Merge branch 'master' into ds_overlap_fix

accelerator/hpu_accelerator.py

@@ -21,8 +21,8 @@ def __init__(self):
         self.apply_hpu_workarounds()
         try:
             import habana_frameworks.torch.hpu as hpu
-            hpu.setDeterministic(True)
             self.hpu = hpu
+            torch.use_deterministic_algorithms(True)
         except ImportError as e:
             raise ValueError(
                 f"HPU_Accelerator requires habana_frameworks.torch.hpu, which is not installed on this system.")

deepspeed/runtime/zero/stage_1_and_2.py

@@ -310,6 +310,7 @@ def __init__(self,
             for param in param_group['params']:
                 if param.requires_grad:
                     param.grad_accum = None
+                    param.param_idx_in_group = len(trainable_parameters)
                     trainable_parameters.append(param)
             self.bit16_groups.append(trainable_parameters)
 
@@ -961,7 +962,7 @@ def reduce_independent_p_g_buckets_and_remove_grads(self, param, i):
         assert grad_reduc is not None, f"rank {dist.get_rank()} - Invalid to reduce Param {param_id} with None gradient"
 
         self.grads_in_ipg_bucket.append(grad_reduc)
-        self.params_in_ipg_bucket.append((i, param, param_id))
+        self.params_in_ipg_bucket.append((i, param.param_idx_in_group, param_id))
 
         #make sure the average tensor function knows how to average the gradients
         if is_moe_param(param):
@@ -1067,7 +1068,8 @@ def average_tensor(self, tensor):
 
             process_group = self.dp_process_group
             # count = 0
-            for i, param, param_id in self.params_in_ipg_bucket:
+            for i, param_idx_in_group, param_id in self.params_in_ipg_bucket:
+                param = self.bit16_groups[i][param_idx_in_group]
 
                 process_group = self.dp_process_group
 
@@ -1383,7 +1385,8 @@ def reduce_ipg_grads(self):
             stream = get_accelerator().current_stream()
 
         with get_accelerator().stream(stream):
-            for _, param, param_id in self.params_in_ipg_bucket:
+            for group_idx, param_idx_in_group, param_id in self.params_in_ipg_bucket:
+                param = self.bit16_groups[group_idx][param_idx_in_group]
 
                 assert self.params_already_reduced[param_id] == False, \
                     f"The parameter {param_id} has already been reduced. \

tests/unit/moe/test_moe.py

@@ -93,7 +93,8 @@ def strict_average_tensor(tensor):
             process_group = optimizer.dp_process_group
             curr_size = 0
             pg_offsets = []
-            for i, param, param_id in optimizer.params_in_ipg_bucket:
+            for i, param_idx, param_id in optimizer.params_in_ipg_bucket:
+                param = optimizer.bit16_groups[i][param_idx]
                 process_group = optimizer.dp_process_group
                 if optimizer.ipg_bucket_has_moe_params:
                     process_group = optimizer.expert_dp_process_group[param.group_name] if is_moe_param(