
|
self.q_data_type = model_runner.dtype
|
|
self.attn_backend = attn_backend
|
|
|
|
# Buffers and wrappers
|
|
self.kv_indptr = attn_backend.kv_indptr
|
|
self.qo_indptr = attn_backend.qo_indptr
|
|
self.req_to_token = model_runner.req_to_token_pool.req_to_token
|
|
self.prefill_wrapper_ragged = attn_backend.prefill_wrapper_ragged
|
|
|
|
def update(
|
|
self,
|
|
req_pool_indices: torch.Tnesor,
|
|
seq_lens: torch.Tensor,
|
|
seq_lens_sum: int,
|
|
prefix_lens: torch.Tensor,
|
|
prefill_wrapper_paged: BatchMLAPagedAttentionWrapper,
|
|
use_ragged: bool,
|
|
spec_info: Optional[Union[EagleDraftInput, EagleVerifyInput]] = None,
|
|
):
|
|
if use_ragged:
|
|
paged_kernel_lens = prefix_lens
|
|
paged_kernel_lens_sum = paged_kernel_lens.sum().item()
|
|
else:
|
|
paged_kernel_lens = seq_lens
|
|
paged_kernel_lens_sum = seq_lens_sum
|
|
|
|
self.call_begin_forward(
|
|
self.prefill_wrapper_ragged,
|
|
prefill_wrapper_paged,
|
|
req_pool_indices,
|
|
paged_kernel_lens,
|
|
paged_kernel_lens_sum,
|
|
seq_lens,
|
|
prefix_lens,
|
|
self.kv_indptr,
|
|
self.qo_indptr,
|
|
use_ragged,
|
|
spec_info,
|
|
)
|
|
|
|
def call_begin_forward(
|
|
self,
|
|
wrapper_ragged: BatchPrefillWithRaggedKVCacheWrapper,
|
|
wrapper_paged: BatchMLAPagedAttentionWrapper,
|
|
req_pool_indices: torch.Tensor,
|
|
paged_kernel_lens: torch.Tensor,
|
|
paged_kernel_lens_sum: int,
|
|
seq_lens: torch.Tensor,
|
|
prefix_lens: torch.Tensor,
|
|
kv_indptr: torch.Tensor,
|
|
qo_indptr: torch.Tensor,
|
|
use_ragged: bool,
|
|
spec_info: Optional[Union[EagleDraftInput, EagleVerifyInput]] = None,
|
|
):
|
|
bs = len(seq_lens)
|
|
sm_scale = self.scaling
|
|
|
|
if spec_info is None:
|
|
assert len(seq_lens) == len(req_pool_indices)
|
|
kv_indptr[1 : bs + 1] = torch.cumsum(paged_kernel_lens, dim=0)
|
|
kv_indptr = kv_indptr[: bs + 1]
|
|
kv_indices = torch.empty(
|
|
paged_kernel_lens_sum,
|
|
dtype=torch.int32,
|
|
device=req_pool_indices.device,
|
|
)
|
|
create_flashinfer_kv_indices_triton[(bs,)](
|
|
self.req_to_token,
|
|
req_pool_indices,
|
|
paged_kernel_lens,
|
|
kv_indptr,
|
|
None,
|
|
kv_indices,
|
|
self.req_to_token.shape[1],
|
|
)
|
|
qo_indptr[1 : bs + 1] = torch.cumsum(seq_lens - prefix_lens, dim=0)
|
|
qo_indptr = qo_indptr[: bs + 1]
|
|
custom_mask = None
|
|
else:
|
|
assert isinstance(spec_info, EagleDraftInput) or isinstance(
|
|
spec_info, EagleVerifyInput
|
|
)
|
|
# TODO: Support topk > 1 with custom mask
|
|
kv_indices, kv_indptr, qo_indptr, custom_mask = (
|
|
spec_info.generate_attn_arg_prefill(
|
|
req_pool_indices,
|
|
paged_kernel_lens,
|
|
paged_kernel_lens_sum,
|
|
self.req_to_token,
|
|
)
|
|
)
|
|
|
|
if use_ragged:
|
|
# ragged prefill
|
|
wrapper_ragged.begin_forward(
|
|
qo_indptr=qo_indptr,
|
|
kv_indptr=qo_indptr,
|
|
num_qo_heads=self.num_local_heads,
|
|
num_kv_heads=self.num_local_heads,
|
|
head_dim_qk=self.qk_nope_head_dim + self.qk_rope_head_dim,
|
|
head_dim_vo=self.v_head_dim,
|
|
q_data_type=self.q_data_type,
|
|
)
|
|
else:
|
|
# mla paged prefill
|
|
kv_len_arr = kv_indptr[1:] - kv_indptr[:-1]
|
|
wrapper_paged.plan(
|
|
qo_indptr,
|
|
kv_indptr,
|
|
kv_indices,
|
|
kv_len_arr,
|
|
self.num_local_heads,
|
|
self.kv_lora_rank,
|
|
self.qk_rope_head_dim,
|
|
1,
|
|
True,
|
|
sm_scale,
|
|
self.q_data_type,
|
|
self.data_type,
|
|
)
|
|
|
|
|
|
class FlashInferMLAMultiStepDraftBackend:
|
|
"""
|
|
Wrap multiple flashinfer mla attention backends as one for multiple consecutive
|
|
draft decoding steps.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
model_runner: ModelRunner,
|
|
topk: int,
|
|
speculative_num_steps: int,
|
|
):
|
|
from sglang.srt.speculative.eagle_utils import generate_draft_decode_kv_indices
|
|
|
|
if topk > 1:
|
|
raise ValueError(
|
|
f"Currently Flashinfer MLA only supports topk=1 for speculative decoding"
|
|
)
|
|
self.topk = topk
|
|
self.speculative_num_steps = speculative_num_steps
|
|
self.generate_draft_decode_kv_indices = generate_draft_decode_kv_indices
|
|
|
|
max_bs = model_runner.req_to_token_pool.size * self.topk
|
|
self.kv_indptr = torch.zeros(
|
|
(
|
|
self.speculative_num_steps,
|
|
max_bs + 1,
|
|
),
|
|
dtype=torch.int32,
|
|
device=model_runner.device,
|
|
)
|
|
self.q_indptr_decode = torch.arange(
|
|
0, max_bs + 1, dtype=torch.int32, device=model_runner.device
|
|
)
|
|
|
|
self.attn_backends = []
|
|
for i in range(self.speculative_num_steps):
|
|
self.attn_backends.append(
|
|
FlashInferMLAAttnBackend(
|
|
model_runner,
|
|
skip_prefill=True,
|
|
kv_indptr_buf=self.kv_indptr[i],
|
|
q_indptr_decode_buf=self.q_indptr_decode,
|
|
)
|
|
)
|
|
|
|
self.max_context_len = self.attn_backends[0].max_context_len
|
|
|
|
# Cached variables for generate_draft_decode_kv_indices
|
|
self.pool_len = model_runner.req_to_token_pool.req_to_token.shape[1]
|
|
|
|
def common_template(
|
|
self,
|
|
forward_batch: ForwardBatch,
|
|
kv_indices_buffer: torch.Tensor,
|
|
call_fn: Callable,
|
|
):
|
|
num_seqs = forward_batch.batch_size
|
|
bs = self.topk * num_seqs
|
|
seq_lens_sum = forward_batch.seq_lens_sum
|
|
|
|
self.generate_draft_decode_kv_indices[
|
|
(self.speculative_num_steps, num_seqs, self.topk)
|
|
](
|
|
forward_batch.req_pool_indices,
|
|
forward_batch.req_to_token_pool.req_to_token,
|
|
forward_batch.seq_lens,
|
|
kv_indices_buffer,
|
|
self.kv_indptr,
|
|
forward_batch.positions,
|
|
num_seqs,
|
|
self.topk,
|
|
self.pool_len,
|
|
kv_indices_buffer.shape[1],
|
|
self.kv_indptr.shape[1],
|
|
triton.next_power_of_2(num_seqs),
|
|
triton.next_power_of_2(self.speculative_num_steps),
|
|
triton.next_power_of_2(bs),
|
|
)
|
|
|
|
assert forward_batch.spec_info is not None
|
|
assert isinstance(forward_batch.spec_info, EagleDraftInput)
|
|
|
|
for i in range(self.speculative_num_steps - 1):
|
|
forward_batch.spec_info.kv_indptr = self.kv_indptr[i, : bs + 1]
|
|
forward_batch.spec_info.kv_indices = kv_indices_buffer[i][
|
|
: seq_lens_sum * self.topk + bs * (i + 1)
|
|
]
|
|
call_fn(i, forward_batch)
|
|
|
|
def init_forward_metadata(self, forward_batch: ForwardBatch):
|
|
kv_indices = torch.zeros(
|
|
(
|
|
self.speculative_num_steps,
|
|
forward_batch.batch_size * self.topk * self.max_context_len,
|
|
),
|
|
dtype=torch.int32,
|
|
device="cuda",
|
|
)
|
|
|
|
def call_fn(i, forward_batch):
|
|
assert forward_batch.spec_info is not None
|
|
assert isinstance(forward_batch.spec_info, EagleDraftInput)
|
|
forward_batch.spec_info.kv_indptr = (
|
|
forward_batch.spec_info.kv_indptr.clone()
|
|
)
|
|
forward_batch.spec_info.kv_indices = (
|
|
forward_batch.spec_info.kv_indices.clone()
|
|
)
|
|
self.attn_backends[i].init_forward_metadata(forward_batch)
|
|
|
|
self.common_template(forward_batch, kv_indices, call_fn)
|
|
|
|
def init_cuda_graph_state(self, max_bs: int):
|
|
self.cuda_graph_kv_indices = torch.zeros(
|
|
(self.speculative_num_steps, max_bs * self.max_context_len),
|
|
dtype=torch.int32,
|
|
device="cuda",
|
|
)
|
|
|
|
for i in range(self.speculative_num_steps):
|
|
self.attn_backends[i].init_cuda_graph_state(
|
|
max_bs, kv_indices_buf=self.cuda_graph_kv_indices[i]
|
|
)
|
|
|
|
def init_forward_metadata_capture_cuda_graph(self, forward_batch: ForwardBatch):
|
|
def call_fn(i, forward_batch):
|
|
self.attn_backends[i].init_forward_metadata_capture_cuda_graph(
|
|
forward_batch.batch_size,
|
|
forward_batch.batch_size * self.topk,
|
|
forward_batch.req_pool_indices,
|
|
forward_batch.seq_lens,
|
|
encoder_lens=None,
|
|
forward_mode=ForwardMode.DECODE,
|
|
spec_info=forward_batch.spec_info,
|
|
)
|
|
|
|
self.common_template(forward_batch, self.cuda_graph_kv_indices, call_fn)
|
|
|
|
def init_forward_metadata_replay_cuda_graph(
|
|
self, forward_batch: ForwardBatch, bs: int
|
|
):
|
|
def call_fn(i, forward_batch):
|
|
self.attn_backends[i].init_forward_metadata_replay_cuda_graph(
|
|
bs,
|
|
forward_batch.req_pool_indices,
|
|
forward_batch.seq_lens,
|
|
seq_lens_sum=-1,
|
|
encoder_lens=None,
|
|
forward_mode=ForwardMode.DECODE,
|
|
spec_info=forward_batch.spec_info,
|
|
seq_lens_cpu=forward_batch.decode_seq_lens_cpu,
|
|
)
|
|
|
|
self.common_template(forward_batch, self.cuda_graph_kv_indices, call_fn)
|
|
|
|
|
|
def fast_mla_decode_plan(
|
|
self,
|
|
qo_indptr_cpu: torch.Tensor,
|
|
kv_indptr_cpu: torch.Tensor,
|
|
kv_indices: torch.Tensor,
|
|
kv_len_arr_cpu: torch.Tensor,
|
|
num_heads: int,
|
|
head_dim_ckv: int,
|
|
head_dim_kpe: int,
|
|
page_size: int,
|
|
causal: bool,
|
|
sm_scale: float,
|
|
q_data_type: torch.dtype,
|
|
kv_data_type: torch.dtype,
|
|
) -> None:
|
|
"""A faster version of BatchMLAPagedAttentionWrapper::plan,
|
|
for skipping the stream synchronization in original plan function during
|
|
cuda graph replaying.
|
|
"""
|
|
self._causal = causal
|
|
self._page_size = page_size
|
|
self._sm_scale = sm_scale
|
|
|
|
with self.device as device:
|
|
stream = torch.cuda.current_stream(device).cuda_stream
|
|
self._cached_module.plan(
|
|
self._float_workspace_buffer,
|
|
self._int_workspace_buffer,
|
|
self._pin_memory_int_workspace_buffer,
|
|
qo_indptr_cpu,
|
|
kv_indptr_cpu,
|
|
kv_len_arr_cpu,
|
|
num_heads,
|
|
head_dim_ckv,
|
|
causal,
|
|
stream,
|
|
)
|