init param

游雁

2024-02-28 7a4816651fd59ba02f780884613c1fbf52031f76

init param

 funasr/auto/auto_model.py

@@ -90,7 +90,7 @@
class AutoModel:
    
    def __init__(self, **kwargs):
        if not kwargs.get("disable_log", False):
        if not kwargs.get("disable_log", True):
            tables.print()
        
        model, kwargs = self.build_model(**kwargs)
@@ -188,7 +188,7 @@
                    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", None),
                    scope_map=kwargs.get("scope_map", "module.,None"),
                    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", None),
                    scope_map=kwargs.get("scope_map", "module.,none"),
                    excludes=kwargs.get("excludes", None),
                )
            else:

 funasr/models/llm_asr_nar/__init__.py

 funasr/models/llm_asr_nar/adaptor.py

 funasr/models/llm_asr_nar/model.py

File was renamed from funasr/models/llm_asr/model.py
@@ -294,24 +294,29 @@
        inputs_embeds = torch.cat((inputs_embeds[None, :, :], encoder_out), dim=1)  # [prompt, audio]
        attention_mask = torch.ones(inputs_embeds.size()[:-1], dtype=torch.long).to(kwargs["device"])
        
        model_outputs = self.llm.generate(
            inputs_embeds=inputs_embeds,
            max_length=kwargs.get("max_length", 200),
            max_new_tokens=kwargs.get("max_new_tokens", 200),
            num_beams=kwargs.get("num_beams", 4),
            do_sample=kwargs.get("do_sample", False),
            min_length=kwargs.get("min_length", 1),
            top_p=kwargs.get("top_p", 1.0),
            repetition_penalty=kwargs.get("repetition_penalty", 1.0),
            length_penalty=kwargs.get("length_penalty", 1.0),
            temperature=kwargs.get("temperature", 1.0),
            attention_mask=attention_mask,
            bos_token_id=tokenizer.bos_token_id,
            eos_token_id=tokenizer.eos_token_id,
            pad_token_id=tokenizer.pad_token_id
        )
        # model_outputs = self.llm.generate(
        #     inputs_embeds=inputs_embeds,
        #     max_length=kwargs.get("max_length", 200),
        #     max_new_tokens=kwargs.get("max_new_tokens", 200),
        #     num_beams=kwargs.get("num_beams", 4),
        #     do_sample=kwargs.get("do_sample", False),
        #     min_length=kwargs.get("min_length", 1),
        #     top_p=kwargs.get("top_p", 1.0),
        #     repetition_penalty=kwargs.get("repetition_penalty", 1.0),
        #     length_penalty=kwargs.get("length_penalty", 1.0),
        #     temperature=kwargs.get("temperature", 1.0),
        #     attention_mask=attention_mask,
        #     bos_token_id=tokenizer.bos_token_id,
        #     eos_token_id=tokenizer.eos_token_id,
        #     pad_token_id=tokenizer.pad_token_id
        # )

        text = tokenizer.batch_decode(model_outputs, add_special_tokens=False, skip_special_tokens=True)

        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]
        # preds = torch.argmax(model_outputs.logits, -1)
        
        ibest_writer = None
        if kwargs.get("output_dir") is not None:

 funasr/train_utils/load_pretrained_model.py

@@ -82,7 +82,7 @@
    ignore_init_mismatch: bool,
    map_location: str = "cpu",
    oss_bucket=None,
    scope_map=None,
    scope_map="module.:none",
    excludes=None,
):
    """Load a model state and set it to the model.
@@ -108,15 +108,40 @@
    
    src_state = src_state["model"] if "model" in src_state else src_state
    
    if isinstance(scope_map, str):
        scope_map = scope_map.split(",")
	
    for k in dst_state.keys():
        if not k.startswith("module.") and "module." + k in src_state.keys():
            k_ddp = "module." + k
        # 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:
            src_prefix = ""
            dst_prefix = ""
            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 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]
        else:
            k_ddp = k
        if k_ddp in src_state:
            dst_state[k] = src_state[k_ddp]
        else:
            print(f"Warning, miss key in ckpt: {k}, mapped: {k_ddp}")
            print(f"Warning, miss key in ckpt: {k}, mapped: {k_src}")
            
    flag = obj.load_state_dict(dst_state, strict=False)
    # print(flag)