[hotfix] adapt ProcessGroup and Optimizer to ColoTensor (#1388)

colossalai/nn/optimizer/cpu_adam.py

@@ -143,9 +143,9 @@ class CPUAdam(NVMeOptimizer):
                     state['step'] = 0
 
                     # gradient momentums
-                    state['exp_avg'] = torch.zeros_like(p.data, dtype=torch.float, device=target_device)
+                    state['exp_avg'] = torch.zeros_like(p, dtype=torch.float, device=target_device)
                     # gradient variances
-                    state['exp_avg_sq'] = torch.zeros_like(p.data, dtype=torch.float, device=target_device)
+                    state['exp_avg_sq'] = torch.zeros_like(p, dtype=torch.float, device=target_device)
                     self._post_state_init(p)
 
                 state['step'] += 1

colossalai/nn/optimizer/fused_adam.py

@@ -122,9 +122,9 @@ class FusedAdam(torch.optim.Optimizer):
                 # State initialization
                 if len(state) == 0:
                     # Exponential moving average of gradient values
-                    state['exp_avg'] = torch.zeros_like(p.data)
+                    state['exp_avg'] = torch.zeros_like(p)
                     # Exponential moving average of squared gradient values
-                    state['exp_avg_sq'] = torch.zeros_like(p.data)
+                    state['exp_avg_sq'] = torch.zeros_like(p)
 
                 if p.dtype not in [torch.float16, torch.float32]:
                     raise RuntimeError('FusedAdam only support fp16 and fp32.')

colossalai/nn/optimizer/fused_lamb.py

@@ -162,9 +162,9 @@ class FusedLAMB(torch.optim.Optimizer):
                 # State initialization
                 if len(state) == 0:
                     # Exponential moving average of gradient values
-                    state['exp_avg'] = torch.zeros_like(p.data)
+                    state['exp_avg'] = torch.zeros_like(p)
                     # Exponential moving average of gradient values
-                    state['exp_avg_sq'] = torch.zeros_like(p.data)
+                    state['exp_avg_sq'] = torch.zeros_like(p)
 
                 if p.dtype == torch.float16:
                     g_16.append(p.grad.data)

colossalai/nn/optimizer/fused_sgd.py

@@ -104,7 +104,7 @@ class FusedSGD(Optimizer):
             # momentum application can be skipped in the main kernel.
             if 'momentum_buffer' not in param_state:
                 first_run = True
-                buf = param_state['momentum_buffer'] = torch.zeros_like(p.data)
+                buf = param_state['momentum_buffer'] = torch.zeros_like(p)
                 momentums.append(buf)
             else:
                 first_run = False

colossalai/nn/optimizer/hybrid_adam.py

@@ -116,9 +116,9 @@ class HybridAdam(NVMeOptimizer):
                     state['step'] = 0
 
                     # gradient momentums
-                    state['exp_avg'] = torch.zeros_like(p.data, dtype=torch.float, device=target_device)
+                    state['exp_avg'] = torch.zeros_like(p, dtype=torch.float, device=target_device)
                     # gradient variances
-                    state['exp_avg_sq'] = torch.zeros_like(p.data, dtype=torch.float, device=target_device)
+                    state['exp_avg_sq'] = torch.zeros_like(p, dtype=torch.float, device=target_device)
                     self._post_state_init(p)
 
                 state['step'] += 1

colossalai/nn/optimizer/lamb.py

@@ -67,9 +67,9 @@ class Lamb(Optimizer):
                 if len(state) == 0:
                     state['step'] = 0
                     # Exponential moving average of gradient values
-                    state['exp_avg'] = torch.zeros_like(p.data)
+                    state['exp_avg'] = torch.zeros_like(p)
                     # Exponential moving average of squared gradient values
-                    state['exp_avg_sq'] = torch.zeros_like(p.data)
+                    state['exp_avg_sq'] = torch.zeros_like(p)
 
                 exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
                 beta1, beta2 = group['betas']

colossalai/tensor/process_group.py

@@ -22,7 +22,6 @@ class PyTorchProcessGroupDict(metaclass=SingletonMeta):
 
             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]
 
@@ -104,10 +103,15 @@ class ProcessGroup:
     def set_cpu_groups(self):
         if self.has_cpu_groups:
             return
-        # self.logger.info(
+
-        #     f'{self._rank} Gloo initialize TP group on {self._tp_rank_list}, DP group on {self._dp_rank_list}')
+        for i in range(self._dp_degree):
-        PYTORCHPGDICT_.get(self._tp_rank_list, 'gloo')
+            i_tp_list = [self._rank_list[i * self._tp_degree + j] for j in range(self._tp_degree)]
-        PYTORCHPGDICT_.get(self._dp_rank_list, 'gloo')
+            PYTORCHPGDICT_.get(i_tp_list, 'gloo')
+
+        for j in range(self._tp_degree):
+            j_dp_list = [self._rank_list[i * self._tp_degree + j] for i in range(self._dp_degree)]
+            PYTORCHPGDICT_.get(j_dp_list, 'gloo')
+
         self._has_cpu_groups = True
 
     @property