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August 10, 2022 07:58
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Converting gpt2-large to onnx with multiple external files and using it later for inference
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#!/usr/bin/python | |
# -*- coding: utf-8 -*- | |
import transformers | |
from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModel, AutoConfig | |
from transformers.onnx import FeaturesManager, convert, export | |
from pathlib import Path | |
import os | |
model_id = 'gpt2-large' | |
export_folder = model_id+'-onnx' | |
print('Loading tokenizer...') | |
tokenizer = AutoTokenizer.from_pretrained(model_id) | |
print('Saving tokenizer to ', export_folder) | |
tokenizer.save_pretrained(export_folder) | |
print('Loading model...') | |
model = AutoModelForCausalLM.from_pretrained(model_id) | |
feature= "causal-lm" | |
model_kind, model_onnx_config = FeaturesManager.check_supported_model_or_raise(model, feature=feature) | |
onnx_config = model_onnx_config(model.config) | |
print("model_kind = {0}\nonx_config = {1}\n".format(model_kind, onnx_config)) | |
onnx_path = Path(export_folder+"/model.onnx") | |
print('Exporting model to ', onnx_path) | |
onnx_inputs, onnx_outputs = export(tokenizer, model, onnx_config, onnx_config.default_onnx_opset, onnx_path) | |
print('Done') |
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#Tested with the following Python package versions: | |
#optimum 1.2.3.dev0 | |
#transformers 4.21.0.dev0 | |
#tokenizers 0.11.6 | |
from transformers import AutoTokenizer | |
from optimum.onnxruntime import ORTModelForCausalLM | |
from optimum.pipelines import pipeline | |
model_name="./gpt2-large-onnx" | |
prompt_text = "Hello, my name is" | |
generated_max_length = 42 | |
print("Loading model...") | |
model = ORTModelForCausalLM.from_pretrained(model_name, from_transformers=False) | |
print('Loading Tokenizer...') | |
tokenizer = AutoTokenizer.from_pretrained(model_name) | |
text_generator = pipeline(task="text-generation", model=model, tokenizer=tokenizer) | |
print("Generating text...") | |
result = text_generator(prompt_text, num_return_sequences=1, batch_size=1, do_sample=True, top_k=40, top_p=0.92, max_length = generated_max_length) | |
print("result = " + str(result)) |
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