[Model] [Quantization] Support quantization for Gemma3n (#21974)

Signed-off-by: Kyle Sayers <kylesayrs@gmail.com>

Author: kylesayrs

vllm/model_executor/models/gemma3n.py

@@ -46,6 +46,7 @@
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.sequence import IntermediateTensors
 
+from .interfaces import SupportsQuant
 from .utils import (AutoWeightsLoader, extract_layer_index,
                     is_pp_missing_parameter, make_layers, maybe_prefix)
 
@@ -68,6 +69,7 @@ def __init__(
         altup_num_inputs: int,
         altup_coef_clip: float,
         altup_active_idx: int,
+        quant_config: QuantizationConfig,
         prefix: str,
     ):
         super().__init__()
@@ -80,20 +82,23 @@ def __init__(
             altup_num_inputs,
             altup_num_inputs,
             bias=False,
+            quant_config=quant_config,
             prefix=f"{prefix}.correction_coefs",
             return_bias=False,
         )
         self.prediction_coefs = ReplicatedLinear(
             altup_num_inputs,
             altup_num_inputs**2,
             bias=False,
+            quant_config=quant_config,
             prefix=f"{prefix}.prediction_coefs",
             return_bias=False,
         )
         self.modality_router = ReplicatedLinear(
             hidden_size,
             altup_num_inputs,
             bias=False,
+            quant_config=quant_config,
             prefix=f"{prefix}.modality_router",
             return_bias=False,
         )
@@ -400,6 +405,7 @@ def __init__(
             altup_num_inputs=config.altup_num_inputs,
             altup_coef_clip=config.altup_coef_clip,
             altup_active_idx=config.altup_active_idx,
+            quant_config=quant_config,
             prefix=f"{prefix}.altup",
         )
         self.self_attn = Gemma3nAttention(
@@ -527,7 +533,7 @@ def forward(
 
 
 @support_torch_compile
-class Gemma3nTextModel(nn.Module):
+class Gemma3nTextModel(nn.Module, SupportsQuant):
 
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
@@ -540,6 +546,7 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         self.embed_tokens = VocabParallelEmbedding(
             config.vocab_size,
             config.hidden_size,
+            quant_config=quant_config,
             prefix=f"{prefix}.embed_tokens",
         )
         self.embed_scale = torch.tensor(
@@ -549,6 +556,7 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         self.embed_tokens_per_layer = VocabParallelEmbedding(
             config.vocab_size_per_layer_input,
             config.num_hidden_layers * config.hidden_size_per_layer_input,
+            quant_config=quant_config,
             prefix=f"{prefix}.per_layer_embed_tokens",
         )
         self.embed_scale_per_layer = torch.tensor(
@@ -582,7 +590,7 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
                 gather_output=True,
                 return_bias=False,
                 quant_config=quant_config,
-                prefix=f"{prefix}.{idx-1}.altup_projections",
+                prefix=f"{prefix}.altup_projections.{idx-1}",
             ) for idx in range(1, self.config.altup_num_inputs)
         ])
         self.altup_unembed_projections = nn.ModuleList([
@@ -593,7 +601,7 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
                 gather_output=True,
                 return_bias=False,
                 quant_config=quant_config,
-                prefix=f"{prefix}.{idx-1}.altup_unembed_projections",
+                prefix=f"{prefix}.altup_unembed_projections.{idx-1}",
             ) for idx in range(1, self.config.altup_num_inputs)
         ])
 
@@ -774,7 +782,7 @@ def forward(
                                    **kwargs)
 
 
-class Gemma3nForConditionalGeneration(nn.Module):
+class Gemma3nForConditionalGeneration(nn.Module, SupportsQuant):
     packed_modules_mapping = {
         "qkv_proj": [
             "q_proj",