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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 gc
import logging
import shutil
from pathlib import Path
import torch
from safetensors.torch import load_file
from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy, StateDictType
from torch.utils.data import DataLoader
from accelerate import Accelerator, FullyShardedDataParallelPlugin
from accelerate.commands.merge import merge_command, merge_command_parser
from accelerate.state import AcceleratorState
from accelerate.test_utils.training import RegressionDataset
from accelerate.utils import merge_fsdp_weights, patch_environment, save_fsdp_model
logging.basicConfig(level=logging.INFO)
parser = merge_command_parser()
class TinyModel(torch.nn.Module):
def __init__(self):
super().__init__()
self.linear1 = torch.nn.Linear(16, 16)
self.activation = torch.nn.ReLU()
self.linear2 = torch.nn.Linear(16, 16)
self.softmax = torch.nn.Softmax()
def forward(self, x):
return self.linear2(self.activation(self.linear1(x)))
def setup():
if AcceleratorState._shared_state != {}:
AcceleratorState()._reset_state()
plugin = FullyShardedDataParallelPlugin(
sharding_strategy=ShardingStrategy.FULL_SHARD, state_dict_type=StateDictType.SHARDED_STATE_DICT
)
model = TinyModel()
with patch_environment(fsdp_auto_wrap_policy="SIZE_BASED_WRAP"):
plugin.set_auto_wrap_policy(model)
accelerator = Accelerator(fsdp_plugin=plugin)
model = accelerator.prepare(model)
return model, plugin, accelerator
def mock_training(accelerator, model):
train_set = RegressionDataset(length=128, seed=42)
train_dl = DataLoader(train_set, batch_size=16, shuffle=False)
optimizer = torch.optim.SGD(model.parameters(), lr=0.1)
train_dl, model, optimizer = accelerator.prepare(train_dl, model, optimizer)
for _ in range(3):
for batch in train_dl:
model.zero_grad()
output = model(batch["x"])
loss = torch.nn.functional.mse_loss(output, batch["y"])
accelerator.backward(loss)
optimizer.step()
return model
def check_weights(operation, state_1, state_2):
for weight_1, weight_2 in zip(state_1.values(), state_2.values()):
if str(weight_1.device) != "cuda":
weight_1 = weight_1.to("cuda")
if str(weight_2.device) != "cuda":
weight_2 = weight_2.to("cuda")
if operation == "same":
assert torch.allclose(weight_1, weight_2)
else:
assert not torch.allclose(weight_1, weight_2)
def check_safetensors_weights(path, model):
safe_state_dict = load_file(path / "model.safetensors")
safe_loaded_model = TinyModel()
check_weights("diff", model.state_dict(), safe_loaded_model.state_dict())
safe_loaded_model.load_state_dict(safe_state_dict)
check_weights("same", model.state_dict(), safe_loaded_model.state_dict())
def check_pytorch_weights(path, model):
nonsafe_state_dict = torch.load(path / "pytorch_model.bin")
nonsafe_loaded_model = TinyModel()
check_weights("diff", model.state_dict(), nonsafe_loaded_model.state_dict())
nonsafe_loaded_model.load_state_dict(nonsafe_state_dict)
check_weights("same", model.state_dict(), nonsafe_loaded_model.state_dict())
def test_merge_weights_safetensors(model, path):
# Should now be saved at `path/merged.safetensors`
merge_fsdp_weights(path / "pytorch_model_fsdp_0", path, safe_serialization=True)
check_safetensors_weights(path, model)
def test_merge_weights_command_safetensors(model, path):
args = parser.parse_args([str(path / "pytorch_model_fsdp_0"), str(path)])
merge_command(args)
check_safetensors_weights(path, model)
def test_merge_weights_pytorch(model, path):
# Should now be saved at `path/merged.bin`
merge_fsdp_weights(path / "pytorch_model_fsdp_0", path, safe_serialization=False)
check_pytorch_weights(path, model)
def test_merge_weights_command_pytorch(model, path):
args = parser.parse_args([str(path / "pytorch_model_fsdp_0"), str(path), "--unsafe_serialization"])
merge_command(args)
check_pytorch_weights(path, model)
if __name__ == "__main__":
# Note this test requires at least two accelerators!
model, plugin, accelerator = setup()
if accelerator.num_processes > 1:
try:
# Initial setup for things
out_path = Path("test_merge_weights_fsdp_weights")
if not out_path.exists():
out_path.mkdir(parents=True, exist_ok=True)
# Train briefly once weights aren't the baseline
model = mock_training(accelerator, model)
accelerator.wait_for_everyone()
gc.collect() # Needed for some lingering refs after training
save_fsdp_model(plugin, accelerator, model, out_path)
accelerator.wait_for_everyone()
# Finally we can test
test_merge_weights_safetensors(model, out_path)
test_merge_weights_command_safetensors(model, out_path)
test_merge_weights_pytorch(model, out_path)
test_merge_weights_command_pytorch(model, out_path)
except Exception:
raise
finally:
# Cleanup in case of any failures
if accelerator.is_main_process:
shutil.rmtree(out_path)
accelerator.wait_for_everyone()
accelerator.end_training()
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