Delete configuration_gemmoe.py

configuration_gemmoe.py

@@ -1,165 +0,0 @@
-# coding=utf-8
-# Copyright 2024 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.
-""" Gemmoe model configuration"""
-
-from transformers.configuration_utils import PretrainedConfig
-from transformers.utils import logging
-
-
-logger = logging.get_logger(__name__)
-
-GEMMOE_PRETRAINED_CONFIG_ARCHIVE_MAP = {
-    "Crystalcareai/GemMoE-Beta-1": "https://huggingface.co/Crystalcareai/GemMoE-Beta-1/resolve/main/config.json",
-}
-
-
-class GemmoeConfig(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a [`GemmoeModel`]. It is used to instantiate a Gemmoe
-    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 Gemmoe-7B.
-
-    e.g. [mhenrichsen/gemmoe-7b](https://huggingface.co/mhenrichsen/gemmoe-7b)
-
-    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 256000):
-            Vocabulary size of the Gemmoe model. Defines the number of different tokens that can be represented by the
-            `inputs_ids` passed when calling [`GemmoeModel`]
-        hidden_size (`int`, *optional*, defaults to 3072):
-            Dimension of the hidden representations.
-        intermediate_size (`int`, *optional*, defaults to 24576):
-            Dimension of the MLP representations.
-        num_hidden_layers (`int`, *optional*, defaults to 28):
-            Number of hidden layers in the Transformer decoder.
-        num_attention_heads (`int`, *optional*, defaults to 16):
-            Number of attention heads for each attention layer in the Transformer decoder.
-        num_key_value_heads (`int`, *optional*, defaults to 16):
-            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
-            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
-            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
-            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
-            by meanpooling all the original heads within that group. For more details checkout [this
-            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to 
-            `num_attention_heads`.
-        head_dim (`int`, *optional*, defaults to 256):
-            The attention head dimension.
-        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
-            The non-linear activation function (function or string) in the decoder.  
-        max_position_embeddings (`int`, *optional*, defaults to 8192):
-            The maximum sequence length that this model might ever be used with.
-        initializer_range (`float`, *optional*, defaults to 0.02):
-            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        rms_norm_eps (`float`, *optional*, defaults to 1e-6):  
-            The epsilon used by the rms 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`.
-        pad_token_id (`int`, *optional*, defaults to 0):
-            Padding token id.
-        eos_token_id (`int`, *optional*, defaults to 1):
-            End of stream token id.  
-        bos_token_id (`int`, *optional*, defaults to 2):
-            Beginning of stream token id.
-        tie_word_embeddings (`bool`, *optional*, defaults to `True`): 
-            Whether to tie weight embeddings
-        rope_theta (`float`, *optional*, defaults to 10000.0):
-            The base period of the RoPE embeddings.
-        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
-            Whether to use a bias in the query, key, value and output projection layers during self-attention.
-        attention_dropout (`float`, *optional*, defaults to 0.0):
-            The dropout ratio for the attention probabilities.
-        num_experts_per_tok (`int`, *optional*, defaults to 2):
-            The number of experts used in the sparse mixture of experts layer.
-        num_local_experts (`int`, *optional*, defaults to 8):  
-            The number of local experts used in the sparse mixture of experts layer.
-        router_aux_loss_coef (`float`, *optional*, defaults to 0.01):
-            The coefficient for the auxiliary loss of the router.
-        output_router_logits (`bool`, *optional*, defaults to `False`):
-            Whether or not to output the logits of the routers. They are useful for computing the router loss, and
-            should not be returned during inference.
-
-    ```python
-    >>> from transformers import GemmoeModel, GemmoeConfig
-
-    >>> # Initializing a Gemmoe gemmoe-7b style configuration
-    >>> configuration = GemmoeConfig()
-
-    >>> # Initializing a model from the gemmoe-7b style configuration
-    >>> model = GemmoeModel(configuration)
-
-    >>> # Accessing the model configuration 
-    >>> configuration = model.config
-    ```"""
-
-    model_type = "gemmoe"
-    keys_to_ignore_at_inference = ["past_key_values"]
-
-    def __init__(
-        self,
-        vocab_size=256000,
-        hidden_size=3072,
-        intermediate_size=24576,
-        num_hidden_layers=28,
-        num_attention_heads=16,
-        num_key_value_heads=16,
-        head_dim=256,
-        hidden_act="gelu",
-        max_position_embeddings=8192,
-        initializer_range=0.02,
-        rms_norm_eps=1e-6,
-        use_cache=True,
-        pad_token_id=0,
-        eos_token_id=1,
-        bos_token_id=2,
-        tie_word_embeddings=True,
-        rope_theta=10000.0,
-        attention_bias=False,
-        attention_dropout=0.0,
-        num_experts_per_tok=2,
-        num_local_experts=8,
-        router_aux_loss_coef=0.02,
-        output_router_logits=False,
-        **kwargs,
-    ):
-        self.vocab_size = vocab_size
-        self.max_position_embeddings = max_position_embeddings        
-        self.hidden_size = hidden_size
-        self.intermediate_size = intermediate_size
-        self.num_hidden_layers = num_hidden_layers
-        self.num_attention_heads = num_attention_heads
-        self.head_dim = head_dim
-        self.num_key_value_heads = num_key_value_heads
-        self.hidden_act = hidden_act
-        self.initializer_range = initializer_range
-        self.rms_norm_eps = rms_norm_eps
-        self.use_cache = use_cache
-        self.rope_theta = rope_theta
-        self.attention_bias = attention_bias 
-        self.attention_dropout = attention_dropout
-        self.num_experts_per_tok = num_experts_per_tok
-        self.num_local_experts = num_local_experts
-        self.router_aux_loss_coef = router_aux_loss_coef
-        self.output_router_logits = output_router_logits
-         
-        super().__init__(
-            pad_token_id=pad_token_id,
-            bos_token_id=bos_token_id,
-            eos_token_id=eos_token_id,
-            tie_word_embeddings=tie_word_embeddings,
-            **kwargs,
-        )