mirror of https://github.com/hpcaitech/ColossalAI
77 lines
2.4 KiB
Python
77 lines
2.4 KiB
Python
|
import torch
|
||
|
|
from torch import nn
|
||
|
|
from torch import autograd
|
||
|
|
from torch.optim import SGD, Adam, RMSprop, Optimizer
|
||
|
|
|
||
|
|
from colossalai.pipeline.rpc.PipelineBase import FillDrainPipelineEngine, OneFOneBPipelineEngine
|
||
|
|
from colossalai.testing import assert_close
|
||
|
|
from rpc_test_utils import rpc_run, parse_args, RpcTestModel
|
||
|
|
|
||
|
|
|
||
|
|
def run_master(args):
|
||
|
|
torch.manual_seed(100)
|
||
|
|
|
||
|
|
device = args.device
|
||
|
|
stage_num = args.world_size
|
||
|
|
chunk = args.chunk
|
||
|
|
actual_stage_num = stage_num * chunk
|
||
|
|
use_checkpoint = args.use_checkpoint
|
||
|
|
num_microbatches = args.num_microbatches
|
||
|
|
optimizer_class = globals()[args.optimizer]
|
||
|
|
|
||
|
|
lr = 1e-3
|
||
|
|
|
||
|
|
sample_num = 1024
|
||
|
|
feat_num = 100
|
||
|
|
h = 100
|
||
|
|
batch_size = 1024
|
||
|
|
|
||
|
|
assert sample_num % batch_size == 0
|
||
|
|
batch_num = sample_num // batch_size
|
||
|
|
|
||
|
|
input_sample = torch.randn((sample_num, feat_num), device=device)
|
||
|
|
|
||
|
|
module_partitions = [RpcTestModel(pp_rank, actual_stage_num, feat_num, h) for pp_rank in range(actual_stage_num)]
|
||
|
|
|
||
|
|
engine = OneFOneBPipelineEngine(module_partitions=module_partitions,
|
||
|
|
stage_num=stage_num,
|
||
|
|
num_microbatches=num_microbatches,
|
||
|
|
device=device,
|
||
|
|
chunk=chunk,
|
||
|
|
checkpoint=use_checkpoint)
|
||
|
|
|
||
|
|
engine.initialize_optimizer(optimizer_class, lr=lr)
|
||
|
|
|
||
|
|
_ = engine.forward_backward(input_sample)
|
||
|
|
|
||
|
|
cuda_rpc_result = []
|
||
|
|
single_result = []
|
||
|
|
actual_stage_num = engine._get_actual_stage_num()
|
||
|
|
|
||
|
|
# compute parameters after updating in cuda rpc
|
||
|
|
parameters = engine.remote_parameters()
|
||
|
|
for stage_id in range(actual_stage_num):
|
||
|
|
for p in parameters[stage_id]:
|
||
|
|
cuda_rpc_result.append(p)
|
||
|
|
|
||
|
|
# compute forward result and backward grad of parameters just in rank_0
|
||
|
|
test_model = nn.Sequential(*module_partitions).to(device)
|
||
|
|
optimizer: Optimizer = optimizer_class(test_model.parameters(), lr=lr)
|
||
|
|
input_sample = input_sample.requires_grad_()
|
||
|
|
out_val = test_model(input_sample).sum()
|
||
|
|
autograd.backward(out_val)
|
||
|
|
optimizer.step()
|
||
|
|
optimizer.zero_grad()
|
||
|
|
|
||
|
|
for p in test_model.parameters():
|
||
|
|
single_result.append(p)
|
||
|
|
|
||
|
|
assert len(cuda_rpc_result) == len(single_result)
|
||
|
|
for r_c, r_s in zip(cuda_rpc_result, single_result):
|
||
|
|
assert_close(r_c, r_s, 0.001, 0.001)
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
args = parse_args()
|
||
|
|
rpc_run(args, run_master)
|