[shardformer] adapted T5 and LLaMa test to use kit (#4049)

* [shardformer] adapted T5 and LLaMa test to use kit

* polish code

tests/test_shardformer/test_model/_utils.py

@@ -0,0 +1,38 @@
+import copy
+
+from colossalai.shardformer import ShardConfig, ShardFormer
+
+
+def build_model(world_size, model_fn):
+    # create new model
+    org_model = model_fn().cuda()
+
+    # shard model
+    shard_config = ShardConfig(tensor_parallel_size=world_size)
+    model_copy = copy.deepcopy(org_model)
+    shard_former = ShardFormer(shard_config=shard_config)
+    shard_former.init_distributed()
+    sharded_model = shard_former.shard_model(model_copy)
+
+    return org_model, sharded_model
+
+
+def run_forward(original_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn):
+    # prepare input
+    data = data_gen_fn()
+    data = {k: v.cuda() for k, v in data.items()}
+
+    # switch to train mode
+    original_model.train()
+    sharded_model.train()
+
+    # run forward
+    org_output = original_model(**data)
+    org_output = output_transform_fn(org_output)
+    org_loss = loss_fn(org_output)
+
+    shard_output = sharded_model(**data)
+    shard_output = output_transform_fn(shard_output)
+    shard_loss = loss_fn(shard_output)
+
+    return org_output, org_loss, shard_output, shard_loss