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2024-07-26 nightly release (dbf87b0)
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| # Copyright (c) Meta Platforms, Inc. and affiliates. | ||
| # All rights reserved. | ||
| # | ||
| # This source code is licensed under the BSD-style license found in the | ||
| # LICENSE file in the root directory of this source tree. | ||
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| # Script to run phi-3-mini model in eager mode. | ||
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| import argparse | ||
| import time | ||
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| import torch | ||
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| from transformers import AutoTokenizer, Phi3ForCausalLM | ||
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| end_of_text_token = 32000 | ||
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| def _generate_token(args, model, prompt_tokens): | ||
| current_token = 0 | ||
| generated_tokens = [] | ||
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| print("Generating tokens:", end="", flush=True) | ||
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| while current_token != end_of_text_token and len(generated_tokens) < args.seq_len: | ||
| outputs = model.forward(input_ids=prompt_tokens) | ||
| current_token = torch.argmax(outputs.logits[:, -1, :], dim=-1).item() | ||
| print(f" {current_token}", end="", flush=True) | ||
| generated_tokens.append(current_token) | ||
| prompt_tokens = torch.cat( | ||
| [prompt_tokens, torch.tensor([[current_token]], dtype=torch.long)], dim=-1 | ||
| ) | ||
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| print("", flush=True) | ||
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| return generated_tokens | ||
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| def _generate_token_with_kv_cache(args, model, prompt_tokens): | ||
| print("Generating tokens:", end="", flush=True) | ||
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| result = model.forward(input_ids=prompt_tokens, use_cache=True, return_dict=True) | ||
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| current_token = torch.argmax(result.logits[:, -1, :], dim=-1).item() | ||
| current_key_value = result.past_key_values | ||
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| print(f" {current_token}", end="", flush=True) | ||
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| generated_tokens = [current_token] | ||
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| while current_token != end_of_text_token and len(generated_tokens) < args.seq_len: | ||
| result = model.forward( | ||
| input_ids=torch.tensor([[current_token]], dtype=torch.long), | ||
| use_cache=True, | ||
| return_dict=True, | ||
| past_key_values=current_key_value, | ||
| ) | ||
| current_token = torch.argmax(result.logits[:, -1, :], dim=-1).item() | ||
| current_key_value = result.past_key_values | ||
| print(f" {current_token}", end="", flush=True) | ||
| generated_tokens.append(current_token) | ||
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| print("", flush=True) | ||
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| return generated_tokens | ||
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| def main(args): | ||
| seed = 42 | ||
| torch.manual_seed(seed) | ||
| model_name = "microsoft/Phi-3-mini-4k-instruct" | ||
| model = Phi3ForCausalLM.from_pretrained(model_name) | ||
| tokenizer = AutoTokenizer.from_pretrained(model_name) | ||
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| tokens = tokenizer.encode(args.prompt, return_tensors="pt") | ||
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| start = time.time() | ||
| generated_tokens = ( | ||
| _generate_token_with_kv_cache(args, model, tokens) | ||
| if args.use_kv_cache | ||
| else _generate_token(args, model, tokens) | ||
| ) | ||
| end = time.time() | ||
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| print( | ||
| "Generated response: \n {}".format( | ||
| tokenizer.decode( | ||
| generated_tokens, | ||
| skip_special_tokens=True, | ||
| clean_up_tokenization_spaces=False, | ||
| ) | ||
| ), | ||
| flush=True, | ||
| ) | ||
| print(f"Time spent: {end - start}", flush=True) | ||
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| if __name__ == "__main__": | ||
| parser = argparse.ArgumentParser() | ||
| parser.add_argument( | ||
| "-s", | ||
| "--seq_len", | ||
| type=int, | ||
| default=128, | ||
| help="Maximum number of tokens to generate", | ||
| ) | ||
| parser.add_argument( | ||
| "-kv", | ||
| "--use_kv_cache", | ||
| default=False, | ||
| action="store_true", | ||
| help="Whether or not to use KV cache", | ||
| ) | ||
| parser.add_argument( | ||
| "-p", | ||
| "--prompt", | ||
| type=str, | ||
| default="Tell me a story", | ||
| help="Prompt as input for the model", | ||
| ) | ||
| main(parser.parse_args()) |
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