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Revert "[Feature] block sparse attention (#3209)" (#3647)

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This reverts commit 646a0c2fd8.
This commit is contained in:
Jiang-Jia-Jun
2025-08-27 17:35:04 +08:00
committed by GitHub
parent b2afdf4fc6
commit c694fa2879
31 changed files with 10 additions and 6507 deletions
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2
fastdeploy/model_executor/layers/attention/__init__.py
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@@ -20,7 +20,6 @@ from .block_multihead_attn_backend import BlockAttentionBackend
from .flash_attn_backend import FlashAttentionBackend
from .iluvatar_attn_backend import IluvatarAttnBackend
from .mla_attention_backend import MLAAttentionBackend
from .moba_attention_backend import MobaAttentionBackend
from .native_paddle_backend import PaddleNativeAttnBackend
from .xpu_attn_backend import XPUAttentionBackend
@@ -35,5 +34,4 @@ __all__ = [
"IluvatarAttnBackend",
"BlockAttentionBackend",
"Attention",
"MobaAttentionBackend",
]

41
fastdeploy/model_executor/layers/attention/attention.py
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@@ -28,11 +28,6 @@ from fastdeploy.model_executor.layers.quantization.quant_base import QuantMethod
if TYPE_CHECKING:
from fastdeploy.model_executor.forward_meta import ForwardMeta
import os
from safetensors import safe_open
from fastdeploy.model_executor.layers.utils import get_tensor
@@ -118,42 +113,6 @@ class Attention(nn.Layer):
self.k_norm_key = f"{self.prefix}.k_norm"
self.init_weight()
if fd_config.moba_attention_config is not None:
mlp_weight_path = os.path.join(
fd_config.model_config.model, fd_config.moba_attention_config.mlp_weight_name
)
self.moba_use_mlp = mlp_weight_path is not None and os.path.exists(mlp_weight_path)
moba_block_size = fd_config.moba_attention_config.moba_block_size
moba_max_seq_length = fd_config.moba_attention_config.moba_max_seq_length
if self.moba_use_mlp:
mlp_weight = {}
with safe_open(mlp_weight_path, framework="np", device="cpu") as f:
for key_name in f.keys():
weight = f.get_tensor(key_name)
weight = paddle.Tensor(weight, zero_copy=True)
weight = weight._copy_to(paddle.framework._current_expected_place(), False)
mlp_weight[key_name] = weight
if self.layer_id < fd_config.model_config.num_hidden_layers - 1:
self.attn_gate_weight = mlp_weight[
f"ernie.layers.{self.layer_id}.self_attn.attn_gate.weight"
].astype(paddle.get_default_dtype())[
fd_config.parallel_config.tensor_parallel_rank
* self.kv_num_heads : (fd_config.parallel_config.tensor_parallel_rank + 1)
* self.kv_num_heads
]
assert self.attn_gate_weight.shape[1] % moba_block_size == 0
self.cache_k_block_means = paddle.zeros(
[
fd_config.parallel_config.max_num_seqs,
moba_max_seq_length // moba_block_size,
self.kv_num_heads,
self.head_dim,
],
dtype=paddle.get_default_dtype(),
)
def init_weight(self):
self.q_norm_weight = self.create_parameter(
shape=[self.qk_head_dim],

198
fastdeploy/model_executor/layers/attention/moba_attention_backend.py
View File

@@ -1,198 +0,0 @@
"""
# 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.
"""
from __future__ import annotations
from dataclasses import dataclass
from typing import TYPE_CHECKING
import paddle
try:
from fastdeploy.model_executor.ops.gpu import get_cur_cu_seq_len_k, moba_attention
except:
moba_attention = None
get_cur_cu_seq_len_k = None
if TYPE_CHECKING:
from fastdeploy.model_executor.forward_meta import ForwardMeta
from fastdeploy.config import FDConfig
from fastdeploy.model_executor.layers.attention.attention import Attention
from fastdeploy.model_executor.layers.attention.base_attention_backend import (
AttentionBackend,
AttentionMetadata,
)
@dataclass
class MobaAttentionMetadata(AttentionMetadata):
"""
AppendAttentionMetadata
"""
q_input: paddle.Tensor = None
k_input: paddle.Tensor = None
v_input: paddle.Tensor = None
cu_seq_q_pack: paddle.Tensor = None
cu_seqlens_k: paddle.Tensor = None
q_pack_tokens: paddle.Tensor = None
max_enc_len_this_time: int = 0
max_dec_len_this_time: int = 0
class MobaAttentionBackend(AttentionBackend):
"""
The backend class that uses paddle native attention implementation.
Which is used only for testing purpose.
"""
def __init__(
self,
fd_config: FDConfig,
kv_num_heads: int,
num_heads: int,
head_dim: int,
encoder_block_shape_q: int = -1,
decoder_block_shape_q: int = -1,
) -> None:
"""
MobaAttentionBackend __init__
"""
super().__init__()
self.attention_metadata: MobaAttentionMetadata = None
assert fd_config.moba_attention_config is not None, "moba_attention_config is None"
self.block_size = fd_config.parallel_config.block_size
self.max_seq_len = fd_config.parallel_config.max_model_len
self.max_num_seqs = fd_config.parallel_config.max_num_seqs
self.kv_num_heads = kv_num_heads
self.num_heads = num_heads
self.head_dim = fd_config.model_config.head_dim
self.num_layers: int = fd_config.model_config.num_hidden_layers
self.attn_block_m = 128
self.moba_block_size = fd_config.moba_attention_config.moba_block_size
self.moba_encoder_top_k_left = int(fd_config.moba_attention_config.moba_encoder_top_k_left)
self.moba_encoder_top_k_right = int(fd_config.moba_attention_config.moba_encoder_top_k_right)
self.moba_use_encoder_seq_limit = int(fd_config.moba_attention_config.moba_use_encoder_seq_limit)
self.moba_decoder_top_k_left = int(fd_config.moba_attention_config.moba_decoder_top_k_left)
self.moba_decoder_top_k_right = int(fd_config.moba_attention_config.moba_decoder_top_k_right)
self.moba_use_decoder_seq_limit = int(fd_config.moba_attention_config.moba_use_decoder_seq_limit)
self.moba_max_seq_length = fd_config.moba_attention_config.moba_max_seq_length
def init_attention_metadata(self, forward_meta: ForwardMeta):
"""Init the metadata for a forward pass."""
metadata = MobaAttentionMetadata()
metadata._dtype = paddle.get_default_dtype()
metadata.cu_seq_q_pack, metadata.cu_seqlens_k, metadata.q_pack_tokens = get_cur_cu_seq_len_k(
forward_meta.seq_lens_encoder,
forward_meta.seq_lens_decoder,
forward_meta.seq_lens_this_time,
int(self.attn_block_m),
)
metadata.max_enc_len_this_time = forward_meta.seq_lens_encoder.max().cpu()
metadata.max_dec_len_this_time = forward_meta.seq_lens_decoder.max().cpu()
q_token_num = int(forward_meta.cu_seqlens_q[-1])
k_token_num = int(metadata.cu_seqlens_k[-1])
metadata.q_input = paddle.zeros(
[q_token_num + self.attn_block_m, self.num_heads * self.head_dim], dtype=metadata._dtype
)
metadata.k_input = paddle.zeros(
[k_token_num + self.attn_block_m, self.kv_num_heads * self.head_dim], dtype=metadata._dtype
)
metadata.v_input = paddle.zeros(
[k_token_num + self.attn_block_m, self.kv_num_heads * self.head_dim], dtype=metadata._dtype
)
self.attention_metadata = metadata
assert self.max_seq_len <= self.moba_max_seq_length
def get_kv_cache_shape(
self,
max_num_blocks: int,
kv_cache_quant_type: str = None,
):
"""
Caculate kv cache shape
"""
if kv_cache_quant_type is not None and kv_cache_quant_type == "int4_zp":
return (
max_num_blocks,
self.kv_num_heads,
self.block_size,
self.head_dim // 2,
)
else:
return (
max_num_blocks,
self.kv_num_heads,
self.block_size,
self.head_dim,
)
def forward_mixed(
self,
q: paddle.Tensor,
k: paddle.Tensor,
v: paddle.Tensor,
qkv: paddle.Tensor,
compressed_kv: paddle.Tensor,
k_pe: paddle.Tensor,
layer: Attention,
forward_meta: ForwardMeta,
) -> paddle.Tensor:
"""
Mixed模式的前向传播
"""
attention_metadata = self.attention_metadata
out = moba_attention(
qkv,
attention_metadata.q_input,
attention_metadata.k_input,
attention_metadata.v_input,
forward_meta.cu_seqlens_q,
attention_metadata.cu_seqlens_k,
attention_metadata.cu_seq_q_pack,
attention_metadata.q_pack_tokens,
forward_meta.seq_lens_encoder,
forward_meta.seq_lens_decoder,
forward_meta.caches[2 * layer.layer_id],
forward_meta.caches[2 * layer.layer_id + 1],
forward_meta.block_tables,
forward_meta.rotary_embs,
layer.cache_k_block_means,
getattr(layer, "attn_gate_weight", None),
layer.qkv_bias,
getattr(layer, "cache_k_scale", None),
getattr(layer, "cache_v_scale", None),
getattr(layer, "cache_k_out_scale", None),
getattr(layer, "cache_v_out_scale", None),
getattr(layer, "cache_k_zp", None),
getattr(layer, "cache_v_zp", None),
self.num_heads,
self.kv_num_heads,
self.head_dim,
self.max_seq_len,
attention_metadata.max_enc_len_this_time,
attention_metadata.max_dec_len_this_time,
self.moba_encoder_top_k_left,
self.moba_encoder_top_k_right,
self.moba_use_encoder_seq_limit,
self.moba_decoder_top_k_left,
self.moba_decoder_top_k_right,
self.moba_use_decoder_seq_limit,
layer.moba_use_mlp,
getattr(layer, "cache_quant_type_str", "none"),
)[0]
return out