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FastDeploy/fastdeploy/model_executor/ops/iluvatar/paged_attention.py

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4.5 KiB
Python

"""
# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
import paddle
try:
from fastdeploy.model_executor.ops.iluvatar import (
mixed_fused_paged_attn,
paged_attn,
prefill_fused_paged_attn,
)
except ImportError:
paged_attn = None
prefill_fused_paged_attn = None
mixed_fused_paged_attn = None
def paged_attention(
q: paddle.Tensor,
k_cache: paddle.Tensor,
v_cache: paddle.Tensor,
block_tables: paddle.Tensor,
seq_lens: paddle.Tensor,
num_heads: int,
head_dim: int,
num_kv_heads: int,
scale: float,
block_size: int,
max_context_len: int,
alibi_slopes: paddle.Tensor = None,
causal: bool = True,
window_left: int = -1,
window_right: int = -1,
softcap: float = 0.0,
use_cuda_graph: bool = False,
use_sqrt_alibi: bool = False,
merged_qkv: bool = False,
k: paddle.Tensor = None,
v: paddle.Tensor = None,
rope_sin: paddle.Tensor = None,
rope_cos: paddle.Tensor = None,
rope_batch_stride: int = 0,
is_interleaved_rope_mode: bool = True,
):
return paged_attn(
q,
k_cache,
v_cache,
block_tables,
seq_lens,
alibi_slopes,
k,
v,
rope_sin,
rope_cos,
num_heads,
head_dim,
num_kv_heads,
scale,
block_size,
max_context_len,
causal,
window_left,
window_right,
softcap,
use_cuda_graph,
use_sqrt_alibi,
merged_qkv,
rope_batch_stride,
is_interleaved_rope_mode,
)
def prefill_fused_paged_attention(
qkv: paddle.Tensor,
k_cache: paddle.Tensor,
v_cache: paddle.Tensor,
block_tables: paddle.Tensor,
cu_seqlens_qkv: paddle.Tensor,
rope_sin: paddle.Tensor,
rope_cos: paddle.Tensor,
num_heads: int,
head_dim: int,
num_kv_heads: int,
block_size: int,
max_seq_len: int,
scale: float,
causal: bool = True,
q_rope: bool = True,
k_rope: bool = True,
v_rope: bool = False,
is_interleaved_rope_mode: bool = True,
):
return prefill_fused_paged_attn(
qkv,
k_cache,
v_cache,
block_tables,
cu_seqlens_qkv,
rope_sin,
rope_cos,
num_heads,
head_dim,
num_kv_heads,
block_size,
max_seq_len,
scale,
causal,
q_rope,
k_rope,
v_rope,
is_interleaved_rope_mode,
)
def mixed_fused_paged_attention(
qkv: paddle.Tensor,
k_cache: paddle.Tensor,
v_cache: paddle.Tensor,
prefill_block_tables: paddle.Tensor,
decode_block_tables: paddle.Tensor,
cu_seqlens_qkv: paddle.Tensor,
seq_lens: paddle.Tensor,
prefill_rope_sin: paddle.Tensor,
prefill_rope_cos: paddle.Tensor,
prefill_num_tokens: int,
num_heads: int,
head_dim: int,
num_kv_heads: int,
block_size: int,
max_seq_len: int,
scale: float,
causal: bool = True,
q_rope: bool = True,
k_rope: bool = True,
v_rope: bool = False,
window_left: int = -1,
window_right: int = -1,
softcap: float = 0.0,
use_cuda_graph: bool = False,
use_sqrt_alibi: bool = False,
decode_rope_sin: paddle.Tensor = None,
decode_rope_cos: paddle.Tensor = None,
rope_batch_stride: int = 0,
is_interleaved_rope_mode: bool = True,
):
return mixed_fused_paged_attn(
qkv,
k_cache,
v_cache,
prefill_block_tables,
decode_block_tables,
cu_seqlens_qkv,
seq_lens,
prefill_rope_sin,
prefill_rope_cos,
decode_rope_sin,
decode_rope_cos,
prefill_num_tokens,
num_heads,
head_dim,
num_kv_heads,
block_size,
max_seq_len,
scale,
causal,
q_rope,
k_rope,
v_rope,
window_left,
window_right,
softcap,
use_cuda_graph,
use_sqrt_alibi,
rope_batch_stride,
is_interleaved_rope_mode,
)