[bin/recognize.py] support numworkers and compute dtype

wenet/bin/recognize.py

@@ -43,6 +43,15 @@ def get_args():
                         type=int,
                         default=-1,
                         help='gpu id for this rank, -1 for cpu')
+    parser.add_argument('--dtype',
+                        type=str,
+                        default='float32',
+                        choices=['float16', 'flaot32', 'bfloat16'],
+                        help='model\'s dtype')
+    parser.add_argument('--num_workers',
+                        default=0,
+                        type=int,
+                        help='num of subprocess workers for reading')
     parser.add_argument('--checkpoint', required=True, help='checkpoint model')
     parser.add_argument('--beam_size',
                         type=int,
@@ -206,7 +215,9 @@ def main():
                            test_conf,
                            partition=False)
 
-    test_data_loader = DataLoader(test_dataset, batch_size=None, num_workers=0)
+    test_data_loader = DataLoader(test_dataset,
+                                  batch_size=None,
+                                  num_workers=args.num_workers)
 
     # Init asr model from configs
     args.jit = False
@@ -216,6 +227,12 @@ def main():
     device = torch.device('cuda' if use_cuda else 'cpu')
     model = model.to(device)
     model.eval()
+    dtype = torch.float32
+    if args.dtype == 'float16':
+        dtype = torch.float16
+    elif args.dtype == 'bfloat16':
+        dtype = torch.bfloat16
+    logging.info("compute dtype is {}".format(dtype))
 
     context_graph = None
     if 'decoding-graph' in args.context_bias_mode:
@@ -237,38 +254,43 @@ def main():
         file_name = os.path.join(dir_name, 'text')
         files[mode] = open(file_name, 'w')
     max_format_len = max([len(mode) for mode in args.modes])
-    with torch.no_grad():
-        for batch_idx, batch in enumerate(test_data_loader):
-            keys = batch["keys"]
-            feats = batch["feats"].to(device)
-            target = batch["target"].to(device)
-            feats_lengths = batch["feats_lengths"].to(device)
-            target_lengths = batch["target_lengths"].to(device)
-            infos = {"tasks": batch["tasks"], "langs": batch["langs"]}
-            results = model.decode(
-                args.modes,
-                feats,
-                feats_lengths,
-                args.beam_size,
-                decoding_chunk_size=args.decoding_chunk_size,
-                num_decoding_left_chunks=args.num_decoding_left_chunks,
-                ctc_weight=args.ctc_weight,
-                simulate_streaming=args.simulate_streaming,
-                reverse_weight=args.reverse_weight,
-                context_graph=context_graph,
-                blank_id=blank_id,
-                blank_penalty=args.blank_penalty,
-                length_penalty=args.length_penalty,
-                infos=infos)
-            for i, key in enumerate(keys):
-                for mode, hyps in results.items():
-                    tokens = hyps[i].tokens
-                    line = '{} {}'.format(key, tokenizer.detokenize(tokens)[0])
-                    logging.info('{} {}'.format(mode.ljust(max_format_len),
-                                                line))
-                    files[mode].write(line + '\n')
-    for mode, f in files.items():
-        f.close()
+
+    with torch.cuda.amp.autocast(enabled=True,
+                                 dtype=dtype,
+                                 cache_enabled=False):
+        with torch.no_grad():
+            for batch_idx, batch in enumerate(test_data_loader):
+                keys = batch["keys"]
+                feats = batch["feats"].to(device)
+                target = batch["target"].to(device)
+                feats_lengths = batch["feats_lengths"].to(device)
+                target_lengths = batch["target_lengths"].to(device)
+                infos = {"tasks": batch["tasks"], "langs": batch["langs"]}
+                results = model.decode(
+                    args.modes,
+                    feats,
+                    feats_lengths,
+                    args.beam_size,
+                    decoding_chunk_size=args.decoding_chunk_size,
+                    num_decoding_left_chunks=args.num_decoding_left_chunks,
+                    ctc_weight=args.ctc_weight,
+                    simulate_streaming=args.simulate_streaming,
+                    reverse_weight=args.reverse_weight,
+                    context_graph=context_graph,
+                    blank_id=blank_id,
+                    blank_penalty=args.blank_penalty,
+                    length_penalty=args.length_penalty,
+                    infos=infos)
+                for i, key in enumerate(keys):
+                    for mode, hyps in results.items():
+                        tokens = hyps[i].tokens
+                        line = '{} {}'.format(key,
+                                              tokenizer.detokenize(tokens)[0])
+                        logging.info('{} {}'.format(mode.ljust(max_format_len),
+                                                    line))
+                        files[mode].write(line + '\n')
+        for mode, f in files.items():
+            f.close()
 
 
 if __name__ == '__main__':