vllm_use_cosyvoice2_model.py

# SPDX-License-Identifier: Apache-2.0

# Adapted from
# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/qwen2/modeling_qwen2.py
# Copyright 2024 The Qwen team.
# Copyright 2023 The vLLM team.
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# 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.
"""Inference-only Qwen2 model compatible with HuggingFace weights."""
from typing import Iterable, List, Optional, Set, Tuple, Union, Iterator, overload, TypedDict, Mapping, Any
from typing_extensions import TypeVar

import torch
from torch import nn

from vllm.attention import AttentionMetadata
from vllm.config import VllmConfig
from vllm.logger import init_logger
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.sequence import IntermediateTensors

from vllm.model_executor.models.interfaces import T, SupportsLoRA, SupportsPP
from vllm.model_executor.models.qwen2 import Qwen2Model

from vllm.model_executor.models.utils import AutoWeightsLoader, maybe_prefix, merge_multimodal_embeddings

logger = init_logger(__name__)

IGNORE_ID = -1


class CosyVoice2Model(nn.Module, SupportsLoRA, SupportsPP):

    packed_modules_mapping = {
        "qkv_proj": [
            "q_proj",
            "k_proj",
            "v_proj",
        ],
        "gate_up_proj": [
            "gate_proj",
            "up_proj",
        ],
    }

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()
        config = vllm_config.model_config.hf_config
        quant_config = vllm_config.quant_config
        lora_config = vllm_config.lora_config

        self.config = config
        self.lora_config = lora_config
        self.quant_config = quant_config

        self.llm_input_size = 896
        self.llm_output_size = 896

        self.speech_token_size = 6561+3
        self.llm_token_size = config.vocab_size

        # 2. build speech token language model related modules
        self.sos_eos = 0
        self.task_id = 1
        self.fill_token = 2


        self.allow_patterns_overrides = ["llm.*"]
        self.llm_embedding = torch.nn.Embedding(2, self.llm_input_size)
        self.model = Qwen2Model(vllm_config=vllm_config,
                              prefix=maybe_prefix(prefix, "model"))

        # self.llm_decoder = nn.Linear(self.llm_output_size, self.speech_token_size)
        self.llm_decoder = ParallelLMHead(self.speech_token_size,
                                      self.llm_output_size,
                                      bias=True,
                                      quant_config=quant_config,
                                      prefix=maybe_prefix(
                                          prefix, "llm_decoder"))
        self.logits_processor = LogitsProcessor(self.speech_token_size)

        # length_normalized_loss: bool = True,
        # lsm_weight: float = 0.0,
        # self.criterion_ce = LabelSmoothingLoss(
        #     size=self.speech_token_size,
        #     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(self.speech_token_size, self.llm_input_size)

        # 4. sampling method
        ## use vllm sampling method
        self.sampler = get_sampler()
        self.make_empty_intermediate_tensors = (
            self.model.make_empty_intermediate_tensors)

        self.mix_ratio: List[int] = [5, 15]

        # 定义特殊token常量
        self.llm_token_id_delta = torch.tensor(self.speech_token_size, dtype=torch.int32)
        self.sos_eos_token_id = torch.tensor((self.llm_token_id_delta + self.llm_token_size + 1), dtype=torch.int32)  # 163840 + 6564 = 170404
        self.task_token_id = self.sos_eos_token_id + torch.tensor(1, dtype=torch.int32)  # 170405
        self.zero_token_id = self.task_token_id + torch.tensor(1, dtype=torch.int32)

        self.zero_embed_buffer = torch.zeros(
            (vllm_config.scheduler_config.max_num_seqs, self.llm_input_size),
            dtype=self.llm_embedding.weight.dtype,
            device=self.llm_embedding.weight.device
        )
        self.inputs_embed_buffer = torch.zeros(
            (vllm_config.scheduler_config.max_num_batched_tokens, self.llm_input_size),
            dtype=self.llm_embedding.weight.dtype,
            device=self.llm_embedding.weight.device,
        )

    def get_sos_eos_emb(self):
        return self.llm_embedding.weight[self.sos_eos].reshape(1, 1, -1)

    def get_task_id_emb(self):
        return self.llm_embedding.weight[self.task_id].reshape(1, 1, -1)

    def get_input_embeddings(
        self,
        input_ids: torch.Tensor,
        multimodal_embeddings: Optional[T] = None,
        attn_metadata: Optional["AttentionMetadata"] = None,
    ) -> torch.Tensor:
        """
        Returns the input embeddings merged from the text embeddings from
        input_ids and the multimodal embeddings generated from multimodal
        kwargs.
        """
        # 创建掩码，标记哪些 token_id 属于音频 Token
        mask = input_ids < self.speech_token_size

        # 获取 input_ids 的原始形状
        input_shape = input_ids.shape
        # 展平 input_ids 和掩码以便统一处理
        flat_input_ids = input_ids.view(-1)
        flat_mask = mask.view(-1)

        inputs_embeds = self.inputs_embed_buffer[:flat_input_ids.shape[0]]
        inputs_embeds.zero_()

        # Process speech tokens
        if flat_mask.any():
            speech_token_ids = flat_input_ids[flat_mask]
            inputs_embeds[flat_mask] = self.speech_embedding(speech_token_ids)

        # 处理大于 delta 的 token_id
        if (~flat_mask).any():
            llm_token_ids = flat_input_ids[~flat_mask]
            llm_embeds = torch.zeros_like(inputs_embeds[~flat_mask])

            sos_eos_mask = llm_token_ids == self.sos_eos_token_id
            task_mask = llm_token_ids == self.task_token_id
            zero_mask = llm_token_ids == self.zero_token_id
            normal_mask = ~(sos_eos_mask | task_mask | zero_mask)

            # 分层处理逻辑
            # 第一优先级：SOS/EOS标记
            if sos_eos_mask.any():
                llm_embeds[sos_eos_mask] = self.llm_embedding.weight[self.sos_eos].unsqueeze(0)

            # 第二优先级：任务标记
            if task_mask.any():
                llm_embeds[task_mask] = self.llm_embedding.weight[self.task_id].unsqueeze(0)

            # 第二优先级：空音频标记
            if zero_mask.any():
                llm_embeds[zero_mask] = self.zero_embed_buffer[:len(llm_embeds[zero_mask])]

            # 常规LLM token
            if normal_mask.any():
                original_ids = llm_token_ids[normal_mask] - self.llm_token_id_delta
                # print('original_ids: ',original_ids)
                llm_embeds[normal_mask] = self.model.get_input_embeddings(original_ids)

            inputs_embeds[~flat_mask] = llm_embeds

        inputs_embeds = inputs_embeds.view(*input_shape, self.llm_input_size)

        # 合并多模态嵌入（如果有）
        if multimodal_embeddings is not None:
            inputs_embeds = merge_multimodal_embeddings(
                input_ids, inputs_embeds, multimodal_embeddings,
                self.config.audio_token_index
            )
        return inputs_embeds

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        kv_caches: List[torch.Tensor],
        attn_metadata: AttentionMetadata,
        intermediate_tensors: Optional[IntermediateTensors] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
    ) -> Union[torch.Tensor, IntermediateTensors]:
        if inputs_embeds is None:
            inputs_embeds = self.get_input_embeddings(
                input_ids,
                attn_metadata=attn_metadata,
            )
        return self.model(input_ids, positions, kv_caches,
                        attn_metadata, intermediate_tensors,
                        inputs_embeds)

    def compute_logits(
        self,
        hidden_states: torch.Tensor,
        sampling_metadata: SamplingMetadata,
    ) -> Optional[torch.Tensor]:
        logits = self.logits_processor(self.llm_decoder, hidden_states,
                                       sampling_metadata)
        return logits

    def sample(
        self,
        logits: torch.Tensor,
        sampling_metadata: SamplingMetadata,
    ) -> Optional[SamplerOutput]:
        next_tokens = self.sampler(logits, sampling_metadata)
        return next_tokens

    @staticmethod
    def convert_weights(weights: Iterable[Tuple[str, torch.Tensor]]) -> Iterable[Tuple[str, torch.Tensor]]:
        for name, param in weights:
            # 处理Qwen2Model核心参数
            if name.startswith("llm."):
                if name.startswith("llm.model.model."):
                    name = name.replace("llm.model.model.", "model.")
                else:
                    continue
            # print('weights name: ', name)
            yield name, param

    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
        weights = self.convert_weights(weights)
        loader = AutoWeightsLoader(self)
        loader.load_weights(weights)