DeepSpeedZeroOptimizer: refactor bit16 flattening to support more accelerators

deepspeed/runtime/zero/stage_1_and_2.py

@@ -75,11 +75,6 @@ def get_alignment_padding(tensor_list, alignment):
     return (alignment - remainder) if remainder else remainder
 
 
-def move_to_cpu(tensor_list):
-    for tensor in tensor_list:
-        tensor.data = tensor.data.cpu()
-
-
 def print_rank_msg(msg):
     print(f"rank {dist.get_rank()} - {msg}")
 
@@ -294,6 +289,7 @@ def __init__(self,
 
         self.round_robin_bit16_groups = []
         self.round_robin_bit16_indices = []
+        self.round_robin_bit16_meta = []
 
         # Use different parallel to do all_to_all_reduce related things
         # padding on each partition for alignment purposes
@@ -316,7 +312,14 @@ def __init__(self,
 
             see_memory_usage(f"Before moving param group {i} to CPU")
             # move all the parameters to cpu to free up GPU space for creating flat buffer
-            move_to_cpu(self.bit16_groups[i])
+
+            # Create temp CPU param copies, free accelerator tensors
+            orig_group_numel = 0
+            for param in self.bit16_groups[i]:
+                param.cpu_data = param.data.cpu()
+                param.data = torch.empty(1).to(param.device)
+                orig_group_numel += param.numel()
+
             empty_cache()
             see_memory_usage(f"After moving param group {i} to CPU", force=False)
 
@@ -334,18 +337,31 @@ def __init__(self,
             self.round_robin_bit16_groups.append(round_robin_tensors)
             self.round_robin_bit16_indices.append(round_robin_indices)
 
-            # create flat buffer in CPU and move to GPU
-            self.bit16_groups_flat.append(
-                self.flatten_dense_tensors_aligned(
-                    self.round_robin_bit16_groups[i],
-                    self.nccl_start_alignment_factor * dist.get_world_size(group=self.real_dp_process_group[i])).to(
-                        get_accelerator().current_device_name()))
+            # Create meta tensors list, ordered according to round_robin_tensors
+            meta_tensors = []
+            for param in round_robin_tensors:
+                meta_tensors.append(torch.zeros_like(param.cpu_data, device="meta"))
+            self.round_robin_bit16_meta.append(meta_tensors)
+
+            # create flat buffer in CPU
+            flattened_buffer = self.flatten_dense_tensors_aligned(
+                self.round_robin_bit16_groups[i],
+                self.nccl_start_alignment_factor * dist.get_world_size(group=self.real_dp_process_group[i]),
+                use_cpu_data=True)
+
+            # free temp CPU params
+            for param in self.bit16_groups[i]:
+                del param.cpu_data
+
+            # Move CPU flat tensor to the accelerator memory.
+            self.bit16_groups_flat.append(flattened_buffer.to(get_accelerator().current_device_name()))
+            del flattened_buffer
+
             see_memory_usage(f"After flattening and moving param group {i} to GPU", force=False)
 
             # Record padding required for alignment
             if partition_id == dist.get_world_size(group=self.real_dp_process_group[i]) - 1:
-                padding = self.bit16_groups_flat[i].numel() - sum(
-                    [t.numel() for t in self.round_robin_bit16_groups[i]])
+                padding = self.bit16_groups_flat[i].numel() - orig_group_numel
             else:
                 padding = 0
             self.groups_padding.append(padding)
@@ -596,8 +612,7 @@ def _configure_moe_settings(self):
         assert self.ep_process_group is not None, "Expert parallel group should be configured with MoE"
 
     def _update_model_bit16_weights(self, group_index):
-        updated_params = self.unflatten(self.bit16_groups_flat[group_index],
-                                        self.round_robin_bit16_groups[group_index])
+        updated_params = self.unflatten(self.bit16_groups_flat[group_index], self.round_robin_bit16_meta[group_index])
         for p, q in zip(self.round_robin_bit16_groups[group_index], updated_params):
             p.data = q.data
 
@@ -887,7 +902,8 @@ def report_ipg_memory_usage(self, tag, param_elems):
         )
 
     # create a flat tensor aligned at the alignment boundary
-    def flatten_dense_tensors_aligned(self, tensor_list, alignment):
+    def flatten_dense_tensors_aligned(self, tensor_list, alignment, use_cpu_data=False):
+        tensor_list = [param.cpu_data for param in tensor_list] if use_cpu_data else tensor_list
         return self.flatten(align_dense_tensors(tensor_list, alignment))
 
     ############### Independent Partition Gradient ########################