[test] reorganize zero/gemini tests (#3445)

tests/test_zero/test_gemini/test_search.py

@@ -0,0 +1,123 @@
+from functools import partial
+
+import pytest
+import torch
+import torch.distributed as dist
+import torch.multiprocessing as mp
+
+import colossalai
+from colossalai.tensor import ComputePattern, ComputeSpec, ProcessGroup, ShardSpec
+from colossalai.testing import rerun_if_address_is_in_use
+from colossalai.utils import free_port, get_current_device
+from colossalai.zero import ColoInitContext
+from colossalai.zero.gemini.chunk import init_chunk_manager, search_chunk_configuration
+from tests.components_to_test.registry import non_distributed_component_funcs
+
+
+def init_1d_row_spec(model, pg: ProcessGroup):
+    tensor_spec = (ShardSpec([0], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D))
+    for n, p in model.named_parameters():
+        if 'weight' in n and 'ln' not in n:
+            p.set_process_group(pg)
+            p.set_tensor_spec(*tensor_spec)
+
+
+def exam_search_chunk_size():
+    world_size = torch.distributed.get_world_size()
+    pg_tp = ProcessGroup(tp_degree=world_size)
+
+    get_components_func = non_distributed_component_funcs.get_callable('gpt2')
+    model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
+
+    # make sure torch_model and model has the same parameter values
+    with ColoInitContext(device=get_current_device()):
+        model = model_builder()
+    init_1d_row_spec(model, pg_tp)
+    config_dict, *_ = search_chunk_configuration(model,
+                                                 search_range_mb=1,
+                                                 search_interval_byte=16,
+                                                 min_chunk_size_mb=0,
+                                                 filter_exlarge_params=True)
+
+    for key in config_dict:
+        chunk_size = config_dict[key]['chunk_size']
+        if world_size == 1:
+            assert chunk_size == 31616
+        else:
+            assert chunk_size == 1024
+
+
+def exam_search_strict_ddp():
+    world_size = torch.distributed.get_world_size()
+    default_shard_pg = ProcessGroup(tp_degree=world_size)
+    default_shard_spec = ShardSpec([-1], [world_size])
+
+    get_components_func = non_distributed_component_funcs.get_callable('gpt2')
+    model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
+    # get the chunk configuration over replicated models
+    with ColoInitContext(device=get_current_device()):
+        ddp_model = model_builder()
+    re_dict, re_total, re_wasted = search_chunk_configuration(ddp_model,
+                                                              search_range_mb=1,
+                                                              search_interval_byte=16,
+                                                              min_chunk_size_mb=0,
+                                                              filter_exlarge_params=True,
+                                                              strict_ddp_flag=False)
+    # get the chunk configuration over sharded ddp models
+    with ColoInitContext(device=get_current_device(), default_pg=default_shard_pg,
+                         default_dist_spec=default_shard_spec):
+        sharded_ddp_model = model_builder()
+    sh_dict, sh_total, sh_wasted = search_chunk_configuration(sharded_ddp_model,
+                                                              search_range_mb=1,
+                                                              search_interval_byte=16,
+                                                              min_chunk_size_mb=0,
+                                                              filter_exlarge_params=True,
+                                                              strict_ddp_flag=True)
+    assert re_dict == sh_dict
+    for key in re_dict:
+        assert re_dict[key] == sh_dict[key]
+
+    assert re_total == sh_total
+    assert re_wasted == sh_wasted
+
+
+def exam_chunk_manager():
+    world_size = torch.distributed.get_world_size()
+    default_shard_pg = ProcessGroup(tp_degree=world_size)
+    default_shard_spec = ShardSpec([-1], [world_size])
+
+    get_components_func = non_distributed_component_funcs.get_callable('gpt2')
+    model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
+
+    with ColoInitContext(device=get_current_device(), default_pg=default_shard_pg,
+                         default_dist_spec=default_shard_spec):
+        sharded_ddp_model = model_builder()
+    chunk_manager = init_chunk_manager(sharded_ddp_model,
+                                       get_current_device(),
+                                       hidden_dim=16,
+                                       search_range_mb=1,
+                                       min_chunk_size_mb=0,
+                                       filter_exlarge_params=True,
+                                       strict_ddp_flag=True)
+    config_dict = chunk_manager.dp_degree_chunk_size_dict
+    assert len(config_dict) == 1
+    assert config_dict[world_size] == 31616
+
+
+def run_dist(rank, world_size, port):
+    colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
+    exam_search_chunk_size()
+    exam_search_strict_ddp()
+    exam_chunk_manager()
+
+
+@pytest.mark.dist
+@pytest.mark.parametrize('world_size', [1, 4])
+@rerun_if_address_is_in_use()
+def test_search(world_size):
+    run_func = partial(run_dist, world_size=world_size, port=free_port())
+    mp.spawn(run_func, nprocs=world_size)
+
+
+if __name__ == '__main__':
+    test_search(4)