mirror of https://github.com/hpcaitech/ColossalAI
258 lines
8.6 KiB
Python
258 lines
8.6 KiB
Python
from tests.components_to_test.registry import non_distributed_component_funcs
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import colossalai
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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 colossalai.testing import parameterize, 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 import ColoInitContext
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from colossalai.tensor import named_params_with_colotensor, TensorSpec, ComputePattern, ParallelAction, ColoTensor, ColoOptimizer
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from colossalai.context import ParallelMode
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from colossalai.core import global_context as gpc
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from functools import partial
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import random
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import os
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import numpy as np
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def set_seed(seed):
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random.seed(seed)
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os.environ['PYTHONHASHSEED'] = str(seed)
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np.random.seed(seed)
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torch.manual_seed(seed)
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torch.cuda.manual_seed(seed)
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torch.backends.cudnn.deterministic = True
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def run_1d_col_tp():
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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('simple_net')
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model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
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rank = gpc.get_local_rank(ParallelMode.PARALLEL_1D)
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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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parallel_action_list_row = [
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ParallelAction(priority=1, compute_pattern=ComputePattern.TP1DRow_Linear, parallel_mode=ParallelMode.PARALLEL_1D)
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]
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spec_row = TensorSpec(parallel_action_list_row)
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parallel_action_list_col = [
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ParallelAction(priority=1, compute_pattern=ComputePattern.TP1DCol_Linear, parallel_mode=ParallelMode.PARALLEL_1D)
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]
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spec_col = TensorSpec(parallel_action_list_col)
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parallel_action_list_embedding_col = [
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ParallelAction(priority=1, compute_pattern=ComputePattern.TP1DCol_Embedding, parallel_mode=ParallelMode.PARALLEL_1D)
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]
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spec_embedding_col = TensorSpec(parallel_action_list_embedding_col)
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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 name, p in named_params_with_colotensor(model):
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if not isinstance(p, ColoTensor):
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continue
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if 'proj1' in name and ('weight' in name or 'bias' in name):
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p.set_spec(spec_col)
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if 'proj2' in name and 'weight' in name:
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p.set_spec(spec_row)
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if 'embed' in name and 'weight' in name:
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p.set_spec(spec_embedding_col)
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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=gpc.get_group(ParallelMode.PARALLEL_1D))
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torch.distributed.broadcast(label, 0, group=gpc.get_group(ParallelMode.PARALLEL_1D))
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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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if rank == 0:
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# print(loss.torch_tensor().item())
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# print('loss torch', loss_torch.item())
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assert torch.allclose(loss.torch_tensor(), loss_torch, rtol=1e-2)
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loss.backward()
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if rank == 0:
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loss_torch.backward()
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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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# 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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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(lazy_memory_allocate=False, device=get_current_device()):
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model = model_builder(checkpoint=True)
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colo_optimizer = ColoOptimizer(dict(model.named_parameters()), torch.optim.SGD, 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():
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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('simple_net')
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model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
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rank = gpc.get_local_rank(ParallelMode.PARALLEL_1D)
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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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parallel_action_list = [
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ParallelAction(priority=1, compute_pattern=ComputePattern.TP1DRow_Linear, parallel_mode=ParallelMode.PARALLEL_1D)
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]
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spec = TensorSpec(parallel_action_list)
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parallel_action_list_embedding_row = [
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ParallelAction(priority=1, compute_pattern=ComputePattern.TP1DRow_Embedding, parallel_mode=ParallelMode.PARALLEL_1D)
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]
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spec_embedding_row = TensorSpec(parallel_action_list_embedding_row)
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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 name, p in model.colo_named_parameters():
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if not isinstance(p, ColoTensor):
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continue
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if 'weight' in name and 'LayerNorm' not in name and 'ln' not in name and 'embed' not in name:
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p.set_spec(spec)
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if 'embed' in name and 'weight' in name:
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p.set_spec(spec_embedding_row)
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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=gpc.get_group(ParallelMode.PARALLEL_1D))
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torch.distributed.broadcast(label, 0, group=gpc.get_group(ParallelMode.PARALLEL_1D))
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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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if rank == 0:
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# print(loss.torch_tensor().item())
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# print('loss torch', loss_torch.item())
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assert torch.allclose(loss.torch_tensor(), loss_torch, rtol=1e-2)
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loss.backward()
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if rank == 0:
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loss_torch.backward()
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if i > 5:
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break
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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_1d_row_tp()
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run_1d_col_tp()
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@pytest.mark.dist
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@parameterize('world_size', [1, 4])
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@rerun_if_address_is_in_use()
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def test_simple_net(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_simple_net()
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# test_model_parameters()
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# test_colo_optimizer()
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