Update configuration_stablelm_epoch.py

configuration_stablelm_epoch.py

@@ -1,5 +1,4 @@
-# coding=utf-8
-# Copyright 2024 Stability AI and The HuggingFace Inc. team. All rights reserved.
+# Copyright 2023 Stability 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.
@@ -12,45 +11,32 @@
 # 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.
-""" StableLM model configuration """
-
-from transformers.configuration_utils import PretrainedConfig
+""" StableLM Epoch model configuration"""
+from transformers import PretrainedConfig
 from transformers.utils import logging
 
 
 logger = logging.get_logger(__name__)
 
-STABLELM_PRETRAINED_CONFIG_ARCHIVE_MAP = {
-    "stabilityai/stablelm-3b-4e1t": "https://huggingface.co/stabilityai/stablelm-3b-4e1t/resolve/main/config.json",
-    # See all StableLM models at https://huggingface.co/models?filter=stablelm
-}
-
 
-class StableLmConfig(PretrainedConfig):
+class StableLMEpochConfig(PretrainedConfig):
     r"""
-    This is the configuration class to store the configuration of a [`~StableLmModel`].
-    It is used to instantiate an StableLM 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 StableLM [stabilityai/stablelm-3b-4e1t](https://huggingface.co/stabilityai/stablelm-3b-4e1t) architecture.
-
-    Configuration objects inherit from  [`PretrainedConfig`] and can be used
-    to control the model outputs. Read the documentation from  [`PretrainedConfig`]
-    for more information.
-
+    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 50304):
+        vocab_size (`int`, *optional*, defaults to 50_304):
             Vocabulary size of the StableLM model. Defines the number of different tokens that
-            can be represented by the `inputs_ids` passed when calling [`StableLmModel`].
+            can be represented by the `inputs_ids` passed when calling [`StableLMEpochModel`].
         intermediate_size (`int`, *optional*, defaults to 6912):
             Dimension of the MLP representations.
         hidden_size (`int`, *optional*, defaults to 2560):
-            Number of hidden layers in the Transformer decoder.
+            Dimension of the decoder layers and the pooler layer.
         num_hidden_layers (`int`, *optional*, defaults to 32):
             Number of hidden layers in the Transformer decoder.
         num_attention_heads (`int`, *optional*, defaults to 32):
             Number of attention heads for each attention layer in the Transformer encoder.
-        num_key_value_heads (`int`, *optional*, defaults to 32):
+        num_key_value_heads (`int`, *optional*):
             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
@@ -60,124 +46,68 @@ class StableLmConfig(PretrainedConfig):
             `num_attention_heads`.
         hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
             The non-linear activation function (function or string).
-        max_position_embeddings (`int`, *optional*, defaults to 4096):
+        rope_pct (`float`, *optional*, defaults to 1.0):
+            Percentage of hidden dimensions to allocate to rotary embeddings.
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        max_position_embeddings (`int`, *optional*, defaults to 2048):
             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).
-        initializer_range (`float`, *optional*, defaults to 0.02):
+        initializer_range (`float`, *optional*, defaults to 1e-5):
             The standard deviation of the truncated_normal_initializer for initializing
              all weight matrices.
-        layer_norm_eps (`float`, *optional*, defaults to 1e-05):
+        norm_eps (`float`, *optional*, defaults to 1e-8):
             The epsilon used by the 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`.
-        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
-            Whether the model's input and output word embeddings should be tied.
-        rope_theta (`float`, *optional*, defaults to `10000.0`):
-            The base period of the RoPE embeddings.
-        rope_scaling (`Dict`, *optional*):
-            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
-            strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
-            `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
-            `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
-            these scaling strategies behave:
-            https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This
-            is an experimental feature, subject to breaking API changes in future versions.
-        use_qkv_bias (`bool`, *optional*, defaults to `False`):
+        use_qkv_bias (`bool`, *optional*, defaults to `True`):
             Whether or not the model should use bias for qkv layers.
-        hidden_dropout (`float`, *optional*, defaults to 0.0):
-            The dropout ratio after applying the MLP to the hidden states.
-        attention_dropout (`float`, *optional*, defaults to 0.0):
-            The dropout ratio for the attention probabilities.
-        partial_rotary_factor (`float`, *optional*, defaults to 0.25):
-            Percentage of the query and keys which will have rotary embedding.
-        bos_token_id (int, *optional*, defaults to 0):
-            The id of the `BOS` token in the vocabulary.
-        eos_token_id (int, *optional*, defaults to 0):
-            The id of the `EOS` token in the vocabulary.
-
-    Example:
-
-    ```python
-    >>> from transformers import StableLmModel, StableLmConfig
-
-    >>> # Initializing a StableLM stablelm-3b style configuration
-    >>> configuration = StableLmConfig()
-    ```"""
-
-    model_type = "stablelm"
+        tie_word_embeddings(`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+    """
+    model_type = "stablelm_epoch"
     keys_to_ignore_at_inference = ["past_key_values"]
 
     def __init__(
         self,
-        vocab_size=50304,
+        vocab_size=50_304,
         intermediate_size=6912,
         hidden_size=2560,
         num_hidden_layers=32,
         num_attention_heads=32,
         num_key_value_heads=32,
         hidden_act="silu",
+        rope_pct=0.25,
+        rope_theta=10_000,
         max_position_embeddings=4096,
         initializer_range=0.02,
-        layer_norm_eps=1.0e-5,
+        norm_eps=1.0e-5,
         use_cache=True,
-        tie_word_embeddings=False,
-        rope_theta=10_000,
-        rope_scaling=None,
-        use_qkv_bias=False,
-        hidden_dropout=0.0,
-        attention_dropout=0.0,
-        partial_rotary_factor=0.25,
+        use_qkv_bias=True,
         bos_token_id=0,
-        eos_token_id=0,
+        eos_token_id=2,
+        tie_word_embeddings=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.hidden_size = hidden_size
         self.num_hidden_layers = num_hidden_layers
         self.num_attention_heads = num_attention_heads
         self.num_key_value_heads = num_key_value_heads
         self.hidden_act = hidden_act
-
+        self.rope_pct = rope_pct
+        self.rope_theta = rope_theta
         self.initializer_range = initializer_range
-        self.layer_norm_eps = layer_norm_eps
+        self.norm_eps = norm_eps
         self.use_cache = use_cache
-        self.rope_theta = rope_theta
-        self.rope_scaling = rope_scaling
         self.use_qkv_bias = use_qkv_bias
-        self.hidden_dropout = hidden_dropout
-        self.attention_dropout = attention_dropout
-        self.partial_rotary_factor = partial_rotary_factor
-        self._rope_scaling_validation()
-
+        self.tie_word_embeddings = tie_word_embeddings
         super().__init__(
             bos_token_id=bos_token_id,
             eos_token_id=eos_token_id,
             tie_word_embeddings=tie_word_embeddings,
             **kwargs,
-        )
-
-    # Copied from transformers.models.llama.configuration_llama.LlamaConfig._rope_scaling_validation
-    def _rope_scaling_validation(self):
-        """
-        Validate the `rope_scaling` configuration.
-        """
-        if self.rope_scaling is None:
-            return
-
-        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
-            raise ValueError(
-                "`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
-                f"got {self.rope_scaling}"
-            )
-        rope_scaling_type = self.rope_scaling.get("type", None)
-        rope_scaling_factor = self.rope_scaling.get("factor", None)
-        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
-            raise ValueError(
-                f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
-            )
-        if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
-            raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")
+        )