make input and output buffer dtypes same

axonn/intra_layer/__init__.py

@@ -43,11 +43,19 @@ def gather(
 OVERLAP_REDUCE_SCATTER = False
 OVERLAP_ALL_REDUCE = False
 ALL_GATHER_ITERATOR = None
-ALL_GATHER_DTYPE = torch.bfloat16
+ALL_GATHER_DTYPE = torch.float32
+REDUCE_SCATTER_DTYPE = torch.bfloat16
 handles = []
 pending_grad_accumulations = []
 weights_cache = {}
 
+def set_all_gather_dtype(dtype):
+    global ALL_GATHER_DTYPE
+    ALL_GATHER_DTYPE = dtype
+
+def set_reduce_scatter_dtype(dtype):
+    global REDUCE_SCATTER_DTYPE
+    REDUCE_SCATTER_DTYPE = dtype
 
 def register_handle(handle):
     # ToDo: This might be unnecesary since

axonn/intra_layer/communication.py

@@ -44,12 +44,13 @@ def _gather(input_, dim, process_group=None, cache=False):
         input_ = input_.contiguous()
         # Size and dimension.
         rank = dist.get_rank(process_group)
+
+        from axonn.intra_layer import ALL_GATHER_DTYPE
 
         tensor_list = [
-            torch.empty_like(input_) for _ in range(dist.get_world_size(process_group))
+            torch.empty_like(input_, dtype=ALL_GATHER_DTYPE) for _ in range(dist.get_world_size(process_group))
         ]
-        tensor_list[rank] = input_
-        dist.all_gather(tensor_list, input_, group=process_group)
+        dist.all_gather(tensor_list, input_.to(ALL_GATHER_DTYPE), group=process_group)
 
         # Note: torch.cat already creates a contiguous tensor.
         output = torch.cat(tensor_list, dim=dim).contiguous()
@@ -70,17 +71,20 @@ def _reduce_scatter(input_, dim, process_group=None, overlap_comm=False):
     assert input_.shape[dim] % total_chunks == 0
     tensor_shape = list(input_.shape)
     tensor_shape[dim] //= total_chunks
+
+    from axonn.intra_layer import REDUCE_SCATTER_DTYPE
+
     output = torch.empty(
-        tensor_shape, dtype=input_.dtype, device=torch.cuda.current_device()
+        tensor_shape, dtype=REDUCE_SCATTER_DTYPE, device=torch.cuda.current_device()
     )
 
     if hasattr(torch.distributed, "reduce_scatter_tensor"):
         handle = torch.distributed.reduce_scatter_tensor(
-            output, input_, group=process_group, async_op=overlap_comm
+            output, input_.to(REDUCE_SCATTER_DTYPE), group=process_group, async_op=overlap_comm
         )
     else:
         handle = torch.distributed._reduce_scatter_base(
-            output, input_, group=process_group, async_op=overlap_comm
+            output, input_.to(REDUCE_SCATTER_DTYPE), group=process_group, async_op=overlap_comm
         )
 
     if overlap_comm:

axonn/intra_layer/fully_connected.py

@@ -79,6 +79,7 @@ def forward(
         ctx.backward_comm_async = backward_comm_async
         if not forward_comm_async:
             output = input_.matmul(weight.t())
+
             dist.all_reduce(output, group=forward_all_reduce_group, async_op=False)
         else:
             assert input_.shape[0] % 2 == 0