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import os |
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from transformers import AutoTokenizer, GPT2Tokenizer |
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from metaseq import checkpoint_utils |
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from transformers import OPTForCausalLM |
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import torch |
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path = "./model" |
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hf_path = "/home/patrick/facebook/opt-125m" |
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vocab_file = os.path.join(path, "gpt2-vocab.json") |
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merges_file = os.path.join(path, "gpt2-merges.txt") |
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tokenizer = GPT2Tokenizer(vocab_file, merges_file) |
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tokenizer.save_pretrained(path) |
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checkpoint = checkpoint_utils.load_model_ensemble_and_task( |
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[os.path.join(path, "restored.pt")], |
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arg_overrides={ |
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"vocab_filename": vocab_file, |
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"merges_filename": merges_file, |
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} |
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) |
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model = checkpoint[0][0].eval() |
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model = model |
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hf_model = OPTForCausalLM.from_pretrained(hf_path) |
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def single_batch_forward_logits(prompts): |
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input_ids = tokenizer(prompts, return_tensors="pt").input_ids |
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input_ids = torch.cat([torch.tensor([[0]]), input_ids], dim=-1) |
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input_ids = input_ids |
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with torch.no_grad(): |
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logits = model(input_ids)[0] |
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return logits |
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def forward_hf(prompts): |
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input_ids = tokenizer(prompts, return_tensors="pt").input_ids |
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input_ids = torch.cat([torch.tensor([[0]]), input_ids], dim=-1) |
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input_ids = input_ids |
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with torch.no_grad(): |
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logits = hf_model(input_ids)[0] |
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return logits |
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prompts = [ |
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"Today is a beautiful day and I want to", |
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"In the city of", |
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"Paris is the capital of France and", |
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"Computers and mobile phones have taken", |
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] |
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print("Next word generation") |
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for prompt in prompts: |
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print("-------------") |
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print(f"Prompt: {prompt}...\n") |
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logits_fsq = single_batch_forward_logits(prompt) |
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pred_next_token = torch.argmax(logits_fsq[0, -1], -1) |
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next_token = tokenizer.convert_ids_to_tokens([pred_next_token]) |
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next_token = next_token[0].replace("Ġ", "") |
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print(f"Next word: {next_token}") |
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print("-------------") |
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logits = forward_hf(prompt) |
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pred_next_token = torch.argmax(logits[0, -1], -1) |
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next_token = tokenizer.convert_ids_to_tokens([pred_next_token]) |
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next_token = next_token[0].replace("Ġ", "") |
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print(f"Next word: {next_token}") |
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print("-------------") |
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print("Is equal:", torch.allclose(logits_fsq.cpu(), logits.cpu(), atol=1e-3)) |
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