add GPT-2 run files

Author: yashbonde

convert.py

@@ -0,0 +1,11 @@
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("gpt2")
+model = AutoModelForCausalLM.from_pretrained("gpt2")
+
+with open("text.en", "r") as f:
+ input_encoder = tokenizer(f.read(), return_tensors="pt")
+
+print(":: Converting to ONNX runtime")
+torch.onnx.export(model, input_encoder["input_ids"], "gpt2.onnx")

run.py

@@ -0,0 +1,66 @@
+#!/usr/bin/env python
+"""
+ Copyright (c) 2018 Intel Corporation
+ 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 os
+import time
+from argparse import ArgumentParser
+
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+from openvino.inference_engine import IECore
+
+if __name__ == '__main__':
+ parser = ArgumentParser()
+ parser.add_argument("--model", help="Path to an .xml file with a trained model.", default = "gpt2.xml", type=str)
+
+ print("-"*70)
+ print("Loading Pytorch model")
+ tokenizer = AutoTokenizer.from_pretrained("gpt2")
+ model = AutoModelForCausalLM.from_pretrained("gpt2")
+ with open("text.en", "r") as f:
+ input_encoder = tokenizer(f.read(), return_tensors="pt")
+
+ st = time.time()
+ model(input_encoder["input_ids"])
+ print(f"Pytorch inference in {time.time() - st:.5f}s")
+ del model, tokenizer
+
+ args = parser.parse_args()
+ print("-"*70)
+ model_xml = args.model
+ model_bin = os.path.splitext(model_xml)[0] + ".bin"
+
+ # Plugin initialization for specified device and load extensions library if specified.
+ print("Creating Inference Engine...")
+ ie = IECore()
+
+ # Read IR
+ print("Loading network")
+ net = ie.read_network(args.model, os.path.splitext(args.model)[0] + ".bin")
+
+ print("Loading IR to the plugin...")
+ exec_net = ie.load_network(network=net, device_name="CPU", num_requests=2)
+ print(f"exec_net: {exec_net}")
+ print("-"*70)
+
+ # this is a bit tricky. So the input to the model is the input from ONNX graph
+ # IECore makes a networkX graph of the "computation graph" and when we run .infer
+ # it passes it through. If you are unsure of what to pass you can always check the
+ # <model>.xml file. In case of pytorch models the value "input.1" is the usual
+ # suspect. Happy Hunting!
+ inputs = input_encoder["input_ids"].tolist()
+ st = time.time()
+ out = exec_net.infer(inputs={"0": [1 for _ in range(127)]})
+ print(f"OpenVino inference in {time.time() - st:.5f}s")
+ print("-"*70)