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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 inspect
import json
from functools import partial
from pathlib import Path
import torch
from transformers import AutoModelForCausalLM
from accelerate import Accelerator, DeepSpeedPlugin
from accelerate.commands.launch import launch_command, launch_command_parser
from accelerate.test_utils.testing import (
AccelerateTestCase,
path_in_accelerate_package,
require_deepspeed,
require_huggingface_suite,
require_multi_device,
require_non_cpu,
run_first,
slow,
)
from accelerate.test_utils.training import RegressionDataset
from accelerate.utils import patch_environment
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler, get_active_deepspeed_plugin
GPT2_TINY = "hf-internal-testing/tiny-random-gpt2"
@require_deepspeed
@require_non_cpu
class DeepSpeedConfigIntegration(AccelerateTestCase):
parser = launch_command_parser()
test_scripts_folder = path_in_accelerate_package("test_utils", "scripts", "external_deps")
def setUp(self):
super().setUp()
self.dist_env = dict(
ACCELERATE_USE_DEEPSPEED="true",
MASTER_ADDR="localhost",
MASTER_PORT="10999",
RANK="0",
LOCAL_RANK="0",
WORLD_SIZE="1",
)
self._test_file_path = inspect.getfile(self.__class__)
path = Path(self._test_file_path).resolve()
self.test_file_dir_str = str(path.parents[0])
self.ds_config_file = dict(
zero2=f"{self.test_file_dir_str}/ds_config_zero2.json",
zero3_inference=f"{self.test_file_dir_str}/ds_config_zero3_model_only.json",
zero3_training=f"{self.test_file_dir_str}/ds_config_zero3.json",
)
with open(self.ds_config_file["zero2"], encoding="utf-8") as f:
self.config_zero2 = json.load(f)
with open(self.ds_config_file["zero3_training"], encoding="utf-8") as f:
self.config_zero3 = json.load(f)
with open(self.ds_config_file["zero3_inference"], encoding="utf-8") as f:
self.config_zero3_inference = json.load(f)
self.model_init = partial(AutoModelForCausalLM.from_pretrained, GPT2_TINY)
def get_ds_plugins(self, zero3_inference=False):
ds_zero2 = DeepSpeedPlugin(
hf_ds_config=self.config_zero2,
)
ds_zero3 = DeepSpeedPlugin(
hf_ds_config=self.config_zero3 if not zero3_inference else self.config_zero3_inference,
)
return {"zero2": ds_zero2, "zero3": ds_zero3}
def test_select_plugin(self):
ds_plugins = self.get_ds_plugins()
ds_zero2, ds_zero3 = ds_plugins.values()
accelerator = Accelerator(
deepspeed_plugin=ds_plugins,
)
# Accelerator's constructor should automatically enable the first plugin
assert ds_zero2.selected
assert not ds_zero3.selected
assert get_active_deepspeed_plugin(accelerator.state) == ds_zero2
assert accelerator.deepspeed_plugin == ds_zero2
assert accelerator.state.get_deepspeed_plugin("zero2") == ds_zero2
accelerator.state.select_deepspeed_plugin("zero3")
assert not ds_zero2.selected
assert ds_zero3.selected
assert get_active_deepspeed_plugin(accelerator.state) == ds_zero3
assert accelerator.deepspeed_plugin == ds_zero3
assert accelerator.state.get_deepspeed_plugin("zero3") == ds_zero3
accelerator.state.select_deepspeed_plugin("zero2")
assert not ds_zero3.selected
assert ds_zero2.selected
assert get_active_deepspeed_plugin(accelerator.state) == ds_zero2
assert accelerator.deepspeed_plugin == ds_zero2
assert accelerator.state.get_deepspeed_plugin("zero2") == ds_zero2
@require_huggingface_suite
def test_config_reference_update(self):
# Make sure that the transformers weakref is updating when we update the config
ds_plugins = self.get_ds_plugins(zero3_inference=True)
zero2, zero3 = ds_plugins.values()
accelerator = Accelerator(deepspeed_plugin=ds_plugins)
from transformers.integrations.deepspeed import deepspeed_config
# Note that these have `auto` values being set so we need to adjust
assert accelerator.deepspeed_plugin is zero2
zero2.deepspeed_config["train_micro_batch_size_per_gpu"] = 1
zero2.deepspeed_config.pop("train_batch_size")
assert deepspeed_config() == accelerator.deepspeed_plugin.hf_ds_config.config
accelerator.state.select_deepspeed_plugin("zero3")
assert accelerator.deepspeed_plugin is zero3
assert deepspeed_config() == accelerator.deepspeed_plugin.hf_ds_config.config
def test_enable_disable_manually_set(self):
ds_plugins = self.get_ds_plugins()
ds_zero2, _ = ds_plugins.values()
with self.assertRaises(ValueError):
ds_zero2.select()
accelerator = Accelerator(deepspeed_plugin=ds_plugins)
accelerator.state.select_deepspeed_plugin("zero2")
with self.assertRaises(NotImplementedError):
ds_zero2.selected = False
assert ds_zero2.selected
def test_multiple_accelerators(self):
ds_plugins = self.get_ds_plugins()
ds_zero2, ds_zero3 = ds_plugins.values()
_ = Accelerator(
deepspeed_plugin=ds_zero2,
)
with self.assertRaises(NotImplementedError):
_ = Accelerator(deepspeed_plugin=ds_zero3)
def test_prepare_multiple_models_zero3_inference(self):
with patch_environment(**self.dist_env):
ds_plugins = self.get_ds_plugins(zero3_inference=True)
accelerator = Accelerator(deepspeed_plugin=ds_plugins)
# Using Zero-2 first
model1 = self.model_init()
optimizer = DummyOptim(model1.parameters())
scheduler = DummyScheduler(optimizer)
dataset = RegressionDataset()
dataloader = torch.utils.data.DataLoader(dataset, batch_size=1)
model1, optimizer, scheduler, dataloader = accelerator.prepare(model1, optimizer, scheduler, dataloader)
accelerator.state.select_deepspeed_plugin("zero3")
model2 = self.model_init()
with self.assertLogs(level="WARNING") as captured:
model2 = accelerator.prepare(model2)
self.assertIn(
"A wrapped DeepSpeed engine reference is currently tied for this `Accelerator()` instance.",
captured.output[0],
)
assert accelerator.deepspeed_engine_wrapped.engine is model1
@run_first
@require_huggingface_suite
@require_multi_device
@slow
def test_train_multiple_models(self):
self.test_file_path = self.test_scripts_folder / "test_ds_multiple_model.py"
args = ["--num_processes=2", "--num_machines=1", str(self.test_file_path)]
args = self.parser.parse_args(args)
launch_command(args)
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