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112 lines
3.6 KiB
Python
Executable File
112 lines
3.6 KiB
Python
Executable File
# SPDX-FileCopyrightText: Copyright (c) 2023-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Utility functions for model type detection and classification."""
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MODEL_NAME_TO_TYPE = {
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"GPT2": "gpt",
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"Mllama": "mllama",
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"Llama4": "llama4",
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"Llama": "llama",
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"Mistral": "llama",
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"GPTJ": "gptj",
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"FalconForCausalLM": "falcon",
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"RWForCausalLM": "falcon",
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"baichuan": "baichuan",
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"MPT": "mpt",
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"Bloom": "bloom",
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"ChatGLM": "chatglm",
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"QWen": "qwen",
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"RecurrentGemma": "recurrentgemma",
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"Gemma3": "gemma3",
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"Gemma2": "gemma2",
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"Gemma": "gemma",
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"phi3small": "phi3small",
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"phi3": "phi3",
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"PhiMoEForCausalLM": "phi3",
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"Phi4MMForCausalLM": "phi4mm",
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"phi": "phi",
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"TLGv4ForCausalLM": "phi",
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"MixtralForCausalLM": "llama",
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"ArcticForCausalLM": "llama",
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"StarCoder": "gpt",
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"Dbrx": "dbrx",
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"T5": "t5",
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"Bart": "bart",
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"GLM": "glm",
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"InternLM2ForCausalLM": "internlm",
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"ExaoneForCausalLM": "exaone",
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"Nemotron": "gpt",
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"Deepseek": "deepseek",
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"Whisper": "whisper",
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"gptoss": "gptoss",
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}
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__doc__ = f"""Utility functions for model type detection and classification.
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.. code-block:: python
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{MODEL_NAME_TO_TYPE=}
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"""
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__all__ = ["get_model_type", "is_multimodal_model"]
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def get_model_type(model):
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"""Try get the model type from the model name. If not found, return None."""
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for k, v in MODEL_NAME_TO_TYPE.items():
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if k.lower() in type(model).__name__.lower():
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return v
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return None
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def is_multimodal_model(model):
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"""Check if a model is a Vision-Language Model (VLM) or multimodal model.
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This function detects various multimodal model architectures by checking for:
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- Standard vision configurations (vision_config)
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- Language model attributes (language_model)
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- Specific multimodal model types (phi4mm)
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- Vision LoRA configurations
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- Audio processing capabilities
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- Image embedding layers
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Args:
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model: The HuggingFace model instance to check
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Returns:
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bool: True if the model is detected as multimodal, False otherwise
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Examples:
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>>> model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct")
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>>> is_multimodal_model(model)
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True
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>>> model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-4-multimodal-instruct")
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>>> is_multimodal_model(model)
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True
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"""
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config = model.config
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return (
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hasattr(config, "vision_config") # Standard vision config (e.g., Qwen2.5-VL)
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or hasattr(model, "language_model") # Language model attribute (e.g., LLaVA)
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or getattr(config, "model_type", "") == "phi4mm" # Phi-4 multimodal
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or hasattr(config, "vision_lora") # Vision LoRA configurations
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or hasattr(config, "audio_processor") # Audio processing capabilities
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or (
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hasattr(config, "embd_layer") and hasattr(config.embd_layer, "image_embd_layer")
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) # Image embedding layers
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)
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