run_model.py

#!/usr/bin/env python3
#!/usr/bin/env python3
import os
from transformers import AutoTokenizer, GPT2Tokenizer
from megatron.initialize import initialize_megatron
from metaseq import checkpoint_utils
import torch

path = "./model"

# just need to initialize args with something, 
# => doesn't need to correspond to the "correct" architecture for this checkpoint
initialize_megatron(args_defaults={
    "micro_batch_size": 1, 
    "num_layers": 12, 
    "hidden_size": 768, 
    "num_attention_heads": 12,
    "max_position_embeddings": 2048,
    "encoder_seq_length": 2048
})

vocab_file = os.path.join(path, "gpt2-vocab.json")
merges_file = os.path.join(path, "gpt2-merges.txt")

tokenizer = GPT2Tokenizer(vocab_file, merges_file)
tokenizer.save_pretrained(path)

checkpoint = checkpoint_utils.load_model_ensemble_and_task(
    [os.path.join(path, "restored.pt")],
    arg_overrides={
        "vocab_filename": vocab_file,
        "merges_filename": merges_file,
    }
)

model = checkpoint[0][0].eval()
model = model.cuda().half()


# forward passes
def single_batch_forward_logits(prompts):
    input_ids = tokenizer(prompts, return_tensors="pt").input_ids
    input_ids = torch.cat([torch.tensor([[0]]), input_ids], dim=-1)
    input_ids = input_ids.cuda()
    with torch.no_grad():
        logits = model(input_ids)[0]
    return logits

prompts = [
    "Today is a beautiful day and I want to",
    "In the city of",
    "Paris is the capital of France and",
    "Computers and mobile phones have taken",
]

print("Next word generation")
for prompt in prompts:
    print("-------------")
    print(f"Prompt: {prompt}...\n")
    logits = single_batch_forward_logits(prompt)
    pred_next_token = torch.argmax(logits[0, -1], -1)
    next_token = tokenizer.convert_ids_to_tokens([pred_next_token])
    next_token = next_token[0].replace("Ġ", "")
    print(f"Next word: {next_token}")
    print("-------------")