[Tensor] add hybrid device demo and fix bugs (#1059)

colossalai/nn/parallel.py

@@ -51,11 +51,17 @@ class ColoDDP(torch.nn.Module):
         free_storage(empty_grad)
         if self.dp_world_size > 1:
             grad = grad / self.dp_world_size
-            self.comm_stream.wait_stream(torch.cuda.current_stream())
-            with torch.cuda.stream(self.comm_stream):
-                dist.all_reduce(grad, group=gpc.get_group(ParallelMode.DATA))
+            if grad.device.type != "cpu":
+                self.comm_stream.wait_stream(torch.cuda.current_stream())
+                with torch.cuda.stream(self.comm_stream):
+                    group = gpc.get_group(ParallelMode.DATA)
+                    dist.all_reduce(grad, group=group)
+                    ColoDDP._save_grad(p, grad)
+                grad.record_stream(self.comm_stream)
+            else:
+                group = gpc.get_cpu_group(ParallelMode.DATA)
+                dist.all_reduce(grad, group=group)
                 ColoDDP._save_grad(p, grad)
-            grad.record_stream(self.comm_stream)
         else:
             ColoDDP._save_grad(p, grad)
         return empty_grad

colossalai/tensor/module_utils.py

@@ -12,13 +12,17 @@ def register_colo_module(module_type: type, colo_module: ColoModule):
 
 def is_colo_module(module: torch.nn.Module):
     global _COLOSSAL_MODULES
-    return type(module) in _COLOSSAL_MODULES
+    for module_type in _COLOSSAL_MODULES.keys():
+        if isinstance(type(module), module_type):
+            return True
+    return False
 
 def get_colo_module(module: torch.nn.Module):
     global _COLOSSAL_MODULES
     if is_colo_module(module):
-        colo_module = _COLOSSAL_MODULES[type(module)]
-        return colo_module
+        for module_type, colo_module in _COLOSSAL_MODULES.items():
+            if isinstance(type(module), module_type):
+                return colo_module
     else:
         return None
 

colossalai/utils/model/colo_init_context.py

@@ -92,4 +92,5 @@ class ColoInitContext(InsertPostInitMethodToModuleSubClasses):
             setattr(submodule, param_name, colo_param)
             colo_param.shared_param_modules.append(submodule)
 
+        module.to(self._device)
         ColoModulize(module)

tests/test_tensor/test_gpt.py

@@ -101,4 +101,4 @@ def test_gpt(world_size, use_ddp):
 
 
 if __name__ == '__main__':
-    test_gpt(4)
+    test_gpt(4, False)

tests/test_tensor/test_hybrid_device.py

+from colossalai.utils import free_port, ColoInitContext, get_current_device
+from colossalai.testing import rerun_if_address_is_in_use
+from colossalai.tensor import TensorSpec, ComputePattern, ParallelAction, init_colo_module
+from functools import partial
+from colossalai.core import global_context as gpc
+from colossalai.context import ParallelMode
+from colossalai.nn.parallel import ColoDDP
+
+import colossalai
+import torch
+import torch.multiprocessing as mp
+import pytest
+
+class Net(torch.nn.Module):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.embed = torch.nn.Embedding(20, 4)
+        self.proj = torch.nn.Linear(4, 8)
+    
+    def forward(self, x):
+        # move input to cpu and restore output
+        current_dev = x.device
+        x = x.to('cpu')
+        x = self.embed(x)
+        x = x.to(current_dev)
+
+        x = self.proj(x)
+        return x
+
+def run_hybrid_device(use_ddp):
+    with ColoInitContext(device=get_current_device()):
+        model = Net()
+
+    real_model = model
+    if use_ddp:
+        model = ColoDDP(model)
+        real_model = model.module
+
+
+    print(f'embedding weight size: {real_model.embed.weight.size()} | device: {real_model.embed.weight.device}')
+    #print(f'linear weight size: {real_model.proj.weight.size()} | device: {real_model.proj.weight.device}')
+    parallel_action = ParallelAction(ComputePattern.TP1D)
+    init_colo_module(model, parallel_action, recursive=True, mode='col')
+
+    # use cpu gloo to handle embedding
+    real_model.embed.to('cpu')
+    gloo_group_tp = gpc.get_cpu_group(ParallelMode.PARALLEL_1D)
+    real_model.embed.weight.spec.dist_spec.process_group = gloo_group_tp
+
+    print(f'embedding weight size: {real_model.embed.weight.size()} | new device: {real_model.embed.weight.device}')
+    #print(f'linear weight size: {real_model.proj.weight.size()} | new device: {real_model.proj.weight.device}')
+    
+    data = torch.randint(low=0, high=20, size=(16,), device=get_current_device())
+    out = model(data)
+    out.sum().backward()
+
+def run_dist(rank, world_size, port, use_ddp):
+    if use_ddp and world_size == 1:
+        return
+    tp_world_size = world_size // 2 if use_ddp else world_size
+    config = dict(parallel=dict(tensor=dict(mode="1d", size=tp_world_size),))
+    colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
+    run_hybrid_device(use_ddp)
+
+@pytest.mark.dist
+@pytest.mark.parametrize('world_size', [1, 4])
+@pytest.mark.parametrize('use_ddp', [False, True])
+@rerun_if_address_is_in_use()
+# Working for simulate the embedding(CPU DP+TP) -> nn(GPU DP+TP)
+def _test_hybrid_device(world_size, use_ddp):
+    run_func = partial(run_dist, world_size=world_size, port=free_port(), use_ddp=use_ddp)
+    mp.spawn(run_func, nprocs=world_size)
+
+if __name__ == '__main__':
+    _test_hybrid_device(1, False)
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