capitalize HF as HF instead of Hf (#11384)

Signed-off-by: Alexandros Koumparoulis <[email protected]>
Signed-off-by: Alexandros Koumparoulis <[email protected]>

examples/llm/peft/hf.py

@@ -76,11 +76,11 @@ def formatting_prompts_func(examples):
         # See: https://github.com/Lightning-AI/pytorch-lightning/blob/8ad3e29816a63d8ce5c00ac104b14729a4176f4f/src/lightning/pytorch/plugins/precision/fsdp.py#L81
         grad_clip = None
     use_dist_samp = False
-    tokenizer = llm.HfAutoModelForCausalLM.configure_tokenizer(args.model)
+    tokenizer = llm.HFAutoModelForCausalLM.configure_tokenizer(args.model)
 
     llm.api.finetune(
-        model=llm.HfAutoModelForCausalLM(args.model),
-        data=llm.HfDatasetDataModule(
+        model=llm.HFAutoModelForCausalLM(args.model),
+        data=llm.HFDatasetDataModule(
             mk_hf_dataset(tokenizer.tokenizer), pad_token_id=tokenizer.tokenizer.eos_token_id
         ),
         trainer=nl.Trainer(

examples/llm/sft/hf.py

@@ -84,7 +84,7 @@ def squad(tokenizer) -> pl.LightningDataModule:
 
     from nemo.lightning.pytorch.accelerate.transformer_engine import te_accelerate
 
-    model = llm.HfAutoModelForCausalLM(model_name=args.model, model_accelerator=model_accelerator)
+    model = llm.HFAutoModelForCausalLM(model_name=args.model, model_accelerator=model_accelerator)
     tokenizer = model.tokenizer
 
     llm.api.finetune(

nemo/collections/llm/__init__.py

@@ -22,7 +22,7 @@
     AlpacaDataModule,
     DollyDataModule,
     FineTuningDataModule,
-    HfDatasetDataModule,
+    HFDatasetDataModule,
     MockDataModule,
     PreTrainingDataModule,
     SquadDataModule,
@@ -64,7 +64,7 @@
     GPTConfig126M,
     GPTConfig175B,
     GPTModel,
-    HfAutoModelForCausalLM,
+    HFAutoModelForCausalLM,
     Llama2Config7B,
     Llama2Config13B,
     Llama2Config70B,
@@ -218,7 +218,7 @@
     "dolly",
     "peft",
     "hf_dataset",
-    "HfAutoModelForCausalLM",
+    "HFAutoModelForCausalLM",
 ]
 
 

nemo/collections/llm/gpt/data/__init__.py

@@ -15,7 +15,7 @@
 from nemo.collections.llm.gpt.data.alpaca import AlpacaDataModule
 from nemo.collections.llm.gpt.data.dolly import DollyDataModule
 from nemo.collections.llm.gpt.data.fine_tuning import FineTuningDataModule
-from nemo.collections.llm.gpt.data.hf_dataset import HfDatasetDataModule
+from nemo.collections.llm.gpt.data.hf_dataset import HFDatasetDataModule
 from nemo.collections.llm.gpt.data.mock import MockDataModule
 from nemo.collections.llm.gpt.data.pre_training import PreTrainingDataModule, build_pretraining_datamodule
 from nemo.collections.llm.gpt.data.squad import SquadDataModule
@@ -28,5 +28,5 @@
     "MockDataModule",
     "PreTrainingDataModule",
     "build_pretraining_datamodule",
-    "HfDatasetDataModule",
+    "HFDatasetDataModule",
 ]

nemo/collections/llm/gpt/data/api.py

@@ -16,7 +16,7 @@
 import nemo_run as run
 
 from nemo.collections.llm.gpt.data.dolly import DollyDataModule
-from nemo.collections.llm.gpt.data.hf_dataset import HfDatasetDataModule
+from nemo.collections.llm.gpt.data.hf_dataset import HFDatasetDataModule
 from nemo.collections.llm.gpt.data.mock import MockDataModule
 from nemo.collections.llm.gpt.data.squad import SquadDataModule
 
@@ -42,7 +42,7 @@ def dolly() -> pl.LightningDataModule:
 @run.cli.factory
 @run.autoconvert
 def hf_dataset(dataset: str) -> pl.LightningDataModule:
-    return HfDatasetDataModule(dataset=dataset, global_batch_size=16, micro_batch_size=2)
+    return HFDatasetDataModule(dataset=dataset, global_batch_size=16, micro_batch_size=2)
 
 
 __all__ = ["mock", "squad", "dolly", "hf_dataset"]

nemo/collections/llm/gpt/data/hf_dataset.py

@@ -18,7 +18,7 @@
 from nemo.lightning.pytorch.plugins import MegatronDataSampler
 
 
-class HfDatasetDataModule(pl.LightningDataModule):
+class HFDatasetDataModule(pl.LightningDataModule):
     def __init__(
         self,
         dataset,
@@ -88,7 +88,7 @@ def train_dataloader(self, collate_fn=None):
         from nemo.lightning.data import add_megatron_sampler
 
         if collate_fn is None:
-            collate_fn = lambda x: HfDatasetDataModule.collate_fn(x, pad_token_id=self.pad_token_id)
+            collate_fn = lambda x: HFDatasetDataModule.collate_fn(x, pad_token_id=self.pad_token_id)
 
         return DataLoader(
             self.dataset,

nemo/collections/llm/gpt/model/__init__.py

@@ -45,7 +45,7 @@
     Gemma2Config27B,
     Gemma2Model,
 )
-from nemo.collections.llm.gpt.model.hf_auto_model_for_causal_lm import HfAutoModelForCausalLM
+from nemo.collections.llm.gpt.model.hf_auto_model_for_causal_lm import HFAutoModelForCausalLM
 from nemo.collections.llm.gpt.model.llama import (
     CodeLlamaConfig7B,
     CodeLlamaConfig13B,
@@ -191,5 +191,5 @@
     "transformer_engine_layer_spec",
     "transformer_engine_full_layer_spec",
     "local_layer_spec",
-    "HfAutoModelForCausalLM",
+    "HFAutoModelForCausalLM",
 ]

nemo/collections/llm/gpt/model/hf_auto_model_for_causal_lm.py

@@ -31,7 +31,7 @@ def masked_cross_entropy(logits, targets, mask=None):
         return F.cross_entropy(logits, targets)
 
 
-class HfAutoModelForCausalLM(pl.LightningModule, io.IOMixin, fn.FNMixin):
+class HFAutoModelForCausalLM(pl.LightningModule, io.IOMixin, fn.FNMixin):
     def __init__(
         self,
         model_name='gpt2',
@@ -57,7 +57,7 @@ def __init__(
     @property
     def tokenizer(self):
         if self._tokenizer is None:
-            self._tokenizer = HfAutoModelForCausalLM.configure_tokenizer(self.model_name, self.trust_remote_code)
+            self._tokenizer = HFAutoModelForCausalLM.configure_tokenizer(self.model_name, self.trust_remote_code)
         return self._tokenizer
 
     @tokenizer.setter

nemo/collections/llm/recipes/hf_auto_model_for_causal_lm.py

@@ -23,7 +23,7 @@
 from nemo import lightning as nl
 from nemo.collections.llm.api import finetune, pretrain
 from nemo.collections.llm.gpt.data.mock import MockDataModule
-from nemo.collections.llm.gpt.model.hf_auto_model_for_causal_lm import HfAutoModelForCausalLM
+from nemo.collections.llm.gpt.model.hf_auto_model_for_causal_lm import HFAutoModelForCausalLM
 from nemo.collections.llm.peft.lora import LoRA
 from nemo.collections.llm.recipes.log.default import default_log, default_resume, tensorboard_logger
 from nemo.collections.llm.recipes.optim.adam import pytorch_adam_with_cosine_annealing
@@ -35,23 +35,23 @@
 @run.cli.factory(name=NAME)
 def model(model_name, load_pretrained_weights) -> run.Config[pl.LightningModule]:
     """
-    Factory function to create HfAutoModelForCausalLM model configurations.
+    Factory function to create HFAutoModelForCausalLM model configurations.
 
     Args:
         model_name (str): Model id on HF.
 
     Returns:
-        run.Config[pl.LightningModule]: Configuration for the HfAutoModelForCausalLM.
+        run.Config[pl.LightningModule]: Configuration for the HFAutoModelForCausalLM.
 
     Examples:
         CLI usage:
-            $ nemo llm pretrain --factory 'HfAutoModelForCausalLM(model_name="mistralai/Mistral-Nemo-Instruct-2407")'
+            $ nemo llm pretrain --factory 'HFAutoModelForCausalLM(model_name="mistralai/Mistral-Nemo-Instruct-2407")'
 
         Python API usage:
             >>> model_config = model(model_name="mistralai/Mistral-Nemo-Instruct-2407")
             >>> print(model_config)
     """
-    return run.Config(HfAutoModelForCausalLM, model_name=model_name, load_pretrained_weights=load_pretrained_weights)
+    return run.Config(HFAutoModelForCausalLM, model_name=model_name, load_pretrained_weights=load_pretrained_weights)
 
 
 def trainer(
@@ -69,7 +69,7 @@ def trainer(
     gradient_clip_val: float = 1.0,
 ) -> run.Config[nl.Trainer]:
     """
-    Configure the NeMo Lightning Trainer for HfAutoModelForCausalLM.
+    Configure the NeMo Lightning Trainer for HFAutoModelForCausalLM.
 
     This function sets up the distributed training strategy and other training parameters.
 
@@ -91,7 +91,7 @@ def trainer(
 
     Examples:
         CLI usage:
-            $ nemo llm pretrain trainer=HfAutoModelForCausalLM ...
+            $ nemo llm pretrain trainer=HFAutoModelForCausalLM ...
 
         Python API usage:
             >>> trainer_config = trainer(num_nodes=2, num_gpus_per_node=8)
@@ -131,7 +131,7 @@ def pretrain_recipe(
     model_name: str = '',
 ) -> run.Partial:
     """
-    Create a pre-training recipe for a HfAutoModelForCausalLM model.
+    Create a pre-training recipe for a HFAutoModelForCausalLM model.
 
     This function sets up a complete configuration for pre-training, including
     model, trainer, data, logging, optimization, and resumption settings.
@@ -148,7 +148,7 @@ def pretrain_recipe(
 
     Examples:
         CLI usage:
-            $ nemo llm pretrain --factory 'HfAutoModelForCausalLM(model_name="mistralai/Mistral-Nemo-Instruct-2407")'
+            $ nemo llm pretrain --factory 'HFAutoModelForCausalLM(model_name="mistralai/Mistral-Nemo-Instruct-2407")'
 
         Python API usage:
             >>> recipe = pretrain_recipe(name="auto_pretrain", num_nodes=2, model_name="mistralai/Mistral-Nemo-Instruct-2407")
@@ -179,7 +179,7 @@ def finetune_recipe(
     model_name: str = '',
 ) -> run.Partial:
     """
-    Create a fine-tuning recipe for a HfAutoModelForCausalLM model.
+    Create a fine-tuning recipe for a HFAutoModelForCausalLM model.
 
     This function sets up a complete configuration for fine-tuning, including
     model, trainer, data, logging, optimization, and resumption settings.

nemo/lightning/pytorch/strategies/megatron_strategy.py

@@ -278,7 +278,7 @@ def connect(self, model: pl.LightningModule) -> None:
         """Attaches a model to strategy."""
         super().connect(model)
 
-        assert not 'is_hf_model' in model.__dict__, "Cannot use HfAutoModelForCausalLM with MegatronParallel"
+        assert not 'is_hf_model' in model.__dict__, "Cannot use HFAutoModelForCausalLM with MegatronParallel"
 
         dtype_config = getattr(self._precision_plugin, "dtype_config", None)
         if self.pipeline_dtype is None and dtype_config: