ColossalAI/tests/test_zero_data_parallel/test_shard_model_v2.py

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#!/usr/bin/env python
# -*- encoding: utf-8 -*-
from functools import partial
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import colossalai
import pytest
import torch
import torch.multiprocessing as mp
from colossalai.testing import parameterize
from colossalai.utils import free_port
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from colossalai.zero.init_ctx import ZeroInitContext
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from colossalai.zero.shard_utils import (BucketTensorShardStrategy, TensorShardStrategy)
from colossalai.zero.sharded_model import ShardedModelV2
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from colossalai.zero.sharded_model._utils import cast_tensor_to_fp16
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from colossalai.zero.sharded_model.utils import col_model_deepcopy
from colossalai.testing import rerun_on_exception
from tests.components_to_test.registry import non_distributed_component_funcs
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from torch.nn.parallel import DistributedDataParallel as DDP
from common import CONFIG, check_grads_padding, run_fwd_bwd
@parameterize("enable_autocast", [True])
@parameterize("shard_strategy_class", [TensorShardStrategy, BucketTensorShardStrategy])
def run_model_test(enable_autocast, shard_strategy_class):
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test_models = ['repeated_computed_layers', 'resnet18', 'bert']
shard_strategy = shard_strategy_class()
for model_name in test_models:
get_components_func = non_distributed_component_funcs.get_callable(model_name)
model_builder, train_dataloader, _, _, criterion = get_components_func()
rm_torch_payload_on_the_fly = False
with ZeroInitContext(convert_fp16=True,
target_device=torch.cuda.current_device(),
shard_strategy=shard_strategy,
shard_param=True,
rm_torch_payload_on_the_fly=rm_torch_payload_on_the_fly):
zero_model = model_builder(checkpoint=True)
zero_model = ShardedModelV2(zero_model, shard_strategy, use_memory_tracer=True)
model = model_builder(checkpoint=True).half()
col_model_deepcopy(zero_model, model)
model = model.cuda()
model = DDP(model)
for i, (data, label) in enumerate(train_dataloader):
if i > 5:
break
data, label = cast_tensor_to_fp16(data).cuda(), label.cuda()
run_fwd_bwd(model, data, label, criterion, enable_autocast)
run_fwd_bwd(zero_model, data, label, criterion, enable_autocast)
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check_grads_padding(model, zero_model, loose=True)
def run_dist(rank, world_size, port):
colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
run_model_test()
@pytest.mark.dist
@pytest.mark.parametrize("world_size", [1, 2])
@rerun_on_exception(exception_type=mp.ProcessRaisedException, pattern=".*Address already in use.*")
def test_shard_model_v2(world_size):
run_func = partial(run_dist, world_size=world_size, port=free_port())
mp.spawn(run_func, nprocs=world_size)
if __name__ == '__main__':
test_shard_model_v2(world_size=2)