Allow finetuning ESM2 with [un]frozen encoder

docs/docs/user-guide/examples/bionemo-esm2/finetune.ipynb

@@ -528,6 +528,7 @@
     "    --num-gpus 1 \\\n",
     "    --val-check-interval 10 \\\n",
     "    --log-every-n-steps 10 \\\n",
+    "    --encoder-frozen \\\n",
     "    --lr 5e-3 \\\n",
     "    --lr-multiplier 1e2 \\\n",
     "    --scale-lr-layer \"regression_head\" \\\n",
@@ -697,6 +698,7 @@
     "    --num-gpus 1 \\\n",
     "    --val-check-interval 10 \\\n",
     "    --log-every-n-steps 10 \\\n",
+    "    --encoder-frozen \\\n",
     "    --lr 5e-3 \\\n",
     "    --lr-multiplier 1e2 \\\n",
     "    --scale-lr-layer \"classification_head\" \\\n",

sub-packages/bionemo-esm2/src/bionemo/esm2/scripts/finetune_esm2.py

@@ -76,6 +76,7 @@ def train_model(
     experiment_name: str,
     resume_if_exists: bool,
     precision: PrecisionTypes,
+    encoder_frozen: bool = False,
     scale_lr_layer: Optional[str] = None,
     lr_multiplier: float = 1.0,
     wandb_entity: Optional[str] = None,
@@ -100,7 +101,7 @@ def train_model(
     dataset_class: Type[InMemoryProteinDataset] = InMemorySingleValueDataset,
     config_class: Type[BioBertConfig] = ESM2FineTuneSeqConfig,
     metric_tracker: Callback | None = None,
-    overlap_grad_reduce: bool = True,
+    overlap_grad_reduce: bool = False,  # Default to False to avoid communication issue in gradient synchronization step
     overlap_param_gather: bool = True,
     average_in_collective: bool = True,
     grad_reduce_in_fp32: bool = False,
@@ -127,6 +128,7 @@ def train_model(
             result_dir that stores the logs and checkpoints.
         resume_if_exists (bool): attempt to resume if the checkpoint exists [FIXME @skothenhill this doesn't work yet]
         precision (PrecisionTypes): Precision type for training (e.g., float16, float32)
+        encoder_frozen (bool): Freeze the encoder parameters. Default is False.
         scale_lr_layer (Optional[str]): layer names for which the lr is scaled by lr_multiplier
         lr_multiplier (float): lr multiplier for parameters in scale_lr_layer
         wandb_entity (Optional[str]): The team posting this run (default: your username or your default team)
@@ -258,6 +260,7 @@ def train_model(
     )
     # Configure the model
     config = config_class(
+        encoder_frozen=encoder_frozen,
         params_dtype=get_autocast_dtype(precision),
         pipeline_dtype=get_autocast_dtype(precision),
         autocast_dtype=get_autocast_dtype(precision),  # setting this speeds things up a lot
@@ -351,6 +354,7 @@ def finetune_esm2_entrypoint():
         tensor_model_parallel_size=args.tensor_model_parallel_size,
         accumulate_grad_batches=args.accumulate_grad_batches,
         precision=args.precision,
+        encoder_frozen=args.encoder_frozen,
         scale_lr_layer=args.scale_lr_layer,
         lr_multiplier=args.lr_multiplier,
         experiment_name=args.experiment_name,
@@ -365,7 +369,7 @@ def finetune_esm2_entrypoint():
         nsys_ranks=args.nsys_ranks,
         dataset_class=args.dataset_class,
         config_class=args.config_class,
-        overlap_grad_reduce=not args.no_overlap_grad_reduce,
+        overlap_grad_reduce=args.overlap_grad_reduce,
         overlap_param_gather=not args.no_overlap_param_gather,
         average_in_collective=not args.no_average_in_collective,
         grad_reduce_in_fp32=args.grad_reduce_in_fp32,
@@ -398,6 +402,12 @@ def get_parser():
         default="bf16-mixed",
         help="Precision type to use for training.",
     )
+    parser.add_argument(
+        "--encoder-frozen",
+        action="store_true",
+        default=False,
+        help="Freeze the encoder parameters",
+    )
     parser.add_argument(
         "--lr",
         type=float,
@@ -596,7 +606,7 @@ def get_parser():
     )
     # DDP config
     parser.add_argument(
-        "--no-overlap-grad-reduce",
+        "--overlap-grad-reduce",
         action="store_true",
         default=False,
     )

sub-packages/bionemo-esm2/tests/bionemo/esm2/scripts/test_finetune_esm2.py

@@ -42,9 +42,11 @@ def data_to_csv(data, tmp_path):
 
 
 @pytest.mark.needs_gpu
+@pytest.mark.parametrize("encoder_frozen", [True, False])
 def test_esm2_finetune_token_classifier(
     tmp_path,
     dummy_data_per_token_classification_ft,
+    encoder_frozen,
     n_steps_train: int = 50,
     seed: int = 42,
 ):
@@ -71,6 +73,7 @@ def test_esm2_finetune_token_classifier(
             accumulate_grad_batches=1,
             resume_if_exists=False,
             precision="bf16-mixed",
+            encoder_frozen=encoder_frozen,
             dataset_class=InMemoryPerTokenValueDataset,
             config_class=ESM2FineTuneTokenConfig,
             metric_tracker=MetricTracker(metrics_to_track_val=["loss"], metrics_to_track_train=["loss"]),
@@ -85,13 +88,17 @@ def test_esm2_finetune_token_classifier(
         encoder_requires_grad = [
             p.requires_grad for name, p in trainer.model.named_parameters() if "classification_head" not in name
         ]
-        assert not all(encoder_requires_grad), "Pretrained model is not fully frozen during fine-tuning"
+        assert (
+            not all(encoder_requires_grad) == encoder_frozen
+        ), f"Conflict in param requires_grad when encoder_frozen={encoder_frozen}"
 
 
 @pytest.mark.needs_gpu
+@pytest.mark.parametrize("encoder_frozen", [True, False])
 def test_esm2_finetune_regressor(
     tmp_path,
     dummy_data_single_value_regression_ft,
+    encoder_frozen,
     n_steps_train: int = 50,
     seed: int = 42,
 ):
@@ -118,6 +125,7 @@ def test_esm2_finetune_regressor(
             accumulate_grad_batches=1,
             resume_if_exists=False,
             precision="bf16-mixed",
+            encoder_frozen=encoder_frozen,
             dataset_class=InMemorySingleValueDataset,
             config_class=ESM2FineTuneSeqConfig,
             metric_tracker=MetricTracker(metrics_to_track_val=["loss"], metrics_to_track_train=["loss"]),
@@ -132,7 +140,9 @@ def test_esm2_finetune_regressor(
         encoder_requires_grad = [
             p.requires_grad for name, p in trainer.model.named_parameters() if "regression_head" not in name
         ]
-        assert not all(encoder_requires_grad), "Pretrained model is not fully frozen during fine-tuning"
+        assert (
+            not all(encoder_requires_grad) == encoder_frozen
+        ), f"Conflict in param requires_grad when encoder_frozen={encoder_frozen}"
 
 
 @pytest.fixture
@@ -258,14 +268,19 @@ def test_get_parser():
             "--nsys-ranks",
             "0",
             "1",
-            "--no-overlap-grad-reduce",
+            "--overlap-grad-reduce",
             "--no-overlap-param-gather",
             "--no-average-in-collective",
             "--grad-reduce-in-fp32",
             "--dataset-class",
             "InMemoryPerTokenValueDataset",
             "--config-class",
             "ESM2FineTuneTokenConfig",
+            "--encoder-frozen",
+            "--lr-multiplier",
+            "1e2",
+            "--scale-lr-layer",
+            "dummy_layer",
         ]
     )
 
@@ -307,9 +322,12 @@ def test_get_parser():
     assert args.nsys_start_step == 10
     assert args.nsys_end_step == 50
     assert args.nsys_ranks == [0, 1]
-    assert args.no_overlap_grad_reduce is True
+    assert args.overlap_grad_reduce is True
     assert args.no_overlap_param_gather is True
     assert args.no_average_in_collective is True
     assert args.grad_reduce_in_fp32 is True
     assert args.dataset_class == InMemoryPerTokenValueDataset
     assert args.config_class == ESM2FineTuneTokenConfig
+    assert args.encoder_frozen is True
+    assert args.lr_multiplier == 100
+    assert args.scale_lr_layer == "dummy_layer"