mirror of https://github.com/hpcaitech/ColossalAI
60 lines
2.4 KiB
Python
60 lines
2.4 KiB
Python
import pytest
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import torch
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import colossalai
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from colossalai.logging import disable_existing_loggers
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from colossalai.testing import assert_hf_output_close, clear_cache_before_run, rerun_if_address_is_in_use, spawn
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from tests.kit.model_zoo import model_zoo
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from tests.test_shardformer.test_model._utils import build_model, run_forward
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def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn):
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# check forward
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org_output, org_loss, shard_output, shard_loss = run_forward(org_model, sharded_model, data_gen_fn,
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output_transform_fn, loss_fn)
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assert_hf_output_close(org_output, shard_output, ignore_keys=['past_key_values'])
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# do backward
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org_loss.backward()
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shard_loss.backward()
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# check grad equality
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if org_model.__class__.__name__ == 'BloomModel':
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org_grad = org_model.h[0].self_attention.query_key_value.weight.grad
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shard_grad = sharded_model.h[0].self_attention.query_key_value.weight.grad
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else:
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org_grad = org_model.transformer.h[0].self_attention.query_key_value.weight.grad
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shard_grad = sharded_model.transformer.h[0].self_attention.query_key_value.weight.grad
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shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)]
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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{all_shard_grad}"
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def check_bloom(rank, world_size, port):
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disable_existing_loggers()
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colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
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sub_model_zoo = model_zoo.get_sub_registry('transformers_bloom')
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for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
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org_model, sharded_model = build_model(world_size, model_fn)
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check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn)
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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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@clear_cache_before_run()
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def test_bloom():
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spawn(check_bloom, 2)
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if __name__ == "__main__":
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test_bloom()
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