CorrDiff integration: Support wandb logging (#316)

* Update blossom-ci.yml (#295)

* Change pip install commands with the correct PyPI package name (#298)

* add wb logging

* formatting

* make mode configurable

---------

Co-authored-by: Kaustubh Tangsali <[email protected]>
Co-authored-by: Abdullah <[email protected]>

examples/generative/corrdiff/conf/config_train_diffusion.yaml

@@ -61,6 +61,8 @@ workers: 4
 
 
 ## I/O-related options
+wandb_mode: offline
+  # Wights & biases mode [online, ofline, disabled]
 desc: ''
   # String to include in result dir name
 tick: 1

examples/generative/corrdiff/conf/config_train_regression.yaml

@@ -61,6 +61,8 @@ workers: 4
 
 
 ## I/O-related options
+wandb_mode: offline
+  # Wights & biases mode [online, ofline, disabled]
 desc: ''
   # String to include in result dir name
 tick: 1

examples/generative/corrdiff/train.py

@@ -69,6 +69,7 @@ def main(cfg: DictConfig) -> None:
     workers = getattr(cfg, "workers", 4)
 
     # Parse I/O-related options
+    wandb_mode = getattr(cfg, "wandb_mode", "disabled")
     desc = getattr(cfg, "desc")
     tick = getattr(cfg, "tick", 1)
     snap = getattr(cfg, "snap", 1)
@@ -80,6 +81,7 @@ def main(cfg: DictConfig) -> None:
     # Parse weather data options
     c = EasyDict()
     c.task = task
+    c.wandb_mode = wandb_mode
     c.train_data_path = getattr(cfg, "train_data_path")
     c.crop_size_x = getattr(cfg, "crop_size_x", 448)
     c.crop_size_y = getattr(cfg, "crop_size_y", 448)

examples/generative/corrdiff/training/training_loop.py

@@ -19,6 +19,7 @@
 import os
 import sys
 import time
+import wandb as wb
 
 import numpy as np
 import psutil
@@ -29,7 +30,11 @@
 sys.path.append("../")
 from module import Module
 from modulus.distributed import DistributedManager
-from modulus.launch.logging import PythonLogger, RankZeroLoggingWrapper
+from modulus.launch.logging import (
+    PythonLogger,
+    RankZeroLoggingWrapper,
+    initialize_wandb,
+)
 from modulus.utils.generative import (
     InfiniteSampler,
     construct_class_by_name,
@@ -81,6 +86,7 @@ def training_loop(
     gridtype="sinusoidal",
     N_grid_channels=4,
     normalization="v1",
+    wandb_mode="disabled",
 ):
     """CorrDiff training loop"""
 
@@ -93,6 +99,15 @@ def training_loop(
     logger0 = RankZeroLoggingWrapper(logger, dist)
     logger.file_logging(file_name=f".logs/training_loop_{dist.rank}.log")
 
+    # wandb logger
+    initialize_wandb(
+        project="Modulus-Generative",
+        entity="Modulus",
+        name="CorrDiff",
+        group="CorrDiff-DDP-Group",
+        mode=wandb_mode,
+    )
+
     # Initialize.
     start_time = time.time()
 
@@ -241,6 +256,7 @@ def training_loop(
     while True:
         # Accumulate gradients.
         optimizer.zero_grad(set_to_none=True)
+        loss_accum = 0
         for round_idx in range(num_accumulation_rounds):
             with ddp_sync(ddp, (round_idx == num_accumulation_rounds - 1)):
                 # Fetch training data: weather
@@ -261,13 +277,17 @@ def training_loop(
                     augment_pipe=augment_pipe,
                 )
                 training_stats.report("Loss/loss", loss)
-                loss.sum().mul(loss_scaling / batch_gpu_total).backward()
+                loss = loss.sum().mul(loss_scaling / batch_gpu_total)
+                loss_accum += loss
+                loss.backward()
+        wb.log({"loss": loss_accum}, step=cur_nimg)
 
         # Update weights.
         for g in optimizer.param_groups:
             g["lr"] = optimizer_kwargs["lr"] * min(
                 cur_nimg / max(lr_rampup_kimg * 1000, 1e-8), 1
             )  # TODO better handling (potential bug)
+            wb.log({"lr": g["lr"]}, step=cur_nimg)
         for param in net.parameters():
             if param.grad is not None:
                 torch.nan_to_num(
@@ -324,8 +344,6 @@ def training_loop(
         torch.cuda.reset_peak_memory_stats()
         logger0.info(" ".join(fields))
 
-        ckpt_dir = run_dir
-
         # Save full dump of the training state.
         if (
             (state_dump_ticks is not None)