[examples]adding tp to PaLM (#2319)

examples/language/palm/train.py

@@ -104,6 +104,48 @@ def gemini_zero_dpp(model: torch.nn.Module, pg: ProcessGroup, placememt_policy:
         raise NotImplemented(f"CAI version {cai_version} is not supported")
     return model
 
+## Parameter Sharding Strategies for Tensor Parallelism
+def split_param_single_dim_tp1d(dim: int, param: ColoParameter, pg: ProcessGroup):
+    spec = (ShardSpec([dim], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D))
+    param.set_tensor_spec(*spec)
+
+
+def split_param_row_tp1d(param: ColoParameter, pg: ProcessGroup):
+    split_param_single_dim_tp1d(0, param, pg)
+
+
+def split_param_col_tp1d(param: ColoParameter, pg: ProcessGroup):
+    split_param_single_dim_tp1d(-1, param, pg)
+
+# Tensor Parallel
+def tensor_parallelize(model: torch.nn.Module, pg: ProcessGroup):
+    """tensor_parallelize
+    Sharding the Model Parameters.
+    Args:
+        model (torch.nn.Module): a torch module to be sharded
+    """
+    for mn, module in model.named_modules():
+        for pn, param in module.named_parameters(recurse=False):
+            if hasattr(param, 'visited'):
+                continue
+            param.set_dist_spec(ReplicaSpec())
+            if 'net.0' in mn:
+                split_param_col_tp1d(param, pg)    # colmn slice
+            elif 'to_q' in mn:
+                split_param_col_tp1d(param, pg)    # colmn slice
+            elif 'to_kv' in mn:
+                split_param_row_tp1d(param, pg)    # row slice
+            elif 'to_out' in mn:
+                split_param_row_tp1d(param, pg)    # row slice
+            elif '1.1' in mn:
+                split_param_col_tp1d(param, pg)    # colmn slice
+            elif '1.2' in mn:
+                split_param_row_tp1d(param, pg)    # row slice
+            else:
+                param.set_dist_spec(ReplicaSpec())
+
+            param.visited = True
+
 
 args = parse_args()
 if args.distplan not in ["colossalai", "pytorch"]:
@@ -150,7 +192,7 @@ if args.distplan == "colossalai":
         model = AutoregressiveWrapper(model, max_seq_len=SEQ_LEN)
 
     pg = default_pg
-    #tensor_parallelize(model, pg)
+    tensor_parallelize(model, pg)
     model = gemini_zero_dpp(model, pg, args.placement)
 
     #optimizer