ColossalAI/tests/test_shardformer/test_layer/test_linear_1d.py

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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 Linear1D_Col, Linear1D_Row
from colossalai.tensor.d_tensor import is_distributed_tensor
from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
@parameterize('lazy_init', [False, True])
def check_linear_1d_col(lazy_init: bool):
ctx = LazyInitContext() if lazy_init else nullcontext()
linear = nn.Linear(32, 128).cuda()
with ctx:
linear_copy = nn.Linear(32, 128).cuda()
linear_col = Linear1D_Col.from_native_module(linear_copy, process_group=None, gather_output=True)
# ensure that the parameters are distributed
assert is_distributed_tensor(linear_col.weight)
assert is_distributed_tensor(linear_col.bias)
assert linear_copy.weight is linear_col.weight
assert linear_copy.bias is linear_col.bias
# ensure the shape is correct
assert linear_col.weight.shape == torch.Size([64, 32])
assert linear_col.bias.shape == torch.Size([64])
# ensure state dict is reversibly loadable
linear.load_state_dict(linear_col.state_dict())
linear_col.load_state_dict(linear.state_dict())
# check computation correctness
x = torch.rand(4, 32).cuda()
x_for_unshard = x.expand_as(x.clone())
x_for_unshard.requires_grad_(True)
x_for_shard = x.expand_as(x.clone())
x_for_shard.requires_grad_(True)
out = linear(x_for_unshard)
gather_out = linear_col(x_for_shard)
assert_close(out, gather_out)
# check backward correctness
out.sum().backward()
gather_out.sum().backward()
rank = dist.get_rank()
target_grad = torch.chunk(linear.weight.grad, 2, dim=0)[rank]
assert_close(target_grad, linear_col.weight.grad)
# check the input gradients
assert x_for_shard.grad is not None
assert x_for_unshard.grad is not None
assert_close(x_for_unshard.grad, x_for_shard.grad)
@parameterize('lazy_init', [False, True])
def check_linear_1d_row(lazy_init: bool):
ctx = LazyInitContext() if lazy_init else nullcontext()
linear = nn.Linear(32, 128).cuda()
with ctx:
linear_copy = nn.Linear(32, 128).cuda()
linear_row = Linear1D_Row.from_native_module(linear_copy, process_group=None, parallel_input=False)
assert linear_row.weight.shape == torch.Size([128, 16])
assert linear_row.bias.shape == torch.Size([128])
assert linear_copy.weight is linear_row.weight
assert linear_copy.bias is linear_row.bias
linear.load_state_dict(linear_row.state_dict())
linear_row.load_state_dict(linear.state_dict())
# check computation correctness
x = torch.rand(4, 32).cuda()
x_for_unshard = x.expand_as(x.clone())
x_for_unshard.requires_grad_(True)
x_for_shard = x.expand_as(x.clone())
x_for_shard.requires_grad_(True)
# run forward
out = linear(x_for_unshard)
gather_out = linear_row(x_for_shard)
assert_close(out, gather_out)
# check backward correctness
out.sum().backward()
gather_out.sum().backward()
rank = dist.get_rank()
target_grad = torch.chunk(linear.weight.grad, 2, dim=1)[rank]
assert_close(target_grad, linear_row.weight.grad)
# check the input gradients
assert x_for_shard.grad is not None
assert x_for_unshard.grad is not None
assert_close(x_for_unshard.grad, x_for_shard.grad)
@parameterize('lazy_init', [False, True])
def check_linear_col_plus_row(lazy_init: bool):
ctx = LazyInitContext() if lazy_init else nullcontext()
linear_1 = nn.Linear(32, 128).cuda()
linear_2 = nn.Linear(128, 32).cuda()
with ctx:
linear_1_copy = nn.Linear(32, 128).cuda()
linear_2_copy = nn.Linear(128, 32).cuda()
linear_col = Linear1D_Col.from_native_module(linear_1_copy, process_group=None, gather_output=False)
linear_row = Linear1D_Row.from_native_module(linear_2_copy, process_group=None, parallel_input=True)
linear_1.load_state_dict(linear_col.state_dict())
linear_col.load_state_dict(linear_1.state_dict())
linear_2.load_state_dict(linear_row.state_dict())
linear_row.load_state_dict(linear_2.state_dict())
# check computation correctness
x = torch.rand(4, 32).cuda()
x_for_unshard = x.expand_as(x.clone())
x_for_unshard.requires_grad_(True)
x_for_shard = x.expand_as(x.clone())
x_for_shard.requires_grad_(True)
# run forward
unshard_out = linear_2(linear_1(x_for_unshard))
shard_out = linear_row(linear_col(x_for_shard))
assert_close(unshard_out, shard_out)
# check backward correctness
unshard_out.sum().backward()
shard_out.sum().backward()
rank = dist.get_rank()
target_1_grad = torch.chunk(linear_1.weight.grad, 2, dim=0)[rank]
assert_close(target_1_grad, linear_col.weight.grad)
# check the input gradients
assert x_for_shard.grad is not None
assert x_for_unshard.grad is not None
assert_close(x_for_unshard.grad, x_for_shard.grad)
def run_dist(rank, world_size, port):
colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
check_linear_1d_col()
check_linear_1d_row()
check_linear_col_plus_row()
@rerun_if_address_is_in_use()
def test_linear():
spawn(run_dist, nprocs=2)
if __name__ == '__main__':
test_linear()