"""InternLM2 Config API."""
from typing import Optional, Union, List
from mindformers import LlamaConfig, TransformerOpParallelConfig
from mindformers.models.utils import convert_mstype
from mindformers.modules.transformer.transformer import default_transformer_config
from mindformers.tools.register import MindFormerRegister, MindFormerModuleType
@MindFormerRegister.register(MindFormerModuleType.CONFIG)
class InternLM2Config(LlamaConfig):
"""InternLM2 config class.
Args:
batch_size (Optional[int]): batch size for input data, use in predict.
seq_length (Optional[int]): The sequence length of input_ids, default is 1024.
vocab_size (`int`, *optional*, defaults to 50257):
Vocabulary size of the BERT model.
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
multiple_of (Optional[int]): Define SwiGLU hidden layer size multiples, default 256.
n_kv_heads (Optional[int]): Define multi group head attention heads number, default None.
ffn_dim_multiplier (Optional[int]): Define ffn layer dim multiples, default None.
rms_norm_eps (Optional[float]): The epsilon value of the denominator. Default 1e-5.
bos_token_id (Optional[int]): The id of the *beginning-of-sequence* token.
eos_token_id (Optional[int]): The id of the *end-of-sequence* token.
pad_token_id (Optional[int]): The id of the *padding* token.
ignore_token_id (Optional[int]): The id of the *ignoring* token.
compute_dtype (Optional[str]):
Linear layer compute dtype, default is "float16".
layernorm_compute_type (Optional[str]):
layernorm compute dtype, default is "float32".
softmax_compute_type (Optional[str]):
softmax compute dtype, default is "float32".
rotary_dtype (Optional[str]):
rope compute dtype, default is "float32".
param_init_type (Optional[str]):
parameter initial dtype, default is "float16".
has_bias (Optional[bool]):
Whether the Query, Key, Value, Out projection has bias.
use_past (`bool`, *optional*, defaults to `False`):
Whether the model should use the past last key/values attentions
(if applicable to the model) to speed up decoding.
parallel_config(TransformerOpParallelConfig):
The parallel configure. Default `default_transformer_config`,
an instance of `TransformerOpParallelConfig` with default args.
extend_method(str): The extend method of seq length of inferencem,default None.
use_flash_attention(bool): Whether enable flash attention ops, default False.
fine_grain_interleave(int): The number of fine grain interleave, default 1.
block_size(int): The shape of blocks in paged attention, default 16.
num_blocks(int): The total number of blocks in paged attention, default 512.
offset(int): Offset of transformer layer when set pipeline stage number.
checkpoint_name_or_path (Optional[str]):
checkpoint path or name used to load to the network.
repetition_penalty (`float`, *optional*, defaults to 1.0):
The parameter for repetition penalty. 1.0 means no penalty. See [this
paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
max_decode_length (`int`, *optional*, defaults to 1024):
The maximum length the generated tokens can have. Corresponds to the length of the input prompt +
`max_new_tokens`. Its effect is overridden by `max_new_tokens`, if also set.
top_k (`int`, *optional*, defaults to 5):
The number of highest probability vocabulary tokens to keep for top-k-filtering.
top_p (`float`, *optional*, defaults to 1.0):
If set to float < 1, only the smallest set of most probable tokens with probabilities
that add up to `top_p` or higher are kept for generation.
do_sample (`bool`, *optional*, defaults to `False`):
Whether to use sampling; use greedy decoding otherwise.
Returns:
Class, LlamaConfig.
"""
model_type = "InternLM2ForCausalLM"
def __init__(self,
batch_size: int = 1,
seq_length: int = 2048,
hidden_size: int = 4096,
num_layers: int = 32,
num_heads: int = 32,
n_kv_heads: Optional[int] = 8,
max_position_embedding: Optional[int] = None,
intermediate_size: Optional[int] = 14336,
vocab_size: int = 92544,
multiple_of: int = 256,
ffn_dim_multiplier: Optional[int] = None,
rms_norm_eps: float = 1e-5,
bos_token_id: int = 1,
eos_token_id: Union[int, List[int]] = 2,
pad_token_id: int = 2,
ignore_token_id: int = -100,
theta: float = 1000000.0,
compute_dtype: str = "float16",
layernorm_compute_type: str = "float32",
softmax_compute_type: str = "float32",
rotary_dtype: str = "float32",
param_init_type: str = "float16",
qkv_concat: bool = True,
parallel_config: TransformerOpParallelConfig = default_transformer_config,
use_past: bool = False,
extend_method: str = "None",
scaling_factor: float = 1.0,
is_dynamic: bool = False,
use_rope_slice: bool = False,
use_flash_attention: bool = False,
fine_grain_interleave: int = 1,
block_size: int = 16,
num_blocks: int = 512,
offset: int = 0,
checkpoint_name_or_path: str = "",
repetition_penalty: float = 1.0,
max_decode_length: int = 512,
top_k: int = 5,
top_p: float = 1.0,
do_sample: bool = True,
**kwargs):
super().__init__(**kwargs)
if isinstance(parallel_config, dict):
parallel_config = TransformerOpParallelConfig(**parallel_config)
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.max_position_embedding = max_position_embedding if max_position_embedding else seq_length
self.intermediate_size = intermediate_size
self.multiple_of = multiple_of
self.n_kv_heads = n_kv_heads
self.ffn_dim_multiplier = ffn_dim_multiplier
self.rms_norm_eps = rms_norm_eps
self.param_init_type = convert_mstype(param_init_type)
self.qkv_concat = qkv_concat
self.layernorm_compute_type = convert_mstype(layernorm_compute_type)
self.softmax_compute_type = convert_mstype(softmax_compute_type)
self.rotary_dtype = convert_mstype(rotary_dtype)
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.extend_method = extend_method
self.scaling_factor = scaling_factor
self.is_dynamic = is_dynamic
self.use_rope_slice = use_rope_slice
self.use_flash_attention = use_flash_attention
self.fine_grain_interleave = fine_grain_interleave
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
self.theta = theta
self.block_size = block_size
self.num_blocks = num_blocks