Skip to content

GitLab

Unverified Commit a39a5c66 authored by Hongxin Liu's avatar Hongxin Liu Committed by GitHub
Browse files

Merge branch 'main' into feature/shardformer

parents e79b1e80 aaeb520c
Hide whitespace changes
Inline Side-by-side
Showing with 3 additions and 2102 deletions
tests/test_ddp/test_ddp_state_dict.py deleted 100644 → 0
View file @ e79b1e80
from collections import OrderedDict
import pytest
import torch
import colossalai
from colossalai.nn.parallel import ColoDDP
from colossalai.tensor import ColoParameter, ProcessGroup
from colossalai.testing import rerun_if_address_is_in_use, spawn
from colossalai.utils.cuda import get_current_device
from colossalai.zero import ColoInitContext
from tests.components_to_test.registry import non_distributed_component_funcs
def check_state_dict_equal(state_dict: OrderedDict, other_state_dict: OrderedDict):
for (k1, t1), (k2, t2) in zip(state_dict.items(), other_state_dict.items()):
assert k1 == k2
if t1.device != t2.device:
temp_t2 = t2.to(t1.device)
else:
temp_t2 = t2
assert torch.equal(t1, temp_t2), "\t{}\n\t{}".format(t1, temp_t2)
def init_ddp(module: torch.nn.Module) -> ColoDDP:
pg = ProcessGroup()
return ColoDDP(module, process_group=pg)
def run_ddp_state_dict():
get_components_func = non_distributed_component_funcs.get_callable('gpt2')
model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
torch_model = model_builder().cuda()
with ColoInitContext(device=get_current_device()):
model = model_builder()
model = init_ddp(model)
torch_state_dict = torch_model.state_dict()
for param in model.parameters():
if isinstance(param, ColoParameter):
assert param.get_process_group() is not None
model.load_state_dict(torch_state_dict)
for param in model.parameters():
if isinstance(param, ColoParameter):
assert param.get_process_group() is not None
state_dict = model.state_dict()
check_state_dict_equal(torch_state_dict, state_dict)
def run_dist(rank, world_size, port):
colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
run_ddp_state_dict()
@pytest.mark.dist
@pytest.mark.parametrize('world_size', [1, 2])
@rerun_if_address_is_in_use()
def test_state_dict(world_size):
spawn(run_dist, world_size)
if __name__ == '__main__':
test_state_dict(2)
tests/test_ddp/test_reducer.py deleted 100644 → 0
View file @ e79b1e80
from functools import partial
import pytest
import torch
import torch.distributed as dist
from torch.distributed.distributed_c10d import _get_default_group
import colossalai
from colossalai.nn.parallel.reducer import Reducer
from colossalai.testing import rerun_if_address_is_in_use, spawn
from colossalai.utils.cuda import get_current_device
REDUCE_CNT = 0
def check_eq(grad, grad_clone):
global REDUCE_CNT
print(f'Rank{dist.get_rank()} check {REDUCE_CNT}')
REDUCE_CNT += 1
assert torch.allclose(grad, grad_clone)
def run_reducer():
grads = [torch.rand(64, i + 1, device=get_current_device()) for i in range(10)]
grads_clone = [g.clone().detach() for g in grads]
for g in grads:
dist.all_reduce(g)
reducer = Reducer(bucket_size_mb=1)
for g, g_clone in zip(grads, grads_clone):
reducer.all_reduce_async(g_clone, _get_default_group(), partial(check_eq, g))
reducer.flush()
def run_dist(rank, world_size, port):
colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
run_reducer()
@pytest.mark.dist
@pytest.mark.parametrize('world_size', [1, 2])
@rerun_if_address_is_in_use()
def test_reducer(world_size):
spawn(run_dist, world_size)
if __name__ == '__main__':
test_reducer(2)
tests/test_ops/test_addmm_tp.py deleted 100644 → 0
View file @ e79b1e80
import pytest
import torch
import torch.nn as nn
import colossalai
from colossalai.tensor import ColoTensor, ColoTensorSpec, ProcessGroup
from colossalai.testing import rerun_if_address_is_in_use, spawn
from tests.test_tensor.common_utils import split_param_col_tp1d, split_param_row_tp1d, tensor_equal, tensor_shard_equal
class Conv1D(nn.Module):
"""
1D-convolutional layer as defined by Radford et al. for OpenAI GPT (and also used in GPT-2).
Basically works like a linear layer but the weights are transposed.
Args:
nf (`int`): The number of output features.
nx (`int`): The number of input features.
"""
def __init__(self, nf, nx):
super().__init__()
self.nf = nf
w = torch.empty(nx, nf)
nn.init.normal_(w, std=0.02)
self.weight = nn.Parameter(w)
self.bias = nn.Parameter(torch.ones(nf))
def forward(self, x):
size_out = x.size()[:-1] + (self.nf,)
x = torch.addmm(self.bias, x.view(-1, x.size(-1)), self.weight)
x = x.view(size_out)
return x
def run_with_spec(spec_init_func, split_bias):
model = Conv1D(4, 16).cuda()
world_size = torch.distributed.get_world_size()
pg = ProcessGroup(tp_degree=world_size)
weight = ColoTensor(torch.nn.Parameter(model.weight.detach()), ColoTensorSpec(pg))
bias = ColoTensor(torch.nn.Parameter(model.bias.detach()), ColoTensorSpec(pg))
spec_init_func(weight, pg)
if split_bias:
spec_init_func(bias, pg)
x = torch.rand(2, 16).cuda()
out = model(x)
colo_out = torch.addmm(bias, x, weight)
colo_out = colo_out.to_replicate()
assert tensor_equal(out, colo_out)
grad = torch.rand_like(out)
out.backward(grad)
colo_out.backward(grad)
tensor_shard_equal(model.weight.grad, weight.grad, pg.tp_local_rank(), pg.tp_world_size())
tensor_shard_equal(model.bias.grad, bias.grad, pg.tp_local_rank(), pg.tp_world_size())
def run_dist(rank, world_size, port):
colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
run_with_spec(spec_init_func=split_param_row_tp1d, split_bias=False)
run_with_spec(spec_init_func=split_param_col_tp1d, split_bias=True)
@pytest.mark.dist
@pytest.mark.parametrize('world_size', [1, 4])
@rerun_if_address_is_in_use()
def test_addmm_1d(world_size):
spawn(run_dist, world_size)
if __name__ == '__main__':
test_addmm_1d(4)
tests/test_ops/test_embedding_bag_tp.py deleted 100644 → 0
View file @ e79b1e80
import pytest
import torch
from torch.nn import functional as F
import colossalai
from colossalai.tensor import ColoParameter, ColoTensorSpec, ProcessGroup
from colossalai.testing import rerun_if_address_is_in_use, spawn
from tests.test_tensor.common_utils import split_param_col_tp1d, tensor_equal, tensor_shard_equal
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(), True, ColoTensorSpec(pg))
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)
colo_out = F.embedding_bag(inputs, weight, offsets=offsets)
assert tensor_equal(out, colo_out)
grad = torch.rand_like(out)
out.backward(grad)
colo_out.backward(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):
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(split_param_col_tp1d)
@pytest.mark.dist
@pytest.mark.parametrize('world_size', [1, 4])
@rerun_if_address_is_in_use()
def test_embedding_bag_1d(world_size):
spawn(run_dist, world_size)
if __name__ == '__main__':
test_embedding_bag_1d(4)
tests/test_ops/test_embedding_tp.py deleted 100644 → 0
View file @ e79b1e80
import pytest
import torch
from torch.nn import functional as F
import colossalai
from colossalai.tensor import ColoTensor, ColoTensorSpec, ProcessGroup
from colossalai.testing import rerun_if_address_is_in_use, spawn
from tests.test_tensor.common_utils import split_param_col_tp1d, split_param_row_tp1d, tensor_equal, tensor_shard_equal
def run_with_spec(spec_init_func, pg: ProcessGroup):
model = torch.nn.Embedding(12, 32).cuda()
weight = ColoTensor(torch.nn.Parameter(model.weight.detach()), ColoTensorSpec(pg))
spec_init_func(weight, pg)
x = torch.tensor((0, 3, 6, 9)).cuda()
out = model(x)
colo_out = F.embedding(x, weight)
assert tensor_equal(out, colo_out)
grad = torch.rand_like(out)
out.backward(grad)
colo_out.backward(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={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
pg = ProcessGroup(tp_degree=world_size)
run_with_spec(split_param_row_tp1d, pg)
run_with_spec(split_param_col_tp1d, pg)
@pytest.mark.dist
@pytest.mark.parametrize('world_size', [1, 4])
@rerun_if_address_is_in_use()
def test_embedding_1d(world_size):
spawn(run_dist, world_size)
if __name__ == '__main__':
test_embedding_1d(4)
tests/test_ops/test_linear_tp.py deleted 100644 → 0
View file @ e79b1e80
import pytest
import torch
import torch.nn.functional as F
import colossalai
from colossalai.tensor import ColoTensor, ColoTensorSpec, ProcessGroup
from colossalai.testing import rerun_if_address_is_in_use, spawn
from tests.test_tensor.common_utils import split_param_col_tp1d, split_param_row_tp1d, tensor_equal, tensor_shard_equal
def run_with_spec(spec_init_func, split_bias):
pg = ProcessGroup(tp_degree=torch.distributed.get_world_size())
model = torch.nn.Linear(4, 8).cuda()
weight = ColoTensor(torch.nn.Parameter(model.weight.detach()), ColoTensorSpec(pg))
bias = ColoTensor(torch.nn.Parameter(model.bias.detach()), ColoTensorSpec(pg))
spec_init_func(weight, pg)
if split_bias:
spec_init_func(bias, pg)
x = torch.rand(2, 4).cuda()
out = model(x)
colo_out = F.linear(x, weight, bias)
colo_out = colo_out.to_replicate()
assert tensor_equal(out, colo_out)
grad = torch.rand_like(out)
out.backward(grad)
colo_out.backward(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):
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(spec_init_func=split_param_col_tp1d, split_bias=False)
run_with_spec(spec_init_func=split_param_row_tp1d, split_bias=True)
@pytest.mark.dist
@pytest.mark.parametrize('world_size', [1, 4])
@rerun_if_address_is_in_use()
def test_linear_1d(world_size):
spawn(run_dist, world_size)
if __name__ == '__main__':
test_linear_1d(4)
tests/test_ops/test_loss_func.py deleted 100644 → 0
View file @ e79b1e80
import pytest
import torch
import torch.nn.functional as F
import colossalai
from colossalai.tensor import ColoTensor, ColoTensorSpec, ComputePattern, ComputeSpec, ProcessGroup, ShardSpec
from colossalai.testing import rerun_if_address_is_in_use, spawn
from colossalai.utils import get_current_device
def check_cross_entropy():
input_t = torch.randn(4, 4, device=get_current_device(), requires_grad=True)
input_ct = torch.randn(4, 4, device=get_current_device(), requires_grad=True)
with torch.no_grad():
input_ct.copy_(input_t)
target = torch.randint(4, (4,), dtype=torch.int64, device=get_current_device())
world_size = torch.distributed.get_world_size()
pg = ProcessGroup(tp_degree=world_size)
input_t_colo = ColoTensor.from_torch_tensor(tensor=input_ct, spec=ColoTensorSpec(pg))
input_shard = input_t_colo.redistribute(ShardSpec([-1], [pg.tp_world_size()]))
input_shard.set_tensor_spec(dist_spec=None, compute_spec=ComputeSpec(ComputePattern.TP1D))
output = F.cross_entropy(input_t, target)
output_colo = F.cross_entropy(input_shard, target)
assert torch.allclose(output_colo, output)
output.backward()
output_colo.backward()
assert torch.allclose(input_t.grad, input_ct.grad)
def run_dist(rank, world_size, port):
colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
check_cross_entropy()
@pytest.mark.dist
@pytest.mark.parametrize('world_size', [1, 2])
@rerun_if_address_is_in_use()
def test_loss_func(world_size):
spawn(run_dist, world_size)
if __name__ == '__main__':
test_loss_func(1)
tests/test_ops/test_op.py deleted 100644 → 0
View file @ e79b1e80
import pytest
import torch
import torch.nn.functional as F
from torch.nn import Parameter
import colossalai
from colossalai.tensor import ColoTensor, ColoTensorSpec, ProcessGroup, ShardSpec
from colossalai.testing import rerun_if_address_is_in_use, spawn
from colossalai.utils import get_current_device
def _run_layer_norm():
ln_op = torch.nn.LayerNorm(2, 3, device=get_current_device())
input_t = torch.randn(3, 2, device=get_current_device())
pg = ProcessGroup(tp_degree=torch.distributed.get_world_size())
input_t_colo = ColoTensor.from_torch_tensor(input_t.clone().detach(), ColoTensorSpec(pg))
# prepare colossalai LN
weight = ColoTensor(Parameter(ln_op.weight.detach()), ColoTensorSpec(pg))
bias = ColoTensor(Parameter(ln_op.bias.detach()), ColoTensorSpec(pg))
output = ln_op(input_t)
output_colo = F.layer_norm(input_t_colo, ln_op.normalized_shape, weight, bias, ln_op.eps)
assert torch.allclose(output_colo, output)
torch.mean(output).backward()
torch.mean(output_colo).backward()
assert torch.allclose(ln_op.weight.grad, weight.grad)
def check_spec_eq(tensor, other):
assert isinstance(tensor, ColoTensor) and isinstance(other, ColoTensor)
for k in dir(tensor.dist_spec):
if not k.startswith('__'):
assert hasattr(other.dist_spec, k), f"{k}"
assert getattr(tensor.dist_spec, k) == getattr(other.dist_spec, k)
def check_element_wise_ops():
world_size = torch.distributed.get_world_size()
pg = ProcessGroup(tp_degree=world_size)
t = torch.rand(2, 2)
x = ColoTensor(t, spec=ColoTensorSpec(pg, ShardSpec([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))
assert torch.equal(torch.abs(x), torch.abs(t))
check_spec_eq(x, F.sigmoid(x))
assert torch.equal(F.sigmoid(x), F.sigmoid(t))
def run_dist(rank, world_size, port):
colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
check_element_wise_ops()
_run_layer_norm()
@pytest.mark.dist
@pytest.mark.parametrize('world_size', [2])
@rerun_if_address_is_in_use()
def test_element_wise_ops(world_size):
spawn(run_dist, world_size)
def run_dist2(rank, world_size, port):
colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
_run_layer_norm()
@pytest.mark.dist
@pytest.mark.parametrize('world_size', [1])
@rerun_if_address_is_in_use()
def test_ln(world_size):
spawn(run_dist2, world_size)
def check_all():
test_element_wise_ops(2)
if __name__ == '__main__':
check_all()
tests/test_ops/test_view.py deleted 100644 → 0
View file @ e79b1e80
import pytest
import torch
import torch.distributed as dist
import colossalai
from colossalai.tensor import ColoTensor, ColoTensorSpec, ProcessGroup, ShardSpec
from colossalai.tensor.distspec import DistPlacementPattern
from colossalai.testing import rerun_if_address_is_in_use, spawn
from colossalai.utils import get_current_device
from tests.test_tensor.common_utils import debug_print, split_param_col_tp1d, split_param_row_tp1d
def exam_view_core(pg):
# the case of replicated ColoTensors
x = torch.randn(4, 4).cuda()
x_colo = ColoTensor(x, ColoTensorSpec(pg))
y = x.view(2, -1, 2)
y_colo = x_colo.view(2, -1, 2)
assert torch.all(y == y_colo)
assert y_colo.dist_spec.placement == DistPlacementPattern.REPLICATE
# the perfect case of col-sliced ColoTensors
split_param_col_tp1d(x_colo, pg)
z = x.view(torch.Size((2, 1, 2, -1)))
z_colo = x_colo.view(torch.Size((2, 1, 2, -1)))
if dist.get_rank() == 0:
z = z[:, :, :, 0:2]
else:
z = z[:, :, :, 2:]
assert torch.all(z == z_colo)
assert z_colo.dist_spec == x_colo.dist_spec
# the perfect case of row-sliced ColoTensors
split_param_row_tp1d(x_colo, pg)
z = x.view(torch.Size((-1, 2, 2)))
z_colo = x_colo.view(torch.Size((-1, 2, 2)))
if dist.get_rank() == 0:
z = z[0:2, :, :]
else:
z = z[2:, :, :]
assert torch.all(z == z_colo)
assert z_colo.dist_spec == x_colo.dist_spec
# the normal case of row-sliced ColoTensors
z = x.view(-1, 2, 2, 2)
z_colo = x_colo.view(-1, 2, 2, 2)
assert torch.all(z == z_colo)
assert y_colo.dist_spec.placement == DistPlacementPattern.REPLICATE
def exam_view_autograd(pg):
x = torch.randn(8, 2, device=get_current_device(), requires_grad=True)
y = torch.randn(8, 2, device=get_current_device(), requires_grad=True)
with torch.no_grad():
y.copy_(x)
y = ColoTensor(y, ColoTensorSpec(pg))
y_slice = y.redistribute(ShardSpec([-1], [pg.tp_world_size()]))
xx = x.view(2, 2, -1)
yy_slice = y_slice.view(2, 2, -1)
yy = yy_slice.to_replicate()
grad = torch.randn(2, 2, 4, device=get_current_device())
xx.backward(grad)
yy.backward(grad)
assert torch.all(x.grad == y.grad)
def exam_view_errors(pg):
x = torch.randn(8, 2, device=get_current_device())
x = ColoTensor(x, ColoTensorSpec(pg))
split_param_row_tp1d(x, pg)
x.view('a', 'b', 'c')
x.view(8, -1)
x.view([-2, -2, -2])
x.view((-1, -1, -1))
def run_dist(rank, world_size, port):
colossalai.launch(config=dict(), rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
pg = ProcessGroup(tp_degree=torch.distributed.get_world_size())
exam_view_core(pg)
exam_view_autograd(pg)
# exam_view_errors(pg)
@pytest.mark.dist
@pytest.mark.parametrize('world_size', [2])
@rerun_if_address_is_in_use()
def test_view(world_size):
spawn(run_dist, world_size)
if __name__ == '__main__':
test_view(2)
tests/test_pipeline/test_pipelinable.py
View file @ a39a5c66
import pytest
import torch
from colossalai.pipeline.pipelinable import PipelinableContext
......@@ -48,6 +49,7 @@ def run_pipelinable(rank, world_size, port):
assert layers_count_in_part_0 + layers_count_in_part_1 == pipelinable.layers_count
@pytest.mark.skip(reason="this is useless")
@rerun_if_address_is_in_use()
def test_pipelinable():
spawn(run_pipelinable, 1)
......
tests/test_shardformer/test_model/test_shard_gpt2.py
View file @ a39a5c66
......@@ -219,6 +219,7 @@ def check_gpt2_3d(rank, world_size, port):
run_gpt2_3d_test()
@pytest.mark.dist
@rerun_if_address_is_in_use()
@clear_cache_before_run()
......
tests/test_tensor/core/test_tensor.py deleted 100644 → 0
View file @ e79b1e80
import pytest
import torch
from numpy import allclose
import colossalai
from colossalai.core import global_context as gpc
from colossalai.tensor import ColoTensor, ColoTensorSpec, ProcessGroup, ReplicaSpec, ShardSpec, distspec
from colossalai.testing import rerun_if_address_is_in_use, spawn
def _run_tensor_indexing():
pg = ProcessGroup()
torch_t = torch.randn(2, 3)
colo_t = ColoTensor(torch_t, ColoTensorSpec(pg))
assert allclose(torch_t[:, 1], colo_t[:, 1])
def _run_wrapped_tensor_func():
pg = ProcessGroup()
t_ref = torch.randn(4, 5)
t = ColoTensor.from_torch_tensor(t_ref.clone(), ColoTensorSpec(pg))
# non-func attr
assert t.is_cuda == t_ref.is_cuda
# return 1 torch.Tensor
t_abs = t.abs()
assert isinstance(t_abs, ColoTensor) and torch.equal(t_abs, t_ref.abs())
# return 1 non-torch.Tensor
assert t.dim() == t_ref.dim()
# return >1 torch.Tensor
assert isinstance(t, ColoTensor)
t_split1, t_split2 = t.split(2)
assert isinstance(t_split1, ColoTensor) and isinstance(t_split2, ColoTensor), f"{type(t_split1)} {type(t_split2)}"
def _run_operand(world_size):
pg = ProcessGroup()
t_ref = torch.randn(4, 5)
t = ColoTensor.from_torch_tensor(t_ref.clone(), ColoTensorSpec(pg))
t_ref_res = t_ref + t_ref
t_res = t + t
assert isinstance(t_res, ColoTensor)
assert torch.allclose(t_ref_res, t_res)
pg = ProcessGroup(tp_degree=world_size)
t = ColoTensor.from_torch_tensor(t_ref.clone(), ColoTensorSpec(pg))
t.set_dist_spec(ShardSpec([0], [world_size]))
t_new = torch.zeros_like(t)
assert isinstance(t_new, ColoTensor)
assert t_new.is_sharded()
#### Test Distributed init a Colotensor
def _run_view(world_size):
t_ref = torch.randn(4, 5)
rank = gpc.get_global_rank()
pg = ProcessGroup(rank, list(range(world_size)), tp_degree=world_size)
t = ColoTensor.from_torch_tensor(
t_ref, ColoTensorSpec(pg, dist_attr=ShardSpec(dims=[0], num_partitions=[pg.tp_world_size()])))
assert t.size_global()[0] == 4 * world_size
assert t.size_global(1) == 5
assert t.size_global() == torch.Size([4 * world_size, 5])
t = t.view(4 * 5 * world_size)
assert t.shape == torch.Size([4 * 5 * world_size])
def _run_tensor_shard_init(world_size):
t_ref = torch.randn(4, 5)
pg = ProcessGroup(tp_degree=world_size)
shard_attr = ShardSpec(dims=[0], num_partitions=[pg.tp_world_size()])
tensor_spec = ColoTensorSpec(pg, dist_attr=shard_attr)
t = ColoTensor.from_torch_tensor(t_ref.clone(), tensor_spec)
t.set_dist_spec(ReplicaSpec())
assert t.shape == torch.Size((4 * world_size, 5)), f"{t.shape} vs ({4 * world_size, 5})"
def _run_tensor_replicated_init(world_size):
t_ref = torch.randn(4 * world_size, 5)
pg = ProcessGroup()
spec = ColoTensorSpec(pg)
t = ColoTensor.from_torch_tensor(t_ref.clone(), spec)
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_redistributed(world_size):
if world_size != 4:
return
pg1 = ProcessGroup(tp_degree=2, dp_degree=2)
pg2 = ProcessGroup(tp_degree=4, dp_degree=1)
spec1 = ColoTensorSpec(pg1)
t1 = ColoTensor.from_torch_tensor(torch.randn(2, 3, 4), spec1)
t1 = t1.redistribute(ShardSpec([0], [pg1.tp_world_size()]))
assert t1.is_sharded()
t1 = t1.redistribute(ShardSpec([-1], [pg2.tp_world_size()]), pg2)
assert t1.is_sharded()
pg3 = ProcessGroup(tp_degree=1, dp_degree=4)
t1 = t1.redistribute(ReplicaSpec(), pg3)
assert t1.is_replicate()
def _run_set_tensor_spec(world_size):
if world_size != 4:
return
pg = ProcessGroup(tp_degree=2, dp_degree=2)
spec1 = ColoTensorSpec(pg)
t1 = ColoTensor.from_torch_tensor(torch.randn(2, 3, 4), spec1)
dist_spec2 = ShardSpec([-1], [pg.tp_world_size()])
assert t1.is_replicate()
t1.set_dist_spec(dist_spec2)
assert t1.is_shard_1dcol()
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)
_run_tensor_indexing()
_run_operand(world_size)
_run_wrapped_tensor_func()
_run_redistributed(world_size)
_run_set_tensor_spec(world_size)
@pytest.mark.dist
@pytest.mark.parametrize('world_size', [1, 2])
@rerun_if_address_is_in_use()
def test_dist_cases(world_size):
spawn(run_dist_tests, world_size)
if __name__ == '__main__':
test_dist_cases(4)
tests/test_tensor/model/test_gpt2.py deleted 100644 → 0
View file @ e79b1e80
import pytest
import torch
from torch.nn.parallel import DistributedDataParallel as DDP
import colossalai
from colossalai.nn.parallel.data_parallel import ColoDDP
from colossalai.tensor import ColoTensor, ColoTensorSpec, ComputePattern, ComputeSpec, ProcessGroup, ShardSpec
from colossalai.testing import rerun_if_address_is_in_use, spawn
from colossalai.utils.cuda import get_current_device
from colossalai.zero import ColoInitContext
from tests.components_to_test.registry import non_distributed_component_funcs
from tests.test_tensor.common_utils import (
debug_print,
set_seed,
split_param_col_tp1d,
split_param_row_tp1d,
tensor_equal,
tensor_shard_equal,
)
def init_1d_row_spec(model, pg: ProcessGroup):
tensor_spec = (ShardSpec([0], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D))
for n, p in model.named_parameters():
p.set_process_group(pg)
if 'weight' in n and 'ln' not in n:
p.set_tensor_spec(*tensor_spec)
def init_1d_col_spec(model, pg: ProcessGroup):
spec = (ShardSpec([-1], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D))
for n, p in model.named_parameters():
p.set_process_group(pg)
if 'ln' not in n and ('weight' in n or 'bias' in n):
p.set_tensor_spec(*spec)
def init_megatron_spec(model, pg: ProcessGroup):
for mn, module in model.named_modules():
# debug_print([0], mn)
for pn, param in module.named_parameters(recurse=False):
# debug_print([0], '\t', pn, param.compute_spec, param.shape)
param.set_process_group(pg)
if 'mlp.c_fc' in mn:
if 'weight' in pn or 'bias' in pn:
split_param_col_tp1d(param, pg)
param.compute_spec.set_output_replicate(False)
else:
raise RuntimeError
elif 'mlp.c_proj' in mn:
if 'weight' in pn:
split_param_row_tp1d(param, pg)
else:
assert 'bias' in pn
elif 'wte' in mn or 'wpe' in mn:
assert 'weight' in pn
split_param_col_tp1d(param, pg)
elif 'c_attn' in mn or 'c_proj' in mn:
split_param_col_tp1d(param, pg)
# debug_print([0], '\t', param.compute_spec, param.shape)
def check_param_equal(model, torch_model, pg: ProcessGroup):
for p, torch_p in zip(model.parameters(), torch_model.parameters()):
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, pg: ProcessGroup):
for p, torch_p in zip(model.parameters(), torch_model.parameters()):
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()
# build a PG with TP and DP hybrid
pg = ProcessGroup(dp_degree=(2 if (use_ddp and world_size >= 2) else 1))
# set seed make processes of the same tp group use the same seed
# set_seed(pg.tp_local_rank())
get_components_func = non_distributed_component_funcs.get_callable('gpt2')
model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
# make sure torch_model and model has the same parameter values
with ColoInitContext(device=get_current_device()):
model = model_builder()
model = model.cuda()
torch_model = model_builder().cuda()
if use_ddp:
torch_model = DDP(torch_model, device_ids=[pg.rank()], process_group=pg.dp_process_group())
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, pg)
check_param_equal(model, torch_model, pg)
# close the dropout in eval mode
model.eval()
torch_model.eval()
set_seed(pg.dp_local_rank())
torch.distributed.barrier()
for i, (input_ids, label) in enumerate(train_dataloader):
colo_input = ColoTensor.from_torch_tensor(input_ids, ColoTensorSpec(pg))
logits = model(colo_input)
torch_logits = torch_model(input_ids)
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:
model.backward(loss)
else:
loss.backward()
torch_loss.backward()
check_grad_equal(model, torch_model, pg)
if i > 0:
break
set_seed(313)
def run_dist(rank, world_size, port, use_ddp):
if use_ddp and world_size == 1:
return
colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
# Comments below tests for speed concern
# run_gpt(init_1d_row_spec, use_ddp)
# run_gpt(init_1d_col_spec, use_ddp)
run_gpt(init_megatron_spec, use_ddp)
@pytest.mark.dist
@pytest.mark.parametrize('world_size', [1, 4])
@pytest.mark.parametrize('use_ddp', [False, True])
@rerun_if_address_is_in_use()
def test_gpt(world_size, use_ddp):
spawn(run_dist, world_size, use_ddp=use_ddp)
if __name__ == '__main__':
test_gpt(4, use_ddp=False)
tests/test_tensor/model/test_model.py deleted 100644 → 0
View file @ e79b1e80
import pytest
import torch
import colossalai
from colossalai.nn.optimizer import ColossalaiOptimizer
from colossalai.tensor import ColoTensor, ProcessGroup
from colossalai.tensor.colo_parameter import ColoParameter
from colossalai.testing import free_port, rerun_if_address_is_in_use, spawn
from colossalai.utils.cuda import get_current_device
from colossalai.zero import ColoInitContext
from tests.components_to_test.registry import non_distributed_component_funcs
from tests.test_tensor.common_utils import (
check_equal,
set_seed,
split_param_col_tp1d,
split_param_row_tp1d,
tensor_shard_equal,
)
def run_1d_hybrid_tp(model_name):
# A simple net with two stacked nn.Linear
get_components_func = non_distributed_component_funcs.get_callable(model_name)
model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
rank = torch.distributed.get_rank()
world_size = torch.distributed.get_world_size()
set_seed(1)
with ColoInitContext(device=get_current_device()):
model = model_builder(checkpoint=True)
if rank == 0:
model_torch = model_builder(checkpoint=True)
model_torch = model_torch.cuda()
optimizer_torch = ColossalaiOptimizer(torch.optim.SGD(model_torch.parameters(), lr=0.1))
# Make two models have the same init params
for p1, p2 in zip(model.parameters(), model_torch.parameters()):
p2.data.copy_(p1.data)
else:
model_torch = None
optimizer_torch = None
pg = ProcessGroup(tp_degree=world_size)
if 'bert' == model_name:
for name, p in model.named_parameters():
if not isinstance(p, ColoTensor):
continue
# num_class = type_vocab_size = 2 | (8, 2)
if 'classifier' in name and 'weight' in name:
split_param_col_tp1d(p, pg)
# num_class = vocab_size = 30524 | (30524, 8)
elif 'word_embeddings' in name and 'weight' in name:
split_param_row_tp1d(p, pg)
# num_class = seq_len = 512 | (512, 8)
elif 'position_embeddings' in name and 'weight' in name:
split_param_row_tp1d(p, pg)
# num_class = type_vocab_size = 2 | (2, 8)
elif 'token_type_embeddings' in name and 'weight' in name:
split_param_col_tp1d(p, pg)
elif "simple_net" == model_name:
# A naive way to set spec for all weights in Linear
for name, p in model.named_parameters():
if not isinstance(p, ColoTensor):
continue
if 'embed' in name and 'weight' in name:
split_param_col_tp1d(p, pg)
if 'proj1' in name and ('weight' in name or 'bias' in name):
split_param_row_tp1d(p, pg)
if 'proj2' in name and 'weight' in name:
split_param_col_tp1d(p, pg)
if 'classifier' in name and ('weight' in name or 'bias' in name):
split_param_row_tp1d(p, pg)
model = model.cuda()
model.eval()
if rank == 0:
model_torch.eval()
colo_optimizer = ColossalaiOptimizer(torch.optim.SGD(model.parameters(), lr=0.1))
for i, (data, label) in enumerate(train_dataloader):
# Zero grad
colo_optimizer.zero_grad()
if rank == 0:
optimizer_torch.zero_grad()
torch.distributed.barrier()
data = data.to(get_current_device())
label = label.to(get_current_device())
torch.distributed.broadcast(data, 0, group=pg.tp_process_group())
torch.distributed.broadcast(label, 0, group=pg.tp_process_group())
# Bcast rank0 data to all processes
if criterion:
output = model(data)
loss = criterion(output, label)
else:
output = model(data, label)
loss = output
# Test output
if rank == 0:
if criterion:
output_torch = model_torch(data)
loss_torch = criterion(output_torch, label)
else:
output_torch = model_torch(data, label)
loss_torch = output_torch
assert torch.allclose(loss, loss_torch, rtol=1e-2), f"model_name {model_name} failed"
torch.distributed.barrier()
loss.backward()
colo_optimizer.step()
if rank == 0:
loss_torch.backward()
optimizer_torch.step()
with torch.no_grad():
# check param
for p, torch_p in zip(model.parameters(), model_torch.parameters()):
assert tensor_shard_equal(torch_p, p, pg.tp_local_rank(), pg.tp_world_size())
torch.distributed.barrier()
if i > 5:
break
# Test the overrided parameters() and named_parameters() member functions
def test_model_parameters():
colossalai.launch(config={}, rank=0, world_size=1, host='localhost', port=free_port(), backend='nccl')
# build a module with 2 Linear, 4 parameters in total.
class Net(torch.nn.Module):
def __init__(self):
super().__init__()
self.fcs = torch.nn.Sequential(torch.nn.Linear(2, 3), torch.nn.Linear(3, 2))
self.extra_param = torch.nn.Parameter(torch.randn(2))
with ColoInitContext(device=get_current_device()):
model = Net()
param_cnt = 0
for name, p in model.named_parameters():
param_cnt += 1
assert param_cnt == 5
for name, colo_p in model.named_parameters():
assert colo_p.is_model_data()
param_cnt = 0
for name, p in model.named_parameters(recurse=False):
param_cnt += 1
assert param_cnt == 1
param_cnt = 0
for p in model.fcs[0].parameters(recurse=False):
param_cnt += 1
assert param_cnt == 2
def test_colo_optimizer():
get_components_func = non_distributed_component_funcs.get_callable('simple_net')
model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
set_seed(1)
with ColoInitContext(device=get_current_device()):
model = model_builder(checkpoint=True)
colo_optimizer = ColossalaiOptimizer(torch.optim.SGD(model.parameters(), lr=0.1))
for i, (data, label) in enumerate(train_dataloader):
colo_optimizer.zero_grad()
data = data.to(get_current_device())
label = label.to(get_current_device())
# Bcast rank0 data to all processes
if criterion:
output = model(data)
loss = criterion(output, label)
else:
output = model(data, label)
loss = output
loss.backward()
colo_optimizer.step()
if i > 5:
break
def run_1d_row_tp(model_name: str):
# A simple net with two stacked nn.Linear
get_components_func = non_distributed_component_funcs.get_callable(model_name)
model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
rank = torch.distributed.get_rank()
set_seed(1)
with ColoInitContext(device=get_current_device()):
model = model_builder(checkpoint=True)
world_size = torch.distributed.get_world_size()
pg = ProcessGroup(tp_degree=world_size)
set_seed(1)
if rank == 0:
model_torch = model_builder(checkpoint=True)
model_torch = model_torch.cuda()
# A naive way to set spec for all weights in Linear
for mo_name, module in model.named_modules():
# print(mo_name)
for pa_name, param in module.named_parameters(recurse=False):
# print('\t', pa_name, param.shape)
if not isinstance(param, ColoTensor):
continue
if 'weight' in pa_name:
if 'embed' in mo_name and 'token' not in mo_name and 'LayerNorm' not in mo_name:
split_param_row_tp1d(param, pg)
elif 'LayerNorm' not in mo_name and 'ln' not in mo_name:
split_param_col_tp1d(param, pg)
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=pg.tp_process_group())
torch.distributed.broadcast(label, 0, group=pg.tp_process_group())
# Bcast rank0 data to all processes
if criterion:
output = model(data)
loss = criterion(output, label)
else:
output = model(data, label)
loss = output
# For reference
if rank == 0:
if criterion:
output_torch = model_torch(data)
loss_torch = criterion(output_torch, label)
else:
output_torch = model_torch(data, label)
loss_torch = output_torch
assert torch.allclose(loss, loss_torch, rtol=1e-2)
torch.distributed.barrier()
loss.backward()
if rank == 0:
loss_torch.backward()
torch.distributed.barrier()
if i > 5:
break
def _run_pretrain_load():
from transformers import BertForMaskedLM
set_seed(1)
model_pretrained = BertForMaskedLM.from_pretrained('bert-base-uncased')
with ColoInitContext(device=get_current_device()):
model = BertForMaskedLM.from_pretrained('bert-base-uncased')
model_pretrained = model_pretrained.cuda()
model = model.cuda()
dict_pretrained = {}
dict_col = {}
c_ref = 0
for name, param in model_pretrained.named_parameters():
dict_pretrained[name] = param
c_ref += 1
c1 = 0
c2 = 0
for name, param in model.named_parameters():
if isinstance(param, ColoParameter):
c1 += 1
else:
c2 += 1
dict_col[name] = param
assert c_ref == c1
assert c2 == 0
if model_pretrained.cls.predictions.decoder.bias is model_pretrained.cls.predictions.bias:
assert model.cls.predictions.decoder.bias is model.cls.predictions.bias
for name, param in dict_pretrained.items():
check_equal(param, dict_col[name])
def run_model_dist(rank, world_size, port):
colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
# Comment below test for speed consideration
# for name in ['bert', 'simple_net']:
# run_1d_row_tp(name)
for name in ['bert', 'simple_net']:
run_1d_hybrid_tp(name)
@pytest.mark.dist
@pytest.mark.parametrize('world_size', [1, 4])
@rerun_if_address_is_in_use()
def test_model(world_size):
spawn(run_model_dist, world_size)
def run_pretrain_load_dist(rank, world_size, port):
colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
_run_pretrain_load()
# The test case has to download huggingface pretrained models from the internet
# So we manually trigger the test.
@pytest.mark.skip
@pytest.mark.dist
@pytest.mark.parametrize('world_size', [1, 4])
@rerun_if_address_is_in_use()
def test_pretrain_load(world_size):
spawn(run_pretrain_load_dist, world_size)
if __name__ == '__main__':
# test_model_parameters()
# test_colo_optimizer()
test_model(4)
# test_pretrain_load(4)
tests/test_tensor/model/test_module_spec.py deleted 100644 → 0
View file @ e79b1e80
from copy import deepcopy
import pytest
import torch
import colossalai
from colossalai.nn.parallel.layers import check_colo_module, init_colo_module
from colossalai.tensor import (
ColoTensor,
ColoTensorSpec,
ComputePattern,
ComputeSpec,
ProcessGroup,
ReplicaSpec,
ShardSpec,
distspec,
)
from colossalai.testing import rerun_if_address_is_in_use, spawn
from colossalai.utils.cuda import get_current_device
from colossalai.zero import ColoInitContext
from tests.components_to_test.registry import non_distributed_component_funcs
from tests.test_tensor.common_utils import set_seed, tensor_equal, tensor_shard_equal
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()
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()):
model = model_builder(checkpoint=False)
if rank == 0:
model_seq = model_builder(checkpoint=False)
model_seq = model_seq.cuda()
# Make two models have the same init params
for p1, p2 in zip(model.parameters(), model_seq.parameters()):
p2.data.copy_(p1.data)
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, 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, 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, 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=pg.tp_process_group())
torch.distributed.broadcast(label, 0, group=pg.tp_process_group())
if criterion:
output = model(data)
loss = criterion(output, label)
else:
output = model(data, label)
loss = output
# For reference
if rank == 0:
if criterion:
output_seq = model_seq(data)
loss_seq = criterion(output_seq, label)
else:
output_seq = model_seq(data, label)
loss_seq = output_seq
if rank == 0:
with torch.no_grad():
assert torch.allclose(loss, loss_seq, rtol=1e-2)
loss.backward()
if rank == 0:
loss_seq.backward()
with torch.no_grad():
# check param
for p1, p2 in zip(model.parameters(), model_seq.parameters()):
if p1.size() == p2.size():
assert torch.allclose(p1, p2)
else:
if p1.size(-1) < p2.size(-1): # col
world_size = p2.size(-1) // p1.size(-1)
split_p2 = torch.chunk(p2, world_size, dim=-1)[0]
elif p1.size(0) < p2.size(0): # row
world_size = p2.size(0) // p1.size(0)
split_p2 = torch.chunk(p2, world_size, dim=0)[0]
assert torch.allclose(p1, split_p2)
if i > 3:
break
def run_linear_with_spec(mode):
with ColoInitContext(device=get_current_device()):
model = torch.nn.Linear(4, 8)
model_handy = deepcopy(model)
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()
colo_x = ColoTensor.from_torch_tensor(x, ColoTensorSpec(pg))
out = model(x)
colo_out = model_handy(colo_x)
assert tensor_equal(out, colo_out)
grad = torch.rand_like(out)
out.backward(grad)
colo_out.backward(grad)
assert tensor_shard_equal(model_handy.weight.grad, model.weight.grad, pg.tp_local_rank(), pg.tp_world_size())
assert tensor_shard_equal(model_handy.bias.grad, model.bias.grad, pg.tp_local_rank(), pg.tp_world_size())
def run_check_shared_param():
from transformers import BertConfig, BertForMaskedLM
hidden_dim = 8
num_head = 4
sequence_length = 12
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,
num_attention_heads=num_head,
max_position_embeddings=sequence_length,
num_hidden_layers=num_layer,
hidden_dropout_prob=0.,
attention_probs_dropout_prob=0.)
with ColoInitContext(device=get_current_device()):
model = BertForMaskedLM(config)
model = model.cuda()
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, 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 = (ShardSpec([0], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D))
# TODO(jiaruifang) optimize this line
if not model.cls.predictions.bias.has_initialized:
model.cls.predictions.bias.pg = pg
model.cls.predictions.bias.dist_spec = ReplicaSpec()
model.cls.predictions.bias.has_initialized = True
model.cls.predictions.bias.set_tensor_spec(*col_spec)
try:
check_colo_module(model.cls.predictions.decoder, pg=pg, recursive=False)
except Exception as e:
assert 'incorrectly sharded' in str(e)
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_linear_with_spec('col')
run_linear_with_spec('row')
def run_dist_model(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')
for model_name in ['simple_net', 'bert']:
run_model_with_spec('col', model_name)
run_model_with_spec('row', model_name)
def run_dist_check(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_check_shared_param()
@pytest.mark.dist
@pytest.mark.parametrize('world_size', [1, 4])
@pytest.mark.skip("for higher testing speed")
@rerun_if_address_is_in_use()
def test_module_linear_1d(world_size):
spawn(run_dist, world_size)
@pytest.mark.dist
@pytest.mark.parametrize('world_size', [1, 4])
@pytest.mark.skip("for higher testing speed")
@rerun_if_address_is_in_use()
def test_module_model(world_size):
spawn(run_dist_model, world_size)
@pytest.mark.dist
@pytest.mark.parametrize('world_size', [1, 2])
@pytest.mark.skip("for higher testing speed")
@rerun_if_address_is_in_use()
def test_module_check(world_size):
spawn(run_dist_check, world_size)
if __name__ == '__main__':
test_module_linear_1d(4)
tests/test_tensor/test_colo_checkpoint_tools.py deleted 100644 → 0
View file @ e79b1e80
import pytest
import torch
import torch.distributed as dist
import colossalai
from colossalai.tensor import ColoTensor, ColoTensorSpec, ComputePattern, ComputeSpec, ProcessGroup, ShardSpec
from colossalai.testing import rerun_if_address_is_in_use, spawn
from colossalai.utils.checkpoint.utils import gather_tensor, scatter_tensor
from tests.test_tensor.common_utils import tensor_shard_equal
def run_dist(rank, world_size, port, dp_degree, tp_degree):
colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
pg = ProcessGroup(dp_degree=dp_degree, tp_degree=tp_degree)
x = torch.randn(4, 4)
param = ColoTensor(torch.nn.Parameter(x), spec=ColoTensorSpec(pg))
spec = ShardSpec([-1], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D)
param.set_tensor_spec(*spec)
gather_tensor(param)
if dist.get_rank() == 0:
assert torch.all(x == param)
else:
assert tensor_shard_equal(x, param.data, pg.tp_local_rank(), pg.tp_world_size())
dist.barrier()
scatter_tensor(param, spec[0])
assert tensor_shard_equal(x, param.data, pg.tp_local_rank(), pg.tp_world_size())
assert param.requires_grad is True
dist.barrier()
@pytest.mark.dist
@pytest.mark.parametrize('world_size', [4])
@rerun_if_address_is_in_use()
def test_checkpoint(world_size):
spawn(run_dist, world_size, dp_degree=2, tp_degree=world_size // 2)
if __name__ == '__main__':
test_checkpoint(world_size=4)
tests/test_tensor/test_context.py deleted 100644 → 0
View file @ e79b1e80
import pytest
import torch
import colossalai
from colossalai.tensor import (
ColoParameter,
ColoTensorSpec,
ComputePattern,
ComputeSpec,
ProcessGroup,
ReplicaSpec,
ShardSpec,
)
from colossalai.testing import rerun_if_address_is_in_use, spawn
from colossalai.utils.cuda import get_current_device
from colossalai.zero import ColoInitContext
from tests.components_to_test.registry import non_distributed_component_funcs
from tests.test_tensor.common_utils import set_seed
def run_colo_init_context(rank: int, world_size: int, port: int):
colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
# make sure seed of each process is the same, so the params are consistent among processes and the params are exactly replicated.
set_seed(42)
get_components_func = non_distributed_component_funcs.get_callable('gpt2')
model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
# keep parameters replicated during init
with ColoInitContext(device=get_current_device()):
model1 = model_builder()
# shard the parameters during init
set_seed(42)
shard_spec = ReplicaSpec()
# If using ShardSpec, the assertations will failed.
# But it is not a bug, the initialized values are not consist with the original one.
# shard_spec = ShardSpec(dims=[0], num_partitions=[world_size])
default_pg = ProcessGroup(tp_degree=world_size)
with ColoInitContext(device=get_current_device(), default_pg=default_pg, default_dist_spec=shard_spec):
model2 = model_builder()
# reshard both models
new_shard = ShardSpec(dims=[-1], num_partitions=[world_size])
for p1, p2 in zip(model1.parameters(), model2.parameters()):
p1: ColoParameter = p1
p1.set_process_group(ProcessGroup(tp_degree=world_size))
p1.set_dist_spec(new_shard)
p2.set_dist_spec(new_shard)
for p1, p2 in zip(model1.parameters(), model2.parameters()):
assert (torch.allclose(p1, p2))
@pytest.mark.dist
@pytest.mark.parametrize('world_size', [1, 4])
@rerun_if_address_is_in_use()
def test_colo_init_context(world_size):
spawn(run_colo_init_context, world_size)
if __name__ == '__main__':
test_colo_init_context(2)
tests/test_tensor/test_sharded_linear.py deleted 100644 → 0
View file @ e79b1e80
import pytest
import torch
import torch.nn.functional as F
import colossalai
from colossalai.device.device_mesh import DeviceMesh
from colossalai.nn._ops._utils import gather_forward_split_backward
from colossalai.tensor import ColoParameter, ColoTensor, ProcessGroup
from colossalai.tensor.sharding_spec import ShardingSpec
from colossalai.testing import rerun_if_address_is_in_use, spawn
def run_dist(rank, world_size, port):
config = {}
colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
# create mlp vars
x = ColoTensor.from_torch_tensor(torch.rand(4, 4, 8, requires_grad=True)).cuda()
w = ColoParameter.from_torch_tensor(torch.rand(16, 8, requires_grad=True)).cuda()
b = ColoParameter.from_torch_tensor(torch.rand(16, requires_grad=True)).cuda()
# run normal forward
out = F.linear(x, w, b)
# create mesh meta
# the mesh is in the following topo
# [[0, 1],
# [2, 3]]
physical_mesh_id = torch.arange(0, 4)
mesh_shape = (2, 2)
device_mesh = DeviceMesh(physical_mesh_id, mesh_shape)
row_id = rank // 2
column_id = rank % 2
# create pg
row_process_group = None
col_process_group = None
row_to_ranks = {0: [0, 1], 1: [2, 3]}
col_to_ranks = {0: [0, 2], 1: [1, 3]}
for idx in range(2):
# row ranks
row_ranks = row_to_ranks[idx]
row_pg = ProcessGroup(ranks=row_ranks, tp_degree=2)
# col ranks
col_ranks = col_to_ranks[idx]
col_pg = ProcessGroup(ranks=col_ranks, tp_degree=2)
if rank in row_ranks:
row_process_group = row_pg
if rank in col_ranks:
col_process_group = col_pg
########################
# RRR x RS0 -> RRS0 #
########################
# w will be transposed in F.linear
x_replica = x.detach().clone()
w_shard = torch.chunk(w.detach().clone(), chunks=2, dim=0)[row_id]
b_shard = torch.chunk(b.detach().clone(), chunks=2, dim=0)[row_id]
# adding sharding spec
x_replica.sharding_spec = ShardingSpec(device_mesh, x.shape, dim_partition_dict={})
w_shard.sharding_spec = ShardingSpec(device_mesh, w.shape, dim_partition_dict={0: [0]})
b_shard.sharding_spec = ShardingSpec(device_mesh, b.shape, dim_partition_dict={0: [0]})
# check sharding spec
assert str(x_replica.sharding_spec.sharding_sequence) == "[R, R, R]"
assert str(w_shard.sharding_spec.sharding_sequence) == "[S0, R]"
assert str(b_shard.sharding_spec.sharding_sequence) == "[S0]"
w_shard.pg_axis0 = col_process_group
w_shard.pg_axis1 = row_process_group
out_shard = F.linear(x_replica, w_shard, b_shard)
assert str(out_shard.sharding_spec.sharding_sequence) == "[R, R, S0]"
# each row only has a mini-batch
expected_out_shard = torch.chunk(out, chunks=2, dim=2)[row_id]
assert torch.allclose(out_shard, expected_out_shard)
########################
# S0RR x RS1 -> S0RS1 #
########################
# w will be transposed in F.linear
x_shard = torch.chunk(x.detach().clone(), chunks=2, dim=0)[row_id]
w_shard = torch.chunk(w.detach().clone(), chunks=2, dim=0)[column_id]
b_shard = torch.chunk(b.detach().clone(), chunks=2, dim=0)[column_id]
# adding sharding spec
x_shard.sharding_spec = ShardingSpec(device_mesh, x.shape, dim_partition_dict={0: [0]})
w_shard.sharding_spec = ShardingSpec(device_mesh, w.shape, dim_partition_dict={0: [1]})
b_shard.sharding_spec = ShardingSpec(device_mesh, b.shape, dim_partition_dict={0: [1]})
# check sharding spec
assert str(x_shard.sharding_spec.sharding_sequence) == "[S0, R, R]"
assert str(w_shard.sharding_spec.sharding_sequence) == "[S1, R]"
assert str(b_shard.sharding_spec.sharding_sequence) == "[S1]"
w_shard.pg_axis0 = col_process_group
w_shard.pg_axis1 = row_process_group
out_shard = F.linear(x_shard, w_shard, b_shard)
# each row only has a mini-batch
expected_out_shard = torch.chunk(out, chunks=2, dim=0)[row_id]
expected_out_shard = torch.chunk(expected_out_shard, chunks=2, dim=2)[column_id]
assert torch.allclose(out_shard, expected_out_shard)
########################
# S0RS1 x S1R -> S0RR #
########################
# w will be transposed in F.linear
x_shard = torch.chunk(x.clone(), chunks=2, dim=0)[row_id]
x_shard = torch.chunk(x_shard, chunks=2, dim=2)[column_id]
w_shard = torch.chunk(w.clone(), chunks=2, dim=1)[column_id]
b_replica = b.clone()
# adding sharding spec
x_shard.sharding_spec = ShardingSpec(device_mesh, x.shape, dim_partition_dict={0: [0], 2: [1]})
w_shard.sharding_spec = ShardingSpec(device_mesh, w.shape, dim_partition_dict={1: [1]})
b_replica.sharding_spec = ShardingSpec(device_mesh, b.shape, dim_partition_dict={})
# check sharding spec
assert str(x_shard.sharding_spec.sharding_sequence) == "[S0, R, S1]"
assert str(w_shard.sharding_spec.sharding_sequence) == "[R, S1]"
assert str(b_replica.sharding_spec.sharding_sequence) == "[R]"
w_shard.pg_axis0 = col_process_group
w_shard.pg_axis1 = row_process_group
out_shard = F.linear(x_shard, w_shard, b_replica)
# each row only has a mini-batch
expected_out_shard = torch.chunk(out, chunks=2, dim=0)[row_id]
assert torch.allclose(out_shard, expected_out_shard)
########################
# RRS0 x S0R -> RRR #
########################
# w will be transposed in F.linear
x_shard = torch.chunk(x.clone(), chunks=2, dim=2)[row_id]
w_shard = torch.chunk(w.clone(), chunks=2, dim=1)[row_id]
b_replica = b.clone()
# adding sharding spec
x_shard.sharding_spec = ShardingSpec(device_mesh, x.shape, dim_partition_dict={2: [0]})
w_shard.sharding_spec = ShardingSpec(device_mesh, w.shape, dim_partition_dict={1: [0]})
b_replica.sharding_spec = ShardingSpec(device_mesh, b.shape, dim_partition_dict={})
# check sharding spec
assert str(x_shard.sharding_spec.sharding_sequence) == "[R, R, S0]"
assert str(w_shard.sharding_spec.sharding_sequence) == "[R, S0]"
assert str(b_replica.sharding_spec.sharding_sequence) == "[R]"
w_shard.pg_axis0 = col_process_group
w_shard.pg_axis1 = row_process_group
out_shard = F.linear(x_shard, w_shard, b_replica)
# each row only has a mini-batch
expected_out_shard = out
assert torch.allclose(out_shard, expected_out_shard)
########################
# RS0S1 x S1R -> RS0R #
########################
# w will be transposed in F.linear
x_shard = torch.chunk(x.clone(), chunks=2, dim=1)[row_id]
x_shard = torch.chunk(x_shard, chunks=2, dim=2)[column_id]
w_shard = torch.chunk(w.clone(), chunks=2, dim=1)[column_id]
b_replica = b.clone()
# adding sharding spec
x_shard.sharding_spec = ShardingSpec(device_mesh, x.shape, dim_partition_dict={1: [0], 2: [1]})
w_shard.sharding_spec = ShardingSpec(device_mesh, w.shape, dim_partition_dict={1: [1]})
b_replica.sharding_spec = ShardingSpec(device_mesh, b.shape, dim_partition_dict={})
# check sharding spec
assert str(x_shard.sharding_spec.sharding_sequence) == "[R, S0, S1]"
assert str(w_shard.sharding_spec.sharding_sequence) == "[R, S1]"
assert str(b_replica.sharding_spec.sharding_sequence) == "[R]"
w_shard.pg_axis0 = col_process_group
w_shard.pg_axis1 = row_process_group
out_shard = F.linear(x_shard, w_shard, b_replica)
# each row only has a mini-batch
expected_out_shard = torch.chunk(out, chunks=2, dim=1)[row_id]
assert torch.allclose(out_shard, expected_out_shard)
########################
# RRS0 x S0S1 -> RRS1 #
########################
# w will be transposed in F.linear
x_shard = torch.chunk(x.clone(), chunks=2, dim=2)[row_id]
w_shard = torch.chunk(w.clone(), chunks=2, dim=1)[row_id]
w_shard = torch.chunk(w_shard, chunks=2, dim=0)[column_id]
b_shard = torch.chunk(b.clone(), chunks=2, dim=0)[column_id]
# adding sharding spec
x_shard.sharding_spec = ShardingSpec(device_mesh, x.shape, dim_partition_dict={2: [0]})
w_shard.sharding_spec = ShardingSpec(device_mesh, w.shape, dim_partition_dict={0: [1], 1: [0]})
b_shard.sharding_spec = ShardingSpec(device_mesh, b.shape, dim_partition_dict={0: [1]})
# check sharding spec
assert str(x_shard.sharding_spec.sharding_sequence) == "[R, R, S0]"
assert str(w_shard.sharding_spec.sharding_sequence) == "[S1, S0]"
assert str(b_shard.sharding_spec.sharding_sequence) == "[S1]"
w_shard.pg_axis0 = col_process_group
w_shard.pg_axis1 = row_process_group
out_shard = F.linear(x_shard, w_shard, b_shard)
# each row only has a mini-batch
expected_out_shard = torch.chunk(out, chunks=2, dim=2)[column_id]
assert torch.allclose(out_shard, expected_out_shard)
@pytest.mark.dist
@pytest.mark.parametrize('world_size', [4])
@rerun_if_address_is_in_use()
def test_sharded_mlp(world_size):
spawn(run_dist, world_size)
if __name__ == '__main__':
test_sharded_mlp(4)
tests/test_tensor/test_tp_with_zero.py deleted 100644 → 0
View file @ e79b1e80
import pytest
import torch
from torch.nn.parallel import DistributedDataParallel as DDP
import colossalai
from colossalai.amp import convert_to_apex_amp
from colossalai.tensor import ColoTensor, ColoTensorSpec, ComputePattern, ComputeSpec, ProcessGroup, ShardSpec
from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
from colossalai.utils.cuda import get_current_device
from colossalai.zero import ColoInitContext, GeminiAdamOptimizer, GeminiDDP, ZeroDDP
from colossalai.zero.gemini import search_chunk_configuration
from tests.components_to_test.registry import non_distributed_component_funcs
from tests.test_tensor.common_utils import set_seed, tensor_shard_equal
from tests.test_tensor.model.test_gpt2 import init_megatron_spec
def check_param(model: ZeroDDP, torch_model: torch.nn.Module, pg: ProcessGroup):
zero_dict = model.state_dict(only_rank_0=False)
torch_dict = torch_model.state_dict()
for key, value in torch_dict.items():
# key is 'module.model.PARAMETER', so we truncate it
key = key[7:]
assert key in zero_dict, "{} not in ZeRO dictionary.".format(key)
temp_zero_value = zero_dict[key].to(device=value.device, dtype=value.dtype)
# debug_print([0], "max range: ", key, torch.max(torch.abs(value - temp_zero_value)))
assert tensor_shard_equal(value, temp_zero_value, pg.tp_local_rank(), pg.tp_world_size()), \
"parameter '{}' has problem.".format(key)
def run_fwd_bwd(model, criterion, optimizer, input_ids):
optimizer.zero_grad()
logits = model(input_ids)
logits = logits.float()
loss = criterion(logits, input_ids)
optimizer.backward(loss)
return logits
def init_1d_row_spec(model, pg: ProcessGroup):
spec = (ShardSpec([0], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D))
for n, p in model.named_parameters():
p.set_process_group(pg)
if 'weight' in n and 'ln' not in n:
p.set_tensor_spec(*spec)
def init_1d_col_spec(model, pg: ProcessGroup):
spec = (ShardSpec([-1], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D))
for n, p in model.named_parameters():
p.set_process_group(pg)
if 'ln' not in n and ('weight' in n or 'bias' in n):
p.set_tensor_spec(*spec)
@parameterize('placement_policy', ['cuda', 'cpu'])
def run_gpt(placement_policy, tp_init_spec_func=None):
set_seed(42)
get_components_func = non_distributed_component_funcs.get_callable('gpt2')
model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
with ColoInitContext(device=get_current_device()):
model = model_builder()
model = model.cuda()
torch_model = model_builder().cuda()
for torch_p, p in zip(torch_model.parameters(), model.parameters()):
torch_p.data.copy_(p.data)
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, pg)
dp_world_size = pg.dp_world_size()
config_dict, *_ = search_chunk_configuration(model, search_range_m=1, search_interval=100)
config_dict[dp_world_size]['chunk_size'] = 5000
config_dict[dp_world_size]['keep_gathered'] = False
if placement_policy != 'cuda':
init_device = torch.device('cpu')
else:
init_device = None
model = GeminiDDP(model, init_device, placement_policy, True, False)
# The same as the following 3 lines
# chunk_manager = ChunkManager(config_dict, init_device=init_device)
# gemini_manager = GeminiManager(placement_policy, chunk_manager)
# model = ZeroDDP(model, gemini_manager, pin_memory=True)
zero_optim = GeminiAdamOptimizer(model, lr=1e-3, initial_scale=1)
# The same as the following 2 lines
# optimizer = HybridAdam(model.parameters(), lr=1e-3)
# zero_optim = ZeroOptimizer(optimizer, model, initial_scale=1)
amp_config = dict(opt_level='O2', keep_batchnorm_fp32=False, loss_scale=1)
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=[pg.rank()], process_group=pg.dp_process_group())
check_param(model, torch_model, pg)
model.eval()
torch_model.eval()
set_seed(pg.dp_local_rank())
for i, (input_ids, label) in enumerate(train_dataloader):
if i > 2:
break
input_ids_colo = ColoTensor.from_torch_tensor(input_ids, ColoTensorSpec(pg))
zero_logits = run_fwd_bwd(model, criterion, zero_optim, input_ids_colo)
torch_logits = run_fwd_bwd(torch_model, criterion, torch_optim, input_ids)
assert torch.allclose(zero_logits, torch_logits, rtol=1e-3, atol=1e-2)
zero_optim.step()
torch_optim.step()
check_param(model, torch_model, pg)
def run_dist(rank, world_size, port):
config = {}
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_megatron_spec)
else:
run_gpt(tp_init_spec_func=init_1d_col_spec)
run_gpt(tp_init_spec_func=init_1d_row_spec)
@pytest.mark.dist
@pytest.mark.parametrize('world_size', [1, 4])
@rerun_if_address_is_in_use()
def test_gpt(world_size):
spawn(run_dist, world_size)
if __name__ == '__main__':
test_gpt(4)
tests/test_utils/test_colo_checkpoint.py deleted 100644 → 0
View file @ e79b1e80
import os
import shutil
from copy import deepcopy
import pytest
import torch
import torch.distributed as dist
from torch.optim.lr_scheduler import CosineAnnealingLR, MultiplicativeLR
import colossalai
from colossalai.nn.lr_scheduler import CosineAnnealingWarmupLR
from colossalai.nn.optimizer import ColossalaiOptimizer
from colossalai.tensor import ColoTensor, ComputePattern, ComputeSpec, ProcessGroup, ShardSpec
from colossalai.testing import rerun_if_address_is_in_use, spawn
from colossalai.utils.checkpoint import load_checkpoint, save_checkpoint
from colossalai.utils.cuda import get_current_device
from colossalai.zero import ColoInitContext
from tests.components_to_test.registry import non_distributed_component_funcs
def init_1d_row_linear(weight: ColoTensor, pg: ProcessGroup):
spec = (ShardSpec([-1], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D))
weight.set_process_group(pg)
weight.set_tensor_spec(*spec)
def init_1d_col_linear(weight, pg):
spec = (ShardSpec([0], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D))
weight.set_process_group(pg)
weight.set_tensor_spec(*spec)
def init_1d_row_embedding(weight, pg):
spec = (ShardSpec([0], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D))
weight.set_process_group(pg)
weight.set_tensor_spec(*spec)
def init_1d_col_embedding(weight, pg):
spec = (ShardSpec([-1], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D))
weight.set_process_group(pg)
weight.set_tensor_spec(*spec)
def init_1d_row_for_linear_weight_spec(model, pg: ProcessGroup):
spec = (ShardSpec([-1], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D))
for name, p in model.named_parameters():
if not isinstance(p, ColoTensor):
continue
if 'embed' in name and 'weight' in name:
init_1d_col_embedding(p, pg)
if 'proj1' in name and ('weight' in name or 'bias' in name):
init_1d_col_linear(p, pg)
if 'proj2' in name and 'weight' in name:
init_1d_row_linear(p, pg)
if 'classifier' in name and ('weight' in name or 'bias' in name):
init_1d_col_linear(p, pg)
def check_param_equal(model, torch_model):
for (n, p), (tn, tp) in zip(model.named_parameters(), torch_model.named_parameters()):
assert torch.all(p.data == tp.data), "{} went wrong.\n {} vs {}\n{}".format(n, p, tp, p.shape)
def remove(path):
""" param <path> could either be relative or absolute. """
if os.path.isfile(path) or os.path.islink(path):
os.remove(path)
elif os.path.isdir(path):
shutil.rmtree(path)
else:
raise ValueError("file {} is not a file or dir.".format(path))
def compare_optims(optim1, optim2):
state1 = optim1.state_dict()['state']
state2 = optim2.state_dict()['state']
for k, p1 in state1.items():
if k not in state2:
continue
p2 = state2[k]
for n, t1 in p1.items():
if n not in p2:
continue
t2 = p2[n]
if isinstance(t1, ColoTensor):
assert isinstance(t2, ColoTensor)
assert torch.allclose(t1, t2, rtol=0, atol=0)
def _run_checkpoint(model_name, init_spec_func, use_ddp, use_mp_reload, test_scheduler, pg):
get_components_func = non_distributed_component_funcs.get_callable(model_name)
model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
rank = torch.distributed.get_rank()
world_size = torch.distributed.get_world_size()
# set_seed(1)
with ColoInitContext(device=get_current_device()):
model = model_builder(checkpoint=True)
if use_mp_reload:
if 'bert' == model_name:
for name, p in model.named_parameters():
if not isinstance(p, ColoTensor):
continue
# num_class = type_vocab_size = 2 | (8, 2)
if 'classifier' in name and 'weight' in name:
init_1d_row_linear(p, pg)
# num_class = vocab_size = 30524 | (30524, 8)
elif 'word_embeddings' in name and 'weight' in name:
init_1d_row_embedding(p, pg)
# num_class = seq_len = 512 | (512, 8)
elif 'position_embeddings' in name and 'weight' in name:
init_1d_row_embedding(p, pg)
# num_class = type_vocab_size = 2 | (2, 8)
elif 'token_type_embeddings' in name and 'weight' in name:
init_1d_col_embedding(p, pg)
elif p.process_group.tp_world_size() == 1:
p.set_process_group(pg)
elif "simple_net" == model_name:
init_spec_func(model, pg)
model_reload = deepcopy(model)
model = model.cuda()
model.eval()
model_reload = model_reload.cuda()
model_reload.eval()
opt_class = torch.optim.Adam
colo_optimizer = ColossalaiOptimizer(opt_class(model.parameters(), lr=0.1))
colo_optimizer_reload = ColossalaiOptimizer(opt_class(model_reload.parameters(), lr=0.1))
for i, (data, label) in enumerate(train_dataloader):
# Zero grad
colo_optimizer.zero_grad()
colo_optimizer_reload.zero_grad()
data = data.to(get_current_device())
label = label.to(get_current_device())
dist.broadcast(data, pg.tp_rank_list()[0], pg.tp_process_group())
dist.broadcast(label, pg.tp_rank_list()[0], pg.tp_process_group())
# Bcast rank0 data to all processes
if criterion:
output = model(data)
output_reload = model_reload(data)
loss = criterion(output, label)
loss_reload = criterion(output_reload, label)
else:
loss = model(data, label)
loss_reload = model_reload(data, label)
loss.backward()
loss_reload.backward()
colo_optimizer.step()
colo_optimizer_reload.step()
if i > 2:
break
if not os.path.isdir('./checkpoint') and rank == 0:
os.mkdir('./checkpoint')
dist.barrier()
save_checkpoint('./checkpoint', 0, model, colo_optimizer, None)
load_checkpoint('./checkpoint', 0, model_reload, colo_optimizer_reload, None)
check_param_equal(model, model_reload)
compare_optims(colo_optimizer, colo_optimizer_reload)
if rank == 0:
remove('./checkpoint')
dist.barrier()
def run_dist(rank, world_size, port, use_ddp, use_mp_reload, test_scheduler):
colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
pg = ProcessGroup(tp_degree=world_size)
# the data loader of BERT is in DDP mode, causing the input data is not replicated in the TP context
for model_name in ['bert']:
_run_checkpoint(model_name,
init_1d_row_for_linear_weight_spec,
use_ddp,
use_mp_reload,
test_scheduler=test_scheduler,
pg=pg)
@pytest.mark.dist
@pytest.mark.parametrize('world_size', [1, 2])
@pytest.mark.parametrize('use_ddp', [False])
@pytest.mark.parametrize('use_mp_reload', [True, False])
# @pytest.mark.parametrize('test_scheduler', ['colossalai_cosine_warmup', 'torch_cosine', 'torch_lambda'])
@rerun_if_address_is_in_use()
def test_checkpoint(world_size, use_ddp, use_mp_reload, test_scheduler=None):
spawn(run_dist, world_size, use_ddp=use_ddp, use_mp_reload=use_mp_reload, test_scheduler=test_scheduler)
if __name__ == '__main__':
test_checkpoint(2, use_ddp=False, use_mp_reload=True, test_scheduler="torch_cosine")
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment