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154 lines
5.6 KiB
154 lines
5.6 KiB
from functools import partial
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import pytest
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import torch
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import torch.multiprocessing as mp
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from torch.nn.parallel import DistributedDataParallel as DDP
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import colossalai
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from colossalai.nn.parallel.data_parallel import ColoDDP
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from colossalai.tensor import ColoTensor, ColoTensorSpec, ComputePattern, ComputeSpec, ProcessGroup, ShardSpec
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from colossalai.testing import rerun_if_address_is_in_use
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from colossalai.utils import free_port
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from colossalai.utils.cuda import get_current_device
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from colossalai.utils.model.colo_init_context 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 (
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debug_print,
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set_seed,
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split_param_col_tp1d,
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split_param_row_tp1d,
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tensor_equal,
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tensor_shard_equal,
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)
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def init_1d_row_spec(model, pg: ProcessGroup):
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tensor_spec = (ShardSpec([0], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D))
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for n, p in model.named_parameters():
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p.set_process_group(pg)
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if 'weight' in n and 'ln' not in n:
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p.set_tensor_spec(*tensor_spec)
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def init_1d_col_spec(model, pg: ProcessGroup):
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spec = (ShardSpec([-1], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D))
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for n, p in model.named_parameters():
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p.set_process_group(pg)
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if 'ln' not in n and ('weight' in n or 'bias' in n):
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p.set_tensor_spec(*spec)
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def init_megatron_spec(model, pg: ProcessGroup):
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for mn, module in model.named_modules():
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# debug_print([0], mn)
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for pn, param in module.named_parameters(recurse=False):
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# debug_print([0], '\t', pn, param.compute_spec, param.shape)
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param.set_process_group(pg)
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if 'mlp.c_fc' in mn:
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if 'weight' in pn or 'bias' in pn:
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split_param_col_tp1d(param, pg)
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param.compute_spec.set_output_replicate(False)
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else:
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raise RuntimeError
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elif 'mlp.c_proj' in mn:
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if 'weight' in pn:
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split_param_row_tp1d(param, pg)
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else:
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assert 'bias' in pn
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elif 'wte' in mn or 'wpe' in mn:
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assert 'weight' in pn
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split_param_col_tp1d(param, pg)
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elif 'c_attn' in mn or 'c_proj' in mn:
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split_param_col_tp1d(param, pg)
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# debug_print([0], '\t', param.compute_spec, param.shape)
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def check_param_equal(model, torch_model, pg: ProcessGroup):
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for p, torch_p in zip(model.parameters(), torch_model.parameters()):
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assert pg.tp_local_rank() is not None, f"{pg.rank()} {pg.tp_world_size()} {pg._tp_degree} {pg.tp_local_rank()}1"
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assert pg.tp_world_size() is not None
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assert tensor_shard_equal(torch_p, p, pg.tp_local_rank(), pg.tp_world_size())
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def check_grad_equal(model, torch_model, pg: ProcessGroup):
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for p, torch_p in zip(model.parameters(), torch_model.parameters()):
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assert tensor_shard_equal(torch_p.grad, p.grad, pg.tp_local_rank(), pg.tp_world_size())
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def run_gpt(init_spec_func, use_ddp):
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world_size = torch.distributed.get_world_size()
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# build a PG with TP and DP hybrid
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pg = ProcessGroup(dp_degree=(2 if (use_ddp and world_size >= 2) else 1))
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# set seed make processes of the same tp group use the same seed
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# set_seed(pg.tp_local_rank())
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get_components_func = non_distributed_component_funcs.get_callable('gpt2')
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model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
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# make sure torch_model and model has the same parameter values
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with ColoInitContext(device=get_current_device()):
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model = model_builder()
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model = model.cuda()
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torch_model = model_builder().cuda()
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if use_ddp:
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torch_model = DDP(torch_model, device_ids=[pg.rank()], process_group=pg.dp_process_group())
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model = ColoDDP(model, process_group=pg)
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for torch_p, p in zip(torch_model.parameters(), model.parameters()):
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torch_p.data.copy_(p)
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init_spec_func(model, pg)
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check_param_equal(model, torch_model, pg)
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# close the dropout in eval mode
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model.eval()
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torch_model.eval()
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set_seed(pg.dp_local_rank())
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torch.distributed.barrier()
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for i, (input_ids, label) in enumerate(train_dataloader):
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colo_input = ColoTensor.from_torch_tensor(input_ids, ColoTensorSpec(pg))
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logits = model(colo_input)
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torch_logits = torch_model(input_ids)
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assert tensor_equal(torch_logits, logits), f"{torch_logits - logits}"
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loss = criterion(logits, input_ids)
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torch_loss = criterion(torch_logits, input_ids)
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if use_ddp:
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model.backward(loss)
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else:
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loss.backward()
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torch_loss.backward()
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check_grad_equal(model, torch_model, pg)
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if i > 0:
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break
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set_seed(313)
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def run_dist(rank, world_size, port, use_ddp):
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if use_ddp and world_size == 1:
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return
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colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
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# Comments below tests for speed concern
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# run_gpt(init_1d_row_spec, use_ddp)
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# run_gpt(init_1d_col_spec, use_ddp)
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run_gpt(init_megatron_spec, use_ddp)
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@pytest.mark.dist
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@pytest.mark.parametrize('world_size', [1, 4])
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@pytest.mark.parametrize('use_ddp', [False, True])
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@rerun_if_address_is_in_use()
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def test_gpt(world_size, use_ddp):
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run_func = partial(run_dist, world_size=world_size, port=free_port(), use_ddp=use_ddp)
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mp.spawn(run_func, nprocs=world_size)
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if __name__ == '__main__':
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test_gpt(4, use_ddp=False)
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