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Unverified Commit 060b917d authored by Jiarui Fang's avatar Jiarui Fang Committed by GitHub
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[refactor] remove gpc dependency in colotensor's _ops (#1189)

parent abf6a262
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Showing with 157 additions and 234 deletions
tests/test_tensor/test_dist_spec_mgr.py
View file @ 060b917d
......@@ -7,12 +7,12 @@ import torch.multiprocessing as mp
from torch.distributed.distributed_c10d import _get_default_group
from colossalai.testing import rerun_if_address_is_in_use
from colossalai.utils import free_port
from colossalai.tensor import DistSpecManager, distspec
from colossalai.tensor import DistSpecManager, distspec, ProcessGroup
from functools import partial
def run():
group = _get_default_group()
group = ProcessGroup(tp_degree=dist.get_world_size())
rank = dist.get_rank()
size = dist.get_world_size()
depth = int(math.sqrt(size))
......@@ -34,7 +34,7 @@ def run():
def check_mem():
group = _get_default_group()
group = ProcessGroup(tp_degree=dist.get_world_size())
size = dist.get_world_size()
assert torch.cuda.memory_allocated() == 0
x = torch.rand(32, 32).cuda()
......
tests/test_tensor/test_embedding_bag_tp.py
View file @ 060b917d
import torch
from colossalai.context.parallel_mode import ParallelMode
from colossalai.tensor import ColoTensor, distspec, ColoParameter
from colossalai.tensor import distspec, ColoParameter
from torch.nn import functional as F
from functools import partial
......@@ -10,23 +9,21 @@ import torch
import torch.multiprocessing as mp
from colossalai.testing import rerun_if_address_is_in_use
from colossalai.utils import free_port
from colossalai.core import global_context as gpc
from colossalai.tensor import TensorSpec, ComputePattern, ComputeSpec, DistSpecManager
from colossalai.tensor import TensorSpec, ComputePattern, ComputeSpec, DistSpecManager, ProcessGroup
from _utils import tensor_equal, tensor_shard_equal
def init_1d_col(weight):
spec = TensorSpec(
distspec.shard(gpc.get_group(ParallelMode.PARALLEL_1D), [-1], [gpc.get_world_size(ParallelMode.PARALLEL_1D)]),
ComputeSpec(ComputePattern.TP1D))
def init_1d_col(weight, pg: ProcessGroup):
spec = TensorSpec(distspec.shard(pg, [-1], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D))
with DistSpecManager.no_grad():
weight.set_tensor_spec(spec)
def run_with_spec(spec_init_func):
pg = ProcessGroup(tp_degree=torch.distributed.get_world_size())
model = torch.nn.EmbeddingBag(10, 4).cuda()
weight = ColoParameter(model.weight.clone())
spec_init_func(weight)
spec_init_func(weight, pg)
inputs = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9]).cuda()
offsets = torch.tensor([0, 4]).cuda()
out = model(inputs, offsets=offsets)
......@@ -35,7 +32,7 @@ def run_with_spec(spec_init_func):
grad = torch.rand_like(out)
out.backward(grad)
colo_out.backward(grad)
assert tensor_shard_equal(model.weight.grad, weight.grad)
assert tensor_shard_equal(model.weight.grad, weight.grad, pg.tp_local_rank(), pg.tp_world_size())
def run_dist(rank, world_size, port):
......
tests/test_tensor/test_embedding_tp.py
View file @ 060b917d
import torch
from colossalai.context.parallel_mode import ParallelMode
from colossalai.tensor import ColoTensor, distspec
from torch.nn import functional as F
from functools import partial
......@@ -11,30 +10,26 @@ import torch.multiprocessing as mp
from colossalai.testing import rerun_if_address_is_in_use
from colossalai.utils import free_port
from colossalai.core import global_context as gpc
from colossalai.tensor import TensorSpec, ComputePattern, ComputeSpec, DistSpecManager
from colossalai.tensor import TensorSpec, ComputePattern, ComputeSpec, DistSpecManager, ProcessGroup
from _utils import tensor_equal, tensor_shard_equal
def init_1d_row(weight):
spec = TensorSpec(
distspec.shard(gpc.get_group(ParallelMode.PARALLEL_1D), [0], [gpc.get_world_size(ParallelMode.PARALLEL_1D)]),
ComputeSpec(ComputePattern.TP1D))
def init_1d_row(weight, pg: ProcessGroup):
spec = TensorSpec(distspec.shard(pg, [0], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D))
with DistSpecManager.no_grad():
weight.set_tensor_spec(spec)
def init_1d_col(weight):
spec = TensorSpec(
distspec.shard(gpc.get_group(ParallelMode.PARALLEL_1D), [-1], [gpc.get_world_size(ParallelMode.PARALLEL_1D)]),
ComputeSpec(ComputePattern.TP1D))
def init_1d_col(weight, pg: ProcessGroup):
spec = TensorSpec(distspec.shard(pg, [-1], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D))
with DistSpecManager.no_grad():
weight.set_tensor_spec(spec)
def run_with_spec(spec_init_func):
def run_with_spec(spec_init_func, pg: ProcessGroup):
model = torch.nn.Embedding(12, 32).cuda()
weight = ColoTensor(torch.nn.Parameter(model.weight.detach()))
spec_init_func(weight)
spec_init_func(weight, pg)
x = torch.tensor((0, 3, 6, 9)).cuda()
out = model(x)
colo_out = F.embedding(x, weight)
......@@ -42,14 +37,16 @@ def run_with_spec(spec_init_func):
grad = torch.rand_like(out)
out.backward(grad)
colo_out.backward(grad)
assert tensor_shard_equal(model.weight.grad, weight.grad)
# compare grad inside a TP group
assert tensor_shard_equal(model.weight.grad, weight.grad, pg.tp_local_rank(), pg.tp_world_size())
def run_dist(rank, world_size, port):
config = dict(parallel=dict(tensor=dict(mode="1d", size=world_size),))
colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
run_with_spec(init_1d_row)
run_with_spec(init_1d_col)
# config = dict(parallel=dict(tensor=dict(mode="1d", size=world_size),))
colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
pg = ProcessGroup(tp_degree=world_size)
run_with_spec(init_1d_row, pg)
run_with_spec(init_1d_col, pg)
@pytest.mark.dist
......
tests/test_tensor/test_gpt.py
View file @ 060b917d
import pytest
import colossalai
from colossalai.context.parallel_mode import ParallelMode
import torch.multiprocessing as mp
from colossalai.testing import rerun_if_address_is_in_use
from colossalai.utils.cuda import get_current_device
from colossalai.utils import free_port
from colossalai.utils.model.colo_init_context import ColoInitContext
from colossalai.tensor import TensorSpec, ComputePattern, ComputeSpec, DistSpecManager, distspec
from colossalai.core import global_context as gpc
from colossalai.tensor import TensorSpec, ComputePattern, ComputeSpec, DistSpecManager, distspec, ProcessGroup
from functools import partial
from _utils import tensor_equal, tensor_shard_equal, set_seed
from tests.components_to_test.registry import non_distributed_component_funcs
import torch
from torch.nn.parallel import DistributedDataParallel as DDP
from colossalai.nn.parallel.data_parallel import ColoDDP
from colossalai.core import global_context as gpc
from colossalai.context.parallel_mode import ParallelMode
def init_1d_row_spec(model):
spec = TensorSpec(
distspec.shard(gpc.get_group(ParallelMode.PARALLEL_1D), [0], [gpc.get_world_size(ParallelMode.PARALLEL_1D)]),
ComputeSpec(ComputePattern.TP1D))
def init_1d_row_spec(model, pg: ProcessGroup):
tensor_spec = TensorSpec(distspec.shard(pg, [0], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D))
with DistSpecManager.no_grad():
for n, p in model.named_parameters():
if 'weight' in n and 'ln' not in n:
p.set_tensor_spec(spec)
p.set_tensor_spec(tensor_spec)
def init_1d_col_spec(model):
spec = TensorSpec(
distspec.shard(gpc.get_group(ParallelMode.PARALLEL_1D), [-1], [gpc.get_world_size(ParallelMode.PARALLEL_1D)]),
ComputeSpec(ComputePattern.TP1D))
def init_1d_col_spec(model, pg: ProcessGroup):
spec = TensorSpec(distspec.shard(pg, [-1], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D))
with DistSpecManager.no_grad():
for n, p in model.named_parameters():
if 'ln' not in n and ('weight' in n or 'bias' in n):
p.set_tensor_spec(spec)
def check_param_equal(model, torch_model):
def check_param_equal(model, torch_model, pg: ProcessGroup):
for p, torch_p in zip(model.parameters(), torch_model.parameters()):
assert tensor_shard_equal(torch_p, p)
assert pg.tp_local_rank() is not None, f"{pg.rank()} {pg.tp_world_size()} {pg._tp_degree} {pg.tp_local_rank()}1"
assert pg.tp_world_size() is not None
assert tensor_shard_equal(torch_p, p, pg.tp_local_rank(), pg.tp_world_size())
def check_grad_equal(model, torch_model):
def check_grad_equal(model, torch_model, pg: ProcessGroup):
for p, torch_p in zip(model.parameters(), torch_model.parameters()):
assert tensor_shard_equal(torch_p.grad, p.grad)
assert tensor_shard_equal(torch_p.grad, p.grad, pg.tp_local_rank(), pg.tp_world_size())
def run_gpt(init_spec_func, use_ddp):
world_size = torch.distributed.get_world_size()
pg = ProcessGroup(dp_degree=(2 if (use_ddp and world_size >= 2) else 1))
get_components_func = non_distributed_component_funcs.get_callable('gpt2')
model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
......@@ -54,21 +57,25 @@ def run_gpt(init_spec_func, use_ddp):
model = model.cuda()
torch_model = model_builder().cuda()
if use_ddp:
model = ColoDDP(model)
# torch_model = DDP(torch_model, device_ids=[pg.rank()], process_group=pg)
# torch.distributed.barrier()
torch_model = DDP(torch_model,
device_ids=[gpc.get_global_rank()],
process_group=gpc.get_group(ParallelMode.DATA))
model = ColoDDP(model, process_group=pg)
for torch_p, p in zip(torch_model.parameters(), model.parameters()):
torch_p.data.copy_(p)
init_spec_func(model)
check_param_equal(model, torch_model)
init_spec_func(model, pg)
check_param_equal(model, torch_model, pg)
model.train()
torch_model.train()
set_seed(gpc.get_local_rank(ParallelMode.DATA))
set_seed(pg.tp_local_rank())
for i, (input_ids, attn_mask) in enumerate(train_dataloader):
logits = model(input_ids, attn_mask)
torch_logits = torch_model(input_ids, attn_mask)
assert tensor_equal(torch_logits, logits)
assert tensor_equal(torch_logits, logits), f"{torch_logits - logits}"
loss = criterion(logits, input_ids)
torch_loss = criterion(torch_logits, input_ids)
if use_ddp:
......@@ -76,7 +83,7 @@ def run_gpt(init_spec_func, use_ddp):
else:
loss.backward()
torch_loss.backward()
check_grad_equal(model, torch_model)
check_grad_equal(model, torch_model, pg)
if i > 0:
break
......@@ -87,11 +94,12 @@ def run_dist(rank, world_size, port, use_ddp):
tp_world_size = world_size // 2 if use_ddp else world_size
config = dict(parallel=dict(tensor=dict(mode="1d", size=tp_world_size),))
colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
run_gpt(init_1d_row_spec, use_ddp)
# run_gpt(init_1d_row_spec, use_ddp)
run_gpt(init_1d_col_spec, use_ddp)
@pytest.mark.dist
@pytest.mark.skip("under development")
@pytest.mark.parametrize('world_size', [1, 4])
@pytest.mark.parametrize('use_ddp', [False, True])
@rerun_if_address_is_in_use()
......
tests/test_tensor/test_hybrid_device.py deleted 100644 → 0
View file @ abf6a262
from colossalai.utils import free_port, get_current_device
from colossalai.utils.model.colo_init_context import ColoInitContext
from colossalai.testing import rerun_if_address_is_in_use
from colossalai.tensor import ComputePattern, ComputeSpec
from functools import partial
from colossalai.core import global_context as gpc
from colossalai.context import ParallelMode
from colossalai.nn.parallel.layers import init_colo_module
from colossalai.nn.parallel.data_parallel import ColoDDP
from colossalai.nn.optimizer import ColoOptimizer
import colossalai
import torch
import torch.multiprocessing as mp
import pytest
class Net(torch.nn.Module):
def __init__(self):
super(Net, self).__init__()
self.embed = torch.nn.Embedding(20, 4)
self.proj = torch.nn.Linear(4, 8)
def forward(self, x):
# move input to cpu and restore output
current_dev = x.device
x = x.to('cpu')
x = self.embed(x)
x = x.to(current_dev)
x = self.proj(x)
return x
def run_hybrid_device(use_ddp, mode):
with ColoInitContext(device=get_current_device()):
model = Net()
real_model = model
if use_ddp:
model = ColoDDP(model)
real_model = model.module
print(f'embedding weight size: {real_model.embed.weight.size()} | device: {real_model.embed.weight.device}')
#print(f'linear weight size: {real_model.proj.weight.size()} | device: {real_model.proj.weight.device}')
parallel_action = ComputeSpec(ComputePattern.TP1D)
init_colo_module(model, parallel_action, recursive=True, mode=mode)
# use cpu gloo to handle embedding
real_model.embed.to('cpu')
gloo_group_tp = gpc.get_cpu_group(ParallelMode.PARALLEL_1D)
real_model.embed.weight.spec.dist_spec.process_group = gloo_group_tp
print(f'embedding weight size: {real_model.embed.weight.size()} | new device: {real_model.embed.weight.device}')
#print(f'linear weight size: {real_model.proj.weight.size()} | new device: {real_model.proj.weight.device}')
optimizer = ColoOptimizer(dict(model.named_parameters()), torch.optim.SGD, lr=0.1)
data = torch.randint(low=0, high=20, size=(16,), device=get_current_device())
out = model(data)
out.sum().backward()
optimizer.step()
def run_dist(rank, world_size, port, use_ddp, mode):
if use_ddp and world_size == 1:
return
tp_world_size = world_size // 2 if use_ddp else world_size
config = dict(parallel=dict(tensor=dict(mode="1d", size=tp_world_size),))
colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
run_hybrid_device(use_ddp, mode)
@pytest.mark.dist
@pytest.mark.parametrize('world_size', [1, 4])
@pytest.mark.parametrize('use_ddp', [False, True])
@pytest.mark.parametrize('mode', ['col', 'row'])
@rerun_if_address_is_in_use()
# Working for simulate the embedding(CPU DP+TP) -> nn(GPU DP+TP)
def _test_hybrid_device(world_size, use_ddp, mode):
run_func = partial(run_dist, world_size=world_size, port=free_port(), use_ddp=use_ddp, mode=mode)
mp.spawn(run_func, nprocs=world_size)
if __name__ == '__main__':
_test_hybrid_device(4, True, 'row')
tests/test_tensor/test_linear_tp.py
View file @ 060b917d
......@@ -12,32 +12,29 @@ import torch.nn.functional as F
from colossalai.testing import rerun_if_address_is_in_use
from colossalai.utils import free_port
from colossalai.core import global_context as gpc
from colossalai.tensor import TensorSpec, ComputePattern, ComputeSpec, DistSpecManager
from colossalai.tensor import TensorSpec, ComputePattern, ComputeSpec, DistSpecManager, ProcessGroup
from _utils import tensor_equal, tensor_shard_equal
def init_1d_row(weight, bias):
spec = TensorSpec(
distspec.shard(gpc.get_group(ParallelMode.PARALLEL_1D), [-1], [gpc.get_world_size(ParallelMode.PARALLEL_1D)]),
ComputeSpec(ComputePattern.TP1D))
def init_1d_row(weight, bias, pg: ProcessGroup):
spec = TensorSpec(distspec.shard(pg, [-1], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D))
with DistSpecManager.no_grad():
weight.set_tensor_spec(spec)
def init_1d_col(weight, bias):
spec = TensorSpec(
distspec.shard(gpc.get_group(ParallelMode.PARALLEL_1D), [0], [gpc.get_world_size(ParallelMode.PARALLEL_1D)]),
ComputeSpec(ComputePattern.TP1D))
def init_1d_col(weight, bias, pg: ProcessGroup):
spec = TensorSpec(distspec.shard(pg, [0], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D))
with DistSpecManager.no_grad():
weight.set_tensor_spec(spec)
bias.set_tensor_spec(spec)
def run_with_spec(spec_init_func):
pg = ProcessGroup(tp_degree=torch.distributed.get_world_size())
model = torch.nn.Linear(4, 8).cuda()
weight = ColoTensor(torch.nn.Parameter(model.weight.detach()))
bias = ColoTensor(torch.nn.Parameter(model.bias.detach()))
spec_init_func(weight, bias)
spec_init_func(weight, bias, pg)
x = torch.rand(2, 4).cuda()
out = model(x)
colo_out = F.linear(x, weight, bias)
......@@ -46,8 +43,8 @@ def run_with_spec(spec_init_func):
grad = torch.rand_like(out)
out.backward(grad)
colo_out.backward(grad)
assert tensor_shard_equal(model.weight.grad, weight.grad)
assert tensor_shard_equal(model.bias.grad, bias.grad)
assert tensor_shard_equal(model.weight.grad, weight.grad, pg.tp_local_rank(), pg.tp_world_size())
assert tensor_shard_equal(model.bias.grad, bias.grad, pg.tp_local_rank(), pg.tp_world_size())
def run_dist(rank, world_size, port):
......
tests/test_tensor/test_model.py
View file @ 060b917d
from colossalai.tensor.colo_parameter import ColoParameter
from tests.components_to_test.registry import non_distributed_component_funcs
import colossalai
import pytest
from functools import partial
from _utils import tensor_shard_equal, set_seed
import torch
import torch.multiprocessing as mp
from colossalai.tensor.colo_parameter import ColoParameter
import colossalai
from colossalai.testing import rerun_if_address_is_in_use
from colossalai.utils.cuda import get_current_device
from colossalai.utils import free_port
......@@ -12,34 +14,30 @@ from colossalai.utils.model.colo_init_context import ColoInitContext
from colossalai.tensor import distspec, TensorSpec, ComputePattern, \
ComputeSpec, ColoTensor, DistSpecManager, ProcessGroup
from colossalai.nn.optimizer import ColoOptimizer
from functools import partial
from _utils import tensor_shard_equal, set_seed
from tests.components_to_test.registry import non_distributed_component_funcs
def init_1d_row_linear(weight, pg: ProcessGroup):
spec = TensorSpec(distspec.shard(pg.tp_process_group(), [-1], [pg.tp_world_size()]),
ComputeSpec(ComputePattern.TP1D))
spec = TensorSpec(distspec.shard(pg, [-1], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D))
with DistSpecManager.no_grad():
weight.set_tensor_spec(spec)
def init_1d_col_linear(weight, pg):
spec = TensorSpec(distspec.shard(pg.tp_process_group(), [0], [pg.tp_world_size()]),
ComputeSpec(ComputePattern.TP1D))
spec = TensorSpec(distspec.shard(pg, [0], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D))
with DistSpecManager.no_grad():
weight.set_tensor_spec(spec)
def init_1d_row_embedding(weight, pg):
spec = TensorSpec(distspec.shard(pg.tp_process_group(), [0], [pg.tp_world_size()]),
ComputeSpec(ComputePattern.TP1D))
spec = TensorSpec(distspec.shard(pg, [0], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D))
with DistSpecManager.no_grad():
weight.set_tensor_spec(spec)
def init_1d_col_embedding(weight, pg):
spec = TensorSpec(distspec.shard(pg.tp_process_group(), [-1], [pg.tp_world_size()]),
ComputeSpec(ComputePattern.TP1D))
spec = TensorSpec(distspec.shard(pg, [-1], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D))
with DistSpecManager.no_grad():
weight.set_tensor_spec(spec)
......@@ -142,7 +140,7 @@ def run_1d_hybrid_tp(model_name):
with torch.no_grad():
# check param
for p, torch_p in zip(model.parameters(), model_torch.parameters()):
assert tensor_shard_equal(torch_p, p)
assert tensor_shard_equal(torch_p, p, pg.tp_local_rank(), pg.tp_world_size())
if i > 5:
break
......
tests/test_tensor/test_module_spec.py
View file @ 060b917d
......@@ -13,12 +13,10 @@ import colossalai
from colossalai.utils.cuda import get_current_device
from colossalai.utils.model.colo_init_context import ColoInitContext
from colossalai.context.parallel_mode import ParallelMode
from colossalai.tensor import distspec
from colossalai.tensor import distspec, ProcessGroup
from colossalai.testing import rerun_if_address_is_in_use
from colossalai.utils import free_port
from colossalai.core import global_context as gpc
from tests.components_to_test.registry import non_distributed_component_funcs
......@@ -26,7 +24,9 @@ from tests.components_to_test.registry import non_distributed_component_funcs
def run_model_with_spec(mode, model_name):
get_components_func = non_distributed_component_funcs.get_callable(model_name)
model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
rank = gpc.get_local_rank(ParallelMode.PARALLEL_1D)
world_size = torch.distributed.get_world_size()
pg = ProcessGroup(tp_degree=world_size)
rank = pg.rank()
set_seed(1)
with ColoInitContext(device=get_current_device()):
......@@ -40,28 +40,28 @@ def run_model_with_spec(mode, model_name):
for p1, p2 in zip(model.parameters(), model_seq.parameters()):
p2.data.copy_(p1.data)
parallel_action = ComputeSpec(ComputePattern.TP1D)
compute_spec = ComputeSpec(ComputePattern.TP1D)
# Not all layers in Bert can be mod by 4.
# e.g. row shard for all layers is invalid because the first dim of some layer is the classification type size 2.
if 'bert' == model_name:
if 'col' == mode:
init_colo_module(model.bert.embeddings, parallel_action, recursive=True, mode=mode)
init_colo_module(model.bert.encoder, parallel_action, recursive=True, mode=mode)
init_colo_module(model.classifier, parallel_action, recursive=True, mode='row')
init_colo_module(model.bert.embeddings, compute_spec, pg=pg, recursive=True, mode=mode)
init_colo_module(model.bert.encoder, compute_spec, pg=pg, recursive=True, mode=mode)
init_colo_module(model.classifier, compute_spec, pg=pg, recursive=True, mode='row')
elif 'row' == mode:
init_colo_module(model.bert.embeddings, parallel_action, recursive=True, mode='col')
init_colo_module(model.bert.encoder, parallel_action, recursive=True, mode=mode)
init_colo_module(model.classifier, parallel_action, recursive=True, mode=mode)
init_colo_module(model.bert.embeddings, compute_spec, pg=pg, recursive=True, mode='col')
init_colo_module(model.bert.encoder, compute_spec, pg=pg, recursive=True, mode=mode)
init_colo_module(model.classifier, compute_spec, pg=pg, recursive=True, mode=mode)
elif 'simple_net' == model_name:
init_colo_module(model, parallel_action, recursive=True, mode=mode)
init_colo_module(model, compute_spec, pg=pg, recursive=True, mode=mode)
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=gpc.get_group(ParallelMode.PARALLEL_1D))
torch.distributed.broadcast(label, 0, group=gpc.get_group(ParallelMode.PARALLEL_1D))
torch.distributed.broadcast(data, 0, group=pg.tp_process_group())
torch.distributed.broadcast(label, 0, group=pg.tp_process_group())
if criterion:
output = model(data)
......@@ -113,9 +113,10 @@ def run_linear_with_spec(mode):
model = torch.nn.Linear(4, 8)
model_handy = copy(model)
parallel_action = ComputeSpec(ComputePattern.TP1D)
init_colo_module(model, parallel_action, recursive=True, mode=mode)
world_size = torch.distributed.get_world_size()
pg = ProcessGroup(tp_degree=world_size)
compute_spec = ComputeSpec(ComputePattern.TP1D)
init_colo_module(model, compute_spec, pg=pg, recursive=True, mode=mode)
x = torch.rand(2, 4).cuda()
out = model(x)
......@@ -124,8 +125,8 @@ def run_linear_with_spec(mode):
grad = torch.rand_like(out)
out.backward(grad)
colo_out.backward(grad)
assert tensor_shard_equal(model.weight.grad, model_handy.weight.grad)
assert tensor_shard_equal(model.bias.grad, model_handy.bias.grad)
assert tensor_shard_equal(model.weight.grad, model_handy.weight.grad, pg.tp_local_rank(), pg.tp_world_size())
assert tensor_shard_equal(model.bias.grad, model_handy.bias.grad, pg.tp_local_rank(), pg.tp_world_size())
def run_check_shared_param():
......@@ -136,6 +137,10 @@ def run_check_shared_param():
num_layer = 2
vocab_size = 24
world_size = torch.distributed.get_world_size()
pg = ProcessGroup(tp_degree=world_size)
rank = pg.rank()
config = BertConfig(vocab_size=vocab_size,
hidden_size=hidden_dim,
intermediate_size=hidden_dim * 4,
......@@ -148,18 +153,16 @@ def run_check_shared_param():
model = BertForMaskedLM(config)
model = model.cuda()
parallel_action = ComputeSpec(ComputePattern.TP1D)
compute_spec = ComputeSpec(ComputePattern.TP1D)
# model.cls.predictions.decoder and model.cls.predictions share the bias, so they should have the same spec
assert len(model.cls.predictions.decoder.bias.shared_param_modules) == 2
# They are all Linear, so both row is allowed. This should pass check.
init_colo_module(model, parallel_action, recursive=True, mode='row')
init_colo_module(model, compute_spec, pg=pg, recursive=True, mode='row')
# This should be detected by check because you can not set weight as row while set bias as col.
col_spec = TensorSpec(
distspec.shard(gpc.get_group(ParallelMode.PARALLEL_1D), [0], [gpc.get_world_size(ParallelMode.PARALLEL_1D)]),
ComputeSpec(ComputePattern.TP1D))
col_spec = TensorSpec(distspec.shard(pg, [0], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D))
model.cls.predictions.bias.set_tensor_spec(col_spec)
try:
check_colo_module(model.cls.predictions.decoder, recursive=False)
check_colo_module(model.cls.predictions.decoder, pg=pg, recursive=False)
except Exception as e:
assert 'incorrectly sharded' in str(e)
......
tests/test_tensor/test_op.py
View file @ 060b917d
......@@ -4,10 +4,9 @@ import colossalai
import torch.nn.functional as F
import torch.multiprocessing as mp
from functools import partial
from colossalai.tensor import ColoTensor, ColoParameter
from colossalai.tensor import ColoTensor, ProcessGroup
from colossalai.utils import get_current_device
from torch.nn import Parameter
from torch.distributed.distributed_c10d import _get_default_group
from colossalai.testing import rerun_if_address_is_in_use
from colossalai.utils import free_port
from colossalai.tensor import distspec, TensorSpec
......@@ -43,9 +42,10 @@ def check_spec_eq(tensor, other):
def check_element_wise_ops():
pg = _get_default_group()
world_size = torch.distributed.get_world_size()
pg = ProcessGroup(tp_degree=world_size)
t = torch.rand(2, 2)
x = ColoTensor(t, spec=TensorSpec(distspec.shard(pg, [0], [pg.size()])))
x = ColoTensor(t, spec=TensorSpec(distspec.shard(pg, [0], [pg.tp_world_size()])))
check_spec_eq(x, x.cuda())
assert torch.equal(x.cuda(), t.cuda())
check_spec_eq(x, torch.abs(x))
......
tests/test_tensor/test_tensor.py
View file @ 060b917d
......@@ -11,7 +11,6 @@ import torch.multiprocessing as mp
from colossalai.testing import rerun_if_address_is_in_use
from colossalai.utils import free_port
from colossalai.tensor import distspec, TensorSpec, ColoTensor, ProcessGroup
from colossalai.context import ParallelMode
from functools import partial
......@@ -55,11 +54,9 @@ def test_operand():
def _run_view(world_size):
t_ref = torch.randn(4, 5)
rank = gpc.get_global_rank()
pg = ProcessGroup(rank, list(range(world_size)))
assert pg.dp_world_size() == world_size, f"{pg.dp_world_size()} vs {world_size}"
pg = ProcessGroup(rank, list(range(world_size)), tp_degree=world_size)
t = ColoTensor.from_torch_tensor(
t_ref,
TensorSpec(distspec.shard(process_group=pg.dp_process_group(), dims=[0], num_partitions=[pg.dp_world_size()])))
t_ref, TensorSpec(distspec.shard(process_group=pg, dims=[0], num_partitions=[pg.tp_world_size()])))
assert t.size_global()[0] == 4 * world_size
assert t.size_global(1) == 5
......@@ -77,12 +74,12 @@ def _run_tensor_shard_init(world_size):
t_ref = torch.randn(4, 5)
rank = gpc.get_global_rank()
pg = ProcessGroup(rank, list(range(world_size)))
shard_spec = distspec.shard(process_group=pg.dp_process_group(), dims=[0], num_partitions=[pg.dp_world_size()])
pg = ProcessGroup(rank, list(range(world_size)), tp_degree=world_size)
shard_spec = distspec.shard(process_group=pg, dims=[0], num_partitions=[pg.tp_world_size()])
tensor_spec = TensorSpec(shard_spec)
t = ColoTensor.from_torch_tensor(t_ref.clone(), tensor_spec)
t.set_tensor_spec(TensorSpec(dist_spec=distspec.replicate()))
assert t.shape == torch.Size((4 * world_size, 5))
assert t.shape == torch.Size((4 * world_size, 5)), f"{t.shape} vs ({4 * world_size, 5})"
def _run_tensor_replicated_init(world_size):
......@@ -92,11 +89,19 @@ def _run_tensor_replicated_init(world_size):
assert t.shape == torch.Size((4 * world_size, 5)), f"{t.shape}"
def _run_process_group(world_size):
pg1 = ProcessGroup()
pg2 = ProcessGroup()
assert pg1 == pg2
def run_dist_tests(rank, world_size, port):
colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
_run_tensor_shard_init(world_size)
_run_tensor_replicated_init(world_size)
_run_view(world_size)
_run_process_group(world_size)
@pytest.mark.dist
......
tests/test_tensor/test_zero_optim.py
View file @ 060b917d
......@@ -2,13 +2,11 @@ import pytest
import colossalai
import torch
import torch.multiprocessing as mp
from colossalai.context.parallel_mode import ParallelMode
from colossalai.testing import rerun_if_address_is_in_use
from colossalai.utils.cuda import get_current_device
from colossalai.utils import free_port
from colossalai.utils.model.colo_init_context import ColoInitContext
from colossalai.gemini import ChunkManager
from colossalai.core import global_context as gpc
from functools import partial
from _utils import tensor_equal, set_seed, tensor_shard_equal
from tests.components_to_test.registry import non_distributed_component_funcs
......@@ -19,20 +17,22 @@ from colossalai.zero import ZeroOptimizer
from colossalai.testing import parameterize
from colossalai.amp import convert_to_apex_amp
from colossalai.gemini.gemini_mgr import GeminiManager
from colossalai.tensor import TensorSpec, ComputePattern, ComputeSpec, DistSpecManager, distspec
from colossalai.tensor import TensorSpec, ComputePattern, ComputeSpec, DistSpecManager, distspec, ProcessGroup
def check_param_equal(model, torch_model):
def check_param_equal(model, torch_model, pg: ProcessGroup):
for p, torch_p in zip(model.parameters(), torch_model.parameters()):
if p.storage().size() > 0:
assert p.dtype == torch.half
assert tensor_shard_equal(torch_p.to(dtype=p.dtype, device=p.device), p), f'{torch_p} vs {p}'
assert tensor_shard_equal(torch_p.to(dtype=p.dtype, device=p.device), p, pg.tp_local_rank(),
pg.tp_world_size()), f'{torch_p} vs {p}'
def check_grad_equal(model, torch_model):
def check_grad_equal(model, torch_model, pg: ProcessGroup):
for p, torch_p in zip(model.parameters(), torch_model.parameters()):
if p.grad is not None:
assert tensor_shard_equal(torch_p.grad.to(dtype=p.grad.dtype, device=p.grad.device), p.grad)
assert tensor_shard_equal(torch_p.grad.to(dtype=p.grad.dtype, device=p.grad.device), p.grad,
pg.tp_local_rank(), pg.tp_world_size())
def run_fwd_bwd(model, criterion, optimizer, input_ids, attn_mask):
......@@ -44,20 +44,16 @@ def run_fwd_bwd(model, criterion, optimizer, input_ids, attn_mask):
return logits
def init_1d_row_spec(model):
spec = TensorSpec(
distspec.shard(gpc.get_group(ParallelMode.PARALLEL_1D), [0], [gpc.get_world_size(ParallelMode.PARALLEL_1D)]),
ComputeSpec(ComputePattern.TP1D))
def init_1d_row_spec(model, pg: ProcessGroup):
spec = TensorSpec(distspec.shard(pg, [0], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D))
with DistSpecManager.no_grad():
for n, p in model.named_parameters():
if 'weight' in n and 'ln' not in n:
p.set_tensor_spec(spec)
def init_1d_col_spec(model):
spec = TensorSpec(
distspec.shard(gpc.get_group(ParallelMode.PARALLEL_1D), [-1], [gpc.get_world_size(ParallelMode.PARALLEL_1D)]),
ComputeSpec(ComputePattern.TP1D))
def init_1d_col_spec(model, pg: ProcessGroup):
spec = TensorSpec(distspec.shard(pg, [-1], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D))
with DistSpecManager.no_grad():
for n, p in model.named_parameters():
if 'ln' not in n and ('weight' in n or 'bias' in n):
......@@ -79,44 +75,51 @@ def run_gpt(use_chunk, use_zero, placement_policy, tp_init_spec_func=None):
for torch_p, p in zip(torch_model.parameters(), model.parameters()):
torch_p.data.copy_(p)
world_size = torch.distributed.get_world_size()
# world size, dp = 2, tp =2, construct a hybrid parallelism.
if world_size == 4:
pg = ProcessGroup(tp_degree=2)
else:
pg = ProcessGroup(tp_degree=world_size)
if tp_init_spec_func:
tp_init_spec_func(model)
tp_init_spec_func(model, pg)
chunk_size = ChunkManager.search_chunk_size(model, 8192, 8) if use_chunk else None
chunk_manager = ChunkManager(chunk_size,
enable_distributed_storage=use_zero,
init_device=GeminiManager.get_default_device(placement_policy))
gemini_manager = GeminiManager(placement_policy, chunk_manager)
model = ZeroDDP(model, gemini_manager)
model = ZeroDDP(model, gemini_manager, pg)
optim = HybridAdam(model.parameters(), lr=1e-3)
optim = ZeroOptimizer(optim, model, initial_scale=32)
amp_config = dict(opt_level='O2', keep_batchnorm_fp32=False, loss_scale=32)
torch_optim = torch.optim.Adam(torch_model.parameters(), lr=1e-3)
torch_model, torch_optim = convert_to_apex_amp(torch_model, torch_optim, amp_config)
torch_model = DDP(torch_model, device_ids=[gpc.get_global_rank()], process_group=gpc.get_group(ParallelMode.DATA))
torch_model = DDP(torch_model, device_ids=[pg.rank()], process_group=pg.dp_process_group())
print(chunk_manager)
check_param_equal(model, torch_model)
# print(chunk_manager)
check_param_equal(model, torch_model, pg)
model.train()
torch_model.train()
set_seed(gpc.get_local_rank(ParallelMode.DATA))
set_seed(pg.dp_local_rank())
for i, (input_ids, attn_mask) in enumerate(train_dataloader):
if i > 2:
break
logits = run_fwd_bwd(model, criterion, optim, input_ids, attn_mask)
torch_logits = run_fwd_bwd(torch_model, criterion, torch_optim, input_ids, attn_mask)
assert tensor_equal(logits, torch_logits)
check_grad_equal(model, torch_model)
check_grad_equal(model, torch_model, pg)
optim.step()
torch_optim.step()
check_param_equal(model, torch_model)
check_param_equal(model, torch_model, pg)
def run_dist(rank, world_size, port):
config = {}
if world_size == 4:
config['parallel'] = {'tensor': {'mode': '1d', 'size': 2}}
colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
if world_size == 4:
run_gpt(tp_init_spec_func=init_1d_col_spec)
......@@ -126,6 +129,7 @@ def run_dist(rank, world_size, port):
@pytest.mark.dist
@pytest.mark.skip("under development")
@pytest.mark.parametrize('world_size', [1, 4])
@rerun_if_address_is_in_use()
def test_gpt(world_size):
......
tests/test_zero/test_sharded_optim_state_dict.py
View file @ 060b917d
import pytest
import colossalai
import torch
from colossalai.context.parallel_mode import ParallelMode
import torch.multiprocessing as mp
from colossalai.testing import rerun_if_address_is_in_use
from colossalai.utils.cuda import get_current_device
from colossalai.utils import free_port
from colossalai.core import global_context as gpc
from functools import partial
from tests.test_tensor._utils import set_seed
from tests.components_to_test.registry import non_distributed_component_funcs
......@@ -16,6 +14,7 @@ from colossalai.zero.init_ctx import ZeroInitContext
from colossalai.zero.shard_utils import TensorShardStrategy
from colossalai.zero.sharded_model import ShardedModelV2
from colossalai.zero.sharded_optim import ShardedOptimizerV2
from colossalai.tensor import ProcessGroup
def init_zero(model_builder, placement_policy):
......@@ -64,7 +63,8 @@ def run_nested_model(placement_policy):
model.train()
model_copy.train()
set_seed(gpc.get_local_rank(ParallelMode.DATA))
pg = ProcessGroup()
set_seed(pg.dp_local_rank())
data_iter = iter(train_dataloader)
data, label = map(lambda x: x.cuda(), next(data_iter))
......
tests/test_zero/test_zero_optim_state_dict.py
View file @ 060b917d
......@@ -16,6 +16,7 @@ from colossalai.gemini import ChunkManager, GeminiManager
from colossalai.testing import parameterize
from colossalai.nn.optimizer import HybridAdam
from colossalai.zero import ZeroOptimizer
from colossalai.tensor import ProcessGroup
def init_zero(model, use_chunk, use_zero, placement_policy):
......@@ -24,7 +25,8 @@ def init_zero(model, use_chunk, use_zero, placement_policy):
enable_distributed_storage=use_zero,
init_device=GeminiManager.get_default_device(placement_policy))
gemini_manager = GeminiManager(placement_policy, chunk_manager)
return ZeroDDP(model, gemini_manager)
pg = ProcessGroup()
return ZeroDDP(model, gemini_manager, pg)
def run_step(model, optim, criterion, data, label):
......
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