fix: use uncompiled feat_encoder for prefill to prevent CUDA Graph dynamic shape accumulation (#209)

app_local.py

@@ -0,0 +1,530 @@
+import os
+import sys
+import logging
+import numpy as np
+import torch
+import gradio as gr
+from typing import Optional, Tuple
+from funasr import AutoModel
+from pathlib import Path
+
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+if os.environ.get("HF_REPO_ID", "").strip() == "":
+    os.environ["HF_REPO_ID"] = "openbmb/VoxCPM2"
+
+import voxcpm
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s - %(levelname)s - %(message)s",
+    handlers=[logging.StreamHandler(sys.stdout)],
+)
+logger = logging.getLogger(__name__)
+
+# ---------- Inline i18n (en + zh-CN only) ----------
+
+_USAGE_INSTRUCTIONS_EN = (
+    "**VoxCPM2 — Three Modes of Speech Generation:**\n\n"
+    "🎨 **Voice Design** — Create a brand-new voice  \n"
+    "No reference audio required. Describe the desired voice characteristics "
+    "(gender, age, tone, emotion, pace …) in **Control Instruction**, and VoxCPM2 "
+    "will craft a unique voice from your description alone.\n\n"
+    "🎛️ **Controllable Cloning** — Clone a voice with optional style guidance  \n"
+    "Upload a reference audio clip, then use **Control Instruction** to steer "
+    "emotion, speaking pace, and overall style while preserving the original timbre.\n\n"
+    "🎙️ **Ultimate Cloning** — Reproduce every vocal nuance through audio continuation  \n"
+    "Turn on **Ultimate Cloning Mode** and provide (or auto-transcribe) the reference audio's transcript. "
+    "The model treats the reference clip as a spoken prefix and seamlessly **continues** from it, faithfully preserving every vocal detail."
+    "Note: This mode will disable Control Instruction."
+)
+
+_EXAMPLES_FOOTER_EN = (
+    "---\n"
+    "**💡 Voice Description Examples:**  \n"
+    "Try the following Control Instructions to explore different voices:  \n\n"
+    "**Example 1 — Gentle & Melancholic Girl**  \n"
+    '`Control Instruction`: *"A young girl with a soft, sweet voice. '
+    'Speaks slowly with a melancholic, slightly tsundere tone."*  \n'
+    '`Target Text`: *"I never asked you to stay… It\'s not like I care or anything. '
+    'But… why does it still hurt so much now that you\'re gone?"*  \n\n'
+    "**Example 2 — Laid-Back Surfer Dude**  \n"
+    '`Control Instruction`: *"Relaxed young male voice, slightly nasal, '
+    'lazy drawl, very casual and chill."*  \n'
+    '`Target Text`: *"Dude, did you see that set? The waves out there are totally gnarly today. '
+    "Just catching barrels all morning — it's like, totally righteous, you know what I mean?\"*"
+)
+
+_USAGE_INSTRUCTIONS_ZH = (
+    "**VoxCPM2 — 三种语音生成方式：**\n\n"
+    "🎨 **声音设计（Voice Design）**  \n"
+    "无需参考音频。在 **Control Instruction** 中描述目标音色特征"
+    "（性别、年龄、语气、情绪、语速等），VoxCPM2 即可为你从零创造独一无二的声音。\n\n"
+    "🎛️ **可控克隆（Controllable Cloning）**  \n"
+    "上传参考音频，同时可选地使用 **Control Instruction** 来指定情绪、语速、风格等表达方式，"
+    "在保留原始音色的基础上灵活控制说话风格。\n\n"
+    "🎙️ **极致克隆（Ultimate Cloning）**  \n"
+    "开启 **极致克隆模式** 并提供参考音频的文字内容（可自动识别）。"
+    "模型会将参考音频视为已说出的前文，以**音频续写**的方式完整还原参考音频中的所有声音细节。"
+    "注意：该模式与可控克隆模式互斥，将禁用Control Instruction。\n\n"
+)
+
+_EXAMPLES_FOOTER_ZH = (
+    "---\n"
+    "**💡 声音描述示例（中英文均可）：**  \n\n"
+    "**示例 1 — 深宫太后**  \n"
+    '`Control Instruction`: *"中老年女性，声音低沉阴冷，语速缓慢而有力，'
+    '字字深思熟虑，带有深不可测的城府与威慑感。"*  \n'
+    '`Target Text`: *"哀家在这深宫待了四十年，什么风浪没见过？你以为瞒得过哀家？"*  \n\n'
+    "**示例 2 — 暴躁驾校教练**  \n"
+    '`Control Instruction`: *"暴躁的中年男声，语速快，充满无奈和愤怒"*  \n'
+    '`Target Text`: *"踩离合！踩刹车啊！你往哪儿开呢？前面是树你看不见吗？'
+    '我教了你八百遍了，打死方向盘！你是不是想把车给我开到沟里去？"*  \n\n'
+    "---\n"
+    "**🗣️ 方言生成指南：**  \n"
+    "要生成地道的方言语音，请在 **Target Text** 中直接使用方言词汇和句式，"
+    "并在 **Control Instruction** 中描述方言特征。  \n\n"
+    "**示例 — 广东话**  \n"
+    '`Control Instruction`: *"粤语，中年男性，语气平淡"*  \n'
+    '✅ 正确（粤语表达）：*"伙計，唔該一個A餐，凍奶茶少甜！"*  \n'
+    '❌ 错误（普通话原文）：*"伙计，麻烦来一个A餐，冻奶茶少甜！"*  \n\n'
+    "**示例 — 河南话**  \n"
+    '`Control Instruction`: *"河南话，接地气的大叔"*  \n'
+    '✅ 正确（河南话表达）：*"恁这是弄啥嘞？晌午吃啥饭？"*  \n'
+    '❌ 错误（普通话原文）：*"你这是在干什么呢？中午吃什么饭？"*  \n\n'
+    "🤖 **小技巧：** 不知道方言怎么写？可以用豆包、DeepSeek、Kimi 等 AI 助手"
+    "将普通话翻译为方言文本，再粘贴到 Target Text 中即可。  \n\n"
+)
+
+_I18N_TRANSLATIONS = {
+    "en": {
+        "reference_audio_label": "🎤 Reference Audio (optional — upload for cloning)",
+        "show_prompt_text_label": "🎙️ Ultimate Cloning Mode (transcript-guided cloning)",
+        "show_prompt_text_info": "Auto-transcribes reference audio for every vocal nuance reproduced. Control Instruction will be disabled when active.",
+        "prompt_text_label": "Transcript of Reference Audio (auto-filled via ASR, editable)",
+        "prompt_text_placeholder": "The transcript of your reference audio will appear here …",
+        "control_label": "🎛️ Control Instruction (optional — supports Chinese & English)",
+        "control_placeholder": "e.g. A warm young woman / 年轻女性，温柔甜美 / Excited and fast-paced",
+        "target_text_label": "✍️ Target Text — the content to speak",
+        "generate_btn": "🔊 Generate Speech",
+        "generated_audio_label": "Generated Audio",
+        "advanced_settings_title": "⚙️ Advanced Settings",
+        "ref_denoise_label": "Reference audio enhancement",
+        "ref_denoise_info": "Apply ZipEnhancer denoising to the reference audio before cloning",
+        "normalize_label": "Text normalization",
+        "normalize_info": "Normalize numbers, dates, and abbreviations via wetext",
+        "cfg_label": "CFG (guidance scale)",
+        "cfg_info": "Higher → closer to the prompt / reference; lower → more creative variation",
+        "dit_steps_label": "LocDiT flow-matching steps",
+        "dit_steps_info": "LocDiT flow-matching steps — more steps → maybe better audio quality, but slower",
+        "usage_instructions": _USAGE_INSTRUCTIONS_EN,
+        "examples_footer": _EXAMPLES_FOOTER_EN,
+    },
+    "zh-CN": {
+        "reference_audio_label": "🎤 参考音频（可选 — 上传后用于克隆）",
+        "show_prompt_text_label": "🎙️ 极致克隆模式（基于文本引导的极致克隆）",
+        "show_prompt_text_info": "自动识别参考音频文本，完整还原音色、节奏、情感等全部声音细节。开启后 Control Instruction 将暂时禁用",
+        "prompt_text_label": "参考音频内容文本（ASR 自动填充，可手动编辑）",
+        "prompt_text_placeholder": "参考音频的文字内容将自动识别并显示在此处 …",
+        "control_label": "🎛️ Control Instruction（可选 — 支持中英文描述）",
+        "control_placeholder": "如：年轻女性，温柔甜美 / A warm young woman / 暴躁老哥，语速飞快",
+        "target_text_label": "✍️ Target Text — 要合成的目标文本",
+        "generate_btn": "🔊 开始生成",
+        "generated_audio_label": "生成结果",
+        "advanced_settings_title": "⚙️ 高级设置",
+        "ref_denoise_label": "参考音频降噪增强",
+        "ref_denoise_info": "克隆前使用 ZipEnhancer 对参考音频进行降噪处理",
+        "normalize_label": "文本规范化",
+        "normalize_info": "自动规范化数字、日期及缩写（基于 wetext）",
+        "cfg_label": "CFG（引导强度）",
+        "cfg_info": "数值越高 → 越贴合提示/参考音色；数值越低 → 生成风格更自由",
+        "dit_steps_label": "LocDiT 流匹配迭代步数",
+        "dit_steps_info": "LocDiT 流匹配生成迭代步数 — 步数越多 → 可能生成更好的音频质量，但速度变慢",
+        "usage_instructions": _USAGE_INSTRUCTIONS_ZH,
+        "examples_footer": _EXAMPLES_FOOTER_ZH,
+    },
+    "zh-Hans": None,  # alias, filled below
+    "zh": None,       # alias, filled below
+}
+_I18N_TRANSLATIONS["zh-Hans"] = _I18N_TRANSLATIONS["zh-CN"]
+_I18N_TRANSLATIONS["zh"] = _I18N_TRANSLATIONS["zh-CN"]
+
+for _d in _I18N_TRANSLATIONS.values():
+    if _d is not None:
+        for _k, _v in _I18N_TRANSLATIONS["en"].items():
+            _d.setdefault(_k, _v)
+
+I18N = gr.I18n(**_I18N_TRANSLATIONS)
+
+DEFAULT_TARGET_TEXT = (
+    "VoxCPM2 is a creative multilingual TTS model from ModelBest, "
+    "designed to generate highly realistic speech."
+)
+
+_CUSTOM_CSS = """
+.logo-container {
+    text-align: center;
+    margin: 0.5rem 0 1rem 0;
+}
+.logo-container img {
+    height: 80px;
+    width: auto;
+    max-width: 200px;
+    display: inline-block;
+}
+
+/* Toggle switch style */
+.switch-toggle {
+    padding: 8px 12px;
+    border-radius: 8px;
+    background: var(--block-background-fill);
+}
+.switch-toggle input[type="checkbox"] {
+    appearance: none;
+    -webkit-appearance: none;
+    width: 44px;
+    height: 24px;
+    background: #ccc;
+    border-radius: 12px;
+    position: relative;
+    cursor: pointer;
+    transition: background 0.3s ease;
+    flex-shrink: 0;
+}
+.switch-toggle input[type="checkbox"]::after {
+    content: "";
+    position: absolute;
+    top: 2px;
+    left: 2px;
+    width: 20px;
+    height: 20px;
+    background: white;
+    border-radius: 50%;
+    transition: transform 0.3s ease;
+    box-shadow: 0 1px 3px rgba(0,0,0,0.2);
+}
+.switch-toggle input[type="checkbox"]:checked {
+    background: var(--color-accent);
+}
+.switch-toggle input[type="checkbox"]:checked::after {
+    transform: translateX(20px);
+}
+"""
+
+_APP_THEME = gr.themes.Soft(
+    primary_hue="blue",
+    secondary_hue="gray",
+    neutral_hue="slate",
+    font=[gr.themes.GoogleFont("Inter"), "Arial", "sans-serif"],
+)
+
+
+# ---------- Model ----------
+
+class VoxCPMDemo:
+    def __init__(self, model_dir: Optional[str] = None) -> None:
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        logger.info(f"Running on device: {self.device}")
+
+        self.asr_model_id = "iic/SenseVoiceSmall"
+        self.asr_model: Optional[AutoModel] = AutoModel(
+            model=self.asr_model_id,
+            disable_update=True,
+            log_level="DEBUG",
+            device="cuda:0" if self.device == "cuda" else "cpu",
+        )
+
+        self.voxcpm_model: Optional[voxcpm.VoxCPM] = None
+        self.explicit_model_dir = model_dir
+
+    def _resolve_model_dir(self) -> str:
+        if self.explicit_model_dir and os.path.isdir(self.explicit_model_dir):
+            return self.explicit_model_dir
+        env_model_dir = os.environ.get("VOXCPM_MODEL_DIR", "").strip()
+        if env_model_dir and os.path.isdir(env_model_dir):
+            return env_model_dir
+        repo_id = os.environ.get("HF_REPO_ID", "").strip()
+        if len(repo_id) > 0:
+            target_dir = os.path.join("models", repo_id.replace("/", "__"))
+            if not os.path.isdir(target_dir):
+                try:
+                    from huggingface_hub import snapshot_download
+                    os.makedirs(target_dir, exist_ok=True)
+                    logger.info(f"Downloading model from HF repo '{repo_id}' to '{target_dir}' ...")
+                    snapshot_download(repo_id=repo_id, local_dir=target_dir, local_dir_use_symlinks=False)
+                except Exception as e:
+                    logger.warning(f"HF download failed: {e}. Falling back to 'models'.")
+                    return "models"
+            return target_dir
+        return "models"
+
+    def get_or_load_voxcpm(self) -> voxcpm.VoxCPM:
+        if self.voxcpm_model is not None:
+            return self.voxcpm_model
+        logger.info("Model not loaded, initializing...")
+        model_dir = self._resolve_model_dir()
+        logger.info(f"Using model dir: {model_dir}")
+        self.voxcpm_model = voxcpm.VoxCPM(voxcpm_model_path=model_dir, optimize=True)
+        logger.info("Model loaded successfully.")
+        return self.voxcpm_model
+
+    def prompt_wav_recognition(self, prompt_wav: Optional[str]) -> str:
+        if prompt_wav is None:
+            return ""
+        res = self.asr_model.generate(input=prompt_wav, language="auto", use_itn=True)
+        return res[0]["text"].split("|>")[-1]
+
+    def _build_generate_kwargs(
+        self,
+        *,
+        final_text: str,
+        audio_path: Optional[str],
+        prompt_text_clean: Optional[str],
+        cfg_value_input: float,
+        do_normalize: bool,
+        denoise: bool,
+        inference_timesteps: int = 10,
+    ) -> dict:
+        generate_kwargs = dict(
+            text=final_text,
+            reference_wav_path=audio_path,
+            cfg_value=float(cfg_value_input),
+            inference_timesteps=inference_timesteps,
+            normalize=do_normalize,
+            denoise=denoise,
+        )
+        if prompt_text_clean and audio_path:
+            generate_kwargs["prompt_wav_path"] = audio_path
+            generate_kwargs["prompt_text"] = prompt_text_clean
+        return generate_kwargs
+
+    def generate_tts_audio(
+        self,
+        text_input: str,
+        control_instruction: str = "",
+        reference_wav_path_input: Optional[str] = None,
+        prompt_text: str = "",
+        cfg_value_input: float = 2.0,
+        do_normalize: bool = True,
+        denoise: bool = True,
+        inference_timesteps: int = 10,
+    ) -> Tuple[int, np.ndarray]:
+        current_model = self.get_or_load_voxcpm()
+
+        text = (text_input or "").strip()
+        if len(text) == 0:
+            raise ValueError("Please input text to synthesize.")
+
+        control = (control_instruction or "").strip()
+        final_text = f"({control}){text}" if control else text
+
+        audio_path = reference_wav_path_input if reference_wav_path_input else None
+        prompt_text_clean = (prompt_text or "").strip() or None
+
+        if audio_path and prompt_text_clean:
+            logger.info(f"[Voice Cloning] prompt_wav + prompt_text + reference_wav")
+        elif audio_path:
+            logger.info(f"[Voice Control] reference_wav only")
+        else:
+            logger.info(f"[Voice Design] control: {control[:50] if control else 'None'}...")
+
+        logger.info(f"Generating audio for text: '{final_text[:80]}...'")
+        generate_kwargs = self._build_generate_kwargs(
+            final_text=final_text,
+            audio_path=audio_path,
+            prompt_text_clean=prompt_text_clean,
+            cfg_value_input=cfg_value_input,
+            do_normalize=do_normalize,
+            denoise=denoise,
+            inference_timesteps=inference_timesteps,
+        )
+        wav = current_model.generate(**generate_kwargs)
+        return (current_model.tts_model.sample_rate, wav)
+
+
+# ---------- UI ----------
+
+def create_demo_interface(demo: VoxCPMDemo):
+    gr.set_static_paths(paths=[Path.cwd().absolute() / "assets"])
+
+    def _generate(
+        text: str,
+        control_instruction: str,
+        ref_wav: Optional[str],
+        use_prompt_text: bool,
+        prompt_text_value: str,
+        cfg_value: float,
+        do_normalize: bool,
+        denoise: bool,
+        dit_steps: int,
+    ):
+        actual_prompt_text = prompt_text_value.strip() if use_prompt_text else ""
+        actual_control = "" if use_prompt_text else control_instruction
+        sr, wav_np = demo.generate_tts_audio(
+            text_input=text,
+            control_instruction=actual_control,
+            reference_wav_path_input=ref_wav,
+            prompt_text=actual_prompt_text,
+            cfg_value_input=cfg_value,
+            do_normalize=do_normalize,
+            denoise=denoise,
+            inference_timesteps=int(dit_steps),
+        )
+        return (sr, wav_np)
+
+    def _on_toggle_instant(checked):
+        """Instant UI toggle — no ASR, no blocking."""
+        if checked:
+            return (
+                gr.update(visible=True, value="", placeholder="Recognizing reference audio..."),
+                gr.update(visible=False),
+            )
+        return (
+            gr.update(visible=False),
+            gr.update(visible=True, interactive=True),
+        )
+
+    def _run_asr_if_needed(checked, audio_path):
+        """Run ASR after the UI has updated. Only when toggled ON."""
+        if not checked or not audio_path:
+            return gr.update()
+        try:
+            logger.info("Running ASR on reference audio...")
+            asr_text = demo.prompt_wav_recognition(audio_path)
+            logger.info(f"ASR result: {asr_text[:60]}...")
+            return gr.update(value=asr_text)
+        except Exception as e:
+            logger.warning(f"ASR recognition failed: {e}")
+            return gr.update(value="")
+
+    with gr.Blocks() as interface:
+        gr.HTML(
+            '<div class="logo-container">'
+            '<img src="/gradio_api/file=assets/voxcpm_logo.png" alt="VoxCPM Logo">'
+            "</div>"
+        )
+
+        gr.Markdown(I18N("usage_instructions"))
+
+        with gr.Row():
+            with gr.Column():
+                reference_wav = gr.Audio(
+                    sources=["upload", "microphone"],
+                    type="filepath",
+                    label=I18N("reference_audio_label"),
+                )
+                show_prompt_text = gr.Checkbox(
+                    value=False,
+                    label=I18N("show_prompt_text_label"),
+                    info=I18N("show_prompt_text_info"),
+                    elem_classes=["switch-toggle"],
+                )
+                prompt_text = gr.Textbox(
+                    value="",
+                    label=I18N("prompt_text_label"),
+                    placeholder=I18N("prompt_text_placeholder"),
+                    lines=2,
+                    visible=False,
+                )
+                control_instruction = gr.Textbox(
+                    value="",
+                    label=I18N("control_label"),
+                    placeholder=I18N("control_placeholder"),
+                    lines=2,
+                )
+                text = gr.Textbox(
+                    value=DEFAULT_TARGET_TEXT,
+                    label=I18N("target_text_label"),
+                    lines=3,
+                )
+
+                with gr.Accordion(I18N("advanced_settings_title"), open=False):
+                    DoDenoisePromptAudio = gr.Checkbox(
+                        value=False,
+                        label=I18N("ref_denoise_label"),
+                        elem_classes=["switch-toggle"],
+                        info=I18N("ref_denoise_info"),
+                    )
+                    DoNormalizeText = gr.Checkbox(
+                        value=False,
+                        label=I18N("normalize_label"),
+                        elem_classes=["switch-toggle"],
+                        info=I18N("normalize_info"),
+                    )
+                    cfg_value = gr.Slider(
+                        minimum=1.0,
+                        maximum=3.0,
+                        value=2.0,
+                        step=0.1,
+                        label=I18N("cfg_label"),
+                        info=I18N("cfg_info"),
+                    )
+                    dit_steps = gr.Slider(
+                        minimum=1,
+                        maximum=50,
+                        value=10,
+                        step=1,
+                        label=I18N("dit_steps_label"),
+                        info=I18N("dit_steps_info"),
+                    )
+
+                run_btn = gr.Button(I18N("generate_btn"), variant="primary", size="lg")
+
+            with gr.Column():
+                audio_output = gr.Audio(label=I18N("generated_audio_label"))
+                gr.Markdown(I18N("examples_footer"))
+
+        show_prompt_text.change(
+            fn=_on_toggle_instant,
+            inputs=[show_prompt_text],
+            outputs=[prompt_text, control_instruction],
+        ).then(
+            fn=_run_asr_if_needed,
+            inputs=[show_prompt_text, reference_wav],
+            outputs=[prompt_text],
+        )
+
+        run_btn.click(
+            fn=_generate,
+            inputs=[
+                text,
+                control_instruction,
+                reference_wav,
+                show_prompt_text,
+                prompt_text,
+                cfg_value,
+                DoNormalizeText,
+                DoDenoisePromptAudio,
+                dit_steps,
+            ],
+            outputs=[audio_output],
+            show_progress=True,
+            api_name="generate",
+        )
+
+    return interface
+
+def run_demo(
+    server_name: str = "0.0.0.0",
+    server_port: int = 8808,
+    show_error: bool = True,
+    model_dir: Optional[str] = None,
+):
+    demo = VoxCPMDemo(model_dir=model_dir)
+    interface = create_demo_interface(demo)
+    interface.queue(max_size=10, default_concurrency_limit=1).launch(
+        server_name=server_name,
+        server_port=server_port,
+        show_error=show_error,
+        i18n=I18N,
+        theme=_APP_THEME,
+        css=_CUSTOM_CSS,
+    )
+
+
+if __name__ == "__main__":
+    import argparse
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model-dir", type=str, default=None, help="Path to VoxCPM2 checkpoint directory")
+    parser.add_argument("--port", type=int, default=8808, help="Server port")
+    args = parser.parse_args()
+    run_demo(model_dir=args.model_dir, server_port=args.port)

src/voxcpm/model/voxcpm.py

@@ -227,6 +227,7 @@ class VoxCPMModel(nn.Module):
             self.residual_lm.forward_step = torch.compile(
                 self.residual_lm.forward_step, mode="reduce-overhead", fullgraph=True
             )
+            self._feat_encoder_raw = self.feat_encoder
             self.feat_encoder = torch.compile(self.feat_encoder, mode="reduce-overhead", fullgraph=True)
             self.feat_decoder.estimator = torch.compile(
                 self.feat_decoder.estimator, mode="reduce-overhead", fullgraph=True
@@ -755,7 +756,8 @@ class VoxCPMModel(nn.Module):
         """
         B, T, P, D = feat.shape
 
-        feat_embed = self.feat_encoder(feat)  # [b, t, h_feat]
+        prefill_encoder = getattr(self, "_feat_encoder_raw", self.feat_encoder)
+        feat_embed = prefill_encoder(feat)  # [b, t, h_feat]
         feat_embed = self.enc_to_lm_proj(feat_embed)
 
         if self.config.lm_config.use_mup:

src/voxcpm/model/voxcpm2.py

@@ -275,6 +275,7 @@ class VoxCPM2Model(nn.Module):
             self.residual_lm.forward_step = torch.compile(
                 self.residual_lm.forward_step, mode="reduce-overhead", fullgraph=True
             )
+            self._feat_encoder_raw = self.feat_encoder
             self.feat_encoder = torch.compile(self.feat_encoder, mode="reduce-overhead", fullgraph=True)
             self.feat_decoder.estimator = torch.compile(
                 self.feat_decoder.estimator, mode="reduce-overhead", fullgraph=True
@@ -997,7 +998,8 @@ class VoxCPM2Model(nn.Module):
         """
         B, T, P, D = feat.shape
 
-        feat_embed = self.feat_encoder(feat)  # [b, t, h_feat]
+        prefill_encoder = getattr(self, "_feat_encoder_raw", self.feat_encoder)
+        feat_embed = prefill_encoder(feat)  # [b, t, h_feat]
         feat_embed = self.enc_to_lm_proj(feat_embed)
 
         if self.config.lm_config.use_mup: