mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2026-04-23 00:17:25 +08:00
周周周
684 lines
23 KiB
Python
684 lines
23 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.
|
|
"""
|
|
|
|
from __future__ import annotations
|
|
|
|
import paddle
|
|
|
|
paddle.enable_compat(scope={"flash_mla"}) # Enable torch proxy before importing flash_mla
|
|
import math
|
|
import os
|
|
from dataclasses import dataclass, field
|
|
from typing import TYPE_CHECKING, List, Optional, Tuple
|
|
|
|
import paddle
|
|
from paddle.nn.functional.flash_attention import flash_attn_unpadded
|
|
|
|
try:
|
|
from paddle.nn.functional.flash_attention import flash_attention_v3_varlen
|
|
except:
|
|
flash_attention_v3_varlen = None
|
|
|
|
from fastdeploy.model_executor.layers.attention.ops import (
|
|
get_block_shape_and_split_kv_block,
|
|
init_kv_signal_per_query,
|
|
init_signal_layerwise,
|
|
open_shm_and_get_meta_signal,
|
|
)
|
|
from fastdeploy.platforms import current_platform
|
|
|
|
if current_platform.is_cuda():
|
|
from fastdeploy.model_executor.ops.gpu import (
|
|
decode_mla_write_cache,
|
|
multi_head_latent_attention,
|
|
prefill_mla_write_cache,
|
|
)
|
|
|
|
if TYPE_CHECKING:
|
|
from fastdeploy.model_executor.forward_meta import ForwardMeta
|
|
|
|
import triton
|
|
import triton.language as tl
|
|
|
|
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,
|
|
)
|
|
from fastdeploy.model_executor.layers.attention.utils import init_rank_and_device_id
|
|
|
|
|
|
@triton.jit()
|
|
def extract_kernel(
|
|
q,
|
|
cu_seqlens_q,
|
|
seq_lens_encoder,
|
|
seq_lens_decoder,
|
|
output,
|
|
cache_seqlens,
|
|
HIDDEN_DIM: tl.constexpr,
|
|
BLOCK_SIZE: tl.constexpr,
|
|
):
|
|
|
|
batch_id = tl.program_id(axis=0)
|
|
cache_kv_len = tl.load(seq_lens_decoder + batch_id)
|
|
|
|
# 这个batch不是decoder，所以不需要动弹
|
|
if cache_kv_len <= 0:
|
|
return
|
|
|
|
cu_len_this_batch = tl.load(cu_seqlens_q + batch_id)
|
|
|
|
read_offsets = tl.arange(0, BLOCK_SIZE)
|
|
q += cu_len_this_batch * HIDDEN_DIM
|
|
|
|
row_data = tl.load(q + read_offsets, mask=read_offsets < HIDDEN_DIM)
|
|
|
|
output += batch_id * HIDDEN_DIM
|
|
|
|
tl.store(output + read_offsets, row_data, mask=read_offsets < HIDDEN_DIM)
|
|
|
|
tl.store(cache_seqlens + batch_id, cache_kv_len + 1)
|
|
|
|
|
|
def extract_decoder_token_from_q(
|
|
q: paddle.Tensor,
|
|
cu_seqlens_q: paddle.Tensor,
|
|
seq_lens_encoder: paddle.Tensor,
|
|
seq_lens_decoder: paddle.Tensor,
|
|
):
|
|
assert len(q.shape) == 2
|
|
assert len(cu_seqlens_q.shape) == 1
|
|
assert len(seq_lens_encoder.shape) == 1
|
|
assert len(seq_lens_decoder.shape) == 1
|
|
|
|
max_bsz = seq_lens_decoder.shape[0]
|
|
|
|
hidden_dim = q.shape[-1]
|
|
out = paddle.empty([max_bsz, hidden_dim], dtype=q.dtype)
|
|
|
|
cache_seqlens = paddle.zeros_like(seq_lens_decoder)
|
|
|
|
BLOCK_SIZE = triton.next_power_of_2(hidden_dim)
|
|
|
|
grid = (max_bsz,)
|
|
|
|
extract_kernel[grid](
|
|
q,
|
|
cu_seqlens_q,
|
|
seq_lens_encoder,
|
|
seq_lens_decoder,
|
|
out,
|
|
cache_seqlens,
|
|
hidden_dim,
|
|
BLOCK_SIZE,
|
|
)
|
|
|
|
return out, cache_seqlens
|
|
|
|
|
|
@triton.jit()
|
|
def insert_kernel(
|
|
decoder_res,
|
|
cu_seqlens_q,
|
|
seq_lens_encoder,
|
|
seq_lens_decoder,
|
|
output,
|
|
HIDDEN_DIM: tl.constexpr,
|
|
BLOCK_SIZE: tl.constexpr,
|
|
):
|
|
|
|
batch_id = tl.program_id(axis=0)
|
|
cache_kv_len = tl.load(seq_lens_decoder + batch_id)
|
|
|
|
# 这个batch不是decoder，所以不需要动弹
|
|
if cache_kv_len <= 0:
|
|
return
|
|
|
|
cu_len_this_batch = tl.load(cu_seqlens_q + batch_id)
|
|
|
|
read_offsets = tl.arange(0, BLOCK_SIZE)
|
|
|
|
decoder_res += batch_id * HIDDEN_DIM
|
|
|
|
row_data = tl.load(decoder_res + read_offsets, mask=read_offsets < HIDDEN_DIM)
|
|
|
|
output += cu_len_this_batch * HIDDEN_DIM
|
|
|
|
tl.store(output + read_offsets, row_data, mask=read_offsets < HIDDEN_DIM)
|
|
|
|
|
|
def insert_decoder_result_back(
|
|
decoder_result: paddle.Tensor,
|
|
cu_seqlens_q: paddle.Tensor,
|
|
seq_lens_encoder: paddle.Tensor,
|
|
seq_lens_decoder: paddle.Tensor,
|
|
mixed_token_num,
|
|
):
|
|
assert len(decoder_result.shape) == 4
|
|
assert len(cu_seqlens_q.shape) == 1
|
|
assert len(seq_lens_encoder.shape) == 1
|
|
|
|
max_bsz = seq_lens_encoder.shape[0]
|
|
|
|
hidden_dim = decoder_result.shape[-2] * decoder_result.shape[-1]
|
|
out = paddle.zeros([mixed_token_num, hidden_dim], dtype=decoder_result.dtype)
|
|
|
|
BLOCK_SIZE = triton.next_power_of_2(hidden_dim)
|
|
|
|
grid = (max_bsz,)
|
|
|
|
insert_kernel[grid](
|
|
decoder_result,
|
|
cu_seqlens_q,
|
|
seq_lens_encoder,
|
|
seq_lens_decoder,
|
|
out,
|
|
hidden_dim,
|
|
BLOCK_SIZE,
|
|
)
|
|
|
|
return out
|
|
|
|
|
|
def yarn_get_mscale(scale=1, mscale=1):
|
|
""" """
|
|
if scale <= 1:
|
|
return 1.0
|
|
return 0.1 * mscale * math.log(scale) + 1.0
|
|
|
|
|
|
@dataclass
|
|
class MLAAttentionMetadata(AttentionMetadata):
|
|
"""
|
|
MLAAttentionMetadata for Multi-Layer Attention
|
|
"""
|
|
|
|
_dtype: paddle.dtype = paddle.bfloat16
|
|
encoder_max_partition_size: int = 32768
|
|
max_partition_size: int = 32768
|
|
block_tables: Optional[paddle.Tensor] = None
|
|
rotary_embs: Optional[paddle.Tensor] = None
|
|
attn_mask: Optional[paddle.Tensor] = None
|
|
_fuse_kernel_compute_dtype: str = "bf16"
|
|
|
|
# pd_disaggregation
|
|
kv_signal_metadata: Optional[paddle.Tensor] = None
|
|
kv_signal_data_list: List[Optional[paddle.Tensor]] = field(default_factory=list)
|
|
|
|
max_enc_len_this_time: Optional[paddle.Tensor] = None
|
|
max_dec_len_this_time: Optional[paddle.Tensor] = None
|
|
max_kv_len_this_time: Optional[paddle.Tensor] = None
|
|
|
|
|
|
class MLAAttentionBackend(AttentionBackend):
|
|
"""
|
|
MLA Attention Backend implementation.
|
|
"""
|
|
|
|
__infer_dynamic_dims_fields__ = ["attention_metadata"]
|
|
attention_metadata: MLAAttentionMetadata
|
|
flash_attn_func: callable = None
|
|
|
|
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:
|
|
"""
|
|
MLAAttentionBackend __init__
|
|
"""
|
|
super().__init__()
|
|
self.attention_metadata: MLAAttentionMetadata = None
|
|
|
|
# 基础配置
|
|
self.block_size: int = fd_config.cache_config.block_size
|
|
self.max_seq_len: int = fd_config.model_config.max_model_len
|
|
self.rope_theta: float = (
|
|
10000.0 if fd_config.model_config.rope_theta is None else fd_config.model_config.rope_theta
|
|
)
|
|
self.rope_3d: bool = getattr(fd_config.model_config, "rope_3d", False)
|
|
self.causal: bool = getattr(fd_config.model_config, "causal", True)
|
|
self.speculative_method: str = fd_config.speculative_config.method
|
|
self.use_speculate: bool = self.speculative_method is not None
|
|
self.speculate_max_draft_token_num: int = fd_config.speculative_config.num_speculative_tokens
|
|
self.keep_pd_step_flag: bool = fd_config.speculative_config.model_type == "mtp"
|
|
self.num_layers_draft_model: int = int(fd_config.speculative_config.method in ["mtp"])
|
|
|
|
self.num_heads: int = num_heads
|
|
self.head_dim: int = fd_config.model_config.head_dim
|
|
self.num_layers: int = fd_config.model_config.num_hidden_layers
|
|
|
|
# For Multi Head Latent Attention
|
|
self.kv_lora_rank: int = fd_config.model_config.kv_lora_rank
|
|
self.qk_rope_head_dim: int = fd_config.model_config.qk_rope_head_dim
|
|
self.qk_head_dim: int = fd_config.model_config.qk_nope_head_dim + fd_config.model_config.qk_rope_head_dim
|
|
self.attn_softmax_scale: float = self.qk_head_dim**-0.5
|
|
if fd_config.model_config.rope_scaling:
|
|
mscale_all_dim = fd_config.model_config.rope_scaling.get("mscale_all_dim", False) # 1.0
|
|
scaling_factor = fd_config.model_config.rope_scaling["factor"] # 40
|
|
mscale = yarn_get_mscale(scaling_factor, float(mscale_all_dim))
|
|
self.attn_softmax_scale = self.attn_softmax_scale * mscale * mscale
|
|
|
|
self.pd_disaggregation_mode: str = fd_config.parallel_config.pd_disaggregation_mode
|
|
|
|
self.start_layer_index: int = fd_config.model_config.start_layer_index
|
|
self.device_id: int = os.getenv("CUDA_VISIBLE_DEVICES", None)
|
|
|
|
self.rank, self.device_id = init_rank_and_device_id(fd_config)
|
|
|
|
self.useless_tensor = paddle.randn([1]).cast("int32")
|
|
|
|
if self.flash_attn_func is None:
|
|
prop = paddle.device.cuda.get_device_properties()
|
|
cc = prop.major * 10 + prop.minor
|
|
is_current_sm_supported = cc >= 90
|
|
is_paddle_supported = any(num >= 90 for num in paddle.version.cuda_archs())
|
|
if is_current_sm_supported and is_paddle_supported:
|
|
self.flash_attn_func = flash_attention_v3_varlen
|
|
print("The current platform supports Flash Attention V3.")
|
|
self.flash_attn_kwargs = {"softmax_scale": self.attn_softmax_scale}
|
|
else:
|
|
self.flash_attn_func = flash_attn_unpadded
|
|
self.flash_attn_kwargs = {"scale": self.attn_softmax_scale, "training": False}
|
|
print(
|
|
"The current platform does not support Flash Attention V3, so Flash Attention V2 will be used instead."
|
|
)
|
|
|
|
def init_attention_metadata(self, forward_meta: ForwardMeta):
|
|
"""Initialize attention metadata hence all layers in the forward pass can reuse it."""
|
|
metadata = MLAAttentionMetadata()
|
|
metadata.max_partition_size = 32768
|
|
metadata.encoder_max_partition_size = self.max_seq_len
|
|
metadata._dtype = paddle.get_default_dtype()
|
|
if metadata._dtype == "bfloat16":
|
|
metadata._fuse_kernel_compute_dtype = "bf16"
|
|
elif metadata._dtype == "float16":
|
|
metadata._fuse_kernel_compute_dtype = "fp16"
|
|
elif metadata._dtype == "float32":
|
|
metadata._fuse_kernel_compute_dtype = "fp32"
|
|
|
|
metadata.block_tables = forward_meta.block_tables
|
|
metadata.rotary_embs = forward_meta.rotary_embs
|
|
metadata.attn_mask = forward_meta.attn_mask
|
|
metadata.pre_caches_length = forward_meta.pre_caches_length
|
|
|
|
get_block_shape_and_split_kv_block(
|
|
forward_meta.seq_lens_encoder,
|
|
forward_meta.seq_lens_decoder,
|
|
forward_meta.seq_lens_this_time,
|
|
forward_meta.decoder_batch_ids,
|
|
forward_meta.decoder_tile_ids_per_batch,
|
|
self.useless_tensor, # not used in mla
|
|
forward_meta.decoder_num_blocks_device,
|
|
forward_meta.decoder_chunk_size_device,
|
|
forward_meta.max_len_tensor_cpu,
|
|
self.useless_tensor, # not used in mla
|
|
self.useless_tensor, # not used in mla
|
|
self.useless_tensor, # not used in mla
|
|
forward_meta.kv_batch_ids,
|
|
forward_meta.kv_tile_ids_per_batch,
|
|
forward_meta.kv_num_blocks_x_cpu,
|
|
-1, # not need.
|
|
-1, # not need.
|
|
-1, # not need.
|
|
self.block_size,
|
|
)
|
|
# MLA
|
|
metadata.max_enc_len_this_time = forward_meta.max_len_tensor_cpu[1]
|
|
metadata.max_dec_len_this_time = forward_meta.max_len_tensor_cpu[2]
|
|
metadata.max_kv_len_this_time = forward_meta.max_len_tensor_cpu[5]
|
|
|
|
# pd_disaggregation
|
|
metadata.kv_signal_data_list = [None] * self.num_layers
|
|
if self.pd_disaggregation_mode == "per_chunk":
|
|
if not self.keep_pd_step_flag and not forward_meta.is_dummy_or_profile_run:
|
|
init_kv_signal_per_query(
|
|
forward_meta.seq_lens_encoder,
|
|
forward_meta.seq_lens_this_time,
|
|
forward_meta.seq_lens_decoder,
|
|
self.rank,
|
|
self.num_layers + self.num_layers_draft_model,
|
|
)
|
|
elif self.pd_disaggregation_mode == "per_query":
|
|
metadata.kv_signal_metadata = open_shm_and_get_meta_signal(
|
|
self.rank, int(self.device_id), self.keep_pd_step_flag
|
|
)
|
|
|
|
self.attention_metadata: AttentionMetadata = metadata
|
|
|
|
def get_attention_meta(self) -> AttentionMetadata:
|
|
"""get_attention_meta"""
|
|
return self.attention_metadata
|
|
|
|
def get_kv_cache_shape(
|
|
self,
|
|
max_num_blocks: int,
|
|
kv_cache_quant_type: str = None,
|
|
) -> Tuple[int, int, int, int]:
|
|
"""
|
|
Calculate kv cache shape for MLA
|
|
"""
|
|
key_cache_shape = [max_num_blocks, 1, self.block_size, self.kv_lora_rank + self.qk_rope_head_dim]
|
|
value_cache_shape = []
|
|
return key_cache_shape, value_cache_shape
|
|
|
|
def forward_extend(
|
|
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:
|
|
"""
|
|
Prefill阶段的前向传播
|
|
"""
|
|
metadata = self.attention_metadata
|
|
|
|
if self.pd_disaggregation_mode == "per_query":
|
|
metadata.kv_signal_data_list[layer.layer_id] = init_signal_layerwise(
|
|
metadata.kv_signal_metadata,
|
|
layer.layer_id + self.start_layer_index,
|
|
)
|
|
|
|
latent_cache = forward_meta.caches[layer.layer_id] if hasattr(forward_meta, "caches") else None
|
|
|
|
# 写入缓存
|
|
prefill_mla_write_cache(
|
|
compressed_kv,
|
|
k_pe,
|
|
latent_cache,
|
|
forward_meta.seq_lens_encoder,
|
|
forward_meta.seq_lens_decoder,
|
|
forward_meta.batch_id_per_token,
|
|
forward_meta.cu_seqlens_q,
|
|
metadata.block_tables,
|
|
metadata.kv_signal_data_list[layer.layer_id],
|
|
"none",
|
|
getattr(forward_meta, "max_input_length", -1),
|
|
)
|
|
|
|
# Flash注意力计算
|
|
fmha_out = self.flash_attn_func(
|
|
q,
|
|
k,
|
|
v,
|
|
forward_meta.cu_seqlens_q,
|
|
forward_meta.cu_seqlens_k,
|
|
metadata.max_enc_len_this_time,
|
|
metadata.max_enc_len_this_time,
|
|
causal=self.causal,
|
|
**self.flash_attn_kwargs,
|
|
)[0]
|
|
|
|
return fmha_out
|
|
|
|
def forward_decode(
|
|
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:
|
|
"""
|
|
Decode阶段的前向传播
|
|
"""
|
|
metadata = self.attention_metadata
|
|
|
|
if self.pd_disaggregation_mode == "per_query":
|
|
metadata.kv_signal_data_list[layer.layer_id] = init_signal_layerwise(
|
|
metadata.kv_signal_metadata,
|
|
layer.layer_id + self.start_layer_index,
|
|
)
|
|
|
|
latent_cache = forward_meta.caches[layer.layer_id] if hasattr(forward_meta, "caches") else None
|
|
|
|
# 获取推测解码参数
|
|
speculate_decoder = self.speculative_method is not None
|
|
speculate_max_tokens = self.speculate_max_draft_token_num
|
|
|
|
# 写入缓存
|
|
decode_mla_write_cache(
|
|
compressed_kv,
|
|
k_pe,
|
|
latent_cache,
|
|
forward_meta.seq_lens_decoder,
|
|
forward_meta.seq_lens_encoder,
|
|
forward_meta.batch_id_per_token,
|
|
forward_meta.cu_seqlens_q,
|
|
metadata.block_tables,
|
|
"none",
|
|
self.max_seq_len,
|
|
speculate_decoder,
|
|
)
|
|
|
|
# 多头潜在注意力计算
|
|
fmha_out = multi_head_latent_attention(
|
|
q,
|
|
latent_cache,
|
|
latent_cache,
|
|
forward_meta.seq_lens_decoder,
|
|
forward_meta.seq_lens_this_time,
|
|
forward_meta.cu_seqlens_q,
|
|
forward_meta.batch_id_per_token,
|
|
metadata.block_tables,
|
|
forward_meta.kv_batch_ids,
|
|
forward_meta.kv_tile_ids_per_batch,
|
|
forward_meta.kv_num_blocks_x_cpu,
|
|
forward_meta.decoder_batch_ids,
|
|
forward_meta.decoder_tile_ids_per_batch,
|
|
forward_meta.decoder_num_blocks_device,
|
|
forward_meta.decoder_chunk_size_device,
|
|
metadata.max_dec_len_this_time,
|
|
metadata.max_kv_len_this_time,
|
|
None, # attn_mask
|
|
None, # qkv_bias
|
|
None, # qkv_out_scales
|
|
None, # cache_k_quant_scales
|
|
None, # cache_v_quant_scales
|
|
None, # cache_k_dequant_scales
|
|
None, # cache_v_dequant_scales
|
|
None, # cache_k_zp
|
|
None, # cache_v_zp
|
|
None, # out_shifts
|
|
None, # out_smooths
|
|
metadata._fuse_kernel_compute_dtype,
|
|
"none", # cache_quant_type
|
|
self.kv_lora_rank,
|
|
self.max_seq_len,
|
|
self.attn_softmax_scale,
|
|
0.0, # quant_max_bound
|
|
0.0, # quant_min_bound
|
|
0.0, # out_linear_in_scale
|
|
speculate_max_tokens,
|
|
True, # causal
|
|
speculate_decoder,
|
|
)
|
|
|
|
return fmha_out
|
|
|
|
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
|
|
"""
|
|
metadata = self.attention_metadata
|
|
speculate_decoder = self.speculative_method is not None
|
|
speculate_max_tokens = self.speculate_max_draft_token_num
|
|
|
|
if self.pd_disaggregation_mode == "per_query":
|
|
metadata.kv_signal_data_list[layer.layer_id] = init_signal_layerwise(
|
|
metadata.kv_signal_metadata,
|
|
layer.layer_id + self.start_layer_index,
|
|
)
|
|
|
|
latent_cache = forward_meta.caches[layer.layer_id] if hasattr(forward_meta, "caches") else None
|
|
|
|
if k is not None:
|
|
prefill_mla_write_cache(
|
|
compressed_kv,
|
|
k_pe,
|
|
latent_cache,
|
|
forward_meta.seq_lens_encoder,
|
|
forward_meta.seq_lens_decoder,
|
|
forward_meta.batch_id_per_token,
|
|
forward_meta.cu_seqlens_q,
|
|
metadata.block_tables,
|
|
metadata.kv_signal_data_list[layer.layer_id],
|
|
"none",
|
|
self.max_seq_len,
|
|
)
|
|
# FA
|
|
fmha_out = self.flash_attn_func(
|
|
q,
|
|
k,
|
|
v,
|
|
forward_meta.cu_seqlens_q,
|
|
forward_meta.cu_seqlens_k,
|
|
metadata.max_enc_len_this_time,
|
|
metadata.max_enc_len_this_time,
|
|
causal=self.causal,
|
|
**self.flash_attn_kwargs,
|
|
)[0]
|
|
|
|
return fmha_out
|
|
|
|
# Decode
|
|
if k is None:
|
|
decode_mla_write_cache(
|
|
compressed_kv,
|
|
k_pe,
|
|
latent_cache,
|
|
forward_meta.seq_lens_decoder,
|
|
forward_meta.seq_lens_encoder,
|
|
forward_meta.batch_id_per_token,
|
|
forward_meta.cu_seqlens_q,
|
|
metadata.block_tables,
|
|
"none",
|
|
self.max_seq_len,
|
|
speculate_decoder,
|
|
)
|
|
|
|
if int(os.getenv("USE_FLASH_MLA", "0")) == 0:
|
|
assert self.num_heads <= 64, "paddle mla attention support failed"
|
|
# 多头潜在注意力计算
|
|
fmha_out = multi_head_latent_attention(
|
|
q,
|
|
latent_cache,
|
|
latent_cache,
|
|
forward_meta.seq_lens_decoder,
|
|
forward_meta.seq_lens_this_time,
|
|
forward_meta.cu_seqlens_q,
|
|
forward_meta.batch_id_per_token,
|
|
metadata.block_tables,
|
|
forward_meta.kv_batch_ids,
|
|
forward_meta.kv_tile_ids_per_batch,
|
|
forward_meta.kv_num_blocks_x_cpu,
|
|
forward_meta.decoder_batch_ids,
|
|
forward_meta.decoder_tile_ids_per_batch,
|
|
forward_meta.decoder_num_blocks_device,
|
|
forward_meta.decoder_chunk_size_device,
|
|
metadata.max_dec_len_this_time,
|
|
metadata.max_kv_len_this_time,
|
|
None, # attn_mask
|
|
None, # qkv_bias
|
|
None, # qkv_out_scales
|
|
None, # cache_k_quant_scales
|
|
None, # cache_v_quant_scales
|
|
None, # cache_k_dequant_scales
|
|
None, # cache_v_dequant_scales
|
|
None, # cache_k_zp
|
|
None, # cache_v_zp
|
|
None, # out_shifts
|
|
None, # out_smooths
|
|
metadata._fuse_kernel_compute_dtype,
|
|
"none", # cache_quant_type
|
|
self.kv_lora_rank,
|
|
self.max_seq_len,
|
|
self.attn_softmax_scale,
|
|
0.0, # quant_max_bound
|
|
0.0, # quant_min_bound
|
|
0.0, # out_linear_in_scale
|
|
speculate_max_tokens,
|
|
True, # causal
|
|
speculate_decoder,
|
|
)
|
|
|
|
return fmha_out
|
|
else:
|
|
import flash_mla
|
|
|
|
decoder_q, cache_seqlens = extract_decoder_token_from_q(
|
|
q,
|
|
forward_meta.cu_seqlens_q,
|
|
forward_meta.seq_lens_encoder,
|
|
forward_meta.seq_lens_decoder,
|
|
)
|
|
|
|
tile_scheduler_metadata, num_splits = flash_mla.get_mla_metadata()
|
|
token_num = q.shape[0]
|
|
decoder_q.reshape_([-1, 1, self.num_heads, 576])
|
|
|
|
new_cache_shape = latent_cache.shape
|
|
assert new_cache_shape[1] == 1
|
|
new_cache_shape[1], new_cache_shape[2] = new_cache_shape[2], new_cache_shape[1]
|
|
|
|
decoder_res, _ = flash_mla.flash_mla_with_kvcache(
|
|
decoder_q,
|
|
# 外面的开源仓库的kv cache存储格式和FD的不同
|
|
# 幸好这里缓存的头是1，直接view即可，否则上上下下要改很多！
|
|
latent_cache.view(new_cache_shape),
|
|
metadata.block_tables,
|
|
cache_seqlens,
|
|
512, # t.dv,
|
|
tile_scheduler_metadata,
|
|
num_splits,
|
|
softmax_scale=self.attn_softmax_scale,
|
|
causal=True,
|
|
)
|
|
|
|
final_res = insert_decoder_result_back(
|
|
decoder_res,
|
|
forward_meta.cu_seqlens_q,
|
|
forward_meta.seq_lens_encoder,
|
|
forward_meta.seq_lens_decoder,
|
|
token_num,
|
|
)
|
|
|
|
return final_res
|