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

* support kit use for bert test

* support kit test for gpt2

tests/test_shardformer/test_model/test_shard_bert.py

@@ -1,86 +1,30 @@
-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")
+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 build_model(world_size, model_fn):
-    config = BertConfig()
-    config.hidden_dropout_prob = 0
-    config.attention_probs_dropout_prob = 0
+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)
 
-    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
-
-
-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)
-
-    #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)
+        check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn)
 
-        if model_fn in backward_lsit:
-            check_backward(org_model, sharded_model)
-
-        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)