mirror of https://github.com/hpcaitech/ColossalAI
107 lines
3.4 KiB
Python
107 lines
3.4 KiB
Python
import copy
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import os
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import random
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import pytest
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import torch
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from transformers import AutoTokenizer, LlamaConfig, LlamaForCausalLM, LlamaModel, LlamaTokenizerFast
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import colossalai
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from colossalai.logging import disable_existing_loggers
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from colossalai.shardformer.shard import ShardConfig, shard_model
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from colossalai.testing import rerun_if_address_is_in_use, spawn
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os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = 'true'
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CONFIG = dict(parallel=dict(data=1, pipeline=1, tensor=dict(size=4, mode='1d')),)
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tokenizer = LlamaTokenizerFast.from_pretrained("hf-internal-testing/llama-tokenizer")
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def build_model(rank, world_size):
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cfg = LlamaConfig(num_hidden_layers=16)
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org_model = LlamaForCausalLM(cfg)
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shardconfig = ShardConfig(
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rank=rank,
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world_size=world_size,
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gather_output=True,
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)
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org_model = org_model.to('cuda')
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org_model_forshard = copy.deepcopy(org_model)
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sharded_model = shard_model(org_model_forshard, shardconfig).to('cuda')
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return org_model, sharded_model
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def check_forward(org_model, sharded_model):
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input = 'Hello, my dog is cute'
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inputs = tokenizer(input, return_tensors='pt').to('cuda')
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del inputs["token_type_ids"]
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del inputs["attention_mask"]
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#orgin model
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org_model.eval()
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org_out = org_model(**inputs)
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#shard model
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sharded_model.eval()
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shard_out = sharded_model(**inputs)
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assert torch.allclose(
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org_out[0], shard_out[0],
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atol=1e-4), f"shard model output is not equal to orgin model output\n{org_out[0]}\n{shard_out[0]}"
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def check_backward(org_model, sharded_model):
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# prepare input
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input = 'Hello, my dog is cute'
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tokenized_input = tokenizer(input, return_tensors='pt').to('cuda')
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del tokenized_input["token_type_ids"]
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del tokenized_input["attention_mask"]
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labels = tokenized_input['input_ids'].clone()
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labels[labels == tokenizer.pad_token_id] = -100
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tokenized_input['labels'] = labels
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#orgin model
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org_model.train()
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org_out = org_model(**tokenized_input)
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org_loss = org_out.loss
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org_loss.backward()
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org_grad = org_model.model.layers[0].self_attn.q_proj.weight.grad
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torch.cuda.empty_cache()
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#shard model
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sharded_model.train()
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shard_out = sharded_model(**tokenized_input)
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shard_loss = shard_out.loss
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shard_loss.backward()
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shard_grad = sharded_model.model.layers[0].self_attn.q_proj.weight.grad
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shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(4)]
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shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad)
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all_shard_grad = torch.cat(shard_grad_list, dim=0)
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assert torch.allclose(org_loss, shard_loss,
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atol=1e-5), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}"
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assert torch.allclose(org_grad, all_shard_grad,
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atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{shard_grad}"
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def check_llama(rank, world_size, port):
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disable_existing_loggers()
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colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
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org_model, sharded_model = build_model(rank, world_size)
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check_forward(org_model, sharded_model)
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check_backward(org_model, sharded_model)
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torch.cuda.empty_cache()
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@pytest.mark.dist
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@rerun_if_address_is_in_use()
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def test_llama():
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spawn(check_llama, 4)
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if __name__ == "__main__":
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test_llama()
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