CosyVoice/cosyvoice/llm/llm.py

# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Zhihao Du)
#               2025 Alibaba Inc (authors: Xiang Lyu, Yabin Li, Qihua, Shengqiang Li)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import queue
import random
import time
import threading
from typing import Dict, Optional, Callable, List, Generator
import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
from transformers import Qwen2ForCausalLM
from torch.nn.utils.rnn import pad_sequence, unpad_sequence
from cosyvoice.utils.common import IGNORE_ID
from cosyvoice.transformer.label_smoothing_loss import LabelSmoothingLoss
from cosyvoice.utils.common import th_accuracy
from cosyvoice.utils.file_utils import logging
from cosyvoice.utils.mask import make_pad_mask


class TransformerLM(torch.nn.Module):
    def __init__(
            self,
            text_encoder_input_size: int,
            llm_input_size: int,
            llm_output_size: int,
            text_token_size: int,
            speech_token_size: int,
            text_encoder: torch.nn.Module,
            llm: torch.nn.Module,
            sampling: Callable,
            length_normalized_loss: bool = True,
            lsm_weight: float = 0.0,
            spk_embed_dim: int = 192,
    ):
        super().__init__()
        self.llm_input_size = llm_input_size
        self.speech_token_size = speech_token_size
        # 1. build text token inputs related modules
        self.text_embedding = torch.nn.Embedding(text_token_size, text_encoder_input_size)
        self.text_encoder = text_encoder
        self.text_encoder_affine_layer = nn.Linear(
            self.text_encoder.output_size(),
            llm_input_size
        )

        # 2. build speech token language model related modules
        self.sos = 0
        self.task_id = 1
        self.eos_token = self.speech_token_size
        self.llm_embedding = torch.nn.Embedding(2, llm_input_size)
        self.llm = llm
        self.llm_decoder = nn.Linear(llm_output_size, speech_token_size + 1)
        self.criterion_ce = LabelSmoothingLoss(
            size=speech_token_size + 1,
            padding_idx=IGNORE_ID,
            smoothing=lsm_weight,
            normalize_length=length_normalized_loss,
        )

        # 3. [Optional] build speech token related modules
        self.speech_embedding = torch.nn.Embedding(speech_token_size, llm_input_size)
        self.spk_embed_affine_layer = torch.nn.Linear(spk_embed_dim, llm_input_size)

        # 4. sampling method
        self.sampling = sampling

    def encode(
            self,
            text: torch.Tensor,
            text_lengths: torch.Tensor,
    ):
        encoder_out, encoder_mask = self.text_encoder(text, text_lengths, decoding_chunk_size=1, num_decoding_left_chunks=-1)
        encoder_out_lens = encoder_mask.squeeze(1).sum(1)
        encoder_out = self.text_encoder_affine_layer(encoder_out)
        return encoder_out, encoder_out_lens

    def pad_unpad_sequence(self, sos_emb, embedding, text_token, text_token_len, task_id_emb, speech_token, speech_token_len):
        text_token = unpad_sequence(text_token, text_token_len.cpu(), batch_first=True)
        speech_token = unpad_sequence(speech_token, speech_token_len.cpu(), batch_first=True)
        lm_input = [torch.concat([sos_emb.squeeze(dim=0), embedding[i], text_token[i], task_id_emb.squeeze(dim=0), speech_token[i]], dim=0)
                    for i in range(len(text_token))]
        lm_input_len = torch.tensor([i.size(0) for i in lm_input], dtype=torch.int32)
        lm_input = pad_sequence(lm_input, batch_first=True, padding_value=IGNORE_ID)
        return lm_input, lm_input_len

    def forward(
            self,
            batch: dict,
            device: torch.device,
    ) -> Dict[str, Optional[torch.Tensor]]:
        """
        Args:
            text: (B, L, D)
            text_lengths: (B,)
            audio: (B, T, N) or (B, T)
            audio_lengths: (B,)
        """
        text_token = batch['text_token'].to(device)
        text_token_len = batch['text_token_len'].to(device)
        speech_token = batch['speech_token'].to(device)
        speech_token_len = batch['speech_token_len'].to(device)
        embedding = batch['embedding'].to(device)

        # 1. prepare llm_target
        lm_target = [torch.tensor([IGNORE_ID] * (2 + text_token_len[i]) + speech_token[i, :speech_token_len[i]].tolist() +
                                  [self.speech_token_size]) for i in range(text_token.size(0))]
        lm_target = pad_sequence(lm_target, batch_first=True, padding_value=IGNORE_ID).to(device)

        # 1. encode text_token
        text_token = self.text_embedding(text_token)
        text_token, text_token_len = self.encode(text_token, text_token_len)

        # 2. embedding projection
        embedding = F.normalize(embedding, dim=1)
        embedding = self.spk_embed_affine_layer(embedding)
        embedding = embedding.unsqueeze(1)

        # 3. sos and task_id
        sos_emb = self.llm_embedding.weight[self.sos].reshape(1, 1, -1)
        task_id_emb = self.llm_embedding.weight[self.task_id].reshape(1, 1, -1)

        # 4. encode speech_token
        speech_token = self.speech_embedding(speech_token)

        # 5. unpad and pad
        lm_input, lm_input_len = self.pad_unpad_sequence(sos_emb, embedding, text_token, text_token_len,
                                                         task_id_emb, speech_token, speech_token_len)

        # 6. run lm forward
        lm_output, lm_output_mask = self.llm(lm_input, lm_input_len.to(device))
        logits = self.llm_decoder(lm_output)
        loss = self.criterion_ce(logits, lm_target)
        acc = th_accuracy(logits.view(-1, self.speech_token_size + 1), lm_target, ignore_label=IGNORE_ID)
        return {'loss': loss, 'acc': acc}

    def sampling_ids(
            self,
            weighted_scores: torch.Tensor,
            decoded_tokens: List,
            sampling: int,
            ignore_eos: bool = True,
    ):
        num_trials, max_trials = 0, 100
        while True:
            top_ids = self.sampling(weighted_scores, decoded_tokens, sampling)
            if (not ignore_eos) or (top_ids < self.speech_token_size):
                break
            num_trials += 1
            if num_trials > max_trials:
                raise RuntimeError('sampling reaches max_trials {} and still get eos when ignore_eos is True, check your input!'.format(max_trials))
        return top_ids

    @torch.inference_mode()
    def inference(
            self,
            text: torch.Tensor,
            text_len: torch.Tensor,
            prompt_text: torch.Tensor,
            prompt_text_len: torch.Tensor,
            prompt_speech_token: torch.Tensor,
            prompt_speech_token_len: torch.Tensor,
            embedding: torch.Tensor,
            sampling: int = 25,
            max_token_text_ratio: float = 20,
            min_token_text_ratio: float = 2,
            uuid: str = '',
    ) -> Generator[torch.Tensor, None, None]:
        device = text.device
        text = torch.concat([prompt_text, text], dim=1)
        text_len += prompt_text_len
        text = self.text_embedding(text)

        # 1. encode text
        text, text_len = self.encode(text, text_len)

        # 2. encode embedding
        if embedding.shape[0] != 0:
            embedding = F.normalize(embedding, dim=1)
            embedding = self.spk_embed_affine_layer(embedding)
            embedding = embedding.unsqueeze(dim=1)
        else:
            embedding = torch.zeros(1, 0, self.llm_input_size, dtype=text.dtype).to(device).to(text.dtype)

        # 3. concat llm_input
        sos_emb = self.llm_embedding.weight[self.sos].reshape(1, 1, -1)
        task_id_emb = self.llm_embedding.weight[self.task_id].reshape(1, 1, -1)
        if prompt_speech_token_len != 0:
            prompt_speech_token_emb = self.speech_embedding(prompt_speech_token)
        else:
            prompt_speech_token_emb = torch.zeros(1, 0, self.llm_input_size, dtype=text.dtype).to(device)
        lm_input = torch.concat([sos_emb, embedding, text, task_id_emb, prompt_speech_token_emb], dim=1)

        # 4. cal min/max_length
        min_len = int((text_len - prompt_text_len) * min_token_text_ratio)
        max_len = int((text_len - prompt_text_len) * max_token_text_ratio)

        # 5. step by step decode
        out_tokens = []
        offset = 0
        att_cache, cnn_cache = torch.zeros((0, 0, 0, 0), device=lm_input.device), torch.zeros((0, 0, 0, 0), device=lm_input.device)
        for i in range(max_len):
            y_pred, att_cache, cnn_cache = self.llm.forward_chunk(lm_input, offset=offset, required_cache_size=-1,
                                                                  att_cache=att_cache, cnn_cache=cnn_cache,
                                                                  att_mask=torch.tril(torch.ones((1, lm_input.shape[1], lm_input.shape[1]),
                                                                                                 device=lm_input.device)).to(torch.bool))
            logp = self.llm_decoder(y_pred[:, -1]).log_softmax(dim=-1)
            top_ids = self.sampling_ids(logp.squeeze(dim=0), out_tokens, sampling, ignore_eos=True if i < min_len else False)
            if top_ids == self.eos_token:
                break
            # in stream mode, yield token one by one
            yield top_ids
            out_tokens.append(top_ids)
            offset += lm_input.size(1)
            lm_input = self.speech_embedding.weight[top_ids].reshape(1, 1, -1)


class Qwen2Encoder(torch.nn.Module):
    def __init__(self, pretrain_path):
        super().__init__()
        self.model = Qwen2ForCausalLM.from_pretrained(pretrain_path)

    def forward(self, xs: torch.Tensor, xs_lens: torch.Tensor):
        T = xs.size(1)
        masks = ~make_pad_mask(xs_lens, T)
        outs = self.model(
            inputs_embeds=xs,
            attention_mask=masks,
            output_hidden_states=True,
            return_dict=True,
        )
        return outs.hidden_states[-1], masks.unsqueeze(1)

    def forward_one_step(self, xs, masks, cache=None):
        input_masks = masks[:, -1, :]
        outs = self.model(
            inputs_embeds=xs,
            attention_mask=input_masks,
            output_hidden_states=True,
            return_dict=True,
            use_cache=True,
            past_key_values=cache,
        )
        xs = outs.hidden_states[-1]
        new_cache = outs.past_key_values
        return xs, new_cache


class Qwen2LM(TransformerLM):
    def __init__(
            self,
            llm_input_size: int,
            llm_output_size: int,
            speech_token_size: int,
            llm: torch.nn.Module,
            sampling: Callable,
            length_normalized_loss: bool = True,
            lsm_weight: float = 0.0,
            mix_ratio: List[int] = [5, 15],
    ):
        torch.nn.Module.__init__(self)
        self.llm_input_size = llm_input_size
        self.llm_output_size = llm_output_size
        self.speech_token_size = speech_token_size
        # 2. build speech token language model related modules
        self.sos = 0
        self.task_id = 1
        self.eos_token = speech_token_size
        self.fill_token = speech_token_size + 2

        self.llm_embedding = torch.nn.Embedding(2, llm_input_size)
        self.llm = llm
        self.llm_decoder = nn.Linear(llm_output_size, speech_token_size + 3)
        self.criterion_ce = LabelSmoothingLoss(
            size=speech_token_size + 3,
            padding_idx=IGNORE_ID,
            smoothing=lsm_weight,
            normalize_length=length_normalized_loss,
        )

        # 3. [Optional] build speech token related modules
        self.speech_embedding = torch.nn.Embedding(speech_token_size + 3, llm_input_size)

        # 4. sampling method
        self.sampling = sampling
        self.mix_ratio = mix_ratio

        # 5. vllm related
        self.stop_token_ids = [speech_token_size + i for i in range(3)]
        self.vllm_output_queue = {}

    def prepare_lm_input_target(self, sos_emb, text_token, text_token_emb, text_token_len, task_id_emb, speech_token, speech_token_emb, speech_token_len, instruct_token=None, instruct_token_emb=None, instruct_token_len=None):
        lm_target, lm_input = [], []
        text_token = unpad_sequence(text_token, text_token_len.cpu(), batch_first=True)
        speech_token = unpad_sequence(speech_token, speech_token_len.cpu(), batch_first=True)
        text_token_emb = unpad_sequence(text_token_emb, text_token_len.cpu(), batch_first=True)
        speech_token_emb = unpad_sequence(speech_token_emb, speech_token_len.cpu(), batch_first=True)
        # NOTE add instruct_token in CosyVoice3
        if instruct_token is not None and instruct_token_emb is not None and instruct_token_len is not None:
            instruct_token = unpad_sequence(instruct_token, instruct_token_len.cpu(), batch_first=True)
            instruct_token_emb = unpad_sequence(instruct_token_emb, instruct_token_len.cpu(), batch_first=True)
        for i in range(len(text_token)):
            # bistream sequence
            if random.random() < 0.5 and speech_token_len[i] / text_token_len[i] > self.mix_ratio[1] / self.mix_ratio[0]:
                this_lm_target, this_lm_input = [IGNORE_ID], [sos_emb.squeeze(dim=0)]
                if instruct_token is not None and instruct_token_emb is not None and instruct_token_len is not None:
                    this_lm_target += [IGNORE_ID] * instruct_token_len[i]
                    this_lm_input.append(instruct_token_emb[i])
                for j in range(((text_token_len[i] + 1) / self.mix_ratio[0]).ceil().int().item()):
                    this_text_token = text_token[i][j * self.mix_ratio[0]: (j + 1) * self.mix_ratio[0]].tolist()
                    this_speech_token = speech_token[i][j * self.mix_ratio[1]: (j + 1) * self.mix_ratio[1]].tolist()
                    if len(this_text_token) == self.mix_ratio[0]:
                        assert len(this_speech_token) == self.mix_ratio[1]
                        this_lm_target += [IGNORE_ID] * (self.mix_ratio[0] - 1)
                        this_lm_target += this_speech_token
                        this_lm_target.append(self.fill_token)
                        this_lm_input.append(text_token_emb[i][j * self.mix_ratio[0]: (j + 1) * self.mix_ratio[0]])
                        this_lm_input.append(speech_token_emb[i][j * self.mix_ratio[1]: (j + 1) * self.mix_ratio[1]])
                    else:
                        this_lm_target += [-1] * len(this_text_token)
                        this_lm_target += speech_token[i][j * self.mix_ratio[1]:].tolist()
                        this_lm_target.append(self.eos_token)
                        this_lm_input.append(text_token_emb[i][j * self.mix_ratio[0]:])
                        this_lm_input.append(task_id_emb.squeeze(dim=0))
                        this_lm_input.append(speech_token_emb[i][j * self.mix_ratio[1]:])
                this_lm_target, this_lm_input = torch.tensor(this_lm_target), torch.concat(this_lm_input, dim=0)
            # unistream sequence
            else:
                this_lm_target = torch.tensor([IGNORE_ID] * (1 + instruct_token_len[i] + text_token_len[i]) + speech_token[i].tolist() + [self.eos_token])
                this_lm_input = torch.concat([sos_emb.squeeze(dim=0), instruct_token_emb[i], text_token_emb[i], task_id_emb.squeeze(dim=0), speech_token_emb[i]], dim=0)
            lm_target.append(this_lm_target)
            lm_input.append(this_lm_input)
        lm_input_len = torch.tensor([i.size(0) for i in lm_input], dtype=torch.int32)
        lm_input = pad_sequence(lm_input, batch_first=True, padding_value=IGNORE_ID)
        lm_target = pad_sequence(lm_target, batch_first=True, padding_value=IGNORE_ID)
        return lm_target, lm_input, lm_input_len

    def forward(
            self,
            batch: dict,
            device: torch.device,
    ) -> Dict[str, Optional[torch.Tensor]]:
        """
        Args:
            text: (B, L, D)
            text_lengths: (B,)
            audio: (B, T, N) or (B, T)
            audio_lengths: (B,)
        """
        text_token = batch['text_token'].to(device)
        text_token_len = batch['text_token_len'].to(device)
        speech_token = batch['speech_token'].to(device)
        speech_token_len = batch['speech_token_len'].to(device)

        # 1. encode text_token
        text_token_emb = self.llm.model.model.embed_tokens(text_token)

        # 3. sos and task_id
        sos_emb = self.llm_embedding.weight[self.sos].reshape(1, 1, -1)
        task_id_emb = self.llm_embedding.weight[self.task_id].reshape(1, 1, -1)

        # 2. encode speech_token
        speech_token_emb = self.speech_embedding(speech_token)

        # 3. prepare llm_input/target
        lm_target, lm_input, lm_input_len = self.prepare_lm_input_target(sos_emb, text_token, text_token_emb, text_token_len, task_id_emb,
                                                                         speech_token, speech_token_emb, speech_token_len)
        lm_target = lm_target.to(device)

        # 4. run lm forward
        lm_output, lm_output_mask = self.llm(lm_input, lm_input_len.to(device))
        logits = self.llm_decoder(lm_output)
        loss = self.criterion_ce(logits, lm_target.to(device))
        acc = th_accuracy(logits.view(-1, self.speech_token_size + 3), lm_target, ignore_label=IGNORE_ID)
        return {'loss': loss, 'acc': acc}

    def forward_dpo(
            self,
            batch: dict,
            device: torch.device,
    ) -> Dict[str, Optional[torch.Tensor]]:
        text_token = batch['text_token'].to(device)
        text_token_len = batch['text_token_len'].to(device)
        speech_token = batch['speech_token'].to(device)
        speech_token_len = batch['speech_token_len'].to(device)
        reject_speech_token = batch['reject_speech_token'].to(device)
        reject_speech_token_len = batch['reject_speech_token_len'].to(device)

        # 1. encode text_token
        text_token_emb = self.llm.model.model.embed_tokens(text_token)

        # 3. sos and task_id
        sos_emb = self.llm_embedding.weight[self.sos].reshape(1, 1, -1)
        task_id_emb = self.llm_embedding.weight[self.task_id].reshape(1, 1, -1)

        # 2. encode speech_token
        speech_token = unpad_sequence(speech_token, speech_token_len.cpu(), batch_first=True)
        reject_speech_token = unpad_sequence(reject_speech_token, reject_speech_token_len.cpu(), batch_first=True)
        speech_token_combined = speech_token + reject_speech_token
        speech_token_combined = pad_sequence(speech_token_combined, batch_first=True, padding_value=0)
        speech_token_combined_len = torch.concat([speech_token_len, reject_speech_token_len], dim=0)
        speech_token_combined_emb = self.speech_embedding(speech_token_combined)

        # 3. prepare llm_input/target
        lm_target, lm_input, lm_input_len = self.prepare_lm_input_target(sos_emb, text_token.repeat(2, 1), text_token_emb.repeat(2, 1, 1), text_token_len.repeat(2),
                                                                         task_id_emb, speech_token_combined, speech_token_combined_emb, speech_token_combined_len)
        lm_target = lm_target.to(device)

        # 4. run lm forward
        lm_output, lm_output_mask = self.llm(lm_input, lm_input_len.to(device))
        logits = self.llm_decoder(lm_output)
        chosen_logits = logits[:text_token.shape[0]]
        rejected_logits = logits[text_token.shape[0]:]
        chosen_lm_target = lm_target[:text_token.shape[0]]
        rejected_lm_target = lm_target[text_token.shape[0]:]
        loss = self.criterion_ce(chosen_logits, chosen_lm_target.to(device))
        acc = th_accuracy(chosen_logits.view(-1, self.speech_token_size + 3), chosen_lm_target, ignore_label=IGNORE_ID)

        # 5. calculate dpo logits
        chosen_lm_mask = chosen_lm_target == IGNORE_ID
        rejected_lm_mask = rejected_lm_target == IGNORE_ID
        chosen_logps = torch.gather(chosen_logits.log_softmax(dim=-1), dim=2, index=chosen_lm_target.masked_fill(chosen_lm_mask, 0).unsqueeze(dim=-1)).squeeze(dim=-1)
        rejected_logps = torch.gather(rejected_logits.log_softmax(dim=-1), dim=2, index=rejected_lm_target.masked_fill(rejected_lm_mask, 0).unsqueeze(dim=-1)).squeeze(dim=-1)
        chosen_logps = (chosen_logps * chosen_lm_mask).sum(dim=-1) / chosen_lm_mask.sum(dim=-1)
        rejected_logps = (rejected_logps * rejected_lm_mask).sum(dim=-1) / rejected_lm_mask.sum(dim=-1)
        return {'loss': loss, 'acc': acc, 'chosen_logps': chosen_logps, 'rejected_logps': rejected_logps}

    @torch.inference_mode()
    def inference(
            self,
            text: torch.Tensor,
            text_len: torch.Tensor,
            prompt_text: torch.Tensor,
            prompt_text_len: torch.Tensor,
            prompt_speech_token: torch.Tensor,
            prompt_speech_token_len: torch.Tensor,
            embedding: torch.Tensor,
            sampling: int = 25,
            max_token_text_ratio: float = 20,
            min_token_text_ratio: float = 2,
            uuid: str = '',
    ) -> Generator[torch.Tensor, None, None]:
        device = text.device
        text = torch.concat([prompt_text, text], dim=1)
        text_len += prompt_text_len
        text = self.llm.model.model.embed_tokens(text)

        # 3. concat llm_input
        sos_emb = self.llm_embedding.weight[self.sos].reshape(1, 1, -1)
        task_id_emb = self.llm_embedding.weight[self.task_id].reshape(1, 1, -1)
        if prompt_speech_token_len != 0:
            prompt_speech_token_emb = self.speech_embedding(prompt_speech_token)
        else:
            prompt_speech_token_emb = torch.zeros(1, 0, self.llm_input_size, dtype=text.dtype).to(device)
        lm_input = torch.concat([sos_emb, text, task_id_emb, prompt_speech_token_emb], dim=1)

        # 4. cal min/max_length
        min_len = int((text_len - prompt_text_len) * min_token_text_ratio)
        max_len = int((text_len - prompt_text_len) * max_token_text_ratio)

        # 5. step by step decode
        for token in self.inference_wrapper(lm_input, sampling, min_len, max_len, uuid):
            yield token

    @torch.inference_mode()
    def inference_wrapper(self, lm_input, sampling, min_len, max_len, uuid):
        if hasattr(self, 'vllm'):
            from vllm import SamplingParams, RequestOutput
            sampling_params = SamplingParams(top_k=sampling,
                                             stop_token_ids=self.stop_token_ids,
                                             min_tokens=min_len,
                                             max_tokens=max_len)
            with self.lock:
                self.vllm.add_request(uuid, {"prompt_embeds": lm_input.squeeze(0).to(torch.bfloat16).to(lm_input.device)}, sampling_params)
                self.vllm_output_queue[uuid] = queue.Queue()
            out_tokens = []
            while True:
                with self.lock:
                    if self.vllm_output_queue[uuid].empty() is True:
                        request_outputs: List[RequestOutput] = self.vllm.step()
                        for request_output in request_outputs:
                            top_ids = list(request_output.outputs[0].token_ids)[-1]
                            self.vllm_output_queue[request_output.request_id].put(top_ids)
                if self.vllm_output_queue[uuid].empty() is False:
                    top_ids = self.vllm_output_queue[uuid].get()
                    if top_ids in self.stop_token_ids:
                        break
                    # in stream mode, yield token one by one
                    yield top_ids
                    out_tokens.append(top_ids)
                    if len(out_tokens) == max_len:
                        break
                time.sleep(0.001)
            with self.lock:
                self.vllm_output_queue.pop(uuid)
        else:
            out_tokens = []
            cache = None
            for i in range(max_len):
                y_pred, cache = self.llm.forward_one_step(lm_input,
                                                          masks=torch.tril(torch.ones((1, lm_input.shape[1], lm_input.shape[1]), device=lm_input.device)).to(torch.bool),
                                                          cache=cache)
                logp = self.llm_decoder(y_pred[:, -1]).log_softmax(dim=-1)
                top_ids = self.sampling_ids(logp.squeeze(dim=0), out_tokens, sampling, ignore_eos=True if i < min_len else False)
                if top_ids in self.stop_token_ids:
                    break
                # in stream mode, yield token one by one
                yield top_ids
                out_tokens.append(top_ids)
                lm_input = self.speech_embedding.weight[top_ids].reshape(1, 1, -1)

    @torch.inference_mode()
    def inference_bistream(
            self,
            text: Generator,
            prompt_text: torch.Tensor,
            prompt_text_len: torch.Tensor,
            prompt_speech_token: torch.Tensor,
            prompt_speech_token_len: torch.Tensor,
            embedding: torch.Tensor,
            sampling: int = 25,
            max_token_text_ratio: float = 20,
            min_token_text_ratio: float = 2,
    ) -> Generator[torch.Tensor, None, None]:

        device = prompt_text.device
        # 1. prepare input
        sos_emb = self.llm_embedding.weight[self.sos].reshape(1, 1, -1)
        task_id_emb = self.llm_embedding.weight[self.task_id].reshape(1, 1, -1)
        if prompt_speech_token_len != 0:
            prompt_speech_token_emb = self.speech_embedding(prompt_speech_token)
        else:
            prompt_speech_token_emb = torch.zeros(1, 0, self.llm_input_size, dtype=prompt_text.dtype).to(device)
        lm_input = torch.concat([sos_emb], dim=1)

        # 2. iterate text
        out_tokens = []
        cache = None
        # NOTE init prompt_text as text_cache as it is basically impossible prompt_speech_token/prompt_text < 15/5
        text_cache = self.llm.model.model.embed_tokens(prompt_text)
        next_fill_index = (int(prompt_speech_token.shape[1] / self.mix_ratio[1]) + 1) * self.mix_ratio[1] - prompt_speech_token.shape[1]
        for this_text in text:
            text_cache = torch.concat([text_cache, self.llm.model.model.embed_tokens(this_text)], dim=1)
            # prompt_speech_token_emb not empty, try append to lm_input
            while prompt_speech_token_emb.size(1) != 0:
                if text_cache.size(1) >= self.mix_ratio[0]:
                    lm_input_text, lm_input_speech = text_cache[:, :self.mix_ratio[0]], prompt_speech_token_emb[:, :self.mix_ratio[1]]
                    logging.info('append {} text token {} speech token'.format(lm_input_text.size(1), lm_input_speech.size(1)))
                    lm_input = torch.concat([lm_input, lm_input_text, lm_input_speech], dim=1)
                    text_cache, prompt_speech_token_emb = text_cache[:, self.mix_ratio[0]:], prompt_speech_token_emb[:, self.mix_ratio[1]:]
                else:
                    logging.info('not enough text token to decode, wait for more')
                    break
            # no prompt_speech_token_emb remain, can decode some speech token
            if prompt_speech_token_emb.size(1) == 0:
                if (len(out_tokens) != 0 and out_tokens[-1] == self.fill_token) or (len(out_tokens) == 0 and lm_input.size(1) == 1):
                    logging.info('get fill token, need to append more text token')
                    if text_cache.size(1) >= self.mix_ratio[0]:
                        lm_input_text = text_cache[:, :self.mix_ratio[0]]
                        logging.info('append {} text token'.format(lm_input_text.size(1)))
                        if len(out_tokens) != 0 and out_tokens[-1] == self.fill_token:
                            lm_input = lm_input_text
                        else:
                            lm_input = torch.concat([lm_input, lm_input_text], dim=1)
                        text_cache = text_cache[:, self.mix_ratio[0]:]
                    else:
                        logging.info('not enough text token to decode, wait for more')
                        continue
                while True:
                    seq_len = lm_input.shape[1] if cache is None else lm_input.shape[1] + cache[0][0].size(2)
                    y_pred, cache = self.llm.forward_one_step(lm_input,
                                                              masks=torch.tril(torch.ones((1, seq_len, seq_len), device=lm_input.device)).to(torch.bool),
                                                              cache=cache)
                    logp = self.llm_decoder(y_pred[:, -1]).log_softmax(dim=-1)
                    if next_fill_index != -1 and len(out_tokens) == next_fill_index:
                        top_ids = self.fill_token
                        next_fill_index += (self.mix_ratio[1] + 1)
                    else:
                        top_ids = self.sampling_ids(logp.squeeze(dim=0), out_tokens, sampling, ignore_eos=True)
                    if top_ids == self.fill_token:
                        next_fill_index = len(out_tokens) + self.mix_ratio[1] + 1
                        logging.info('fill_token index {} next fill_token index {}'.format(len(out_tokens), next_fill_index))
                    out_tokens.append(top_ids)
                    if top_ids >= self.speech_token_size:
                        if top_ids == self.fill_token:
                            break
                        else:
                            raise ValueError('should not get token {}'.format(top_ids))
                    yield top_ids
                    lm_input = self.speech_embedding.weight[top_ids].reshape(1, 1, -1)

        # 3. final decode
        lm_input = torch.concat([lm_input, text_cache, task_id_emb], dim=1)
        logging.info('no more text token, decode until met eos')
        while True:
            seq_len = lm_input.shape[1] if cache is None else lm_input.shape[1] + cache[0][0].size(2)
            y_pred, cache = self.llm.forward_one_step(lm_input,
                                                      masks=torch.tril(torch.ones((1, seq_len, seq_len), device=lm_input.device)).to(torch.bool),
                                                      cache=cache)
            logp = self.llm_decoder(y_pred[:, -1]).log_softmax(dim=-1)
            top_ids = self.sampling_ids(logp.squeeze(dim=0), out_tokens, sampling, ignore_eos=False)
            out_tokens.append(top_ids)
            if top_ids >= self.speech_token_size:
                if top_ids == self.eos_token:
                    break
                else:
                    raise ValueError('should not get token {}'.format(top_ids))
            # in stream mode, yield token one by one
            yield top_ids
            lm_input = self.speech_embedding.weight[top_ids].reshape(1, 1, -1)


class CosyVoice3LM(Qwen2LM):
    def __init__(
            self,
            llm_input_size: int,
            llm_output_size: int,
            speech_token_size: int,
            llm: torch.nn.Module,
            sampling: Callable,
            length_normalized_loss: bool = True,
            lsm_weight: float = 0.0,
            mix_ratio: List[int] = [5, 15],
    ):
        torch.nn.Module.__init__(self)
        self.llm_input_size = llm_input_size
        self.llm_output_size = llm_output_size
        self.speech_token_size = speech_token_size
        # 2. build speech token language model related modules
        self.sos = speech_token_size + 0
        self.eos_token = speech_token_size + 1
        self.task_id = speech_token_size + 2
        self.fill_token = speech_token_size + 3

        self.llm = llm
        self.llm_decoder = nn.Linear(llm_output_size, speech_token_size + 200, bias=False)
        self.criterion_ce = LabelSmoothingLoss(
            size=speech_token_size + 200,
            padding_idx=IGNORE_ID,
            smoothing=lsm_weight,
            normalize_length=length_normalized_loss,
        )

        # 3. [Optional] build speech token related modules
        self.speech_embedding = torch.nn.Embedding(speech_token_size + 200, llm_input_size)

        # 4. sampling method
        self.sampling = sampling
        self.mix_ratio = mix_ratio

        # 5. vllm related
        self.stop_token_ids = [speech_token_size + i for i in range(200)]
        self.vllm_output_queue = {}

    def forward(
            self,
            batch: dict,
            device: torch.device,
    ) -> Dict[str, Optional[torch.Tensor]]:
        """
        Args:
            text: (B, L, D)
            text_lengths: (B,)
            audio: (B, T, N) or (B, T)
            audio_lengths: (B,)
        """
        text_token = batch['text_token'].to(device)
        text_token_len = batch['text_token_len'].to(device)
        speech_token = batch['speech_token'].to(device)
        speech_token_len = batch['speech_token_len'].to(device)
        # NOTE should append instruct_token to sequence, not implemented yet
        instruct_token = batch['instruct_token'].to(device)
        instruct_token_len = batch['instruct_token_len'].to(device)

        # 1. encode text_token
        text_token_emb = self.llm.model.model.embed_tokens(text_token)
        instruct_token_emb = self.llm.model.model.embed_tokens(instruct_token)

        # 3. sos and task_id
        sos_emb = self.speech_embedding.weight[self.sos].reshape(1, 1, -1)
        task_id_emb = self.speech_embedding.weight[self.task_id].reshape(1, 1, -1)

        # 2. encode speech_token
        speech_token_emb = self.speech_embedding(speech_token)

        # 3. prepare llm_input/target
        lm_target, lm_input, lm_input_len = self.prepare_lm_input_target(sos_emb, text_token, text_token_emb, text_token_len, task_id_emb,
                                                                         speech_token, speech_token_emb, speech_token_len, instruct_token, instruct_token_emb, instruct_token_len)
        lm_target = lm_target.to(device)

        # 4. run lm forward
        lm_output, lm_output_mask = self.llm(lm_input, lm_input_len.to(device))
        logits = self.llm_decoder(lm_output)
        loss = self.criterion_ce(logits, lm_target.to(device))
        acc = th_accuracy(logits.view(-1, self.speech_token_size + 200), lm_target, ignore_label=IGNORE_ID)
        return {'loss': loss, 'acc': acc}

    @torch.inference_mode()
    def inference(
            self,
            text: torch.Tensor,
            text_len: torch.Tensor,
            prompt_text: torch.Tensor,
            prompt_text_len: torch.Tensor,
            prompt_speech_token: torch.Tensor,
            prompt_speech_token_len: torch.Tensor,
            embedding: torch.Tensor,
            sampling: int = 25,
            max_token_text_ratio: float = 20,
            min_token_text_ratio: float = 2,
            uuid: str = '',
    ) -> Generator[torch.Tensor, None, None]:
        device = text.device
        text = torch.concat([prompt_text, text], dim=1)
        text_len += prompt_text_len
        text = self.llm.model.model.embed_tokens(text)

        # 3. concat llm_input
        sos_emb = self.speech_embedding.weight[self.sos].reshape(1, 1, -1)
        task_id_emb = self.speech_embedding.weight[self.task_id].reshape(1, 1, -1)
        if prompt_speech_token_len != 0:
            prompt_speech_token_emb = self.speech_embedding(prompt_speech_token)
        else:
            prompt_speech_token_emb = torch.zeros(1, 0, self.llm_input_size, dtype=text.dtype).to(device)
        lm_input = torch.concat([sos_emb, text, task_id_emb, prompt_speech_token_emb], dim=1)

        # 4. cal min/max_length
        min_len = int((text_len - prompt_text_len) * min_token_text_ratio)
        max_len = int((text_len - prompt_text_len) * max_token_text_ratio)

        # 5. step by step decode
        for token in self.inference_wrapper(lm_input, sampling, min_len, max_len, uuid):
            yield token