ColossalAI/tests/test_shardformer/test_layer/test_embedding.py

57 lines
1.7 KiB
Python

from contextlib import nullcontext
import torch
import torch.distributed as dist
import torch.nn as nn
from torch.testing import assert_close
import colossalai
from colossalai.lazy import LazyInitContext
from colossalai.shardformer.layer import Embedding1D
from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
@parameterize('lazy_init', [False, True])
def check_embedding_1d(lazy_init: bool):
ctx = LazyInitContext() if lazy_init else nullcontext()
embedding = nn.Embedding(32, 128).cuda()
with ctx:
embedding_copy = nn.Embedding(32, 128).cuda()
embedding_1d = Embedding1D.from_native_module(embedding_copy, process_group=None)
assert embedding_1d.weight.shape == torch.Size([32, 64])
assert embedding_1d.weight is embedding_copy.weight
# ensure state dict is reversibly loadable
embedding.load_state_dict(embedding_1d.state_dict())
embedding_1d.load_state_dict(embedding.state_dict())
# check computation correctness
x = torch.randint(low=0, high=32, size=(4, 32)).cuda()
out = embedding(x)
gather_out = embedding_1d(x)
assert_close(out, gather_out)
# check backward correctness
out.sum().backward()
gather_out.sum().backward()
rank = dist.get_rank()
target_grad = torch.chunk(embedding.weight.grad, 2, dim=1)[rank]
assert_close(target_grad, embedding_1d.weight.grad)
def run_dist(rank, world_size, port):
colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
check_embedding_1d()
@rerun_if_address_is_in_use()
def test_embedding_1d():
spawn(run_dist, nprocs=2)
if __name__ == '__main__':
test_embedding_1d()