# Copyright 2023 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
"""Llama Config API."""
from mindformers.modules.transformer.transformer import default_transformer_config, TransformerOpParallelConfig
from mindformers.tools.register import MindFormerRegister, MindFormerModuleType
from ..utils import convert_mstype
from ..base_config import BaseConfig
from ...mindformer_book import MindFormerBook
__all__ = ['LlamaConfig']
[文档]@MindFormerRegister.register(MindFormerModuleType.CONFIG)
class LlamaConfig(BaseConfig):
"""
LLaMA config class which defines the model size.
"""
_support_list = MindFormerBook.get_config_support_list()['llama']
def __init__(self,
batch_size: int = 1,
seq_length: int = 2048,
hidden_size: int = 4096,
num_layers: int = 32,
num_heads: int = 32,
vocab_size: int = 32000, # defined later by tokenizer
multiple_of: int = 256, # make SwiGLU hidden layer size multiple of large power of 2
rms_norm_eps: float = 1e-5,
bos_token_id: int = 1,
eos_token_id: int = 2,
pad_token_id: int = 32000,
ignore_token_id: int = -100,
compute_dtype: str = "float16",
layernorm_compute_type: str = "float32",
softmax_compute_type: str = "float32",
param_init_type: str = "float16",
parallel_config: TransformerOpParallelConfig = default_transformer_config,
use_past: bool = False,
offset: int = 0,
checkpoint_name_or_path: str = "",
repetition_penalty: float = 1.0,
max_decode_length: int = 1024,
top_k: int = 5,
top_p: float = 1.0,
do_sample: bool = True,
**kwargs):
super(LlamaConfig, self).__init__(**kwargs)
self.batch_size = batch_size
self.seq_length = seq_length
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_layers = num_layers
self.num_heads = num_heads
self.multiple_of = multiple_of
self.rms_norm_eps = rms_norm_eps
self.param_init_type = convert_mstype(param_init_type)
self.layernorm_compute_type = convert_mstype(layernorm_compute_type)
self.softmax_compute_type = convert_mstype(softmax_compute_type)
self.compute_dtype = convert_mstype(compute_dtype)
self.parallel_config = parallel_config
self.checkpoint_name_or_path = checkpoint_name_or_path
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
self.pad_token_id = pad_token_id
self.ignore_token_id = ignore_token_id
self.use_past = use_past
self.offset = offset
self.repetition_penalty = repetition_penalty
self.max_decode_length = max_decode_length
self.top_k = top_k
self.top_p = top_p
self.do_sample = do_sample