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>
238 lines
9.6 KiB
Python
238 lines
9.6 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 typing import TYPE_CHECKING, Dict, Optional
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import numpy as np
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import paddle
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from paddle import nn
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from paddleformers.utils.log import logger
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from fastdeploy.config import FDConfig
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from fastdeploy.model_executor.layers.quantization.quant_base import QuantMethodBase
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if TYPE_CHECKING:
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from fastdeploy.model_executor.forward_meta import ForwardMeta
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import os
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from safetensors import safe_open
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from fastdeploy.model_executor.layers.utils import get_tensor
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from fastdeploy.model_executor.utils import default_weight_loader
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class Attention(nn.Layer):
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"""
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The AttentionLayer.
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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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layer_id: int,
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v_head_dim: int = -1,
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rope_type: str = "",
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qkv_bias: Optional[paddle.Tensor] = None,
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qkv_scale: Optional[paddle.Tensor] = None,
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prefix: str = "",
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out_scale: float = -1.0,
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linear_shift: paddle.Tensor = None,
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linear_smooth: paddle.Tensor = None,
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use_neox_rotary_style: bool = False,
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use_qk_norm: bool = False,
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rms_norm_eps: float = 1e-6,
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) -> None:
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"""
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Initializes `LMLayer` with the given parameters.
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Args:
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fd_config (dict): The config of LM model.
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layer_id (int): The id of current layer.
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v_head_dim (int, optional): The head dim of value. Defaults to -1.
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rope_type (str, optional): The type of RoPE. Defaults to "".
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qkv_bias (Optional[paddle.Tensor], optional): The bias of QKV. Defaults to None.
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qkv_scale (Optional[paddle.Tensor], optional): The scale of QKV. Defaults to None.
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prefix (str, optional): The name of current layer. Defaults to "".
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linear_shift (Optional[paddle.Tensor], optional): The shift of linear. Defaults to None.
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linear_smooth (Optional[paddle.Tensor], optional): The smooth of linear. Defaults to None.
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use_qk_norm (bool, optional): Whether to apply rmsnorm on QA after rope. Defaults to False.
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rms_norm_eps (float, optional): The epsilon of RMSNorm. Defaults to 1e-6.
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Raises:
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ValueError: If the `v_head_dim` is less than 0.
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"""
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super().__init__()
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self.fd_config = fd_config
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self.num_heads: int = (
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fd_config.model_config.num_attention_heads // fd_config.parallel_config.tensor_parallel_size
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)
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self.head_dim: int = fd_config.model_config.head_dim
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self.kv_num_heads: int = max(
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1,
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fd_config.model_config.num_key_value_heads // fd_config.parallel_config.tensor_parallel_size,
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)
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self.layer_id: int = layer_id
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self.v_head_dim: int = v_head_dim if v_head_dim > 0 else self.head_dim
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self.rope_type: str = rope_type
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self.qk_head_dim: int = self.head_dim
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self.prefix: str = prefix
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# not use
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self.linear_shift: paddle.Tensor | None = linear_shift
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self.linear_smooth: paddle.Tensor | None = linear_smooth
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self.qkv_bias: paddle.Tensor | None = qkv_bias
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self.qkv_scale: paddle.Tensor | None = qkv_scale
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self._dtype = self._helper.get_default_dtype()
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self.out_scale: float = out_scale
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self.use_neox_rotary_style: bool = use_neox_rotary_style
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if fd_config.quant_config and hasattr(fd_config.quant_config, "kv_cache_quant_type"):
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self.quant_method: QuantMethodBase = fd_config.quant_config.get_quant_method(self)
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else:
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self.quant_method = None
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if self.quant_method is None:
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logger.info(f"Attention is running in cache kv {self._dtype} mode")
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else:
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logger.info(f"Attention is running in cache kv {self.quant_method.cache_quant_config.quant_type} mode")
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self.use_qk_norm = use_qk_norm
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self.rms_norm_eps = rms_norm_eps
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if self.use_qk_norm:
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self.q_norm_key = f"{self.prefix}.q_norm"
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self.k_norm_key = f"{self.prefix}.k_norm"
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self.init_weight()
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if (
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fd_config.moba_attention_config is not None
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and fd_config.moba_attention_config.moba_encoder_top_k_left is not None
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and fd_config.moba_attention_config.moba_encoder_top_k_right is not None
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and fd_config.moba_attention_config.moba_decoder_top_k_left is not None
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and fd_config.moba_attention_config.moba_decoder_top_k_right is not None
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):
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mlp_weight_path = os.path.join(
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fd_config.model_config.model, fd_config.moba_attention_config.mlp_weight_name
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)
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self.moba_use_mlp = mlp_weight_path is not None and os.path.exists(mlp_weight_path)
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moba_block_size = fd_config.moba_attention_config.moba_block_size
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moba_max_seq_length = fd_config.moba_attention_config.moba_max_seq_length
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if self.moba_use_mlp:
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mlp_weight = {}
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with safe_open(mlp_weight_path, framework="np", device="cpu") as f:
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for key_name in f.keys():
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weight = f.get_tensor(key_name)
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weight = paddle.Tensor(weight, zero_copy=True)
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weight = weight._copy_to(paddle.framework._current_expected_place(), False)
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mlp_weight[key_name] = weight
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if self.layer_id < fd_config.model_config.num_hidden_layers - 1:
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self.attn_gate_weight = mlp_weight[
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f"ernie.layers.{self.layer_id}.self_attn.attn_gate.weight"
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].astype(paddle.get_default_dtype())[
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fd_config.parallel_config.tensor_parallel_rank
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* self.kv_num_heads : (fd_config.parallel_config.tensor_parallel_rank + 1)
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* self.kv_num_heads
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]
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assert self.attn_gate_weight.shape[1] % moba_block_size == 0
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self.cache_k_block_means = paddle.zeros(
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[
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fd_config.scheduler_config.max_num_seqs,
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moba_max_seq_length // moba_block_size,
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self.kv_num_heads,
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self.head_dim,
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],
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dtype=paddle.get_default_dtype(),
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)
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def init_weight(self):
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if self.quant_method is not None:
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self.quant_method.create_weights(
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self,
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weight_loader=(
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self.weight_loader if hasattr(self, "weight_loader") else default_weight_loader(self.fd_config)
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),
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)
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if self.use_qk_norm:
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self.q_norm_weight = self.create_parameter(
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shape=[self.qk_head_dim],
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dtype="float32",
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is_bias=False,
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default_initializer=paddle.nn.initializer.Constant(0),
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)
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self.k_norm_weight = self.create_parameter(
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shape=[self.qk_head_dim],
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dtype="float32",
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is_bias=False,
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default_initializer=paddle.nn.initializer.Constant(0),
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)
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def load_state_dict(self, state_dict: Dict[str, paddle.Tensor | np.ndarray]):
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"""
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Attention only have quant related scales not other parameters.
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"""
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if self.quant_method is not None:
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self.quant_method.process_loaded_weights(self, state_dict)
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if self.use_qk_norm:
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q_norm_weight_tensor = paddle.to_tensor(get_tensor(state_dict.pop(self.q_norm_key + ".weight")))
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k_norm_weight_tensor = paddle.to_tensor(get_tensor(state_dict.pop(self.k_norm_key + ".weight")))
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self.q_norm_weight.set_value(q_norm_weight_tensor.astype("float32"))
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self.k_norm_weight.set_value(k_norm_weight_tensor.astype("float32"))
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def weight_loader(self, param, loaded_weight, loaded_shard_id: Optional[str] = None):
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loaded_weight = get_tensor(loaded_weight).cast(paddle.get_default_dtype())
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if self.quant_method.cache_quant_config.has_zero_point: # cache_int4_zp
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loaded_weight = 1.0 / loaded_weight
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else:
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loaded_weight = self.quant_method.cache_quant_config.max_bound / loaded_weight
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param.copy_(loaded_weight, False)
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def forward(
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self,
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q: paddle.Tensor = None,
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k: paddle.Tensor = None,
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v: paddle.Tensor = None,
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qkv: paddle.Tensor = None,
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compressed_kv: paddle.Tensor = None,
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k_pe: paddle.Tensor = None,
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forward_meta: ForwardMeta = None,
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) -> paddle.Tensor:
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"""
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The forward function of attention layer.
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args:
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q: the query tensor
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k: the key tensor
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v: the value tensor
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forward_meta: the forward meta data
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compressed_kv: optional compressed key-value cache (for MLA)
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k_pe: optional key positional encoding (for MLA)
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"""
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return forward_meta.attn_backend.forward(
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q,
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k,
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v,
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qkv,
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compressed_kv,
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k_pe,
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self,
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forward_meta,
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)
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