Update onnxgenairun.py
Browse files- onnxgenairun.py +31 -23
onnxgenairun.py
CHANGED
@@ -3,16 +3,23 @@ import argparse
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import time
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import re
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def main(args):
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if args.verbose: print("Loading model...")
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if args.timings:
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started_timestamp = 0
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first_token_timestamp = 0
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if args.verbose: print("Model loaded")
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tokenizer = og.Tokenizer(model)
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tokenizer_stream = tokenizer.create_stream()
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if args.verbose: print("Tokenizer created")
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@@ -26,6 +33,10 @@ def main(args):
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chat_template = '<|user|>\n{input} <|end|>\n<|assistant|>'
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# Keep asking for input prompts in a loop
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while True:
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text = input("Input: ")
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@@ -40,10 +51,8 @@ def main(args):
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input_tokens = tokenizer.encode(prompt)
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params = og.GeneratorParams(model)
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params.set_search_options(**search_options)
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# params.input_ids = input_tokens
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generator = og.Generator(model, params)
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if args.verbose: print("Generator created")
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if args.verbose: print("Running generation loop ...")
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@@ -52,14 +61,13 @@ def main(args):
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new_tokens = []
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print()
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print("Output
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try:
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vPreviousDecoded = ""
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vNewDecoded = ""
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generator.append_tokens(input_tokens)
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while not generator.is_done():
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# generator.compute_logits()
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generator.generate_next_token()
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if args.timings:
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if first:
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@@ -67,26 +75,25 @@ def main(args):
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first = False
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new_token = generator.get_next_tokens()[0]
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###print(tokenizer_stream.decode(new_token), end='', flush=True)
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vNewDecoded = tokenizer_stream.decode(new_token)
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if re.findall("^[\x2E\x3A\x3B]$", vPreviousDecoded) and vNewDecoded.startswith(" ") and (not vNewDecoded.startswith(" *")) :
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vPreviousDecoded = vNewDecoded
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if args.timings: new_tokens.append(new_token)
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except KeyboardInterrupt:
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print(" --control+c pressed, aborting generation--")
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print()
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print()
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# Delete the generator to free the captured graph for the next generator, if graph capture is enabled
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del generator
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if args.timings:
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prompt_time = first_token_timestamp - started_timestamp
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run_time = time.time() - first_token_timestamp
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@@ -95,7 +102,8 @@ def main(args):
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(argument_default=argparse.SUPPRESS, description="End-to-end AI Question/Answer example for gen-ai")
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parser.add_argument('-m', '--
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parser.add_argument('-i', '--min_length', type=int, help='Min number of tokens to generate including the prompt')
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parser.add_argument('-l', '--max_length', type=int, help='Max number of tokens to generate including the prompt')
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parser.add_argument('-ds', '--do_sample', action='store_true', default=False, help='Do random sampling. When false, greedy or beam search are used to generate the output. Defaults to false')
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@@ -106,4 +114,4 @@ if __name__ == "__main__":
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parser.add_argument('-v', '--verbose', action='store_true', default=False, help='Print verbose output and timing information. Defaults to false')
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parser.add_argument('-g', '--timings', action='store_true', default=False, help='Print timing information for each generation step. Defaults to false')
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args = parser.parse_args()
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main(args)
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import time
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import re
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def main(args):
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if args.verbose: print("Loading model...")
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if args.timings:
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started_timestamp = 0
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first_token_timestamp = 0
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config = og.Config(args.model_path)
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config.clear_providers()
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# if args.execution_provider != "cpu":
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# if args.verbose: print(f"Setting model to {args.execution_provider}")
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# config.append_provider(args.execution_provider)
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config.append_provider("dml")
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model = og.Model(config)
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if args.verbose: print("Model loaded")
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tokenizer = og.Tokenizer(model)
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tokenizer_stream = tokenizer.create_stream()
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if args.verbose: print("Tokenizer created")
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chat_template = '<|user|>\n{input} <|end|>\n<|assistant|>'
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params = og.GeneratorParams(model)
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params.set_search_options(**search_options)
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# generator = og.Generator(model, params)
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# Keep asking for input prompts in a loop
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while True:
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text = input("Input: ")
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input_tokens = tokenizer.encode(prompt)
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generator = og.Generator(model, params)
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generator.append_tokens(input_tokens)
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if args.verbose: print("Generator created")
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if args.verbose: print("Running generation loop ...")
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new_tokens = []
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print()
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print("Output: ", end='', flush=True)
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vPreviousDecoded = ""
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vNewDecoded = ""
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try:
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while not generator.is_done():
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generator.generate_next_token()
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if args.timings:
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if first:
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first = False
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new_token = generator.get_next_tokens()[0]
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#print(tokenizer_stream.decode(new_token), end='', flush=True)
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vNewDecoded = tokenizer_stream.decode(new_token)
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#if re.findall("^[\x2E\x3A\x3B]$", vPreviousDecoded) and vNewDecoded.startswith(" ") and (not vNewDecoded.startswith(" *")) :
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if re.fullmatch("^[\x2E\x3A\x3B]$", vPreviousDecoded) and vNewDecoded.startswith(" ") and (not vNewDecoded.startswith(" *")) :
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# vNewDecoded = "\n" + vNewDecoded.replace(" ", "", 1)
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print("\n" + vNewDecoded.replace(" ", "", 1), end='', flush=True)
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else :
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print(vNewDecoded, end='', flush=True)
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vPreviousDecoded = vNewDecoded
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if args.timings: new_tokens.append(new_token)
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except KeyboardInterrupt:
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print(" --control+c pressed, aborting generation--")
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print()
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print()
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if args.timings:
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prompt_time = first_token_timestamp - started_timestamp
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run_time = time.time() - first_token_timestamp
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(argument_default=argparse.SUPPRESS, description="End-to-end AI Question/Answer example for gen-ai")
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parser.add_argument('-m', '--model_path', type=str, required=True, help='Onnx model folder path (must contain genai_config.json and model.onnx)')
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# parser.add_argument('-e', '--execution_provider', type=str, required=True, choices=["cpu", "cuda", "dml"], help="Execution provider to run ONNX model with")
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parser.add_argument('-i', '--min_length', type=int, help='Min number of tokens to generate including the prompt')
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parser.add_argument('-l', '--max_length', type=int, help='Max number of tokens to generate including the prompt')
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parser.add_argument('-ds', '--do_sample', action='store_true', default=False, help='Do random sampling. When false, greedy or beam search are used to generate the output. Defaults to false')
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parser.add_argument('-v', '--verbose', action='store_true', default=False, help='Print verbose output and timing information. Defaults to false')
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parser.add_argument('-g', '--timings', action='store_true', default=False, help='Print timing information for each generation step. Defaults to false')
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args = parser.parse_args()
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main(args)
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