mirror of https://github.com/hpcaitech/ColossalAI
108 lines
4.7 KiB
Python
108 lines
4.7 KiB
Python
import os
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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.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor
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from colossalai.testing import (
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assert_hf_output_close,
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clear_cache_before_run,
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parameterize,
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rerun_if_address_is_in_use,
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spawn,
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)
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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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# the value "past_key_values" is sharded, so we ignore
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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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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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# check attention grad
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org_grad = org_model.encoder.block[0].layer[0].SelfAttention.q.weight.grad
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shard_grad = sharded_model.encoder.block[0].layer[0].SelfAttention.q.weight.grad
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shard_weight = sharded_model.encoder.block[0].layer[0].SelfAttention.q.weight
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if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight):
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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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else:
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all_shard_grad = shard_grad
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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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# check self attention embed
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org_grad = org_model.encoder.block[0].layer[0].SelfAttention.relative_attention_bias.weight.grad
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shard_grad = sharded_model.encoder.block[0].layer[0].SelfAttention.relative_attention_bias.weight.grad
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shard_weight = sharded_model.encoder.block[0].layer[0].SelfAttention.relative_attention_bias.weight
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if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight):
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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=1)
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else:
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all_shard_grad = shard_grad
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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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# check token embedding grad
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org_grad = org_model.shared.weight.grad
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# check weights are tied
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if hasattr(org_model, 'lm_head'):
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assert org_model.shared.weight.data.data_ptr() == org_model.lm_head.weight.data.data_ptr()
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assert sharded_model.shared.weight.data.data_ptr() == sharded_model.lm_head.weight.data.data_ptr()
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shard_grad = sharded_model.shared.weight.grad
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shard_weight = sharded_model.shared.weight
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if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight):
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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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else:
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all_shard_grad = shard_grad
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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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@parameterize('enable_fused_normalization', [True, False])
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@parameterize('enable_tensor_parallelism', [True, False])
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def run_t5_test(enable_fused_normalization, enable_tensor_parallelism):
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sub_model_zoo = model_zoo.get_sub_registry('transformers_t5')
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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(model_fn, enable_fused_normalization, enable_tensor_parallelism)
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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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def check_t5(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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run_t5_test()
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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_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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