feat: support nemotron_parse vlm

examples/vlm_finetune/nemotron/nemotron_parse_v1_1.yaml

@@ -0,0 +1,96 @@
+# 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.
+
+
+step_scheduler:
+  global_batch_size: 16
+  local_batch_size: 2
+  ckpt_every_steps: 100
+  val_every_steps: 100  # will run every x number of gradient steps
+  max_steps: 1000
+
+dist_env:
+  backend: nccl
+  timeout_minutes: 10
+
+rng:
+  _target_: nemo_automodel.components.training.rng.StatefulRNG
+  seed: 42
+  ranked: true
+
+model:
+  _target_: nemo_automodel.NeMoAutoModelForImageTextToText.from_pretrained
+  pretrained_model_name_or_path: /lustre/fsw/portfolios/coreai/projects/coreai_dlalgo_llm/users/huiyingl/bulb/Automodel-bulb/my/NVIDIA-Nemotron-Parse-v1.1
+  torch_dtype: torch.bfloat16
+  use_liger_kernel: false
+  attn_implementation: eager
+  trust_remote_code: True
+  local_files_only: True
+
+processor:
+  _target_: transformers.AutoProcessor.from_pretrained
+  pretrained_model_name_or_path: nvidia/NVIDIA-Nemotron-Parse-v1.1
+  trust_remote_code: True
+
+checkpoint:
+  enabled: true
+  checkpoint_dir: vlm_checkpoints/
+  model_save_format: safetensors
+  save_consolidated: true
+
+distributed:
+  _target_: nemo_automodel.components.distributed.fsdp2.FSDP2Manager
+  dp_size: none
+  tp_size: 1
+  cp_size: 1
+  sequence_parallel: false
+
+loss_fn:
+  _target_: nemo_automodel.components.loss.masked_ce.MaskedCrossEntropy
+
+dataset:
+  _target_: nemo_automodel.components.datasets.vlm.datasets.make_cord_v2_dataset
+  path_or_dataset: naver-clova-ix/cord-v2
+  split: train
+
+dataloader:
+  _target_: torchdata.stateful_dataloader.StatefulDataLoader
+  num_workers: 0
+  pin_memory: true
+  collate_fn:
+    _target_: nemo_automodel.components.datasets.vlm.collate_fns.nemotron_parse_collate_fn
+
+validation_dataset:
+  _target_: nemo_automodel.components.datasets.vlm.datasets.make_cord_v2_dataset
+  path_or_dataset: naver-clova-ix/cord-v2
+  split: validation
+
+validation_dataloader:
+  _target_: torchdata.stateful_dataloader.StatefulDataLoader
+
+optimizer:
+  _target_: torch.optim.AdamW
+  lr: 1e-5
+  weight_decay: 0.01
+  betas: [0.9, 0.95]
+
+freeze_config:
+  freeze_embeddings: true
+  freeze_vision_tower: true
+  freeze_language_model: false
+
+# wandb:
+#   project: <your_project_name>  
+#   entity: <your_entity_name>
+#   name: <your_experiment_name>

nemo_automodel/components/datasets/vlm/collate_fns.py

@@ -253,6 +253,88 @@ def has_data(modality_list):
     return batch
 
 
+def nemotron_parse_collate_fn(
+    examples: Sequence[Dict[str, Any]],
+    processor,
+    task_prompt: str = "</s><s><predict_bbox><predict_classes><output_markdown>",
+) -> Dict[str, torch.Tensor]:
+    """
+    Collate function for NVIDIA Nemotron-Parse models.
+
+    The Nemotron-Parse processor does not expose a chat template, so we build the
+    prompt + answer string manually, mask the prompt tokens, and keep the
+    image preprocessing handled by the processor.
+    """
+
+    conversations = [example["conversation"] for example in examples]
+
+    images: List[Any] = []
+    targets: List[str] = []
+    for conversation in conversations:
+        image = None
+        assistant_text = ""
+
+        for message in conversation:
+            role = message.get("role")
+            content = message.get("content")
+
+            if role == "user":
+                if isinstance(content, list):
+                    for item in content:
+                        if isinstance(item, dict) and item.get("type") == "image":
+                            image = item.get("image")
+                            break
+            elif role == "assistant" and not assistant_text:
+                assistant_text = _extract_assistant_text(message)
+
+            if image is not None and assistant_text:
+                break
+
+        images.append(image)
+        targets.append(assistant_text)
+
+    texts = [f"{task_prompt}{target}" for target in targets]
+
+    batch = processor(images=images, text=texts, padding=True, return_tensors="pt")
+
+    if "pixel_values" in batch:
+        batch["pixel_values"] = batch["pixel_values"].to(torch.bfloat16)
+
+    labels = build_labels(
+        batch["input_ids"],
+        conversations,
+        processor,
+    )
+
+    batch["labels"] = labels[:, 1:]
+
+    tokenizer = getattr(processor, "tokenizer", processor)
+    pad_token_id = getattr(tokenizer, "pad_token_id", None)
+    decoder_start_token_id = getattr(tokenizer, "decoder_start_token_id", None) or getattr(
+        tokenizer, "bos_token_id", None
+    )
+    if decoder_start_token_id is None:
+        decoder_start_token_id = getattr(tokenizer, "eos_token_id", None)
+    if pad_token_id is None or decoder_start_token_id is None:
+        raise ValueError("Nemotron-Parse collate_fn requires pad_token_id and decoder_start_token_id.")
+
+    decoder_input_ids = batch["input_ids"].clone()
+    decoder_input_ids[:, 0] = decoder_start_token_id
+    decoder_input_ids[:, 1:] = batch["input_ids"][:, :-1]
+
+    decoder_attention_mask = (decoder_input_ids != pad_token_id).long()
+
+    batch["decoder_input_ids"] = decoder_input_ids[:, 1:]
+    batch["decoder_attention_mask"] = decoder_attention_mask[:, 1:]
+
+    input_shape = batch["input_ids"].shape
+    for key, value in list(batch.items()):
+        if isinstance(value, torch.Tensor) and value.shape == input_shape:
+            batch[key] = value[:, :-1]
+
+    return batch
+
+
 def default_collate_fn(
     examples: Sequence[Dict[str, Any]],
     processor,
@@ -297,5 +379,6 @@ def default_collate_fn(
 COLLATE_FNS = {
     "Qwen2_5_VLProcessor": qwen2_5_collate_fn,
     "Qwen3OmniMoeProcessor": qwen3_omni_collate_fn,
+    "NemotronParseProcessor": nemotron_parse_collate_fn,
     "default": default_collate_fn,
 }