Delete configuration_shell.py

configuration_shell.py

@@ -1,150 +0,0 @@
-# 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.
-""" Shell configuration"""
-
-from transformers.configuration_utils import PretrainedConfig
-from transformers.utils import logging
-
-
-logger = logging.get_logger(__name__)
-
-
-class ShellConfig(PretrainedConfig):
-    """
-    This is the configuration class to store the configuration of a [`ShellModel`]. It is used to instantiate a
-    Shell 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 Shell
-    [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 [`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 transformers import ShellConfig, ShellModel
-
-    >>> # Initializing a Shell configuration
-    >>> configuration = ShellConfig()
-
-    >>> # Initializing a model (with random weights) from the configuration
-    >>> model = ShellModel(configuration)
-
-    >>> # Accessing the model configuration
-    >>> configuration = model.config
-    ```"""
-
-    model_type = "kclgpt"
-    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,
-        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)