enable lr scheduler and fix benchmark timing

examples/mlperf/model_train.py

@@ -377,7 +377,6 @@ def train_retinanet():
     return x.shard(GPUS, axis=0).realize(), y_bboxes.shard(GPUS, axis=0), y_labels.shard(GPUS, axis=0), matches.shard(GPUS, axis=0), anchors.shard(GPUS, axis=0), cookie
   
   def _create_lr_scheduler(optim, start_iter, warmup_iters, warmup_factor):
-    # TODO: refactor this a bit more so we don't have to recreate it, unlike what MLPerf script is doing
     def _lr_lambda(e):
       e = e + start_iter
       if e >= warmup_iters: return 1.0
@@ -394,7 +393,7 @@ def train_retinanet():
 
     loss.backward()
     optim.step()
-    # lr_scheduler.step()
+    lr_scheduler.step()
 
     return loss.realize(), losses
 
@@ -435,6 +434,8 @@ def train_retinanet():
 
   for p in params: p.to_(GPUS)
 
+  step_times, start_epoch = [], 0
+
   # ** optimizer **
   optim = Adam(params, lr=lr)
 
@@ -442,21 +443,17 @@ def train_retinanet():
   train_dataset = COCO(download_dataset(BASE_DIR, "train"))
   val_dataset = COCO(download_dataset(BASE_DIR, "validation"))
 
+  # ** lr scheduler **
   config["steps_in_train_epoch"] = steps_in_train_epoch = round_up(len(train_dataset.imgs.keys()), bs) // bs
-  step_times = []
+  start_iter, warmup_iters = start_epoch * steps_in_train_epoch, lr_warmup_epochs * steps_in_train_epoch
+  lr_scheduler = _create_lr_scheduler(optim, start_iter, warmup_iters, lr_warmup_factor)
 
   # ** training loop **
-  for e in range(1, num_epochs + 1):
+  for e in range(start_epoch, num_epochs):
     train_dataloader = batch_load_retinanet(train_dataset, False, Path(BASE_DIR), batch_size=bs, seed=seed)
     it = iter(tqdm(train_dataloader, total=steps_in_train_epoch, desc=f"epoch {e}", disable=BENCHMARK))
     i, proc = 0, _data_get(it)
 
-    # if e < LR_WARMUP_EPOCHS:
-    #   start_iter, warmup_iters = e * train_dataset_len, LR_WARMUP_EPOCHS * train_dataset_len
-    #   lr_scheduler = _create_lr_scheduler(optim, start_iter, warmup_iters, LR_WARMUP_FACTOR)
-    # else: lr_scheduler = None
-    lr_scheduler = None
-
     prev_cookies = []
     st = time.perf_counter()
 
@@ -484,7 +481,7 @@ def train_retinanet():
 
       tqdm.write(
         f"{i:5} {((cl - st)) * 1000.0:7.2f} ms run, {(pt - st) * 1000.0:7.2f} ms python, {(dt - pt) * 1000.0:6.2f} ms fetch data, "
-        f"{(cl - dt) * 1000.0:7.2f} ms {device_str}, {loss:5.2f} loss, {losses['classification_loss'].item():5.2f} classification loss, {losses['regression_loss'].item():5.2f} regression loss, "
+        f"{(cl - dt) * 1000.0:7.2f} ms {device_str}, {loss:5.2f} loss, {losses['classification_loss'].item():5.4f} classification loss, {losses['regression_loss'].item():5.4f} regression loss, "
         f"{optim.lr.numpy()[0]:.6f} LR, {GlobalCounters.mem_used / 1e9:.2f} GB used, {GlobalCounters.global_ops * 1e-9 / (cl - st):9.2f} GFLOPS"
       )
 
@@ -501,7 +498,7 @@ def train_retinanet():
       if i == BENCHMARK:
         assert not math.isnan(loss)
         median_step_time = sorted(step_times)[(BENCHMARK + 1) // 2]  # in seconds
-        estimated_total_minutes = int(median_step_time * steps_in_train_epoch * e / 60)
+        estimated_total_minutes = int(median_step_time * steps_in_train_epoch * num_epochs / 60)
         print(f"Estimated training time: {estimated_total_minutes // 60}h{estimated_total_minutes % 60}m")
         print(f"epoch global_ops: {steps_in_train_epoch * GlobalCounters.global_ops:_}, "
               f"epoch global_mem: {steps_in_train_epoch * GlobalCounters.global_mem:_}")