add model: qwen2-audio (#7596)

python/sglang/srt/configs/model_config.py

@@ -593,6 +593,7 @@ multimodal_model_archs = [
     "Mistral3ForConditionalGeneration",
     "MultiModalityCausalLM",
     "MllamaForConditionalGeneration",
+    "Qwen2AudioForConditionalGeneration",
     "Qwen2VLForConditionalGeneration",
     "Qwen2_5_VLForConditionalGeneration",
     "KimiVLForConditionalGeneration",

python/sglang/srt/conversation.py

@@ -59,6 +59,7 @@ class SeparatorStyle(IntEnum):
     METAMATH = auto()
     DeepSeekVL2 = auto()
     QWEN2_VL_EMBED = auto()
+    QWEN2_AUDIO = auto()
     GEMMA3 = auto()
     MPT = auto()
 
@@ -350,6 +351,23 @@ class Conversation:
                 else:
                     ret += role
             return ret
+        elif self.sep_style == SeparatorStyle.QWEN2_AUDIO:
+            ret = "" if system_prompt == "" else system_prompt + self.sep
+
+            counter = 1
+            for role, message in self.messages:
+                if message:
+                    while self.audio_token in message:
+                        message = message.replace(
+                            self.audio_token, self.audio_token.format(idx=counter), 1
+                        )
+                        counter += 1
+
+                    ret += role + "\n" + message + self.sep
+                else:
+                    ret += role + "\n"
+
+            return ret
         else:
             raise ValueError(f"Invalid style: {self.sep_style}")
 
@@ -904,6 +922,20 @@ register_conv_template(
 )
 
 
+register_conv_template(
+    Conversation(
+        name="qwen2-audio",
+        system_template="<|im_start|>system\n{system_message}",
+        system_message="You are a helpful assistant.",
+        roles=("<|im_start|>user", "<|im_start|>assistant"),
+        sep="<|im_end|>\n",
+        sep_style=SeparatorStyle.QWEN2_AUDIO,
+        stop_str=["<|im_end|>"],
+        audio_token="Audio {idx}: <|audio_bos|><|AUDIO|><|audio_eos|>\n",
+    )
+)
+
+
 @register_conv_template_matching_function
 def match_internvl(model_path: str):
     if re.search(r"internvl2_5", model_path, re.IGNORECASE):
@@ -956,6 +988,8 @@ def match_qwen_chat_ml(model_path: str):
         return "gme-qwen2-vl"
     if re.search(r"qwen.*vl", model_path, re.IGNORECASE):
         return "qwen2-vl"
+    if re.search(r"qwen.*audio", model_path, re.IGNORECASE):
+        return "qwen2-audio"
     if re.search(
         r"llava-v1\.6-34b|llava-v1\.6-yi-34b|llava-next-video-34b|llava-onevision-qwen2",
         model_path,

python/sglang/srt/managers/multimodal_processors/qwen_audio.py

+import re
+from typing import List, Union
+
+import torch
+
+from sglang.srt.managers.multimodal_processors.base_processor import (
+    BaseMultimodalProcessor,
+    MultimodalSpecialTokens,
+)
+from sglang.srt.managers.schedule_batch import Modality, MultimodalDataItem
+from sglang.srt.models.qwen2_audio import Qwen2AudioForConditionalGeneration
+
+
+class Qwen2AudioMultimodalProcessor(BaseMultimodalProcessor):
+    models = [Qwen2AudioForConditionalGeneration]
+
+    def __init__(self, hf_config, server_args, _processor):
+        super().__init__(hf_config, server_args, _processor)
+        self.AUDIO_TOKEN = "<|audio_bos|><|AUDIO|><|audio_eos|>"
+        self.AUDIO_TOKEN_REGEX = re.compile(
+            r"<\|audio_bos\|>(?:<\|AUDIO\|>)+<\|audio_eos\|>"
+        )
+
+    async def process_mm_data_async(
+        self,
+        image_data: List[Union[str, bytes]],
+        input_text,
+        request_obj,
+        max_req_input_len,
+        **kwargs,
+    ):
+        audio_data = request_obj.audio_data
+        if not isinstance(audio_data, list):
+            audio_data = [audio_data]
+
+        base_output = self.load_mm_data(
+            prompt=input_text,
+            max_req_input_len=max_req_input_len,
+            audio_data=audio_data,
+            multimodal_tokens=MultimodalSpecialTokens(
+                audio_token=self.AUDIO_TOKEN,
+                audio_token_regex=self.AUDIO_TOKEN_REGEX,
+            ),
+        )
+        if base_output is None:
+            return None
+
+        res = self.process_mm_data(
+            input_text=base_output.input_text,
+            audio=base_output.audios,
+        )
+
+        # Collect special token ids
+        tokenizer = self._processor.tokenizer
+        audio_start_id = tokenizer.convert_tokens_to_ids("<|audio_bos|>")
+        audio_token_id = tokenizer.convert_tokens_to_ids("<|AUDIO|>")
+        audio_end_id = tokenizer.convert_tokens_to_ids("<|audio_eos|>")
+
+        items = []
+        input_ids = res["input_ids"].flatten()
+
+        if (
+            "input_features" in res
+            and res["input_features"] is not None
+            and len(res["input_features"]) != 0
+        ):
+            if audio_start_id is not None and audio_end_id is not None:
+                audio_offsets = self.get_mm_items_offset_by_pair(
+                    input_ids=input_ids,
+                    mm_start_id=audio_start_id,
+                    mm_end_id=audio_end_id,
+                )
+            else:
+                audio_offsets = None
+
+            input_lengths = res["feature_attention_mask"].sum(dim=-1)
+            input_lengths = (input_lengths - 1) // 2 + 1
+            output_lengths = (input_lengths - 2) // 2 + 1
+
+            item = MultimodalDataItem(
+                audio_features=res["input_features"],
+                audio_feature_lens=output_lengths,
+                audio_offsets=audio_offsets,
+                modality=Modality.AUDIO,
+            )
+            items += [item]
+
+        return {
+            "mm_items": items,
+            "input_ids": input_ids.tolist(),
+            "audio_start_id": audio_start_id,
+            "audio_token_id": audio_token_id,
+            "audio_end_id": audio_end_id,
+        }

python/sglang/srt/models/qwen2.py

@@ -425,6 +425,7 @@ class Qwen2ForCausalLM(nn.Module):
                     quant_config=quant_config,
                     prefix=add_prefix("lm_head", prefix),
                 )
+
         else:
             # ranks other than the last rank will have a placeholder layer
             self.lm_head = PPMissingLayer()

python/sglang/srt/models/qwen2_audio.py

+# coding=utf-8
+# Adapted from
+# https://github.com/huggingface/transformers/blob/1d45d90e5d1552eccb6d8cc9b7bba283ccefb808/src/transformers/models/qwen2_audio/modeling_qwen2_audio.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-Audio model compatible with HuggingFace weights."""
+import logging
+import math
+from functools import lru_cache, partial
+from typing import Any, Iterable, List, Optional, Tuple, Type, TypedDict
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+from transformers import AutoTokenizer, Qwen2AudioEncoderConfig, Qwen2Config
+from transformers.activations import ACT2FN
+from transformers.models.qwen2_audio.configuration_qwen2_audio import Qwen2AudioConfig
+from transformers.models.qwen2_audio.modeling_qwen2_audio import (
+    Qwen2AudioEncoder,
+    Qwen2AudioMultiModalProjector,
+)
+
+from sglang.srt.hf_transformers_utils import get_processor
+from sglang.srt.layers.activation import QuickGELU
+from sglang.srt.layers.attention.vision import VisionAttention
+from sglang.srt.layers.linear import ColumnParallelLinear, RowParallelLinear
+from sglang.srt.layers.logits_processor import LogitsProcessor
+from sglang.srt.layers.pooler import Pooler, PoolingType
+from sglang.srt.layers.quantization.base_config import QuantizationConfig
+from sglang.srt.layers.utils import get_layer_id
+from sglang.srt.layers.vocab_parallel_embedding import ParallelLMHead
+from sglang.srt.managers.mm_utils import (
+    MultiModalityDataPaddingPatternMultimodalTokens,
+    general_mm_embed_routine,
+)
+from sglang.srt.managers.schedule_batch import MultimodalDataItem, MultimodalInputs
+from sglang.srt.model_executor.forward_batch_info import ForwardBatch
+from sglang.srt.model_loader.weight_utils import default_weight_loader
+from sglang.srt.models.qwen2 import Qwen2ForCausalLM
+from sglang.srt.utils import add_prefix
+
+logger = logging.getLogger(__name__)
+
+
+class Qwen2AudioForConditionalGeneration(nn.Module):
+    # BitandBytes specific attributes
+    default_bitsandbytes_target_modules = [
+        ".gate_proj.",
+        ".down_proj.",
+        ".up_proj.",
+        ".q_proj.",
+        ".k_proj.",
+        ".v_proj.",
+        ".o_proj.",
+    ]
+    bitsandbytes_stacked_params_mapping = {
+        # shard_name, weight_name, index
+        "q_proj": ("qkv_proj", 0),
+        "k_proj": ("qkv_proj", 1),
+        "v_proj": ("qkv_proj", 2),
+        "gate_proj": ("gate_up_proj", 0),
+        "up_proj": ("gate_up_proj", 1),
+    }
+
+    def __init__(
+        self,
+        config: Qwen2AudioConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        self.config = config
+
+        if getattr(self.config, "audio_config", None) is None:
+            self.config.audio_config = Qwen2AudioEncoderConfig(
+                self.config._name_or_path
+            )
+
+        if getattr(self.config, "text_config", None) is None:
+            self.config.text_config = Qwen2Config(self.config._name_or_path)
+
+        self.audio_tower = Qwen2AudioEncoder(
+            config.audio_config,
+        )
+        self.multi_modal_projector = Qwen2AudioMultiModalProjector(config)
+        self.language_model = Qwen2ForCausalLM(
+            config.text_config, quant_config, prefix=add_prefix("model", prefix)
+        )
+
+    def pad_input_ids(self, input_ids: List[int], mm_inputs: MultimodalInputs):
+        # Get all special token IDs for audio
+        audio_token_id: int = getattr(
+            mm_inputs, "audio_token_id", mm_inputs.im_token_id
+        )
+
+        pattern = MultiModalityDataPaddingPatternMultimodalTokens([audio_token_id])
+        return pattern.pad_input_tokens(input_ids, mm_inputs)
+
+    def get_audio_feature(self, items: List[MultimodalDataItem]) -> torch.Tensor:
+        # Extract audio features from input items
+        input_features = torch.cat([item.audio_features for item in items], dim=0).type(
+            self.audio_tower.dtype
+        )
+
+        audio_embeds = self.audio_tower(input_features).last_hidden_state
+        audio_embeds = self.multi_modal_projector(audio_embeds)
+
+        audio_feature_lens = torch.cat([item.audio_feature_lens for item in items])
+        new_embeds = []
+        for i, d in zip(audio_feature_lens, audio_embeds):
+            new_embeds.append(d[: i.item()])
+
+        return torch.cat(new_embeds, dim=0)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        **kwargs: Any,
+    ) -> torch.Tensor:
+        hidden_states = general_mm_embed_routine(
+            input_ids=input_ids,
+            forward_batch=forward_batch,
+            language_model=self.language_model,
+            audio_data_embedding_func=self.get_audio_feature,
+            positions=positions,
+        )
+
+        return hidden_states
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+        params_dict = dict(self.named_parameters(remove_duplicate=False))
+
+        for name, loaded_weight in weights:
+            if "rotary_emb.inv_freq" in name:
+                continue
+            if "rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name:
+                # Models trained using ColossalAI may include these tensors in
+                # the checkpoint. Skip them.
+                continue
+
+            if self.config.text_config.tie_word_embeddings and "lm_head.weight" in name:
+                continue
+
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not in name or "audio_tower" in name:
+                    continue
+                name_tmp = name.replace(weight_name, param_name)
+
+                # Skip loading extra bias for GPTQ models.
+                if name_tmp.endswith(".bias") and name_tmp not in params_dict:
+                    continue
+                param = params_dict[name_tmp]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                try:
+                    # Skip loading extra bias for GPTQ models.
+                    if name.endswith(".bias") and name not in params_dict:
+                        continue
+                    param = params_dict[name]
+                except KeyError:
+                    print(params_dict.keys())
+                    raise
+
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                weight_loader(param, loaded_weight)
+
+
+EntryClass = Qwen2AudioForConditionalGeneration