Enable mixtral 8x7b autotp

deepspeed/module_inject/auto_tp.py

@@ -133,7 +133,7 @@ def is_load_module(module):
         load_layers = [nn.Linear, nn.Embedding, nn.LayerNorm]
         load_layer_names = [
             "LPLayerNorm", "SharedEmbedding", "OPTLearnedPositionalEmbedding", "LlamaRMSNorm", "FalconLinear",
-            "MistralRMSNorm", "T5LayerNorm"
+            "MistralRMSNorm", "T5LayerNorm", "MixtralRMSNorm"
         ]
         return module.__class__ in load_layers or module._get_name() in load_layer_names
 
@@ -303,6 +303,9 @@ def tp_parser(model):
                 elif 'self_attention.dense' in layer and 'falcon' in str(
                         type(module)):  # this is a hack to get the right linear layer for this model!
                     gem_list = gem_list + [layer]
+                # Mixtral-7x8b used w2*act(w1*w3) linear. need to replace w2 to linearallreduce.
+                elif 'w2' in layer and 'Mixtral' in str(type(module)):
+                    gem_list = gem_list + [layer]
 
             layer_list = []
             if gem_list != []:
@@ -322,6 +325,9 @@ def _replace(self, child, name, conv_linear_layer):
             return
         weight_shape = child.weight.shape
         mp_replace = ReplaceWithTensorSlicing(mp_group=self.mp_group)
+        # For mixtral-7x8b, need to skip MoE gate linear replace.
+        if name == "block_sparse_moe.gate":
+            return child
         if name in self.all_reduce_linears:
             # if conv_linear_layer [weight_shape[1], weight_shape[0] // mp_size]
             # else [weight_shape[0], weight_shape[1] // mp_size]