feat: Add NeMo Biencoder

examples/biencoder/finetune.py

@@ -0,0 +1,36 @@
+# 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.
+
+from __future__ import annotations
+
+from nemo_automodel.components.config._arg_parser import parse_args_and_load_config
+from nemo_automodel.recipes.biencoder import TrainBiencoderRecipe
+
+
+def main(default_config_path="examples/biencoder/llama3_2_1b_biencoder.yaml"):
+    """Main entry point for the biencoder fine-tuning recipe.
+
+    Loads the configuration, sets up the recipe, and initiates the training loop.
+
+    Args:
+        default_config_path: Path to the default configuration file
+    """
+    cfg = parse_args_and_load_config(default_config_path)
+    recipe = TrainBiencoderRecipe(cfg)
+    recipe.setup()
+    recipe.run_train_validation_loop()
+
+
+if __name__ == "__main__":
+    main()

examples/biencoder/llama3_2_1b_biencoder.yaml

@@ -0,0 +1,132 @@
+# 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.
+
+# To run this recipe, please use the following command:
+# python examples/biencoder/finetune.py --config examples/biencoder/llama3_2_1b_biencoder.yaml
+# Or with torchrun for multi-GPU:
+# torchrun --nproc-per-node=8 examples/biencoder/finetune.py --config examples/biencoder/llama3_2_1b_biencoder.yaml
+
+seed: 42
+
+step_scheduler:
+  global_batch_size: 128
+  local_batch_size: 4
+  ckpt_every_steps: 500
+  val_every_steps: 500
+  num_epochs: 1
+
+dist_env:
+  backend: nccl
+  timeout_minutes: 1
+
+model:
+  _target_: nemo_automodel.components.models.biencoder.NeMoAutoModelBiencoder.from_pretrained
+  pretrained_model_name_or_path: meta-llama/Llama-3.2-1B
+  share_encoder: true
+  add_linear_pooler: false
+  out_dimension: 768
+  do_gradient_checkpointing: false
+  train_n_passages: 5
+  eval_negative_size: 4
+  pooling: avg
+  l2_normalize: true
+  t: 0.02
+  use_liger_kernel: true
+  use_sdpa_patching: true
+  torch_dtype: bfloat16
+
+tokenizer:
+  _target_: transformers.AutoTokenizer.from_pretrained
+  pretrained_model_name_or_path: meta-llama/Llama-3.2-1B
+
+dataloader:
+  _target_: torchdata.stateful_dataloader.StatefulDataLoader
+  dataset:
+    _target_: nemo_automodel.components.datasets.llm.make_retrieval_dataset
+    data_dir_list:
+      - training_datasets/nqsh_shuffled_50k.json
+      - training_datasets/miracl_train_es_llama3_1b_4m_512len.json
+      - training_datasets/mldr_en_perc95_small.json
+    data_type: train
+    train_n_passages: 5
+    eval_negative_size: 4
+    seed: 42
+    do_shuffle: true
+  collate_fn:
+    _target_: nemo_automodel.components.datasets.llm.RetrievalBiencoderCollator
+    q_max_len: 512
+    p_max_len: 512
+    query_prefix: "query:"
+    passage_prefix: "passage:"
+    pad_to_multiple_of: 8
+  shuffle: true
+  num_workers: 0
+
+# Optional: Uncomment to enable validation
+# validation_dataloader:
+#   _target_: torchdata.stateful_dataloader.StatefulDataLoader
+#   dataset:
+#     _target_: nemo_automodel.components.datasets.llm.make_retrieval_dataset
+#     data_dir_list: training_datasets/validation.json
+#     data_type: eval
+#     train_n_passages: 5
+#     eval_negative_size: 4
+#     seed: 42
+#     do_shuffle: false
+#     max_train_samples: 1000
+#     train_data_select_offset: 0
+#   collate_fn:
+#     _target_: nemo_automodel.components.datasets.llm.RetrievalBiencoderCollator
+#     q_max_len: 512
+#     p_max_len: 512
+#     query_prefix: "query:"
+#     passage_prefix: "passage:"
+#     padding: longest
+#     pad_to_multiple_of: 8
+#   batch_size: 2
+#   shuffle: false
+#   num_workers: 0
+
+optimizer:
+  _target_: transformer_engine.pytorch.optimizers.fused_adam.FusedAdam
+  lr: 5.0e-6
+  weight_decay: 0.01
+  adam_w_mode: true
+  bias_correction: true
+  master_weights: true
+
+# Learning rate scheduler
+lr_scheduler:
+  lr_warmup_steps: 100
+
+checkpoint:
+  enabled: true
+  checkpoint_dir: ./output/llama3_2_1b_biencoder/checkpoints
+  model_save_format: torch_save
+  save_consolidated: false
+
+distributed:
+  _target_: nemo_automodel.components.distributed.fsdp2.FSDP2Manager
+  dp_size: none
+  dp_replicate_size: 1
+  tp_size: 1
+  cp_size: 1
+  sequence_parallel: false
+
+# Uncomment and configure for W&B logging
+# wandb:
+#   project: biencoder-finetuning
+#   entity: your_entity
+#   name: llama3_2_1b_biencoder
+

nemo_automodel/components/checkpoint/stateful_wrappers.py

@@ -139,7 +139,8 @@ def load_state_dict(self, state_dict: dict[str, Any], strict: bool = True) -> No
         if self.is_tied_lm_head and not self.is_peft:
             # PP models don't have tied embeddings. Safe to pass in model[0] here.
             lm_head_weight, lm_head_param_name = _get_lm_head_weight_and_name(self.model[0])
-            if lm_head_param_name not in state_dict:
+            # Skip for Biencoder models as it doesn't have a lm_head at the top level
+            if lm_head_weight is not None and lm_head_param_name not in state_dict:
                 # weight tying guarantees this is identical to the embedding weight
                 state_dict[lm_head_param_name] = lm_head_weight.detach()
 

nemo_automodel/components/datasets/llm/__init__.py

@@ -14,12 +14,16 @@
 
 from .column_mapped_text_instruction_dataset import ColumnMappedTextInstructionDataset  # noqa: F401
 from .nanogpt_dataset import NanogptDataset  # noqa: F401
+from .retrieval_collator import RetrievalBiencoderCollator  # noqa: F401
+from .retrieval_dataset import make_retrieval_dataset  # noqa: F401
 from .squad import make_squad_dataset  # noqa: F401
 from .tool_calling_chat_dataset import ToolCallingChatDataset  # noqa: F401
 
 __all__ = [
     "NanogptDataset",
     "make_squad_dataset",
+    "make_retrieval_dataset",
+    "RetrievalBiencoderCollator",
     "ColumnMappedTextInstructionDataset",
     "ToolCallingChatDataset",
 ]