Delete configuration_glm.py

configuration_glm.py

@@ -1,136 +0,0 @@
-# coding=utf-8
-# Copyright 2022 shunxing1234 and The 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.
-""" GLM model configuration """
-
-from transformers.configuration_utils import PretrainedConfig
-from transformers.utils import logging
-
-logger = logging.get_logger(__name__)
-
-GLM_PRETRAINED_CONFIG_ARCHIVE_MAP = {
-    "shunxing1234/GLM": "https://huggingface.co/shunxing1234/GLM/resolve/main/config.json",
-    # See all GLM models at https://huggingface.co/models?filter=glm
-}
-
-
-class GLMConfig(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a [`~GLMModel`].
-    It is used to instantiate an GLM 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 GLM [shunxing1234/GLM-base-cased](https://huggingface.co/shunxing1234/GLM-base-cased) 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 30522):
-            Vocabulary size of the GLM model. Defines the number of different tokens that can be represented by the
-            `inputs_ids` passed when calling [`~GLMModel`] or
-            [`~TFGLMModel`].
-        hidden_size (`int`, *optional*, defaults to 768):
-            Dimension of the encoder layers and the pooler layer.
-        num_hidden_layers (`int`, *optional*, defaults to 12):
-            Number of hidden layers in the Transformer encoder.
-        num_attention_heads (`int`, *optional*, defaults to 12):
-            Number of attention heads for each attention layer in the Transformer encoder.
-        intermediate_size (`int`, *optional*, defaults to 3072):
-            Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
-        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
-            The non-linear activation function (function or string) in the encoder and pooler.
-            If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported.
-        hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
-            The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
-        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
-            The dropout ratio for the attention probabilities.
-        max_position_embeddings (`int`, *optional*, defaults to 512):
-            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).
-        type_vocab_size (`int`, *optional*, defaults to 2):
-            The vocabulary size of the `token_type_ids` passed when calling [`~GLMModel`] or
-            [`~TFGLMModel`].
-        initializer_range (`float`, *optional*, defaults to 0.02):
-            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
-            The epsilon used by the layer normalization layers.
-        use_cache (`bool`, *optional*, defaults to `True`):
-            Whether or not the model should return the last key/values attentions (not used by all models). Only
-            relevant if `config.is_decoder=True`.
-        Example:
-
-    ```python
-    >>> from transformers import GLMModel, GLMConfig
-
-    >>> # Initializing a GLM shunxing1234/GLM-base-cased style configuration
-    >>> configuration = GLMConfig()
-
-    >>> # Initializing a model from the shunxing1234/GLM-base-cased style configuration
-    >>> model = GLMModel(configuration)
-
-    >>> # Accessing the model configuration
-    >>> configuration = model.config
-    ```
-"""
-    model_type = "glm"
-    attribute_map = {
-        "num_hidden_layers": "num_layers"
-    }
-
-    def __init__(
-            self,
-            num_layers=24,
-            vocab_size=30592,
-            hidden_size=1024,
-            num_attention_heads=16,
-            embedding_dropout_prob=0.1,
-            attention_dropout_prob=0.1,
-            output_dropout_prob=0.1,
-            max_sequence_length=512,
-            checkpoint_activations=False,
-            checkpoint_num_layers=1,
-            parallel_output=True,
-            relative_encoding=False,
-            block_position_encoding=True,
-            output_predict=False,
-            spell_length=None,
-            spell_func="lstm",
-            attention_scale=1.0,
-            initializer_range=0.02,
-            pool_token="cls",
-            **kwargs
-    ):
-        self.num_layers = num_layers
-        self.vocab_size = vocab_size
-        self.hidden_size = hidden_size
-        self.num_attention_heads = num_attention_heads
-        self.embedding_dropout_prob = embedding_dropout_prob
-        self.attention_dropout_prob = attention_dropout_prob
-        self.output_dropout_prob = output_dropout_prob
-        self.max_sequence_length = max_sequence_length
-        self.checkpoint_activations = checkpoint_activations
-        self.checkpoint_num_layers = checkpoint_num_layers
-        self.parallel_output = parallel_output
-        self.relative_encoding = relative_encoding
-        self.block_position_encoding = block_position_encoding
-        self.output_predict = output_predict
-        self.spell_length = spell_length
-        self.spell_func = spell_func
-        self.attention_scale = attention_scale
-        self.initializer_range = initializer_range
-        self.pool_token = pool_token
-
-        super().__init__(**kwargs)