feat: Add Evo2 fine-tuning partial-conv benchmarking

ci/benchmarks/partial-conv/evo2_finetuning.yaml

@@ -0,0 +1,98 @@
+scope: partial-conv
+time_limit: 14400
+key_segments:
+  # Modify keys to be renamed (str) or excluded (False) from run identifier. By default, all args under script_args are included.
+  dataset_config: False
+  dataset_dir: False
+  data_base_path: False
+  num_workers: False
+  limit_val_batches: False
+  val_check_interval: False
+  experiment_name: False
+  workspace: False
+  restore_from_checkpoint_path: False
+  activation_checkpoint_layers: False
+  lora_enabled: False
+  lr: False
+  min_lr: False
+  warmup_steps: False
+  accumulate_grad_batches: False
+  clip_grad: False
+  weight_decay: False
+  attention_dropout: False
+  hidden_dropout: False
+  precision: False
+  seq_length: False
+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: /workspace/bionemo2
+  data_base_path: /data/evo2
+  restore_from_checkpoint_path: checkpoints/nemo2_evo2_1b_8k
+  nodes: 1
+  model: evo2
+  config_name: 1b
+  num_workers: 1
+  limit_val_batches: 20
+  dataset_config: training_data_config.yaml
+  dataset_dir: preprocessed_data
+  val_check_interval: 5
+  seq_length: 8192
+  warmup_steps: 10
+  activation_checkpoint_layers: 2
+  lr: 0.000015
+  min_lr: 0.0000149
+  accumulate_grad_batches: 4
+  max_steps: 1000
+  gpus: 1
+  clip_grad: 250
+  weight_decay: 0.001
+  attention_dropout: 0.01
+  hidden_dropout: 0.01
+  stop_steps: 100
+  batch_size: 2
+  variant: finetune
+  precision: fp8
+  products:
+    - variant: finetune
+      lora_enabled: ""
+      task: finetune_from_ckpt
+      experiment_name: evo2-finetune
+    - variant: lora_finetune
+      lora_enabled: "--lora-finetune"
+      task: lora_finetune_from_ckpt
+      experiment_name: evo2-lora-finetune
+script: |-
+  WANDB_API_KEY=$BIONEMO_WANDB_API_KEY train_${model} \
+    -d ${data_base_path}/${dataset_config} \
+    --dataset-dir=${data_base_path}/${dataset_dir} \
+    --ckpt-dir=${data_base_path}/${restore_from_checkpoint_path} \
+    ${lora_enabled} \
+    --model-size=${config_name} \
+    --max-steps=${max_steps} \
+    --experiment-name=${experiment_name}_${batch_size}bs_${nodes}node_${gpus}gpu_${max_steps}s \
+    --lr=${lr} \
+    --min-lr=${min_lr} \
+    --warmup-steps=${warmup_steps} \
+    --result-dir=${tensorboard_dir} \
+    --micro-batch-size=${batch_size} \
+    --grad-acc-batches=${accumulate_grad_batches} \
+    --limit-val-batches=${limit_val_batches} \
+    --seq-length=${seq_length} \
+    --clip-grad=${clip_grad} \
+    --wd=${weight_decay} \
+    --attention-dropout=${attention_dropout} \
+    --hidden-dropout=${hidden_dropout} \
+    --num-layers 4 \
+    --hybrid-override-pattern 'SDH*' \
+    --devices=${gpus} \
+    --num-nodes=${nodes} \
+    --val-check-interval=${val_check_interval} \
+    --wandb-project=${wandb_project_name} \
+    --wandb-group=${model}_${variant}_${config_name}_${task}_${target} \
+    --create-tensorboard-logger \
+    --activation-checkpoint-recompute-num-layers=${activation_checkpoint_layers} \
+    --disable-checkpointing \
+    --early-stop-on-step=${stop_steps} \
+    --garbage-collect-at-inference;

ci/benchmarks/partial-conv/evo2_pretrain.yaml

@@ -15,7 +15,7 @@ script_args:
   # See jet/core/configs.py for the specification of the configuration class
   workspace: /workspace/bionemo2
   data_path: /data/evo2
-  artefacts_url: https://__token__:${JET_GITLAB_TOKEN}@gitlab-master.nvidia.com/api/v4/projects/180496/packages/pypi/simple
+  artefacts_url: https://__token__:${{JET_GITLAB_TOKEN}}@gitlab-master.nvidia.com/api/v4/projects/180496/packages/pypi/simple
   file_name_wheel: subquadratic-ops
   model: evo2
   variant: train

sub-packages/bionemo-evo2/src/bionemo/evo2/run/train.py

@@ -48,6 +48,7 @@
 
 from bionemo.evo2.models.mamba import MAMBA_MODEL_OPTIONS, MambaModel, mamba_no_weight_decay_cond_with_embeddings
 from bionemo.evo2.run.peft import Evo2LoRA
+from bionemo.evo2.utils.callbacks import GarbageCollectAtInferenceTime
 from bionemo.evo2.utils.config import hyena_no_weight_decay_cond_with_embeddings
 from bionemo.evo2.utils.logging.callbacks import TEVCallback
 from bionemo.llm.utils.datamodule_utils import infer_global_batch_size
@@ -506,6 +507,12 @@ def parse_args(args: Optional[List[str]] = None) -> argparse.Namespace:
         default=False,
         help="Skip checking for NaNs in gradients. Only use this for debugging purposes.",
     )
+    parser.add_argument(
+        "--garbage-collect-at-inference",
+        action="store_true",
+        default=False,
+        help="Enable CUDA memory cleanup before validation to prevent initialization errors.",
+    )
 
     recompute_group = parser.add_mutually_exclusive_group(required=False)
     recompute_group.add_argument("--no-activation-checkpointing", action="store_true", default=False)
@@ -645,6 +652,9 @@ def train(args: argparse.Namespace) -> nl.Trainer:
         TEVCallback(),
     ]
 
+    if args.garbage_collect_at_inference:
+        callbacks.append(GarbageCollectAtInferenceTime())
+
     if args.lora_finetune:
         callbacks.append(ModelTransform())
     if args.enable_preemption:

sub-packages/bionemo-evo2/src/bionemo/evo2/utils/callbacks.py

@@ -0,0 +1,36 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-Apache2
+#
+# 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.
+
+import gc
+
+import torch
+from lightning.pytorch import Callback
+
+
+class GarbageCollectAtInferenceTime(Callback):
+    """Callback to clean up CUDA memory before validation to prevent initialization errors."""
+
+    def on_validation_start(self, trainer, pl_module) -> None:
+        """Clean up CUDA memory before validation to prevent initialization errors."""
+        if torch.cuda.is_available():
+            try:
+                torch.cuda.empty_cache()
+                torch.cuda.synchronize()
+                current_device = torch.cuda.current_device()
+                torch.cuda.set_device(current_device)
+                torch.cuda.synchronize()
+                gc.collect()
+            except Exception as e:
+                print(f"Warning: CUDA cleanup failed: {e}")