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# coding=utf-8
# Copyright 2024 The PLM team and The HuggingFace Inc. All rights reserved.
#
# 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.
"""PLM model configuration"""
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
class PLMConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`PLMModel`]. It is used to instantiate a
PLM model according to the specified arguments, defining the model architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information. Instantiating a configuration with the
defaults will yield a similar configuration to that of the PLM model.
Args:
vocab_size (`int`, *optional*, defaults to 151936):
Vocabulary size of the PLM model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`PLMModel`]
hidden_size (`int`, *optional*, defaults to 4096):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 8192):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder.
num_key_value_heads (`int`, *optional*, defaults to 16):
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
by meanpooling all the original heads within that group. For more details checkout [this
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `32`.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder.
pretraining_tp (`int`, *optional*, defaults to 1):
Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
issue](https://github.com/pytorch/pytorch/issues/76232).
max_position_embeddings (`int`, *optional*, defaults to 4096):
The maximum sequence length that this model might ever be used with.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
rms_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the rms normalization layers.
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`.
tie_word_embeddings (`bool`, *optional*, defaults to `True`):
Whether the model's input and output word embeddings should be tied.
rope_scaling (`Dict`, *optional*):
Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports normal rope.
rope_theta (`float`, *optional*, defaults to 100000.0):
The base period of the RoPE embeddings.
attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
Whether to use a bias in the query, key, value and output projection layers during self-attention.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
```python
>>> from transformers import PLMModel, PLMConfig
>>> # Initializing a PLM style configuration
>>> configuration = PLMConfig()
>>> # Initializing a model from the PLM style configuration
>>> model = PLMModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "plm"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=151936,
hidden_size=2048,
intermediate_size=8192,
num_hidden_layers=32,
num_attention_heads=16,
num_key_value_heads=16,
kv_lora_rank = 512,
q_lora_rank = None,
qk_rope_head_dim = 64,
v_head_dim = 128,
qk_nope_head_dim = 128,
hidden_act="relu2",
max_position_embeddings=4096,
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=True,
pretraining_tp=1,
tie_word_embeddings=True,
rope_theta=100000.0,
rope_scaling=None,
attention_bias=False,
attention_dropout=0.0,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.kv_lora_rank = kv_lora_rank
self.q_lora_rank = q_lora_rank
self.qk_rope_head_dim = qk_rope_head_dim
self.v_head_dim = v_head_dim
self.qk_nope_head_dim = qk_nope_head_dim
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.pretraining_tp = pretraining_tp
self.use_cache = use_cache
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self.attention_bias = attention_bias
self.attention_dropout = attention_dropout
super().__init__(
tie_word_embeddings=tie_word_embeddings,
**kwargs,
) |