[shardformer] add gpt2 test and layer class refactor (#4041)

* add gpt2 test and layer class refactor

* add dropout in gpt2 policy

tests/test_shardformer/test_model/test_shard_bert.py

@@ -108,7 +108,7 @@ def check_bert(rank, world_size, port):
     backward_lsit = [BertForMaskedLM, BertLMHeadModel]
 
     for model_fn in forward_list:
-        org_model, sharded_model = build_model(model_fn)
+        org_model, sharded_model = build_model(world_size, model_fn)
         check_forward(org_model, sharded_model)
 
         if model_fn in backward_lsit:

tests/test_shardformer/test_model/test_shard_gpt2.py

@@ -0,0 +1,118 @@
+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
+
+
+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
+    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
+
+    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_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}"
+
+
+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 = [
+        GPT2Model,
+
+    # TODO: do not work yet
+    # BertModel,
+    # BertForSequenceClassification
+    # BertForNextSentencePrediction,
+    ]
+    backward_lsit = []
+
+    for model_fn in forward_list:
+        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)
+
+        torch.cuda.empty_cache()
+
+
+@pytest.mark.dist
+@rerun_if_address_is_in_use()
+def test_gpt2():
+    spawn(check_bert, 2)
+
+
+if __name__ == "__main__":
+    test_gpt2()