[zero] add zero context manager to change config during initialization (#546)

tests/test_moe/test_moe_zero_init.py

@@ -0,0 +1,97 @@
+from functools import partial
+
+import colossalai
+import pytest
+import torch
+import torch.multiprocessing as mp
+import torch.nn as nn
+from colossalai.logging import get_dist_logger
+from colossalai.testing import parameterize
+from colossalai.utils import free_port
+from colossalai.context import MOE_CONTEXT
+from colossalai.nn.layer import MoeModule
+from colossalai.zero.init_ctx import ZeroInitContext
+from colossalai.zero.shard_utils import (BucketTensorShardStrategy, TensorShardStrategy)
+
+from colossalai.testing import rerun_on_exception
+from colossalai.utils import get_current_device
+from tests.test_zero_data_parallel.common import CONFIG
+
+
+class MoeModel(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+        self.proj1 = nn.Linear(4, 8)
+        expert_cls = nn.Linear
+        expert_args_dict = dict(in_features=8, out_features=8)
+        self.moe = MoeModule(dim_model=8,
+                             num_experts=8,
+                             noisy_policy='Jitter',
+                             use_residual=True,
+                             expert_cls=expert_cls,
+                             **expert_args_dict)
+        self.proj2 = nn.Linear(8, 4)
+
+    def forward(self, x):
+        x = self.proj(x)
+        x = self.moe(x)
+        x = self.proj2(x)
+        return x
+
+
+@parameterize("init_device_type", ['cpu', 'cuda'])
+@parameterize("shard_strategy_class", [TensorShardStrategy, BucketTensorShardStrategy])
+def run_moe_zero_init(init_device_type, shard_strategy_class):
+    logger = get_dist_logger("test_moe_zero_init")
+
+    if init_device_type == 'cuda':
+        init_device = torch.device(f"cuda:{get_current_device()}")
+    elif init_device_type == 'cpu':
+        init_device = torch.device("cpu")
+    else:
+        raise NotImplementedError("Unknown device found.")
+
+    model_numel_tensor = torch.zeros(1, dtype=torch.int)
+    with ZeroInitContext(target_device=init_device,
+                         shard_strategy=shard_strategy_class(),
+                         shard_param=True,
+                         model_numel_tensor=model_numel_tensor,
+                         rm_torch_payload_on_the_fly=False):
+        model = MoeModel()
+
+        for name, param in model.named_parameters():
+            assert hasattr(param, 'col_attr')
+
+            # the weights in the gate should be fp32
+            if 'gate' in name:
+                assert param.col_attr.sharded_data_tensor.dtype == torch.float32
+            else:
+                assert param.col_attr.sharded_data_tensor.dtype == torch.half
+
+            # the parameters in moe experts and its gate should not be sharded
+            if ('experts' in name) or ('gate' in name) or ('residual_combine' in name):
+                assert not param.col_attr.sharded_data_tensor.is_sharded
+            else:
+                assert param.col_attr.sharded_data_tensor.is_sharded
+
+            assert param.col_attr.sharded_data_tensor.payload.device.type == init_device.type, \
+                f'{param.col_attr.sharded_data_tensor.payload.device.type} vs. {init_device.type}'
+
+
+def _run_dist(rank, world_size, port):
+    colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
+    MOE_CONTEXT.setup(seed=42)
+    run_moe_zero_init()
+
+
+@pytest.mark.dist
+@pytest.mark.parametrize("world_size", [2, 4])
+@rerun_on_exception(exception_type=mp.ProcessRaisedException, pattern=".*Address already in use.*")
+def test_moe_zero_init(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_moe_zero_init(world_size=2)