Upload apex-master/tests/L0/run_transformer/test_data.py with huggingface_hub

apex-master/tests/L0/run_transformer/test_data.py

@@ -0,0 +1,64 @@
+import logging
+
+import torch.testing
+from torch.testing._internal import common_utils
+
+logging.getLogger("torch").setLevel(logging.WARNING)
+
+from apex.transformer import parallel_state
+from apex.transformer.tensor_parallel import data as data_utils
+from apex.transformer.testing.distributed_test_base import NcclDistributedTestBase
+from apex.transformer.testing.distributed_test_base import UccDistributedTestBase
+
+logging.getLogger("torch").setLevel(logging.WARNING)
+
+
+class BroadcastDataTestBase:
+    def test_broadcast_data(self):
+        tensor_model_parallel_world_size: int = self.world_size // (
+            1 + self.world_size > 1
+        )
+        parallel_state.initialize_model_parallel(
+            tensor_model_parallel_size_=tensor_model_parallel_world_size
+        )
+
+        target_key_size = {
+            "key1": [7, 11],
+            "key2": [8, 2, 1],
+            "key3": [13],
+            "key4": [5, 1, 2],
+            "key5": [5, 12],
+        }
+        keys = [k for k in target_key_size]
+
+        data = {}
+        data_t = {}
+        with torch.no_grad():
+            for key in target_key_size:
+                data[key] = torch.randint(0, 1000, size=target_key_size[key])
+                data_t[key] = data[key].clone()
+            # "key_x" is supposed to be ignored.
+            data["key_x"] = torch.rand(5)
+            data_t["key_x"] = data["key_x"].clone()
+        if parallel_state.get_tensor_model_parallel_rank() != 0:
+            data = None
+
+        data_utils._check_data_types(keys, data_t, torch.int64)
+        key_size, _, _ = data_utils._build_key_size_numel_dictionaries(keys, data)
+
+        for key in keys:
+            self.assertEqual(target_key_size[key], key_size[key])
+
+        broadcasted_data = data_utils.broadcast_data(keys, data, torch.int64)
+        for key in keys:
+            self.assertEqual(broadcasted_data[key], data_t[key].cuda())
+
+        parallel_state.destroy_model_parallel()
+
+
+class NcclBroadcastDataTest(BroadcastDataTestBase, NcclDistributedTestBase): pass
+class UccBroadcastDataTest(BroadcastDataTestBase, UccDistributedTestBase): pass
+
+
+if __name__ == "__main__":
+    common_utils.run_tests()