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# coding=utf-8
# Copyright 2023 WisdomShell 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.
# This code is based on Bigcode's GPTBigCode configuration. It has been modified from
# its original forms to accommodate minor architectural differences compared to
# GPTBigCode Configuration that trained the model.
# Copyright 2023 The BigCode team and HuggingFace Inc. team.
#
# 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.
""" Shell configuration"""
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
class CodeShellConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`CodeShellModel`]. It is used to instantiate a
CodeShell 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.
Args:
vocab_size (`int`, *optional*, defaults to 50257):
Vocabulary size of the GPT-2 model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`ShellModel`].
n_positions (`int`, *optional*, defaults to 1024):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
n_embd (`int`, *optional*, defaults to 768):
Dimensionality of the embeddings and hidden states.
n_layer (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
n_head (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
n_inner (`int`, *optional*, defaults to None):
Dimensionality of the inner feed-forward layers. `None` will set it to 4 times n_embd
activation_function (`str`, *optional*, defaults to `"gelu_pytorch_tanh"`):
Activation function, to be selected in the list `["relu", "silu", "gelu", "tanh", "gelu_new",
"gelu_pytorch_tanh"]`.
resid_pdrop (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
embd_pdrop (`float`, *optional*, defaults to 0.1):
The dropout ratio for the embeddings.
attn_pdrop (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention.
layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
The epsilon to use in the layer normalization layers.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
scale_attn_weights (`bool`, *optional*, defaults to `True`):
Scale attention weights by dividing by sqrt(hidden_size)..
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models).
attention_softmax_in_fp32 (`bool`, *optional*, defaults to `True`):
Whether to call the fused softmax in float32.
scale_attention_softmax_in_fp32 (`bool`, *optional*, defaults to `True`):
Whether to scale the attention softmax in float32.
attention_type (`bool`, *optional*, defaults to `True`):
Whether to use Multi-Query Attion (`True`) or Multi-Head Attention (`False`).
Example:
```python
>>> from configuration_codeshell import CodeShellConfig
>>> from modeling_codeshell import CodeShellForCausalLM
>>> # Initializing a CodeShell configuration
>>> configuration = CodeShellConfig()
>>> # Initializing a model (with random weights) from the configuration
>>> model = CodeShellForCausalLM(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "codeshell"
keys_to_ignore_at_inference = ["past_key_values"]
attribute_map = {
"hidden_size": "n_embd",
"max_position_embeddings": "n_positions",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__(
self,
vocab_size=70144,
n_positions=8192,
n_embd=4096,
n_layer=42,
n_head=32,
n_inner=None,
activation_function="gelu_pytorch_tanh",
resid_pdrop=0.1,
embd_pdrop=0.1,
attn_pdrop=0.1,
layer_norm_epsilon=1e-5,
initializer_range=0.02,
scale_attn_weights=True,
use_cache=True,
bos_token_id=70000,
eos_token_id=70000,
attention_softmax_in_fp32=True,
scale_attention_softmax_in_fp32=True,
group_query_attention=True,
num_query_groups=1,
position_embedding_type="learned_absolute",
rope_scaling=None,
**kwargs,
):
self.vocab_size = vocab_size
self.n_positions = n_positions
self.n_embd = n_embd
self.n_layer = n_layer
self.n_head = n_head
self.n_inner = n_inner
self.activation_function = activation_function
self.resid_pdrop = resid_pdrop
self.embd_pdrop = embd_pdrop
self.attn_pdrop = attn_pdrop
self.layer_norm_epsilon = layer_norm_epsilon
self.initializer_range = initializer_range
self.scale_attn_weights = scale_attn_weights
self.use_cache = use_cache
self.attention_softmax_in_fp32 = attention_softmax_in_fp32
self.scale_attention_softmax_in_fp32 = scale_attention_softmax_in_fp32
self.group_query_attention = group_query_attention
self.num_query_groups = num_query_groups
self.position_embedding_type = position_embedding_type
self.rope_scaling = rope_scaling
assert self.position_embedding_type in [
"learned_absolute", "rope"
], "position_embedding_type must be one of ['learned_absolute', 'rope']"
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
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