Merge pull request #1825 from modelscope/dev_libt

Shi Xian

2024-06-18 6c467e6f0abfc6d20d0621fbbf67b4dbd81776cc

 funasr/models/llm_asr/model.py

@@ -6,7 +6,7 @@
import torch.nn as nn
import torch.nn.functional as F
from torch.cuda.amp import autocast

import re
from funasr.models.scama.utils import sequence_mask
from funasr.losses.label_smoothing_loss import LabelSmoothingLoss
from funasr.models.ctc.ctc import CTC
@@ -18,6 +18,8 @@
from funasr.utils import postprocess_utils
from funasr.utils.datadir_writer import DatadirWriter
from funasr.register import tables
from funasr.train_utils.device_funcs import to_device
import traceback


@tables.register("model_classes", "LLMASR")
@@ -164,8 +166,6 @@
                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:
@@ -468,7 +468,7 @@
        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.audio_encoder(speech.permute(0, 2, 1), speech_lengths)
@@ -480,13 +480,32 @@
        inputs_embeds = self.llm.model.get_input_embeddings()(input_ids)

        batch_size, token_num, dims = inputs_embeds.shape
        _, l, _ = encoder_out.shape
        fbank_mask[fbank_mask < 0] = 0
        fbank_fake_lens = fbank_mask.sum(-1).to(torch.int32)
        # _, l, _ = encoder_out.shape
        for batch_idx in range(batch_size):

            fbank_fake_len = fbank_fake_lens[batch_idx].item()
            fbank_beg_idx = fbank_beg[batch_idx, 0].item()
            inputs_embeds[batch_idx, fbank_beg_idx : fbank_beg_idx + l, :] = encoder_out[
                batch_idx, :l, :
            ]
            min_len = min(fbank_fake_len, inputs_embeds.shape[1] - fbank_beg_idx)

            try:
                inputs_embeds[batch_idx, fbank_beg_idx : fbank_beg_idx + min_len, :] = encoder_out[
                    batch_idx, :min_len, :
                ]
            except Exception as e:
                logging.error(f"{str(e)}, {traceback.format_exc()}")
                logging.info(
                    f"batch_idx: {batch_idx}, inputs_embeds: {inputs_embeds.shape}, fbank_beg_idx: {fbank_beg_idx}, min_len: {min_len}, fbank_fake_len: {fbank_fake_len}, encoder_out: {encoder_out.shape}, encoder_out_lens: {encoder_out_lens[batch_idx].item()}"
                )
                fbank_fake_len = encoder_out_lens[batch_idx].item()
                min_len = min(fbank_fake_len, min_len)
                inputs_embeds[batch_idx, fbank_beg_idx : fbank_beg_idx + min_len, :] = encoder_out[
                    batch_idx, :min_len, :
                ]

        labels_ids[labels_ids == -1] = -100

        model_outputs = self.llm(
            inputs_embeds=inputs_embeds, attention_mask=attention_mask, labels=labels_ids
        )
@@ -499,12 +518,146 @@
            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((labels_ids > 0 + 1).sum())
        loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
        return loss, stats, weight

    def data_template(self, data):
        system, user, assistant = [], [], []
        for i, item in enumerate(data):
            role = item["role"]
            content = item["content"]
            if role == "system":
                system.append(content)
            elif role == "user":
                user.append(content)
            elif role == "assistant":
                assistant.append(content)

        system = system * len(user)

        contents = {
            "system": system,
            "user": user,
            "assistant": assistant,
        }

        return contents

    def data_load_speech(self, contents: dict, tokenizer, frontend, meta_data={}, **kwargs):

        system = contents["system"]
        user = contents["user"]
        assistant = contents["assistant"]
        pattern = re.compile(r"(<\|startofspeech\|>.*?<\|endofspeech\|>)")
        input_ids, labels, source_ids, target_ids, fbank, fbank_lens, fbank_mask, fbank_beg = (
            [],
            [],
            [],
            [],
            [],
            [],
            [],
            [],
        )

        for i, (system_prompt, user_prompt, target_out) in enumerate(zip(system, user, assistant)):

            source_input = f"<|im_start|>system\n{system_prompt}<|im_end|>\n<|im_start|>user\n{user_prompt}<|im_end|>\n<|im_start|>assistant\n"

            splits = pattern.split(source_input)
            source_ids_i = []
            fbank_mask_i = []
            fbank_beg_i = []
            fbank_lens_i = []
            # target_ids_i = []
            for k, sub_str in enumerate(splits):
                if not sub_str.startswith("<|startofspeech|>"):
                    sub_token = tokenizer.encode(sub_str)
                    source_ids_i += sub_token
                    fbank_mask_i += [0] * len(sub_token)
                else:
                    sub_str = sub_str.replace("<|startofspeech|>", "").replace(
                        "<|endofspeech|>", ""
                    )
                    if sub_str.startswith("!"):
                        try:
                            time1 = time.perf_counter()
                            data_src = load_audio_text_image_video(sub_str[1:], fs=frontend.fs)
                            time2 = time.perf_counter()
                            meta_data["load_data"] = f"{time2 - time1:0.3f}"
                        except Exception as e:
                            logging.error(f"Loading wav failed! {str(e)}, {traceback.format_exc()}")

                        speech, speech_lengths = extract_fbank(
                            data_src,
                            data_type=kwargs.get("data_type", "sound"),
                            frontend=frontend,
                            is_final=True,
                        )  # speech: [b, T, d]

                        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
                        )

                        if kwargs.get("permute", True):
                            speech = speech.permute(0, 2, 1)

                        olens = 1 + (speech_lengths[0].item() - 3 + 2 * 1) // 2
                        olens = 1 + (olens - 3 + 2 * 1) // 2
                        sub_token_len = (olens - 1) // 2 + 1
                        sub_token = [0] * sub_token_len
                        fbank_beg_i = [len(source_ids_i)]
                        source_ids_i += sub_token
                        fbank_mask_i += [1] * len(sub_token)

            source_mask = [-100] * len(source_ids_i)
            target_out = f"{target_out}<|im_end|>"
            target_ids = tokenizer.encode(target_out)
            input_ids += source_ids_i + target_ids
            labels += source_mask + target_ids
            fbank_mask += fbank_mask_i
            fbank_beg.append(fbank_beg_i)

        input_ids = torch.tensor(input_ids, dtype=torch.int64)  # [: self.max_token_length]
        attention_mask = torch.tensor([1] * len(input_ids), dtype=torch.int32)
        labels = torch.tensor(labels, dtype=torch.int64)  # [: self.max_token_length]
        source_ids = torch.tensor(source_ids_i, dtype=torch.int64)
        target_ids = torch.tensor(target_ids, dtype=torch.int64)

        fbank = speech[0, :, :]
        fbank_lens = speech_lengths
        fbank_mask = torch.tensor(fbank_mask, dtype=torch.float32)
        fbank_beg = torch.tensor(fbank_beg, dtype=torch.int32)

        output = {
            "speech": fbank[None, :, :],
            "speech_lengths": fbank_lens[:, None],
            "fbank_mask": fbank_mask[None, :],
            "fbank_beg": fbank_beg[None,],
            "input_ids": input_ids[None, :],
            "attention_mask": attention_mask[None, :],
            "labels_ids": labels[None, :],
            "source_ids": source_ids[None, :],
            "target_ids": target_ids[None, :],
        }

        return output

    def inference(
        self,
@@ -516,92 +669,86 @@
        **kwargs,
    ):

        prompt = kwargs.get("prompt", "Transcribe speech to text.")
        meta_data = {}
        prompt = kwargs.get("prompt", None)

        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
            )
        contents = self.data_template(data_in[0])
        output = self.data_load_speech(contents, tokenizer, frontend, meta_data=meta_data, **kwargs)
        batch = to_device(output, kwargs["device"])

        speech = speech.to(device=kwargs["device"])
        speech_lengths = speech_lengths.to(device=kwargs["device"])
        # audio encoder
        speech = batch["speech"]
        speech_lengths = batch["speech_lengths"][:, 0]
        # fp16
        if kwargs.get("fp16", False):
            speech = speech.to(torch.float16)
            encoder_out_lens = encoder_out_lens.to(torch.float16)
        elif kwargs.get("bf16", False):
            speech = speech.to(torch.bfloat16)
            encoder_out_lens = encoder_out_lens.to(torch.bfloat16)
        encoder_out, encoder_out_lens = self.audio_encoder(speech.permute(0, 2, 1), speech_lengths)

        # Encoder
        encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
        # audio_adaptor
        encoder_out, encoder_out_lens = self.audio_adaptor(encoder_out, encoder_out_lens)

        # adaptor
        encoder_out = self.audio_adaptor(encoder_out)
        input_ids = batch["input_ids"]
        source_ids = batch["source_ids"]
        if not kwargs.get("tearchforing", False):
            input_ids = source_ids
        input_ids[input_ids < 0] = 0
        inputs_embeds = self.llm.model.get_input_embeddings()(input_ids)

        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"])
        batch_size, token_num, dims = inputs_embeds.shape
        fbank_beg = batch["fbank_beg"]
        for batch_idx in range(batch_size):

        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)
            min_len = encoder_out_lens[batch_idx].item()
            fbank_beg_idx = fbank_beg[batch_idx]
            inputs_embeds[batch_idx, fbank_beg_idx : fbank_beg_idx + min_len, :] = encoder_out[
                batch_idx, :min_len, :
            ]

        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"]
        )
        llm_dtype = kwargs.get("llm_dtype", "fp32")
        dtype_map = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}
        with torch.cuda.amp.autocast(dtype=dtype_map[llm_dtype]):
            label = contents["assistant"][0]
            # self.llm = self.llm.to(dtype_map[llm_dtype])
            # inputs_embeds = inputs_embeds.to(dtype_map[llm_dtype])

        preds = 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,
        )
            if not kwargs.get("tearchforing", False):

        text = tokenizer.batch_decode(preds, add_special_tokens=False, skip_special_tokens=True)
                generated_ids = self.llm.generate(
                    inputs_embeds=inputs_embeds, max_new_tokens=kwargs.get("max_length", 512)
                )
                # generated_ids = [
                #     output_ids[len(input_id) :]
                #     for input_id, output_ids in zip(input_ids, generated_ids)
                # ]
                response = tokenizer.batch_decode(
                    generated_ids, skip_special_tokens=kwargs.get("skip_special_tokens", True)
                )[0]

        text = text[0].split(": ")[-1]
        text = text.strip()
                loss = None
            else:

        # preds = torch.argmax(model_outputs.logits, -1)
                labels_ids = batch["labels_ids"]
                labels_ids[labels_ids == -1] = -100
                attention_mask = batch.get("attention_mask", None)
                # attention_mask = attention_mask.to(dtype_map[llm_dtype])
                model_outputs = self.llm(
                    inputs_embeds=inputs_embeds, attention_mask=attention_mask, labels=labels_ids
                )

                preds = torch.argmax(model_outputs.logits, -1)[:, source_ids.shape[1] :]
                response = tokenizer.batch_decode(
                    preds,
                    add_special_tokens=False,
                    skip_special_tokens=kwargs.get("skip_special_tokens", True),
                )[0]
                loss = model_outputs.loss.item()

        ibest_writer = None
        if kwargs.get("output_dir") is not None:
@@ -610,10 +757,15 @@
            ibest_writer = self.writer[f"{0 + 1}best_recog"]

        results = []
        result_i = {"key": key[0], "text": text}
        response_clean = re.sub("[^\w\s\u3000\u4e00-\u9fff]+", "", response)
        result_i = {"key": key[0], "text": response, "text_tn": response_clean, "label": label}
        if loss is not None:
            result_i["loss"] = loss
        results.append(result_i)

        if ibest_writer is not None:
            ibest_writer["text"][key[0]] = text
            ibest_writer["text"][key[0]] = response
            ibest_writer["label"][key[0]] = label
            ibest_writer["text_tn"][key[0]] = response_clean

        return results, meta_data