mirror of https://github.com/hpcaitech/ColossalAI
147 lines
5.9 KiB
Python
147 lines
5.9 KiB
Python
import pytest
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import colossalai
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import torch
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import torch.multiprocessing as mp
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from colossalai.testing import rerun_if_address_is_in_use
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from colossalai.utils.cuda import get_current_device
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from colossalai.utils import free_port
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from colossalai.utils.model.colo_init_context import ColoInitContext
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from colossalai.gemini import ChunkManager
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from functools import partial
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from tests.test_tensor.common_utils import tensor_equal, set_seed, tensor_shard_equal
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from tests.components_to_test.registry import non_distributed_component_funcs
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from torch.nn.parallel import DistributedDataParallel as DDP
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from colossalai.nn.parallel import ZeroDDP
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from colossalai.nn.optimizer import HybridAdam
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from colossalai.zero import ZeroOptimizer
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from colossalai.testing import parameterize
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from colossalai.amp import convert_to_apex_amp
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from colossalai.gemini.gemini_mgr import GeminiManager
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from colossalai.tensor import ColoTensorSpec, ShardSpec, ComputePattern, ComputeSpec, ProcessGroup, ColoTensor
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from tests.test_tensor.model.test_gpt2 import init_megatron_spec
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def check_param_equal(model, torch_model, pg: ProcessGroup):
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for (n, p), (tn, tp) in zip(model.named_parameters(), torch_model.named_parameters()):
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if p.storage().size() > 0:
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assert p.dtype == torch.float16
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assert tensor_shard_equal(tp.to(dtype=p.dtype, device=p.device), p, pg.tp_local_rank(),
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pg.tp_world_size()), f'{tp} vs {p}\n{n}:\n\t{tp.shape} vs {p.shape}'
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def check_grad_equal(model, torch_model, pg: ProcessGroup):
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for (n, p), (tn, tp) in zip(model.named_parameters(), torch_model.named_parameters()):
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if p.grad is not None:
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assert tensor_shard_equal(tp.grad.to(dtype=p.grad.dtype, device=p.grad.device), p.grad,
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pg.tp_local_rank(), pg.tp_world_size()), \
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f'{tp.grad} vs {p.grad}\n{n}:\n\t{tp.grad.shape} vs {p.grad.shape} in {pg.rank()}'
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def run_fwd_bwd(model, criterion, optimizer, input_ids, attn_mask):
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optimizer.zero_grad()
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logits = model(input_ids, attn_mask)
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logits = logits.float()
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loss = criterion(logits, input_ids)
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optimizer.backward(loss)
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return logits
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def init_1d_row_spec(model, pg: ProcessGroup):
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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(*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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@parameterize('use_chunk', [False, True])
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@parameterize('use_zero', [False, True])
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@parameterize('placement_policy', ['cuda', 'cpu'])
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def run_gpt(use_chunk, use_zero, placement_policy, tp_init_spec_func=None):
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set_seed(42)
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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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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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for torch_p, p in zip(torch_model.parameters(), model.parameters()):
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torch_p.data.copy_(p.data)
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world_size = torch.distributed.get_world_size()
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# world size, dp = 2, tp =2, construct a hybrid parallelism.
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if world_size == 4:
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pg = ProcessGroup(tp_degree=2)
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else:
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pg = ProcessGroup(tp_degree=world_size)
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if tp_init_spec_func:
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tp_init_spec_func(model, pg)
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chunk_size = ChunkManager.search_chunk_size(model, 8192, 8) if use_chunk else None
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chunk_manager = ChunkManager(chunk_size,
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pg,
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enable_distributed_storage=use_zero,
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init_device=GeminiManager.get_default_device(placement_policy))
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gemini_manager = GeminiManager(placement_policy, chunk_manager)
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model = ZeroDDP(model, gemini_manager)
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optim = HybridAdam(model.parameters(), lr=1e-3)
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optim = ZeroOptimizer(optim, model, initial_scale=1)
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amp_config = dict(opt_level='O2', keep_batchnorm_fp32=False, loss_scale=1)
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torch_optim = torch.optim.Adam(torch_model.parameters(), lr=1e-3)
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torch_model, torch_optim = convert_to_apex_amp(torch_model, torch_optim, amp_config)
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torch_model = DDP(torch_model, device_ids=[pg.rank()], process_group=pg.dp_process_group())
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print(chunk_manager)
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check_param_equal(model, torch_model, pg)
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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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for i, (input_ids, attn_mask) in enumerate(train_dataloader):
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if i > 2:
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break
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input_ids_colo = ColoTensor.from_torch_tensor(input_ids, ColoTensorSpec(pg))
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logits = run_fwd_bwd(model, criterion, optim, input_ids_colo, attn_mask)
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torch_logits = run_fwd_bwd(torch_model, criterion, torch_optim, input_ids, attn_mask)
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assert tensor_equal(logits, torch_logits)
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check_grad_equal(model, torch_model, pg)
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optim.step()
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torch_optim.step()
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check_param_equal(model, torch_model, pg)
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def run_dist(rank, world_size, port):
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config = {}
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colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
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if world_size == 4:
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run_gpt(tp_init_spec_func=init_megatron_spec)
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else:
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run_gpt(tp_init_spec_func=init_1d_col_spec)
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run_gpt(tp_init_spec_func=init_1d_row_spec)
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@pytest.mark.dist
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@pytest.mark.parametrize('world_size', [1, 4])
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@rerun_if_address_is_in_use()
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def test_gpt(world_size):
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run_func = partial(run_dist, world_size=world_size, port=free_port())
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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)
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