[Model] NemotronH Support (#22349)

Signed-off-by: Daniel Afrimi <[email protected]>

Author: danielafrimi

vllm/model_executor/models/nemotron_h.py

@@ -64,20 +64,32 @@ class NemotronHMLP(nn.Module):
     def __init__(
         self,
         config: NemotronHConfig,
+        layer_idx: int,
         quant_config: Optional[QuantizationConfig] = None,
         bias: bool = False,
         prefix: str = "",
     ) -> None:
         super().__init__()
+
+        hybrid_override_pattern = config.hybrid_override_pattern
+        mlp_index = hybrid_override_pattern[:layer_idx + 1].count("-") - 1
+        if isinstance(config.intermediate_size, list):
+            if len(config.intermediate_size) == 1:
+                intermediate_size = config.intermediate_size[0]
+            else:
+                intermediate_size = config.intermediate_size[mlp_index]
+        else:
+            intermediate_size = config.intermediate_size
+
         self.up_proj = ColumnParallelLinear(
             input_size=config.hidden_size,
-            output_size=config.intermediate_size,
+            output_size=intermediate_size,
             bias=bias,
             quant_config=quant_config,
             prefix=f"{prefix}.up_proj",
         )
         self.down_proj = RowParallelLinear(
-            input_size=config.intermediate_size,
+            input_size=intermediate_size,
             output_size=config.hidden_size,
             bias=bias,
             quant_config=quant_config,
@@ -110,6 +122,7 @@ def __init__(
             quant_config=quant_config,
             bias=config.mlp_bias,
             prefix=f"{prefix}.mixer",
+            layer_idx=layer_idx,
         )
 
         self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
@@ -146,7 +159,7 @@ def __init__(
             hidden_size=config.hidden_size,
             ssm_state_size=config.ssm_state_size,
             conv_kernel_size=config.conv_kernel,
-            intermediate_size=config.expand * config.hidden_size,
+            intermediate_size=config.mamba_num_heads * config.mamba_head_dim,
             use_conv_bias=config.use_conv_bias,
             use_bias=config.use_bias,
             n_groups=config.n_groups,
@@ -205,7 +218,10 @@ def __init__(
             # the KV heads across multiple tensor parallel GPUs.
             assert tp_size % self.total_num_kv_heads == 0
         self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
-        self.head_dim = config.hidden_size // self.total_num_heads
+        if hasattr(config, "head_dim") and config.head_dim is not None:
+            self.head_dim = config.head_dim
+        else:
+            self.head_dim = config.hidden_size // self.total_num_heads
         self.q_size = self.num_heads * self.head_dim
         self.kv_size = self.num_kv_heads * self.head_dim
         self.scaling = self.head_dim**-0.5
@@ -481,7 +497,7 @@ def get_mamba_state_shape_from_config(
         """
         parallel_config = vllm_config.parallel_config
         hf_config = vllm_config.model_config.hf_config
-        intermediate_size = hf_config.expand * hf_config.hidden_size
+        intermediate_size = hf_config.mamba_num_heads * hf_config.mamba_head_dim
 
         return MambaStateShapeCalculator.mamba2_state_shape(
             intermediate_size=intermediate_size,

vllm/transformers_utils/configs/nemotron_h.py

@@ -151,7 +151,7 @@ def __init__(
         num_hidden_layers=52,
         hybrid_override_pattern="M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M-",
         num_attention_heads=32,
-        attention_head_dim=128,
+        head_dim=128,
         num_key_value_heads=8,  # nemo: num_query_groups
         mlp_hidden_act="relu2",
         attention_bias=False,
@@ -194,7 +194,7 @@ def __init__(
         self.num_hidden_layers = num_hidden_layers
         self.hybrid_override_pattern = hybrid_override_pattern
         self.num_attention_heads = num_attention_heads
-        self.attention_head_dim = attention_head_dim
+        self.head_dim = head_dim
         self.sliding_window = sliding_window
         self.max_position_embeddings = max_position_embeddings
         self.attention_dropout = attention_dropout