Delete configuration_falcon.py

configuration_falcon.py

@@ -1,147 +0,0 @@
-# coding=utf-8
-# Copyright 2023 the Falcon authors and HuggingFace Inc. team.  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.
-""" Falcon configuration"""
-from transformers.configuration_utils import PretrainedConfig
-from transformers.utils import logging
-
-
-logger = logging.get_logger(__name__)
-
-FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP = {
-    "tiiuae/falcon-40b": "https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json",
-    "tiiuae/falcon-7b": "https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json",
-}
-
-
-class FalconConfig(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a [`FalconModel`]. It is used to instantiate a Falcon
-    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
-    [tiiuae/falcon-7b](https://huggingface.co/tiiuae/falcon-7b) 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 65024):
-            Vocabulary size of the Falcon model. Defines the number of different tokens that can be represented by the
-            `inputs_ids` passed when calling [`FalconModel`]
-        hidden_size (`int`, *optional*, defaults to 4544):
-            Dimension of the hidden representations.
-        num_hidden_layers (`int`, *optional*, defaults to 32):
-            Number of hidden layers in the Transformer decoder.
-        num_attention_heads (`int`, *optional*, defaults to 71):
-            Number of attention heads for each attention layer in the Transformer encoder.
-        initializer_range (`float`, *optional*, defaults to 0.02):
-            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        use_cache (`bool`, *optional*, defaults to `True`):
-            Whether the model should return the last key/values attentions (not used by all models). Only relevant if
-            `config.is_decoder=True`.
-        layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
-            The epsilon used by the layer normalization layers.
-        hidden_dropout (`float`, *optional*, defaults to 0.0):
-            The dropout probability for MLP layers.
-        attention_dropout (`float`, *optional*, defaults to 0.0):
-            The dropout probability for attention layers.
-        num_kv_heads (`int`, *optional*):
-            Number of key-value heads to use per attention layer. If unset, defaults to the same value as
-            `num_attention_heads`.
-        alibi (`bool`, *optional*, defaults to `False`):
-            Whether to use ALiBi positional biases during self-attention.
-        new_decoder_architecture (`bool`, *optional*, defaults to `False`):
-            Whether to use the new (Falcon-40B) decoder architecture. If `True`, the `multi_query` and `parallel_attn`
-            arguments are ignored, as the new decoder always uses parallel attention.
-        multi_query (`bool`, *optional*, defaults to `True`):
-            Whether to use multi-query attention in the decoder. Ignored when `new_decoder_architecture` is `True`.
-        parallel_attn (`bool`, *optional*, defaults to `True`):
-            Whether to compute attention in parallel with the feedforward layer. If False, they are consecutive
-            instead, as in the original Transformer architecture. Ignored when `new_decoder_architecture` is `True`.
-        bias (`bool`, *optional*, defaults to `False`):
-            Whether to use bias on Linear layers.
-        bos_token_id (`int`, *optional*, defaults to 11):
-            The id of the "beginning-of-sequence" token.
-        eos_token_id (`int`, *optional*, defaults to 11):
-            The id of the "end-of-sequence" token.
-
-    Example:
-
-    ```python
-    >>> from transformers import FalconModel, FalconConfig
-
-    >>> # Initializing a small (2-layer) Falcon configuration
-    >>> configuration = FalconConfig(num_hidden_layers=2)
-
-    >>> # Initializing a model from the small configuration
-    >>> model = FalconModel(configuration)
-
-    >>> # Accessing the model configuration
-    >>> configuration = model.config
-    ```"""
-    model_type = "falcon"
-    keys_to_ignore_at_inference = ["past_key_values"]
-
-    def __init__(
-        self,
-        vocab_size=65024,
-        hidden_size=4544,
-        num_hidden_layers=32,
-        num_attention_heads=71,
-        layer_norm_epsilon=1e-5,
-        initializer_range=0.02,
-        use_cache=True,
-        hidden_dropout=0.0,
-        attention_dropout=0.0,
-        num_kv_heads=None,
-        alibi=False,
-        new_decoder_architecture=False,
-        multi_query=True,
-        parallel_attn=True,
-        bias=False,
-        bos_token_id=11,
-        eos_token_id=11,
-        **kwargs,
-    ):
-        self.vocab_size = vocab_size
-        # Backward compatibility with n_embed kwarg
-        n_embed = kwargs.pop("n_embed", None)
-        self.hidden_size = hidden_size if n_embed is None else n_embed
-        self.num_hidden_layers = num_hidden_layers
-        self.num_attention_heads = num_attention_heads
-        self.layer_norm_epsilon = layer_norm_epsilon
-        self.initializer_range = initializer_range
-        self.use_cache = use_cache
-        self.hidden_dropout = hidden_dropout
-        self.attention_dropout = attention_dropout
-
-        self.bos_token_id = bos_token_id
-        self.eos_token_id = eos_token_id
-        self.num_kv_heads = num_attention_heads if num_kv_heads is None else num_kv_heads
-        self.alibi = alibi
-        self.new_decoder_architecture = new_decoder_architecture
-        self.multi_query = multi_query  # Ignored when new_decoder_architecture is True
-        self.parallel_attn = parallel_attn
-        self.bias = bias
-
-        super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
-
-    @property
-    def head_dim(self):
-        return self.hidden_size // self.num_attention_heads
-
-    @property
-    def rotary(self):
-        return not self.alibi