ColossalAI/tests/test_tensor/model/test_model.py

from functools import partial

import pytest
import torch
import torch.multiprocessing as mp

import colossalai
from colossalai.nn.optimizer import ColossalaiOptimizer
from colossalai.tensor import ColoTensor, ProcessGroup
from colossalai.tensor.colo_parameter import ColoParameter
from colossalai.testing import rerun_if_address_is_in_use
from colossalai.utils import free_port
from colossalai.utils.cuda import get_current_device
from colossalai.utils.model.colo_init_context import ColoInitContext
from tests.components_to_test.registry import non_distributed_component_funcs
from tests.test_tensor.common_utils import (
    check_equal,
    set_seed,
    split_param_col_tp1d,
    split_param_row_tp1d,
    tensor_shard_equal,
)


def run_1d_hybrid_tp(model_name):
    # A simple net with two stacked nn.Linear
    get_components_func = non_distributed_component_funcs.get_callable(model_name)
    model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()

    rank = torch.distributed.get_rank()
    world_size = torch.distributed.get_world_size()

    set_seed(1)
    with ColoInitContext(device=get_current_device()):
        model = model_builder(checkpoint=True)

    if rank == 0:
        model_torch = model_builder(checkpoint=True)
        model_torch = model_torch.cuda()

        optimizer_torch = ColossalaiOptimizer(torch.optim.SGD(model_torch.parameters(), lr=0.1))

        # Make two models have the same init params
        for p1, p2 in zip(model.parameters(), model_torch.parameters()):
            p2.data.copy_(p1.data)
    else:
        model_torch = None
        optimizer_torch = None

    pg = ProcessGroup(tp_degree=world_size)
    if 'bert' == model_name:
        for name, p in model.named_parameters():
            if not isinstance(p, ColoTensor):
                continue

            # num_class = type_vocab_size = 2 | (8, 2)
            if 'classifier' in name and 'weight' in name:
                split_param_col_tp1d(p, pg)
            # num_class = vocab_size = 30524 | (30524, 8)
            elif 'word_embeddings' in name and 'weight' in name:
                split_param_row_tp1d(p, pg)
            # num_class = seq_len = 512 | (512, 8)
            elif 'position_embeddings' in name and 'weight' in name:
                split_param_row_tp1d(p, pg)
            # num_class = type_vocab_size = 2 | (2, 8)
            elif 'token_type_embeddings' in name and 'weight' in name:
                split_param_col_tp1d(p, pg)

    elif "simple_net" == model_name:
        # A naive way to set spec for all weights in Linear
        for name, p in model.named_parameters():
            if not isinstance(p, ColoTensor):
                continue
            if 'embed' in name and 'weight' in name:
                split_param_col_tp1d(p, pg)
            if 'proj1' in name and ('weight' in name or 'bias' in name):
                split_param_row_tp1d(p, pg)
            if 'proj2' in name and 'weight' in name:
                split_param_col_tp1d(p, pg)
            if 'classifier' in name and ('weight' in name or 'bias' in name):
                split_param_row_tp1d(p, pg)

    model = model.cuda()
    model.eval()
    if rank == 0:
        model_torch.eval()

    colo_optimizer = ColossalaiOptimizer(torch.optim.SGD(model.parameters(), lr=0.1))

    for i, (data, label) in enumerate(train_dataloader):

        # Zero grad
        colo_optimizer.zero_grad()
        if rank == 0:
            optimizer_torch.zero_grad()
        torch.distributed.barrier()

        data = data.to(get_current_device())
        label = label.to(get_current_device())

        torch.distributed.broadcast(data, 0, group=pg.tp_process_group())
        torch.distributed.broadcast(label, 0, group=pg.tp_process_group())

        # Bcast rank0 data to all processes
        if criterion:
            output = model(data)
            loss = criterion(output, label)
        else:
            output = model(data, label)
            loss = output

        # Test output
        if rank == 0:
            if criterion:
                output_torch = model_torch(data)
                loss_torch = criterion(output_torch, label)
            else:
                output_torch = model_torch(data, label)
                loss_torch = output_torch
            assert torch.allclose(loss, loss_torch, rtol=1e-2), f"model_name {model_name} failed"
        torch.distributed.barrier()

        loss.backward()
        colo_optimizer.step()

        if rank == 0:
            loss_torch.backward()
            optimizer_torch.step()

            with torch.no_grad():
                # check param
                for p, torch_p in zip(model.parameters(), model_torch.parameters()):
                    assert tensor_shard_equal(torch_p, p, pg.tp_local_rank(), pg.tp_world_size())
        torch.distributed.barrier()
        if i > 5:
            break


# Test the overrided parameters() and named_parameters() member functions
def test_model_parameters():
    colossalai.launch(config={}, rank=0, world_size=1, host='localhost', port=free_port(), backend='nccl')

    # build a module with 2 Linear, 4 parameters in total.
    class Net(torch.nn.Module):

        def __init__(self):
            super().__init__()
            self.fcs = torch.nn.Sequential(torch.nn.Linear(2, 3), torch.nn.Linear(3, 2))
            self.extra_param = torch.nn.Parameter(torch.randn(2))

    with ColoInitContext(device=get_current_device()):
        model = Net()

    param_cnt = 0
    for name, p in model.named_parameters():
        param_cnt += 1
    assert param_cnt == 5

    for name, colo_p in model.named_parameters():
        assert colo_p.is_model_data()

    param_cnt = 0
    for name, p in model.named_parameters(recurse=False):
        param_cnt += 1
    assert param_cnt == 1

    param_cnt = 0
    for p in model.fcs[0].parameters(recurse=False):
        param_cnt += 1
    assert param_cnt == 2


def test_colo_optimizer():
    get_components_func = non_distributed_component_funcs.get_callable('simple_net')
    model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
    set_seed(1)
    with ColoInitContext(device=get_current_device()):
        model = model_builder(checkpoint=True)

    colo_optimizer = ColossalaiOptimizer(torch.optim.SGD(model.parameters(), lr=0.1))
    for i, (data, label) in enumerate(train_dataloader):
        colo_optimizer.zero_grad()
        data = data.to(get_current_device())
        label = label.to(get_current_device())

        # Bcast rank0 data to all processes
        if criterion:
            output = model(data)
            loss = criterion(output, label)
        else:
            output = model(data, label)
            loss = output

        loss.backward()
        colo_optimizer.step()

        if i > 5:
            break


def run_1d_row_tp(model_name: str):
    # A simple net with two stacked nn.Linear
    get_components_func = non_distributed_component_funcs.get_callable(model_name)
    model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
    rank = torch.distributed.get_rank()

    set_seed(1)
    with ColoInitContext(device=get_current_device()):
        model = model_builder(checkpoint=True)

    world_size = torch.distributed.get_world_size()
    pg = ProcessGroup(tp_degree=world_size)

    set_seed(1)
    if rank == 0:
        model_torch = model_builder(checkpoint=True)
        model_torch = model_torch.cuda()

    # A naive way to set spec for all weights in Linear
    for mo_name, module in model.named_modules():
        # print(mo_name)
        for pa_name, param in module.named_parameters(recurse=False):
            # print('\t', pa_name, param.shape)
            if not isinstance(param, ColoTensor):
                continue
            if 'weight' in pa_name:
                if 'embed' in mo_name and 'token' not in mo_name and 'LayerNorm' not in mo_name:
                    split_param_row_tp1d(param, pg)
                elif 'LayerNorm' not in mo_name and 'ln' not in mo_name:
                    split_param_col_tp1d(param, pg)

    model = model.cuda()

    for i, (data, label) in enumerate(train_dataloader):
        data = data.to(get_current_device())
        label = label.to(get_current_device())

        torch.distributed.broadcast(data, 0, group=pg.tp_process_group())
        torch.distributed.broadcast(label, 0, group=pg.tp_process_group())

        # Bcast rank0 data to all processes
        if criterion:
            output = model(data)
            loss = criterion(output, label)
        else:
            output = model(data, label)
            loss = output

        # For reference
        if rank == 0:
            if criterion:
                output_torch = model_torch(data)
                loss_torch = criterion(output_torch, label)
            else:
                output_torch = model_torch(data, label)
                loss_torch = output_torch
            assert torch.allclose(loss, loss_torch, rtol=1e-2)
        torch.distributed.barrier()

        loss.backward()

        if rank == 0:
            loss_torch.backward()
        torch.distributed.barrier()

        if i > 5:
            break


def _run_pretrain_load():
    from transformers import BertForMaskedLM
    set_seed(1)
    model_pretrained = BertForMaskedLM.from_pretrained('bert-base-uncased')
    with ColoInitContext(device=get_current_device()):
        model = BertForMaskedLM.from_pretrained('bert-base-uncased')

    model_pretrained = model_pretrained.cuda()
    model = model.cuda()

    dict_pretrained = {}
    dict_col = {}
    c_ref = 0
    for name, param in model_pretrained.named_parameters():
        dict_pretrained[name] = param
        c_ref += 1
    c1 = 0
    c2 = 0
    for name, param in model.named_parameters():
        if isinstance(param, ColoParameter):
            c1 += 1
        else:
            c2 += 1
        dict_col[name] = param
    assert c_ref == c1
    assert c2 == 0
    if model_pretrained.cls.predictions.decoder.bias is model_pretrained.cls.predictions.bias:
        assert model.cls.predictions.decoder.bias is model.cls.predictions.bias

    for name, param in dict_pretrained.items():
        check_equal(param, dict_col[name])


def run_model_dist(rank, world_size, port):
    colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
    # Comment below test for speed consideration
    # for name in ['bert', 'simple_net']:
    #     run_1d_row_tp(name)
    for name in ['bert', 'simple_net']:
        run_1d_hybrid_tp(name)


@pytest.mark.dist
@pytest.mark.parametrize('world_size', [1, 4])
@rerun_if_address_is_in_use()
def test_model(world_size):
    run_func = partial(run_model_dist, world_size=world_size, port=free_port())
    mp.spawn(run_func, nprocs=world_size)


def run_pretrain_load_dist(rank, world_size, port):
    colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
    _run_pretrain_load()


# The test case has to download huggingface pretrained models from the internet
# So we manually trigger the test.
@pytest.mark.skip
@pytest.mark.dist
@pytest.mark.parametrize('world_size', [1, 4])
@rerun_if_address_is_in_use()
def test_pretrain_load(world_size):
    run_func = partial(run_pretrain_load_dist, world_size=world_size, port=free_port())
    mp.spawn(run_func, nprocs=world_size)


if __name__ == '__main__':
    # test_model_parameters()
    # test_colo_optgimizer()
    test_model(4)
    # test_pretrain_load(4)