add initial configs for perf testing on ESM2 in JET (bionemo2)

ci/benchmarks/partial-conv/esm2_pretrain.yaml

@@ -0,0 +1,54 @@
+scope: partial-conv
+time_limit: 14400
+script_args:
+  # All arguments referenced in the script string must be specified here.
+  # Arguments not referenced in the script string must have the 'arg' field specified.
+  # See jet/core/configs.py for the specification of the configuration class
+  workspace:
+    value: /workspace/bionemo2
+    key_segment: False
+  data_path:
+    value: /data/20240809_uniref_2024_03/data
+    key_segment: False
+  model:
+    value: esm2
+  variant:
+    value: train
+  config_name:
+    value: 650M
+  precision:
+    value: [bf16-mixed]
+  nodes:
+    value: [4]
+  gpus:
+    value: 8
+  batch_size:
+    value: 16
+  max_steps:
+    value: 26500
+script: |-
+  WANDB_API_KEY=$BIONEMO_WANDB_API_KEY ${variant}_${model} \
+    --train-cluster-path=${data_path}/train_clusters.parquet \
+    --train-database-path=${data_path}/train.db \
+    --valid-cluster-path=${data_path}/valid_clusters.parquet \
+    --valid-database-path=${data_path}/validation.db \
+    --micro-batch-size=${batch_size} \
+    --num-nodes=${nodes} \
+    --num-gpus=${gpus} \
+    --val-check-interval=1000 \
+    --limit-val-batches=1 \
+    --num-steps=${max_steps} \
+    --min-seq-length=1024 \
+    --max-seq-length=1024 \
+    --num-layers=33 \
+    --hidden-size=1280 \
+    --num-attention-heads=20 \
+    --ffn-hidden-size=5120 \
+    --create-tensorboard-logger \
+    --experiment-name=${batch_size}bs_${nodes}node_${gpus}gpu_${max_steps}s_${precision}prec \
+    --result-dir=${tensorboard_dir} \
+    --wandb-project=${wandb_project_name} \
+    --wandb-group=${model}_${variant}_${config_name} \
+    --wandb-job-type=${pipeline_label} \
+    --log-every-n-steps=50 \
+    --disable-checkpointing;

ci/benchmarks/perf/esm2_pretrain.yaml

@@ -0,0 +1,65 @@
+scope: perf
+time_limit: 1800
+script_args:
+  # All arguments referenced in the script string must be specified here.
+  # Arguments not referenced in the script string must have the 'arg' field specified.
+  # See jet/core/configs.py for the specification of the configuration class
+  workspace:
+    value: /workspace/bionemo2
+    key_segment: False
+  data_path:
+    value: /data/20240809_uniref_2024_03/data
+    key_segment: False
+  model: esm2
+  variant: train
+  config_name: 650M
+  precision: bf16-mixed
+  max_steps: 200
+  gpus: 8
+  acc_grad: 1
+  products:
+    - nodes: 1
+      batch_size: 16
+      pp: 1
+      tp: 1
+    - nodes: 2
+      batch_size: 16
+      pp: 2
+      tp: 1
+    - nodes: 2
+      batch_size: 16
+      pp: 1
+      tp: 2
+    - nodes: 2
+      batch_size: 16
+      pp: 1
+      tp: 1
+script: |-
+  WANDB_API_KEY=$BIONEMO_WANDB_API_KEY ${variant}_${model} \
+    --train-cluster-path=${data_path}/train_clusters.parquet \
+    --train-database-path=${data_path}/train.db \
+    --valid-cluster-path=${data_path}/valid_clusters.parquet \
+    --valid-database-path=${data_path}/validation.db \
+    --micro-batch-size=${batch_size} \
+    --num-nodes=${nodes} \
+    --num-gpus=${gpus} \
+    --val-check-interval=50 \
+    --limit-val-batches=1 \
+    --num-steps=${max_steps} \
+    --min-seq-length=1024 \
+    --max-seq-length=1024 \
+    --num-layers=33 \
+    --hidden-size=1280 \
+    --num-attention-heads=20 \
+    --ffn-hidden-size=5120 \
+    --create-tensorboard-logger \
+    --experiment-name=${batch_size}bs_${nodes}node_${gpus}gpu_${max_steps}s_${precision}prec \
+    --result-dir=${tensorboard_dir} \
+    --wandb-project=${wandb_project_name} \
+    --wandb-group=${model}_${variant}_${config_name} \
+    --wandb-job-type=${pipeline_label} \
+    --log-every-n-steps=10 \
+    --accumulate-grad-batches=${acc_grad} \
+    --pipeline-model-parallel-size=${pp} \
+    --tensor-model-parallel-size={tp} \
+    --disable-checkpointing;

sub-packages/bionemo-esm2/src/bionemo/esm2/run/main.py

@@ -78,6 +78,13 @@ def parse_args():
             default=[0],
             help="Enable nsys profiling for these ranks.",
         )
+        parser.add_argument(
+            "--disable-checkpointing",
+            action="store_false",
+            default=True,
+            dest="create_checkpoint_callback",
+            help="Disable creating a ModelCheckpoint callback.",
+        )
         return parser.parse_args()
 
     def string_to_class(path: str):
@@ -87,7 +94,12 @@ def string_to_class(path: str):
         module = importlib.import_module(module_path)
         return getattr(module, class_name)
 
-    def load_config(config_path: str, model_config_cls: Optional[str], data_config_cls: Optional[str]) -> MainConfig:
+    def load_config(
+        config_path: str,
+        model_config_cls: Optional[str],
+        data_config_cls: Optional[str],
+        create_checkpoint_callback: bool,
+    ) -> MainConfig:
         with open(config_path, "r") as f:
             config_dict = yaml.safe_load(f)
 
@@ -109,10 +121,15 @@ def load_config(config_path: str, model_config_cls: Optional[str], data_config_c
         elif isinstance(data_config_cls, str):
             data_config_cls = string_to_class(data_config_cls)
 
+        # disable checkpointing if called from the command line
+        if not create_checkpoint_callback:
+            config_dict["training_config"]["enable_checkpointing"] = create_checkpoint_callback
+            config_dict["experiment_config"]["create_checkpoint_callback"] = create_checkpoint_callback
+
         return MainConfig[model_config_cls, data_config_cls](**config_dict)
 
     args = parse_args()
-    config = load_config(args.config, args.model_config_cls, args.data_config_cls)
+    config = load_config(args.config, args.model_config_cls, args.data_config_cls, args.create_checkpoint_callback)
 
     if args.nsys_profiling:
         nsys_config = NsysConfig(

sub-packages/bionemo-esm2/src/bionemo/esm2/scripts/train_esm2.py

@@ -70,13 +70,15 @@ def main(
     wandb_offline: bool = False,
     wandb_tags: Optional[List[str]] = None,
     wandb_group: Optional[str] = None,
+    wandb_job_type: Optional[str] = None,
     wandb_id: Optional[str] = None,
     wandb_anonymous: Optional[bool] = False,
     wandb_log_model: bool = False,
     pipeline_model_parallel_size: int = 1,
     tensor_model_parallel_size: int = 1,
     create_tensorboard_logger: bool = False,
     nemo1_init_path: Optional[Path] = None,
+    create_checkpoint_callback: bool = True,
     restore_from_checkpoint_path: Optional[str] = None,
     save_best_checkpoint: bool = True,
     save_last_checkpoint: bool = True,
@@ -124,13 +126,15 @@ def main(
         wandb_offline (bool): Run offline (data can be streamed later to wandb servers).
         wandb_tags (Optional[List[str]]): Tags associated with this run
         wandb_group (Optional[str]): A unique string shared by all runs in a given group
+        wandb_job_type (Optional[str]): Type of run, which is useful when you're grouping runs together into larger experiments using group.
         wandb_id (Optional[str]): Sets the version, mainly used to resume a previous run
         wandb_anonymous (Optional[bool]): Enables or explicitly disables anonymous logging
         wandb_log_model (bool): Save checkpoints in wandb dir to upload on W&B servers
         pipeline_model_parallel_size (int): pipeline model parallel size
         tensor_model_parallel_size (int): tensor model parallel size
         create_tensorboard_logger (bool): create the tensorboard logger
         nemo1_init_path (Optional[Path]): Nemo 1 initialization path
+        create_checkpoint_callback (bool): create a ModelCheckpoint callback and attach it to the pytorch lightning trainer
         restore_from_checkpoint_path (Optional[str]): If set, restores the model from the directory passed in. Expects the
             checkpoint to be created by using the ModelCheckpoint class and always_save_context=True.
         save_best_checkpoint (bool): whether to save the best checkpoint
@@ -182,6 +186,7 @@ def main(
             entity=wandb_entity,
             tags=wandb_tags,
             group=wandb_group,
+            job_type=wandb_job_type,
             id=wandb_id,
             anonymous=wandb_anonymous,
             log_model=wandb_log_model,
@@ -213,6 +218,7 @@ def main(
         log_every_n_steps=log_every_n_steps,
         num_nodes=num_nodes,
         callbacks=callbacks,
+        enable_checkpointing=create_checkpoint_callback,
         plugins=nl.MegatronMixedPrecision(precision=precision),
     )
 
@@ -275,14 +281,17 @@ def main(
     )
 
     # Configure our custom Checkpointer
-    checkpoint_callback = nl_callbacks.ModelCheckpoint(
-        save_last=save_last_checkpoint,
-        monitor=metric_to_monitor_for_checkpoints,  # "val_loss",
-        save_top_k=save_top_k,
-        every_n_train_steps=val_check_interval,
-        always_save_context=True,  # Enables the .nemo file-like checkpointing where all IOMixins are under SerDe
-        filename="{epoch}-{val_loss:.2f}-{step}-{consumed_samples}",  # Including step and consumed_samples in the checkpoint filename prevents duplicate filenames and bugs related to this.
-    )
+    if create_checkpoint_callback:
+        checkpoint_callback = nl_callbacks.ModelCheckpoint(
+            save_last=save_last_checkpoint,
+            monitor=metric_to_monitor_for_checkpoints,  # "val_loss",
+            save_top_k=save_top_k,
+            every_n_train_steps=val_check_interval,
+            always_save_context=True,  # Enables the .nemo file-like checkpointing where all IOMixins are under SerDe
+            filename="{epoch}-{val_loss:.2f}-{step}-{consumed_samples}",  # Including step and consumed_samples in the checkpoint filename prevents duplicate filenames and bugs related to this.
+        )
+    else:
+        checkpoint_callback = None
 
     # Setup the logger and train the model
     nemo_logger = setup_nemo_lightning_logger(
@@ -325,6 +334,7 @@ def train_esm2_entrypoint():
         wandb_project=args.wandb_project,
         wandb_tags=args.wandb_tags,
         wandb_group=args.wandb_group,
+        wandb_job_type=args.wandb_job_type,
         wandb_id=args.wandb_id,
         wandb_anonymous=args.wandb_anonymous,
         wandb_log_model=args.wandb_log_model,
@@ -346,6 +356,7 @@ def train_esm2_entrypoint():
         experiment_name=args.experiment_name,
         resume_if_exists=args.resume_if_exists,
         nemo1_init_path=args.nemo1_init_path,
+        create_checkpoint_callback=args.create_checkpoint_callback,
         restore_from_checkpoint_path=args.restore_from_checkpoint_path,
         save_best_checkpoint=args.save_best_checkpoint,
         save_last_checkpoint=args.save_last_checkpoint,
@@ -432,6 +443,12 @@ def get_parser():
     parser.add_argument(
         "--wandb-group", type=str, default=None, help="A unique string shared by all runs in a given group"
     )
+    parser.add_argument(
+        "--wandb-job-type",
+        type=str,
+        default=None,
+        help="A unique string representing a type of run, which is useful when you're grouping runs together into larger experiments using group.",
+    )
     parser.add_argument(
         "--wandb-id", type=str, default=None, help="Sets the version, mainly used to resume a previous run"
     )
@@ -553,6 +570,13 @@ def get_parser():
         required=False,
         help="Path to nemo1 file, if desired to load at init time.",
     )
+    parser.add_argument(
+        "--disable-checkpointing",
+        action="store_false",
+        default=True,
+        dest="create_checkpoint_callback",
+        help="Disable creating a ModelCheckpoint callback.",
+    )
     parser.add_argument(
         "--save-best-checkpoint",
         action="store_true",

sub-packages/bionemo-esm2/tests/bionemo/esm2/scripts/test_train_esm2.py

@@ -82,7 +82,10 @@ def dummy_parquet_train_val_inputs(tmp_path):
     return train_cluster_path, valid_cluster_path
 
 
-def test_main_runs(monkeypatch, tmpdir, dummy_protein_dataset, dummy_parquet_train_val_inputs):
+@pytest.mark.parametrize("create_checkpoint_callback", [True, False])
+def test_main_runs(
+    monkeypatch, tmpdir, dummy_protein_dataset, dummy_parquet_train_val_inputs, create_checkpoint_callback
+):
     train_cluster_path, valid_cluster_path = dummy_parquet_train_val_inputs
 
     result_dir = Path(tmpdir.mkdir("results"))
@@ -119,19 +122,28 @@ def test_main_runs(monkeypatch, tmpdir, dummy_protein_dataset, dummy_parquet_tra
             num_attention_heads=2,
             hidden_size=4,
             ffn_hidden_size=4 * 4,
+            create_checkpoint_callback=create_checkpoint_callback,
         )
 
     assert (result_dir / "test_experiment").exists(), "Could not find test experiment directory."
     assert (result_dir / "test_experiment").is_dir(), "Test experiment directory is supposed to be a directory."
     children = list((result_dir / "test_experiment").iterdir())
     assert len(children) == 1, f"Expected 1 child in test experiment directory, found {children}."
     uq_rundir = children[0]  # it will be some date.
-    assert (
-        result_dir / "test_experiment" / uq_rundir / "checkpoints"
-    ).exists(), "Could not find test experiment checkpoints directory."
-    assert (
-        result_dir / "test_experiment" / uq_rundir / "checkpoints"
-    ).is_dir(), "Test experiment checkpoints directory is supposed to be a directory."
+
+    # checking directory with checkpoints
+    expected_exists = create_checkpoint_callback
+    actual_exists = (result_dir / "test_experiment" / uq_rundir / "checkpoints").exists()
+    assert expected_exists == actual_exists, (
+        f"Checkpoints directory existence mismatch. "
+        f"Expected: {'exists' if expected_exists else 'does not exist'}, "
+        f"Found: {'exists' if actual_exists else 'does not exist'}."
+    )
+
+    if create_checkpoint_callback:
+        assert (
+            result_dir / "test_experiment" / uq_rundir / "checkpoints"
+        ).is_dir(), "Test experiment checkpoints directory is supposed to be a directory."
     assert (
         result_dir / "test_experiment" / uq_rundir / "nemo_log_globalrank-0_localrank-0.txt"
     ).is_file(), "Could not find experiment log."

sub-packages/bionemo-geneformer/src/bionemo/geneformer/run/main.py

@@ -82,6 +82,13 @@ def parse_args():
             default=[0],
             help="Enable nsys profiling for these ranks.",
         )
+        parser.add_argument(
+            "--disable-checkpointing",
+            action="store_false",
+            default=True,
+            dest="create_checkpoint_callback",
+            help="Disable creating a ModelCheckpoint callback.",
+        )
 
         return parser.parse_args()
 
@@ -92,7 +99,12 @@ def string_to_class(path: str):
         module = importlib.import_module(module_path)
         return getattr(module, class_name)
 
-    def load_config(config_path: str, model_config_cls: Optional[str], data_config_cls: Optional[str]) -> MainConfig:
+    def load_config(
+        config_path: str,
+        model_config_cls: Optional[str],
+        data_config_cls: Optional[str],
+        create_checkpoint_callback: bool,
+    ) -> MainConfig:
         with open(config_path, "r") as f:
             config_dict = yaml.safe_load(f)
 
@@ -106,14 +118,19 @@ def load_config(config_path: str, model_config_cls: Optional[str], data_config_c
             # We assume we get a string to some importable config... e.g. in the sub-package jensen, 'bionemo.jensen.configs.MyConfig'
             model_config_cls = string_to_class(model_config_cls)
 
+        # disable checkpointing if called from the command line
+        if not create_checkpoint_callback:
+            config_dict["training_config"]["enable_checkpointing"] = create_checkpoint_callback
+            config_dict["experiment_config"]["create_checkpoint_callback"] = create_checkpoint_callback
+
         if data_config_cls is None:
             data_config_cls = GeneformerPretrainingDataConfig
         elif isinstance(data_config_cls, str):
             data_config_cls = string_to_class(data_config_cls)
         return MainConfig[model_config_cls, data_config_cls](**config_dict)
 
     args = parse_args()
-    config = load_config(args.config, args.model_config_cls, args.data_config_cls)
+    config = load_config(args.config, args.model_config_cls, args.data_config_cls, args.create_checkpoint_callback)
 
     if args.nsys_profiling:
         nsys_config = NsysConfig(