Fix error caused by all_reduce call in domino (#6880)

Fix #6851 
Initialize communication backend to fix error caused by all_reduce call
in the Domino transformer layer.
Verified correctness in local test.

---------

Co-authored-by: Olatunji Ruwase <[email protected]>
Co-authored-by: Logan Adams <[email protected]>

deepspeed/runtime/domino/transformer.py

@@ -6,8 +6,7 @@
 import torch
 import torch.nn.functional as F
 from torch.nn.parameter import Parameter
-import deepspeed
-from deepspeed import comm as dist
+import deepspeed.comm as dist
 from deepspeed.accelerator import get_accelerator
 
 
@@ -97,7 +96,7 @@ def backward(ctx, grad_output):
             return grad_output
 
         # Async All-reduce.
-        handle = deepspeed.comm.all_reduce(grad_output, group=ctx.mpu.get_tensor_model_parallel_group(), async_op=True)
+        handle = dist.all_reduce(grad_output, group=ctx.mpu.get_tensor_model_parallel_group(), async_op=True)
         ctx.handle_dic[ctx.h_id] = handle
         return None, grad_output, None, None
 
@@ -249,6 +248,10 @@ def __init__(self,
                  output_bias=None):
         super(DominoTransformerLayer, self).__init__()
 
+        if not dist.is_initialized():
+            dist.init_distributed()
+            assert dist.is_initialized(), "deepspeed.comm is not initialized!"
+
         self.llama_model = config.llama_model
         self.layer_number = layer_number
         self.layer_type = layer_type
@@ -358,18 +361,14 @@ def forward(self, hidden_states, attention_mask, rotary_pos_emb=None):
                 layernorm_output0,
                 attention_mask,
                 rotary_pos_emb=rotary_pos_emb)
-        handle0 = deepspeed.comm.all_reduce(attention_output0,
-                                            group=self.mpu.get_tensor_model_parallel_group(),
-                                            async_op=True)
+        handle0 = dist.all_reduce(attention_output0, group=self.mpu.get_tensor_model_parallel_group(), async_op=True)
 
         attention_output1, attention_bias1 = \
             self.self_attention(
             layernorm_output1,
             attention_mask,
             rotary_pos_emb=rotary_pos_emb)
-        handle1 = deepspeed.comm.all_reduce(attention_output1,
-                                            group=self.mpu.get_tensor_model_parallel_group(),
-                                            async_op=True)
+        handle1 = dist.all_reduce(attention_output1, group=self.mpu.get_tensor_model_parallel_group(), async_op=True)
         handle0.wait()
 
         # Residual0 connection.
@@ -413,7 +412,7 @@ def forward(self, hidden_states, attention_mask, rotary_pos_emb=None):
             output0 = output0 + bias_c
         output0 = self.mlp_activation_func(output0)
         output0 = torch.matmul(output0, self.weight_r.t())
-        handle2 = deepspeed.comm.all_reduce(output0, group=self.mpu.get_tensor_model_parallel_group(), async_op=True)
+        handle2 = dist.all_reduce(output0, group=self.mpu.get_tensor_model_parallel_group(), async_op=True)
 
         handle1.wait()
 
@@ -425,7 +424,7 @@ def forward(self, hidden_states, attention_mask, rotary_pos_emb=None):
         if bias_c is not None:
             output1 = output1 + bias_c
         output1 = torch.matmul(output1, self.weight_r.t())
-        deepspeed.comm.all_reduce(output1, group=self.mpu.get_tensor_model_parallel_group())
+        dist.all_reduce(output1, group=self.mpu.get_tensor_model_parallel_group())
 
         handle2.wait()