Dev gzf (#1402)

zhifu gao

2024-02-28 b9cfd9953a88db445e3fd499d9fc40d713672152

Dev gzf (#1402)

* init param

 funasr/auto/auto_model.py

@@ -193,7 +193,7 @@
                    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 @@
                    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 @@
        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 @@
                )
    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 @@
    
    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:
@@ -125,65 +88,24 @@
                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)
                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():
            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]
                
        # 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]

        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)