mirror of https://github.com/hpcaitech/ColossalAI
132 lines
4.4 KiB
Python
132 lines
4.4 KiB
Python
import pytest
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import torch
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from torch import distributed as dist
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import colossalai
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from colossalai.logging import disable_existing_loggers
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from colossalai.shardformer.layer.utils import Randomizer
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from colossalai.tensor.d_tensor.api import clear_layout_converter
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from colossalai.testing import clear_cache_before_run, parameterize, 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 (
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build_model_from_hybrid_plugin,
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check_grad,
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check_loss,
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check_output_hidden_state,
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check_weight,
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run_forward_backward_with_hybrid_plugin,
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unwrap_model,
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)
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def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config):
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org_model, org_optimizer, sharded_model, sharded_optimizer, criterion, booster = \
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build_model_from_hybrid_plugin(model_fn, loss_fn, test_config)
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org_loss, org_output, sharded_loss, sharded_output = \
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run_forward_backward_with_hybrid_plugin(
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org_model,
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sharded_model,
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sharded_optimizer,
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data_gen_fn,
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output_transform_fn,
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criterion,
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booster)
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stage_manager = booster.plugin.stage_manager
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tp_group = booster.plugin.tp_group
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# check last hidden state & loss
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if stage_manager is None or stage_manager.is_last_stage():
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if test_config['precision'] == 'fp32':
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atol, rtol = 1e-5, 1e-3
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else:
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atol, rtol = 5e-3, 5e-3
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if org_model.__class__.__name__ == 'BertModel':
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check_output_hidden_state(org_output, sharded_output, stage_manager, atol=atol, rtol=rtol)
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check_loss(org_loss, sharded_loss, atol=atol, rtol=rtol)
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bert = unwrap_model(org_model, 'BertModel', 'bert')
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sharded_bert = unwrap_model(sharded_model, 'BertModel', 'bert')
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col_layer_for_check = ['encoder.layer[0].output.dense']
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row_layer_for_check = ['embeddings.word_embeddings', 'encoder.layer[0].intermediate.dense']
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if test_config['precision'] == 'fp32':
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atol, rtol = 1e-4, 1e-3
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else:
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atol, rtol = 5e-3, 5e-3
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if stage_manager is None or stage_manager.is_first_stage():
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#check_weight(bert.embeddings.word_embeddings, sharded_bert.embeddings.word_embeddings, tp_group, atol=1e-5, rtol=1e-3)
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#check_weight(bert.encoder.layer[0].attention.self.query, sharded_bert.encoder.layer[0].attention.self.query, tp_group, atol=5e-3, rtol=1e-3)
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check_grad(bert, sharded_bert, col_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=1, verbose=False)
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check_grad(bert, sharded_bert, row_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=0, verbose=False)
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# check weights after optimizer.step()
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org_optimizer.step()
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sharded_optimizer.step()
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if test_config['precision'] == 'fp32':
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atol, rtol = 5e-3, 1e-3
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else:
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atol, rtol = 5e-3, 5e-3
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if stage_manager is None or stage_manager.is_first_stage():
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check_weight(bert, sharded_bert, col_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=1, verbose=False)
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torch.cuda.empty_cache()
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@parameterize('test_config', [{
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'tp_size': 1,
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'pp_size': 2,
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'num_microbatches': 4,
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'use_lazy_init': True,
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'precision': 'fp32',
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}, {
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'tp_size': 2,
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'pp_size': 2,
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'num_microbatches': 2,
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'enable_all_optimization': True,
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'use_lazy_init': True,
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'precision': 'fp16',
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'initial_scale': 1,
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}, {
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'tp_size': 4,
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'pp_size': 1,
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'enable_all_optimization': True,
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'use_lazy_init': False,
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'precision': 'fp32',
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}, {
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'tp_size': 2,
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'pp_size': 1,
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'enable_all_optimization': True,
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'use_lazy_init': False,
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'precision': 'fp32'
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}])
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def run_bert_test(test_config):
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sub_model_zoo = model_zoo.get_sub_registry('transformers_bert')
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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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check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config)
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clear_layout_converter()
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Randomizer.reset_index()
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torch.cuda.empty_cache()
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def check_bert(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_bert_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_bert():
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spawn(check_bert, 4)
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if __name__ == "__main__":
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test_bert()
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