mirror of
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2e9e53ff7e
* remove max_num_batched_tokens in parallel config * remove max_num_seqs * update test case * fix test * fix --------- Co-authored-by: Jiang-Jia-Jun <163579578+Jiang-Jia-Jun@users.noreply.github.com>
199 lines
7.3 KiB
Python
199 lines
7.3 KiB
Python
"""
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# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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from typing import TYPE_CHECKING
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import paddle
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try:
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from fastdeploy.model_executor.ops.gpu import get_cur_cu_seq_len_k, moba_attention
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except:
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moba_attention = None
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get_cur_cu_seq_len_k = None
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if TYPE_CHECKING:
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from fastdeploy.model_executor.forward_meta import ForwardMeta
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from fastdeploy.config import FDConfig
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from fastdeploy.model_executor.layers.attention.attention import Attention
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from fastdeploy.model_executor.layers.attention.base_attention_backend import (
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AttentionBackend,
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AttentionMetadata,
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)
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@dataclass
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class MobaAttentionMetadata(AttentionMetadata):
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"""
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AppendAttentionMetadata
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"""
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q_input: paddle.Tensor = None
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k_input: paddle.Tensor = None
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v_input: paddle.Tensor = None
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cu_seq_q_pack: paddle.Tensor = None
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cu_seqlens_k: paddle.Tensor = None
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q_pack_tokens: paddle.Tensor = None
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max_enc_len_this_time: int = 0
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max_dec_len_this_time: int = 0
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class MobaAttentionBackend(AttentionBackend):
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"""
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The backend class that uses paddle native attention implementation.
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Which is used only for testing purpose.
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"""
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def __init__(
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self,
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fd_config: FDConfig,
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kv_num_heads: int,
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num_heads: int,
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head_dim: int,
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encoder_block_shape_q: int = -1,
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decoder_block_shape_q: int = -1,
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) -> None:
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"""
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MobaAttentionBackend __init__
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"""
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super().__init__()
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self.attention_metadata: MobaAttentionMetadata = None
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assert fd_config.moba_attention_config is not None, "moba_attention_config is None"
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self.block_size = fd_config.parallel_config.block_size
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self.max_seq_len = fd_config.parallel_config.max_model_len
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self.max_num_seqs = fd_config.scheduler_config.max_num_seqs
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self.kv_num_heads = kv_num_heads
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self.num_heads = num_heads
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self.head_dim = fd_config.model_config.head_dim
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self.num_layers: int = fd_config.model_config.num_hidden_layers
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self.attn_block_m = 128
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self.moba_block_size = fd_config.moba_attention_config.moba_block_size
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self.moba_encoder_top_k_left = int(fd_config.moba_attention_config.moba_encoder_top_k_left)
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self.moba_encoder_top_k_right = int(fd_config.moba_attention_config.moba_encoder_top_k_right)
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self.moba_use_encoder_seq_limit = int(fd_config.moba_attention_config.moba_use_encoder_seq_limit)
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self.moba_decoder_top_k_left = int(fd_config.moba_attention_config.moba_decoder_top_k_left)
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self.moba_decoder_top_k_right = int(fd_config.moba_attention_config.moba_decoder_top_k_right)
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self.moba_use_decoder_seq_limit = int(fd_config.moba_attention_config.moba_use_decoder_seq_limit)
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self.moba_max_seq_length = fd_config.moba_attention_config.moba_max_seq_length
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def init_attention_metadata(self, forward_meta: ForwardMeta):
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"""Init the metadata for a forward pass."""
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metadata = MobaAttentionMetadata()
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metadata._dtype = paddle.get_default_dtype()
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metadata.cu_seq_q_pack, metadata.cu_seqlens_k, metadata.q_pack_tokens = get_cur_cu_seq_len_k(
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forward_meta.seq_lens_encoder,
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forward_meta.seq_lens_decoder,
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forward_meta.seq_lens_this_time,
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int(self.attn_block_m),
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)
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metadata.max_enc_len_this_time = forward_meta.seq_lens_encoder.max().cpu()
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metadata.max_dec_len_this_time = forward_meta.seq_lens_decoder.max().cpu()
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q_token_num = int(forward_meta.cu_seqlens_q[-1])
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k_token_num = int(metadata.cu_seqlens_k[-1])
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metadata.q_input = paddle.zeros(
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[q_token_num + self.attn_block_m, self.num_heads * self.head_dim], dtype=metadata._dtype
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)
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metadata.k_input = paddle.zeros(
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[k_token_num + self.attn_block_m, self.kv_num_heads * self.head_dim], dtype=metadata._dtype
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)
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metadata.v_input = paddle.zeros(
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[k_token_num + self.attn_block_m, self.kv_num_heads * self.head_dim], dtype=metadata._dtype
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)
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self.attention_metadata = metadata
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assert self.max_seq_len <= self.moba_max_seq_length
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def get_kv_cache_shape(
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self,
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max_num_blocks: int,
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kv_cache_quant_type: str = None,
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):
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"""
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Calculate kv cache shape
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"""
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if kv_cache_quant_type is not None and kv_cache_quant_type == "int4_zp":
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return (
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max_num_blocks,
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self.kv_num_heads,
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self.block_size,
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self.head_dim // 2,
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)
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else:
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return (
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max_num_blocks,
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self.kv_num_heads,
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self.block_size,
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self.head_dim,
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)
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def forward_mixed(
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self,
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q: paddle.Tensor,
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k: paddle.Tensor,
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v: paddle.Tensor,
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qkv: paddle.Tensor,
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compressed_kv: paddle.Tensor,
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k_pe: paddle.Tensor,
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layer: Attention,
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forward_meta: ForwardMeta,
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) -> paddle.Tensor:
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"""
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Mixed模式的前向传播
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"""
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attention_metadata = self.attention_metadata
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out = moba_attention(
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qkv,
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attention_metadata.q_input,
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attention_metadata.k_input,
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attention_metadata.v_input,
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forward_meta.cu_seqlens_q,
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attention_metadata.cu_seqlens_k,
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attention_metadata.cu_seq_q_pack,
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attention_metadata.q_pack_tokens,
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forward_meta.seq_lens_encoder,
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forward_meta.seq_lens_decoder,
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forward_meta.caches[2 * layer.layer_id],
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forward_meta.caches[2 * layer.layer_id + 1],
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forward_meta.block_tables,
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forward_meta.rotary_embs,
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layer.cache_k_block_means,
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getattr(layer, "attn_gate_weight", None),
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layer.qkv_bias,
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getattr(layer, "cache_k_scale", None),
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getattr(layer, "cache_v_scale", None),
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getattr(layer, "cache_k_out_scale", None),
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getattr(layer, "cache_v_out_scale", None),
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getattr(layer, "cache_k_zp", None),
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getattr(layer, "cache_v_zp", None),
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self.num_heads,
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self.kv_num_heads,
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self.head_dim,
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self.max_seq_len,
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attention_metadata.max_enc_len_this_time,
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attention_metadata.max_dec_len_this_time,
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self.moba_encoder_top_k_left,
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self.moba_encoder_top_k_right,
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self.moba_use_encoder_seq_limit,
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self.moba_decoder_top_k_left,
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self.moba_decoder_top_k_right,
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self.moba_use_decoder_seq_limit,
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layer.moba_use_mlp,
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getattr(layer, "cache_quant_type_str", "none"),
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)[0]
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return out
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