support kit use for bert/gpt test (#4055)

* support kit use for bert test

* support kit test for gpt2

colossalai/shardformer/policies/autopolicy.py

@@ -25,17 +25,19 @@ class PolicyLocation:
 _POLICY_LIST = {
     # BERT
     "transformers.models.bert.modeling_bert.BertModel":
-        PolicyLocation(file_name="bert", class_name="BertPolicy"),
+        PolicyLocation(file_name="bert", class_name="BertModelPolicy"),
     "transformers.models.bert.modeling_bert.BertForPreTraining":
         PolicyLocation(file_name="bert", class_name="BertForPretrainingPolicy"),
-    "transformers.models.bert.modeling_bert.BertForMaskedLM":
-        PolicyLocation(file_name="bert", class_name="BertForMaskedLMPolicy"),
     "transformers.models.bert.modeling_bert.BertLMHeadModel":
         PolicyLocation(file_name="bert", class_name="BertLMHeadModelPolicy"),
-    "transformers.models.bert.modeling_bert.BertForNextSentencePrediction":
-        PolicyLocation(file_name="bert", class_name="BertForNextSentencePredictionPolicy"),
+    "transformers.models.bert.modeling_bert.BertForMaskedLM":
+        PolicyLocation(file_name="bert", class_name="BertForMaskedLMPolicy"),
     "transformers.models.bert.modeling_bert.BertForSequenceClassification":
         PolicyLocation(file_name="bert", class_name="BertForSequenceClassificationPolicy"),
+    "transformers.models.bert.modeling_bert.BertForTokenClassification":
+        PolicyLocation(file_name="bert", class_name="BertForTokenClassificationPolicy"),
+    "transformers.models.bert.modeling_bert.BertForNextSentencePrediction":
+        PolicyLocation(file_name="bert", class_name="BertForNextSentencePredictionPolicy"),
     "transformers.models.bert.modeling_bert.BertForMultipleChoice":
         PolicyLocation(file_name="bert", class_name="BertForMultipleChoicePolicy"),
 
@@ -58,6 +60,14 @@ _POLICY_LIST = {
     # GPT2
     "transformers.models.gpt2.modeling_gpt2.GPT2Model":
         PolicyLocation(file_name="gpt2", class_name="GPT2ModelPolicy"),
+    "transformers.models.gpt2.modeling_gpt2.GPT2LMHeadModel":
+        PolicyLocation(file_name="gpt2", class_name="GPT2LMHeadModelPolicy"),
+    "transformers.models.gpt2.modeling_gpt2.GPT2DoubleHeadsModel":
+        PolicyLocation(file_name="gpt2", class_name="GPT2DoubleHeadsModelPolicy"),
+    "transformers.models.gpt2.modeling_gpt2.GPT2ForTokenClassification":
+        PolicyLocation(file_name="gpt2", class_name="GPT2ForTokenClassificationPolicy"),
+    "transformers.models.gpt2.modeling_gpt2.GPT2ForSequenceClassification":
+        PolicyLocation(file_name="gpt2", class_name="GPT2ForSequenceClassificationPolicy"),
 }
 
 

colossalai/shardformer/policies/bert.py

@@ -131,8 +131,8 @@ class BertForPretrainingPolicy(BertPolicy):
         return self.model
 
 
-# BertForMaskedLM
-class BertForMaskedLMPolicy(BertPolicy):
+# BertLMHeadModel
+class BertLMHeadModelPolicy(BertPolicy):
 
     def __init__(self) -> None:
         super().__init__()
@@ -162,8 +162,8 @@ class BertForMaskedLMPolicy(BertPolicy):
         return self.model
 
 
-# BertLMHeadModel
-class BertLMHeadModelPolicy(BertPolicy):
+# BertForMaskedLM
+class BertForMaskedLMPolicy(BertPolicy):
 
     def __init__(self) -> None:
         super().__init__()
@@ -193,15 +193,22 @@ class BertLMHeadModelPolicy(BertPolicy):
         return self.model
 
 
-# BertForNextSentencePrediction
-class BertForNextSentencePredictionPolicy(BertPolicy):
+# BertForSequenceClassification
+class BertForSequenceClassificationPolicy(BertPolicy):
 
     def __init__(self) -> None:
         super().__init__()
 
 
-# BertForSequenceClassification
-class BertForSequenceClassificationPolicy(BertPolicy):
+# BertForTokenClassification
+class BertForTokenClassificationPolicy(BertPolicy):
+
+    def __init__(self) -> None:
+        super().__init__()
+
+
+# BertForNextSentencePrediction
+class BertForNextSentencePredictionPolicy(BertPolicy):
 
     def __init__(self) -> None:
         super().__init__()

colossalai/shardformer/policies/gpt2.py

-from transformers.models.gpt2.modeling_gpt2 import GPT2Block, GPT2Model
+import torch.nn as nn
+from transformers.models.gpt2.modeling_gpt2 import GPT2Block, GPT2DoubleHeadsModel, GPT2LMHeadModel, GPT2Model
 
 import colossalai.shardformer.layer as col_nn
 
+from .._utils import getattr_, setattr_
 from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
 
 
@@ -82,7 +84,6 @@ class GPT2Policy(Policy):
         }
 
     def new_model_class(self):
-
         return self.model
 
     def postprocess(self):
@@ -94,3 +95,79 @@ class GPT2ModelPolicy(GPT2Policy):
 
     def __init__(self) -> None:
         super().__init__()
+
+
+# GPT2LMHeadModel
+class GPT2LMHeadModelPolicy(GPT2Policy):
+
+    def __init__(self) -> None:
+        super().__init__()
+
+    def module_policy(self):
+        module_policy = super().module_policy()
+        addon_module = {
+            GPT2LMHeadModel:
+                ModulePolicyDescription(attribute_replacement={},
+                                        param_replacement=[],
+                                        sub_module_replacement=[
+                                            SubModuleReplacementDescription(suffix="lm_head",
+                                                                            target_module=col_nn.Linear1D_Col,
+                                                                            kwargs={"gather_output": True})
+                                        ])
+        }
+        module_policy.update(addon_module)
+        return module_policy
+
+    def postprocess(self):
+        binding_map = {"transformer.wte.weight": "lm_head.weight"}
+        for k, v in binding_map.items():
+            param = getattr_(self.model, k)
+            param = nn.Parameter(param)
+            setattr_(self.model, k, param)
+            setattr_(self.model, v, param)
+        return self.model
+
+
+# GPT22DoubleHeadsModel
+class GPT2DoubleHeadsModelPolicy(GPT2Policy):
+
+    def __init__(self) -> None:
+        super().__init__()
+
+    def module_policy(self):
+        module_policy = super().module_policy()
+        addon_module = {
+            GPT2DoubleHeadsModel:
+                ModulePolicyDescription(attribute_replacement={},
+                                        param_replacement=[],
+                                        sub_module_replacement=[
+                                            SubModuleReplacementDescription(suffix="lm_head",
+                                                                            target_module=col_nn.Linear1D_Col,
+                                                                            kwargs={"gather_output": True})
+                                        ])
+        }
+        module_policy.update(addon_module)
+        return module_policy
+
+    def postprocess(self):
+        binding_map = {"transformer.wte.weight": "lm_head.weight"}
+        for k, v in binding_map.items():
+            param = getattr_(self.model, k)
+            param = nn.Parameter(param)
+            setattr_(self.model, k, param)
+            setattr_(self.model, v, param)
+        return self.model
+
+
+# GPT2ForTokenClassification
+class GPT2ForTokenClassificationPolicy(GPT2Policy):
+
+    def __init__(self) -> None:
+        super().__init__()
+
+
+# GPT2ForSequenceClassification
+class GPT2ForSequenceClassificationPolicy(GPT2Policy):
+
+    def __init__(self) -> None:
+        super().__init__()

tests/kit/model_zoo/transformers/bert.py

@@ -6,83 +6,147 @@ from ..registry import ModelAttribute, model_zoo
 # ===============================
 # Register single-sentence BERT
 # ===============================
-BATCH_SIZE = 2
-SEQ_LENGTH = 16
 
 
-def data_gen_fn():
-    input_ids = torch.zeros((BATCH_SIZE, SEQ_LENGTH), dtype=torch.int64)
-    token_type_ids = torch.zeros((BATCH_SIZE, SEQ_LENGTH), dtype=torch.int64)
-    attention_mask = torch.zeros((BATCH_SIZE, SEQ_LENGTH), dtype=torch.int64)
+# define data gen function
+def data_gen():
+    # Generated from following code snippet
+    #
+    # from transformers import BertTokenizer
+    # input = 'Hello, my dog is cute'
+    # tokenized_input = tokenizer(input, return_tensors='pt')
+    # input_ids = tokenized_input['input_ids']
+    # attention_mask = tokenized_input['attention_mask']
+    # token_type_ids = tokenized_input['token_type_ids']
+    input_ids = torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 10140, 102]], dtype=torch.int64)
+    token_type_ids = torch.tensor([[0, 0, 0, 0, 0, 0, 0, 0]], dtype=torch.int64)
+    attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1]], dtype=torch.int64)
     return dict(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask)
 
 
+def data_gen_for_lm():
+    # LM data gen
+    # the `labels` of LM is the token of the output, cause no padding, use `input_ids` as `labels`
+    data = data_gen()
+    data['labels'] = data['input_ids'].clone()
+    return data
+
+
+def data_gen_for_pretraining():
+    # pretraining data gen
+    # `next_sentence_label` is the label for next sentence prediction, 0 or 1
+    data = data_gen_for_lm()
+    data['next_sentence_label'] = torch.tensor([1], dtype=torch.int64)
+    return data
+
+
+def data_gen_for_sequence_classification():
+    # sequence classification data gen
+    # `labels` is the label for sequence classification, 0 or 1
+    data = data_gen()
+    data['labels'] = torch.tensor([1], dtype=torch.int64)
+    return data
+
+
+def data_gen_for_token_classification():
+    # token classification data gen
+    # `labels` is the type not the token id for token classification, 0 or 1
+    data = data_gen()
+    data['labels'] = torch.tensor([[1, 0, 0, 0, 0, 0, 0, 0]], dtype=torch.int64)
+    return data
+
+
+def data_gen_for_mcq():
+    # multiple choice question data gen
+    # Generated from following code snippet
+    #
+    # tokenizer = transformers.BertTokenizer.from_pretrained("bert-base-uncased")
+    # prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
+    # choice0 = "It is eaten with a fork and a knife."
+    # choice1 = "It is eaten while held in the hand."
+    # data = tokenizer([prompt, prompt], [choice0, choice1], return_tensors="pt", padding=True)
+    # data = {k: v.unsqueeze(0) for k, v in encoding.items()}
+    # data['labels'] = torch.tensor([0], dtype=torch.int64)
+    input_ids = torch.tensor([[[
+        101, 1999, 3304, 1010, 10733, 2366, 1999, 5337, 10906, 1010, 2107, 2004, 2012, 1037, 4825, 1010, 2003, 3591,
+        4895, 14540, 6610, 2094, 1012, 102, 2009, 2003, 8828, 2007, 1037, 9292, 1998, 1037, 5442, 1012, 102
+    ],
+                               [
+                                   101, 1999, 3304, 1010, 10733, 2366, 1999, 5337, 10906, 1010, 2107, 2004, 2012, 1037,
+                                   4825, 1010, 2003, 3591, 4895, 14540, 6610, 2094, 1012, 102, 2009, 2003, 8828, 2096,
+                                   2218, 1999, 1996, 2192, 1012, 102, 0
+                               ]]])
+    token_type_ids = torch.tensor(
+        [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+          [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0]]])
+    attention_mask = torch.tensor(
+        [[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+          [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0]]])
+    labels = torch.tensor([0], dtype=torch.int64)
+
+    return dict(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask, labels=labels)
+
+
+# define output transform function
 output_transform_fn = lambda x: x
 
-config = transformers.BertConfig(hidden_size=128, num_hidden_layers=2, num_attention_heads=4, intermediate_size=256)
+# define loss funciton
+loss_fn_for_bert_model = lambda x: x.pooler_output.mean()
+loss_fn = lambda x: x.loss
+
+config = transformers.BertConfig(hidden_size=128,
+                                 num_hidden_layers=2,
+                                 num_attention_heads=4,
+                                 intermediate_size=256,
+                                 hidden_dropout_prob=0,
+                                 attention_probs_dropout_prob=0)
 
 # register the BERT variants
 model_zoo.register(name='transformers_bert',
                    model_fn=lambda: transformers.BertModel(config),
-                   data_gen_fn=data_gen_fn,
+                   data_gen_fn=data_gen,
                    output_transform_fn=output_transform_fn,
+                   loss_fn=loss_fn_for_bert_model,
                    model_attribute=ModelAttribute(has_control_flow=True))
 model_zoo.register(name='transformers_bert_for_pretraining',
                    model_fn=lambda: transformers.BertForPreTraining(config),
-                   data_gen_fn=data_gen_fn,
+                   data_gen_fn=data_gen_for_pretraining,
                    output_transform_fn=output_transform_fn,
+                   loss_fn=loss_fn,
                    model_attribute=ModelAttribute(has_control_flow=True))
 model_zoo.register(name='transformers_bert_lm_head_model',
                    model_fn=lambda: transformers.BertLMHeadModel(config),
-                   data_gen_fn=data_gen_fn,
+                   data_gen_fn=data_gen_for_lm,
                    output_transform_fn=output_transform_fn,
+                   loss_fn=loss_fn,
                    model_attribute=ModelAttribute(has_control_flow=True))
 model_zoo.register(name='transformers_bert_for_masked_lm',
                    model_fn=lambda: transformers.BertForMaskedLM(config),
-                   data_gen_fn=data_gen_fn,
+                   data_gen_fn=data_gen_for_lm,
                    output_transform_fn=output_transform_fn,
+                   loss_fn=loss_fn,
                    model_attribute=ModelAttribute(has_control_flow=True))
 model_zoo.register(name='transformers_bert_for_sequence_classification',
                    model_fn=lambda: transformers.BertForSequenceClassification(config),
-                   data_gen_fn=data_gen_fn,
+                   data_gen_fn=data_gen_for_sequence_classification,
                    output_transform_fn=output_transform_fn,
+                   loss_fn=loss_fn,
                    model_attribute=ModelAttribute(has_control_flow=True))
 model_zoo.register(name='transformers_bert_for_token_classification',
                    model_fn=lambda: transformers.BertForTokenClassification(config),
-                   data_gen_fn=data_gen_fn,
+                   data_gen_fn=data_gen_for_token_classification,
                    output_transform_fn=output_transform_fn,
+                   loss_fn=loss_fn,
                    model_attribute=ModelAttribute(has_control_flow=True))
-
-
-# ===============================
-# Register multi-sentence BERT
-# ===============================
-def data_gen_for_next_sentence():
-    tokenizer = transformers.BertTokenizer.from_pretrained("bert-base-uncased")
-    prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
-    next_sentence = "The sky is blue due to the shorter wavelength of blue light."
-    encoding = tokenizer(prompt, next_sentence, return_tensors="pt")
-    return encoding
-
-
-def data_gen_for_mcq():
-    tokenizer = transformers.BertTokenizer.from_pretrained("bert-base-uncased")
-    prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
-    choice0 = "It is eaten with a fork and a knife."
-    choice1 = "It is eaten while held in the hand."
-    encoding = tokenizer([prompt, prompt], [choice0, choice1], return_tensors="pt", padding=True)
-    encoding = {k: v.unsqueeze(0) for k, v in encoding.items()}
-    return encoding
-
-
-# register the following models
 model_zoo.register(name='transformers_bert_for_next_sentence',
                    model_fn=lambda: transformers.BertForNextSentencePrediction(config),
-                   data_gen_fn=data_gen_for_next_sentence,
+                   data_gen_fn=data_gen_for_sequence_classification,
                    output_transform_fn=output_transform_fn,
+                   loss_fn=loss_fn,
                    model_attribute=ModelAttribute(has_control_flow=True))
 model_zoo.register(name='transformers_bert_for_mcq',
                    model_fn=lambda: transformers.BertForMultipleChoice(config),
                    data_gen_fn=data_gen_for_mcq,
                    output_transform_fn=output_transform_fn,
+                   loss_fn=loss_fn,
                    model_attribute=ModelAttribute(has_control_flow=True))

tests/kit/model_zoo/transformers/gpt.py

@@ -11,47 +11,86 @@ SEQ_LENGTH = 16
 
 
 def data_gen():
-    input_ids = torch.zeros((BATCH_SIZE, SEQ_LENGTH), dtype=torch.int64)
-    token_type_ids = torch.zeros((BATCH_SIZE, SEQ_LENGTH), dtype=torch.int64)
-    attention_mask = torch.zeros((BATCH_SIZE, SEQ_LENGTH), dtype=torch.int64)
-    return dict(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask)
+    # Generated from following code snippet
+    #
+    # from transformers import GPT2Tokenizer
+    # input = 'Hello, my dog is cute'
+    # tokenized_input = tokenizer(input, return_tensors='pt')
+    # input_ids = tokenized_input['input_ids']
+    # attention_mask = tokenized_input['attention_mask']
+    input_ids = torch.tensor([[15496, 11, 616, 3290, 318, 13779]], dtype=torch.int64)
+    attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1]], dtype=torch.int64)
+    return dict(input_ids=input_ids, attention_mask=attention_mask)
 
 
-def seq_classification_data_gen():
-    # batch sizes should be 1 if no padding token is defined.
-    input_ids = torch.zeros((1, SEQ_LENGTH), dtype=torch.int64)
-    token_type_ids = torch.zeros((1, SEQ_LENGTH), dtype=torch.int64)
-    attention_mask = torch.zeros((1, SEQ_LENGTH), dtype=torch.int64)
-    return dict(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask)
+def data_gen_for_lm():
+    # LM data gen
+    # the `labels` of LM is the token of the output, cause no padding, use `input_ids` as `labels`
+    data = data_gen()
+    data['labels'] = data['input_ids'].clone()
+    return data
 
 
+def data_gen_for_token_classification():
+    # token classification data gen
+    # `labels` is the type not the token id for token classification, 0 or 1
+    data = data_gen()
+    data['labels'] = torch.tensor([[0, 0, 0, 0, 0, 0]], dtype=torch.int64)
+    return data
+
+
+def data_gen_for_sequence_classification():
+    # sequence classification data gen
+    data = data_gen()
+    data['labels'] = torch.tensor([0], dtype=torch.int64)
+    return data
+
+
+# define output transform function
 output_transform_fn = lambda x: x
 
-config = transformers.GPT2Config(n_position=64, n_layer=2, n_head=4)
+# define loss function
+loss_fn_for_gpt2_model = lambda x: x.last_hidden_state.mean()
+loss_fn = lambda x: x.loss
+
+config = transformers.GPT2Config(n_layer=2,
+                                 n_head=4,
+                                 vocab_size=50258,
+                                 attn_pdrop=0,
+                                 embd_pdrop=0,
+                                 resid_pdrop=0,
+                                 summary_first_dropout=0,
+                                 hidden_dropout=0,
+                                 problem_type="single_label_classification")
 
 # register the following models
 model_zoo.register(name='transformers_gpt',
                    model_fn=lambda: transformers.GPT2Model(config),
                    data_gen_fn=data_gen,
                    output_transform_fn=output_transform_fn,
+                   loss_fn=loss_fn_for_gpt2_model,
                    model_attribute=ModelAttribute(has_control_flow=True))
 model_zoo.register(name='transformers_gpt_lm',
                    model_fn=lambda: transformers.GPT2LMHeadModel(config),
-                   data_gen_fn=data_gen,
+                   data_gen_fn=data_gen_for_lm,
                    output_transform_fn=output_transform_fn,
+                   loss_fn=loss_fn,
                    model_attribute=ModelAttribute(has_control_flow=True))
 model_zoo.register(name='transformers_gpt_double_heads',
                    model_fn=lambda: transformers.GPT2DoubleHeadsModel(config),
-                   data_gen_fn=data_gen,
+                   data_gen_fn=data_gen_for_lm,
                    output_transform_fn=output_transform_fn,
+                   loss_fn=loss_fn,
                    model_attribute=ModelAttribute(has_control_flow=True))
 model_zoo.register(name='transformers_gpt_for_token_classification',
                    model_fn=lambda: transformers.GPT2ForTokenClassification(config),
-                   data_gen_fn=data_gen,
+                   data_gen_fn=data_gen_for_token_classification,
                    output_transform_fn=output_transform_fn,
+                   loss_fn=loss_fn,
                    model_attribute=ModelAttribute(has_control_flow=True))
 model_zoo.register(name='transformers_gpt_for_sequence_classification',
                    model_fn=lambda: transformers.GPT2ForSequenceClassification(config),
-                   data_gen_fn=seq_classification_data_gen,
+                   data_gen_fn=data_gen_for_sequence_classification,
                    output_transform_fn=output_transform_fn,
+                   loss_fn=loss_fn,
                    model_attribute=ModelAttribute(has_control_flow=True))

tests/test_shardformer/test_model/test_shard_bert.py

-import copy
-import os
-
 import pytest
 import torch
-from transformers import (
-    AutoTokenizer,
-    BertConfig,
-    BertForMaskedLM,
-    BertForNextSentencePrediction,
-    BertForPreTraining,
-    BertForSequenceClassification,
-    BertLMHeadModel,
-    BertModel,
-)
 
 import colossalai
 from colossalai.logging import disable_existing_loggers
-from colossalai.shardformer import ShardConfig, ShardFormer
-from colossalai.testing import rerun_if_address_is_in_use, spawn
-
-os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = 'true'
-CONFIG = dict(parallel=dict(data=1, pipeline=1, tensor=dict(size=2, mode='1d')),)
-tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
-
-
-def build_model(world_size, model_fn):
-    config = BertConfig()
-    config.hidden_dropout_prob = 0
-    config.attention_probs_dropout_prob = 0
-
-    org_model = model_fn(config=config)
-    org_model_forshard = copy.deepcopy(org_model)
-
-    org_model.to('cuda')
-    # TODO: no need to transfer to cuda
-    org_model_forshard.to('cuda')
-    shard_config = ShardConfig(tensor_parallel_size=world_size,)
-    shard_former = ShardFormer(shard_config=shard_config)
-    shard_former.init_distributed()
-    sharded_model = shard_former.shard_model(org_model_forshard).to('cuda')
-
-    return org_model, sharded_model
-
+from colossalai.testing import assert_hf_output_close, clear_cache_before_run, 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
 
-def check_forward(org_model, sharded_model):
-    input = 'Hello, my dog is cute'
-    tokenized_input = tokenizer(input, return_tensors='pt').to('cuda')
 
-    #orgin model
-    org_model.eval()
-    org_out = org_model(**tokenized_input)
+def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn):
+    # check forward
+    org_output, org_loss, shard_output, shard_loss = run_forward(org_model, sharded_model, data_gen_fn,
+                                                                 output_transform_fn, loss_fn)
+    assert_hf_output_close(org_output, shard_output)
 
-    #shard model
-    sharded_model.eval()
-    shard_out = sharded_model(**tokenized_input)
-
-    assert torch.allclose(
-        org_out[0], shard_out[0],
-        atol=1e-5), f"shard model output is not equal to orgin model output\n{org_out[0]}\n{shard_out[0]}"
-
-
-def check_backward(org_model, sharded_model):
-    # prepare input
-    input = 'Hello, my dog is cute'
-    tokenized_input = tokenizer(input, return_tensors='pt').to('cuda')
-    labels = tokenized_input['input_ids'].clone()
-    labels[labels == tokenizer.pad_token_id] = -100
-    tokenized_input['labels'] = labels
-
-    #orgin model
-    org_model.train()
-    org_out = org_model(**tokenized_input)
-    org_loss = org_out.loss
+    # do backward
     org_loss.backward()
-    org_grad = org_model.bert.encoder.layer[0].attention.self.query.weight.grad
-
-    #shard model
-    sharded_model.train()
-    shard_out = sharded_model(**tokenized_input)
-    shard_loss = shard_out.loss
     shard_loss.backward()
-    shard_grad = sharded_model.bert.encoder.layer[0].attention.self.query.weight.grad
+
+    # check grad equality
+    if org_model.__class__.__name__ == 'BertModel':
+        org_grad = org_model.encoder.layer[0].attention.self.query.weight.grad
+        shard_grad = sharded_model.encoder.layer[0].attention.self.query.weight.grad
+    else:
+        org_grad = org_model.bert.encoder.layer[0].attention.self.query.weight.grad
+        shard_grad = sharded_model.bert.encoder.layer[0].attention.self.query.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)
@@ -89,36 +33,24 @@ def check_backward(org_model, sharded_model):
     assert torch.allclose(org_loss, shard_loss,
                           atol=1e-5), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}"
     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}"
+                          atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}"
 
 
 def check_bert(rank, world_size, port):
     disable_existing_loggers()
-    colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
-    forward_list = [
-        BertForMaskedLM,
-        BertForPreTraining,
-        BertLMHeadModel,
+    colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
 
-    # TODO: do not work yet
-    # BertModel,
-    # BertForSequenceClassification
-    # BertForNextSentencePrediction,
-    ]
-    backward_lsit = [BertForMaskedLM, BertLMHeadModel]
-
-    for model_fn in forward_list:
+    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(world_size, model_fn)
-        check_forward(org_model, sharded_model)
-
-        if model_fn in backward_lsit:
-            check_backward(org_model, sharded_model)
+        check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn)
 
-        torch.cuda.empty_cache()
+    torch.cuda.empty_cache()
 
 
 @pytest.mark.dist
 @rerun_if_address_is_in_use()
+@clear_cache_before_run()
 def test_bert():
     spawn(check_bert, 2)
 

tests/test_shardformer/test_model/test_shard_gpt2.py

-import copy
-import os
-
 import pytest
 import torch
-from transformers import AutoTokenizer, GPT2Config, GPT2Model
 
 import colossalai
 from colossalai.logging import disable_existing_loggers
-from colossalai.shardformer import ShardConfig, ShardFormer
-from colossalai.testing import rerun_if_address_is_in_use, spawn
-
-os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = 'true'
-CONFIG = dict(parallel=dict(data=1, pipeline=1, tensor=dict(size=2, mode='1d')),)
-tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
-
-
-def build_model(world_size, model_fn):
-    config = GPT2Config()
-    config.attn_pdrop = 0
-    config.embd_pdrop = 0
-    config.resid_pdrop = 0
-    config.summary_first_dropout
-
-    org_model = model_fn(config=config)
-    org_model_forshard = copy.deepcopy(org_model)
-
-    org_model.to('cuda')
-    # TODO: no need to transfer to cuda
-    org_model_forshard.to('cuda')
-    shard_config = ShardConfig(tensor_parallel_size=world_size,)
-    shard_former = ShardFormer(shard_config=shard_config)
-    shard_former.init_distributed()
-    sharded_model = shard_former.shard_model(org_model_forshard).to('cuda')
-
-    return org_model, sharded_model
-
+from colossalai.testing import assert_hf_output_close, clear_cache_before_run, 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
 
-def check_forward(org_model, sharded_model):
-    input = 'Hello, my dog is cute'
-    tokenized_input = tokenizer(input, return_tensors='pt').to('cuda')
 
-    #orgin model
-    org_model.eval()
-    org_out = org_model(**tokenized_input)
+def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn):
+    # check forward
+    org_output, org_loss, shard_output, shard_loss = run_forward(org_model, sharded_model, data_gen_fn,
+                                                                 output_transform_fn, loss_fn)
+    assert_hf_output_close(org_output, shard_output, ignore_keys=['past_key_values'])
 
-    #shard model
-    sharded_model.eval()
-    shard_out = sharded_model(**tokenized_input)
-
-    assert torch.allclose(
-        org_out[0], shard_out[0],
-        atol=1e-5), f"shard model output is not equal to orgin model output\n{org_out[0]}\n{shard_out[0]}"
-
-
-def check_backward(org_model, sharded_model):
-    # prepare input
-    input = 'Hello, my dog is cute'
-    tokenized_input = tokenizer(input, return_tensors='pt').to('cuda')
-    labels = tokenized_input['input_ids'].clone()
-    labels[labels == tokenizer.pad_token_id] = -100
-    # tokenized_input['labels'] = labels
-
-    #orgin model
-    org_model.train()
-    org_out = org_model(**tokenized_input)
-    org_loss = org_out.loss
+    # do backward
     org_loss.backward()
-    org_grad = org_model.h[0].attn.c_attn.weight.grad
-
-    #shard model
-    sharded_model.train()
-    shard_out = sharded_model(**tokenized_input)
-    shard_loss = shard_out.loss
     shard_loss.backward()
-    shard_grad = sharded_model.h[0].attn.c_attn.weight.grad
+
+    # check grad equality
+    if org_model.__class__.__name__ == 'GPT2Model':
+        org_grad = org_model.h[0].attn.c_attn.weight.grad
+        shard_grad = sharded_model.h[0].attn.c_attn.weight.grad.transpose(0, 1).contiguous()
+    else:
+        org_grad = org_model.transformer.h[0].mlp.c_fc.weight.grad
+        shard_grad = sharded_model.transformer.h[0].mlp.c_fc.weight.grad.transpose(0, 1).contiguous()
 
     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)
+    all_shard_grad = torch.cat(shard_grad_list, dim=1)
 
     assert torch.allclose(org_loss, shard_loss,
                           atol=1e-5), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}"
     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}"
+                          atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}"
 
 
-def check_bert(rank, world_size, port):
+def check_gpt2(rank, world_size, port):
     disable_existing_loggers()
-    colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
-    forward_list = [
-        GPT2Model,
+    colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
 
-    # TODO: do not work yet
-    # BertModel,
-    # BertForSequenceClassification
-    # BertForNextSentencePrediction,
-    ]
-    backward_lsit = []
-
-    for model_fn in forward_list:
+    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():
+        print(name)
+        # if name == 'transformers_gpt':
+        #     continue
         org_model, sharded_model = build_model(world_size, model_fn)
-        check_forward(org_model, sharded_model)
-
-        if model_fn in backward_lsit:
-            check_backward(org_model, sharded_model)
+        check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn)
 
-        torch.cuda.empty_cache()
+    torch.cuda.empty_cache()
 
 
 @pytest.mark.dist
 @rerun_if_address_is_in_use()
+@clear_cache_before_run()
 def test_gpt2():
-    spawn(check_bert, 2)
+    spawn(check_gpt2, 2)
 
 
 if __name__ == "__main__":