Torchmetrics usage improvements with classes instead of functionals

.github/workflows/test.yml

@@ -49,6 +49,9 @@ jobs:
       - name: Install library
         run: python -m pip install --no-deps -e . # `-e` required for correct `coverage` run.
 
+      - name: Install test dependencies
+        run: micromamba install -c conda-forge pytdc  # Required to run the `test_finetuning.py`
+
       - name: Install C++ library
         run: cd graphium/graphium_cpp && git clone https://github.com/pybind/pybind11.git && export PYTHONPATH=$PYTHONPATH:./pybind11 && python -m pip install . && cd ../..
 

.gitignore

@@ -39,6 +39,7 @@ graphium/data/cache/
 graphium/data/b3lyp/
 graphium/data/PCQM4Mv2/
 graphium/data/PCQM4M/
+graphium/data/largemix/
 graphium/data/neurips2023/small-dataset/
 graphium/data/neurips2023/large-dataset/
 graphium/data/neurips2023/dummy-dataset/

docs/api/graphium.ipu.md

@@ -22,11 +22,6 @@ Code for adapting to run on IPU
 ::: graphium.ipu.ipu_losses
 
 
-## IPU Metrics
-------------
-::: graphium.ipu.ipu_metrics
-
-
 ## IPU Simple Lightning
 ------------
 ::: graphium.ipu.ipu_simple_lightning

env.yml

@@ -31,7 +31,7 @@ dependencies:
   - cuda-version # works also with CPU-only system.
   - pytorch >=1.12
   - lightning >=2.0
-  - torchmetrics >=0.7.0,<0.11
+  - torchmetrics
   - ogb
   - pytorch_geometric >=2.0 # Use `pyg` for Windows instead of `pytorch_geometric`
   - wandb

expts/hydra-configs/tasks/loss_metrics_datamodule/toymix.yaml

@@ -1,6 +1,8 @@
 # @package _global_
 
 predictor:
+  target_nan_mask: ignore
+  multitask_handling: flatten
   metrics_on_progress_bar:
     qm9: ["mae"]
     tox21: ["auroc"]
@@ -13,29 +15,31 @@ predictor:
 metrics:
   qm9: &qm9_metrics
     - name: mae
-      metric: mae_ipu
-      target_nan_mask: null
+      metric: mae
+      target_nan_mask: ignore
       multitask_handling: flatten
       threshold_kwargs: null
     - name: pearsonr
-      metric: pearsonr_ipu
+      metric: pearsonr
       threshold_kwargs: null
-      target_nan_mask: null
+      target_nan_mask: ignore
       multitask_handling: mean-per-label
     - name: r2_score
-      metric: r2_score_ipu
-      target_nan_mask: null
+      metric: r2_score
+      target_nan_mask: ignore
       multitask_handling: mean-per-label
       threshold_kwargs: null
   tox21:
     - name: auroc
-      metric: auroc_ipu
+      metric: auroc
       task: binary
+      target_nan_mask: ignore
       multitask_handling: mean-per-label
       threshold_kwargs: null
     - name: avpr
-      metric: average_precision_ipu
+      metric: averageprecision
       task: binary
+      target_nan_mask: ignore
       multitask_handling: mean-per-label
       threshold_kwargs: null
     - name: f1 > 0.5
@@ -44,6 +48,8 @@ metrics:
       target_to_int: True
       num_classes: 2
       average: micro
+      task: binary
+      target_nan_mask: ignore
       threshold_kwargs: &threshold_05
         operator: greater
         threshold: 0.5
@@ -53,6 +59,8 @@ metrics:
       metric: precision
       multitask_handling: mean-per-label
       average: micro
+      task: binary
+      target_nan_mask: ignore
       threshold_kwargs: *threshold_05
   zinc: *qm9_metrics
 

expts/hydra-configs/training/toymix.yaml

@@ -23,4 +23,4 @@ trainer:
     precision: 16
     max_epochs: ${constants.max_epochs}
     min_epochs: 1
-    check_val_every_n_epoch: 20
+    check_val_every_n_epoch: 2

expts/run_validation_test.py

@@ -8,7 +8,7 @@
 import timeit
 from loguru import logger
 from datetime import datetime
-from pytorch_lightning.utilities.model_summary import ModelSummary
+from lightning.pytorch.utilities.model_summary import ModelSummary
 
 # Current project imports
 import graphium

graphium/cli/train_finetune_test.py

@@ -52,41 +52,6 @@ def cli(cfg: DictConfig) -> None:
     return run_training_finetuning_testing(cfg)
 
 
-def get_replication_factor(cfg):
-    try:
-        ipu_config = cfg.get("accelerator", {}).get("ipu_config", [])
-        for item in ipu_config:
-            if "replicationFactor" in item:
-                # Extract the number between parentheses
-                start = item.find("(") + 1
-                end = item.find(")")
-                if start != 0 and end != -1:
-                    return int(item[start:end])
-    except Exception as e:
-        print(f"An error occurred: {e}")
-
-    # Return default value if replicationFactor is not found or an error occurred
-    return 1
-
-
-def get_gradient_accumulation_factor(cfg):
-    """
-    WARNING: This MUST be called after accelerator overrides have been applied
-    (i.e. after `load_accelerator` has been called)
-    """
-    try:
-        # Navigate through the nested dictionaries and get the gradient accumulation factor
-        grad_accumulation_factor = cfg.get("trainer", {}).get("trainer", {}).get("accumulate_grad_batches", 1)
-
-        # Ensure that the extracted value is an integer
-        return int(grad_accumulation_factor)
-    except Exception as e:
-        print(f"An error occurred: {e}")
-
-    # Return default value if an error occurred
-    return 1
-
-
 def get_training_batch_size(cfg):
     """
     WARNING: This MUST be called after accelerator overrides have been applied
@@ -195,14 +160,6 @@ def run_training_finetuning_testing(cfg: DictConfig) -> None:
         ## Metrics
         metrics = load_metrics(cfg)
 
-        # Note: these MUST be called after `cfg, accelerator = load_accelerator(cfg)`
-        replicas = get_replication_factor(cfg)
-        gradient_acc = get_gradient_accumulation_factor(cfg)
-        micro_bs = get_training_batch_size(cfg)
-        device_iterations = get_training_device_iterations(cfg)
-
-        global_bs = replicas * gradient_acc * micro_bs * device_iterations
-
         ## Predictor
         predictor = load_predictor(
             config=cfg,
@@ -213,17 +170,15 @@ def run_training_finetuning_testing(cfg: DictConfig) -> None:
             accelerator_type=accelerator_type,
             featurization=datamodule.featurization,
             task_norms=datamodule.task_norms,
-            replicas=replicas,
-            gradient_acc=gradient_acc,
-            global_bs=global_bs,
         )
 
     logger.info(predictor.model)
     logger.info(ModelSummary(predictor, max_depth=4))
+    metrics_on_progress_bar = predictor.get_metrics_on_progress_bar
 
     ## Trainer
     date_time_suffix = datetime.now().strftime("%d.%m.%Y_%H.%M.%S")
-    trainer = load_trainer(cfg, accelerator_type, date_time_suffix)
+    trainer = load_trainer(cfg, accelerator_type, date_time_suffix, metrics_on_progress_bar=metrics_on_progress_bar)
 
     if not testing_only:
         # Add the fine-tuning callback to trainer

graphium/config/_loader.py

@@ -15,7 +15,7 @@
 # Misc
 import os
 from copy import deepcopy
-from typing import Any, Callable, Dict, Mapping, Optional, Tuple, Type, Union
+from typing import Any, Callable, Dict, Mapping, Optional, Tuple, Type, Union, Iterable
 
 import joblib
 import mup
@@ -40,10 +40,10 @@
 from graphium.trainer.metrics import MetricWrapper
 from graphium.trainer.predictor import PredictorModule
 from graphium.utils.command_line_utils import get_anchors_and_aliases, update_config
+from graphium.trainer.progress_bar import ProgressBarMetrics
 
 # Graphium
 from graphium.utils.mup import set_base_shapes
-from graphium.utils.spaces import DATAMODULE_DICT, GRAPHIUM_PRETRAINED_MODELS_DICT
 from graphium.utils import fs
 
 
@@ -111,6 +111,8 @@ def load_datamodule(
         datamodule: The datamodule used to process and load the data
     """
 
+    from graphium.utils.spaces import DATAMODULE_DICT # Avoid circular imports with `spaces.py`
+
     cfg_data = config["datamodule"]["args"]
 
     # Instanciate the datamodule
@@ -298,9 +300,6 @@ def load_predictor(
     accelerator_type: str,
     featurization: Dict[str, str] = None,
     task_norms: Optional[Dict[Callable, Any]] = None,
-    replicas: int = 1,
-    gradient_acc: int = 1,
-    global_bs: int = 1,
 ) -> PredictorModule:
     """
     Defining the predictor module, which handles the training logic from `lightning.LighningModule`
@@ -326,9 +325,6 @@ def load_predictor(
         task_levels=task_levels,
         featurization=featurization,
         task_norms=task_norms,
-        replicas=replicas,
-        gradient_acc=gradient_acc,
-        global_bs=global_bs,
         **cfg_pred,
     )
 
@@ -345,9 +341,6 @@ def load_predictor(
             task_levels=task_levels,
             featurization=featurization,
             task_norms=task_norms,
-            replicas=replicas,
-            gradient_acc=gradient_acc,
-            global_bs=global_bs,
             **cfg_pred,
         )
 
@@ -384,6 +377,7 @@ def load_trainer(
     config: Union[omegaconf.DictConfig, Dict[str, Any]],
     accelerator_type: str,
     date_time_suffix: str = "",
+    metrics_on_progress_bar: Optional[Iterable[str]] = None,
 ) -> Trainer:
     """
     Defining the pytorch-lightning Trainer module.
@@ -449,12 +443,15 @@ def load_trainer(
             name += f"_{date_time_suffix}"
         trainer_kwargs["logger"] = WandbLogger(name=name, **wandb_cfg)
 
-    trainer_kwargs["callbacks"] = callbacks
+    progress_bar_callback = ProgressBarMetrics(metrics_on_progress_bar = metrics_on_progress_bar)
+    callbacks.append(progress_bar_callback)
+
     trainer = Trainer(
         detect_anomaly=True,
         strategy=strategy,
         accelerator=accelerator_type,
         devices=devices,
+        callbacks=callbacks,
         **cfg_trainer["trainer"],
         **trainer_kwargs,
     )
@@ -625,6 +622,8 @@ def get_checkpoint_path(config: Union[omegaconf.DictConfig, Dict[str, Any]]) ->
     Otherwise, assume it refers to a file in the checkpointing dir.
     """
 
+    from graphium.utils.spaces import GRAPHIUM_PRETRAINED_MODELS_DICT # Avoid circular imports with `spaces.py`
+
     cfg_trainer = config["trainer"]
 
     path = config.get("ckpt_name_for_testing", "last.ckpt")

graphium/config/dummy_finetuning_from_gnn.yaml

@@ -55,7 +55,7 @@ finetuning:
 
 constants:
   seed: 42
-  max_epochs: 2
+  max_epochs: 5
 
 accelerator:
   float32_matmul_precision: medium
@@ -64,14 +64,14 @@ accelerator:
 predictor:
   random_seed: ${constants.seed}
   optim_kwargs:
-    lr: 4.e-5
+    lr: 1.e-3
   scheduler_kwargs: null
-  target_nan_mask: null
+  target_nan_mask: ignore
   multitask_handling: flatten # flatten, mean-per-label
 
   torch_scheduler_kwargs:
     module_type: WarmUpLinearLR
-    max_num_epochs: 2
+    max_num_epochs: 4
     warmup_epochs: 1
     verbose: False
 
@@ -84,30 +84,30 @@ metrics:
   lipophilicity_astrazeneca:
     - name: mae
       metric: mae
-      target_nan_mask: null
+      target_nan_mask: ignore
       multitask_handling: flatten
       threshold_kwargs: null
     - name: spearman
       metric: spearmanr
       threshold_kwargs: null
-      target_nan_mask: null
+      target_nan_mask: ignore
       multitask_handling: mean-per-label
     - name: pearson
       metric: pearsonr
       threshold_kwargs: null
-      target_nan_mask: null
+      target_nan_mask: ignore
       multitask_handling: mean-per-label
     - name: r2_score
       metric: r2_score
-      target_nan_mask: null
+      target_nan_mask: ignore
       multitask_handling: mean-per-label
       threshold_kwargs: null
 
 trainer:
   seed: ${constants.seed}
   trainer:
     precision: 32
-    max_epochs: 2
+    max_epochs: 5
     min_epochs: 1
     check_val_every_n_epoch: 1
     accumulate_grad_batches: 1
@@ -122,12 +122,12 @@ datamodule:
 
   module_type: "ADMETBenchmarkDataModule"
   args:
+    processed_graph_data_path: datacache/processed_graph_data/dummy_finetuning_from_gnn
     # TDC specific
     tdc_benchmark_names: [lipophilicity_astrazeneca]
     tdc_train_val_seed: ${constants.seed}
-
-    batch_size_training: 200
-    batch_size_inference: 200
+    batch_size_training: 20
+    batch_size_inference: 20
     num_workers: 0
 
     persistent_workers: False