mirror of
https://github.com/opencv/opencv.git
synced 2026-07-29 07:13:02 +04:00
Jorge Velez
a49a293d3c
GSoC 2025: Add Tokenizer Support to DNN Module #27534 merge with https://github.com/opencv/opencv_extra/pull/1276 ### Summary This pull request introduces initial support for a tokenizer module under `modules/dnn/src/tokenizer` as part of Google Summer of Code 2025 (Project: Tokenization for OpenCV DNN). ### Status - [x] Project structure in place - [x] Initial BPE tokenizer loading - [x] Regex splitting (in progress) - [x] Encoding logic for GPT-2 tokenizer (in progress) - [ ] Documentation (to be improved) ### Goals The goal is to support Hugging Face-compatible tokenization (e.g., GPT-2) natively in C++ to be integrated with DNN inference pipelines. The core pipeline lives in `dnn/src/tokenizer/core_bpe.hpp` and `dnn/src/tokenizer/encoding.hpp`. For Unicode handling I’m using `dnn/src/tokenizer/unicode.hpp`, which is adapted from llama.cpp. ### Feedback Please share early feedback on: - General design structure - Integration strategy with `dnn` - Code organization or naming conventions ### Reference Project: https://summerofcode.withgoogle.com/programs/2025/projects/79SW6eNK
90 lines
3.0 KiB
Python
90 lines
3.0 KiB
Python
'''
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This is a sample script to run GPT-2 inference in OpenCV using ONNX model.
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The script loads the GPT-2 model and runs inference on a given prompt.
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Currently script only works with fixed size window, that means
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you will have to specify prompt of the same length as when model was exported to ONNX.
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Exporting GPT-2 model to ONNX.
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To export GPT-2 model to ONNX, you can use the following procedure:
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1. Clone fork of Andrej Karpathy's GPT-2 repository:
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git clone -b fix-dynamic-axis-export https://github.com/nklskyoy/build-nanogpt
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2. Install the required dependencies:
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pip install -r requirements.txt
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3 Export the model to ONNX:
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python export2onnx.py --promt=<Any-promt-you-want>
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Run the script:
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1. Install the required dependencies:
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pip install tiktoken==0.7.0 numpy tqdm
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2. Run the script:
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python gpt2_inference.py --model=<path-to-onnx-model> --tokenizer_path=<path-to-tokenizer-config> --prompt=<use-promt-of-the-same-length-used-while-exporting>
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'''
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import numpy as np
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import argparse
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import cv2 as cv
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def parse_args():
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parser = argparse.ArgumentParser(description='Use this script to run GPT-2 inference in OpenCV',
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formatter_class=argparse.ArgumentDefaultsHelpFormatter)
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parser.add_argument('--model', type=str, required=True, help='Path to GPT-2 model ONNX model file.')
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parser.add_argument('--tokenizer_path', type=str, required=True, help='Path to GPT-2 tokenizer config file.')
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parser.add_argument("--prompt", type=str, default="Hello, I'm a language model,", help="Prompt to start with.")
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parser.add_argument("--max_seq_len", type=int, default=32, help="Number of tokens to continue.")
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parser.add_argument("--seed", type=int, default=0, help="Random seed")
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return parser.parse_args()
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def stable_softmax(logits):
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exp_logits = np.exp(logits - np.max(logits, axis=-1, keepdims=True))
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return exp_logits / np.sum(exp_logits, axis=-1, keepdims=True)
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def gpt2_inference(net, prompt, max_length, tokenizer):
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print("Inferencing GPT-2 model...")
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tokens = tokenizer.encode(prompt).reshape(1,-1)
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stop_tokens = (50256, ) ## could be extended to include more stop tokens
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while 0 < max_length and tokens[:, -1] not in stop_tokens:
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net.setInputsNames(['idx'])
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net.setInput(tokens, 'idx')
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logits = net.forward()
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logits = logits[:, -1, :] # (B, vocab_size)
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# use hard sampling
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new_idx = np.argmax(logits.reshape(-1)).reshape(1,1)
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tokens = np.concatenate((tokens, new_idx), axis=1)
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max_length -= 1
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return tokens
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if __name__ == '__main__':
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args = parse_args()
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print("Preparing GPT-2 model...")
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max_length = args.max_seq_len
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prompt = args.prompt
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tokenizer_path = args.tokenizer_path
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net = cv.dnn.readNetFromONNX(args.model, cv.dnn.ENGINE_NEW)
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tokenizer = cv.dnn.Tokenizer.load(tokenizer_path)
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tokens = gpt2_inference(net, prompt, max_length, tokenizer)
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print(tokenizer.decode(tokens[0]))
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