update ultravox model to support v0.5 release

Signed-off-by: Farzad Abdolhosseini <[email protected]>

vllm/model_executor/models/ultravox.py

@@ -18,7 +18,7 @@
 from vllm import envs
 from vllm.attention import AttentionMetadata
 from vllm.config import VllmConfig
-from vllm.model_executor.layers.activation import MulAndSilu, get_act_fn
+from vllm.model_executor.layers.activation import SiluAndMul, get_act_fn
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
 from vllm.model_executor.model_loader.loader import DefaultModelLoader
@@ -252,33 +252,50 @@ def forward(self, audio_embeds: torch.Tensor) -> torch.Tensor:
         return audio_embeds
 
 
+class FlippedSiluAndMul(SiluAndMul):
+    """Ultravox is trained with SwiGLU with flipped halves."""
+
+    def forward(self, x: torch.Tensor):
+        a, b = x.chunk(2, dim=-1)
+        flipped = torch.cat((b, a), dim=-1)
+        return super().forward(flipped)
+
+
 class UltravoxProjector(nn.Module):
 
     def __init__(self, config: UltravoxConfig):
         super().__init__()
         self.hidden_dim = config.hidden_size
         self._pad_and_stack = StackAudioFrames(config.stack_factor)
-        dim = config.audio_config.hidden_size * config.stack_factor
-        self.ln_pre = RMSNorm(dim)
-        self.linear_1 = nn.Linear(dim, self.hidden_dim, bias=False)
-        dim = self.hidden_dim
+        dim_in = config.audio_config.hidden_size * config.stack_factor
+        self.ln_pre = RMSNorm(dim_in)
+        self.linear_1 = nn.Linear(dim_in, self.hidden_dim, bias=False)
+        dim_mid = self.hidden_dim
 
         if config.projector_act == "swiglu":
-            self.act = MulAndSilu()
-            dim = dim // 2
+            self.act = FlippedSiluAndMul()
+            dim_mid = dim_mid // 2
         else:
             self.act = get_act_fn(config.projector_act)
 
-        self.linear_2 = nn.Linear(dim,
-                                  config.text_config.hidden_size,
-                                  bias=False)
-        self.ln_post = RMSNorm(config.text_config.hidden_size)
+        dim_out = config.text_config.hidden_size
+        self.linear_2 = nn.Linear(dim_mid, dim_out, bias=False)
+
+        # Ultravox v0.4.1 and below uses layer_norm after the second linear layer,
+        # while v0.5.0 and above uses layer_norm after the first linear layer.
+        if config.projector_ln_mid:
+            self.ln_mid: nn.Module = RMSNorm(dim_mid)
+            self.ln_post = nn.Identity()
+        else:
+            self.ln_mid = nn.Identity()
+            self.ln_post = RMSNorm(dim_out)
 
     def forward(self, audio_features: torch.Tensor) -> torch.Tensor:
         audio_features = self._pad_and_stack(audio_features)
         audio_features = self.ln_pre(audio_features)
         hidden_states = self.linear_1(audio_features)
         hidden_states = self.act(hidden_states)
+        hidden_states = self.ln_mid(hidden_states)
         hidden_states = self.linear_2(hidden_states)
         hidden_states = self.ln_post(hidden_states)
         return hidden_states

vllm/transformers_utils/configs/ultravox.py

@@ -37,6 +37,12 @@ class UltravoxConfig(transformers.PretrainedConfig):
             The LoRA configuration for finetuning the text model.
         audio_model_lora_config (`LoraConfigSimplified`, *optional*):
             The LoRA configuration for finetuning the audio model.
+        projector_ln_mid (`bool`, *optional*, defaults to `False`):
+            Whether to apply layer normalization at the middle of the
+            projector or at the end. Versions v0.4.1 and below
+            use `False`, but v0.5 and above use `True`.
+        audio_latency_block_size (`int`, *optional*, defaults to `None`):
+            The latency block size for simulating audio streaming.
     """
 
     model_type = "ultravox"
@@ -56,6 +62,8 @@ def __init__(
         projector_act: str = "swiglu",
         text_model_lora_config: Optional[Dict[str, Any]] = None,
         audio_model_lora_config: Optional[Dict[str, Any]] = None,
+        projector_ln_mid: bool = False,
+        audio_latency_block_size: Optional[int] = None,
         **kwargs,
     ):
         self.ignore_index = ignore_index
@@ -68,6 +76,8 @@ def __init__(
         self.stack_factor = stack_factor
         self.norm_init = norm_init
         self.projector_act = projector_act
+        self.projector_ln_mid = projector_ln_mid
+        self.audio_latency_block_size = audio_latency_block_size
 
         if text_model_id is not None:
             # Avoid circular import