1184 lines
43 KiB
Python
1184 lines
43 KiB
Python
from __future__ import annotations
|
|
|
|
"""
|
|
end to end attention solution with aiter kernels
|
|
"""
|
|
|
|
import math
|
|
import os
|
|
from dataclasses import dataclass
|
|
from enum import Enum, auto
|
|
from functools import partial
|
|
from typing import TYPE_CHECKING, List, Optional, Union
|
|
|
|
import torch
|
|
import triton
|
|
import triton.language as tl
|
|
|
|
from sglang.global_config import global_config
|
|
from sglang.srt.layers.attention.base_attn_backend import AttentionBackend
|
|
from sglang.srt.layers.attention.utils import create_flashinfer_kv_indices_triton
|
|
from sglang.srt.layers.dp_attention import (
|
|
get_attention_tp_size,
|
|
is_dp_attention_enabled,
|
|
)
|
|
from sglang.srt.model_executor.forward_batch_info import ForwardBatch, ForwardMode
|
|
|
|
if TYPE_CHECKING:
|
|
from sglang.srt.layers.radix_attention import RadixAttention
|
|
from sglang.srt.model_executor.model_runner import ModelRunner
|
|
from sglang.srt.speculative.spec_info import SpecInfo
|
|
|
|
try:
|
|
from aiter import (
|
|
flash_attn_varlen_func,
|
|
mha_batch_prefill_func,
|
|
paged_attention_ragged,
|
|
)
|
|
from aiter.mla import mla_decode_fwd, mla_prefill_fwd
|
|
except ImportError:
|
|
print(
|
|
"aiter is AMD specific kernel library. Please make sure aiter is installed on your AMD device."
|
|
)
|
|
|
|
from sglang.srt.configs.model_config import AttentionArch
|
|
|
|
|
|
class WrapperDispatch(Enum):
|
|
SLIDING_WINDOW = auto()
|
|
CROSS_ATTENTION = auto()
|
|
|
|
|
|
@dataclass
|
|
class ForwardMetadata:
|
|
kv_indptr: torch.Tensor
|
|
kv_indices: torch.Tensor
|
|
qo_indptr: torch.Tensor
|
|
kv_last_page_len: torch.Tensor
|
|
max_q_len: int
|
|
max_kv_len: Optional[int]
|
|
|
|
|
|
global_workspace_buffer = None
|
|
|
|
_AITER_PARTITION_SIZE_ROCM = 256
|
|
|
|
|
|
class AiterAttnBackend(AttentionBackend):
|
|
def __init__(
|
|
self,
|
|
model_runner: ModelRunner,
|
|
skip_prefill: bool = False,
|
|
kv_indptr_buf: Optional[torch.Tensor] = None,
|
|
):
|
|
super().__init__()
|
|
# Lazy import to avoid the initialization of cuda context
|
|
from sglang.srt.layers.attention.triton_ops.extend_attention import (
|
|
extend_attention_fwd,
|
|
)
|
|
|
|
self.extend_attention_fwd = torch.compiler.disable(extend_attention_fwd)
|
|
|
|
self.device = model_runner.device
|
|
self.is_multimodal = model_runner.model_config.is_multimodal
|
|
self.num_draft_tokens = model_runner.server_args.speculative_num_draft_tokens
|
|
self.speculative_num_steps = model_runner.server_args.speculative_num_steps
|
|
self.num_head = (
|
|
model_runner.model_config.num_attention_heads // get_attention_tp_size()
|
|
)
|
|
self.head_dim = model_runner.model_config.head_dim
|
|
self.v_head_dim = model_runner.token_to_kv_pool.get_value_buffer(0).shape[-1]
|
|
self.num_kv_head = model_runner.model_config.get_num_kv_heads(
|
|
get_attention_tp_size()
|
|
)
|
|
self.kv_cache_dtype = model_runner.kv_cache_dtype
|
|
|
|
self.req_to_token = model_runner.req_to_token_pool.req_to_token
|
|
|
|
self.use_mla = model_runner.model_config.attention_arch == AttentionArch.MLA
|
|
|
|
# Parse constants
|
|
self.max_context_len = model_runner.model_config.context_len
|
|
self.skip_prefill = skip_prefill
|
|
|
|
max_bs = model_runner.req_to_token_pool.size
|
|
|
|
if kv_indptr_buf is None:
|
|
self.kv_indptr = torch.zeros(
|
|
(max_bs + 1,), dtype=torch.int32, device=model_runner.device
|
|
)
|
|
else:
|
|
self.kv_indptr = kv_indptr_buf
|
|
|
|
self.kv_last_page_len = torch.ones(
|
|
(max_bs,), dtype=torch.int32, device=model_runner.device
|
|
)
|
|
self.qo_indptr = torch.zeros(
|
|
(max_bs + 1,), dtype=torch.int32, device=model_runner.device
|
|
)
|
|
|
|
# Create prefill indices updater
|
|
if not skip_prefill:
|
|
self.indices_updater_prefill = AiterIndicesUpdaterPrefill(
|
|
model_runner, self
|
|
)
|
|
if self.use_mla:
|
|
self.mla_indices_updater_prefill = AiterMlaIndicesUpdaterPrefill(
|
|
model_runner, self
|
|
)
|
|
|
|
# aiter kernel related initialization
|
|
self.max_num_partitions = (
|
|
self.max_context_len + _AITER_PARTITION_SIZE_ROCM - 1
|
|
) // _AITER_PARTITION_SIZE_ROCM
|
|
|
|
nbyes_per_qo_elem = torch.finfo(torch.float32).bits // 8
|
|
|
|
if not self.use_mla:
|
|
self.workspace_buffer = torch.empty(
|
|
(max_bs * self.num_head * self.max_num_partitions * self.head_dim)
|
|
* nbyes_per_qo_elem
|
|
+ 2 * (max_bs * self.num_head * self.max_num_partitions) * 4,
|
|
dtype=torch.uint8,
|
|
device=self.device,
|
|
)
|
|
|
|
self.scale = float(1.0 / (self.head_dim**0.5))
|
|
self.k_scale = self.v_scale = torch.tensor([1.0], dtype=torch.float32).to(
|
|
self.device
|
|
)
|
|
|
|
self.logits_soft_cap = 0.0
|
|
|
|
self.forward_metadata: ForwardMetadata = None
|
|
|
|
if self.use_mla:
|
|
self.qo_indptr_ = torch.zeros(
|
|
(max_bs + 1,), dtype=torch.int32, device=model_runner.device
|
|
)
|
|
|
|
self.enable_dp_attention = is_dp_attention_enabled()
|
|
|
|
def init_forward_metadata(self, forward_batch: ForwardBatch):
|
|
"""Init auxiliary variables for triton attention backend."""
|
|
|
|
bs = forward_batch.batch_size
|
|
kv_indptr = self.kv_indptr
|
|
spec_info = forward_batch.spec_info
|
|
qo_indptr = None
|
|
kv_last_page_len = None
|
|
max_q_len = None
|
|
|
|
if forward_batch.forward_mode.is_decode_or_idle():
|
|
if spec_info is None:
|
|
kv_indptr[1 : bs + 1] = torch.cumsum(forward_batch.seq_lens, dim=0)
|
|
kv_indptr = kv_indptr[: bs + 1]
|
|
kv_indices = torch.empty(
|
|
forward_batch.seq_lens_sum, dtype=torch.int32, device=self.device
|
|
)
|
|
create_flashinfer_kv_indices_triton[(bs,)](
|
|
self.req_to_token,
|
|
forward_batch.req_pool_indices,
|
|
forward_batch.seq_lens,
|
|
kv_indptr,
|
|
None,
|
|
kv_indices,
|
|
self.req_to_token.stride(0),
|
|
)
|
|
else:
|
|
kv_indptr, kv_indices = spec_info.kv_indptr, spec_info.kv_indices
|
|
bs = kv_indptr.shape[0] - 1
|
|
|
|
if self.use_mla:
|
|
qo_indptr = self.qo_indptr_[: bs + 1]
|
|
qo_indptr[1 : bs + 1] = torch.cumsum(self.kv_last_page_len[:bs], dim=0)
|
|
kv_last_page_len = self.kv_last_page_len[:bs]
|
|
max_q_len = 1
|
|
|
|
self.forward_metadata = ForwardMetadata(
|
|
kv_indptr,
|
|
kv_indices,
|
|
qo_indptr,
|
|
kv_last_page_len,
|
|
max_q_len,
|
|
None,
|
|
)
|
|
|
|
elif forward_batch.forward_mode.is_draft_extend():
|
|
if self.use_mla:
|
|
kv_indices, kv_indptr, qo_indptr, custom_mask = (
|
|
spec_info.generate_attn_arg_prefill(
|
|
forward_batch.req_pool_indices,
|
|
forward_batch.seq_lens,
|
|
forward_batch.seq_lens_sum,
|
|
self.req_to_token,
|
|
)
|
|
)
|
|
self.forward_metadata = ForwardMetadata(
|
|
kv_indptr,
|
|
kv_indices,
|
|
qo_indptr,
|
|
# self.mla_indices_updater_prefill.kv_last_page_len,
|
|
self.kv_last_page_len[:bs],
|
|
max(forward_batch.extend_seq_lens_cpu),
|
|
forward_batch.seq_lens_cpu.max().item(),
|
|
)
|
|
else:
|
|
self.indices_updater_prefill.update(
|
|
forward_batch.req_pool_indices,
|
|
forward_batch.seq_lens,
|
|
forward_batch.seq_lens_sum,
|
|
prefix_lens=None,
|
|
encoder_lens=forward_batch.encoder_lens,
|
|
spec_info=forward_batch.spec_info,
|
|
)
|
|
self.forward_metadata = ForwardMetadata(
|
|
self.indices_updater_prefill.kv_indptr,
|
|
self.indices_updater_prefill.kv_indices,
|
|
None,
|
|
None,
|
|
self.indices_updater_prefill.max_q_len,
|
|
self.indices_updater_prefill.max_kv_len,
|
|
)
|
|
elif forward_batch.forward_mode.is_target_verify():
|
|
if self.use_mla:
|
|
draft_num = spec_info.draft_token_num
|
|
kv_lens = forward_batch.seq_lens + draft_num
|
|
kv_lens_sum = forward_batch.seq_lens_sum + draft_num * bs
|
|
device = forward_batch.seq_lens.device
|
|
|
|
qo_indptr = torch.arange(
|
|
0,
|
|
(1 + bs) * draft_num,
|
|
step=draft_num,
|
|
dtype=torch.int32,
|
|
device=device,
|
|
)
|
|
kv_indptr = self.kv_indptr
|
|
kv_indptr[1 : bs + 1] = torch.cumsum(kv_lens, dim=0)
|
|
kv_indptr = kv_indptr[: bs + 1]
|
|
kv_indices = torch.empty(
|
|
kv_lens_sum,
|
|
dtype=torch.int32,
|
|
device=device,
|
|
)
|
|
create_flashinfer_kv_indices_triton[(bs,)](
|
|
self.req_to_token,
|
|
forward_batch.req_pool_indices,
|
|
kv_lens,
|
|
kv_indptr,
|
|
None,
|
|
kv_indices,
|
|
self.req_to_token.stride(0),
|
|
)
|
|
self.forward_metadata = ForwardMetadata(
|
|
kv_indptr,
|
|
kv_indices,
|
|
qo_indptr,
|
|
# self.mla_indices_updater_prefill.kv_last_page_len,
|
|
self.kv_last_page_len[:bs],
|
|
draft_num,
|
|
None,
|
|
)
|
|
else:
|
|
self.indices_updater_prefill.update(
|
|
forward_batch.req_pool_indices,
|
|
forward_batch.seq_lens,
|
|
forward_batch.seq_lens_sum,
|
|
prefix_lens=None,
|
|
encoder_lens=forward_batch.encoder_lens,
|
|
spec_info=forward_batch.spec_info,
|
|
)
|
|
self.forward_metadata = ForwardMetadata(
|
|
self.indices_updater_prefill.kv_indptr,
|
|
self.indices_updater_prefill.kv_indices,
|
|
None,
|
|
None,
|
|
self.indices_updater_prefill.max_q_len,
|
|
self.indices_updater_prefill.max_kv_len,
|
|
)
|
|
else:
|
|
prefix_lens = forward_batch.extend_prefix_lens
|
|
|
|
if self.is_multimodal:
|
|
extend_no_prefix = False
|
|
else:
|
|
extend_no_prefix = not any(forward_batch.extend_prefix_lens_cpu)
|
|
if self.use_mla:
|
|
self.mla_indices_updater_prefill.update(
|
|
forward_batch.req_pool_indices,
|
|
forward_batch.seq_lens,
|
|
forward_batch.seq_lens_sum,
|
|
forward_batch.extend_seq_lens,
|
|
forward_batch.extend_seq_lens.max().item(),
|
|
forward_batch.seq_lens.max().item(),
|
|
spec_info=None,
|
|
)
|
|
|
|
kv_indices = self.mla_indices_updater_prefill.kv_indices
|
|
|
|
self.forward_metadata = ForwardMetadata(
|
|
self.mla_indices_updater_prefill.kv_indptr,
|
|
kv_indices,
|
|
self.mla_indices_updater_prefill.qo_indptr,
|
|
self.kv_last_page_len[:bs],
|
|
self.mla_indices_updater_prefill.max_q_len,
|
|
self.mla_indices_updater_prefill.max_kv_len,
|
|
)
|
|
else:
|
|
self.indices_updater_prefill.update(
|
|
forward_batch.req_pool_indices,
|
|
forward_batch.seq_lens,
|
|
forward_batch.seq_lens_sum,
|
|
prefix_lens,
|
|
encoder_lens=forward_batch.encoder_lens,
|
|
spec_info=None,
|
|
)
|
|
self.forward_metadata = ForwardMetadata(
|
|
self.indices_updater_prefill.kv_indptr,
|
|
self.indices_updater_prefill.kv_indices,
|
|
None,
|
|
None,
|
|
self.indices_updater_prefill.max_q_len,
|
|
self.indices_updater_prefill.max_kv_len,
|
|
)
|
|
|
|
def init_cuda_graph_state(
|
|
self,
|
|
max_bs: int,
|
|
max_num_tokens: int,
|
|
kv_indices_buf: Optional[torch.Tensor] = None,
|
|
):
|
|
self.cuda_graph_kv_last_page_len = torch.ones(max_bs, dtype=torch.int)
|
|
if kv_indices_buf is None:
|
|
self.cuda_graph_kv_indices = torch.zeros(
|
|
(max_bs * self.max_context_len),
|
|
dtype=torch.int32,
|
|
device=self.device,
|
|
)
|
|
else:
|
|
self.cuda_graph_kv_indices = kv_indices_buf
|
|
|
|
if not self.skip_prefill:
|
|
self.cuda_graph_custom_mask = torch.zeros(
|
|
(max_num_tokens * self.max_context_len),
|
|
dtype=torch.uint8,
|
|
device=self.device,
|
|
)
|
|
|
|
def init_forward_metadata_capture_cuda_graph(
|
|
self,
|
|
bs: int,
|
|
num_tokens: int,
|
|
req_pool_indices: torch.Tensor,
|
|
seq_lens: torch.Tensor,
|
|
encoder_lens: Optional[torch.Tensor],
|
|
forward_mode: ForwardMode,
|
|
spec_info: Optional[SpecInfo],
|
|
):
|
|
if forward_mode.is_decode_or_idle():
|
|
qo_indptr = None
|
|
kv_last_page_len = None
|
|
max_q_len = None
|
|
|
|
if spec_info is None:
|
|
kv_indptr = self.kv_indptr
|
|
kv_indptr[1 : bs + 1] = torch.cumsum(seq_lens, dim=0)
|
|
kv_indptr = kv_indptr[: bs + 1]
|
|
kv_indices = self.cuda_graph_kv_indices
|
|
create_flashinfer_kv_indices_triton[(bs,)](
|
|
self.req_to_token,
|
|
req_pool_indices,
|
|
seq_lens,
|
|
kv_indptr,
|
|
None,
|
|
kv_indices,
|
|
self.req_to_token.stride(0),
|
|
)
|
|
else:
|
|
kv_indptr, kv_indices = spec_info.kv_indptr, spec_info.kv_indices
|
|
|
|
if self.use_mla:
|
|
qo_indptr = self.qo_indptr_[: bs + 1]
|
|
qo_indptr[1 : bs + 1] = torch.cumsum(
|
|
self.cuda_graph_kv_last_page_len[:bs], dim=0
|
|
)
|
|
kv_last_page_len = self.cuda_graph_kv_last_page_len[:bs]
|
|
max_q_len = 1
|
|
|
|
self.forward_metadata = ForwardMetadata(
|
|
kv_indptr,
|
|
kv_indices,
|
|
qo_indptr,
|
|
kv_last_page_len,
|
|
max_q_len,
|
|
None,
|
|
)
|
|
|
|
elif forward_mode.is_target_verify():
|
|
if self.use_mla:
|
|
qo_indptr = self.qo_indptr[: bs + 1]
|
|
qo_indptr[: bs + 1] = torch.arange(
|
|
0,
|
|
(1 + bs) * self.num_draft_tokens,
|
|
step=self.num_draft_tokens,
|
|
dtype=torch.int32,
|
|
device=self.device,
|
|
)
|
|
kv_indptr = self.kv_indptr[: bs + 1]
|
|
kv_indptr[1 : bs + 1] = torch.cumsum(seq_lens, dim=0)
|
|
kv_indices = self.cuda_graph_kv_indices
|
|
create_flashinfer_kv_indices_triton[(bs,)](
|
|
self.req_to_token,
|
|
req_pool_indices,
|
|
seq_lens,
|
|
kv_indptr,
|
|
None,
|
|
kv_indices,
|
|
self.req_to_token.stride(0),
|
|
)
|
|
kv_last_page_len = self.cuda_graph_kv_last_page_len[:bs]
|
|
max_q_len = self.num_draft_tokens
|
|
|
|
self.forward_metadata = ForwardMetadata(
|
|
kv_indptr,
|
|
kv_indices,
|
|
qo_indptr,
|
|
kv_last_page_len,
|
|
max_q_len,
|
|
None,
|
|
)
|
|
else:
|
|
seq_lens_sum = seq_lens.sum().item()
|
|
self.indices_updater_prefill.update(
|
|
req_pool_indices,
|
|
seq_lens,
|
|
seq_lens_sum,
|
|
prefix_lens=None,
|
|
encoder_lens=encoder_lens,
|
|
spec_info=spec_info,
|
|
)
|
|
self.forward_metadata = ForwardMetadata(
|
|
self.indices_updater_prefill.kv_indptr,
|
|
self.indices_updater_prefill.kv_indices,
|
|
None,
|
|
None,
|
|
self.indices_updater_prefill.max_q_len,
|
|
self.indices_updater_prefill.max_kv_len,
|
|
)
|
|
elif forward_mode.is_draft_extend():
|
|
num_tokens_per_bs = self.speculative_num_steps + 1
|
|
qo_indptr = self.qo_indptr[: bs + 1]
|
|
qo_indptr[: bs + 1] = torch.arange(
|
|
0,
|
|
bs * num_tokens_per_bs + 1,
|
|
step=num_tokens_per_bs,
|
|
dtype=torch.int32,
|
|
device=self.device,
|
|
)
|
|
kv_indptr = self.kv_indptr[: bs + 1]
|
|
kv_indptr[1 : bs + 1] = torch.cumsum(seq_lens, dim=0)
|
|
kv_indices = self.cuda_graph_kv_indices
|
|
create_flashinfer_kv_indices_triton[(bs,)](
|
|
self.req_to_token,
|
|
req_pool_indices,
|
|
seq_lens,
|
|
kv_indptr,
|
|
None,
|
|
kv_indices,
|
|
self.req_to_token.stride(0),
|
|
)
|
|
kv_last_page_len = self.cuda_graph_kv_last_page_len[:bs]
|
|
max_q_len = num_tokens_per_bs
|
|
self.forward_metadata = ForwardMetadata(
|
|
kv_indptr,
|
|
kv_indices,
|
|
qo_indptr,
|
|
kv_last_page_len,
|
|
max_q_len,
|
|
None,
|
|
)
|
|
else:
|
|
raise ValueError(f"Invalid mode: {forward_mode=}")
|
|
|
|
def init_forward_metadata_replay_cuda_graph(
|
|
self,
|
|
bs: int,
|
|
req_pool_indices: torch.Tensor,
|
|
seq_lens: torch.Tensor,
|
|
seq_lens_sum: int,
|
|
encoder_lens: Optional[torch.Tensor],
|
|
forward_mode: ForwardMode,
|
|
spec_info: Optional[SpecInfo],
|
|
seq_lens_cpu: Optional[torch.Tensor],
|
|
):
|
|
if forward_mode.is_decode_or_idle():
|
|
kv_indptr = self.kv_indptr
|
|
kv_indices = self.cuda_graph_kv_indices
|
|
if spec_info is None:
|
|
kv_indptr[1 : bs + 1] = torch.cumsum(seq_lens[:bs], dim=0)
|
|
kv_indptr = kv_indptr[: bs + 1]
|
|
create_flashinfer_kv_indices_triton[(bs,)](
|
|
self.req_to_token,
|
|
req_pool_indices[:bs],
|
|
seq_lens[:bs],
|
|
kv_indptr,
|
|
None,
|
|
kv_indices,
|
|
self.req_to_token.stride(0),
|
|
)
|
|
else:
|
|
kv_indptr[: spec_info.kv_indptr.shape[0]] = spec_info.kv_indptr
|
|
kv_indices[: spec_info.kv_indices.shape[0]] = spec_info.kv_indices
|
|
|
|
elif forward_mode.is_target_verify():
|
|
bs = len(req_pool_indices)
|
|
qo_indptr = self.qo_indptr[: bs + 1]
|
|
qo_indptr[: bs + 1] = torch.arange(
|
|
0,
|
|
(1 + bs) * self.num_draft_tokens,
|
|
step=self.num_draft_tokens,
|
|
dtype=torch.int32,
|
|
device=self.device,
|
|
)
|
|
kv_lens = seq_lens + self.num_draft_tokens
|
|
kv_indptr = self.kv_indptr[: bs + 1]
|
|
kv_indptr[1 : bs + 1] = torch.cumsum(kv_lens, dim=0)
|
|
kv_indices = self.cuda_graph_kv_indices
|
|
create_flashinfer_kv_indices_triton[(bs,)](
|
|
self.req_to_token,
|
|
req_pool_indices,
|
|
kv_lens,
|
|
kv_indptr,
|
|
None,
|
|
kv_indices,
|
|
self.req_to_token.stride(0),
|
|
)
|
|
elif forward_mode.is_draft_extend():
|
|
seq_lens = seq_lens[:bs]
|
|
accept_lens = spec_info.accept_length[:bs]
|
|
qo_indptr = self.qo_indptr[: bs + 1]
|
|
qo_indptr[1 : bs + 1] = torch.cumsum(accept_lens, dim=0)
|
|
kv_indptr = self.kv_indptr[: bs + 1]
|
|
kv_indptr[1 : bs + 1] = torch.cumsum(seq_lens, dim=0)
|
|
kv_indices = self.cuda_graph_kv_indices
|
|
create_flashinfer_kv_indices_triton[(bs,)](
|
|
self.req_to_token,
|
|
req_pool_indices,
|
|
seq_lens,
|
|
kv_indptr,
|
|
None,
|
|
kv_indices,
|
|
self.req_to_token.stride(0),
|
|
)
|
|
else:
|
|
raise ValueError("Invalid forward mode")
|
|
|
|
def get_cuda_graph_seq_len_fill_value(self):
|
|
return 1
|
|
|
|
def forward_extend(
|
|
self,
|
|
q: torch.Tensor,
|
|
k: torch.Tensor,
|
|
v: torch.Tensor,
|
|
layer: RadixAttention,
|
|
forward_batch: ForwardBatch,
|
|
save_kv_cache=True,
|
|
):
|
|
cache_loc = (
|
|
forward_batch.out_cache_loc
|
|
if not layer.is_cross_attention
|
|
else forward_batch.encoder_out_cache_loc
|
|
)
|
|
|
|
self.logits_soft_cap = layer.logit_cap
|
|
|
|
if k is not None:
|
|
assert v is not None
|
|
if save_kv_cache:
|
|
if self.use_mla:
|
|
forward_batch.token_to_kv_pool.set_kv_buffer(layer, cache_loc, k, v)
|
|
else:
|
|
forward_batch.token_to_kv_pool.set_kv_buffer(
|
|
layer, cache_loc, k, v, layer.k_scale, layer.v_scale
|
|
)
|
|
|
|
if self.use_mla:
|
|
max_q_len = self.forward_metadata.max_q_len
|
|
max_kv_len = self.forward_metadata.max_kv_len
|
|
kv_indptr = self.forward_metadata.kv_indptr
|
|
kv_indices = self.forward_metadata.kv_indices
|
|
qo_indptr = self.forward_metadata.qo_indptr
|
|
K_Buffer = forward_batch.token_to_kv_pool.get_key_buffer(layer.layer_id)
|
|
V_Buffer = forward_batch.token_to_kv_pool.get_value_buffer(layer.layer_id)
|
|
kv_lora_rank = V_Buffer.shape[-1]
|
|
qk_rope_head_dim = K_Buffer.shape[-1] - kv_lora_rank
|
|
qk_nope_head_dim = k.shape[-1] - qk_rope_head_dim
|
|
assert len(q.shape) == 3
|
|
assert len(k.shape) == 3
|
|
assert len(v.shape) == 3
|
|
|
|
if (
|
|
forward_batch.forward_mode.is_extend()
|
|
and not forward_batch.forward_mode.is_target_verify()
|
|
and not forward_batch.forward_mode.is_draft_extend()
|
|
):
|
|
if kv_indices.shape[0] == 0:
|
|
o = flash_attn_varlen_func(
|
|
q,
|
|
k,
|
|
v,
|
|
qo_indptr,
|
|
qo_indptr,
|
|
max_q_len,
|
|
max_q_len,
|
|
softmax_scale=layer.scaling,
|
|
causal=True,
|
|
)
|
|
return o
|
|
elif layer.qk_head_dim != (kv_lora_rank + qk_rope_head_dim):
|
|
K_Buffer = torch.index_select(K_Buffer, 0, kv_indices)
|
|
kvc, k_pe = torch.split(
|
|
K_Buffer, [kv_lora_rank, qk_rope_head_dim], dim=-1
|
|
)
|
|
kvprefix = layer.kv_b_proj(kvc.contiguous())[0]
|
|
|
|
kvprefix = kvprefix.view(
|
|
-1, layer.tp_k_head_num, qk_nope_head_dim + layer.v_head_dim
|
|
)
|
|
k_prefix, v_prefix = torch.split(
|
|
kvprefix, [qk_nope_head_dim, layer.v_head_dim], dim=-1
|
|
)
|
|
k_prefix = torch.cat(
|
|
[
|
|
k_prefix,
|
|
torch.broadcast_to(
|
|
k_pe,
|
|
(k_pe.shape[0], layer.tp_k_head_num, k_pe.shape[2]),
|
|
),
|
|
],
|
|
dim=-1,
|
|
)
|
|
assert (
|
|
forward_batch.extend_prefix_lens.shape
|
|
== forward_batch.extend_seq_lens.shape
|
|
)
|
|
|
|
k = k_prefix
|
|
v = v_prefix
|
|
|
|
o = flash_attn_varlen_func(
|
|
q,
|
|
k,
|
|
v,
|
|
qo_indptr,
|
|
kv_indptr,
|
|
max_q_len,
|
|
max_kv_len,
|
|
softmax_scale=layer.scaling,
|
|
causal=True,
|
|
)
|
|
return o
|
|
|
|
else:
|
|
if layer.qk_head_dim != layer.v_head_dim:
|
|
o = q.new_empty(
|
|
(q.shape[0], layer.tp_q_head_num * layer.v_head_dim)
|
|
)
|
|
else:
|
|
o = torch.empty_like(q)
|
|
|
|
mla_prefill_fwd(
|
|
q.view(-1, layer.tp_q_head_num, layer.qk_head_dim),
|
|
K_Buffer.view(-1, 1, 1, layer.qk_head_dim),
|
|
o.view(-1, layer.tp_q_head_num, layer.v_head_dim),
|
|
qo_indptr,
|
|
kv_indptr,
|
|
kv_indices,
|
|
self.forward_metadata.kv_last_page_len,
|
|
self.forward_metadata.max_q_len,
|
|
layer.scaling,
|
|
layer.logit_cap,
|
|
)
|
|
K_Buffer = K_Buffer.view(-1, layer.tp_k_head_num, layer.qk_head_dim)
|
|
return o
|
|
elif forward_batch.forward_mode.is_target_verify():
|
|
o = q.new_empty((q.shape[0], layer.tp_q_head_num, layer.v_head_dim))
|
|
mla_decode_fwd(
|
|
q,
|
|
K_Buffer.view(-1, 1, 1, layer.qk_head_dim),
|
|
o,
|
|
self.forward_metadata.qo_indptr,
|
|
self.forward_metadata.kv_indptr,
|
|
self.forward_metadata.kv_indices,
|
|
self.forward_metadata.kv_last_page_len,
|
|
self.forward_metadata.max_q_len,
|
|
layer.scaling,
|
|
layer.logit_cap,
|
|
)
|
|
K_Buffer = K_Buffer.view(-1, 1, layer.qk_head_dim)
|
|
return o
|
|
elif forward_batch.forward_mode.is_draft_extend():
|
|
o = q.new_empty((q.shape[0], layer.tp_q_head_num, layer.v_head_dim))
|
|
causal = True
|
|
sliding_window_size = -1
|
|
kv_indptr = self.forward_metadata.kv_indptr
|
|
kv_indices = self.forward_metadata.kv_indices
|
|
mla_prefill_fwd(
|
|
q,
|
|
K_Buffer.view(-1, 1, 1, layer.qk_head_dim),
|
|
o,
|
|
self.forward_metadata.qo_indptr,
|
|
self.forward_metadata.kv_indptr,
|
|
self.forward_metadata.kv_indices,
|
|
self.forward_metadata.kv_last_page_len,
|
|
self.forward_metadata.max_q_len,
|
|
layer.scaling,
|
|
layer.logit_cap,
|
|
)
|
|
K_Buffer = K_Buffer.view(-1, 1, layer.qk_head_dim)
|
|
return o
|
|
# self.extend_attention_fwd(
|
|
# q.view(-1, layer.tp_q_head_num, layer.qk_head_dim),
|
|
# k.contiguous(),
|
|
# v.contiguous(),
|
|
# o.view(-1, layer.tp_q_head_num, layer.v_head_dim),
|
|
# forward_batch.token_to_kv_pool.get_key_buffer(layer.layer_id),
|
|
# forward_batch.token_to_kv_pool.get_value_buffer(layer.layer_id),
|
|
# self.forward_metadata.qo_indptr,
|
|
# kv_indptr,
|
|
# kv_indices,
|
|
# None,
|
|
# causal,
|
|
# None,
|
|
# self.forward_metadata.max_q_len,
|
|
# layer.scaling,
|
|
# layer.logit_cap,
|
|
# sliding_window_size,
|
|
# )
|
|
# return o
|
|
else:
|
|
raise ValueError(
|
|
f"Invalid forward mode for MLA prefill: {forward_batch.forward_mode=}"
|
|
)
|
|
else:
|
|
k_cache, v_cache = forward_batch.token_to_kv_pool.get_kv_buffer(
|
|
layer.layer_id
|
|
)
|
|
|
|
bs0 = forward_batch.batch_size + 1
|
|
|
|
o = mha_batch_prefill_func(
|
|
q.contiguous().view(-1, layer.tp_q_head_num, layer.head_dim),
|
|
k_cache,
|
|
v_cache,
|
|
self.qo_indptr[:bs0],
|
|
self.forward_metadata.kv_indptr[:bs0],
|
|
self.forward_metadata.kv_indices,
|
|
self.forward_metadata.max_q_len,
|
|
self.forward_metadata.max_kv_len,
|
|
causal=True,
|
|
logits_soft_cap=self.logits_soft_cap,
|
|
alibi_slopes=None,
|
|
return_lse=False,
|
|
return_attn_probs=False,
|
|
)
|
|
|
|
return o.view(-1, layer.tp_q_head_num * layer.head_dim)
|
|
|
|
def forward_decode(
|
|
self,
|
|
q: torch.Tensor,
|
|
k: torch.Tensor,
|
|
v: torch.Tensor,
|
|
layer: RadixAttention,
|
|
forward_batch: ForwardBatch,
|
|
save_kv_cache=True,
|
|
):
|
|
|
|
q = q.reshape(-1, layer.tp_q_head_num * layer.qk_head_dim)
|
|
|
|
if layer.qk_head_dim != layer.v_head_dim:
|
|
o = q.new_empty((q.shape[0], layer.tp_q_head_num * layer.v_head_dim))
|
|
else:
|
|
o = torch.empty_like(q)
|
|
|
|
if save_kv_cache:
|
|
forward_batch.token_to_kv_pool.set_kv_buffer(
|
|
layer, forward_batch.out_cache_loc, k, v
|
|
)
|
|
|
|
if self.use_mla:
|
|
k_buffer = forward_batch.token_to_kv_pool.get_key_buffer(layer.layer_id)
|
|
mla_decode_fwd(
|
|
q.view(-1, layer.tp_q_head_num, layer.qk_head_dim),
|
|
k_buffer.view(-1, 1, 1, layer.qk_head_dim),
|
|
o.view(-1, layer.tp_q_head_num, layer.v_head_dim),
|
|
self.forward_metadata.qo_indptr,
|
|
self.forward_metadata.kv_indptr,
|
|
self.forward_metadata.kv_indices,
|
|
self.forward_metadata.kv_last_page_len,
|
|
self.forward_metadata.max_q_len,
|
|
layer.scaling,
|
|
layer.logit_cap,
|
|
)
|
|
k_buffer = k_buffer.view(-1, 1, layer.qk_head_dim)
|
|
else:
|
|
self.logits_soft_cap = layer.logit_cap
|
|
paged_attention_ragged(
|
|
o.view(-1, layer.tp_q_head_num, layer.qk_head_dim),
|
|
self.workspace_buffer,
|
|
q.view(-1, layer.tp_q_head_num, layer.qk_head_dim),
|
|
forward_batch.token_to_kv_pool.get_key_buffer(layer.layer_id).view(
|
|
-1, 1, layer.tp_k_head_num, layer.qk_head_dim
|
|
),
|
|
forward_batch.token_to_kv_pool.get_value_buffer(layer.layer_id).view(
|
|
-1, 1, layer.tp_v_head_num, layer.v_head_dim
|
|
),
|
|
self.scale,
|
|
self.forward_metadata.kv_indptr,
|
|
self.forward_metadata.kv_indices,
|
|
self.kv_last_page_len,
|
|
1,
|
|
self.max_num_partitions,
|
|
None,
|
|
"auto",
|
|
"NHD",
|
|
self.logits_soft_cap,
|
|
self.k_scale,
|
|
self.v_scale,
|
|
None,
|
|
_AITER_PARTITION_SIZE_ROCM,
|
|
)
|
|
|
|
return o
|
|
|
|
|
|
class AiterIndicesUpdaterPrefill:
|
|
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.update = self.update_single_wrapper
|
|
|
|
self.kv_indices = None
|
|
self.max_q_len = 0
|
|
self.max_kv_len = 0
|
|
|
|
def update(
|
|
self,
|
|
req_pool_indices: torch.Tensor,
|
|
seq_lens: torch.Tensor,
|
|
seq_lens_sum: int,
|
|
prefix_lens: torch.Tensor,
|
|
encoder_lens: Optional[torch.Tensor],
|
|
spec_info: Optional[SpecInfo],
|
|
):
|
|
# 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,
|
|
encoder_lens: Optional[torch.Tensor],
|
|
spec_info: Optional[SpecInfo],
|
|
):
|
|
|
|
kv_start_idx = None
|
|
kv_indptr = self.kv_indptr
|
|
qo_indptr = self.qo_indptr
|
|
paged_kernel_lens = seq_lens
|
|
paged_kernel_lens_sum = seq_lens_sum
|
|
|
|
bs = len(req_pool_indices)
|
|
if spec_info is None:
|
|
# Normal extend
|
|
kv_indptr[1 : bs + 1] = torch.cumsum(paged_kernel_lens, dim=0)
|
|
kv_indptr = kv_indptr[: bs + 1]
|
|
|
|
# (TODO: Kk) WA - CI test_moe_eval_accuracy_large.py
|
|
# mha_batch_prefill reads 128 data to do computatoin
|
|
# if real data is not long enough then original padding value 0 is used
|
|
# but the 0 location will be made nan (noqa) in cuda graph capture mode
|
|
# this will cause the output tensor value becomes nan
|
|
# WA is to assure that last index of pool not changed
|
|
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],
|
|
)
|
|
|
|
token_num = kv_indptr[-1]
|
|
kv_indices[token_num:] = kv_indices[0]
|
|
|
|
self.max_kv_len = torch.max(paged_kernel_lens).item()
|
|
|
|
extend_lens = seq_lens - prefix_lens
|
|
self.max_q_len = torch.max(extend_lens).item()
|
|
|
|
qo_indptr[1 : bs + 1] = torch.cumsum(extend_lens, dim=0)
|
|
qo_indptr = qo_indptr[: bs + 1]
|
|
custom_mask = None
|
|
else:
|
|
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,
|
|
)
|
|
)
|
|
|
|
self.kv_indices = kv_indices
|
|
|
|
|
|
class AiterMlaIndicesUpdaterPrefill:
|
|
def __init__(self, model_runner: ModelRunner, attn_backend: AttentionBackend):
|
|
# Parse Constants
|
|
self.attn_backend = attn_backend
|
|
|
|
# Buffers and wrappers
|
|
self.req_to_token = model_runner.req_to_token_pool.req_to_token
|
|
self.update = self.update_single_wrapper
|
|
|
|
self.kv_indptr = None
|
|
self.kv_indices = None
|
|
self.qo_indptr = None
|
|
self.kv_last_page_len = None
|
|
self.max_q_len = 0
|
|
self.max_kv_len = 0
|
|
|
|
def update(
|
|
self,
|
|
req_pool_indices: torch.Tensor,
|
|
kv_lens: torch.Tensor,
|
|
kv_lens_sum: int,
|
|
extend_lens: torch.Tensor,
|
|
max_q_len: int,
|
|
max_kv_len: int,
|
|
spec_info: Optional[SpecInfo],
|
|
):
|
|
# Keep the signature for type checking. It will be assigned during runtime.
|
|
raise NotImplementedError()
|
|
|
|
def update_single_wrapper(
|
|
self,
|
|
req_pool_indices: torch.Tensor,
|
|
kv_lens: torch.Tensor,
|
|
kv_lens_sum: int,
|
|
extend_lens: torch.Tensor,
|
|
max_q_len: int,
|
|
max_kv_len: int,
|
|
spec_info: Optional[SpecInfo],
|
|
):
|
|
bs = len(req_pool_indices)
|
|
|
|
kv_indptr = self.attn_backend.kv_indptr
|
|
|
|
if spec_info is None:
|
|
# Normal extend
|
|
kv_indptr[1 : bs + 1] = torch.cumsum(kv_lens, dim=0)
|
|
kv_indptr = kv_indptr[: bs + 1]
|
|
kv_indices = torch.empty(
|
|
kv_lens_sum,
|
|
dtype=torch.int32,
|
|
device=req_pool_indices.device,
|
|
)
|
|
create_flashinfer_kv_indices_triton[(bs,)](
|
|
self.req_to_token,
|
|
req_pool_indices,
|
|
kv_lens,
|
|
kv_indptr,
|
|
None,
|
|
kv_indices,
|
|
self.req_to_token.stride(0),
|
|
)
|
|
|
|
qo_indptr = self.attn_backend.qo_indptr
|
|
qo_indptr[1 : bs + 1] = torch.cumsum(extend_lens, dim=0)
|
|
qo_indptr = qo_indptr[: bs + 1]
|
|
else:
|
|
kv_indices, kv_indptr, qo_indptr, custom_mask = (
|
|
spec_info.generate_attn_arg_prefill(
|
|
req_pool_indices,
|
|
kv_lens,
|
|
kv_lens_sum,
|
|
self.req_to_token,
|
|
)
|
|
)
|
|
|
|
self.kv_indptr = kv_indptr
|
|
self.kv_indices = kv_indices
|
|
self.qo_indptr = qo_indptr
|
|
self.max_q_len = max_q_len
|
|
self.max_kv_len = max_kv_len
|
|
|
|
|
|
class AiterMultiStepDraftBackend:
|
|
"""
|
|
Wrap multiple triton 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.attn_backends = []
|
|
for i in range(self.speculative_num_steps):
|
|
self.attn_backends.append(
|
|
AiterAttnBackend(
|
|
model_runner,
|
|
skip_prefill=True,
|
|
kv_indptr_buf=self.kv_indptr[i],
|
|
)
|
|
)
|
|
self.max_context_len = self.attn_backends[0].max_context_len
|
|
self.num_head = (
|
|
model_runner.model_config.num_attention_heads // get_attention_tp_size()
|
|
)
|
|
self.device = model_runner.device
|
|
# Cached variables for generate_draft_decode_kv_indices
|
|
self.pool_len = model_runner.req_to_token_pool.req_to_token.shape[1]
|
|
self.page_size = model_runner.server_args.page_size
|
|
assert self.page_size == 1, "Page size must be 1"
|
|
|
|
def common_template(
|
|
self, forward_batch: ForwardBatch, kv_indices_buffer: torch.Tensor, call_fn: int
|
|
):
|
|
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,
|
|
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),
|
|
self.page_size,
|
|
)
|
|
|
|
for i in range(self.speculative_num_steps):
|
|
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.empty(
|
|
(
|
|
self.speculative_num_steps,
|
|
forward_batch.batch_size * self.topk * self.max_context_len,
|
|
),
|
|
dtype=torch.int32,
|
|
device=self.device,
|
|
)
|
|
|
|
def call_fn(i, forward_batch):
|
|
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, max_num_tokens: int):
|
|
self.cuda_graph_kv_indices = torch.zeros(
|
|
(self.speculative_num_steps, max_num_tokens * self.max_context_len),
|
|
dtype=torch.int32,
|
|
device=self.device,
|
|
)
|
|
for i in range(self.speculative_num_steps):
|
|
self.attn_backends[i].init_cuda_graph_state(
|
|
max_bs, max_num_tokens, 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)
|