Dev gzf (#1402)

* init param

funasr/auto/auto_model.py

@@ -193,7 +193,7 @@ class AutoModel:
                     path=init_param,
                     ignore_init_mismatch=kwargs.get("ignore_init_mismatch", False),
                     oss_bucket=kwargs.get("oss_bucket", None),
-                    scope_map=kwargs.get("scope_map", "module.,None"),
+                    scope_map=kwargs.get("scope_map", []),
                     excludes=kwargs.get("excludes", None),
                 )
             else:

funasr/bin/train.py

@@ -105,7 +105,7 @@ def main(**kwargs):
                     path=p,
                     ignore_init_mismatch=kwargs.get("ignore_init_mismatch", True),
                     oss_bucket=kwargs.get("oss_bucket", None),
-                    scope_map=kwargs.get("scope_map", "module.,none"),
+                    scope_map=kwargs.get("scope_map", []),
                     excludes=kwargs.get("excludes", None),
                 )
             else:

funasr/models/llm_asr_nar/model.py

@@ -315,7 +315,7 @@ class LLMASRNAR(nn.Module):
         model_outputs = self.llm(inputs_embeds=inputs_embeds, attention_mask=attention_mask, labels=None)
         preds = torch.argmax(model_outputs.logits, -1)
         text = tokenizer.batch_decode(preds, add_special_tokens=False, skip_special_tokens=True)
-        text = text.split(': "\n')[-1]
+        text = text[0].split(': \n')[-1]
         # preds = torch.argmax(model_outputs.logits, -1)
         
         ibest_writer = None

funasr/train_utils/load_pretrained_model.py

@@ -38,52 +38,17 @@ def filter_state_dict(
 				)
 	return match_state
 
-def assigment_scope_map(dst_state: dict, src_state: dict, scope_map: str=None):
-	"""Compute the union of the current variables and checkpoint variables."""
-	import collections
-	import re
-
-	# current model variables
-	name_to_variable = collections.OrderedDict()
-	for name, var in dst_state.items():
-		name_to_variable[name] = var
-	
-	scope_map_num = 0
-	if scope_map is not None:
-		scope_map = scope_map.split(",")
-		scope_map_num = len(scope_map) // 2
-		for scope_map_idx in range(scope_map_num):
-			scope_map_id = scope_map_idx * 2
-			logging.info('assignment_map from scope {} to {}'.format(scope_map[scope_map_id], scope_map[scope_map_id+1]))
-	
-	assignment_map = {}
-	for name, var in src_state.items():
-
-		if scope_map:
-			for scope_map_idx in range(scope_map_num):
-				scope_map_id = scope_map_idx * 2
-				try:
-					idx = name.index(scope_map[scope_map_id])
-					new_name = scope_map[scope_map_id+1] + name[idx + len(scope_map[scope_map_id]):]
-					if new_name in name_to_variable:
-						assignment_map[name] = var
-				except:
-					continue
-		else:
-			if name in name_to_variable:
-				assignment_map[name] = var
-	
-	return assignment_map
-
 
 def load_pretrained_model(
 	path: str,
 	model: torch.nn.Module,
-	ignore_init_mismatch: bool,
+	ignore_init_mismatch: bool=True,
 	map_location: str = "cpu",
 	oss_bucket=None,
-	scope_map="module.:none",
+	scope_map=[],
 	excludes=None,
+	ignore_mismatch=False,
+	**kwargs,
 ):
 	"""Load a model state and set it to the model.
 
@@ -110,12 +75,10 @@ def load_pretrained_model(
 	
 	if isinstance(scope_map, str):
 		scope_map = scope_map.split(",")
+	scope_map += ["module.", "None"]
 	
 	for k in dst_state.keys():
-		# if not k.startswith("module.") and "module." + k in src_state.keys():
-		# 	k_ddp = "module." + k
-		# else:
-		# 	k_ddp = k
+		
 		k_src = k
 
 		if scope_map is not None:
@@ -124,66 +87,25 @@ def load_pretrained_model(
 			for i in range(0, len(scope_map), 2):
 				src_prefix = scope_map[i] if scope_map[i].lower() != "none" else ""
 				dst_prefix = scope_map[i+1] if scope_map[i+1].lower() != "none" else ""
-
-				if k.startswith(dst_prefix) and k.replace(dst_prefix, src_prefix) in src_state.keys():
-					k_src = k.replace(dst_prefix, src_prefix)
-					print(f"init param, map: {k} from {k_src} in ckpt")
+				
+				if dst_prefix == "" and (src_prefix + k) in src_state.keys():
+					k_src = src_prefix + k
+					if not k_src.startswith("module."):
+						print(f"init param, map: {k} from {k_src} in ckpt")
+				elif k.startswith(dst_prefix) and k.replace(dst_prefix, src_prefix, 1) in src_state.keys():
+					k_src = k.replace(dst_prefix, src_prefix, 1)
+					if not k_src.startswith("module."):
+						print(f"init param, map: {k} from {k_src} in ckpt")
 					
 		if k_src in src_state.keys():
-			dst_state[k] = src_state[k_src]
-				
-		# if k_ddp.startswith("audio_encoder"):
-		# 	if k_ddp.replace("audio_encoder", "encoder.model") in src_state.keys():
-		# 		k_ddp = k_ddp.replace("audio_encoder", "encoder.model")
-		# if k_ddp.startswith("adaptor"):
-		# 	if k_ddp.replace("adaptor", "encoder_projector") in src_state.keys():
-		# 		k_ddp = k_ddp.replace("adaptor", "encoder_projector")
-		# if k_ddp in src_state:
-		# 	dst_state[k] = src_state[k_ddp]
+			if ignore_init_mismatch and dst_state[k].shape != src_state[k_src].shape:
+				print(f"ignore_mismatch:{ignore_mismatch}, dst: {k, dst_state[k].shape}, src: {k_src, src_state[k_src].shape}")
+			else:
+				dst_state[k] = src_state[k_src]
+
+
 		else:
 			print(f"Warning, miss key in ckpt: {k}, mapped: {k_src}")
 			
-	flag = obj.load_state_dict(dst_state, strict=False)
+	flag = obj.load_state_dict(dst_state, strict=True)
 	# print(flag)
-
-# def load_pretrained_model(
-# 	path: str,
-# 	model: torch.nn.Module,
-# 	ignore_init_mismatch: bool,
-# 	map_location: str = "cpu",
-# 	oss_bucket=None,
-# 	scope_map=None,
-# 	excludes=None,
-# ):
-# 	"""Load a model state and set it to the model.
-#
-# 	Args:
-# 		init_param: <file_path>:<src_key>:<dst_key>:<exclude_Keys>
-#
-# 	Examples:
-#
-# 	"""
-#
-# 	obj = model
-#
-# 	if oss_bucket is None:
-# 		src_state = torch.load(path, map_location=map_location)
-# 	else:
-# 		buffer = BytesIO(oss_bucket.get_object(path).read())
-# 		src_state = torch.load(buffer, map_location=map_location)
-# 	src_state = src_state["model"] if "model" in src_state else src_state
-#
-# 	if excludes is not None:
-# 		for e in excludes.split(","):
-# 			src_state = {k: v for k, v in src_state.items() if not k.startswith(e)}
-#
-# 	dst_state = obj.state_dict()
-# 	src_state = assigment_scope_map(dst_state, src_state, scope_map)
-#
-# 	if ignore_init_mismatch:
-# 		src_state = filter_state_dict(dst_state, src_state)
-#
-# 	logging.debug("Loaded src_state keys: {}".format(src_state.keys()))
-# 	logging.debug("Loaded dst_state keys: {}".format(dst_state.keys()))
-# 	dst_state.update(src_state)
-# 	obj.load_state_dict(dst_state, strict=True)