mirror of https://github.com/hpcaitech/ColossalAI
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
112 lines
3.4 KiB
112 lines
3.4 KiB
#!/usr/bin/env python
|
|
# -*- encoding: utf-8 -*-
|
|
|
|
from functools import partial
|
|
|
|
import pytest
|
|
import torch
|
|
import torch.distributed as dist
|
|
import torch.nn as nn
|
|
from torch.nn.parallel import DistributedDataParallel as DDP
|
|
from torch.nn.utils import clip_grad_norm_
|
|
|
|
import colossalai
|
|
from colossalai.logging import disable_existing_loggers
|
|
from colossalai.testing import rerun_if_address_is_in_use, spawn
|
|
from colossalai.utils import checkpoint, clip_grad_norm_fp32
|
|
from colossalai.zero.legacy.shard_utils.tensor_shard_strategy import TensorShardStrategy
|
|
from colossalai.zero.legacy.sharded_model.sharded_model_v2 import ShardedModelV2
|
|
|
|
|
|
def checkpoint_wrapper(module, enable=True):
|
|
if enable:
|
|
module.forward = partial(checkpoint, module.forward, False)
|
|
return module
|
|
|
|
|
|
class Net(nn.Module):
|
|
|
|
def __init__(self, checkpoint=False) -> None:
|
|
super().__init__()
|
|
self.fc1 = nn.Linear(5, 5)
|
|
self.fc2 = nn.Linear(5, 5)
|
|
self.fc3 = nn.Linear(5, 1)
|
|
if checkpoint:
|
|
self.fc1 = checkpoint_wrapper(self.fc1)
|
|
self.layers = [self.fc1, self.fc2, self.fc1, self.fc2, self.fc3]
|
|
|
|
def forward(self, x):
|
|
for layer in self.layers:
|
|
x = layer(x)
|
|
return x
|
|
|
|
|
|
def run_step(model, optimizer, x, enable_autocast=False, norm_type=2.0):
|
|
model.train()
|
|
optimizer.zero_grad()
|
|
with torch.cuda.amp.autocast(enabled=enable_autocast):
|
|
y = model(x)
|
|
loss = y.sum()
|
|
loss = loss.float()
|
|
loss.backward()
|
|
clip_grad(model, norm_type)
|
|
optimizer.step()
|
|
|
|
|
|
def clip_grad(model, norm_type):
|
|
if isinstance(model, DDP):
|
|
clip_grad_norm_(model.parameters(), max_norm=1.0, norm_type=norm_type)
|
|
else:
|
|
clip_grad_norm_fp32(model.parameters(), max_norm=1.0, norm_type=norm_type)
|
|
|
|
|
|
def allclose(tensor_a: torch.Tensor, tensor_b: torch.Tensor, loose=False) -> bool:
|
|
if loose:
|
|
return torch.allclose(tensor_a, tensor_b, atol=1e-3, rtol=1e-3)
|
|
return torch.allclose(tensor_a, tensor_b)
|
|
|
|
|
|
def check_grads(model, zero_model, loose=False):
|
|
rank = dist.get_rank()
|
|
for p, zero_p in zip(model.parameters(), zero_model.parameters()):
|
|
zero_grad = zero_p.grad.clone().to(p.device)
|
|
chunks = torch.flatten(p.grad).chunk(4)
|
|
if rank >= len(chunks):
|
|
continue
|
|
grad = chunks[rank]
|
|
if zero_p.zero_shard_padding > 0:
|
|
zero_grad = zero_grad[:-zero_p.zero_shard_padding]
|
|
assert grad.dtype == zero_grad.dtype
|
|
assert allclose(grad, zero_grad, loose=loose)
|
|
|
|
|
|
def check_params(model, zero_model, loose=False):
|
|
rank = dist.get_rank()
|
|
for p, zero_p in zip(model.parameters(), zero_model.parameters()):
|
|
zero_shard_padding = zero_p.zero_shard_padding
|
|
zero_p = zero_p.clone().to(p.device)
|
|
chunks = torch.flatten(p).chunk(4)
|
|
if rank >= len(chunks):
|
|
continue
|
|
p = chunks[rank]
|
|
if zero_shard_padding > 0:
|
|
zero_p = zero_p[:-zero_shard_padding]
|
|
assert p.dtype == zero_p.dtype
|
|
assert allclose(p, zero_p, loose=loose)
|
|
|
|
|
|
def run_dist(rank, world_size, port):
|
|
disable_existing_loggers()
|
|
colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
|
|
|
|
|
|
@pytest.mark.dist
|
|
@rerun_if_address_is_in_use()
|
|
def test_zero_clip_grad():
|
|
world_size = 4
|
|
spawn(run_dist, world_size)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
test_zero_clip_grad()
|