[Model] Add support for Nemotron architecture (mlc-ai#3069)

This PR adds support for Nemotron architecture, and is in reference
to mlc-ai#2901 [Request for Nemotron-Mini-4B-Instruct]

Based on my analysis of the Nemotron architecture in
the huggingface repository, it appears to share similarities
with the Llama architecture, but with the following key distinctions:

- The activation function used in the MLP is `relu2` (squared ReLU).
- The MLP includes `up_proj` and `down_proj`, but does not have
a `gate_proj` as seen in Llama.
- It uses `layernorm1p`, and the normalization layer incorporates a bias term.
- The architecture employs a `partial_rotary_factor`, which is similar
to the approach used in the Phi architecture.

python/mlc_llm/conversation_template/__init__.py

@@ -19,6 +19,7 @@
     llama,
     llava,
     mistral,
+    nemotron,
     oasst,
     olmo,
     orion,

python/mlc_llm/conversation_template/nemotron.py

@@ -0,0 +1,27 @@
+"""nemotron default templates"""
+
+from mlc_llm.protocol.conversation_protocol import Conversation, MessagePlaceholders
+
+from .registry import ConvTemplateRegistry
+
+# Nemotron template
+# https://huggingface.co/nvidia/Nemotron-Mini-4B-Instruct/blob/6a417790c444fd65a3da6a5c8821de6afc9654a6/tokenizer_config.json#L8030
+ConvTemplateRegistry.register_conv_template(
+    Conversation(
+        name="nemotron",
+        system_template=("<extra_id_0>System\n" f"{MessagePlaceholders.SYSTEM.value}\n\n"),
+        system_message="",
+        roles={
+            "user": "<extra_id_1>User",
+            "assistant": "<extra_id_1>Assistant",
+            "tool": "<extra_id_1>Tool",
+        },
+        seps=["\n"],
+        role_content_sep="\n",
+        role_empty_sep="\n",
+        stop_str=["</s>"],
+        stop_token_ids=[3],
+        system_prefix_token_ids=[2],
+        add_role_after_system_message=True,
+    )
+)

python/mlc_llm/interface/gen_config.py

@@ -310,4 +310,5 @@ def gen_config(  # pylint: disable=too-many-locals,too-many-arguments,too-many-b
     "deepseek_v2",
     "deepseek",
     "olmo",
+    "nemotron",
 }

python/mlc_llm/model/model.py

@@ -29,6 +29,7 @@
 from .minicpm import minicpm_loader, minicpm_model, minicpm_quantization
 from .mistral import mistral_loader, mistral_model, mistral_quantization
 from .mixtral import mixtral_loader, mixtral_model, mixtral_quantization
+from .nemotron import nemotron_loader, nemotron_model, nemotron_quantization
 from .olmo import olmo_loader, olmo_model, olmo_quantization
 from .orion import orion_loader, orion_model, orion_quantization
 from .phi import phi_loader, phi_model, phi_quantization
@@ -565,4 +566,20 @@ class Model:
             "per-tensor-quant": olmo_quantization.per_tensor_quant,
         },
     ),
+    "nemotron": Model(
+        name="nemotron",
+        model=nemotron_model.NemotronForCausalLM,
+        config=nemotron_model.NemotronConfig,
+        source={
+            "huggingface-torch": nemotron_loader.huggingface,
+            "huggingface-safetensor": nemotron_loader.huggingface,
+        },
+        quantize={
+            "no-quant": nemotron_quantization.no_quant,
+            "group-quant": nemotron_quantization.group_quant,
+            "ft-quant": nemotron_quantization.ft_quant,
+            "awq": nemotron_quantization.awq_quant,
+            "per-tensor-quant": nemotron_quantization.per_tensor_quant,
+        },
+    ),
 }

python/mlc_llm/model/nemotron/nemotron_loader.py

@@ -0,0 +1,76 @@
+"""
+This file specifies how MLC's Nemotron parameter maps from other formats, for example HuggingFace
+PyTorch, HuggingFace safetensors.
+"""
+
+import functools
+
+import numpy as np
+
+from mlc_llm.loader import ExternMapping
+from mlc_llm.quantization import Quantization
+
+from .nemotron_model import NemotronConfig, NemotronForCausalLM
+
+
+def huggingface(model_config: NemotronConfig, quantization: Quantization) -> ExternMapping:
+    """Returns a parameter mapping that maps from the names of MLC LLM parameters to
+    the names of HuggingFace PyTorch parameters.
+
+    Parameters
+    ----------
+    model_config : NemotronConfig
+        The configuration of the Nemotron model.
+
+    quantization : Quantization
+        The quantization configuration.
+
+    Returns
+    -------
+    param_map : ExternMapping
+        The parameter mapping from MLC to HuggingFace PyTorch.
+    """
+    model = NemotronForCausalLM(model_config)
+    if quantization is not None:
+        model.to(quantization.model_dtype)
+    _, _named_params, _ = model.export_tvm(  # type: ignore[misc]
+        spec=model.get_default_spec(),
+        allow_extern=True,
+    )
+    named_parameters = dict(_named_params)
+
+    mapping = ExternMapping()
+
+    for i in range(model_config.num_hidden_layers):
+        # Add QKV in self attention
+        attn = f"model.layers.{i}.self_attn"
+        mlc_name = f"{attn}.qkv_proj.weight"
+        mlc_param = named_parameters[mlc_name]
+        mapping.add_mapping(
+            mlc_name,
+            [
+                f"{attn}.q_proj.weight",
+                f"{attn}.k_proj.weight",
+                f"{attn}.v_proj.weight",
+            ],
+            functools.partial(
+                lambda q, k, v, dtype: np.concatenate([q, k, v], axis=0).astype(dtype),
+                dtype=mlc_param.dtype,
+            ),
+        )
+
+        # inv_freq is not used in the model
+        mapping.add_unused(f"{attn}.rotary_emb.inv_freq")
+
+    for mlc_name, mlc_param in named_parameters.items():
+        if mlc_name not in mapping.param_map:
+            mapping.add_mapping(
+                mlc_name,
+                [mlc_name],
+                functools.partial(
+                    lambda x, dtype: x.astype(dtype),
+                    dtype=mlc_param.dtype,
+                ),
+            )
+
+    return mapping