[polish] polish ColoTensor and its submodules (#2537)

colossalai/tensor/colo_parameter.py

@@ -71,7 +71,7 @@ class ColoParameter(ColoTensor, torch.nn.Parameter):
         return tensor
 
     def __repr__(self):
-        return f'ColoParameter: {ColoTensor.__repr__(self)}'
+        return super(ColoParameter, self).__repr__()
 
     @classmethod
     def __torch_function__(cls, func, types, args=..., kwargs=None):

colossalai/tensor/colo_tensor.py

@@ -189,7 +189,12 @@ class ColoTensor(torch.Tensor):
                 return _convert_output(ret, colo_spec)
 
     def __repr__(self):
-        return f'ColoTensor:\n{super().__repr__()}\n{self.dist_spec}\n{self.process_group}\n{self.compute_spec}'
+        output_list = [super(ColoTensor, self).__repr__()]
+        output_list.append(str(self.process_group))
+        output_list.append(str(self.dist_spec))
+        if self.compute_spec is not None:
+            output_list.append(str(self.compute_spec))
+        return "\n".join(output_list)
 
     def _redistribute(self, dist_spec: _DistSpec) -> None:
         """_redistribute

colossalai/tensor/compute_spec.py

@@ -23,7 +23,7 @@ class ComputeSpec(object):
         self.output_replicate = True
 
     def __repr__(self):
-        return f'Compute pattern: {self.compute_pattern}'
+        return f'ComputeSpec(pattern={self.compute_pattern}, replicate_output={self.output_replicate})'
 
     def set_output_replicate(self, flag: bool = True):
         self.output_replicate = flag

colossalai/tensor/distspec.py

@@ -39,11 +39,12 @@ class _DistSpec:
         return True
 
     def __repr__(self) -> str:
-        res_list = ["DistSpec:"]
+        attr_list = []
         for attr in dir(self):
             if not attr.startswith('__'):
-                res_list.append(f'\n\t{attr}: {str(getattr(self, attr))}')
-        return ''.join(res_list)
+                attr_list.append(f'{attr}={str(getattr(self, attr))}')
+        attr_str = ", ".join(attr_list)
+        return "DistSpec(" + attr_str + ")"
 
 
 def ReplicaSpec() -> _DistSpec:

colossalai/tensor/process_group.py

@@ -1,29 +1,36 @@
-import torch
 from typing import List, Optional
-from colossalai.logging import get_dist_logger
+
+import torch
+
 from colossalai.context.singleton_meta import SingletonMeta
+from colossalai.logging import get_dist_logger
 
 
 class PyTorchProcessGroupDict(metaclass=SingletonMeta):
 
     def __init__(self):
         # distributed settings
+        # use this dict to record all Pytorch ProcessGroups
         self.dict = {}
+        # set a distributed logger
+        self.logger = get_dist_logger('ProcessGroup')
+
+    def log_pg_init(self, rank_list: List[int], backend: str):
+        str_list = ["Pytorch ProcessGroup Init:"]
+        str_list.append(f"backend: {backend}")
+        str_list.append(f"ranks: {rank_list}")
+        self.logger.info("\n\t".join(str_list), ranks=[0])
 
     def get(self, rank_list: List[int], backend: str = 'nccl'):
         """Reuse Pytorch ProcessGroup when such a group is initialized
         """
-        rank_tuple = tuple(rank_list)
         # we need to convert the passed list to a tuple
         # since List is unhashable
-        pg_key = (backend, rank_tuple)
-
-        if pg_key not in self.dict:
-
-            self.logger = get_dist_logger('ProcessGroup')
-            self.logger.info(f'NCCL initialize ProcessGroup on {rank_list}', ranks=[0])
-            self.dict[pg_key] = torch.distributed.new_group(ranks=rank_list, backend=backend)
-        return self.dict[pg_key]
+        processgroup_key = (backend, tuple(rank_list))
+        if processgroup_key not in self.dict:
+            self.log_pg_init(rank_list=rank_list, backend=backend)
+            self.dict[processgroup_key] = torch.distributed.new_group(ranks=rank_list, backend=backend)
+        return self.dict[processgroup_key]
 
 
 PYTORCHPGDICT_ = PyTorchProcessGroupDict()
@@ -54,10 +61,10 @@ class ProcessGroup:
             return
 
         assert torch.distributed.is_initialized(), f"ProcessGroup must be used after distributed initialized"
-        if rank is None:
-            self._rank = torch.distributed.get_rank()
-        else:
-            self._rank = rank
+
+        self._rank = torch.distributed.get_rank()
+        if rank is not None:
+            assert self._rank == rank    # make sure that the global rank is correct
 
         if ranks is None:
             self._rank_list = list(range(torch.distributed.get_world_size()))
@@ -132,8 +139,9 @@ class ProcessGroup:
 
     def __repr__(self):
         if self.is_init:
-            return "ProcessGroup:\n\tRank: {}, World size: {}, DP degree: {}, TP degree: {}\n\tRanks in group: {}".\
-                format(self._rank, self._world_size, self._dp_degree, self._tp_degree, self._rank_list)
+            ranks_str = f"ProcessGroup(ranks={self._rank_list},\n"
+            personal_str = f"             rank={self._rank}, dp={self._dp_degree}, tp={self._tp_degree})"
+            return ranks_str + personal_str
         else:
             return "ProcessGroup not initialized"
 

colossalai/utils/model/colo_init_context.py

@@ -43,7 +43,6 @@ def _convert_to_coloparam(param: torch.nn.Parameter,
     else:
         colo_param = ColoParameter(param.to(device=device, dtype=dtype), requires_grad=requires_grad)
 
-
     # if default_shard_plan exists, shard the param during initialization.
     # This can reduce the model size after initialization.
     # NOTE() embedding usually can not be correctly sharded. So I use except to handle
@@ -130,30 +129,27 @@ class ColoInitContext(InsertPostInitMethodToModuleSubClasses):
             setattr(submodule, param_name, colo_param)
             colo_param.shared_param_modules.append(submodule)
 
-        meta_param_flag = 0
-        meta_buffer_flag = 0
+        param_number = 0
+        meta_param_number = 0
+        buffer_number = 0
+        meta_buffer_number = 0
+
         for param in module.parameters():
-            if param.device.type=="meta":
-                meta_param_flag = 1
-            if meta_param_flag == 1 and param.device.type!="meta":
-                raise ValueError("Meta parameters and valued parameters can not  be in the same model")
+            param_number += 1
+            meta_param_number += (param.device.type == 'meta')
 
         for buffer in module.buffers():
-            if buffer.device.type=="meta":
-                meta_buffer_flag = 1
-            if meta_buffer_flag == 1 and buffer.device.type!="meta":
-                raise ValueError("Meta buffers and valued buffers can not be in the same model")
+            buffer_number += 1
+            meta_buffer_number += (buffer.device.type == 'meta')
 
-        if meta_param_flag==1 and meta_buffer_flag==1:
-            pass
-        elif meta_buffer_flag==0 and meta_param_flag==1:
-             for name, buf in module.named_buffers():
-                module._buffers[name] = module._buffers[name].to(device=self._device)
-        elif meta_param_flag==0 and meta_buffer_flag==1:
-            for name, param in module.named_parameters():
-                module._parameters[name] = module._parameters[name].to(device=self._device)
-        else:
-            module.to(self._device)
+        if meta_param_number > 0 and meta_param_number != param_number:
+            raise ValueError("Meta parameters and valued parameters can not  be in the same model")
+        if meta_buffer_number > 0 and meta_buffer_number != buffer_number:
+            raise ValueError("Meta buffers and valued buffers can not be in the same model")
+
+        if meta_buffer_number == 0:
+            for buffer in module.buffers():
+                buffer.data = buffer.data.to(device=self._device)
 
 
 def post_process_colo_init_ctx(model: torch.nn.Module,