[Refactor] GLM-ASR Modeling (#31779)

Signed-off-by: JaredforReal <w13431838023@gmail.com>
Co-authored-by: Isotr0py <mozf@mail2.sysu.edu.cn>

vllm/model_executor/models/glmasr.py

@@ -8,18 +8,22 @@ import numpy as np
 import torch
 import torch.nn as nn
 from transformers import BatchFeature
-from transformers.models.glmasr import GlmAsrConfig, GlmAsrEncoder, GlmAsrProcessor
+from transformers.models.glmasr import GlmAsrConfig, GlmAsrProcessor
 from transformers.models.whisper import WhisperFeatureExtractor
 
+from vllm.attention.layers.mm_encoder_attention import MMEncoderAttention
 from vllm.config import ModelConfig, SpeechToTextConfig, VllmConfig
 from vllm.config.multimodal import BaseDummyOptions
+from vllm.distributed.parallel_state import get_tensor_model_parallel_world_size
 from vllm.inputs.data import PromptType
 from vllm.model_executor.layers.activation import get_act_fn
 from vllm.model_executor.layers.linear import (
     ColumnParallelLinear,
+    QKVParallelLinear,
     RowParallelLinear,
 )
 from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding.common import ApplyRotaryEmb
 from vllm.model_executor.models.module_mapping import MultiModelKeys
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.multimodal.inputs import (
@@ -35,6 +39,8 @@ from vllm.multimodal.parse import (
     MultiModalDataParser,
 )
 from vllm.multimodal.processing import (
+    BaseMultiModalProcessor,
+    BaseProcessingInfo,
     PromptReplacement,
     PromptUpdate,
     PromptUpdateDetails,
@@ -45,21 +51,12 @@ from vllm.tokenizers import cached_tokenizer_from_config
 from vllm.transformers_utils.processor import cached_processor_from_config
 from vllm.utils.tensor_schema import TensorSchema, TensorShape
 
-from .audioflamingo3 import (
-    AudioFlamingo3MultiModalDataParser,
-    AudioFlamingo3MultiModalProcessor,
-    AudioFlamingo3ProcessingInfo,
-)
-from .audioflamingo3 import (
-    _audioflamingo3_field_config as _glmasr_field_config,
-)
 from .glmasr_utils import (
     DEFAULT_CONV_PARAMS,
     DEFAULT_MAX_AUDIO_LEN_S,
     DEFAULT_MERGE_FACTOR,
     _flatten_audio_features_by_length,
     _get_audio_output_lengths_for_tower,
-    _get_num_features_for_item,
     _group_audio_embeddings,
     _normalize_chunk_counts,
 )
@@ -74,6 +71,460 @@ from .utils import AutoWeightsLoader, init_vllm_registered_model, maybe_prefix
 from .whisper import ISO639_1_SUPPORTED_LANGS
 
 
+class GlmAsrEncoderRotaryEmbedding(nn.Module):
+    """
+    Rotary Position Embedding for GLM-ASR encoder.
+
+    Computes rotary position embeddings on-demand for efficiency.
+    Only caches inv_freq as a buffer; cos/sin are computed during forward
+    to avoid wasted computation during initialization and ensure correct
+    device placement.
+    """
+
+    def __init__(self, config) -> None:
+        super().__init__()
+
+        # Compute inverse frequencies following transformers implementation
+        head_dim = getattr(
+            config, "head_dim", config.hidden_size // config.num_attention_heads
+        )
+
+        # Handle rope_parameters if present (for compatibility with transformers config)
+        if hasattr(config, "rope_parameters") and config.rope_parameters:
+            base = config.rope_parameters.get("rope_theta", 10000.0)
+            partial_rotary_factor = config.rope_parameters.get(
+                "partial_rotary_factor", 1.0
+            )
+            dim = int(head_dim * partial_rotary_factor)
+            self.attention_scaling = config.rope_parameters.get(
+                "attention_scaling", 1.0
+            )
+        else:
+            base = getattr(config, "rope_theta", 10000.0)
+            dim = head_dim
+            self.attention_scaling = 1.0
+
+        self.dim = dim
+        self.head_dim = head_dim
+
+        # Only cache inv_freq; cos/sin computed on-demand in correct device
+        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float) / dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+    def forward(self, seq_len: int) -> torch.Tensor:
+        """
+        Compute rotary position frequencies for given sequence length.
+
+        Args:
+            seq_len: The sequence length to compute embeddings for.
+
+        Returns:
+            Frequency tensor with shape [seq_len, dim/2]. Use .cos() and
+            .sin() to get the rotary embedding components.
+        """
+        # Compute on the same device as inv_freq (automatically correct after .to())
+        seq = torch.arange(
+            seq_len, device=self.inv_freq.device, dtype=self.inv_freq.dtype
+        )
+        freqs = torch.outer(seq, self.inv_freq)
+        return freqs * self.attention_scaling
+
+
+class GlmAsrEncoderAttention(nn.Module):
+    """
+    Optimized Multi-headed Grouped Query Attention for GLM-ASR encoder.
+
+    Uses vLLM's QKVParallelLinear for fused projections, ApplyRotaryEmb for
+    rotary position embeddings, and MMEncoderAttention for hardware-optimized
+    attention computation with automatic backend selection.
+    """
+
+    def __init__(
+        self,
+        config,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.num_kv_heads = getattr(
+            config, "num_key_value_heads", config.num_attention_heads
+        )
+        self.head_dim = self.hidden_size // self.num_heads
+
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.num_heads_per_rank = self.num_heads // self.tp_size
+        self.num_kv_heads_per_rank = max(1, self.num_kv_heads // self.tp_size)
+
+        # Use QKVParallelLinear for fused QKV projection
+        # Note: GLM-ASR uses bias on Q and V, but not K
+        # For simplicity with QKVParallelLinear, we use bias=True for all
+        self.qkv_proj = QKVParallelLinear(
+            self.hidden_size,
+            self.head_dim,
+            self.num_heads,
+            self.num_kv_heads,
+            bias=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
+        )
+
+        self.o_proj = RowParallelLinear(
+            self.hidden_size,
+            self.hidden_size,
+            bias=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.o_proj",
+        )
+
+        # Use vLLM's ApplyRotaryEmb CustomOp
+        # enforce_enable=True ensures the op is always enabled (important for ViT)
+        self.apply_rotary_emb = ApplyRotaryEmb(enforce_enable=True)
+
+        # Use vLLM's MMEncoderAttention for hardware-optimized attention
+        # Automatically selects Flash Attention, SDPA, or Pallas based on device
+        self.attn = MMEncoderAttention(
+            num_heads=self.num_heads_per_rank,
+            head_size=self.head_dim,
+            num_kv_heads=self.num_kv_heads_per_rank,
+            prefix=f"{prefix}.attn",
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        rotary_pos_emb_cos: torch.Tensor,
+        rotary_pos_emb_sin: torch.Tensor,
+    ) -> torch.Tensor:
+        """
+        Args:
+            hidden_states: [batch_size, seq_len, hidden_size]
+            rotary_pos_emb_cos: [seq_len, rotary_dim/2] - cosine of rotary embeddings
+            rotary_pos_emb_sin: [seq_len, rotary_dim/2] - sine of rotary embeddings
+
+        Returns:
+            [batch_size, seq_len, hidden_size]
+        """
+        batch_size, seq_len, _ = hidden_states.shape
+
+        # QKV projection - fused for efficiency
+        qkv, _ = self.qkv_proj(hidden_states)
+
+        # Split into q, k, v
+        q_size = self.num_heads_per_rank * self.head_dim
+        kv_size = self.num_kv_heads_per_rank * self.head_dim
+        q, k, v = qkv.split([q_size, kv_size, kv_size], dim=-1)
+
+        # Reshape to [batch, seq, num_heads, head_dim] for ApplyRotaryEmb
+        q = q.view(batch_size, seq_len, self.num_heads_per_rank, self.head_dim)
+        k = k.view(batch_size, seq_len, self.num_kv_heads_per_rank, self.head_dim)
+        v = v.view(batch_size, seq_len, self.num_kv_heads_per_rank, self.head_dim)
+
+        # Apply rotary position embeddings using vLLM's ApplyRotaryEmb
+        # ApplyRotaryEmb expects x: [batch, seq, heads, head_dim]
+        # cos/sin: [seq_len, rotary_dim/2]
+        q = self.apply_rotary_emb(q, rotary_pos_emb_cos, rotary_pos_emb_sin)
+        k = self.apply_rotary_emb(k, rotary_pos_emb_cos, rotary_pos_emb_sin)
+
+        # MMEncoderAttention expects [batch, seq, num_heads, head_dim]
+        # It handles GQA internally via repeat_interleave
+        attn_output = self.attn(q, k, v)
+
+        # Reshape back to [batch, seq, hidden_size]
+        attn_output = attn_output.view(batch_size, seq_len, -1)
+
+        # Output projection
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class GlmAsrEncoderMLP(nn.Module):
+    """
+    Optimized MLP for GLM-ASR encoder.
+    Uses vLLM's parallel linear layers for better performance.
+    """
+
+    def __init__(
+        self,
+        config,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+
+        self.fc1 = ColumnParallelLinear(
+            self.hidden_size,
+            self.intermediate_size,
+            bias=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.fc1",
+        )
+
+        self.act_fn = get_act_fn(config.hidden_act)
+
+        self.fc2 = RowParallelLinear(
+            self.intermediate_size,
+            self.hidden_size,
+            bias=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.fc2",
+        )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states, _ = self.fc1(hidden_states)
+        hidden_states = self.act_fn(hidden_states)
+        hidden_states, _ = self.fc2(hidden_states)
+        return hidden_states
+
+
+class GlmAsrEncoderLayer(nn.Module):
+    """
+    Optimized Transformer encoder layer for GLM-ASR.
+    Combines attention and MLP with residual connections and layer norms.
+    """
+
+    def __init__(
+        self,
+        config,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+
+        self.self_attn = GlmAsrEncoderAttention(
+            config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.self_attn",
+        )
+
+        self.mlp = GlmAsrEncoderMLP(
+            config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.mlp",
+        )
+
+        layer_norm_eps = getattr(config, "layer_norm_eps", 1e-5)
+        self.input_layernorm = nn.LayerNorm(self.hidden_size, eps=layer_norm_eps)
+        self.post_attention_layernorm = nn.LayerNorm(
+            self.hidden_size, eps=layer_norm_eps
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        rotary_pos_emb_cos: torch.Tensor,
+        rotary_pos_emb_sin: torch.Tensor,
+    ) -> torch.Tensor:
+        """
+        Args:
+            hidden_states: [batch_size, seq_len, hidden_size]
+            rotary_pos_emb_cos: [seq_len, rotary_dim/2] - cosine of rotary embeddings
+            rotary_pos_emb_sin: [seq_len, rotary_dim/2] - sine of rotary embeddings
+
+        Returns:
+            [batch_size, seq_len, hidden_size]
+        """
+        # Self-attention with residual
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        hidden_states = self.self_attn(
+            hidden_states=hidden_states,
+            rotary_pos_emb_cos=rotary_pos_emb_cos,
+            rotary_pos_emb_sin=rotary_pos_emb_sin,
+        )
+        hidden_states = residual + hidden_states
+
+        # MLP with residual
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        return hidden_states
+
+
+class _GlmAsrEncoderOutput:
+    """
+    Simple output container compatible with transformers' BaseModelOutput.
+
+    This lightweight container holds the encoder output and is compatible
+    with the transformers library's output format while being more efficient
+    than a full dataclass.
+
+    Attributes:
+        last_hidden_state: Final layer hidden states from the encoder.
+            Shape: [batch_size, seq_len, hidden_size]
+    """
+
+    __slots__ = ("last_hidden_state",)
+
+    def __init__(self, last_hidden_state: torch.Tensor):
+        self.last_hidden_state = last_hidden_state
+
+
+class GlmAsrEncoder(nn.Module):
+    """
+    Optimized GLM-ASR Audio Encoder with vLLM native implementation.
+
+    This encoder processes audio features through convolutional layers
+    followed by transformer layers with rotary position embeddings.
+    Optimized for performance with:
+    - QKVParallelLinear for fused attention projections
+    - Tensor parallelism support via ColumnParallelLinear/RowParallelLinear
+    - Quantization support
+    - Flash Attention (SDPA)
+    """
+
+    # Mapping for weight loading: transformers uses separate q/k/v, we use fused qkv
+    packed_modules_mapping = {
+        "qkv_proj": ["q_proj", "k_proj", "v_proj"],
+    }
+
+    def __init__(
+        self,
+        config,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.config = config
+
+        # Convolutional feature extraction layers
+        self.conv1 = nn.Conv1d(
+            config.num_mel_bins,
+            config.hidden_size,
+            kernel_size=3,
+            padding=1,
+        )
+        self.conv2 = nn.Conv1d(
+            config.hidden_size,
+            config.hidden_size,
+            kernel_size=3,
+            stride=2,
+            padding=1,
+        )
+
+        # Transformer encoder layers
+        self.layers = nn.ModuleList(
+            [
+                GlmAsrEncoderLayer(
+                    config,
+                    quant_config=quant_config,
+                    prefix=f"{prefix}.layers.{layer_idx}",
+                )
+                for layer_idx in range(config.num_hidden_layers)
+            ]
+        )
+
+        # Final layer norm
+        layer_norm_eps = getattr(config, "layer_norm_eps", 1e-5)
+        self.norm = nn.LayerNorm(config.hidden_size, eps=layer_norm_eps)
+
+        # Rotary position embeddings
+        self.rotary_emb = GlmAsrEncoderRotaryEmbedding(config)
+
+    def _get_feat_extract_output_lengths(
+        self, input_lengths: torch.Tensor
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """
+        Compute the output length after convolutions.
+
+        Args:
+            input_lengths: Input sequence lengths [batch_size]
+
+        Returns:
+            Tuple of (output after conv1, output after conv2)
+        """
+        # Conv1: kernel=3, stride=1, padding=1
+        output_lengths_conv1 = (input_lengths + 2 * 1 - 3) // 1 + 1
+
+        # Conv2: kernel=3, stride=2, padding=1
+        output_lengths_conv2 = (output_lengths_conv1 + 2 * 1 - 3) // 2 + 1
+
+        return output_lengths_conv1, output_lengths_conv2
+
+    def forward(self, input_features: torch.Tensor) -> _GlmAsrEncoderOutput:
+        """
+        Forward pass through the encoder.
+
+        Args:
+            input_features: [batch_size, num_mel_bins, seq_len]
+
+        Returns:
+            _GlmAsrEncoderOutput: Object with .last_hidden_state attribute \
+                containing [batch_size, seq_len', hidden_size] where seq_len' \
+                is the sequence length after convolutions
+        """
+        # Apply convolutional layers with GELU activation
+        hidden_states = torch.nn.functional.gelu(self.conv1(input_features))
+        hidden_states = torch.nn.functional.gelu(self.conv2(hidden_states))
+
+        # Transpose to [batch_size, seq_len, hidden_size]
+        hidden_states = hidden_states.transpose(1, 2)
+        output_seq_len = hidden_states.shape[1]
+
+        # Compute rotary position embeddings on-demand
+        rotary_pos_emb = self.rotary_emb(output_seq_len)
+        rotary_pos_emb_cos = rotary_pos_emb.cos().to(dtype=hidden_states.dtype)
+        rotary_pos_emb_sin = rotary_pos_emb.sin().to(dtype=hidden_states.dtype)
+
+        # Apply transformer layers
+        for encoder_layer in self.layers:
+            hidden_states = encoder_layer(
+                hidden_states, rotary_pos_emb_cos, rotary_pos_emb_sin
+            )
+
+        # Final layer norm
+        hidden_states = self.norm(hidden_states)
+
+        # Return in a format compatible with transformers' BaseModelOutput
+        return _GlmAsrEncoderOutput(last_hidden_state=hidden_states)
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        """Custom weight loading to handle q_proj/k_proj/v_proj -> qkv_proj mapping."""
+        from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+        ]
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+
+        for name, loaded_weight in weights:
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                # Default weight loading for non-stacked params
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                if name not in params_dict:
+                    continue
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+
 class GlmAsrFeatureInputs(TensorSchema):
     """
     Dimensions:
@@ -117,6 +568,19 @@ GlmAsrInputs: TypeAlias = GlmAsrFeatureInputs | GlmAsrEmbeddingInputs
 
 
 class GlmAsrMultiModalProjector(nn.Module):
+    """
+    Projects audio encoder outputs to language model hidden space.
+
+    This projector uses a two-layer MLP to map audio features from the
+    encoder's intermediate size to the language model's hidden size.
+    Uses vLLM's parallel linear layers for tensor parallelism support.
+
+    Architecture:
+        - Linear layer: intermediate_size -> hidden_size * 2
+        - Activation function (e.g., GELU)
+        - Linear layer: hidden_size * 2 -> hidden_size
+    """
+
     def __init__(
         self,
         config: GlmAsrConfig,
@@ -145,7 +609,14 @@ class GlmAsrMultiModalProjector(nn.Module):
         return hidden_states
 
 
-class GlmAsrProcessingInfo(AudioFlamingo3ProcessingInfo):
+class GlmAsrProcessingInfo(BaseProcessingInfo):
+    """
+    Processing information provider for GLM-ASR model.
+
+    Provides access to model configuration, processor, and feature extractor
+    needed for audio preprocessing and multimodal integration.
+    """
+
     def get_hf_config(self) -> GlmAsrConfig:
         return self.ctx.get_hf_config(GlmAsrConfig)
 
@@ -153,13 +624,21 @@ class GlmAsrProcessingInfo(AudioFlamingo3ProcessingInfo):
         return self.ctx.get_hf_processor(GlmAsrProcessor, **kwargs)
 
     def get_feature_extractor(self, **kwargs: object) -> WhisperFeatureExtractor:
-        # Reuse parent implementation, but add type annotation and assertion
-        feature_extractor = super().get_feature_extractor(**kwargs)
-        assert isinstance(feature_extractor, WhisperFeatureExtractor)
-        return feature_extractor
+        return self.get_hf_processor(**kwargs).feature_extractor
+
+    def get_supported_mm_limits(self) -> Mapping[str, int | None]:
+        return {"audio": None}
 
 
 class GlmAsrDummyInputsBuilder(BaseDummyInputsBuilder[GlmAsrProcessingInfo]):
+    """
+    Builder for dummy inputs used in profiling and testing.
+
+    Generates dummy text prompts and audio data that match the expected
+    format for GLM-ASR model inputs. Used for memory profiling and
+    performance benchmarking.
+    """
+
     def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
         num_audios = mm_counts.get("audio", 0)
         hf_processor = self.info.get_hf_processor()
@@ -188,7 +667,51 @@ class GlmAsrDummyInputsBuilder(BaseDummyInputsBuilder[GlmAsrProcessingInfo]):
         }
 
 
-class GlmAsrMultiModalDataParser(AudioFlamingo3MultiModalDataParser):
+def _glmasr_field_config(
+    hf_inputs: Mapping[str, torch.Tensor],
+) -> dict[str, MultiModalFieldConfig]:
+    """
+    Configure multimodal field batching strategy for GLM-ASR.
+
+    Determines how to batch audio inputs based on whether chunking is used.
+    When chunk_counts is present, features are flattened across chunks;
+    otherwise, they are batched normally.
+
+    Args:
+        hf_inputs: Dictionary of preprocessed inputs from HuggingFace processor.
+
+    Returns:
+        Dictionary mapping field names to MultiModalFieldConfig objects \
+            that specify batching behavior.
+    """
+    chunk_counts = hf_inputs.get("chunk_counts")
+    if chunk_counts is not None:
+        return dict(
+            audio_embeds=MultiModalFieldConfig.batched("audio"),
+            input_features=MultiModalFieldConfig.flat_from_sizes(
+                "audio", chunk_counts, dim=0
+            ),
+            feature_attention_mask=MultiModalFieldConfig.flat_from_sizes(
+                "audio", chunk_counts, dim=0
+            ),
+            chunk_counts=MultiModalFieldConfig.batched("audio"),
+        )
+    return dict(
+        audio_embeds=MultiModalFieldConfig.batched("audio"),
+        input_features=MultiModalFieldConfig.batched("audio"),
+        feature_attention_mask=MultiModalFieldConfig.batched("audio"),
+        chunk_counts=MultiModalFieldConfig.batched("audio"),
+    )
+
+
+class GlmAsrMultiModalDataParser(MultiModalDataParser):
+    """
+    Custom parser for GLM-ASR multimodal data.
+
+    Extends the base parser to handle GLM-ASR specific audio data formats,
+    including both pre-computed audio embeddings and raw audio features.
+    """
+
     def _parse_audio_data(
         self,
         data: dict[str, torch.Tensor] | ModalityData[Any],
@@ -203,7 +726,12 @@ class GlmAsrMultiModalDataParser(AudioFlamingo3MultiModalDataParser):
         return super()._parse_audio_data(data)
 
 
-class GlmAsrMultiModalProcessor(AudioFlamingo3MultiModalProcessor):
+class GlmAsrMultiModalProcessor(BaseMultiModalProcessor["GlmAsrProcessingInfo"]):
+    """
+    GLM-ASR processor that inherits directly from BaseMultiModalProcessor
+    for better performance and cleaner implementation.
+    """
+
     def _get_data_parser(self) -> MultiModalDataParser:
         feature_extractor = self.info.get_feature_extractor()
         return GlmAsrMultiModalDataParser(target_sr=feature_extractor.sampling_rate)
@@ -214,7 +742,6 @@ class GlmAsrMultiModalProcessor(AudioFlamingo3MultiModalProcessor):
         feature_extractor: WhisperFeatureExtractor,
         processor: GlmAsrProcessor,
     ) -> list[int]:
-        """Calculate chunk counts for each audio."""
         sampling_rate = feature_extractor.sampling_rate
         chunk_length = feature_extractor.chunk_length
         max_audio_len = getattr(processor, "max_audio_len", DEFAULT_MAX_AUDIO_LEN_S)
@@ -248,10 +775,14 @@ class GlmAsrMultiModalProcessor(AudioFlamingo3MultiModalProcessor):
             prompt_ids = self._apply_hf_processor_tokens_only(prompt_ids)
             return BatchFeature(dict(input_ids=[prompt_ids]), tensor_type="pt")
 
-        # Get processor for chunk counts calculation
-        processor = self.info.get_hf_processor(**mm_kwargs)
+        # Handle sampling_rate
+        feature_extractor = self.info.get_feature_extractor(**mm_kwargs)
+        mm_kwargs = dict(
+            **mm_kwargs,
+            sampling_rate=feature_extractor.sampling_rate,
+        )
 
-        # Call parent method (it will handle sampling_rate)
+        # Call parent method
         outputs = super()._call_hf_processor(
             prompt=prompt,
             mm_data=mm_data,
@@ -259,9 +790,24 @@ class GlmAsrMultiModalProcessor(AudioFlamingo3MultiModalProcessor):
             tok_kwargs=tok_kwargs,
         )
 
-        # Postprocess: rename mask and add chunk counts.
-        if "input_features_mask" in outputs:
-            outputs["feature_attention_mask"] = outputs.pop("input_features_mask")
+        # Postprocess: rename mask and add chunk counts
+        # Handle different key names from different transformers versions
+        if "input_feature_mask" in outputs:
+            outputs["feature_attention_mask"] = outputs.pop("input_feature_mask")
+        elif "feature_attention_mask" not in outputs and "input_features" in outputs:
+            # If no mask is provided, create one from input_features
+            input_features = outputs["input_features"]
+            if isinstance(input_features, torch.Tensor):
+                # Create a mask of all ones matching the sequence length
+                mask = torch.ones(
+                    input_features.shape[0],
+                    input_features.shape[-1],
+                    dtype=torch.long,
+                )
+                outputs["feature_attention_mask"] = mask
+
+        # Get processor for chunk counts calculation
+        processor = self.info.get_hf_processor(**mm_kwargs)
 
         # Override chunk counts calculation with GLM-ASR specific logic
         chunk_counts = self._calculate_chunk_counts(
@@ -295,20 +841,56 @@ class GlmAsrMultiModalProcessor(AudioFlamingo3MultiModalProcessor):
             audio_token_id = processor.audio_token_id
 
         merge_factor = getattr(config, "merge_factor", DEFAULT_MERGE_FACTOR)
+        conv_params = getattr(config, "conv_params", DEFAULT_CONV_PARAMS)
         out_mm_data = out_mm_kwargs.get_data()
         feature_attention_mask = out_mm_data.get("feature_attention_mask")
         chunk_counts = out_mm_data.get("chunk_counts")
 
+        # Pre-compute audio output lengths if feature_attention_mask is available
+        audio_output_lengths: list[int] = []
+        if feature_attention_mask is not None:
+            # Compute output lengths for all audio items
+            from .glmasr_utils import (
+                _as_list_chunk_counts,
+                _get_audio_output_lengths_from_mask,
+            )
+
+            if chunk_counts is not None:
+                start_idx = 0
+                for count in _as_list_chunk_counts(chunk_counts):
+                    end_idx = start_idx + count
+                    mask = feature_attention_mask[start_idx:end_idx]
+                    if isinstance(mask, list):
+                        mask = torch.stack(mask)
+
+                    lengths = _get_audio_output_lengths_from_mask(
+                        mask, merge_factor, conv_params
+                    )
+                    audio_output_lengths.append(int(lengths.sum().item()))
+                    start_idx = end_idx
+            else:
+                # Single chunk per audio
+                for idx in range(len(feature_attention_mask)):
+                    mask = feature_attention_mask[idx : idx + 1]
+                    if isinstance(mask, list):
+                        mask = torch.tensor(mask).unsqueeze(0)
+                    lengths = _get_audio_output_lengths_from_mask(
+                        mask, merge_factor, conv_params
+                    )
+                    audio_output_lengths.append(int(lengths.sum().item()))
+
         def get_replacement_glmasr(item_idx: int):
-            conv_params = getattr(config, "conv_params", DEFAULT_CONV_PARAMS)
+            # Use pre-computed lengths if available, otherwise fall back to audio_embeds
+            if audio_output_lengths:
+                num_features = audio_output_lengths[item_idx]
+            else:
                 audio_embeds = out_mm_data.get("audio_embeds")
-            num_features = _get_num_features_for_item(
-                feature_attention_mask,
-                chunk_counts,
-                item_idx,
-                audio_embeds,
-                merge_factor,
-                conv_params,
+                if audio_embeds is not None:
+                    embed = audio_embeds[item_idx]
+                    num_features = embed.shape[0]
+                else:
+                    raise ValueError(
+                        "Either feature_attention_mask or audio_embeds must be provided"
                     )
 
             if num_features == 0:
@@ -352,7 +934,12 @@ class GlmAsrForConditionalGeneration(
         self.config = config
         self.multimodal_config = multimodal_config
 
-        self.audio_tower = GlmAsrEncoder(config.audio_config)
+        # Use optimized vLLM native encoder
+        self.audio_tower = GlmAsrEncoder(
+            config.audio_config,
+            quant_config=quant_config,
+            prefix=maybe_prefix(prefix, "audio_tower"),
+        )
         self.multi_modal_projector = GlmAsrMultiModalProjector(
             config,
             quant_config=quant_config,
@@ -419,12 +1006,31 @@ class GlmAsrForConditionalGeneration(
             audio_input.get("chunk_counts"), num_chunks=num_chunks
         )
 
+        # Convert input_features to model dtype (e.g., bfloat16) to match model weights
+        input_features = input_features.to(dtype=self.audio_tower.conv1.weight.dtype)
+
+        # audio_tower returns [batch_size, seq_len, hidden_size] where hidden_size=1280
         audio_hidden_states = self.audio_tower(input_features).last_hidden_state
+
+        # GLM-ASR merges consecutive frames: 4 frames with hidden_size=1280
+        # -> 1 frame with intermediate_size=5120
+        hidden_size = self.config.audio_config.hidden_size
+        intermediate_size = self.config.audio_config.intermediate_size
+        merge_ratio = intermediate_size // hidden_size
+
+        # Truncate sequence length to be divisible by merge_ratio
+        seq_len = audio_hidden_states.shape[1]
+        seq_len_truncated = (seq_len // merge_ratio) * merge_ratio
+        if seq_len_truncated < seq_len:
+            audio_hidden_states = audio_hidden_states[:, :seq_len_truncated, :]
+
+        # Reshape to merge consecutive frames
         audio_hidden_states = audio_hidden_states.reshape(
             num_chunks,
             -1,
-            self.config.audio_config.intermediate_size,
+            intermediate_size,
         )
+
         audio_features = self.multi_modal_projector(audio_hidden_states)
 
         merge_factor = getattr(self.config, "merge_factor", DEFAULT_MERGE_FACTOR)
@@ -453,7 +1059,9 @@ class GlmAsrForConditionalGeneration(
         audio_input = self._parse_and_validate_audio_input(**kwargs)
         if audio_input is None:
             return []
+
         masked_audio_features = self._process_audio_input(audio_input)
+
         return masked_audio_features
 
     def forward(

vllm/model_executor/models/glmasr_utils.py

@@ -71,15 +71,38 @@ def _get_audio_output_lengths_for_tower(
     merge_factor: int,
     conv_params: list[tuple[int, int, int]],
 ) -> torch.Tensor:
+    """
+    Calculate the output lengths after audio processing.
+
+    The output length accounts for:
+    1. Convolution layers (downsampling)
+    2. Merge factor (further downsampling during projection)
+
+    Args:
+        audio_tower: The audio encoder module
+        audio_lengths: Input feature lengths [batch_size]
+        merge_factor: Factor for merging adjacent features
+        conv_params: List of (padding, kernel_size, stride) for each conv layer
+
+    Returns:
+        Output lengths after all processing [batch_size]
+    """
+    # First, calculate the output length after convolutions
     if hasattr(audio_tower, "_get_feat_extract_output_lengths"):
-        _, audio_output_lengths = audio_tower._get_feat_extract_output_lengths(
+        _, conv_output_lengths = audio_tower._get_feat_extract_output_lengths(
             audio_lengths
         )
-        return audio_output_lengths
-    return _get_audio_output_lengths_from_lengths(
-        audio_lengths, merge_factor, conv_params
+    else:
+        conv_output_lengths = audio_lengths
+        for padding, kernel_size, stride in conv_params:
+            conv_output_lengths = _calculate_conv_output_length(
+                conv_output_lengths, padding, kernel_size, stride
             )
 
+    # Then, apply merge_factor to get final output length
+    # Formula: (conv_output_lengths - merge_factor) // merge_factor + 1
+    return (conv_output_lengths - merge_factor) // merge_factor + 1
+
 
 def _flatten_audio_features_by_length(
     audio_features: torch.Tensor,