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| """ | |
| This script provides an example to use DeepSpeed for classification inference. | |
| """ | |
| import sys | |
| import os | |
| import torch | |
| import argparse | |
| import collections | |
| import torch.nn as nn | |
| import deepspeed | |
| import torch.distributed as dist | |
| tencentpretrain_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "..")) | |
| sys.path.append(tencentpretrain_dir) | |
| from tencentpretrain.opts import deepspeed_opts | |
| from inference.run_classifier_infer import * | |
| def main(): | |
| parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) | |
| infer_opts(parser) | |
| parser.add_argument("--labels_num", type=int, required=True, | |
| help="Number of prediction labels.") | |
| tokenizer_opts(parser) | |
| parser.add_argument("--output_logits", action="store_true", help="Write logits to output file.") | |
| parser.add_argument("--output_prob", action="store_true", help="Write probabilities to output file.") | |
| deepspeed_opts(parser) | |
| parser.add_argument("--mp_size", type=int, default=1, help="Model parallel size.") | |
| args = parser.parse_args() | |
| # Load the hyperparameters from the config file. | |
| args = load_hyperparam(args) | |
| # Build tokenizer. | |
| args.tokenizer = str2tokenizer[args.tokenizer](args) | |
| # Build classification model and load parameters. | |
| args.soft_targets, args.soft_alpha = False, False | |
| deepspeed.init_distributed() | |
| model = Classifier(args) | |
| if args.load_model_path: | |
| model = load_model(model, args.load_model_path) | |
| model = deepspeed.init_inference(model=model, mp_size=args.mp_size, replace_method=None) | |
| rank = dist.get_rank() | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| if rank == 0: | |
| dataset = read_dataset(args, args.test_path) | |
| src = torch.LongTensor([sample[0] for sample in dataset]) | |
| seg = torch.LongTensor([sample[1] for sample in dataset]) | |
| batch_size = args.batch_size | |
| instances_num = src.size()[0] | |
| print("The number of prediction instances: ", instances_num) | |
| model.eval() | |
| with open(args.prediction_path, mode="w", encoding="utf-8") as f: | |
| f.write("label") | |
| if args.output_logits: | |
| f.write("\t" + "logits") | |
| if args.output_prob: | |
| f.write("\t" + "prob") | |
| f.write("\n") | |
| for i, (src_batch, seg_batch) in enumerate(batch_loader(batch_size, src, seg)): | |
| src_batch = src_batch.to(device) | |
| seg_batch = seg_batch.to(device) | |
| with torch.no_grad(): | |
| _, logits = model(src_batch, None, seg_batch) | |
| pred = torch.argmax(logits, dim=1) | |
| pred = pred.cpu().numpy().tolist() | |
| prob = nn.Softmax(dim=1)(logits) | |
| logits = logits.cpu().numpy().tolist() | |
| prob = prob.cpu().numpy().tolist() | |
| for j in range(len(pred)): | |
| f.write(str(pred[j])) | |
| if args.output_logits: | |
| f.write("\t" + " ".join([str(v) for v in logits[j]])) | |
| if args.output_prob: | |
| f.write("\t" + " ".join([str(v) for v in prob[j]])) | |
| f.write("\n") | |
| if __name__ == "__main__": | |
| main() | |