text stringlengths 1 93.6k |
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print(f"Loaded {len(predictions)} Original Predictions")
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for pred in predictions:
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if prompt in pred:
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s = pred.index(prompt)
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e = s + len(prompt)
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# print(f"Original Pred: {pred}")
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trimmed_pred = pred[: s] + pred[e: ]
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if "\n\ndef" in trimmed_pred:
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trimmed_pred = trimmed_pred[: trimmed_pred.index("\n\ndef")]
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if trimmed_pred.startswith("return "):
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trimmed_pred = trimmed_pred[len("return "): ]
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if verbose:
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print(f"Trimmed Pred: \n{trimmed_pred}")
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else:
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trimmed_pred = pred
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trimmed_predictions.append(trimmed_pred)
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print(f"Collected {len(trimmed_predictions)} (trimmed) predictions")
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return trimmed_predictions
|
def evaluate(model, dataloader, tokenizer, args):
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model.eval()
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if hasattr(model, "module"):
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model = model.module
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gen_kwargs = {
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"max_length": args.max_length,
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"num_beams": args.num_beams,
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"num_return_sequences": args.num_return_sequences,
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"temperature": args.temperature,
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"top_p": args.top_p,
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}
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total = 0
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write_path = Path(args.output_dir) / f"{args.language}-{args.model_size}-{args.model_data}-predictions"
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fw = open(write_path / (f"{args.global_rank}.jsonl"), 'a')
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print(f"Create sub-file: {write_path / (f'{args.global_rank}.jsonl')}")
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with torch.no_grad():
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for i, batch_inputs in enumerate(dataloader):
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batch_prompts = batch_inputs["prompt"]
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batch_inputs = {
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k:v.to(model.device) for k,v in batch_inputs.items()
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if k != "prompt"
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}
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outputs = model.generate(**batch_inputs, **gen_kwargs)
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s, e = 0, gen_kwargs["num_return_sequences"]
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batch_size = batch_inputs["input_ids"].size(0)
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for j in range(batch_size):
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j_preds = tokenizer.batch_decode(
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outputs[s: e],
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skip_special_tokens=True,
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truncate_before_pattern=TRUC_PATTERN_LIST,
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)
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j_prompt = batch_prompts[j]
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j_preds = remove_input_from_outputs(j_preds, j_prompt, args.verbose)
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j_dict = {"predictions": j_preds}
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fw.write(json.dumps(j_dict) + '\n')
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s += gen_kwargs["num_return_sequences"]
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e += gen_kwargs["num_return_sequences"]
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total += 1
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if (i + 1) % args.eval_print_freq == 0:
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log = f"Process rank: {args.global_rank}, {i+1} / {len(dataloader)}"
|
logger.warning(log)
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logger.warning(f"Process rank:{args.global_rank}, total {total} ")
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if args.is_distributed:
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torch.distributed.barrier()
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def main():
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torch.cuda.empty_cache()
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gc.collect()
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model_kwargs = {}
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if args.world_size > 1:
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model_kwargs["device_map"] = "balanced_low_0"
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if args.dtype is not None:
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if args.dtype == "int8":
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model_kwargs["load_in_8bit"] = True
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else:
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model_kwargs["torch_dtype"] = torch.bfloat16 # else torch.float16
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print(f"[Model Kwargs] {model_kwargs}")
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tokenizer = AutoTokenizer.from_pretrained(args.model_name, padding_side='left')
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tokenizer.pad_token = tokenizer.eos_token # '50256' use eos as pad token
|
eval_examples = src.data.load_data(
|
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