feat: nano-v3 custom model (#1091)

Signed-off-by: adil-a <adil.asif2000@hotmail.com>
Signed-off-by: adil-a <adil-a@users.noreply.github.com>
Signed-off-by: Dong Hyuk Chang <donghyukc@nvidia.com>
Signed-off-by: thomasdhc <thomasdhc@users.noreply.github.com>
Co-authored-by: adil-a <adil-a@users.noreply.github.com>
Co-authored-by: Dong Hyuk Chang <donghyukc@nvidia.com>
Co-authored-by: thomasdhc <thomasdhc@users.noreply.github.com>

Author: adil-a

.github/actions/build-container/action.yml

@@ -130,6 +130,7 @@ runs:
         context: .
         build-args: |
           BASE_IMAGE=pytorch
+          INSTALL_MAMBA=True
         cache-from: |
           type=registry,ref=${{ env.container-registry }}/${{ env.REPO_LOWER }}:${{ fromJSON(steps.get-pr-info.outputs.pr-info || '{}').number || 0 }}-buildcache,mode=max
           type=registry,ref=${{ env.container-registry }}/${{ env.REPO_LOWER }}:main-buildcache,mode=max

.github/actions/test-template/action.yml

@@ -254,4 +254,4 @@ runs:
         path: |
           coverage.xml
           .coverage
-        include-hidden-files: true
+        include-hidden-files: true

docker/common/uv-pytorch.lock

@@ -575,6 +575,17 @@ wheels = [
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 ]
 
+[[package]]
+name = "causal-conv1d"
+version = "1.6.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "ninja" },
+    { name = "packaging" },
+    { name = "torch", marker = "sys_platform == 'never'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/db/df/63a384c49743b9fc8fec4c05dbd0b515e1c1c2b07e4559acc4fc37c69223/causal_conv1d-1.6.0.tar.gz", hash = "sha256:4eae3220d08e1e88238f3a0a88783147cbdf47f612cc610add75127c7a37ca3e", size = 29356, upload-time = "2026-01-12T17:33:32.794Z" }
+
 [[package]]
 name = "certifi"
 version = "2025.10.5"
@@ -2388,6 +2399,21 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/87/fb/99f81ac72ae23375f22b7afdb7642aba97c00a713c217124420147681a2f/mako-1.3.10-py3-none-any.whl", hash = "sha256:baef24a52fc4fc514a0887ac600f9f1cff3d82c61d4d700a1fa84d597b88db59", size = 78509, upload-time = "2025-04-10T12:50:53.297Z" },
 ]
 
+[[package]]
+name = "mamba-ssm"
+version = "2.3.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "einops" },
+    { name = "ninja" },
+    { name = "packaging" },
+    { name = "setuptools" },
+    { name = "torch", marker = "sys_platform == 'never'" },
+    { name = "transformers" },
+    { name = "triton", marker = "sys_platform == 'never'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/54/69/a87f06d9dba78c041adb81f2228e978aab179477c64f1a210c0fe0d63e8d/mamba_ssm-2.3.0.tar.gz", hash = "sha256:8294e12125f76021e4e190f4137e84a84935920eeda5d0037a6917524456b303", size = 121116, upload-time = "2026-01-12T17:07:22.152Z" }
+
 [[package]]
 name = "markdown-it-py"
 version = "3.0.0"
@@ -3068,7 +3094,9 @@ all = [
     { name = "torchcodec", marker = "platform_machine == 'x86_64' and sys_platform != 'darwin'" },
 ]
 cuda = [
+    { name = "causal-conv1d" },
     { name = "flash-attn", marker = "sys_platform == 'never'" },
+    { name = "mamba-ssm" },
     { name = "transformer-engine", extra = ["pytorch"] },
 ]
 delta-databricks = [
@@ -3126,6 +3154,7 @@ test = [
 requires-dist = [
     { name = "albumentations", marker = "extra == 'vlm'" },
     { name = "backoff", marker = "extra == 'vlm'" },
+    { name = "causal-conv1d", marker = "extra == 'cuda'" },
     { name = "databricks-sql-connector", marker = "extra == 'delta-databricks'", specifier = ">=3.0.0" },
     { name = "datasets", specifier = ">=4.0.0" },
     { name = "deltalake", marker = "extra == 'delta-databricks'", specifier = ">=1.0.0" },
@@ -3135,6 +3164,7 @@ requires-dist = [
     { name = "ftfy" },
     { name = "imageio-ffmpeg" },
     { name = "liger-kernel", marker = "platform_machine == 'x86_64' and sys_platform != 'darwin'", specifier = ">=0.5.9" },
+    { name = "mamba-ssm", marker = "extra == 'cuda'" },
     { name = "megatron-fsdp" },
     { name = "mistral-common", extras = ["opencv"], marker = "extra == 'vlm'" },
     { name = "mlflow" },
@@ -3216,6 +3246,32 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/eb/8d/776adee7bbf76365fdd7f2552710282c79a4ead5d2a46408c9043a2b70ba/networkx-3.5-py3-none-any.whl", hash = "sha256:0030d386a9a06dee3565298b4a734b68589749a544acbb6c412dc9e2489ec6ec", size = 2034406, upload-time = "2025-05-29T11:35:04.961Z" },
 ]
 
+[[package]]
+name = "ninja"
+version = "1.13.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/43/73/79a0b22fc731989c708068427579e840a6cf4e937fe7ae5c5d0b7356ac22/ninja-1.13.0.tar.gz", hash = "sha256:4a40ce995ded54d9dc24f8ea37ff3bf62ad192b547f6c7126e7e25045e76f978", size = 242558, upload-time = "2025-08-11T15:10:19.421Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/3c/74/d02409ed2aa865e051b7edda22ad416a39d81a84980f544f8de717cab133/ninja-1.13.0-py3-none-macosx_10_9_universal2.whl", hash = "sha256:fa2a8bfc62e31b08f83127d1613d10821775a0eb334197154c4d6067b7068ff1", size = 310125, upload-time = "2025-08-11T15:09:50.971Z" },
+    { url = "https://files.pythonhosted.org/packages/8e/de/6e1cd6b84b412ac1ef327b76f0641aeb5dcc01e9d3f9eee0286d0c34fd93/ninja-1.13.0-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:3d00c692fb717fd511abeb44b8c5d00340c36938c12d6538ba989fe764e79630", size = 177467, upload-time = "2025-08-11T15:09:52.767Z" },
+    { url = "https://files.pythonhosted.org/packages/c8/83/49320fb6e58ae3c079381e333575fdbcf1cca3506ee160a2dcce775046fa/ninja-1.13.0-py3-none-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:be7f478ff9f96a128b599a964fc60a6a87b9fa332ee1bd44fa243ac88d50291c", size = 187834, upload-time = "2025-08-11T15:09:54.115Z" },
+    { url = "https://files.pythonhosted.org/packages/56/c7/ba22748fb59f7f896b609cd3e568d28a0a367a6d953c24c461fe04fc4433/ninja-1.13.0-py3-none-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:60056592cf495e9a6a4bea3cd178903056ecb0943e4de45a2ea825edb6dc8d3e", size = 202736, upload-time = "2025-08-11T15:09:55.745Z" },
+    { url = "https://files.pythonhosted.org/packages/79/22/d1de07632b78ac8e6b785f41fa9aad7a978ec8c0a1bf15772def36d77aac/ninja-1.13.0-py3-none-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:1c97223cdda0417f414bf864cfb73b72d8777e57ebb279c5f6de368de0062988", size = 179034, upload-time = "2025-08-11T15:09:57.394Z" },
+    { url = "https://files.pythonhosted.org/packages/ed/de/0e6edf44d6a04dabd0318a519125ed0415ce437ad5a1ec9b9be03d9048cf/ninja-1.13.0-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:fb46acf6b93b8dd0322adc3a4945452a4e774b75b91293bafcc7b7f8e6517dfa", size = 180716, upload-time = "2025-08-11T15:09:58.696Z" },
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+]
+
 [[package]]
 name = "nodeenv"
 version = "1.9.1"

…une/nemotron/nemotron_nano_v3_squad.yaml …nemotron/nemotron_nano_v3_hellaswag.yamlexamples/llm_finetune/nemotron/nemotron_nano_v3_squad.yaml renamed to examples/llm_finetune/nemotron/nemotron_nano_v3_hellaswag.yaml examples/llm_finetune/nemotron/nemotron_nano_v3_squad.yaml renamed to examples/llm_finetune/nemotron/nemotron_nano_v3_hellaswag.yaml

@@ -35,34 +35,31 @@ rng:
   seed: 1111
   ranked: true
 
+parallelizer:
+  _target_: nemo_automodel.components.moe.parallelizer.parallelize_model
+  activation_checkpointing: false
+
 model:
   _target_: nemo_automodel.NeMoAutoModelForCausalLM.from_pretrained
   pretrained_model_name_or_path: nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16
   trust_remote_code: true
 
-# torch.compile configuration
-compile:
-  enabled: false
-  mode: "default"  # Options: "default", "reduce-overhead", "max-autotune"
-  fullgraph: false
-  dynamic: true  # Set to false for better performance with fixed shapes
-  backend: null  # Use default backend (inductor)
-
 distributed:
   _target_: nemo_automodel.components.distributed.fsdp2.FSDP2Manager
   dp_size: none
   dp_replicate_size: 1 # dp_shard_size = dp_size / dp_replicate_size and dp_shard_size < dp_size. For DDP usecase, use DDPManager
   tp_size: 1
   cp_size: 1
+  ep_size: 8
   sequence_parallel: false
   defer_fsdp_grad_sync: false
 
 loss_fn:
   _target_: nemo_automodel.components.loss.masked_ce.MaskedCrossEntropy
 
 dataset:
-  _target_: nemo_automodel.components.datasets.llm.squad.make_squad_dataset
-  dataset_name: rajpurkar/squad
+  _target_: nemo_automodel.components.datasets.llm.hellaswag.HellaSwag
+  path_or_dataset: rowan/hellaswag
   split: train
 
 packed_sequence:
@@ -74,10 +71,10 @@ dataloader:
   shuffle: True
 
 validation_dataset:
-  _target_: nemo_automodel.components.datasets.llm.squad.make_squad_dataset
-  dataset_name: rajpurkar/squad
+  _target_: nemo_automodel.components.datasets.llm.hellaswag.HellaSwag
+  path_or_dataset: rowan/hellaswag
   split: validation
-  limit_dataset_samples: 64
+  num_samples_limit: 64
 
 validation_dataloader:
   _target_: torchdata.stateful_dataloader.StatefulDataLoader

…emotron/nemotron_nano_v3_squad_peft.yaml …ron/nemotron_nano_v3_hellaswag_peft.yamlexamples/llm_finetune/nemotron/nemotron_nano_v3_squad_peft.yaml renamed to examples/llm_finetune/nemotron/nemotron_nano_v3_hellaswag_peft.yaml examples/llm_finetune/nemotron/nemotron_nano_v3_squad_peft.yaml renamed to examples/llm_finetune/nemotron/nemotron_nano_v3_hellaswag_peft.yaml

@@ -67,8 +67,8 @@ loss_fn:
   _target_: nemo_automodel.components.loss.masked_ce.MaskedCrossEntropy
 
 dataset:
-  _target_: nemo_automodel.components.datasets.llm.squad.make_squad_dataset
-  dataset_name: rajpurkar/squad
+  _target_: nemo_automodel.components.datasets.llm.hellaswag.HellaSwag
+  path_or_dataset: rowan/hellaswag
   split: train
 
 packed_sequence:
@@ -80,10 +80,10 @@ dataloader:
   shuffle: True
 
 validation_dataset:
-  _target_: nemo_automodel.components.datasets.llm.squad.make_squad_dataset
-  dataset_name: rajpurkar/squad
+  _target_: nemo_automodel.components.datasets.llm.hellaswag.HellaSwag
+  path_or_dataset: rowan/hellaswag
   split: validation
-  limit_dataset_samples: 64
+  num_samples_limit: 64
 
 validation_dataloader:
   _target_: torchdata.stateful_dataloader.StatefulDataLoader

nemo_automodel/_transformers/auto_model.py

@@ -131,6 +131,30 @@ def wrapper(self, *args, **kwargs):
     return obj
 
 
+def _is_config_compatible_with_custom_model(arch_name: str, config) -> bool:
+    """
+    Check if a HuggingFace config is compatible with our custom model implementation.
+
+    Some architectures (e.g., NemotronHForCausalLM) are shared between different model versions
+    (v2 vs v3) but our custom implementation only supports specific versions. This function
+    validates that the config has the required attributes for the custom implementation.
+
+    Args:
+        arch_name: The architecture name (e.g., "NemotronHForCausalLM")
+        config: The HuggingFace config object
+
+    Returns:
+        True if the config is compatible with our custom implementation, False otherwise
+    """
+    # NemotronHForCausalLM: Our custom implementation is for v3 (MoE model)
+    # v3 requires n_routed_experts, v2 does not have this attribute
+    if arch_name == "NemotronHForCausalLM":
+        return hasattr(config, "n_routed_experts") and config.n_routed_experts is not None
+
+    # All other architectures are assumed compatible
+    return True
+
+
 def _patch_liger_kernel(model):
     """
     Patches a model with liger-kernel and sdpa_kernel
@@ -503,7 +527,12 @@ def _retry(**override):
             )
         architectures = get_architectures(hf_config)
         # 2. If we have a custom model implementation available, we prioritize that over HF
-        if len(architectures) > 0 and architectures[0] in ModelRegistry.model_arch_name_to_cls:
+        arch_name = architectures[0] if len(architectures) > 0 else None
+        if (
+            arch_name is not None
+            and arch_name in ModelRegistry.model_arch_name_to_cls
+            and _is_config_compatible_with_custom_model(arch_name, hf_config)
+        ):
             # if we are able to init the custom model, we will now download the model weights on local rank 0
             _download_model_weights(hf_config, pretrained_model_name_or_path)
             logger.info(f"Using custom model implementation for {architectures[0]}")
@@ -673,7 +702,12 @@ def _retry(**override):
 
         # 2. If we have a custom model implementation available, we prioritize that over HF
         architectures = get_architectures(config)
-        if len(architectures) > 0 and architectures[0] in ModelRegistry.model_arch_name_to_cls:
+        arch_name = architectures[0] if len(architectures) > 0 else None
+        if (
+            arch_name is not None
+            and arch_name in ModelRegistry.model_arch_name_to_cls
+            and _is_config_compatible_with_custom_model(arch_name, config)
+        ):
             model_cls = ModelRegistry.model_arch_name_to_cls[architectures[0]]
             init_param_names = _get_init_param_names(model_cls)
             _consume_config_overrides(config, kwargs, init_param_names=init_param_names)

nemo_automodel/components/checkpoint/checkpointing.py

@@ -358,15 +358,19 @@ def load_base_model(
         # But because we initialize on meta device, these are erroneously set to True.
         # We need to set them to False and call initialize_weights to re-initialize the weights.
 
-        # Gemma3ForConditionalGeneration cannot be pretrained currently. The pinned torch version
-        # doesn't support initialize_weights when the model is sharded. This is because Gemma's
-        # initialize_weights method requires setting a row to zeros in the embedding matrix.
-        # This index selection op is not supported for DTensors in the pinned torch version.
+        # Some models cannot call initialize_weights when sharded with DTensors:
+        # - Gemma3ForConditionalGeneration: requires setting a row to zeros in the embedding matrix,
+        #   which is not supported for DTensors in the pinned torch version.
+        # - NemotronHForCausalLM: the HF remote code's _init_weights uses dt_bias.copy_()
+        #   which fails with DTensors. Note: v3 (MoE) has n_routed_experts and uses our custom
+        #   implementation which handles this correctly.
         try:
             model_class = model.config.architectures[0]
         except:
             model_class = ""
-        if model_class not in ["Gemma3ForConditionalGeneration", "NemotronHForCausalLM"]:
+        is_nemotron_v2 = model_class == "NemotronHForCausalLM" and not getattr(model.config, "n_routed_experts", None)
+        skip_initialize_weights = model_class in ["Gemma3ForConditionalGeneration"] or is_nemotron_v2
+        if not skip_initialize_weights:
             for _, module in model.named_modules():
                 if hasattr(module, "_is_hf_initialized"):
                     module._is_hf_initialized = False
@@ -651,9 +655,15 @@ def _get_original_model_path(self, model_state: ModelState) -> str | None:
         """
         Get the path to the original model from the Hugging Face checkpoint.
         """
-        if not hasattr(model_state.model[0], "name_or_path"):
+        if not hasattr(model_state.model[0], "name_or_path") and not hasattr(
+            getattr(model_state.model[0], "config", None), "name_or_path"
+        ):
             return None
-        pretrained_model_name_or_path = getattr(model_state.model[0], "name_or_path")
+        pretrained_model_name_or_path = getattr(model_state.model[0], "name_or_path", None) or getattr(
+            getattr(model_state.model[0], "config", None), "name_or_path", None
+        )
+        if os.path.isdir(pretrained_model_name_or_path):
+            return pretrained_model_name_or_path
         return get_safetensors_index_path(
             getattr(self.config, "original_model_root_dir", None) or TRANSFORMERS_CACHE, pretrained_model_name_or_path
         )

nemo_automodel/components/models/common/te_utils.py

@@ -81,6 +81,11 @@ def __init__(self, normalized_shape, eps, device, params_dtype):
         object.__setattr__(self, "te_norm", te_norm)
         object.__setattr__(self, "torch_norm", torch_norm)
 
+    def reset_parameters(self) -> None:
+        """Reset parameters by delegating to the underlying torch norm."""
+        torch_norm = object.__getattribute__(self, "torch_norm")
+        torch_norm.reset_parameters()
+
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         if is_tensor_unallocated(x):
             # Shape inference only - return empty tensor with same shape
@@ -113,6 +118,8 @@ def __init__(self, in_features, out_features, bias, device, params_dtype):
         if bias:
             self.bias = te_linear.bias
             torch_linear.bias = self.bias
+        else:
+            self.bias = None
 
         # Use object.__setattr__ to prevent submodules from being registered in self._modules.
         object.__setattr__(self, "te_linear", te_linear)

nemo_automodel/components/models/nemotron_v3/__init__.py

@@ -0,0 +1,13 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.