add

游雁

2024-02-29 cce5d9999dabaf257347fbadb7ccc2473c9a757a

add

 funasr/datasets/llm_datasets/datasets.py

@@ -129,3 +129,129 @@
                
                outputs[key] = torch.nn.utils.rnn.pad_sequence(data_list, batch_first=True, padding_value=pad_value)
        return outputs


@tables.register("dataset_classes", "AudioLLMARDataset")
class AudioLLMARDataset(torch.utils.data.Dataset):
    """
    AudioLLMDataset
    """
    
    def __init__(self,
                 path,
                 index_ds: str = None,
                 frontend=None,
                 tokenizer=None,
                 int_pad_value: int = -1,
                 float_pad_value: float = 0.0,
                 **kwargs):
        super().__init__()
        index_ds_class = tables.index_ds_classes.get(index_ds)
        self.index_ds = index_ds_class(path, **kwargs)
        preprocessor_speech = kwargs.get("preprocessor_speech", None)
        if preprocessor_speech:
            preprocessor_speech_class = tables.preprocessor_classes.get(preprocessor_speech)
            preprocessor_speech = preprocessor_speech_class(**kwargs.get("preprocessor_speech_conf", {}))
        self.preprocessor_speech = preprocessor_speech
        preprocessor_text = kwargs.get("preprocessor_text", None)
        if preprocessor_text:
            preprocessor_text_class = tables.preprocessor_classes.get(preprocessor_text)
            preprocessor_text = preprocessor_text_class(**kwargs.get("preprocessor_text_conf", {}))
        self.preprocessor_text = preprocessor_text
        
        self.frontend = frontend
        self.fs = 16000 if frontend is None else frontend.fs
        self.data_type = "sound"
        self.tokenizer = tokenizer
        
        self.float_pad_value = float_pad_value
        self.prompt = kwargs.get("prompt", "Transcribe speech to text.")
        self.prompt_pre = "USER: \nINSTRUCTION: {}\nINPUT: ".format(
            self.prompt)  # "USER: \nINSTRUCTION: {}\nnINPUT: {}\nASSISTANT: "
        self.prompt_af = ""
        self.IGNORE_INDEX = kwargs.get("IGNORE_INDEX", -100)
        self.int_pad_value = self.IGNORE_INDEX
    
    def get_source_len(self, index):
        item = self.index_ds[index]
        return self.index_ds.get_source_len(item)
    
    def get_target_len(self, index):
        item = self.index_ds[index]
        return self.index_ds.get_target_len(item)
    
    def __len__(self):
        return len(self.index_ds)
    
    def __getitem__(self, index):
        item = self.index_ds[index]
        # import pdb;
        # pdb.set_trace()
        source = item["source"]
        data_src = load_audio_text_image_video(source, fs=self.fs)
        if self.preprocessor_speech:
            data_src = self.preprocessor_speech(data_src, fs=self.fs)
        speech, speech_lengths = extract_fbank(data_src, data_type=self.data_type, frontend=self.frontend,
                                               is_final=True)  # speech: [b, T, d]
        speech = speech.squeeze(0)
        
        target = item["target"]
        if self.preprocessor_text:
            target = self.preprocessor_text(target)
        
        prompt_ids_pre = self.tokenizer.encode(self.prompt_pre)  # [bos,prompt]
        prompt_pre_length = len(prompt_ids_pre)
        
        prompt_input = "{}{}".format(self.prompt_pre, target)
        prompt_input_ids = self.tokenizer.encode(prompt_input)
        audio_length = len(prompt_input_ids) - prompt_pre_length
        input_ids = prompt_input_ids + [self.tokenizer.pad_token_id]
        input_ids = torch.tensor(input_ids, dtype=torch.int64)  # [bos, prompt, input, pad]
        input_ids[prompt_pre_length:] = -1  # [bos, prompt,-1,-1]
        attention_mask = input_ids.ge(-1)  # [true, true, true, true], length mask
        
        prompt_answer = "{}{}".format(self.prompt_pre, target)
        prompt_answer_ids = self.tokenizer.encode(prompt_answer)
        answer_length = len(prompt_answer_ids) - prompt_pre_length
        labels_ids = copy.deepcopy(prompt_input_ids) + [self.tokenizer.eos_token_id]
        labels_ids = torch.tensor(labels_ids, dtype=torch.int64)  # [bos, prompt, input, eos]
        labels_ids[:prompt_pre_length] = -1  # [-1, -1, input, eos]
        label_mask = labels_ids.ge(0)  # [False,False,True,True]
        labels_ids[~label_mask] = self.IGNORE_INDEX  # [-100,-100,input,eos]
        
        audio_mask = [0] * prompt_pre_length + [1] * audio_length + [0]
        audio_mask = torch.tensor(audio_mask, dtype=torch.float32)
        
        ids = self.tokenizer.encode(target)  # token ids is different from labels_ids
        text = torch.tensor(ids, dtype=torch.int64)
        text_lengths = torch.tensor([len(ids)], dtype=torch.int32)
        
        return {"speech": speech,
                "speech_lengths": speech_lengths,
                "text": text,
                "text_lengths": text_lengths,
                "input_ids": input_ids,
                "attention_mask": attention_mask,
                "labels_ids": labels_ids,
                "label_mask": label_mask,
                "audio_mask": audio_mask,
                }
    
    def collator(self, samples: list = None):
        outputs = {}
        for sample in samples:
            for key in sample.keys():
                if key not in outputs:
                    outputs[key] = []
                outputs[key].append(sample[key])
        
        for key, data_list in outputs.items():
            if isinstance(data_list[0], torch.Tensor):
                if data_list[0].dtype == torch.int64:
                    
                    pad_value = self.int_pad_value
                else:
                    pad_value = self.float_pad_value
                
                outputs[key] = torch.nn.utils.rnn.pad_sequence(data_list, batch_first=True, padding_value=pad_value)
        return outputs

 funasr/models/llm_asr/__init__.py

 funasr/models/llm_asr/adaptor.py

New file
@@ -0,0 +1,62 @@
import torch
import torch.nn as nn

from funasr.register import tables

@tables.register("adaptor_classes", "Linear")
class Linear(nn.Module):
    def __init__(self, downsample_rate, encoder_dim, llm_dim, ffn_dim: int = 2048, **kwargs):
        super().__init__()
        self.k = downsample_rate
        self.encoder_dim = encoder_dim
        self.llm_dim = llm_dim
        self.linear1 = nn.Linear(self.encoder_dim * self.k, ffn_dim)
        self.relu = nn.ReLU()
        self.linear2 = nn.Linear(ffn_dim, self.llm_dim)

    def forward(self, x):
        batch_size, seq_len, dim = x.size()
        num_frames_to_discard = seq_len % self.k
        if num_frames_to_discard > 0:
            x = x[:, :-num_frames_to_discard, :]
        seq_len = x.size(1)
        
        x = x.contiguous()
        x = x.view(batch_size, seq_len // self.k, dim * self.k)
        x = self.linear1(x)
        x = self.relu(x)
        x = self.linear2(x)
        return x

@tables.register("adaptor_classes", "QFormer")
class EncoderProjectorQFormer(nn.Module):
    def __init__(self, downsample_rate, encoder_dim, llm_dim, ffn_dim: int = 2048, **kwargs):
        super().__init__()
        self.encoder_dim = encoder_dim
        self.llm_dim = llm_dim
        from transformers import Blip2QFormerConfig, Blip2QFormerModel
        configuration = Blip2QFormerConfig()
        configuration.encoder_hidden_size = self.encoder_dim
        configuration.num_hidden_layers = 2
        
        self.query_len = 64
        self.query = nn.Parameter(torch.zeros(1, self.query_len, configuration.hidden_size))
        self.query.data.normal_(mean=0.0, std=1.0)
        self.qformer = Blip2QFormerModel(configuration)
        
        self.linear = nn.Linear(configuration.hidden_size, self.llm_dim)
        self.norm = nn.LayerNorm(self.llm_dim, eps=1e-5)
    
    def forward(self, x, atts):
        query = self.query.expand(x.shape[0], -1, -1)
        
        query_output = self.qformer(
            query_embeds=query,
            encoder_hidden_states=x,
            encoder_attention_mask=atts,
            return_dict=True,
        )
        
        query_proj = self.norm(self.linear(query_output.last_hidden_state))
        
        return query_proj

 funasr/models/llm_asr/model.py

New file
@@ -0,0 +1,341 @@
import logging
from typing import Union, Dict, List, Tuple, Optional

import time
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.cuda.amp import autocast

from funasr.models.scama.utils import sequence_mask
from funasr.losses.label_smoothing_loss import LabelSmoothingLoss
from funasr.models.ctc.ctc import CTC
from funasr.models.transformer.utils.add_sos_eos import add_sos_eos
from funasr.metrics.compute_acc import th_accuracy, compute_accuracy
# from funasr.models.e2e_asr_common import ErrorCalculator
from funasr.train_utils.device_funcs import force_gatherable
from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
from funasr.utils import postprocess_utils
from funasr.utils.datadir_writer import DatadirWriter
from funasr.register import tables


@tables.register("model_classes", "LLMASR")
class LLMASR(nn.Module):
    """ """
    
    def __init__(
        self,
        specaug: str = None,
        specaug_conf: dict = None,
        normalize: str = None,
        normalize_conf: dict = None,
        encoder: str = None,
        encoder_conf: dict = None,
        decoder: str = None,
        decoder_conf: dict = None,
        ctc: str = None,
        ctc_conf: dict = None,
        ctc_weight: float = 0.5,
        llm: str = None,
        llm_conf: dict = None,
        adaptor: str = None,
        adaptor_conf: dict = None,
        input_size: int = 80,
        vocab_size: int = -1,
        ignore_id: int = -1,
        blank_id: int = 0,
        sos: int = 1,
        eos: int = 2,
        lsm_weight: float = 0.0,
        length_normalized_loss: bool = False,
        report_cer: bool = True,
        report_wer: bool = True,
        sym_space: str = "<space>",
        sym_blank: str = "<blank>",
        # extract_feats_in_collect_stats: bool = True,
        share_embedding: bool = False,
        # preencoder: Optional[AbsPreEncoder] = None,
        # postencoder: Optional[AbsPostEncoder] = None,
        **kwargs,
    ):
        
        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 = encoder_conf.get("hub", None)
        if hub == "funasr":
            from funasr import AutoModel
            init_param_path = encoder_conf.get("init_param_path", "iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch")
            model = AutoModel(model=init_param_path, model_revision="v2.0.4")
            # frontend = model.kwargs.get("frontend")
            model.model.decoder = None
            
            self.audio_encoder = model.model
            # self.frontend = frontend
            
        elif hub == "hf":
            pass
        else:
            encoder_class = tables.encoder_classes.get(encoder)
            encoder = encoder_class(input_size=input_size, **encoder_conf)
            encoder_output_size = encoder.output_size()

        # 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
        
        # adaptor
        adaptor_class = tables.adaptor_classes.get(adaptor)
        adaptor = adaptor_class(**adaptor_conf)
        
        self.adaptor = 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.encoder = encoder


        self.criterion_att = LabelSmoothingLoss(
            size=vocab_size,
            padding_idx=ignore_id,
            smoothing=lsm_weight,
            normalize_length=length_normalized_loss,
        )
        #
        # if report_cer or report_wer:
        #     self.error_calculator = ErrorCalculator(
        #         token_list, sym_space, sym_blank, report_cer, report_wer
        #     )
        #
        self.error_calculator = None

        self.length_normalized_loss = length_normalized_loss
        self.beam_search = None
    
    def forward(
        self,
        speech: torch.Tensor,
        speech_lengths: torch.Tensor,
        text: torch.Tensor,
        text_lengths: torch.Tensor,
        input_ids: torch.Tensor,
        attention_mask:torch.Tensor,
        labels_ids: torch.Tensor,
        label_mask: torch.Tensor,
        audio_mask: torch.Tensor,
        **kwargs,
    ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
        """Encoder + Decoder + Calc loss
        Args:
                speech: (Batch, Length, ...)
                speech_lengths: (Batch, )
                text: (Batch, Length)
                text_lengths: (Batch,)
        """
        # import pdb;
        # pdb.set_trace()
        if len(text_lengths.size()) > 1:
            text_lengths = text_lengths[:, 0]
        if len(speech_lengths.size()) > 1:
            speech_lengths = speech_lengths[:, 0]
        
        batch_size = speech.shape[0]
        
        # audio encoder
        encoder_out, encoder_out_lens = self.encode(speech, speech_lengths, audio_mask=audio_mask)
        
        # adaptor
        encoder_out = self.adaptor(encoder_out)

        if input_ids is not None:
            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)

            if audio_mask is not None:
                batch_size, token_num, dims = inputs_embeds.shape
                _, l, _ = encoder_out.shape
                encoder_outs_pad = F.pad(encoder_out, (0, 0, token_num-l-1, 1, 0, 0), value=0.0)
                inputs_embeds = encoder_outs_pad * audio_mask[:, :, None] + inputs_embeds * (1.0-audio_mask[:, :, None])
                inputs_embeds = F.pad(inputs_embeds[:, 1:, :], (0, 0, 0, 1, 0, 0), value=0.0)

        model_outputs = self.llm(inputs_embeds=inputs_embeds, attention_mask=attention_mask, labels=labels_ids)
        loss = model_outputs.loss


        stats = {}
        with torch.no_grad():
            preds = torch.argmax(model_outputs.logits, -1)
            acc_att = compute_accuracy(preds[:, :-1], labels_ids[:, 1:], ignore_label=-100)
            stats["acc"] = acc_att

        stats["loss"] = torch.clone(loss.detach())

        # force_gatherable: to-device and to-tensor if scalar for DataParallel
        if self.length_normalized_loss:
            batch_size = int((text_lengths + 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,
    ) -> Tuple[torch.Tensor, torch.Tensor]:
    
        audio_mask = kwargs.get("audio_mask", None)
        audio_token_lengths = audio_mask.sum(-1) if audio_mask is not None else None

        batch = {"speech": speech, "speech_lengths": speech_lengths}
        enc, enc_lens = self.audio_encoder.encode(**batch)
        with autocast(False):
            enc_mask = sequence_mask(enc_lens, enc.size(1), device=enc.device)[:, None, :]
            pre_acoustic_embeds, pre_token_length, _, _ = self.audio_encoder.predictor(enc,
                                                                               mask=enc_mask,
                                                                               target_label_length=audio_token_lengths,
                                                                               )

        return pre_acoustic_embeds, pre_token_length


    def inference(self,
                  data_in,
                  data_lengths=None,
                  key: list = None,
                  tokenizer=None,
                  frontend=None,
                  **kwargs,
                  ):
        
        prompt = kwargs.get("prompt", "Transcribe speech to text.")
        
        if kwargs.get("batch_size", 1) > 1:
            raise NotImplementedError("batch decoding is not implemented")


        
        meta_data = {}
        if isinstance(data_in, torch.Tensor) and kwargs.get("data_type", "sound") == "fbank":  # fbank
            speech, speech_lengths = data_in, data_lengths
            if len(speech.shape) < 3:
                speech = speech[None, :, :]
            if speech_lengths is None:
                speech_lengths = speech.shape[1]
        else:
            # extract fbank feats
            time1 = time.perf_counter()
            audio_sample_list = load_audio_text_image_video(data_in, fs=frontend.fs, audio_fs=kwargs.get("fs", 16000),
                                                            data_type=kwargs.get("data_type", "sound"),
                                                            tokenizer=tokenizer)
            time2 = time.perf_counter()
            meta_data["load_data"] = f"{time2 - time1:0.3f}"
            speech, speech_lengths = extract_fbank(audio_sample_list, data_type=kwargs.get("data_type", "sound"),
                                                   frontend=frontend)
            time3 = time.perf_counter()
            meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
            meta_data["batch_data_time"] = speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000
        
        speech = speech.to(device=kwargs["device"])
        speech_lengths = speech_lengths.to(device=kwargs["device"])
        
        # Encoder
        encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)

        # adaptor
        encoder_out = self.adaptor(encoder_out)
        
    
        prompt_pre = "USER: \nINSTRUCTION: {}\nINPUT: ".format(prompt)
        prompt_ids = tokenizer.encode(prompt_pre)
        prompt_length = len(prompt_ids)
        prompt_ids = torch.tensor(prompt_ids, dtype=torch.int64).to(kwargs["device"])


        if hasattr(self.llm.model, "embed_tokens"):
            inputs_embeds = self.llm.model.embed_tokens(prompt_ids)
        elif hasattr(self.llm.model.model, "embed_tokens"):
            inputs_embeds = self.llm.model.model.embed_tokens(prompt_ids)
        else:
            inputs_embeds = self.llm.model.model.model.embed_tokens(prompt_ids)

        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(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[0].split(': ')[-1]
        text = text.strip()
        
        # preds = torch.argmax(model_outputs.logits, -1)
        
        ibest_writer = None
        if kwargs.get("output_dir") is not None:
            if not hasattr(self, "writer"):
                self.writer = DatadirWriter(kwargs.get("output_dir"))
            ibest_writer = self.writer[f"{0 + 1}best_recog"]

        results = []
        result_i = {"key": key[0], "text": text}
        results.append(result_i)

        if ibest_writer is not None:
            ibest_writer["text"][key[0]] = text
        
        
        
        
        return results, meta_data