fix moe bugs in zero optimizer

internlm/solver/optimizer/hybrid_zero_optim.py

@@ -166,21 +166,20 @@ class HybridZeroOptimizer(BaseOptimizer):
         # partition these param groups for data parallel training
         # and add buffers to parameter store for future access
         for group_id, param_group in enumerate(self.optim.param_groups):
-            if "moe" in param_group.keys() and param_group["moe"]:
-                print("true", flush=True)
-                continue
-
             group_params = param_group["params"]
 
             # add the fp16 params to fp16_param_groups for bookkeeping
             self._fp16_param_groups[group_id] = group_params
 
             # assign parameters to ranks the params in the list are sorted
-            params_per_rank, no_params_ranks = self._partition_param_list(group_params)
+            params_per_rank, no_params_ranks = self._partition_param_list(param_group)
             self.param_group_no_params_ranks.append(no_params_ranks)
             self.param_group_has_params.append(self._zero_local_rank not in no_params_ranks)
 
-            # store the mapping between param to rank each param should belong to only one rank
+            # store the mapping between param to rank each param should belong to only one rank.
+            # we can skip the moe param and do not keep them in _param_store to save memory
+            # (means we need to deal with moe param in a different way), but it will increase
+            # complexity and reduce code readablity.
             for rank, params in enumerate(params_per_rank):
                 # check whether any rank is not assigned params.
                 if len(params) != 0:
@@ -261,32 +260,47 @@ class HybridZeroOptimizer(BaseOptimizer):
     def num_param_groups(self):
         return len(self._fp16_param_groups)
 
-    def _partition_param_list(self, param_list):
+    def _get_real_dp_process_group(self, param_groups):
+        if "moe" in param_groups.keys() and param_groups["moe"]:
+            return ParallelMode.EXPERT_DATA
+        else:
+            return ParallelMode.DATA
+
+    def _partition_param_list(self, param_group):
         no_params_ranks = []
         params_per_rank = [[] for _ in range(self._zero_world_size)]
         numel_per_rank = [0 for _ in range(self._zero_world_size)]
         self.params_per_rank_id_dict.append([[] for _ in range(self._zero_world_size)])
+        param_list = param_group["params"]
 
-        sorted_params = sorted(param_list, key=lambda x: x.numel(), reverse=True)
-        for i, param in enumerate(sorted_params):
-            global_id = str(i)
-            for j in range(len(param.size())):
-                global_id = "_".join([global_id, str(param.size()[j])])
+        if "moe" in param_group.keys() and param_group["moe"]:
+            # just add current params to params_per_rank[_zero_local_rank]
+            params_per_rank[self._zero_local_rank] = list(param_list)
+            self.params_per_rank_id_dict[-1][self._zero_local_rank].append(None)
+            no_params_ranks = list(range(self._zero_world_size))
+            no_params_ranks.pop(self._zero_world_size)
 
-            rank_to_go = numel_per_rank.index(min(numel_per_rank))
-            params_per_rank[rank_to_go].append(param)
-            self.params_per_rank_id_dict[-1][rank_to_go].append(global_id)
-            numel_per_rank[rank_to_go] += param.numel()
+        else:
+            sorted_params = sorted(param_list, key=lambda x: x.numel(), reverse=True)
+            for i, param in enumerate(sorted_params):
+                global_id = str(i)
+                for j in range(len(param.size())):
+                    global_id = "_".join([global_id, str(param.size()[j])])
 
-        # check whether any rank is not assigned to parameters.
-        for rank, params in enumerate(params_per_rank):
-            if len(params) == 0:
-                no_params_ranks.append(rank)
+                rank_to_go = numel_per_rank.index(min(numel_per_rank))
+                params_per_rank[rank_to_go].append(param)
+                self.params_per_rank_id_dict[-1][rank_to_go].append(global_id)
+                numel_per_rank[rank_to_go] += param.numel()
 
-        if gpc.is_rank_for_log():
-            logger.info(  # pylint: disable=W1203
-                f"Number of elements on ranks: {numel_per_rank}, rank:{gpc.get_global_rank()}"
-            )
+            # check whether any rank is not assigned to parameters.
+            for rank, params in enumerate(params_per_rank):
+                if len(params) == 0:
+                    no_params_ranks.append(rank)
+
+            if gpc.is_rank_for_log():
+                logger.info(  # pylint: disable=W1203
+                    f"Number of elements on ranks: {numel_per_rank}, rank:{gpc.get_global_rank()}"
+                )
 
         return params_per_rank, set(no_params_ranks)
 
@@ -296,6 +310,7 @@ class HybridZeroOptimizer(BaseOptimizer):
         for group_id in range(self.num_param_groups):
             param_group = self._fp16_param_groups[group_id]
             for param in param_group:
+                # we should not reduce the param in moe
                 if param.requires_grad and not is_moe_param(param):
                     reduce_rank = None
 
@@ -496,6 +511,7 @@ class HybridZeroOptimizer(BaseOptimizer):
         if not self._overlap_communication:
             for group_id in range(len(self._fp16_param_groups)):
                 for param in self._fp16_param_groups[group_id]:
+                    # we should not reduce the param in moe
                     if param.grad is not None and not is_moe_param(param):
                         self._store_and_try_reduce_grads_by_bucket(param)
 

internlm/utils/parallel.py

@@ -43,7 +43,7 @@ def sync_tensor(tensor, parallel_mode):
 
 
 # TODO: will be used in expert data parallel, may can also used in sync_model_param_within_tp
-def sync_model_param_within_ep(model):
+def sync_model_param_with_ep(model):
     r"""Make sure data parameters are consistent during Data Parallel Mode.
 
     Args: