mirror of https://github.com/hpcaitech/ColossalAI
100 lines
3.3 KiB
Python
100 lines
3.3 KiB
Python
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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, BertConfig, BertForMaskedLM, T5Config, T5ForConditionalGeneration, T5Tokenizer
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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=2, mode='1d')),)
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tokenizer = T5Tokenizer.from_pretrained("t5-small")
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def build_model(rank, world_size):
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config = T5Config.from_pretrained("t5-small")
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config.dropout_rate = 0
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org_model = T5ForConditionalGeneration.from_pretrained("t5-small", config=config).to('cuda')
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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_for_shard = copy.deepcopy(org_model)
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sharded_model = shard_model(org_model_for_shard, 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_ids = tokenizer("translate English to German: The house is wonderful.",
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return_tensors="pt").input_ids.to('cuda')
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#orgin model
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org_model.eval()
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org_output = org_model.generate(input_ids)
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#shard model
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sharded_model.eval()
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shard_output = sharded_model.generate(input_ids)
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assert torch.allclose(
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org_output[0], shard_output[0],
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atol=1e-5), 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_ids = tokenizer("translate English to German: The house is wonderful.",
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return_tensors="pt").input_ids.to('cuda')
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labels = tokenizer("Das Haus ist wunderbar.", return_tensors="pt").input_ids.to('cuda')
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#orgin model
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org_model.train()
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org_loss = org_model(input_ids=input_ids, labels=labels).loss
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org_loss.backward()
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org_grad = org_model.encoder.block[0].layer[0].SelfAttention.q.weight.grad
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#shard model
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sharded_model.train()
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shard_loss = sharded_model(input_ids=input_ids, labels=labels).loss
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shard_loss.backward()
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shard_grad = sharded_model.encoder.block[0].layer[0].SelfAttention.q.weight.grad
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shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)]
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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_t5(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_t5():
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spawn(check_t5, 2)
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if __name__ == "__main__":
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test_t5()
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