Delete configuration_telechat.py

configuration_telechat.py

@@ -1,94 +0,0 @@
-# coding=utf-8
-# Copyright 2022 the Big Science Workshop 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.
-
-""" Telechat configuration"""
-
-from packaging import version
-from collections import OrderedDict
-from transformers.utils import is_torch_available, logging
-from transformers.configuration_utils import PretrainedConfig
-from typing import TYPE_CHECKING, Any, List, Mapping, Optional
-
-logger = logging.get_logger(__name__)
-
-class TelechatConfig(PretrainedConfig):
-    """
-    Args:
-        vocab_size (`int`, *optional*, defaults to 160256): Vocabulary size of the Telechat model.
-        hidden_size (`int`, *optional*, defaults to 4096): Dimensionality of the embeddings and hidden states.
-        ffn_hidden_size (`int`, *optional*, defaults to 12288): Dimensionality of the feed-forward hidden states.
-        n_layer (`int`, *optional*, defaults to 30): Number of hidden layers in the Transformer
-        n_head (`int`, *optional*, defaults to 32): Number of attention heads for each attention layer.
-        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.
-        apply_residual_connection_post_layernorm (`bool`, *optional*, defaults to `False`): If enabled, use the layer norm of the hidden states as the residual in the transformer blocks
-        hidden_dropout (`float`, *optional*, defaults to 0.0): Dropout rate of the dropout function on the bias dropout.
-        attention_dropout (`float`, *optional*, defaults to 0.0): Dropout rate applied to the attention probs
-        use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions.
-        training_seqlen (`int`, *optional*, defaults to 8192): Sequence length during last finetuning.
-        logn (`bool`, *optional*, defaults to `True`): Whether or not to use logN during extrapolation.
-        embed_layernorm (`bool`, *optional*, defaults to `True`): Whether or not to use embedding layernorm.
-
-    """
-
-    model_type = "telechat"
-    keys_to_ignore_at_inference = ["past_key_values"]
-    attribute_map = {
-        "num_hidden_layers": "n_layer",
-        "num_attention_heads": "n_head",
-    }
-
-    def __init__(
-        self,
-        vocab_size=160256,
-        hidden_size=4096,
-        n_layer=30,
-        n_head=32,
-        layer_norm_epsilon=1e-5,
-        initializer_range=0.02,
-        use_cache=True,
-        bos_token_id=1,
-        eos_token_id=2,
-        apply_residual_connection_post_layernorm=False,
-        hidden_dropout=0.0,
-        attention_dropout=0.0,
-        ffn_hidden_size=12288,
-        training_seqlen = 8192,
-        logn = True,
-        embed_layernorm = False,
-        **kwargs,
-    ):
-        self.vocab_size = vocab_size
-        n_embed = kwargs.pop("n_embed", None)
-        self.hidden_size = hidden_size if n_embed is None else n_embed
-        self.n_layer = n_layer
-        self.n_head = n_head
-        self.layer_norm_epsilon = layer_norm_epsilon
-        self.initializer_range = initializer_range
-        self.use_cache = use_cache
-        self.apply_residual_connection_post_layernorm = apply_residual_connection_post_layernorm
-        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.logn = logn
-        self.ffn_hidden_size = ffn_hidden_size
-        self.training_seqlen = training_seqlen
-        self.embed_layernorm = embed_layernorm
-        self.num_key_value_heads= kwargs.pop("num_key_value_heads", None)
-
-
-        super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
-