[shardformer] made tensor parallelism configurable (#4144)

* [shardformer] made tensor parallelism configurable

* polish code

tests/test_shardformer/test_model/_utils.py

@@ -3,12 +3,13 @@ import copy
 from colossalai.shardformer import ShardConfig, ShardFormer
 
 
-def build_model(model_fn):
+def build_model(model_fn, enable_fused_normalization=True, enable_tensor_parallelism=True):
     # create new model
     org_model = model_fn().cuda()
 
     # shard model
-    shard_config = ShardConfig(enable_fused_normalization=True)
+    shard_config = ShardConfig(enable_fused_normalization=enable_fused_normalization,
+                               enable_tensor_parallelism=enable_tensor_parallelism)
     model_copy = copy.deepcopy(org_model)
     shard_former = ShardFormer(shard_config=shard_config)
     sharded_model = shard_former.optimize(model_copy).cuda()

tests/test_shardformer/test_model/test_shard_bert.py

@@ -3,7 +3,14 @@ import torch
 
 import colossalai
 from colossalai.logging import disable_existing_loggers
-from colossalai.testing import assert_hf_output_close, clear_cache_before_run, rerun_if_address_is_in_use, spawn
+from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor
+from colossalai.testing import (
+    assert_hf_output_close,
+    clear_cache_before_run,
+    parameterize,
+    rerun_if_address_is_in_use,
+    spawn,
+)
 from tests.kit.model_zoo import model_zoo
 from tests.test_shardformer.test_model._utils import build_model, run_forward
 
@@ -33,34 +40,48 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo
     # compare self attention grad
     org_grad = bert.encoder.layer[0].attention.self.query.weight.grad
     shard_grad = sharded_bert.encoder.layer[0].attention.self.query.weight.grad
+    shard_weight = sharded_bert.encoder.layer[0].attention.self.query.weight
 
-    shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)]
-    shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad)
-    all_shard_grad = torch.cat(shard_grad_list, dim=0)
+    if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight):
+        shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)]
+        shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad)
+        all_shard_grad = torch.cat(shard_grad_list, dim=0)
+    else:
+        all_shard_grad = shard_grad
     assert torch.allclose(org_grad, all_shard_grad,
                           atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}"
 
     # compare embedding grad
     org_grad = bert.embeddings.word_embeddings.weight.grad
     shard_grad = sharded_bert.embeddings.word_embeddings.weight.grad
+    shard_weight = sharded_bert.embeddings.word_embeddings.weight
+
+    if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight):
+        shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)]
+        shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad)
+        all_shard_grad = torch.cat(shard_grad_list, dim=0)
+    else:
+        all_shard_grad = shard_grad
 
-    shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)]
-    shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad)
-    all_shard_grad = torch.cat(shard_grad_list, dim=0)
     assert torch.allclose(org_grad, all_shard_grad,
                           atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}"
 
 
+@parameterize('enable_fused_normalization', [True, False])
+@parameterize('enable_tensor_parallelism', [True, False])
+def run_bert_test(enable_fused_normalization, enable_tensor_parallelism):
+    sub_model_zoo = model_zoo.get_sub_registry('transformers_bert')
+    for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
+        org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism)
+        check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn)
+
+    torch.cuda.empty_cache()
+
+
 def check_bert(rank, world_size, port):
     disable_existing_loggers()
     colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
-
-    sub_model_zoo = model_zoo.get_sub_registry('transformers_bert')
-    for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
-        org_model, sharded_model = build_model(model_fn)
-        check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn)
-
-    torch.cuda.empty_cache()
+    run_bert_test()
 
 
 @pytest.mark.dist

tests/test_shardformer/test_model/test_shard_bloom.py

@@ -3,7 +3,14 @@ import torch
 
 import colossalai
 from colossalai.logging import disable_existing_loggers
-from colossalai.testing import assert_hf_output_close, clear_cache_before_run, rerun_if_address_is_in_use, spawn
+from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor
+from colossalai.testing import (
+    assert_hf_output_close,
+    clear_cache_before_run,
+    parameterize,
+    rerun_if_address_is_in_use,
+    spawn,
+)
 from tests.kit.model_zoo import model_zoo
 from tests.test_shardformer.test_model._utils import build_model, run_forward
 
@@ -32,10 +39,14 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo
     # check attention grad
     org_grad = bloom.h[0].self_attention.query_key_value.weight.grad
     shard_grad = sharded_bloom.h[0].self_attention.query_key_value.weight.grad
+    shard_weight = sharded_bloom.h[0].self_attention.query_key_value.weight
 
-    shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)]
-    torch.distributed.all_gather(shard_grad_list, shard_grad)
-    all_shard_grad = torch.cat(shard_grad_list, dim=0)
+    if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight):
+        shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)]
+        torch.distributed.all_gather(shard_grad_list, shard_grad)
+        all_shard_grad = torch.cat(shard_grad_list, dim=0)
+    else:
+        all_shard_grad = shard_grad
 
     assert torch.allclose(org_grad, all_shard_grad,
                           atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}"
@@ -43,25 +54,33 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo
     # check embedding weights
     org_grad = bloom.word_embeddings.weight.grad
     shard_grad = sharded_bloom.word_embeddings.weight.grad
+    shard_weight = sharded_bloom.word_embeddings.weight
 
-    shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)]
-    torch.distributed.all_gather(shard_grad_list, shard_grad)
-    all_shard_grad = torch.cat(shard_grad_list, dim=0)
+    if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight):
+        shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)]
+        torch.distributed.all_gather(shard_grad_list, shard_grad)
+        all_shard_grad = torch.cat(shard_grad_list, dim=0)
+    else:
+        all_shard_grad = shard_grad
 
     assert torch.allclose(org_grad, all_shard_grad,
                           atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}"
 
 
+@parameterize('enable_fused_normalization', [True, False])
+@parameterize('enable_tensor_parallelism', [True, False])
+def run_bloom_test(enable_fused_normalization, enable_tensor_parallelism):
+    sub_model_zoo = model_zoo.get_sub_registry('transformers_bloom')
+    for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
+        org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism)
+        check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn)
+    torch.cuda.empty_cache()
+
+
 def check_bloom(rank, world_size, port):
     disable_existing_loggers()
     colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
-
-    sub_model_zoo = model_zoo.get_sub_registry('transformers_bloom')
-    for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
-        org_model, sharded_model = build_model(model_fn)
-        check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn)
-
-    torch.cuda.empty_cache()
+    run_bloom_test()
 
 
 @pytest.mark.dist

tests/test_shardformer/test_model/test_shard_gpt2.py

@@ -3,7 +3,14 @@ import torch
 
 import colossalai
 from colossalai.logging import disable_existing_loggers
-from colossalai.testing import assert_hf_output_close, clear_cache_before_run, rerun_if_address_is_in_use, spawn
+from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor
+from colossalai.testing import (
+    assert_hf_output_close,
+    clear_cache_before_run,
+    parameterize,
+    rerun_if_address_is_in_use,
+    spawn,
+)
 from tests.kit.model_zoo import model_zoo
 from tests.test_shardformer.test_model._utils import build_model, run_forward
 
@@ -32,11 +39,14 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo
     # check mlp grad
     org_grad = org_model.h[0].mlp.c_fc.weight.grad
     shard_grad = sharded_model.h[0].mlp.c_fc.weight.grad
+    shard_weight = sharded_model.h[0].mlp.c_fc.weight
 
-    shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)]
-    shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad)
-    all_shard_grad = torch.cat(shard_grad_list, dim=1)
-
+    if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight):
+        shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)]
+        shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad)
+        all_shard_grad = torch.cat(shard_grad_list, dim=1)
+    else:
+        all_shard_grad = shard_grad
     assert torch.allclose(
         org_grad, all_shard_grad,
         atol=1e-5), f"shard model grad is not equal to origin model grad\n{org_grad}\n{all_shard_grad}"
@@ -44,25 +54,33 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo
     # check embedding weights
     org_grad = org_model.wte.weight.grad
     shard_grad = sharded_model.wte.weight.grad
-    shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)]
-    shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad)
-    all_shard_grad = torch.cat(shard_grad_list, dim=0)
+    shard_weight = sharded_model.wte.weight
 
+    if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight):
+        shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)]
+        shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad)
+        all_shard_grad = torch.cat(shard_grad_list, dim=0)
+    else:
+        all_shard_grad = shard_grad
     assert torch.allclose(
         org_grad, all_shard_grad,
         atol=1e-5), f"shard model grad is not equal to origin model grad\n{org_grad}\n{all_shard_grad}"
 
 
+@parameterize('enable_fused_normalization', [True, False])
+@parameterize('enable_tensor_parallelism', [True, False])
+def run_gpt2_test(enable_fused_normalization, enable_tensor_parallelism):
+    sub_model_zoo = model_zoo.get_sub_registry('transformers_gpt')
+    for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
+        org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism)
+        check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn)
+    torch.cuda.empty_cache()
+
+
 def check_gpt2(rank, world_size, port):
     disable_existing_loggers()
     colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
-
-    sub_model_zoo = model_zoo.get_sub_registry('transformers_gpt')
-    for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
-        org_model, sharded_model = build_model(model_fn)
-        check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn)
-
-    torch.cuda.empty_cache()
+    run_gpt2_test()
 
 
 @pytest.mark.dist

tests/test_shardformer/test_model/test_shard_llama.py

@@ -5,7 +5,14 @@ import torch
 
 import colossalai
 from colossalai.logging import disable_existing_loggers
-from colossalai.testing import assert_hf_output_close, clear_cache_before_run, rerun_if_address_is_in_use, spawn
+from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor
+from colossalai.testing import (
+    assert_hf_output_close,
+    clear_cache_before_run,
+    parameterize,
+    rerun_if_address_is_in_use,
+    spawn,
+)
 from tests.kit.model_zoo import model_zoo
 from tests.test_shardformer.test_model._utils import build_model, run_forward
 
@@ -37,33 +44,46 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo
     # check attention grad
     org_grad = llama_model.layers[0].self_attn.q_proj.weight.grad
     shard_grad = shard_llama_model.layers[0].self_attn.q_proj.weight.grad
-    shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(4)]
-    shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad)
-    all_shard_grad = torch.cat(shard_grad_list, dim=0)
+    shard_weight = shard_llama_model.layers[0].self_attn.q_proj.weight
+
+    if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight):
+        shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(4)]
+        shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad)
+        all_shard_grad = torch.cat(shard_grad_list, dim=0)
+    else:
+        all_shard_grad = shard_grad
     assert torch.allclose(org_grad, all_shard_grad,
                           atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{shard_grad}"
 
     # check embedding grad
     org_grad = llama_model.embed_tokens.weight.grad
     shard_grad = shard_llama_model.embed_tokens.weight.grad
-    shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(4)]
-    shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad)
-    all_shard_grad = torch.cat(shard_grad_list, dim=0)
+    shard_weight = shard_llama_model.embed_tokens.weight
+
+    if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight):
+        shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(4)]
+        shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad)
+        all_shard_grad = torch.cat(shard_grad_list, dim=0)
+    else:
+        all_shard_grad = shard_grad
     assert torch.allclose(org_grad, all_shard_grad,
                           atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{shard_grad}"
 
 
+@parameterize('enable_fused_normalization', [True, False])
+@parameterize('enable_tensor_parallelism', [True, False])
+def run_gpt2_llama(enable_fused_normalization, enable_tensor_parallelism):
+    sub_model_zoo = model_zoo.get_sub_registry('transformers_llama')
+    for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
+        org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism)
+        check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn)
+    torch.cuda.empty_cache()
+
+
 def check_llama(rank, world_size, port):
     disable_existing_loggers()
     colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
-
-    sub_model_zoo = model_zoo.get_sub_registry('transformers_llama')
-
-    for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
-        org_model, sharded_model = build_model(model_fn)
-        check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn)
-
-    torch.cuda.empty_cache()
+    run_gpt2_llama()
 
 
 @pytest.mark.dist

tests/test_shardformer/test_model/test_shard_opt.py

@@ -6,10 +6,11 @@ import torch
 
 import colossalai
 from colossalai.logging import disable_existing_loggers
+from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor
 from colossalai.testing import (
     assert_hf_output_close,
-    check_state_dict_equal,
     clear_cache_before_run,
+    parameterize,
     rerun_if_address_is_in_use,
     spawn,
 )
@@ -42,32 +43,46 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo
     # check attention grad
     org_grad = opt_model.decoder.layers[0].self_attn.q_proj.weight.grad
     shard_grad = shard_opt_model.decoder.layers[0].self_attn.q_proj.weight.grad
-    shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(4)]
-    torch.distributed.all_gather(shard_grad_list, shard_grad)
-    all_shard_grad = torch.cat(shard_grad_list, dim=0)
+    shard_weight = shard_opt_model.decoder.layers[0].self_attn.q_proj.weight
+
+    if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight):
+        shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(4)]
+        torch.distributed.all_gather(shard_grad_list, shard_grad)
+        all_shard_grad = torch.cat(shard_grad_list, dim=0)
+    else:
+        all_shard_grad = shard_grad
     assert torch.allclose(org_grad, all_shard_grad,
                           atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}"
 
     # check embedding grad
     org_grad = opt_model.decoder.embed_tokens.weight.grad
     shard_grad = shard_opt_model.decoder.embed_tokens.weight.grad
-    shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(4)]
-    torch.distributed.all_gather(shard_grad_list, shard_grad)
-    all_shard_grad = torch.cat(shard_grad_list, dim=0)
+    shard_weight = shard_opt_model.decoder.embed_tokens.weight
+
+    if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight):
+        shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(4)]
+        torch.distributed.all_gather(shard_grad_list, shard_grad)
+        all_shard_grad = torch.cat(shard_grad_list, dim=0)
+    else:
+        all_shard_grad = shard_grad
     assert torch.allclose(org_grad, all_shard_grad,
                           atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}"
 
 
+@parameterize('enable_fused_normalization', [True, False])
+@parameterize('enable_tensor_parallelism', [True, False])
+def run_t5_test(enable_fused_normalization, enable_tensor_parallelism):
+    sub_model_zoo = model_zoo.get_sub_registry('transformers_opt')
+    for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
+        org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism)
+        check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn)
+    torch.cuda.empty_cache()
+
+
 def check_OPTModel(rank, world_size, port):
     disable_existing_loggers()
     colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
-
-    sub_model_zoo = model_zoo.get_sub_registry('transformers_opt')
-    for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
-        org_model, sharded_model = build_model(model_fn)
-        check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn)
-
-    torch.cuda.empty_cache()
+    run_t5_test()
 
 
 @pytest.mark.dist

tests/test_shardformer/test_model/test_shard_t5.py

@@ -5,7 +5,14 @@ import torch
 
 import colossalai
 from colossalai.logging import disable_existing_loggers
-from colossalai.testing import assert_hf_output_close, clear_cache_before_run, rerun_if_address_is_in_use, spawn
+from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor
+from colossalai.testing import (
+    assert_hf_output_close,
+    clear_cache_before_run,
+    parameterize,
+    rerun_if_address_is_in_use,
+    spawn,
+)
 from tests.kit.model_zoo import model_zoo
 from tests.test_shardformer.test_model._utils import build_model, run_forward
 
@@ -27,19 +34,28 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo
     # check attention grad
     org_grad = org_model.encoder.block[0].layer[0].SelfAttention.q.weight.grad
     shard_grad = sharded_model.encoder.block[0].layer[0].SelfAttention.q.weight.grad
+    shard_weight = sharded_model.encoder.block[0].layer[0].SelfAttention.q.weight
 
-    shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)]
-    shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad)
-    all_shard_grad = torch.cat(shard_grad_list, dim=0)
+    if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight):
+        shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)]
+        shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad)
+        all_shard_grad = torch.cat(shard_grad_list, dim=0)
+    else:
+        all_shard_grad = shard_grad
     assert torch.allclose(org_grad, all_shard_grad,
                           atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{shard_grad}"
 
     # check self attention embed
     org_grad = org_model.encoder.block[0].layer[0].SelfAttention.relative_attention_bias.weight.grad
     shard_grad = sharded_model.encoder.block[0].layer[0].SelfAttention.relative_attention_bias.weight.grad
-    shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)]
-    torch.distributed.all_gather(shard_grad_list, shard_grad)
-    all_shard_grad = torch.cat(shard_grad_list, dim=1)
+    shard_weight = sharded_model.encoder.block[0].layer[0].SelfAttention.relative_attention_bias.weight
+
+    if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight):
+        shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)]
+        torch.distributed.all_gather(shard_grad_list, shard_grad)
+        all_shard_grad = torch.cat(shard_grad_list, dim=1)
+    else:
+        all_shard_grad = shard_grad
     assert torch.allclose(org_grad, all_shard_grad,
                           atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}"
 
@@ -52,23 +68,32 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo
         assert sharded_model.shared.weight.data.data_ptr() == sharded_model.lm_head.weight.data.data_ptr()
 
     shard_grad = sharded_model.shared.weight.grad
-    shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)]
-    torch.distributed.all_gather(shard_grad_list, shard_grad)
-    all_shard_grad = torch.cat(shard_grad_list, dim=0)
+    shard_weight = sharded_model.shared.weight
+
+    if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight):
+        shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)]
+        torch.distributed.all_gather(shard_grad_list, shard_grad)
+        all_shard_grad = torch.cat(shard_grad_list, dim=0)
+    else:
+        all_shard_grad = shard_grad
     assert torch.allclose(org_grad, all_shard_grad,
                           atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}"
 
 
+@parameterize('enable_fused_normalization', [True, False])
+@parameterize('enable_tensor_parallelism', [True, False])
+def run_t5_test(enable_fused_normalization, enable_tensor_parallelism):
+    sub_model_zoo = model_zoo.get_sub_registry('transformers_t5')
+    for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
+        org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism)
+        check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn)
+    torch.cuda.empty_cache()
+
+
 def check_t5(rank, world_size, port):
     disable_existing_loggers()
     colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
-
-    sub_model_zoo = model_zoo.get_sub_registry('transformers_t5')
-
-    for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
-        org_model, sharded_model = build_model(model_fn)
-        check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn)
-    torch.cuda.empty_cache()
+    run_t5_test()
 
 
 @pytest.mark.dist