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models/mPLUG_owl/mplug_owl/configuration_mplug_owl.py

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+# coding=utf-8
+# Copyright 2023 Alibaba Inc. 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.
+
+
+import copy
+import os
+from typing import Union
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.models.auto import CONFIG_MAPPING
+from transformers.models.auto.modeling_auto import \
+    MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+
+class mPLUG_OwlVisualAbstractorConfig(PretrainedConfig):
+
+    model_type = "mPLUG_OwlVisualAbstractor"
+
+    def __init__(
+        self,
+        vocab_size=30522,
+        hidden_size=1024,
+        num_hidden_layers=6,
+        num_attention_heads=8,
+        intermediate_size=3072,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.1,
+        attention_probs_dropout_prob=0.1,
+        max_position_embeddings=512,
+        initializer_range=0.02,
+        layer_norm_eps=1e-5,
+        pad_token_id=0,
+        position_embedding_type="absolute",
+        classifier_dropout=None,
+        cross_attention_frequency=2,
+        encoder_hidden_size=1024,
+        **kwargs,
+    ):
+        super().__init__(pad_token_id=pad_token_id, **kwargs)
+
+        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.classifier_dropout = classifier_dropout
+        self.cross_attention_frequency = cross_attention_frequency
+        self.encoder_hidden_size = encoder_hidden_size
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
+        config_dict, kwargs = cls.get_config_dict(
+            pretrained_model_name_or_path, **kwargs)
+
+        if config_dict.get("model_type") == "mplug_owl":
+            config_dict = config_dict["abstractor_config"]
+
+        if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
+            logger.warning(
+                f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
+                f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
+            )
+
+        return cls.from_dict(config_dict, **kwargs)
+
+
+class mPLUG_OwlConfig(PretrainedConfig):
+
+    model_type = "mplug_owl"
+    is_composition = True
+
+    def __init__(self, vision_config=None, visual_abstractor_config=None, text_config=None, num_query_tokens=64, **kwargs):
+        super().__init__(**kwargs)
+        from clip.configuration_clip import CLIPVisionConfig
+        if vision_config is None:
+            # By defalt we use openai-clip large patch14
+
+            vision_config = CLIPVisionConfig(
+                **vision_config_dict, layer_norm_eps=1e-6).to_dict()
+            logger.info(
+                "vision_config is None.")
+
+        if visual_abstractor_config is None:
+            visual_abstractor_config = {}
+            logger.info(
+                "abstractor_config is None. ")
+
+        if text_config is None:
+            # we use LLAMA 7b by default
+            from transformers.models.llama.configuration_llama import \
+                LlamaConfig
+            text_config = LlamaConfig(pad_token_id=2).to_dict()
+            logger.info("text_config is None.")
+
+        self.vision_config = CLIPVisionConfig(**vision_config)
+        self.visual_abstractor_config = mPLUG_OwlVisualAbstractorConfig(
+            **visual_abstractor_config)
+        self.visual_abstractor_config.layer_norm_eps = 1e-6
+        text_model_type = text_config["model_type"] if "model_type" in text_config else "opt"
+        self.text_config = CONFIG_MAPPING[text_model_type](**text_config)
+
+        self.tie_word_embeddings = self.text_config.tie_word_embeddings
+        self.is_encoder_decoder = self.text_config.is_encoder_decoder
+
+        self.num_query_tokens = num_query_tokens
+        self.visual_abstractor_config.encoder_hidden_size = self.vision_config.hidden_size
+        self.use_decoder_only_language_model = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
+        self.initializer_factor = 1.0
+        self.initializer_range = 0.02
+
+    def to_dict(self):
+        """
+        Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
+
+        Returns:
+            `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
+        """
+        output = copy.deepcopy(self.__dict__)
+        output["vision_config"] = self.vision_config.to_dict()
+        output["abstractor_config"] = self.visual_abstractor_config.to_dict()
+        output["text_config"] = self.text_config.to_dict()
+        output["model_type"] = self.__class__.model_type
+        return output
+
+
+vision_config_dict = {
+    "hidden_size": 1024,
+    "intermediate_size": 4096,
+    "num_attention_heads": 8,
+    "num_hidden_layers": 24,
+    "patch_size": 14,
+    "projection_dim": 768}