ColossalAI/tests/test_shardformer/test_model/test_shard_t5.py

126 lines
3.8 KiB
Python

import pytest
import torch
import colossalai
from colossalai.logging import disable_existing_loggers
from colossalai.shardformer.layer.utils import Randomizer
from colossalai.tensor.d_tensor.api import clear_layout_converter
from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn
from tests.kit.model_zoo import model_zoo
from tests.test_shardformer.test_model._utils import (
build_model_from_hybrid_plugin,
check_grad,
check_loss,
check_output_hidden_state,
check_weight,
run_forward_backward_with_hybrid_plugin,
)
def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config):
org_model, org_optimizer, sharded_model, sharded_optimizer, criterion, booster = \
build_model_from_hybrid_plugin(model_fn, loss_fn, test_config)
org_loss, org_output, sharded_loss, sharded_output = \
run_forward_backward_with_hybrid_plugin(
org_model,
sharded_model,
sharded_optimizer,
data_gen_fn,
output_transform_fn,
criterion,
booster)
stage_manager = booster.plugin.stage_manager
tp_group = booster.plugin.tp_group
# check last hidden state & loss
if stage_manager is None or stage_manager.is_last_stage():
if org_model.__class__.__name__ != 'T5ForConditionalGeneration':
check_output_hidden_state(org_output, sharded_output, stage_manager, atol=1e-5, rtol=1e-3)
check_loss(org_loss, sharded_loss, atol=1e-5, rtol=1e-3)
# unwrap model
t5 = org_model
sharded_t5 = sharded_model.unwrap()
row_layer_for_check = ['shared', 'encoder.block[0].layer[0].SelfAttention.q']
# check weights and gradients
if stage_manager is None or stage_manager.is_first_stage():
check_grad(t5, sharded_t5, row_layer_for_check, tp_group, atol=1e-5, rtol=1e-3, dim=0)
# check weights after optimizer.step()
org_optimizer.step()
sharded_optimizer.step()
if stage_manager is None or stage_manager.is_first_stage():
check_weight(t5, sharded_t5, row_layer_for_check, tp_group, atol=1e-4, rtol=1e-3, dim=0, verbose=False)
torch.cuda.empty_cache()
@parameterize('test_config', [{
'tp_size': 2,
'pp_size': 2,
'num_microbatches': 2,
'enable_fused_normalization': True,
'use_lazy_init': True
}, {
'tp_size': 1,
'pp_size': 2,
'num_microbatches': 4,
'use_lazy_init': False
}, {
'tp_size': 4,
'pp_size': 1,
'enable_fused_normalization': True,
'use_lazy_init': False
}, {
'tp_size': 1,
'pp_size': 4,
'num_microbatches': 4,
'use_lazy_init': False
}])
@clear_cache_before_run()
def run_t5_test(test_config):
# TODO: add plugin_config for TP+DP after supporting & debugging it
# {'tp_size': 2, 'pp_size': 1, 'enable_fused_normalization': True}
# TODO: add test_config for flash attention & jit operator after supporting
sub_model_zoo = model_zoo.get_sub_registry('transformers_t5')
test_config['precision'] = 'float' # Do not use fp16/bf16 in testing
for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
# skip 4-stage pp test for t5_encoder
if test_config['pp_size'] > 2 and name == 'transformers_t5_encoder_model':
continue
check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config)
clear_layout_converter()
Randomizer.reset_index()
torch.cuda.empty_cache()
def check_t5(rank, world_size, port):
disable_existing_loggers()
colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
run_t5_test()
@pytest.mark.dist
@rerun_if_address_is_in_use()
@clear_cache_before_run()
def test_t5():
spawn(check_t5, 4)
if __name__ == "__main__":
test_t5()