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| # coding=utf-8 | |
| # 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. | |
| """ GPTBigCode configuration""" | |
| from transformers.configuration_utils import PretrainedConfig | |
| from transformers.utils import logging | |
| logger = logging.get_logger(__name__) | |
| class GPTBigCodeConfig(PretrainedConfig): | |
| """ | |
| This is the configuration class to store the configuration of a [`GPTBigCodeModel`]. It is used to instantiate a | |
| GPTBigCode model according to the specified arguments, defining the model architecture. Instantiating a | |
| configuration with the defaults will yield a similar configuration to that of the GPTBigCode | |
| [gpt_bigcode](https://huggingface.co/gpt_bigcode) 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 [`GPTBigCodeModel`]. | |
| 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 transformers import GPTBigCodeConfig, GPTBigCodeModel | |
| >>> # Initializing a GPTBigCode configuration | |
| >>> configuration = GPTBigCodeConfig() | |
| >>> # Initializing a model (with random weights) from the configuration | |
| >>> model = GPTBigCodeModel(configuration) | |
| >>> # Accessing the model configuration | |
| >>> configuration = model.config | |
| ```""" | |
| model_type = "gpt_bigcode" | |
| 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=50257, | |
| n_positions=1024, | |
| n_embd=768, | |
| n_layer=12, | |
| n_head=12, | |
| 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=50256, | |
| eos_token_id=50256, | |
| attention_softmax_in_fp32=True, | |
| scale_attention_softmax_in_fp32=True, | |
| multi_query=True, | |
| **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.multi_query = multi_query | |
| 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) | |