auto frontend

游雁

2024-06-07 0ba1bdd476c2079f1220904d5f2a217d78bdb64a

 funasr/models/llm_asr/model.py

@@ -385,13 +385,6 @@

        super().__init__()

        if specaug is not None:
            specaug_class = tables.specaug_classes.get(specaug)
            specaug = specaug_class(**specaug_conf)
        if normalize is not None:
            normalize_class = tables.normalize_classes.get(normalize)
            normalize = normalize_class(**normalize_conf)

        # audio encoder
        hub = audio_encoder_conf.get("hub", None)
        if hub == "ms":
@@ -422,23 +415,23 @@
        # llm
        hub = llm_conf.get("hub", "hf")
        self.llm = None
        # if hub == "hf":
        #     from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig
        #
        #     init_param_path = llm_conf.get("init_param_path", "vicuna-7b-v1.5")
        #
        #     model = AutoModelForCausalLM.from_pretrained(
        #         init_param_path,
        #         load_in_8bit=None,
        #         device_map=None,
        #         use_cache=None,
        #     )
        #     freeze = llm_conf.get("freeze", True)
        #     if freeze:
        #         for name, param in model.named_parameters():
        #             param.requires_grad = False
        #         model.eval()
        #     self.llm = model
        if hub == "hf":
            from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig

            init_param_path = llm_conf.get("init_param_path", "vicuna-7b-v1.5")

            model = AutoModelForCausalLM.from_pretrained(
                init_param_path,
                load_in_8bit=None,
                device_map=None,
                use_cache=None,
            )
            freeze = llm_conf.get("freeze", True)
            if freeze:
                for name, param in model.named_parameters():
                    param.requires_grad = False
                model.eval()
            self.llm = model

        # adaptor
        adaptor_class = tables.adaptor_classes.get(audio_adaptor)
@@ -446,21 +439,6 @@
        audio_adaptor = adaptor_class(**audio_adaptor_conf)

        self.audio_adaptor = audio_adaptor

        self.blank_id = blank_id
        self.sos = sos if sos is not None else vocab_size - 1
        self.eos = eos if eos is not None else vocab_size - 1
        self.vocab_size = vocab_size
        self.ignore_id = ignore_id
        self.specaug = specaug
        self.normalize = normalize

        self.criterion_att = LabelSmoothingLoss(
            size=vocab_size,
            padding_idx=ignore_id,
            smoothing=lsm_weight,
            normalize_length=length_normalized_loss,
        )

        self.error_calculator = None

@@ -490,22 +468,16 @@
        if len(speech_lengths.size()) > 1:
            speech_lengths = speech_lengths[:, 0]

        batch_size = speech.shape[0]
        batch_size, frames, _ = speech.shape

        # audio encoder
        encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
        encoder_out, encoder_out_lens = self.audio_encoder(speech.permute(0, 2, 1), speech_lengths)

        # audio_adaptor
        encoder_out = self.audio_adaptor(encoder_out)
        encoder_out, encoder_out_lens = self.audio_adaptor(encoder_out, encoder_out_lens)

        input_ids[input_ids == -1] = 0
        input_ids[input_ids == -100] = 0
        if hasattr(self.llm.model, "embed_tokens"):
            inputs_embeds = self.llm.model.embed_tokens(input_ids)
        elif hasattr(self.llm.model.model, "embed_tokens"):
            inputs_embeds = self.llm.model.model.embed_tokens(input_ids)
        else:
            inputs_embeds = self.llm.model.model.model.embed_tokens(input_ids)
        input_ids[input_ids < 0] = 0
        inputs_embeds = self.llm.model.get_input_embeddings()(input_ids)

        batch_size, token_num, dims = inputs_embeds.shape
        _, l, _ = encoder_out.shape
@@ -514,7 +486,7 @@
            inputs_embeds[batch_idx, fbank_beg_idx : fbank_beg_idx + l, :] = encoder_out[
                batch_idx, :l, :
            ]

        labels_ids[labels_ids == -1] = -100
        model_outputs = self.llm(
            inputs_embeds=inputs_embeds, attention_mask=attention_mask, labels=labels_ids
        )
@@ -527,26 +499,19 @@
            stats["acc"] = acc_att

        stats["loss"] = torch.clone(loss.detach())
        stats["batch_size"] = batch_size
        stats["batch_size_x_frames"] = frames * batch_size
        stats["batch_size_real_frames"] = speech_lengths.sum().item()
        stats["padding_frames"] = stats["batch_size_x_frames"] - stats["batch_size_real_frames"]
        stats["batch_size_x_tokens"] = token_num * batch_size
        stats["batch_size_real_tokens"] = attention_mask.sum().item()
        stats["padding_tokens"] = stats["batch_size_x_tokens"] - stats["batch_size_real_tokens"]

        # force_gatherable: to-device and to-tensor if scalar for DataParallel
        if self.length_normalized_loss:
            batch_size = int((text_lengths + 1).sum())
            batch_size = int((labels_ids > 0 + 1).sum())
        loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
        return loss, stats, weight

    def encode(
        self,
        speech: torch.Tensor,
        speech_lengths: torch.Tensor,
        **kwargs,
    ):
        speech = speech.permute(0, 2, 1)
        res = self.audio_encoder(speech)
        if isinstance(res, (list, tuple)):
            encoder_out, encoder_out_lens = res[0], res[1]
        else:
            encoder_out, encoder_out_lens = res, speech_lengths
        return encoder_out, encoder_out_lens

    def inference(
        self,