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* save cpu memory * update * update * update * update * refine * update * update --------- Co-authored-by: Your Name <you@example.com>
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198
...ects/mmrazor_large/examples/model_examples/language_models/Llama/llama_sparse_gpt_fsdp.py
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Original file line number | Diff line number | Diff line change |
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import functools | ||
import os | ||
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import torch | ||
import torch.distributed as dist | ||
import torch.multiprocessing as mp | ||
import torch.nn as nn | ||
from datautils import build_language_loader, get_loaders | ||
from llama_sparse_gpt import get_model | ||
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP | ||
from torch.distributed.fsdp.api import ShardingStrategy | ||
from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload | ||
from torch.distributed.fsdp.wrap import size_based_auto_wrap_policy | ||
from utils import init_on_meta, opt_eval_fsdp, opt_infer_fsdp | ||
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from mmrazor.implementations.pruning import sparse_gpt | ||
from mmrazor.utils import print_log | ||
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def setup(rank, world_size): | ||
os.environ['MASTER_ADDR'] = 'localhost' | ||
os.environ['MASTER_PORT'] = '12356' | ||
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dist.init_process_group('nccl', rank=rank, world_size=world_size) | ||
torch.cuda.set_device(rank) | ||
print_log(f'init {rank}/{world_size}', only_rank0=False) | ||
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def init_fn_wrapper(model: nn.Module, model_copy: nn.Module): | ||
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def find_module_in_model_copy(module: nn.Module): | ||
name2module = dict(model.named_modules()) | ||
module2name = dict([(v, k) for k, v in name2module.items()]) | ||
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name = module2name[module] | ||
return dict(model_copy.named_modules())[name] | ||
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def _materialize_meta_module(module: nn.Module, ): | ||
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def meta_to_empty(p: torch.Tensor): | ||
if p.device == torch.device('meta'): | ||
return p.new_empty(p.shape, device='cpu') | ||
else: | ||
return p | ||
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module._apply(meta_to_empty) | ||
if dist.get_rank() == 0: | ||
assert model_copy is not None | ||
module_copy = find_module_in_model_copy(module) | ||
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name2p = dict(module_copy.named_parameters(remove_duplicate=False)) | ||
for n, p in module.named_parameters(): | ||
if '_flat_param' not in n: | ||
n = n.replace('_fsdp_wrapped_module.', '') | ||
try: | ||
p.data.copy_(name2p[n]) | ||
except Exception: | ||
pass | ||
name2p = dict(module_copy.named_buffers(remove_duplicate=False)) | ||
for n, p in module.named_buffers(): | ||
if '_flat_param' not in n: | ||
n = n.replace('_fsdp_wrapped_module.', '') | ||
try: | ||
p.data.copy_(name2p[n]) | ||
except Exception: | ||
pass | ||
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return _materialize_meta_module | ||
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def main(rank, world_size=8, args=None): | ||
setup(rank, world_size) | ||
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model_name = args.model | ||
batch_size = args.batch_size | ||
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def build(): | ||
model = get_model(model_name) | ||
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# init mutator | ||
mutator = sparse_gpt.SparseGptMutator() | ||
mutator.prepare_from_supernet(model.model.layers) | ||
return model, mutator | ||
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with init_on_meta(enable=True): | ||
model, mutator = build() | ||
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if rank == 0: | ||
model_copy, _ = build() # init on cpu | ||
else: | ||
model_copy = None | ||
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# init fsdp | ||
size_based_auto_wrap_policy_x = functools.partial( | ||
size_based_auto_wrap_policy, min_num_params=int(1e8)) | ||
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model = FSDP( | ||
model, | ||
auto_wrap_policy=size_based_auto_wrap_policy_x, | ||
cpu_offload=CPUOffload(True), | ||
sharding_strategy=ShardingStrategy.FULL_SHARD, | ||
device_id=rank, | ||
param_init_fn=init_fn_wrapper(model, model_copy), | ||
sync_module_states=True) | ||
print_log(model) | ||
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# init hessian | ||
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mutator.init_hessian(device='cuda') | ||
mutator.start_init_hessian() | ||
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_, testloader = get_loaders( | ||
args.dataset, seed=args.seed, model=model_name, seqlen=model.seqlen) | ||
testloader = build_language_loader( | ||
testloader, world_size, rank, model, batch_size=batch_size) | ||
opt_infer_fsdp(model, testloader) | ||
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mutator.end_init_hessian() | ||
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# prune | ||
name2module = dict(model.named_modules()) | ||
module2name = {} | ||
module2name = dict([(v, k) for k, v in name2module.items()]) | ||
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with torch.no_grad(): | ||
for fsdp in FSDP.fsdp_modules(model): | ||
fsdp._reset_lazy_init() | ||
with FSDP.summon_full_params(fsdp, recurse=False): | ||
fsdp_name = module2name[fsdp] | ||
for name, op in fsdp.named_modules(): | ||
if name.count('_fsdp_wrapped_module') <= 1: | ||
if isinstance(op, sparse_gpt.SparseGptMixIn): | ||
try: | ||
op.prune(0.5, prunen=2, prunem=4) | ||
print_log( | ||
f'prune {fsdp_name}.{name} successfully.', # noqa | ||
only_rank0=True) | ||
except Exception as e: | ||
print_log( | ||
f'prune {fsdp_name}.{name} failed, as {e}', # noqa | ||
only_rank0=True) | ||
fsdp._reset_lazy_init() | ||
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# save | ||
if args.save: | ||
print_log(f'save model in {args.save}') | ||
model._reset_lazy_init() | ||
with FSDP.summon_full_params(model, rank0_only=True, writeback=False): | ||
if dist.get_rank() == 0: | ||
model.save_pretrained(args.save) | ||
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# val | ||
torch.cuda.empty_cache() | ||
model._reset_lazy_init() | ||
for dataset in ['wikitext2', 'ptb', 'c4']: | ||
_, testloader = get_loaders( | ||
dataset, seed=args.seed, model=model_name, seqlen=model.seqlen) | ||
testloader = build_language_loader( | ||
testloader, world_size, rank, model, batch_size=batch_size) | ||
print_log(dataset) | ||
opt_eval_fsdp(model, testloader, torch.device('cuda')) | ||
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if __name__ == '__main__': | ||
import argparse | ||
parser = argparse.ArgumentParser() | ||
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parser.add_argument( | ||
'model', type=str, help='OPT model to load; pass `facebook/opt-X`.') | ||
parser.add_argument( | ||
'dataset', | ||
type=str, | ||
choices=['wikitext2', 'ptb', 'c4'], | ||
help='Where to extract calibration data from.') | ||
parser.add_argument( | ||
'--seed', | ||
type=int, | ||
default=0, | ||
help='Seed for sampling the calibration data.') | ||
parser.add_argument( | ||
'--nsamples', | ||
type=int, | ||
default=128, | ||
help='Number of calibration data samples.') | ||
parser.add_argument( | ||
'--batch_size', | ||
type=int, | ||
default=64, | ||
help='Batchsize for calibration and evaluation.') | ||
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parser.add_argument( | ||
'--save', type=str, default='', help='Path to saved model.') | ||
parser.add_argument( | ||
'--world_size', type=int, default=1, help='Number of GPUs to use.') | ||
args = parser.parse_args() | ||
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WORLD_SIZE = args.world_size | ||
mp.spawn(main, args=(WORLD_SIZE, args), nprocs=WORLD_SIZE, join=True) |
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