[transformer] add microbatches test (pytorch#1349)

* add test

* destroy model parallel was missing

apex/transformer/microbatches.py

@@ -60,8 +60,7 @@ def build_num_microbatches_calculator(
                     global_batch_size,
                     batch_size_increment,
                     ramup_samples,
-                ),
-                flush=True,
+                )
             )
         num_microbatches_calculator = RampupBatchsizeNumMicroBatches(
             start_batch_size,

tests/L0/run_transformer/test_microbatches.py

@@ -0,0 +1,80 @@
+import logging
+from typing import List, Optional
+
+from torch.testing._internal import common_utils
+
+logging.getLogger("torch").setLevel(logging.WARNING)
+
+from apex.transformer import parallel_state
+from apex.transformer.pipeline_parallel.utils import (
+    _reconfigure_microbatch_calculator,
+    get_micro_batch_size,
+    get_num_microbatches,
+    get_current_global_batch_size,
+    update_num_microbatches,
+)
+from apex.transformer.testing.distributed_test_base import DistributedTestBase
+
+logging.getLogger("apex").setLevel(logging.WARNING)
+
+
+class MicrobatchCalculatorTest(DistributedTestBase):
+
+    GLOBAL_BATCH_SIZE: int = 1024
+    MICRO_BATCH_SIZE: int = 1
+
+    def _test(self, rampup_batch_size: Optional[List[int]]) -> None:
+        for data_parallel_size in range(1, self.world_size + 1):
+
+            expected_global_batch_size = MicrobatchCalculatorTest.GLOBAL_BATCH_SIZE
+            expected_micro_batch_size = MicrobatchCalculatorTest.MICRO_BATCH_SIZE
+            if rampup_batch_size:
+                expected_global_batch_size = rampup_batch_size[0]
+                num_consumed_samples = 0
+                step_of_global_batch_size = rampup_batch_size[1]
+                threshold = rampup_batch_size[2]
+
+            if data_parallel_size > 1 and data_parallel_size % 2 != 0:
+                continue
+            if self.world_size % data_parallel_size != 0:
+                continue
+            with self.subTest(data_parallel_size=data_parallel_size):
+                parallel_state.initialize_model_parallel(
+                    tensor_model_parallel_size_=self.world_size // data_parallel_size,
+                    pipeline_model_parallel_size_=1,
+                )
+                self.assertEqual(data_parallel_size, parallel_state.get_data_parallel_world_size())
+
+                _reconfigure_microbatch_calculator(
+                    self.rank,
+                    rampup_batch_size,
+                    MicrobatchCalculatorTest.GLOBAL_BATCH_SIZE,
+                    MicrobatchCalculatorTest.MICRO_BATCH_SIZE,
+                    data_parallel_size,
+                )
+
+                self.assertEqual(get_micro_batch_size(), expected_micro_batch_size)
+                self.assertEqual(get_num_microbatches(), expected_global_batch_size / expected_micro_batch_size / data_parallel_size)
+                current_global_batch_size = get_current_global_batch_size()
+                self.assertEqual(current_global_batch_size, expected_global_batch_size)
+
+                # Make sure `global_batch_size` equals to the final global batch size after
+                # certain number of updates.
+                if rampup_batch_size:
+                    update_num_microbatches(current_global_batch_size)
+                    for i in range(100):
+                        current_global_batch_size = get_current_global_batch_size()
+                        update_num_microbatches(current_global_batch_size)
+                    current_global_batch_size = get_current_global_batch_size()
+                    self.assertEqual(get_current_global_batch_size(), MicrobatchCalculatorTest.GLOBAL_BATCH_SIZE)
+                parallel_state.destroy_model_parallel()
+
+    def test_constant_microbatch_calculator(self):
+        self._test(rampup_batch_size=None)
+
+    def test_dynamic_microbatch_calculator(self):
+        self._test(rampup_batch_size=[256, 128, 500])
+
+
+if __name__ == "__main__":
+    common_utils.run_tests()