[Model] Optimize nemotron_h implementation (#19249)

Signed-off-by: Jee Jee Li <pandaleefree@gmail.com>

vllm/model_executor/models/nemotron_h.py

 # SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 # Adapted from https://github.com/vllm-project/vllm/blob/94d8ec8d2bcb4ec55e33022b313c7e978edf05e1/vllm/model_executor/models/bamba.py
 # Copyright 2024 HuggingFace Inc. team. All rights reserved.
@@ -29,7 +30,7 @@ from vllm.distributed.parallel_state import get_pp_group
 from vllm.forward_context import get_forward_context
 from vllm.model_executor.layers.activation import ReLUSquaredActivation
 from vllm.model_executor.layers.layernorm import RMSNorm
-from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
                                                QKVParallelLinear,
                                                RowParallelLinear)
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
@@ -63,19 +64,22 @@ class NemotronHMLP(nn.Module):
         config: NemotronHConfig,
         quant_config: Optional[QuantizationConfig] = None,
         bias: bool = False,
+        prefix: str = "",
     ) -> None:
         super().__init__()
-        self.up_proj = MergedColumnParallelLinear(
+        self.up_proj = ColumnParallelLinear(
             input_size=config.hidden_size,
-            output_sizes=[config.intermediate_size],
+            output_size=config.intermediate_size,
             bias=bias,
             quant_config=quant_config,
+            prefix=f"{prefix}.up_proj",
         )
         self.down_proj = RowParallelLinear(
             input_size=config.intermediate_size,
             output_size=config.hidden_size,
             bias=bias,
             quant_config=quant_config,
+            prefix=f"{prefix}.down_proj",
         )
         self.act_fn = ReLUSquaredActivation()
 
@@ -99,9 +103,12 @@ class NemotronHMLPDecoderLayer(nn.Module):
         super().__init__()
         self.config = config
 
-        self.mixer = NemotronHMLP(config,
-                                  quant_config=quant_config,
-                                  bias=config.mlp_bias)
+        self.mixer = NemotronHMLP(
+            config,
+            quant_config=quant_config,
+            bias=config.mlp_bias,
+            prefix=f"{prefix}.mixer",
+        )
 
         self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
 
@@ -207,12 +214,14 @@ class NemotronHAttention(nn.Module):
             self.total_num_kv_heads,
             bias=False,
             quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
         )
         self.o_proj = RowParallelLinear(
             self.total_num_heads * self.head_dim,
             config.hidden_size,
             bias=False,
             quant_config=quant_config,
+            prefix=f"{prefix}.o_proj",
         )
 
         self.attn = Attention(
@@ -253,7 +262,7 @@ class NemotronHAttentionDecoderLayer(nn.Module):
             layer_idx,
             cache_config,
             quant_config,
-            prefix,
+            prefix=f"{prefix}.mixer",
         )
 
         self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
@@ -435,7 +444,6 @@ class NemotronHForCausalLM(nn.Module, HasInnerState, SupportsLoRA, SupportsPP,
             "k_proj",
             "v_proj",
         ],
-        "gate_up_proj": ["up_proj", "down_proj"]
     }
 
     # LoRA specific attributes