169 lines
6.1 KiB
Python
169 lines
6.1 KiB
Python
"""Radix attention."""
|
|
|
|
import torch
|
|
from flashinfer.cascade import merge_state
|
|
from torch import nn
|
|
|
|
from sglang.global_config import global_config
|
|
from sglang.srt.layers.extend_attention import extend_attention_fwd
|
|
from sglang.srt.layers.token_attention import token_attention_fwd
|
|
from sglang.srt.managers.controller.model_runner import ForwardMode, InputMetadata
|
|
from sglang.srt.server import global_server_args_dict
|
|
|
|
|
|
class RadixAttention(nn.Module):
|
|
def __init__(
|
|
self,
|
|
num_heads: int,
|
|
head_dim: int,
|
|
scaling: float,
|
|
num_kv_heads: int,
|
|
layer_id: int,
|
|
logit_cap: int = -1,
|
|
):
|
|
super().__init__()
|
|
self.tp_q_head_num = num_heads
|
|
self.tp_k_head_num = num_kv_heads
|
|
self.tp_v_head_num = num_kv_heads
|
|
self.head_dim = head_dim
|
|
self.scaling = scaling
|
|
self.layer_id = layer_id
|
|
|
|
if not global_server_args_dict.get("disable_flashinfer", False):
|
|
self.extend_forward = self.extend_forward_flashinfer
|
|
self.decode_forward = self.decode_forward_flashinfer
|
|
else:
|
|
self.extend_forward = self.extend_forward_triton
|
|
self.decode_forward = self.decode_forward_triton
|
|
|
|
self.logit_cap = logit_cap if logit_cap is not None and logit_cap > 0 else 0
|
|
|
|
def extend_forward_triton(self, q, k, v, input_metadata: InputMetadata):
|
|
o = torch.empty_like(q)
|
|
self.store_kv_cache(k, v, input_metadata)
|
|
extend_attention_fwd(
|
|
q.view(-1, self.tp_q_head_num, self.head_dim),
|
|
k.contiguous(),
|
|
v.contiguous(),
|
|
o.view(-1, self.tp_q_head_num, self.head_dim),
|
|
input_metadata.token_to_kv_pool.get_key_buffer(self.layer_id),
|
|
input_metadata.token_to_kv_pool.get_value_buffer(self.layer_id),
|
|
input_metadata.req_to_token_pool.req_to_token,
|
|
input_metadata.req_pool_indices,
|
|
input_metadata.triton_start_loc,
|
|
input_metadata.seq_lens,
|
|
input_metadata.triton_prefix_lens,
|
|
input_metadata.extend_start_loc,
|
|
input_metadata.extend_seq_lens,
|
|
input_metadata.triton_max_seq_len,
|
|
input_metadata.triton_max_extend_len,
|
|
sm_scale=self.scaling,
|
|
logit_cap=self.logit_cap,
|
|
)
|
|
|
|
return o
|
|
|
|
def decode_forward_triton(self, q, k, v, input_metadata: InputMetadata):
|
|
o = torch.empty_like(q)
|
|
self.store_kv_cache(k, v, input_metadata)
|
|
|
|
token_attention_fwd(
|
|
q.view(-1, self.tp_q_head_num, self.head_dim),
|
|
input_metadata.token_to_kv_pool.get_key_buffer(self.layer_id),
|
|
input_metadata.token_to_kv_pool.get_value_buffer(self.layer_id),
|
|
o.view(-1, self.tp_q_head_num, self.head_dim),
|
|
input_metadata.req_to_token_pool.req_to_token,
|
|
input_metadata.req_pool_indices,
|
|
input_metadata.triton_start_loc,
|
|
input_metadata.seq_lens,
|
|
input_metadata.triton_max_seq_len,
|
|
input_metadata.total_num_tokens,
|
|
sm_scale=self.scaling,
|
|
logit_cap=self.logit_cap,
|
|
)
|
|
|
|
return o
|
|
|
|
def extend_forward_flashinfer(self, q, k, v, input_metadata: InputMetadata):
|
|
o1, s1 = input_metadata.flashinfer_prefill_wrapper_ragged.forward_return_lse(
|
|
q.contiguous().view(-1, self.tp_q_head_num, self.head_dim),
|
|
k.contiguous().view(-1, self.tp_k_head_num, self.head_dim),
|
|
v.contiguous().view(-1, self.tp_v_head_num, self.head_dim),
|
|
causal=True,
|
|
sm_scale=self.scaling,
|
|
logits_soft_cap=self.logit_cap,
|
|
)
|
|
|
|
if input_metadata.extend_no_prefix:
|
|
o = o1
|
|
else:
|
|
o2, s2 = input_metadata.flashinfer_prefill_wrapper_paged.forward_return_lse(
|
|
q.contiguous().view(-1, self.tp_q_head_num, self.head_dim),
|
|
input_metadata.token_to_kv_pool.kv_data[self.layer_id],
|
|
causal=False,
|
|
sm_scale=self.scaling,
|
|
logits_soft_cap=self.logit_cap,
|
|
)
|
|
|
|
o, _ = merge_state(o1, s1, o2, s2)
|
|
|
|
self.store_kv_cache(k, v, input_metadata)
|
|
|
|
if input_metadata.total_num_tokens >= global_config.layer_sync_threshold:
|
|
torch.cuda.synchronize()
|
|
|
|
return o.view(-1, self.tp_q_head_num * self.head_dim)
|
|
|
|
def decode_forward_flashinfer(self, q, k, v, input_metadata: InputMetadata):
|
|
self.store_kv_cache(k, v, input_metadata)
|
|
|
|
o = input_metadata.flashinfer_decode_wrapper.forward(
|
|
q.contiguous().view(-1, self.tp_q_head_num, self.head_dim),
|
|
input_metadata.token_to_kv_pool.kv_data[self.layer_id],
|
|
sm_scale=self.scaling,
|
|
logits_soft_cap=self.logit_cap,
|
|
)
|
|
|
|
return o.view(-1, self.tp_q_head_num * self.head_dim)
|
|
|
|
def forward(self, q, k, v, input_metadata: InputMetadata):
|
|
k = k.view(-1, self.tp_k_head_num, self.head_dim)
|
|
v = v.view(-1, self.tp_v_head_num, self.head_dim)
|
|
|
|
if input_metadata.forward_mode == ForwardMode.EXTEND:
|
|
return self.extend_forward(q, k, v, input_metadata)
|
|
elif input_metadata.forward_mode == ForwardMode.DECODE:
|
|
return self.decode_forward(q, k, v, input_metadata)
|
|
|
|
def store_kv_cache(self, cache_k, cache_v, input_metadata: InputMetadata):
|
|
kv_cache = input_metadata.token_to_kv_pool.kv_data[self.layer_id]
|
|
_store_kv_cache(cache_k, cache_v, kv_cache, input_metadata.out_cache_loc)
|
|
|
|
|
|
try:
|
|
|
|
@torch.library.custom_op("mylib::store_kv_cache", mutates_args={"kv_cache"})
|
|
def _store_kv_cache(
|
|
k: torch.Tensor,
|
|
v: torch.Tensor,
|
|
kv_cache: torch.Tensor,
|
|
cache_loc: torch.Tensor,
|
|
) -> None:
|
|
kv_cache[cache_loc, 0] = k
|
|
kv_cache[cache_loc, 1] = v
|
|
|
|
@_store_kv_cache.register_fake
|
|
def _(k, v, kv_cache, cache_loc):
|
|
pass
|
|
|
|
except:
|
|
|
|
def _store_kv_cache(
|
|
k: torch.Tensor,
|
|
v: torch.Tensor,
|
|
kv_cache: torch.Tensor,
|
|
cache_loc: torch.Tensor,
|
|
) -> None:
|
|
kv_cache[cache_loc, 0] = k
|
|
kv_cache[cache_loc, 1] = v
|