mirror of https://github.com/hpcaitech/ColossalAI
341 lines
11 KiB
Python
341 lines
11 KiB
Python
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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import colossalai
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from colossalai.nn.optimizer import ColossalaiOptimizer
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from colossalai.tensor import ColoTensor, ProcessGroup
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from colossalai.tensor.colo_parameter import ColoParameter
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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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check_equal,
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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_shard_equal,
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)
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def run_1d_hybrid_tp(model_name):
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# A simple net with two stacked nn.Linear
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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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rank = torch.distributed.get_rank()
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world_size = torch.distributed.get_world_size()
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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=True)
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if rank == 0:
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model_torch = model_builder(checkpoint=True)
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model_torch = model_torch.cuda()
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optimizer_torch = ColossalaiOptimizer(torch.optim.SGD(model_torch.parameters(), lr=0.1))
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# Make two models have the same init params
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for p1, p2 in zip(model.parameters(), model_torch.parameters()):
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p2.data.copy_(p1.data)
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else:
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model_torch = None
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optimizer_torch = None
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pg = ProcessGroup(tp_degree=world_size)
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if 'bert' == model_name:
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for name, p in model.named_parameters():
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if not isinstance(p, ColoTensor):
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continue
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# num_class = type_vocab_size = 2 | (8, 2)
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if 'classifier' in name and 'weight' in name:
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split_param_col_tp1d(p, pg)
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# num_class = vocab_size = 30524 | (30524, 8)
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elif 'word_embeddings' in name and 'weight' in name:
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split_param_row_tp1d(p, pg)
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# num_class = seq_len = 512 | (512, 8)
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elif 'position_embeddings' in name and 'weight' in name:
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split_param_row_tp1d(p, pg)
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# num_class = type_vocab_size = 2 | (2, 8)
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elif 'token_type_embeddings' in name and 'weight' in name:
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split_param_col_tp1d(p, pg)
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elif "simple_net" == model_name:
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# A naive way to set spec for all weights in Linear
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for name, p in model.named_parameters():
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if not isinstance(p, ColoTensor):
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continue
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if 'embed' in name and 'weight' in name:
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split_param_col_tp1d(p, pg)
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if 'proj1' in name and ('weight' in name or 'bias' in name):
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split_param_row_tp1d(p, pg)
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if 'proj2' in name and 'weight' in name:
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split_param_col_tp1d(p, pg)
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if 'classifier' in name and ('weight' in name or 'bias' in name):
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split_param_row_tp1d(p, pg)
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model = model.cuda()
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model.eval()
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if rank == 0:
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model_torch.eval()
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colo_optimizer = ColossalaiOptimizer(torch.optim.SGD(model.parameters(), lr=0.1))
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for i, (data, label) in enumerate(train_dataloader):
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# Zero grad
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colo_optimizer.zero_grad()
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if rank == 0:
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optimizer_torch.zero_grad()
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torch.distributed.barrier()
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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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# Bcast rank0 data to all processes
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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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# Test output
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if rank == 0:
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if criterion:
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output_torch = model_torch(data)
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loss_torch = criterion(output_torch, label)
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else:
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output_torch = model_torch(data, label)
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loss_torch = output_torch
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assert torch.allclose(loss, loss_torch, rtol=1e-2)
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torch.distributed.barrier()
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loss.backward()
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colo_optimizer.step()
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if rank == 0:
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loss_torch.backward()
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optimizer_torch.step()
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with torch.no_grad():
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# check param
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for p, torch_p in zip(model.parameters(), model_torch.parameters()):
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assert tensor_shard_equal(torch_p, p, pg.tp_local_rank(), pg.tp_world_size())
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torch.distributed.barrier()
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if i > 5:
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break
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# Test the overrided parameters() and named_parameters() member functions
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def test_model_parameters():
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colossalai.launch(config={}, rank=0, world_size=1, host='localhost', port=free_port(), backend='nccl')
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# build a module with 2 Linear, 4 parameters in total.
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class Net(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.fcs = torch.nn.Sequential(torch.nn.Linear(2, 3), torch.nn.Linear(3, 2))
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self.extra_param = torch.nn.Parameter(torch.randn(2))
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with ColoInitContext(device=get_current_device()):
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model = Net()
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param_cnt = 0
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for name, p in model.named_parameters():
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param_cnt += 1
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assert param_cnt == 5
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for name, colo_p in model.named_parameters():
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assert colo_p.is_model_data()
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param_cnt = 0
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for name, p in model.named_parameters(recurse=False):
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param_cnt += 1
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assert param_cnt == 1
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param_cnt = 0
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for p in model.fcs[0].parameters(recurse=False):
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param_cnt += 1
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assert param_cnt == 2
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def test_colo_optimizer():
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get_components_func = non_distributed_component_funcs.get_callable('simple_net')
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model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
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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=True)
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colo_optimizer = ColossalaiOptimizer(torch.optim.SGD(model.parameters(), lr=0.1))
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for i, (data, label) in enumerate(train_dataloader):
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colo_optimizer.zero_grad()
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data = data.to(get_current_device())
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label = label.to(get_current_device())
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# Bcast rank0 data to all processes
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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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loss.backward()
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colo_optimizer.step()
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if i > 5:
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break
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def run_1d_row_tp(model_name: str):
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# A simple net with two stacked nn.Linear
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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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rank = torch.distributed.get_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=True)
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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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set_seed(1)
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if rank == 0:
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model_torch = model_builder(checkpoint=True)
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model_torch = model_torch.cuda()
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# A naive way to set spec for all weights in Linear
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for mo_name, module in model.named_modules():
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# print(mo_name)
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for pa_name, param in module.named_parameters(recurse=False):
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# print('\t', pa_name, param.shape)
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if not isinstance(param, ColoTensor):
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continue
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if 'weight' in pa_name:
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if 'embed' in mo_name and 'token' not in mo_name and 'LayerNorm' not in mo_name:
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split_param_row_tp1d(param, pg)
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elif 'LayerNorm' not in mo_name and 'ln' not in mo_name:
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split_param_col_tp1d(param, pg)
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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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# Bcast rank0 data to all processes
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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_torch = model_torch(data)
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loss_torch = criterion(output_torch, label)
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else:
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output_torch = model_torch(data, label)
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loss_torch = output_torch
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assert torch.allclose(loss, loss_torch, rtol=1e-2)
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torch.distributed.barrier()
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loss.backward()
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if rank == 0:
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loss_torch.backward()
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torch.distributed.barrier()
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if i > 5:
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break
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def _run_pretrain_load():
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from transformers import BertForMaskedLM
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set_seed(1)
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model_pretrained = BertForMaskedLM.from_pretrained('bert-base-uncased')
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with ColoInitContext(device=get_current_device()):
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model = BertForMaskedLM.from_pretrained('bert-base-uncased')
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model_pretrained = model_pretrained.cuda()
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model = model.cuda()
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dict_pretrained = {}
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dict_col = {}
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c_ref = 0
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for name, param in model_pretrained.named_parameters():
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dict_pretrained[name] = param
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c_ref += 1
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c1 = 0
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c2 = 0
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for name, param in model.named_parameters():
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if isinstance(param, ColoParameter):
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c1 += 1
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else:
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c2 += 1
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dict_col[name] = param
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assert c_ref == c1
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assert c2 == 0
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if model_pretrained.cls.predictions.decoder.bias is model_pretrained.cls.predictions.bias:
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assert model.cls.predictions.decoder.bias is model.cls.predictions.bias
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for name, param in dict_pretrained.items():
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check_equal(param, dict_col[name])
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def run_model_dist(rank, world_size, port):
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colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
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# Comment below test for speed consideration
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# for name in ['bert', 'simple_net']:
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# run_1d_row_tp(name)
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for name in ['bert', 'simple_net']:
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run_1d_hybrid_tp(name)
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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_model(world_size):
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run_func = partial(run_model_dist, world_size=world_size, port=free_port())
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mp.spawn(run_func, nprocs=world_size)
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def run_pretrain_load_dist(rank, world_size, port):
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colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
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_run_pretrain_load()
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# The test case has to download huggingface pretrained models from the internet
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# So we manually trigger the test.
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@pytest.mark.skip
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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_pretrain_load(world_size):
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run_func = partial(run_pretrain_load_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_model_parameters()
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# test_colo_optgimizer()
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test_model(4)
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# test_pretrain_load(4)
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