1260 lines
45 KiB
Python
1260 lines
45 KiB
Python
from __future__ import annotations
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"""
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Support different attention backends.
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Now there are two backends: FlashInfer and Triton.
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FlashInfer is faster and Triton is easier to customize.
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Each backend supports two operators: extend (i.e. prefill with cached prefix) and decode.
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"""
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import os
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from dataclasses import dataclass
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from enum import Enum, auto
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from functools import partial
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from typing import TYPE_CHECKING, Callable, List, 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.utils import create_flashinfer_kv_indices_triton
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from sglang.srt.layers.dp_attention import get_attention_tp_size
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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 get_bool_env_var, 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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if is_flashinfer_available():
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from flashinfer import (
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BatchDecodeWithPagedKVCacheWrapper,
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BatchPrefillWithPagedKVCacheWrapper,
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BatchPrefillWithRaggedKVCacheWrapper,
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)
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from flashinfer.cascade import merge_state
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from flashinfer.decode import _get_range_buf, get_seq_lens
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class WrapperDispatch(Enum):
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SLIDING_WINDOW = auto()
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CROSS_ATTENTION = auto()
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@dataclass
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class DecodeMetadata:
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decode_wrappers: List[BatchDecodeWithPagedKVCacheWrapper]
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@dataclass
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class PrefillMetadata:
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prefill_wrappers: List[BatchPrefillWithPagedKVCacheWrapper]
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use_ragged: bool
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extend_no_prefix: 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 FlashInferAttnBackend(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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kv_last_page_len_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.decode_use_tensor_cores = should_use_tensor_core(
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kv_cache_dtype=model_runner.kv_cache_dtype,
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num_attention_heads=model_runner.model_config.num_attention_heads
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// get_attention_tp_size(),
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num_kv_heads=model_runner.model_config.get_num_kv_heads(
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get_attention_tp_size()
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),
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)
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self.max_context_len = model_runner.model_config.context_len
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self.skip_prefill = skip_prefill
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self.is_multimodal = model_runner.model_config.is_multimodal
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assert not (
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model_runner.sliding_window_size is not None
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and model_runner.model_config.is_encoder_decoder
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), "Sliding window and cross attention are not supported together"
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if model_runner.sliding_window_size is not None:
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self.num_wrappers = 2
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self.dispatch_reason = WrapperDispatch.SLIDING_WINDOW
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elif model_runner.model_config.is_encoder_decoder:
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self.num_wrappers = 2
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self.dispatch_reason = WrapperDispatch.CROSS_ATTENTION
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else:
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self.num_wrappers = 1
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self.dispatch_reason = None
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# Qwen2 models require higher flashinfer workspace size
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if "Qwen2ForCausalLM" in model_runner.model_config.hf_config.architectures:
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global_config.flashinfer_workspace_size = 512 * 1024 * 1024
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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 = [
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torch.zeros(
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(max_bs + 1,), dtype=torch.int32, device=model_runner.device
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)
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for _ in range(self.num_wrappers)
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]
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else:
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assert self.num_wrappers == 1
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self.kv_indptr = [kv_indptr_buf]
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if kv_last_page_len_buf is None:
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self.kv_last_page_len = torch.ones(
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(max_bs,), dtype=torch.int32, device=model_runner.device
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)
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else:
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assert self.num_wrappers == 1
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self.kv_last_page_len = kv_last_page_len_buf
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if not self.skip_prefill:
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self.qo_indptr = [
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torch.zeros(
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(max_bs + 1,), dtype=torch.int32, device=model_runner.device
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)
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for _ in range(self.num_wrappers)
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]
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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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# Two wrappers: one for sliding window attention and one for full attention.
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# Using two wrappers is unnecessary in the current PR, but are prepared for future PRs
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self.prefill_wrappers_paged = []
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self.prefill_wrappers_verify = []
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self.decode_wrappers = []
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for _ in range(self.num_wrappers):
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if not skip_prefill:
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self.prefill_wrappers_paged.append(
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BatchPrefillWithPagedKVCacheWrapper(
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self.workspace_buffer,
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"NHD",
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backend="fa2",
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)
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)
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self.prefill_wrappers_verify.append(
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BatchPrefillWithPagedKVCacheWrapper(
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self.workspace_buffer,
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"NHD",
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)
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)
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self.decode_wrappers.append(
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BatchDecodeWithPagedKVCacheWrapper(
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self.workspace_buffer,
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"NHD",
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use_tensor_cores=self.decode_use_tensor_cores,
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)
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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 = FlashInferIndicesUpdaterPrefill(
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model_runner, self
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) # for verify
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self.indices_updater_decode = FlashInferIndicesUpdaterDecode(model_runner, self)
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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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self.draft_extend_cuda_graph_metadata = {} # For draft extend
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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_wrappers=self.decode_wrappers,
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encoder_lens=forward_batch.encoder_lens,
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spec_info=forward_batch.spec_info,
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)
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self.forward_metadata = DecodeMetadata(self.decode_wrappers)
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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_wrappers=self.prefill_wrappers_paged,
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use_ragged=False,
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encoder_lens=forward_batch.encoder_lens,
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spec_info=forward_batch.spec_info,
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)
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self.forward_metadata = PrefillMetadata(
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self.prefill_wrappers_paged, False, False
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)
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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_wrappers=self.prefill_wrappers_verify,
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use_ragged=False,
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encoder_lens=forward_batch.encoder_lens,
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spec_info=forward_batch.spec_info,
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)
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self.forward_metadata = PrefillMetadata(
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self.prefill_wrappers_verify, False, False
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)
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else:
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prefix_lens = forward_batch.extend_prefix_lens
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if self.is_multimodal:
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use_ragged = False
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extend_no_prefix = False
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else:
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use_ragged = True
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extend_no_prefix = not any(forward_batch.extend_prefix_lens_cpu)
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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_wrappers=self.prefill_wrappers_paged,
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use_ragged=use_ragged,
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encoder_lens=forward_batch.encoder_lens,
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spec_info=None,
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)
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self.forward_metadata = PrefillMetadata(
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self.prefill_wrappers_paged, use_ragged, extend_no_prefix
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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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cuda_graph_kv_indices.clone() for _ in range(self.num_wrappers - 1)
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]
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if not self.skip_prefill:
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self.cuda_graph_custom_mask = torch.zeros(
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(max_bs * self.max_context_len),
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dtype=torch.uint8,
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device="cuda",
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)
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self.cuda_graph_qk_indptr = [x.clone() for x in self.kv_indptr]
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self.cuda_graph_qo_indptr = [x.clone() for x in self.kv_indptr]
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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[Union[EagleDraftInput, EagleVerifyInput]],
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):
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if forward_mode.is_decode_or_idle():
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decode_wrappers = []
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for i in range(self.num_wrappers):
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decode_wrappers.append(
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BatchDecodeWithPagedKVCacheWrapper(
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self.workspace_buffer,
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"NHD",
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use_cuda_graph=True,
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use_tensor_cores=self.decode_use_tensor_cores,
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paged_kv_indptr_buffer=self.kv_indptr[i][: num_tokens + 1],
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paged_kv_indices_buffer=self.cuda_graph_kv_indices[i],
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paged_kv_last_page_len_buffer=self.kv_last_page_len[
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:num_tokens
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],
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)
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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_wrappers=decode_wrappers,
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encoder_lens=encoder_lens,
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spec_info=spec_info,
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)
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self.decode_cuda_graph_metadata[bs] = decode_wrappers
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self.forward_metadata = DecodeMetadata(decode_wrappers)
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for i in range(self.num_wrappers):
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decode_wrappers[i].begin_forward = partial(
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fast_decode_plan, decode_wrappers[i]
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)
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elif forward_mode.is_target_verify():
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prefill_wrappers = []
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for i in range(self.num_wrappers):
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prefill_wrappers.append(
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BatchPrefillWithPagedKVCacheWrapper(
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self.workspace_buffer,
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"NHD",
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use_cuda_graph=True,
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qo_indptr_buf=self.cuda_graph_qo_indptr[i][: bs + 1],
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paged_kv_indptr_buf=self.kv_indptr[i][: bs + 1],
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paged_kv_indices_buf=self.cuda_graph_kv_indices[i],
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paged_kv_last_page_len_buf=self.kv_last_page_len[:bs],
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custom_mask_buf=self.cuda_graph_custom_mask,
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mask_indptr_buf=self.cuda_graph_qk_indptr[i][: bs + 1],
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)
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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_wrappers=prefill_wrappers,
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use_ragged=False,
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encoder_lens=encoder_lens,
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spec_info=spec_info,
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)
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self.prefill_cuda_graph_metadata[bs] = prefill_wrappers
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self.forward_metadata = PrefillMetadata(prefill_wrappers, False, 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[Union[EagleDraftInput, EagleVerifyInput]],
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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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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_wrappers=self.decode_cuda_graph_metadata[bs],
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encoder_lens=encoder_lens[:bs] if encoder_lens is not None else None,
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spec_info=spec_info,
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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_wrappers=self.prefill_cuda_graph_metadata[bs],
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use_ragged=False,
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encoder_lens=encoder_lens[:bs] if encoder_lens is not None else None,
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spec_info=spec_info,
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)
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else:
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raise ValueError("Invalid 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=True,
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):
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prefill_wrapper_paged = self.forward_metadata.prefill_wrappers[
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self._get_wrapper_idx(layer)
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]
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cache_loc = (
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forward_batch.out_cache_loc
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if not layer.is_cross_attention
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else forward_batch.encoder_out_cache_loc
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)
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|
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logits_soft_cap = layer.logit_cap
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|
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if not self.forward_metadata.use_ragged:
|
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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, cache_loc, k, v, layer.k_scale, layer.v_scale
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)
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o = prefill_wrapper_paged.forward(
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q.contiguous().view(-1, layer.tp_q_head_num, layer.head_dim),
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forward_batch.token_to_kv_pool.get_kv_buffer(layer.layer_id),
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causal=not layer.is_cross_attention,
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sm_scale=layer.scaling,
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window_left=layer.sliding_window_size,
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logits_soft_cap=logits_soft_cap,
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k_scale=layer.k_scale,
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v_scale=layer.v_scale,
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)
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else:
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o1, s1 = self.prefill_wrapper_ragged.forward_return_lse(
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q.view(-1, layer.tp_q_head_num, layer.head_dim),
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k.view(-1, layer.tp_k_head_num, layer.head_dim),
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v.view(-1, layer.tp_v_head_num, layer.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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|
|
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if self.forward_metadata.extend_no_prefix:
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o = o1
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else:
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o2, s2 = prefill_wrapper_paged.forward_return_lse(
|
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q.contiguous().view(-1, layer.tp_q_head_num, layer.head_dim),
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forward_batch.token_to_kv_pool.get_kv_buffer(layer.layer_id),
|
|
causal=False,
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|
sm_scale=layer.scaling,
|
|
logits_soft_cap=logits_soft_cap,
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)
|
|
|
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o, _ = merge_state(o1, s1, o2, s2)
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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, cache_loc, k, v, layer.k_scale, layer.v_scale
|
|
)
|
|
|
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return o.view(-1, layer.tp_q_head_num * layer.head_dim)
|
|
|
|
def forward_decode(
|
|
self,
|
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q: torch.Tensor,
|
|
k: torch.Tensor,
|
|
v: torch.Tensor,
|
|
layer: RadixAttention,
|
|
forward_batch: ForwardBatch,
|
|
save_kv_cache=True,
|
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):
|
|
decode_wrapper = self.forward_metadata.decode_wrappers[
|
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self._get_wrapper_idx(layer)
|
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]
|
|
cache_loc = (
|
|
forward_batch.out_cache_loc
|
|
if not layer.is_cross_attention
|
|
else forward_batch.encoder_out_cache_loc
|
|
)
|
|
|
|
if k is not None:
|
|
assert v is not None
|
|
if save_kv_cache:
|
|
forward_batch.token_to_kv_pool.set_kv_buffer(
|
|
layer, cache_loc, k, v, layer.k_scale, layer.v_scale
|
|
)
|
|
|
|
o = decode_wrapper.forward(
|
|
q.contiguous().view(-1, layer.tp_q_head_num, layer.head_dim),
|
|
forward_batch.token_to_kv_pool.get_kv_buffer(layer.layer_id),
|
|
sm_scale=layer.scaling,
|
|
logits_soft_cap=layer.logit_cap,
|
|
k_scale=layer.k_scale,
|
|
v_scale=layer.v_scale,
|
|
)
|
|
|
|
return o.view(-1, layer.tp_q_head_num * layer.head_dim)
|
|
|
|
def _get_wrapper_idx(self, layer: RadixAttention):
|
|
if self.num_wrappers == 1:
|
|
return 0
|
|
|
|
if self.dispatch_reason == WrapperDispatch.SLIDING_WINDOW:
|
|
return layer.sliding_window_size == -1
|
|
if self.dispatch_reason == WrapperDispatch.CROSS_ATTENTION:
|
|
return layer.is_cross_attention
|
|
|
|
raise ValueError(f"Unknown dispatch reason: {self.dispatch_reason}")
|
|
|
|
|
|
class FlashInferIndicesUpdaterDecode:
|
|
def __init__(self, model_runner: ModelRunner, attn_backend: AttentionBackend):
|
|
# Parse Constants
|
|
self.num_qo_heads = (
|
|
model_runner.model_config.num_attention_heads // get_attention_tp_size()
|
|
)
|
|
self.num_kv_heads = model_runner.model_config.get_num_kv_heads(
|
|
get_attention_tp_size()
|
|
)
|
|
self.head_dim = model_runner.model_config.head_dim
|
|
self.data_type = model_runner.kv_cache_dtype
|
|
self.q_data_type = model_runner.dtype
|
|
self.sliding_window_size = model_runner.sliding_window_size
|
|
self.attn_backend = attn_backend
|
|
|
|
# Buffers and wrappers
|
|
self.kv_indptr = attn_backend.kv_indptr
|
|
self.kv_last_page_len = attn_backend.kv_last_page_len
|
|
self.req_to_token = model_runner.req_to_token_pool.req_to_token
|
|
|
|
# Dispatch the update function
|
|
if self.attn_backend.dispatch_reason == WrapperDispatch.SLIDING_WINDOW:
|
|
self.update = self.update_sliding_window
|
|
elif self.attn_backend.dispatch_reason == WrapperDispatch.CROSS_ATTENTION:
|
|
self.update = self.update_cross_attention
|
|
else:
|
|
assert self.attn_backend.num_wrappers == 1
|
|
self.update = self.update_single_wrapper
|
|
|
|
def update(
|
|
self,
|
|
req_pool_indices: torch.Tensor,
|
|
seq_lens: torch.Tensor,
|
|
seq_lens_sum: int,
|
|
decode_wrappers: List[BatchDecodeWithPagedKVCacheWrapper],
|
|
encoder_lens: Optional[torch.Tensor],
|
|
spec_info: Optional[Union[EagleDraftInput, EagleVerifyInput]],
|
|
):
|
|
# Keep the signature for type checking. It will be assigned during runtime.
|
|
raise NotImplementedError()
|
|
|
|
def update_single_wrapper(
|
|
self,
|
|
req_pool_indices: torch.Tensor,
|
|
seq_lens: torch.Tensor,
|
|
seq_lens_sum: int,
|
|
decode_wrappers: List[BatchDecodeWithPagedKVCacheWrapper],
|
|
encoder_lens: Optional[torch.Tensor],
|
|
spec_info: Optional[Union[EagleDraftInput, EagleVerifyInput]],
|
|
):
|
|
decode_wrappers = decode_wrappers or self.decode_wrappers
|
|
self.call_begin_forward(
|
|
decode_wrappers[0],
|
|
req_pool_indices,
|
|
seq_lens,
|
|
seq_lens_sum,
|
|
self.kv_indptr[0],
|
|
None,
|
|
spec_info,
|
|
)
|
|
|
|
def update_sliding_window(
|
|
self,
|
|
req_pool_indices: torch.Tensor,
|
|
seq_lens: torch.Tensor,
|
|
seq_lens_sum: int,
|
|
decode_wrappers: List[BatchDecodeWithPagedKVCacheWrapper],
|
|
encoder_lens: Optional[torch.Tensor],
|
|
spec_info: Optional[Union[EagleDraftInput, EagleVerifyInput]],
|
|
):
|
|
for wrapper_id in range(2):
|
|
if wrapper_id == 0:
|
|
# Sliding window attention
|
|
paged_kernel_lens_tmp = torch.minimum( # TODO: replace this with clamp
|
|
seq_lens,
|
|
torch.tensor(self.sliding_window_size + 1),
|
|
)
|
|
paged_kernel_lens_sum_tmp = paged_kernel_lens_tmp.sum().item()
|
|
kv_start_idx_tmp = seq_lens - paged_kernel_lens_tmp
|
|
else:
|
|
# Full attention
|
|
paged_kernel_lens_tmp = seq_lens
|
|
paged_kernel_lens_sum_tmp = seq_lens_sum
|
|
kv_start_idx_tmp = None
|
|
|
|
self.call_begin_forward(
|
|
decode_wrappers[wrapper_id],
|
|
req_pool_indices,
|
|
paged_kernel_lens_tmp,
|
|
paged_kernel_lens_sum_tmp,
|
|
self.kv_indptr[wrapper_id],
|
|
kv_start_idx_tmp,
|
|
spec_info,
|
|
)
|
|
|
|
def update_cross_attention(
|
|
self,
|
|
req_pool_indices: torch.Tensor,
|
|
seq_lens: torch.Tensor,
|
|
seq_lens_sum: int,
|
|
decode_wrappers: List[BatchDecodeWithPagedKVCacheWrapper],
|
|
encoder_lens: Optional[torch.Tensor],
|
|
spec_info: Optional[Union[EagleDraftInput, EagleVerifyInput]],
|
|
):
|
|
for wrapper_id in range(2):
|
|
if wrapper_id == 0:
|
|
# Normal attention
|
|
paged_kernel_lens = seq_lens
|
|
kv_start_idx = encoder_lens
|
|
else:
|
|
# Cross attention
|
|
paged_kernel_lens = encoder_lens
|
|
kv_start_idx = torch.zeros_like(encoder_lens)
|
|
seq_lens_sum = encoder_lens.sum().item()
|
|
|
|
self.call_begin_forward(
|
|
decode_wrappers[wrapper_id],
|
|
req_pool_indices,
|
|
paged_kernel_lens,
|
|
seq_lens_sum,
|
|
self.kv_indptr[wrapper_id],
|
|
kv_start_idx,
|
|
spec_info,
|
|
)
|
|
|
|
def call_begin_forward(
|
|
self,
|
|
wrapper: BatchDecodeWithPagedKVCacheWrapper,
|
|
req_pool_indices: torch.Tensor,
|
|
paged_kernel_lens: torch.Tensor,
|
|
paged_kernel_lens_sum: int,
|
|
kv_indptr: torch.Tensor,
|
|
kv_start_idx: torch.Tensor,
|
|
spec_info: Optional[Union[EagleDraftInput, EagleVerifyInput]],
|
|
):
|
|
if spec_info is None:
|
|
bs = len(req_pool_indices)
|
|
kv_indptr[1 : bs + 1] = torch.cumsum(paged_kernel_lens, dim=0)
|
|
kv_indptr = kv_indptr[: bs + 1]
|
|
|
|
if wrapper.is_cuda_graph_enabled:
|
|
# Directly write to the cuda graph input buffer
|
|
kv_indices = wrapper._paged_kv_indices_buf
|
|
else:
|
|
kv_indices = torch.empty(
|
|
paged_kernel_lens_sum, dtype=torch.int32, device="cuda"
|
|
)
|
|
|
|
create_flashinfer_kv_indices_triton[(bs,)](
|
|
self.req_to_token,
|
|
req_pool_indices,
|
|
paged_kernel_lens,
|
|
kv_indptr,
|
|
kv_start_idx,
|
|
kv_indices,
|
|
self.req_to_token.shape[1],
|
|
)
|
|
else:
|
|
kv_indptr, kv_indices = spec_info.kv_indptr, spec_info.kv_indices
|
|
bs = kv_indptr.shape[0] - 1
|
|
|
|
wrapper.begin_forward(
|
|
kv_indptr,
|
|
kv_indices,
|
|
self.kv_last_page_len[:bs],
|
|
self.num_qo_heads,
|
|
self.num_kv_heads,
|
|
self.head_dim,
|
|
1,
|
|
data_type=self.data_type,
|
|
q_data_type=self.q_data_type,
|
|
non_blocking=True,
|
|
)
|
|
|
|
|
|
class FlashInferIndicesUpdaterPrefill:
|
|
def __init__(self, model_runner: ModelRunner, attn_backend: AttentionBackend):
|
|
# Parse Constants
|
|
self.num_qo_heads = (
|
|
model_runner.model_config.num_attention_heads // get_attention_tp_size()
|
|
)
|
|
self.num_kv_heads = model_runner.model_config.get_num_kv_heads(
|
|
get_attention_tp_size()
|
|
)
|
|
self.head_dim = model_runner.model_config.head_dim
|
|
self.data_type = model_runner.kv_cache_dtype
|
|
self.q_data_type = model_runner.dtype
|
|
self.sliding_window_size = model_runner.sliding_window_size
|
|
self.attn_backend = attn_backend
|
|
|
|
# Buffers and wrappers
|
|
self.kv_indptr = attn_backend.kv_indptr
|
|
self.kv_last_page_len = attn_backend.kv_last_page_len
|
|
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
|
|
|
|
# Dispatch the update function
|
|
if self.attn_backend.dispatch_reason == WrapperDispatch.SLIDING_WINDOW:
|
|
self.update = self.update_sliding_window
|
|
elif self.attn_backend.dispatch_reason == WrapperDispatch.CROSS_ATTENTION:
|
|
self.update = self.update_cross_attention
|
|
else:
|
|
assert self.attn_backend.num_wrappers == 1
|
|
self.update = self.update_single_wrapper
|
|
|
|
def update(
|
|
self,
|
|
req_pool_indices: torch.Tensor,
|
|
seq_lens: torch.Tensor,
|
|
seq_lens_sum: int,
|
|
prefix_lens: torch.Tensor,
|
|
prefill_wrappers: List[BatchPrefillWithPagedKVCacheWrapper],
|
|
use_ragged: bool,
|
|
encoder_lens: Optional[torch.Tensor],
|
|
spec_info: Optional[Union[EagleDraftInput, EagleVerifyInput]],
|
|
):
|
|
# Keep the signature for type checking. It will be assigned during runtime.
|
|
raise NotImplementedError()
|
|
|
|
def update_single_wrapper(
|
|
self,
|
|
req_pool_indices: torch.Tensor,
|
|
seq_lens: torch.Tensor,
|
|
seq_lens_sum: int,
|
|
prefix_lens: torch.Tensor,
|
|
prefill_wrappers: List[BatchPrefillWithPagedKVCacheWrapper],
|
|
use_ragged: bool,
|
|
encoder_lens: Optional[torch.Tensor],
|
|
spec_info: Optional[Union[EagleDraftInput, EagleVerifyInput]],
|
|
):
|
|
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_wrappers[0],
|
|
req_pool_indices,
|
|
paged_kernel_lens,
|
|
paged_kernel_lens_sum,
|
|
seq_lens,
|
|
prefix_lens,
|
|
None,
|
|
self.kv_indptr[0],
|
|
self.qo_indptr[0],
|
|
use_ragged,
|
|
spec_info,
|
|
)
|
|
|
|
def update_sliding_window(
|
|
self,
|
|
req_pool_indices: torch.Tensor,
|
|
seq_lens: torch.Tensor,
|
|
seq_lens_sum: int,
|
|
prefix_lens: torch.Tensor,
|
|
prefill_wrappers: List[BatchPrefillWithPagedKVCacheWrapper],
|
|
use_ragged: bool,
|
|
encoder_lens: Optional[torch.Tensor],
|
|
spec_info: Optional[Union[EagleDraftInput, EagleVerifyInput]],
|
|
):
|
|
for wrapper_id in range(2):
|
|
if wrapper_id == 0:
|
|
# window attention use paged only
|
|
paged_kernel_lens = torch.minimum(
|
|
seq_lens,
|
|
torch.tensor(self.sliding_window_size) + seq_lens - prefix_lens,
|
|
)
|
|
paged_kernel_lens_sum = paged_kernel_lens.sum().item()
|
|
else:
|
|
# full attention
|
|
paged_kernel_lens = seq_lens
|
|
paged_kernel_lens_sum = seq_lens_sum
|
|
|
|
kv_start_idx = seq_lens - paged_kernel_lens
|
|
|
|
self.call_begin_forward(
|
|
self.prefill_wrapper_ragged,
|
|
prefill_wrappers[wrapper_id],
|
|
req_pool_indices,
|
|
paged_kernel_lens,
|
|
paged_kernel_lens_sum,
|
|
seq_lens,
|
|
prefix_lens,
|
|
kv_start_idx,
|
|
self.kv_indptr[wrapper_id],
|
|
self.qo_indptr[wrapper_id],
|
|
use_ragged,
|
|
spec_info,
|
|
)
|
|
|
|
def update_cross_attention(
|
|
self,
|
|
req_pool_indices: torch.Tensor,
|
|
seq_lens: torch.Tensor,
|
|
seq_lens_sum: int,
|
|
prefix_lens: torch.Tensor,
|
|
prefill_wrappers: List[BatchPrefillWithPagedKVCacheWrapper],
|
|
use_ragged: bool,
|
|
encoder_lens: Optional[torch.Tensor],
|
|
spec_info: Optional[Union[EagleDraftInput, EagleVerifyInput]],
|
|
):
|
|
for wrapper_id in range(2):
|
|
if wrapper_id == 0:
|
|
# normal attention
|
|
paged_kernel_lens = seq_lens
|
|
kv_start_idx = encoder_lens
|
|
paged_kernel_lens_sum = seq_lens_sum
|
|
else:
|
|
# cross attention
|
|
paged_kernel_lens = encoder_lens
|
|
kv_start_idx = torch.zeros_like(encoder_lens)
|
|
paged_kernel_lens_sum = paged_kernel_lens.sum().item()
|
|
|
|
self.call_begin_forward(
|
|
self.prefill_wrapper_ragged,
|
|
prefill_wrappers[wrapper_id],
|
|
req_pool_indices,
|
|
paged_kernel_lens,
|
|
paged_kernel_lens_sum,
|
|
seq_lens,
|
|
prefix_lens,
|
|
kv_start_idx,
|
|
self.kv_indptr[wrapper_id],
|
|
self.qo_indptr[wrapper_id],
|
|
use_ragged,
|
|
spec_info,
|
|
)
|
|
|
|
def call_begin_forward(
|
|
self,
|
|
wrapper_ragged: BatchPrefillWithRaggedKVCacheWrapper,
|
|
wrapper_paged: BatchPrefillWithPagedKVCacheWrapper,
|
|
req_pool_indices: torch.Tensor,
|
|
paged_kernel_lens: torch.Tensor,
|
|
paged_kernel_lens_sum: int,
|
|
seq_lens: torch.Tensor,
|
|
prefix_lens: torch.Tensor,
|
|
kv_start_idx: torch.Tensor,
|
|
kv_indptr: torch.Tensor,
|
|
qo_indptr: torch.Tensor,
|
|
use_ragged: bool,
|
|
spec_info: Optional[Union[EagleDraftInput, EagleVerifyInput]],
|
|
):
|
|
bs = len(seq_lens)
|
|
if spec_info is None:
|
|
assert len(seq_lens) == len(req_pool_indices)
|
|
# Normal extend
|
|
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 + 256,
|
|
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,
|
|
kv_start_idx,
|
|
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
|
|
)
|
|
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,
|
|
)
|
|
)
|
|
|
|
# extend part
|
|
if use_ragged:
|
|
wrapper_ragged.begin_forward(
|
|
qo_indptr,
|
|
qo_indptr,
|
|
self.num_qo_heads,
|
|
self.num_kv_heads,
|
|
self.head_dim,
|
|
q_data_type=self.q_data_type,
|
|
)
|
|
|
|
# cached part
|
|
wrapper_paged.begin_forward(
|
|
qo_indptr,
|
|
kv_indptr,
|
|
kv_indices,
|
|
self.kv_last_page_len[:bs],
|
|
self.num_qo_heads,
|
|
self.num_kv_heads,
|
|
self.head_dim,
|
|
1,
|
|
q_data_type=self.q_data_type,
|
|
kv_data_type=self.data_type,
|
|
custom_mask=custom_mask,
|
|
non_blocking=True,
|
|
)
|
|
|
|
|
|
# Use as a fast path to override the indptr in flashinfer's plan function
|
|
# This is used to remove some host-to-device copy overhead.
|
|
global global_override_indptr_cpu
|
|
|
|
|
|
class FlashInferMultiStepDraftBackend:
|
|
"""
|
|
Wrap multiple flashinfer 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
|
|
|
|
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.kv_last_page_len = torch.ones(
|
|
(max_bs,), dtype=torch.int32, device=model_runner.device
|
|
)
|
|
self.attn_backends = []
|
|
for i in range(self.speculative_num_steps):
|
|
self.attn_backends.append(
|
|
FlashInferAttnBackend(
|
|
model_runner,
|
|
skip_prefill=True,
|
|
kv_indptr_buf=self.kv_indptr[i],
|
|
kv_last_page_len_buf=self.kv_last_page_len,
|
|
)
|
|
)
|
|
|
|
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)
|
|
|
|
# Copy the kv_indptr once to avoid multiple device-to-host copies in flashinfer's plan.
|
|
indptr_cpu_whole = self.kv_indptr[:, : bs + 1].cpu()
|
|
global global_override_indptr_cpu
|
|
|
|
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)
|
|
]
|
|
global_override_indptr_cpu = indptr_cpu_whole[i]
|
|
call_fn(i, forward_batch)
|
|
|
|
global_override_indptr_cpu = None
|
|
|
|
def init_forward_metadata(self, forward_batch: ForwardBatch):
|
|
kv_indices = torch.empty(
|
|
(
|
|
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=None,
|
|
)
|
|
|
|
self.common_template(forward_batch, self.cuda_graph_kv_indices, call_fn)
|
|
|
|
|
|
def should_use_tensor_core(
|
|
kv_cache_dtype: torch.dtype,
|
|
num_attention_heads: int,
|
|
num_kv_heads: int,
|
|
) -> bool:
|
|
"""
|
|
Determine whether to use tensor cores for attention computation.
|
|
|
|
Args:
|
|
kv_cache_dtype: Data type of the KV cache
|
|
num_attention_heads: Number of attention heads
|
|
num_kv_heads: Number of key/value heads
|
|
|
|
Returns:
|
|
bool: Whether to use tensor cores
|
|
"""
|
|
# Try to use environment variable first
|
|
env_override = os.environ.get("SGLANG_FLASHINFER_USE_TENSOR_CORE")
|
|
if env_override is not None:
|
|
return env_override.lower() == "true"
|
|
|
|
# Try to use _grouped_size_compiled_for_decode_kernels if available
|
|
# This is for flashinfer <=0.1.6. Otherwise, there is an accuracy bug
|
|
try:
|
|
from flashinfer.decode import _grouped_size_compiled_for_decode_kernels
|
|
|
|
if not _grouped_size_compiled_for_decode_kernels(
|
|
num_attention_heads,
|
|
num_kv_heads,
|
|
):
|
|
return True
|
|
else:
|
|
return False
|
|
except (ImportError, AttributeError):
|
|
pass
|
|
|
|
# Calculate GQA group size
|
|
gqa_group_size = num_attention_heads // num_kv_heads
|
|
|
|
# Determine based on dtype and GQA group size
|
|
if kv_cache_dtype in (torch.float8_e4m3fn, torch.float8_e5m2):
|
|
return True
|
|
elif kv_cache_dtype in (torch.float16, torch.half, torch.bfloat16):
|
|
return gqa_group_size > 4
|
|
else:
|
|
return False
|
|
|
|
|
|
# Use as a fast path to override the indptr in flashinfer's plan function
|
|
# This is used to remove some host-to-device copy overhead.
|
|
global_override_indptr_cpu = None
|
|
|
|
|
|
def fast_decode_plan(
|
|
self,
|
|
indptr: torch.Tensor,
|
|
indices: torch.Tensor,
|
|
last_page_len: torch.Tensor,
|
|
num_qo_heads: int,
|
|
num_kv_heads: int,
|
|
head_dim: int,
|
|
page_size: int,
|
|
pos_encoding_mode: str = "NONE",
|
|
window_left: int = -1,
|
|
logits_soft_cap: Optional[float] = None,
|
|
data_type: Union[str, torch.dtype] = "float16",
|
|
q_data_type: Optional[Union[str, torch.dtype]] = None,
|
|
sm_scale: Optional[float] = None,
|
|
rope_scale: Optional[float] = None,
|
|
rope_theta: Optional[float] = None,
|
|
non_blocking: bool = True,
|
|
) -> None:
|
|
"""
|
|
A faster version of BatchDecodeWithPagedKVCacheWrapper::plan used for FlashInferMultiStepDraftBackend.
|
|
Modifications:
|
|
- Remove unnecessary device-to-device copy for the cuda graph buffers.
|
|
- Remove unnecessary host-to-device copy for the metadata buffers.
|
|
"""
|
|
batch_size = len(last_page_len)
|
|
if logits_soft_cap is None:
|
|
logits_soft_cap = 0.0
|
|
|
|
if self.use_tensor_cores:
|
|
qo_indptr_host = _get_range_buf(batch_size + 1, "cpu")
|
|
|
|
if self.is_cuda_graph_enabled:
|
|
if batch_size != self._fixed_batch_size:
|
|
raise ValueError(
|
|
"The batch size should be fixed in cudagraph mode, the runtime batch size {} "
|
|
" mismatches the batch size set during initialization {}".format(
|
|
batch_size, self._fixed_batch_size
|
|
)
|
|
)
|
|
if len(indices) > len(self._paged_kv_indices_buf):
|
|
raise ValueError(
|
|
"The size of indices should be less than or equal to the allocated buffer"
|
|
)
|
|
# Skip these copies because we directly write to them during prepartion
|
|
# self._paged_kv_indptr_buf.copy_(indptr)
|
|
# self._paged_kv_indices_buf[: len(indices)] = indices
|
|
# self._paged_kv_last_page_len_buf.copy_(last_page_len)
|
|
else:
|
|
self._paged_kv_indptr_buf = indptr
|
|
self._paged_kv_indices_buf = indices
|
|
self._paged_kv_last_page_len_buf = last_page_len
|
|
self._qo_indptr_buf = qo_indptr_host.to(self.device, non_blocking=non_blocking)
|
|
|
|
# NOTE(Zihao): the following tensors acts as placeholder to pass dtype info
|
|
if not q_data_type:
|
|
q_data_type = data_type
|
|
|
|
if not hasattr(self, "empty_q_data"):
|
|
self.empty_q_data = torch.empty(
|
|
0,
|
|
dtype=(
|
|
getattr(torch, q_data_type)
|
|
if isinstance(q_data_type, str)
|
|
else q_data_type
|
|
),
|
|
)
|
|
self.empty_kv_cache = torch.empty(
|
|
0,
|
|
dtype=(
|
|
getattr(torch, data_type) if isinstance(data_type, str) else data_type
|
|
),
|
|
)
|
|
self.last_page_len = torch.ones(32768, dtype=torch.int32)
|
|
|
|
indptr_host = (
|
|
global_override_indptr_cpu
|
|
if global_override_indptr_cpu is not None
|
|
else indptr.cpu()
|
|
)
|
|
|
|
if self.use_tensor_cores:
|
|
kv_lens_arr_host = get_seq_lens(
|
|
indptr_host, self.last_page_len[:batch_size], page_size
|
|
)
|
|
|
|
self._plan_info = self._cached_module.plan(
|
|
self._float_workspace_buffer,
|
|
self._int_workspace_buffer,
|
|
self._pin_memory_int_workspace_buffer,
|
|
qo_indptr_host,
|
|
indptr_host,
|
|
kv_lens_arr_host,
|
|
batch_size, # total_num_rows
|
|
batch_size,
|
|
num_qo_heads,
|
|
num_kv_heads,
|
|
page_size,
|
|
self.is_cuda_graph_enabled,
|
|
head_dim,
|
|
head_dim,
|
|
False, # causal
|
|
torch.cuda.current_stream().cuda_stream,
|
|
)
|
|
else:
|
|
self._plan_info = self._cached_module.plan(
|
|
self._float_workspace_buffer,
|
|
self._int_workspace_buffer,
|
|
self._pin_memory_int_workspace_buffer,
|
|
indptr_host,
|
|
batch_size,
|
|
num_qo_heads,
|
|
num_kv_heads,
|
|
page_size,
|
|
self.is_cuda_graph_enabled,
|
|
window_left,
|
|
logits_soft_cap,
|
|
head_dim,
|
|
head_dim,
|
|
self.empty_q_data,
|
|
self.empty_kv_cache,
|
|
torch.cuda.current_stream().cuda_stream,
|
|
)
|
|
|
|
self._pos_encoding_mode = pos_encoding_mode
|
|
self._window_left = window_left
|
|
self._logits_soft_cap = logits_soft_cap
|
|
self._sm_scale = sm_scale
|
|
self._rope_scale = rope_scale
|
|
self._rope_theta = rope_theta
|