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228 lines
8.4 KiB
228 lines
8.4 KiB
from copy import deepcopy
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import pytest
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import torch
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import colossalai
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from colossalai.nn.parallel.layers import check_colo_module, init_colo_module
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from colossalai.tensor import (
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ColoTensor,
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ColoTensorSpec,
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ComputePattern,
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ComputeSpec,
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ProcessGroup,
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ReplicaSpec,
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ShardSpec,
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distspec,
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)
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from colossalai.testing import rerun_if_address_is_in_use, spawn
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from colossalai.utils.cuda import get_current_device
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from colossalai.zero import ColoInitContext
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from tests.components_to_test.registry import non_distributed_component_funcs
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from tests.test_tensor.common_utils import set_seed, tensor_equal, tensor_shard_equal
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def run_model_with_spec(mode, model_name):
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get_components_func = non_distributed_component_funcs.get_callable(model_name)
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model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
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world_size = torch.distributed.get_world_size()
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pg = ProcessGroup(tp_degree=world_size)
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rank = pg.rank()
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set_seed(1)
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with ColoInitContext(device=get_current_device()):
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model = model_builder(checkpoint=False)
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if rank == 0:
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model_seq = model_builder(checkpoint=False)
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model_seq = model_seq.cuda()
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# Make two models have the same init params
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for p1, p2 in zip(model.parameters(), model_seq.parameters()):
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p2.data.copy_(p1.data)
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compute_spec = ComputeSpec(ComputePattern.TP1D)
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# Not all layers in Bert can be mod by 4.
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# e.g. row shard for all layers is invalid because the first dim of some layer is the classification type size 2.
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if 'bert' == model_name:
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if 'col' == mode:
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init_colo_module(model.bert.embeddings, compute_spec, pg=pg, recursive=True, mode=mode)
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init_colo_module(model.bert.encoder, compute_spec, pg=pg, recursive=True, mode=mode)
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init_colo_module(model.classifier, compute_spec, pg=pg, recursive=True, mode='row')
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elif 'row' == mode:
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init_colo_module(model.bert.embeddings, compute_spec, pg=pg, recursive=True, mode='col')
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init_colo_module(model.bert.encoder, compute_spec, pg=pg, recursive=True, mode=mode)
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init_colo_module(model.classifier, compute_spec, pg=pg, recursive=True, mode=mode)
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elif 'simple_net' == model_name:
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init_colo_module(model, compute_spec, pg=pg, recursive=True, mode=mode)
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model = model.cuda()
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for i, (data, label) in enumerate(train_dataloader):
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data = data.to(get_current_device())
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label = label.to(get_current_device())
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torch.distributed.broadcast(data, 0, group=pg.tp_process_group())
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torch.distributed.broadcast(label, 0, group=pg.tp_process_group())
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if criterion:
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output = model(data)
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loss = criterion(output, label)
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else:
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output = model(data, label)
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loss = output
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# For reference
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if rank == 0:
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if criterion:
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output_seq = model_seq(data)
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loss_seq = criterion(output_seq, label)
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else:
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output_seq = model_seq(data, label)
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loss_seq = output_seq
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if rank == 0:
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with torch.no_grad():
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assert torch.allclose(loss, loss_seq, rtol=1e-2)
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loss.backward()
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if rank == 0:
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loss_seq.backward()
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with torch.no_grad():
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# check param
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for p1, p2 in zip(model.parameters(), model_seq.parameters()):
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if p1.size() == p2.size():
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assert torch.allclose(p1, p2)
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else:
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if p1.size(-1) < p2.size(-1): # col
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world_size = p2.size(-1) // p1.size(-1)
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split_p2 = torch.chunk(p2, world_size, dim=-1)[0]
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elif p1.size(0) < p2.size(0): # row
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world_size = p2.size(0) // p1.size(0)
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split_p2 = torch.chunk(p2, world_size, dim=0)[0]
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assert torch.allclose(p1, split_p2)
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if i > 3:
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break
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def run_linear_with_spec(mode):
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with ColoInitContext(device=get_current_device()):
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model = torch.nn.Linear(4, 8)
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model_handy = deepcopy(model)
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world_size = torch.distributed.get_world_size()
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pg = ProcessGroup(tp_degree=world_size)
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compute_spec = ComputeSpec(ComputePattern.TP1D)
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init_colo_module(model, compute_spec, pg=pg, recursive=True, mode=mode)
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x = torch.rand(2, 4).cuda()
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colo_x = ColoTensor.from_torch_tensor(x, ColoTensorSpec(pg))
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out = model(x)
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colo_out = model_handy(colo_x)
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assert tensor_equal(out, colo_out)
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grad = torch.rand_like(out)
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out.backward(grad)
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colo_out.backward(grad)
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assert tensor_shard_equal(model_handy.weight.grad, model.weight.grad, pg.tp_local_rank(), pg.tp_world_size())
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assert tensor_shard_equal(model_handy.bias.grad, model.bias.grad, pg.tp_local_rank(), pg.tp_world_size())
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def run_check_shared_param():
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from transformers import BertConfig, BertForMaskedLM
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hidden_dim = 8
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num_head = 4
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sequence_length = 12
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num_layer = 2
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vocab_size = 24
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world_size = torch.distributed.get_world_size()
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pg = ProcessGroup(tp_degree=world_size)
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rank = pg.rank()
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config = BertConfig(vocab_size=vocab_size,
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hidden_size=hidden_dim,
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intermediate_size=hidden_dim * 4,
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num_attention_heads=num_head,
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max_position_embeddings=sequence_length,
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num_hidden_layers=num_layer,
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hidden_dropout_prob=0.,
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attention_probs_dropout_prob=0.)
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with ColoInitContext(device=get_current_device()):
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model = BertForMaskedLM(config)
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model = model.cuda()
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compute_spec = ComputeSpec(ComputePattern.TP1D)
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# model.cls.predictions.decoder and model.cls.predictions share the bias, so they should have the same spec
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assert len(model.cls.predictions.decoder.bias.shared_param_modules) == 2
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# They are all Linear, so both row is allowed. This should pass check.
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init_colo_module(model, compute_spec, pg=pg, recursive=True, mode='row')
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# This should be detected by check because you can not set weight as row while set bias as col.
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col_spec = (ShardSpec([0], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D))
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# TODO(jiaruifang) optimize this line
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if not model.cls.predictions.bias.has_initialized:
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model.cls.predictions.bias.pg = pg
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model.cls.predictions.bias.dist_spec = ReplicaSpec()
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model.cls.predictions.bias.has_initialized = True
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model.cls.predictions.bias.set_tensor_spec(*col_spec)
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try:
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check_colo_module(model.cls.predictions.decoder, pg=pg, recursive=False)
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except Exception as e:
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assert 'incorrectly sharded' in str(e)
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def run_dist(rank, world_size, port):
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config = dict(parallel=dict(tensor=dict(mode="1d", size=world_size),))
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colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
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run_linear_with_spec('col')
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run_linear_with_spec('row')
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def run_dist_model(rank, world_size, port):
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config = dict(parallel=dict(tensor=dict(mode="1d", size=world_size),))
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colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
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for model_name in ['simple_net', 'bert']:
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run_model_with_spec('col', model_name)
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run_model_with_spec('row', model_name)
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def run_dist_check(rank, world_size, port):
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config = dict(parallel=dict(tensor=dict(mode="1d", size=world_size),))
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colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
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run_check_shared_param()
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@pytest.mark.dist
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@pytest.mark.parametrize('world_size', [1, 4])
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@pytest.mark.skip("for higher testing speed")
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@rerun_if_address_is_in_use()
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def test_module_linear_1d(world_size):
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spawn(run_dist, world_size)
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@pytest.mark.dist
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@pytest.mark.parametrize('world_size', [1, 4])
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@pytest.mark.skip("for higher testing speed")
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@rerun_if_address_is_in_use()
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def test_module_model(world_size):
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spawn(run_dist_model, world_size)
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@pytest.mark.dist
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@pytest.mark.parametrize('world_size', [1, 2])
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@pytest.mark.skip("for higher testing speed")
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@rerun_if_address_is_in_use()
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def test_module_check(world_size):
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spawn(run_dist_check, world_size)
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if __name__ == '__main__':
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test_module_linear_1d(4)
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