# Copyright 2025 The EasyDeL Author @erfanzar (Erfan Zare Chavoshi).
#
# 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
#
# https://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 eformer.common_types import ColumnWise, Replicated, RowWise
from easydel.infra.base_module import EasyDeLBaseConfig
from easydel.infra.etils import EasyDeLGradientCheckPointers
from easydel.infra.factory import register_config
[docs]@register_config("internlm2")
class InternLM2Config(EasyDeLBaseConfig):
"""
Configuration objects inherit from [`EasyDeLBaseConfig`] and can be used to control the model outputs. Read
the documentation from [`EasyDeLBaseConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 32000):
Vocabulary size of the InternLM2 model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed to the forward method.
hidden_size (`int`, *optional*, defaults to 4096):
Dimensionality of the encoder layers and the pooler layer.
intermediate_size (`int`, *optional*, defaults to 11008):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 32):
Number of attention heads for each attention layer in the Transformer encoder.
num_key_value_heads (`int`, *optional*):
Number of key and value heads for each attention layer in the Transformer encoder. Will default to
`number_rep_kv * num_attention_heads` if not set.
max_position_embeddings (`int`, *optional*, defaults to 2048):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 2048 or 4096).
rms_norm_eps (`float`, *optional*, defaults to 1e-6):
The epsilon used by the rms normalization layers.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
pad_token_id (`int`, *optional*, defaults to 0):
The id of the *pad* token.
bos_token_id (`int`, *optional*, defaults to 1):
The id of the *beginning-of-sequence* token.
eos_token_id (`int`, *optional*, defaults to 2):
The id of the *end-of-sequence* token.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
rope_theta (`float`, *optional*, defaults to 10000.0):
The theta value to use for rotary position embeddings.
bias (`bool`, *optional*, defaults to `False`):
Whether to use attention bias.
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether to tie the weights of the input embeddings and the output embeddings.
gradient_checkpointing (`str`, *optional*, defaults to `"nothing_saveable"`):
The gradient checkpointing configuration.
fcm_min_ratio (`float`, *optional*, defaults to -1):
The minimum ratio for Flash Attention.
fcm_max_ratio (`float`, *optional*, defaults to -1):
The maximum ratio for Flash Attention.
rope_scaling (`tp.Dict[str, tp.Union[str, float]]`, *optional*):
The configuration for rope scaling.
scan_mlp_chunk_size (`int`, *optional*, defaults to 1024):
The chunk size to use when scanning the MLP.
bits (`int`, *optional*):
The number of bits to quantize the model to.
hidden_act (`str`, *optional*, defaults to `"silu"`):
The hidden activation function to use.
pretraining_tp (`int`, *optional*, defaults to 1):
The tensor parallelism degree used during pretraining.
mlp_bias (`bool`, *optional*, defaults to `False`):
Whether to use bias in the MLP.
scan_layers (`bool`, *optional*, defaults to `False`):
Whether to use the scan implementation for the layers.
"""
model_type: str = "internlm2"
def __init__(
self,
vocab_size=103168,
hidden_size=4096,
intermediate_size=11008,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=None,
hidden_act="silu",
max_position_embeddings=2048,
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=True,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
pretraining_tp=1,
tie_word_embeddings=False,
bias=True,
rope_theta=10000,
rope_scaling=None,
gradient_checkpointing: EasyDeLGradientCheckPointers = EasyDeLGradientCheckPointers.NONE,
fcm_min_ratio: float = -1,
fcm_max_ratio: float = -1,
scan_mlp_chunk_size: int = 1024,
bits: int | None = None,
scan_layers: bool = False,
**kwargs,
):
"""Initializes an InternLM2Config object.
Args:
vocab_size (int, optional): Vocabulary size. Defaults to 103168.
hidden_size (int, optional): Hidden size. Defaults to 4096.
intermediate_size (int, optional): Intermediate size of the feed-forward network. Defaults to 11008.
num_hidden_layers (int, optional): Number of hidden layers. Defaults to 32.
num_attention_heads (int, optional): Number of attention heads. Defaults to 32.
num_key_value_heads (int, optional):
Number of key/value heads (for GQA). Defaults to None (uses num_attention_heads).
hidden_act (str, optional): Activation function. Defaults to "silu".
max_position_embeddings (int, optional): Maximum sequence length. Defaults to 2048.
initializer_range (float, optional): Initializer range. Defaults to 0.02.
rms_norm_eps (float, optional): Epsilon for RMS normalization. Defaults to 1e-6.
use_cache (bool, optional): Whether to use KV cache. Defaults to True.
pad_token_id (int, optional): Padding token ID. Defaults to 0.
bos_token_id (int, optional): Beginning-of-sequence token ID. Defaults to 1.
eos_token_id (int, optional): End-of-sequence token ID. Defaults to 2.
pretraining_tp (int, optional): Tensor parallelism degree during pretraining. Defaults to 1.
tie_word_embeddings (bool, optional): Whether to tie input/output embeddings. Defaults to False.
bias (bool, optional): Whether to use bias in linear layers. Defaults to True.
rope_theta (float, optional): Base value for RoPE. Defaults to 10000.
rope_scaling (dict, optional): RoPE scaling configuration. Defaults to None.
gradient_checkpointing (EasyDeLGradientCheckPointers, optional): Gradient checkpointing strategy.
Defaults to EasyDeLGradientCheckPointers.NONE.
fcm_min_ratio (float, optional): Minimum ratio for Flash Attention. Defaults to -1.
fcm_max_ratio (float, optional): Maximum ratio for Flash Attention. Defaults to -1.
scan_mlp_chunk_size (int, optional): Chunk size for scan MLP. Defaults to 1024.
bits (tp.Optional[int], optional): Quantization bits. Defaults to None.
scan_layers (bool, optional): Whether to use scan for layers. Defaults to False.
**kwargs: Additional keyword arguments.
"""
num_key_value_heads = num_key_value_heads or num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.initializer_range = initializer_range
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.rope_theta = rope_theta
self.bias = bias
self.num_attention_heads = num_attention_heads
self.max_position_embeddings = max_position_embeddings
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.pretraining_tp = pretraining_tp
self.gradient_checkpointing = gradient_checkpointing
self.fcm_min_ratio = fcm_min_ratio
self.hidden_act = hidden_act
self.fcm_max_ratio = fcm_max_ratio
self.rope_scaling = rope_scaling
self.bits = bits
self.scan_layers = scan_layers
self.attn_implementation = "eager"
# HF: AttributeError: 'InternLM2Config' object has no attribute 'attn_implementation'.
# Did you mean: '_attn_implementation'?
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
scan_mlp_chunk_size=scan_mlp_chunk_size,
bits=bits,
**kwargs,
)
[docs] def get_partition_rules(self, *args, **kwargs):
"""
Get the partition rules for the model.
Returns:
`tp.Tuple[tp.Tuple[str, PartitionSpec]]`: The partition rules.
"""
pmag = self.partition_manager
return (
(r"tok_embeddings/embedding", pmag.resolve(ColumnWise)),
(r"attention/wqkv/kernel", pmag.resolve(ColumnWise)),
(r"attention/wo/kernel", pmag.resolve(RowWise)),
(r"feed_forward/(w1|w3)/kernel", pmag.resolve(ColumnWise)),
(r"feed_forward/w2/kernel", pmag.resolve(RowWise)),
(r".*/(attention_norm|ffn_norm|norm)/kernel", pmag.resolve(Replicated)),
(r"output/kernel", pmag.resolve(ColumnWise)),
(r"score/kernel", pmag.resolve(RowWise)),
(r".*/(wqkv|wo|w1|w3|w2|output|score)/bias", pmag.resolve(Replicated)),
(r".*", pmag.resolve(Replicated)),
)
@property
def granted_freq_max_position_embedding(self) -> int:
"""Returns the maximum position embedding size specifically for frequency-based position embeddings.
If `freq_max_position_embeddings` is set, it returns that value. Otherwise, it falls back to
`max_position_embeddings`.
Returns:
int: The granted maximum position embedding size for frequency encoding.
"""
return getattr(
self,
"freq_max_position_embeddings",
self.max_position_embeddings,
)
@property
def granted_mask_max_position_embedding(self) -> int:
"""Returns the maximum position embedding size specifically for mask-based position embeddings.
If `mask_max_position_embeddings` is set, it returns that value. Otherwise, it falls back to
`max_position_embeddings`.
Returns:
int: The granted maximum position embedding size for mask encoding.
"""
return getattr(
self,
"mask_max_position_embeddings",
self.max_position_embeddings,
)