Create configuration_xglm.py

configuration_xglm.py

@@ -0,0 +1,141 @@
+# coding=utf-8
+# Copyright 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.
+""" XGLM model configuration"""
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/config.json",
+    # See all XGLM models at https://huggingface.co/models?filter=xglm
+}
+
+
+class XGLMConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`XGLMModel`]. It is used to instantiate an XGLM
+    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 XGLM
+    [facebook/xglm-564M](https://huggingface.co/facebook/xglm-564M) 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 256008):
+            Vocabulary size of the XGLM model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`XGLMModel`] or [`FlaxXGLMModel`].
+        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).
+        d_model (`int`, *optional*, defaults to 1024):
+            Dimension of the layers and the pooler layer.
+        ffn_dim (`int`, *optional*, defaults to 4096):
+            Dimension of the "intermediate" (often named feed-forward) layer in decoder.
+        num_layers (`int`, *optional*, defaults to 24):
+            Number of hidden layers Transformer decoder.
+        attention_heads (`int`, *optional*, defaults to 16):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
+            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+            `"relu"`, `"silu"` and `"gelu_new"` are supported.
+        dropout (`float`, *optional*, defaults to 0.1):
+            The dropout probability for all fully connected layers in the embeddings, dencoder, and pooler.
+        attention_dropout (`float`, *optional*, defaults to 0.1):
+            The dropout ratio for the attention probabilities.
+        activation_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for activations inside the fully connected layer.
+        layerdrop (`float`, *optional*, defaults to 0.0):
+            The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
+            for more details.
+        init_std (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        scale_embedding (`bool`, *optional*, defaults to `True`):
+            Scale embeddings by diving by sqrt(d_model).
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models).
+
+    Example:
+
+    ```python
+    >>> from transformers import XGLMModel, XGLMConfig
+
+    >>> # Initializing a XGLM facebook/xglm-564M style configuration
+    >>> configuration = XGLMConfig()
+
+    >>> # Initializing a model from the facebook/xglm-564M style configuration
+    >>> model = XGLMModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "xglm"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    attribute_map = {
+        "num_attention_heads": "attention_heads",
+        "hidden_size": "d_model",
+        "num_hidden_layers": "num_layers",
+    }
+
+    def __init__(
+        self,
+        vocab_size=256008,
+        max_position_embeddings=2048,
+        d_model=1024,
+        ffn_dim=4096,
+        num_layers=24,
+        attention_heads=16,
+        activation_function="gelu",
+        dropout=0.1,
+        attention_dropout=0.1,
+        activation_dropout=0.0,
+        layerdrop=0.0,
+        init_std=0.02,
+        scale_embedding=True,
+        use_cache=True,
+        decoder_start_token_id=2,
+        pad_token_id=1,
+        bos_token_id=0,
+        eos_token_id=2,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.d_model = d_model
+        self.ffn_dim = ffn_dim
+        self.num_layers = num_layers
+        self.attention_heads = attention_heads
+        self.activation_function = activation_function
+        self.dropout = dropout
+        self.attention_dropout = attention_dropout
+        self.activation_dropout = activation_dropout
+        self.layerdrop = layerdrop
+        self.init_std = init_std
+        self.scale_embedding = scale_embedding  # scale factor will be sqrt(d_model) if True
+        self.use_cache = use_cache
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            decoder_start_token_id=decoder_start_token_id,
+            **kwargs,
+        )