Support omnivision inference

nexa/transformers/README.md

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+# transformers support for Nexa AI models
+
+```
+python run_omnivision.py
+```
+
+## Acknowledgements
+We thank the [Hugging Face Transformers](https://github.com/huggingface/transformers) for their amazing work on the Transformers library.

nexa/transformers/omnivision/configuration.py

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+# Copyright (c) 2024 Nexa AI Inc., Alibaba Group (Qwen team), and HuggingFace Inc.
+# 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.
+
+""" Qwen2 model configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+from typing import Union
+from transformers import PretrainedConfig
+import os
+from transformers.models.auto import CONFIG_MAPPING
+
+logger = logging.get_logger(__name__)
+
+
+class SigLipVisionConfig(PretrainedConfig):
+    model_type = "siglip_vision_model"
+    def __init__(
+            self,
+            hidden_size=1152,
+            image_mean=(0.5, 0.5, 0.5),
+            intermediate_size=4304,
+            num_hidden_layers=27,
+            num_attention_heads=16,
+            num_channels=3,
+            image_size=384,
+            patch_size=14,
+            hidden_act="gelu_pytorch_tanh",
+            layer_norm_eps=1e-6,
+            attention_dropout=0.0,
+            **kwargs,
+    ):
+        super().__init__(**kwargs)
+        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.num_channels = num_channels
+        self.patch_size = patch_size
+        self.image_size = image_size
+        self.attention_dropout = attention_dropout
+        self.layer_norm_eps = layer_norm_eps
+        self.hidden_act = hidden_act
+        self.image_mean = image_mean
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
+        cls._set_token_in_kwargs(kwargs)
+
+        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
+
+        # get the vision config dict if we are loading from SigLipConfig
+        if config_dict.get("model_type") == "siglip":
+            config_dict = config_dict["vision_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)
+
+
+""" Nexa AI model configuration"""
+class OminiVLMConfig(PretrainedConfig):
+    model_type = "nano-omini-vlm"
+
+    model_type = "omini_vlm"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vision_config=None,
+        text_config=None,
+        hidden_size=4096,
+        mm_hidden_size=1152,
+        mm_projector_lr=None,
+        mm_projector_type="mlp2x_gelu",
+        image_token_index=151655,
+        initializer_range=0.02,
+        **kwargs,
+    ):
+        self.hidden_size = hidden_size
+        self.mm_hidden_size = mm_hidden_size
+        self.mm_projector_lr = mm_projector_lr
+        self.mm_projector_type = mm_projector_type
+        self.image_token_index = image_token_index
+        self.initializer_range = initializer_range
+        if isinstance(vision_config, dict):
+            vision_config = SigLipVisionConfig(**vision_config)
+        elif vision_config is None:
+            vision_config = SigLipVisionConfig(
+                hidden_size=1152,
+                image_mean=(0.5, 0.5, 0.5),
+                intermediate_size=4304,
+                num_hidden_layers=27,
+                num_attention_heads=16,
+                num_channels=3,
+                image_size=384,
+                patch_size=14,
+                hidden_act="gelu_pytorch_tanh",
+                layer_norm_eps=1e-6,
+                attention_dropout=0.0,
+            )
+        self.vision_config = vision_config
+
+        if isinstance(text_config, dict):
+            text_config["model_type"] = (
+                text_config["model_type"] if "model_type" in text_config else "qwen2"
+            )
+            text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config)
+        elif text_config is None:
+            text_config = CONFIG_MAPPING["qwen2"]()
+
+        self.text_config = text_config
+
+        super().__init__(**kwargs)
+