Initial script for the model validation tool

tools/python/model_validation/README.md

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+# ONNX Runtime GenAI Model Validation  Example
+
+## Setup
+
+Clone this repository and navigate to the `tools/python/model_validation folder`.
+
+```bash
+git clone https://github.com/microsoft/onnxruntime-genai.git
+cd tools/python/model_validation
+```
+
+In the model_validation folder, you should find the validation_tool.py script, validation_config.json file, and this README.md.
+
+### Current Support
+* Gemma
+* Llama 
+* Mistral
+* Phi
+* Qwen
+
+### Usage - Build the Model
+This step creates optimized and quantized ONNX models that run with ONNX Runtime GenAI.
+
+1. In the validation_config.json file, enter the supported Hugging Face model name. Models can be found here.
+2. Include the path to the output folder, precision, and execution provider.
+
+Once the model is built, you can find it in path_to_output_folder/{model_name}. This should include the ONNX model data and tokenizer.
+
+### Run the Model Validation Script 
+```bash
+python validation_tool.py -j validation_config.json
+```
+

tools/python/model_validation/validation_config.json

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+{
+    "models": [
+    ],
+    "inputs": [
+    ],
+    "max_length": 512,
+    "min_length": 0,
+    "do_sample": false,
+    "top_p": 0.0,
+    "top_k": 1,
+    "temperature": 1.0,
+    "repetition_penalty": 1.0,
+    "verbose": false,
+    "output_directory": "",
+    "cache_directory": "",
+    "precision": "",
+    "executive_provider": ""
+}

tools/python/model_validation/validation_tool.py

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+import onnxruntime_genai as og
+import argparse
+import time
+from onnxruntime_genai.models.builder import create_model
+import json
+import os
+import pandas as pd
+
+def create_table(output):
+    df = pd.DataFrame(output, columns=['Model Name', 'Validation Completed', 'Exceptions / Failures'])
+    return df
+
+def validate_model(config, model_directory):
+    if config["verbose"]: print("Loading model...")
+
+    model = og.Model(f'{model_directory}')
+
+    if config["verbose"]: print("Model loaded")
+    tokenizer = og.Tokenizer(model)
+    tokenizer_stream = tokenizer.create_stream() 
+    if config["verbose"]: print("Tokenizer created")
+    if config["verbose"]: print()  
+
+    chat_template = get_chat_template(model_directory)
+
+    for input in config["inputs"]:
+
+        complete_text = ''
+
+        prompt = f'{chat_template.format(input=input)}'
+
+        input_tokens = tokenizer.encode(prompt)
+
+        params = og.GeneratorParams(model)
+        params.input_ids = input_tokens
+
+        generator = og.Generator(model, params)
+        if config["verbose"]: print("Generator created")
+
+        if config["verbose"]: print("Running generation loop ...")
+
+        print()
+        print("Output: ", end='', flush=True)
+
+        generation_successful = True
+
+        try:
+            while not generator.is_done():
+                generator.compute_logits()
+                generator.generate_next_token()
+
+                new_token = generator.get_next_tokens()[0]
+
+                value_to_save = tokenizer_stream.decode(new_token)
+
+                complete_text += value_to_save
+
+                print(tokenizer_stream.decode(new_token), end='', flush=True)
+
+        except KeyboardInterrupt:
+            print("  --control+c pressed, aborting generation--")
+            generation_successful = False
+        except Exception as e:
+            print(f"An error occurred: {e}")
+            generation_successful = False
+
+        with open(f'{model_directory}/output.txt', 'a') as file:
+            file.write(complete_text)
+
+        # Delete the generator to free the captured graph for the next generator, if graph capture is enabled
+        del generator
+
+    return generation_successful
+
+def get_chat_template(output_directory):
+    tokenizer_json = output_directory + '/tokenizer_config.json'
+    with open(tokenizer_json, 'r') as file:
+        config = json.load(file)
+    return config["chat_template"]
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(argument_default=argparse.SUPPRESS, description="End-to-end AI Question/Answer example for gen-ai")
+
+    parser.add_argument('-j', '--json', type=str, required=True, help='Path to the JSON file containing the arguments')
+
+    args = parser.parse_args()
+
+    with open(args.json, 'r') as file:
+        config = json.load(file)
+
+    os.makedirs(config["output_directory"], exist_ok=True)
+    os.makedirs(config["cache_directory"], exist_ok=True)
+
+    output = []
+
+    validation_complete = False
+
+    for model in config["models"]:
+
+        print(f"We are validating {model}")
+        adjusted_model = model.replace("/", "_")
+        output_path = config["output_directory"] + f'/{adjusted_model}'
+        # From the output directory, there exist a file named tokenizer_config.json which contains the chat 
+        cache_path = config["cache_directory"] + f'/{adjusted_model}'
+
+        try:
+            create_model(model, '', output_path, config["precision"], config["executive_provider"], cache_path)
+        except Exception as e:
+            print(f'Failure after create model {e}')
+            output.append([model, validation_complete, e])
+            continue
+        try:          
+            validation_complete = validate_model(config, output_path)
+        except Exception as e:
+            print(f'Failure after validation model {e}')
+            output.append([model, validation_complete, e])
+
+
+    df = create_table(output)
+
+    df.to_csv("models.csv")
+
+    print(df)
+
+    # From the folder name, get the chat template