[example] gpt, shard init on all processes (#2366)

colossalai/tensor/colo_tensor.py

@@ -117,7 +117,7 @@ class ColoTensor(torch.Tensor):
     def set_process_group(self, pg: ProcessGroup):
         """set_process_group
         change the pg of the ColoTensor. Note that the valid use cases is limited.
-        Only existing pg is DP and dist spec is REPLICaTE is valid.
+        It works for the target pg is DP and TP only and current dist spec of the Tensor is Replica.
 
         Args:
             pg (ProcessGroup): target pg
@@ -127,10 +127,10 @@ class ColoTensor(torch.Tensor):
         # if the new pg is the same as the old pg, just returns
         if self.process_group == pg:
             return
-        assert self.process_group.tp_world_size() == 1, \
-            "Can not set_process_group on a ColoTensor whose process_group has tp world group"
+        assert self.process_group.tp_world_size() == 1 or self.process_group.dp_world_size() == 1, \
+            "Can not set_process_group on a ColoTensor whose process_group is both tp > 1 and world group > 1"
         assert self.dist_spec.placement.value == 'r', \
-            "Can not set_process_group on a ColoTensor whose dist spec is not REPLICATE"
+            "Can not set_process_group on a ColoTensor whose dist spec is not Replica"
 
         self.process_group = pg
 

examples/language/gpt/gemini/train_gpt_demo.py

@@ -148,10 +148,16 @@ def tensor_parallelize(model: torch.nn.Module, pg: ProcessGroup):
     """
     for mn, module in model.named_modules():
         for pn, param in module.named_parameters(recurse=False):
-            # NOTE() a param maybe shared by tow modules
+            # NOTE() a param maybe shared by two modules
             if hasattr(param, 'visited'):
                 continue
+
+            # if shard init, then convert param to replica and use the dp-only ProcessGroup
+            param: ColoParameter = param
             param.set_dist_spec(ReplicaSpec())
+            param.set_process_group(pg)
+
+            # shard it w.r.t tp pattern
             if 'mlp.c_fc' in mn:
                 if 'weight' in pn or 'bias' in pn:
                     split_param_col_tp1d(param, pg)    # colmn slice
@@ -170,7 +176,6 @@ def tensor_parallelize(model: torch.nn.Module, pg: ProcessGroup):
                 split_param_col_tp1d(param, pg)    # colmn slice
             else:
                 param.set_dist_spec(ReplicaSpec())
-
             param.visited = True
 
 
@@ -248,27 +253,28 @@ def main():
     torch.manual_seed(123)
     if args.distplan == "colossalai":
         # all param must use the same process group.
-        default_pg = ProcessGroup(tp_degree=args.tp_degree)
-        default_dist_spec = ShardSpec([-1], [args.tp_degree]) if args.shardinit else None
+        world_size = torch.distributed.get_world_size()
+        shard_pg = ProcessGroup(tp_degree=world_size)
+        default_dist_spec = ShardSpec([-1], [world_size]) if args.shardinit else None
 
         # build GPT model
         if version.parse(CAI_VERSION) > version.parse("0.1.10"):
             with ColoInitContext(device=get_current_device(),
                                  dtype=torch.half,
                                  default_dist_spec=default_dist_spec,
-                                 default_pg=default_pg):
+                                 default_pg=shard_pg):
                 model = model_builder(args.model_type)(checkpoint=True)
         else:
             with ColoInitContext(device=get_current_device()):
                 model = model_builder(args.model_type)(checkpoint=True)
 
-        pg = default_pg
+        tp_pg = ProcessGroup(tp_degree=args.tp_degree)
         # Tensor Parallelism (TP)
-        tensor_parallelize(model, pg)
+        tensor_parallelize(model, tp_pg)
 
         # build a Gemini model and a highly optimized cpu optimizer
         # Gemini + ZeRO DP, Note it must be used after TP
-        model, optimizer = build_gemini(model, pg, args.placement)
+        model, optimizer = build_gemini(model, tp_pg, args.placement)
 
         logger.info(get_mem_info(prefix='After init optim, '), ranks=[0])
     else: