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configuration_git.py

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+# coding=utf-8
+# Copyright 2022 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.
+
+import os
+from typing import Union
+
+from transformers.configuration_utils import PretrainedConfig
+import transformers.models.git.configuration_git as configuration_git
+
+
+GIT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "microsoft/git-base": "https://huggingface.co/microsoft/git-base/resolve/main/config.json",
+}
+
+
+class GitVisionConfig(configuration_git.GitVisionConfig, dict):
+    def __init__(self, *args, **kwargs):
+        configuration_git.GitVisionConfig.__init__(
+                self, *args, **kwargs)
+        dict.__init__(self, **self.__dict__)
+
+    def toJSON(self):
+        return json.dumps(
+            self,
+            default=lambda o: o.__dict__, 
+            sort_keys=True,
+            indent=4)
+
+
+class GitConfig(PretrainedConfig, dict):
+    r"""
+    This is the configuration class to store the configuration of a [`GitModel`]. It is used to instantiate a GIT 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 GIT
+    [microsoft/git-base](https://huggingface.co/microsoft/git-base) architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        vision_config (`dict`, *optional*):
+            Dictionary of configuration options used to initialize [`GitVisionConfig`].
+        vocab_size (`int`, *optional*, defaults to 30522):
+            Vocabulary size of the GIT model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`GitModel`].
+        hidden_size (`int`, *optional*, defaults to 768):
+            Dimensionality of the encoder layers and the pooler layer.
+        num_hidden_layers (`int`, *optional*, defaults to 6):
+            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):
+            Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
+        hidden_act (`str` or `Callable`, *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.
+        hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
+            The dropout probability 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 1024):
+            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):
+            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.
+        position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
+            Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
+            positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
+            [Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
+            For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
+            with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models).
+        num_image_with_embedding (`int`, *optional*):
+            The number of temporal embeddings to add, in case the model is used for video captioning/VQA.
+
+    Examples:
+
+    ```python
+    >>> from transformers import GitConfig, GitModel
+
+    >>> # Initializing a GIT microsoft/git-base style configuration
+    >>> configuration = GitConfig()
+
+    >>> # Initializing a model (with random weights) from the microsoft/git-base style configuration
+    >>> model = GitModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "git"
+
+    def __init__(
+        self,
+        vision_config=None,
+        vocab_size=32778,
+        hidden_size=768,
+        num_hidden_layers=6,
+        num_attention_heads=12,
+        intermediate_size=3072,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.1,
+        attention_probs_dropout_prob=0.1,
+        max_position_embeddings=1024,
+        initializer_range=0.02,
+        layer_norm_eps=1e-12,
+        pad_token_id=0,
+        position_embedding_type="absolute",
+        use_cache=True,
+        tie_word_embeddings=True,
+        bos_token_id=101,
+        eos_token_id=102,
+        num_image_with_embedding=None,
+        **kwargs,
+    ):
+        PretrainedConfig.__init__(
+                self,
+                bos_token_id=bos_token_id, eos_token_id=eos_token_id, pad_token_id=pad_token_id, **kwargs)
+
+        if vision_config is None:
+            vision_config = {}
+        self.vision_config = GitVisionConfig(**vision_config)
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.hidden_act = hidden_act
+        self.intermediate_size = intermediate_size
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.max_position_embeddings = max_position_embeddings
+        self.initializer_range = initializer_range
+        self.layer_norm_eps = layer_norm_eps
+        self.position_embedding_type = position_embedding_type
+        self.use_cache = use_cache
+        self.tie_word_embeddings = tie_word_embeddings
+        self.num_image_with_embedding = num_image_with_embedding
+
+        self.bos_token_id = bos_token_id
+        self.eos_token_id = eos_token_id
+
+        dict.__init__(self, **self.__dict__)
+
+    def toJSON(self):
+        return json.dumps(
+            self,
+            default=lambda o: o.__dict__, 
+            sort_keys=True,
+            indent=4)