[shardformer] support llama model using shardformer (#3969)

adjust layer attr

tests/test_shardformer/test_model/test_shard_llama.py

@@ -0,0 +1,106 @@
+import copy
+import os
+import random
+
+import pytest
+import torch
+from transformers import AutoTokenizer, LlamaConfig, LlamaForCausalLM, LlamaModel, LlamaTokenizerFast
+
+import colossalai
+from colossalai.logging import disable_existing_loggers
+from colossalai.shardformer.shard import ShardConfig, shard_model
+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=4, mode='1d')),)
+tokenizer = LlamaTokenizerFast.from_pretrained("hf-internal-testing/llama-tokenizer")
+
+
+def build_model(rank, world_size):
+    cfg = LlamaConfig(num_hidden_layers=16)
+    org_model = LlamaForCausalLM(cfg)
+
+    shardconfig = ShardConfig(
+        rank=rank,
+        world_size=world_size,
+        gather_output=True,
+    )
+    org_model = org_model.to('cuda')
+
+    org_model_forshard = copy.deepcopy(org_model)
+    sharded_model = shard_model(org_model_forshard, shardconfig).to('cuda')
+
+    return org_model, sharded_model
+
+
+def check_forward(org_model, sharded_model):
+    input = 'Hello, my dog is cute'
+    inputs = tokenizer(input, return_tensors='pt').to('cuda')
+    del inputs["token_type_ids"]
+    del inputs["attention_mask"]
+    #orgin model
+    org_model.eval()
+    org_out = org_model(**inputs)
+
+    #shard model
+    sharded_model.eval()
+    shard_out = sharded_model(**inputs)
+
+    assert torch.allclose(
+        org_out[0], shard_out[0],
+        atol=1e-4), 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')
+    del tokenized_input["token_type_ids"]
+    del tokenized_input["attention_mask"]
+    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.model.layers[0].self_attn.q_proj.weight.grad
+
+    torch.cuda.empty_cache()
+    #shard model
+    sharded_model.train()
+    shard_out = sharded_model(**tokenized_input)
+    shard_loss = shard_out.loss
+    shard_loss.backward()
+    shard_grad = sharded_model.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)
+
+    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_llama(rank, world_size, port):
+    disable_existing_loggers()
+    colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
+
+    org_model, sharded_model = build_model(rank, world_size)
+    check_forward(org_model, sharded_model)
+    check_backward(org_model, sharded_model)
+
+    torch.cuda.empty_cache()
+
+
+@pytest.mark.dist
+@rerun_if_address_is_in_use()
+def test_llama():
+    spawn(check_llama, 4)
+
+
+if __name__ == "__main__":
+    test_llama()