[booster] fix no_sync method (#3709)

* [booster] fix no_sync method

* [booster] add test for ddp no_sync

* [booster] fix merge

* [booster] update unit test

* [booster] update unit test

* [booster] update unit test

tests/test_booster/test_plugin/test_dp_plugin_base.py

@@ -1,4 +1,4 @@
-from typing import Callable, List, Tuple, Union
+from typing import Callable, Iterator, List, Tuple, Union
 
 import torch
 import torch.distributed as dist
@@ -49,6 +49,9 @@ class DPPluginWrapper(DPPluginBase):
     def supported_precisions(self) -> List[str]:
         pass
 
+    def no_sync(self, model: nn.Module) -> Iterator[None]:
+        pass
+
 
 def check_dataloader_sharding():
     plugin = DPPluginWrapper()

tests/test_booster/test_plugin/test_torch_ddp_plugin.py

@@ -1,5 +1,8 @@
+from contextlib import nullcontext
+
 import torch
 import torch.distributed as dist
+import torch.nn as nn
 from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.optim import SGD
 
@@ -44,10 +47,67 @@ def check_torch_ddp_plugin():
         torch.cuda.empty_cache()
 
 
+class DummyModel(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+        self.weight = nn.Parameter(torch.rand(1))
+
+    def forward(self, x):
+        return self.weight * x
+
+
+def check_torch_ddp_no_sync():
+    plugin = TorchDDPPlugin()
+    booster = Booster(plugin=plugin)
+
+    model = DummyModel()
+    criterion = lambda x: x.mean()
+    optimizer = SGD(model.parameters(), lr=1e-3)
+    # create a custom dasetset with 0 to 10
+    dataset = torch.arange(0, 10)
+    train_dataloader = plugin.prepare_dataloader(dataset, batch_size=2)
+    model, optimizer, criterion, train_dataloader, _ = booster.boost(model,
+                                                                     optimizer,
+                                                                     criterion,
+                                                                     dataloader=train_dataloader)
+
+    def fwd_bwd():
+        output = model(batch.cuda())
+        loss = criterion(output)
+        booster.backward(loss, optimizer)
+
+    def get_grad_set_over_all_ranks():
+        for p in model.parameters():
+            # grad shape is (1, )
+            assert p.grad.shape == (1,)
+            grad_list = [torch.empty_like(p.grad) for _ in range(dist.get_world_size())]
+            dist.all_gather(grad_list, p.grad)
+            # get grad set of all ranks
+            grad_set = set([grad.item() for grad in grad_list])
+            # as the model only has one parameter, we can return here
+            return grad_set
+
+    for i, batch in enumerate(train_dataloader):
+        if i > 1:
+            # only check the first two batches
+            break
+        # no_sync for the first batch, sync for the second batch
+        ctx = booster.no_sync(model) if i == 0 else nullcontext()
+        with ctx:
+            fwd_bwd()
+        grad_set = get_grad_set_over_all_ranks()
+        # for the first batch, all ranks should have different grads
+        # for the second batch, as grad is synchronized,all ranks should have the same grads
+        target_num_different_grad = dist.get_world_size() if i == 0 else 1
+        assert len(grad_set) == target_num_different_grad
+
+
 def run_dist(rank, world_size, port):
     # init dist env
     colossalai.launch(config=dict(), rank=rank, world_size=world_size, port=port, host='localhost')
     check_torch_ddp_plugin()
+    check_torch_ddp_no_sync()
 
 
 @rerun_if_address_is_in_use()