add depth export (PaddlePaddle#853)

examples/model_compression/ofa/export_model.py

@@ -15,6 +15,7 @@
 import argparse
 import logging
 import os
+import math
 import random
 import time
 import json
@@ -85,6 +86,11 @@ def parse_args():
         type=float,
         default=1.0,
         help="width mult you want to export")
+    parser.add_argument(
+        '--depth_mult',
+        type=float,
+        default=1.0,
+        help="depth mult you want to export")
     args = parser.parse_args()
     return args
 
@@ -106,6 +112,18 @@ def do_train(args):
     model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
     config_path = os.path.join(args.model_name_or_path, 'model_config.json')
     cfg_dict = dict(json.loads(open(config_path).read()))
+
+    if args.depth_mult < 1.0:
+        depth = round(cfg_dict["init_args"][0]['num_hidden_layers'] * args.depth_mult)
+        cfg_dict["init_args"][0]['num_hidden_layers'] = depth 
+        kept_layers_index = {}
+        for idx, i in enumerate(range(1, depth+1)):
+            kept_layers_index[idx] = math.floor(i / args.depth_mult) - 1
+
+    os.rename(config_path, config_path+'_bak')
+    with open(config_path, "w", encoding="utf-8") as f:
+        f.write(json.dumps(cfg_dict, ensure_ascii=False))
+
     num_labels = cfg_dict['num_classes']
 
     model = model_class.from_pretrained(
@@ -114,14 +132,24 @@ def do_train(args):
     origin_model = model_class.from_pretrained(
         args.model_name_or_path, num_classes=num_labels)
 
+    os.rename(config_path+'_bak', config_path)
+
     sp_config = supernet(expand_ratio=[1.0, args.width_mult])
     model = Convert(sp_config).convert(model)
 
     ofa_model = OFA(model)
 
     sd = paddle.load(
         os.path.join(args.model_name_or_path, 'model_state.pdparams'))
-    ofa_model.model.set_state_dict(sd)
+
+    for name, params in ofa_model.model.named_parameters():
+        if 'encoder' not in name:
+            params.set_value(sd[name])
+        else:
+            idx = int(name.strip().split('.')[3])
+            mapping_name = name.replace('.'+str(idx)+'.', '.'+str(kept_layers_index[idx])+'.')
+            params.set_value(sd[mapping_name])
+
     best_config = utils.dynabert_config(ofa_model, args.width_mult)
     ofa_model.export(
         best_config,