149 lines
6.8 KiB
Python
149 lines
6.8 KiB
Python
# coding=utf-8
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# Copyright 2024 BigCode and the HuggingFace Inc. team. 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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""" Starcoder2 model configuration"""
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from ...configuration_utils import PretrainedConfig
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from ...utils import logging
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logger = logging.get_logger(__name__)
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from ..deprecated._archive_maps import STARCODER2_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class Starcoder2Config(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`Starcoder2Model`]. It is used to instantiate a
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Starcoder2 model according to the specified arguments, defining the model architecture. Instantiating a configuration
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with the defaults will yield a similar configuration to that of the [bigcode/starcoder2-7b_16k](https://huggingface.co/bigcode/starcoder2-7b_16k) model.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 49152):
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Vocabulary size of the Starcoder2 model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`Starcoder2Model`]
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hidden_size (`int`, *optional*, defaults to 3072):
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Dimension of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to 12288):
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Dimension of the MLP representations.
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num_hidden_layers (`int`, *optional*, defaults to 30):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 24):
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Number of attention heads for each attention layer in the Transformer encoder.
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num_key_value_heads (`int`, *optional*, defaults to 2):
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This is the number of key_value heads that should be used to implement Grouped Query Attention. If
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`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
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`num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
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converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
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by meanpooling all the original heads within that group. For more details checkout [this
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paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `8`.
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hidden_act (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`):
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The non-linear activation function (function or string) in the decoder.
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max_position_embeddings (`int`, *optional*, defaults to 4096):
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The maximum sequence length that this model might ever be used with. Starcoder2's sliding window attention
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allows sequence of up to 4096*32 tokens.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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norm_epsilon (`float`, *optional*, defaults to 1e-05):
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Epsilon value for the layer norm
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the model should return the last key/values attentions (not used by all models). Only
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relevant if `config.is_decoder=True`.
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bos_token_id (`int`, *optional*, defaults to 50256):
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The id of the "beginning-of-sequence" token.
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eos_token_id (`int`, *optional*, defaults to 50256):
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The id of the "end-of-sequence" token.
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rope_theta (`float`, *optional*, defaults to 10000.0):
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The base period of the RoPE embeddings.
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sliding_window (`int`, *optional*):
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Sliding window attention window size. If not specified, will default to `None` (no sliding window).
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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residual_dropout (`float`, *optional*, defaults to 0.0):
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Residual connection dropout value.
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embedding_dropout (`float`, *optional*, defaults to 0.0):
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Embedding dropout.
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use_bias (`bool`, *optional*, defaults to `True`):
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Whether to use bias term on linear layers of the model.
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```python
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>>> from transformers import Starcoder2Model, Starcoder2Config
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>>> # Initializing a Starcoder2 7B style configuration
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>>> configuration = Starcoder2Config()
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>>> # Initializing a model from the Starcoder2 7B style configuration
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>>> model = Starcoder2Model(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "starcoder2"
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keys_to_ignore_at_inference = ["past_key_values"]
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def __init__(
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self,
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vocab_size=49152,
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hidden_size=3072,
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intermediate_size=12288,
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num_hidden_layers=30,
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num_attention_heads=24,
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num_key_value_heads=2,
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hidden_act="gelu_pytorch_tanh",
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max_position_embeddings=4096,
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initializer_range=0.018042,
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norm_epsilon=1e-5,
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use_cache=True,
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bos_token_id=50256,
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eos_token_id=50256,
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rope_theta=10000.0,
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sliding_window=None,
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attention_dropout=0.0,
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residual_dropout=0.0,
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embedding_dropout=0.0,
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use_bias=True,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.max_position_embeddings = max_position_embeddings
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.sliding_window = sliding_window
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self.use_bias = use_bias
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self.num_key_value_heads = num_key_value_heads
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self.hidden_act = hidden_act
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self.initializer_range = initializer_range
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self.norm_epsilon = norm_epsilon
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self.use_cache = use_cache
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self.rope_theta = rope_theta
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self.attention_dropout = attention_dropout
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self.residual_dropout = residual_dropout
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self.embedding_dropout = embedding_dropout
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super().__init__(
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bos_token_id=bos_token_id,
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eos_token_id=eos_token_id,
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**kwargs,
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)
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