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# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
from typing import Any, Callable
from transformers import is_torch_available, is_torch_xpu_available
from transformers.testing_utils import (
TestCasePlus,
backend_device_count,
backend_torch_accelerator_module,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_accelerator,
torch_device,
)
from transformers.utils import is_ccl_available, is_ipex_available
if is_torch_available():
import functools
import torch
if is_torch_xpu_available():
if is_ipex_available():
import intel_extension_for_pytorch # noqa: F401
if is_ccl_available():
import oneccl_bindings_for_pytorch # noqa: F401
import torch.distributed
from torch.distributed._composable.fsdp import fully_shard, register_fsdp_forward_method
from torch.distributed.device_mesh import init_device_mesh
from torch.distributed.fsdp import FullyShardedDataParallel
from torch.distributed.fsdp.wrap import transformer_auto_wrap_policy
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.models.gpt2.modeling_gpt2 import GPT2Block
data = 4 * [
"Hello world!",
"The quick brown fox jumps over the lazy dog.",
]
def manage_process_group(func: Callable[..., Any]) -> Callable[..., Any]:
"""Manage the creation and destruction of the distributed process group for the wrapped function."""
def wrapped(*args: Any, **kwargs: Any) -> Any:
device_count = backend_device_count(torch_device)
torch.distributed.init_process_group(world_size=device_count)
try:
return func(*args, **kwargs)
finally:
torch.distributed.destroy_process_group()
return wrapped
@manage_process_group
def fsdp_generate():
torch_accelerator_module = backend_torch_accelerator_module(torch_device)
torch_accelerator_module.set_device(device := torch.device(rank := torch.distributed.get_rank()))
model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(device)
fsdp_model = FullyShardedDataParallel(
model,
auto_wrap_policy=functools.partial(transformer_auto_wrap_policy, transformer_layer_cls={GPT2Block}),
limit_all_gathers=True,
use_orig_params=True,
)
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
batch = tokenizer(data[rank], return_tensors="pt", return_attention_mask=True).to(device)
with FullyShardedDataParallel.summon_full_params(fsdp_model):
_ = fsdp_model.module.generate(
input_ids=batch["input_ids"],
attention_mask=batch["attention_mask"],
max_length=30,
)
@manage_process_group
def fsdp2_generate():
torch_accelerator_module = backend_torch_accelerator_module(torch_device)
torch_accelerator_module.set_device(device := torch.device(rank := torch.distributed.get_rank()))
model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(device)
mesh = init_device_mesh(device.type, (torch.distributed.get_world_size(),))
for submodule in model.modules():
if isinstance(submodule, GPT2Block):
fully_shard(submodule, mesh=mesh)
fully_shard(model, mesh=mesh)
register_fsdp_forward_method(model, "generate")
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
batch = tokenizer(data[rank], return_tensors="pt", return_attention_mask=True).to(device)
_ = model.generate(
input_ids=batch["input_ids"],
attention_mask=batch["attention_mask"],
max_length=30,
)
class TestFSDPGeneration(TestCasePlus):
@require_torch_multi_accelerator
def test_fsdp_generate(self):
device_count = backend_device_count(torch_device)
distributed_args = f"""--nproc_per_node={device_count}
--master_port={get_torch_dist_unique_port()}
{self.test_file_dir}/test_fsdp.py
""".split()
args = "--fsdp".split()
cmd = ["torchrun"] + distributed_args + args
execute_subprocess_async(cmd, env=self.get_env())
# successful return here == success - any errors would have caused an error in the sub-call
@require_torch_multi_accelerator
def test_fsdp2_generate(self):
device_count = backend_device_count(torch_device)
distributed_args = f"""--nproc_per_node={device_count}
--master_port={get_torch_dist_unique_port()}
{self.test_file_dir}/test_fsdp.py
""".split()
args = "--fsdp2".split()
cmd = ["torchrun"] + distributed_args + args
execute_subprocess_async(cmd, env=self.get_env())
# successful return here == success - any errors would have caused an error in the sub-call
if __name__ == "__main__":
# The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
#
# PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/generation/test_fsdp.py --fsdp
class CLIArgs(argparse.Namespace):
fsdp: bool
fsdp2: bool
parser = argparse.ArgumentParser()
group = parser.add_mutually_exclusive_group()
group.add_argument("--fsdp", action="store_true")
group.add_argument("--fsdp2", action="store_true")
args = parser.parse_args(namespace=CLIArgs())
if args.fsdp:
fsdp_generate()
elif args.fsdp2:
fsdp2_generate()
else:
raise ValueError("Missing test selection")
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