Fix+tests for get_lr from lr_scheduler before training starts (#310)

* add fix and tests for get_lr from lr_scheduler before training starts

deepspeed/pt/deepspeed_lr_schedules.py

@@ -676,6 +676,10 @@ class WarmupLR(object):
         self.last_batch_iteration = last_batch_iteration
 
     def get_lr(self):
+        if self.last_batch_iteration < 0:
+            logger.warning(
+                "Attempting to get learning rate from scheduler before it has started")
+            return 0.0
         gamma = self._get_gamma()
         return [
             min_lr + (delta_lr * gamma) for min_lr,

tests/unit/test_lr_schedulers.py

+import torch
+import deepspeed
+import argparse
+import pytest
+import json
+import os
+from common import distributed_test
+from simple_model import SimpleModel, SimpleOptimizer, random_dataloader, args_from_dict
+
+
+@pytest.mark.parametrize("scheduler_type,params",
+                         [("WarmupLR",
+                           {}),
+                          ("OneCycle",
+                           {
+                               'cycle_min_lr': 0,
+                               'cycle_max_lr': 0
+                           }),
+                          ("LRRangeTest",
+                           {})])
+def test_get_lr_before_train(tmpdir, scheduler_type, params):
+    config_dict = {
+        "train_batch_size": 2,
+        "steps_per_print": 1,
+        "optimizer": {
+            "type": "Adam",
+            "params": {
+                "lr": 0.00015
+            },
+        },
+        "scheduler": {
+            "type": scheduler_type,
+            "params": params
+        },
+        "gradient_clipping": 1.0
+    }
+    args = args_from_dict(tmpdir, config_dict)
+    hidden_dim = 10
+
+    model = SimpleModel(hidden_dim, empty_grad=False)
+
+    @distributed_test(world_size=[1])
+    def _test_get_lr_before_train(args, model, hidden_dim):
+        model, _, _, lr_scheduler = deepspeed.initialize(args=args,
+                                             model=model,
+                                             model_parameters=model.parameters())
+        data_loader = random_dataloader(model=model,
+                                        total_samples=50,
+                                        hidden_dim=hidden_dim,
+                                        device=model.device,
+                                        dtype=torch.float)
+        for n, batch in enumerate(data_loader):
+            # get lr before training starts
+            lr_scheduler.get_lr()
+            loss = model(batch[0], batch[1])
+            model.backward(loss)
+            model.step()
+
+    _test_get_lr_before_train(args=args, model=model, hidden_dim=hidden_dim)