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[DeepSpeed] ZeRO Stage 3 (#10753)

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* synced gpus

* fix

* fix

* need to use t5-small for quality tests

* notes

* complete merge

* fix a disappearing std stream problem

* start zero3 tests

* wip

* tune params

* sorting out the pre-trained model loading

* reworking generate loop wip

* wip

* style

* fix tests

* split the tests

* refactor tests

* wip

* parameterized

* fix

* workout the resume from non-ds checkpoint pass + test

* cleanup

* remove no longer needed code

* split getter/setter functions

* complete the docs

* suggestions

* gpus and their compute capabilities link

* Apply suggestions from code review

Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* style

* remove invalid paramgd

* automatically configure zero3 params that rely on hidden size

* make _get_resized_embeddings zero3-aware

* add test exercising resize_token_embeddings()

* add docstring

Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
This commit is contained in:
Stas Bekman
2021-04-08 09:53:01 -07:00
committed by GitHub
parent acc851e1ff
commit c6d664849b
10 changed files with 1307 additions and 268 deletions
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2
examples/tests/deepspeed/ds_config.json → examples/tests/deepspeed/ds_config_zero2.json
View File

@@ -3,7 +3,7 @@
"enabled": true,
"loss_scale": 0,
"loss_scale_window": 1000,
"initial_scale_power": 32,
"initial_scale_power": 16,
"hysteresis": 2,
"min_loss_scale": 1
},

48
examples/tests/deepspeed/ds_config_zero3.json Normal file
View File

@@ -0,0 +1,48 @@
{
"fp16": {
"enabled": true,
"loss_scale": 0,
"loss_scale_window": 1000,
"initial_scale_power": 16,
"hysteresis": 2,
"min_loss_scale": 1
},
"zero_optimization": {
"stage": 3,
"cpu_offload": true,
"cpu_offload_params": true,
"cpu_offload_use_pin_memory" : true,
"overlap_comm": true,
"contiguous_gradients": true,
"sub_group_size": 1e14,
"reduce_bucket_size": 0,
"stage3_prefetch_bucket_size": 0,
"stage3_param_persistence_threshold": 0,
"stage3_max_live_parameters": 1e9,
"stage3_max_reuse_distance": 1e9,
"stage3_gather_fp16_weights_on_model_save": true
},
"optimizer": {
"type": "AdamW",
"params": {
"lr": 3e-5,
"betas": [0.8, 0.999],
"eps": 1e-8,
"weight_decay": 3e-7
}
},
"scheduler": {
"type": "WarmupLR",
"params": {
"warmup_min_lr": 0,
"warmup_max_lr": 3e-5,
"warmup_num_steps": 500
}
},
"steps_per_print": 2000,
"wall_clock_breakdown": false
}

415
examples/tests/deepspeed/test_deepspeed.py
View File

@@ -20,11 +20,12 @@ import sys
import unittest
from copy import deepcopy
from parameterized import parameterized
from transformers import TrainingArguments
from transformers.file_utils import WEIGHTS_NAME
from transformers.integrations import is_deepspeed_available
from transformers.testing_utils import (
CaptureStd,
CaptureLogger,
TestCasePlus,
execute_subprocess_async,
get_gpu_count,
@@ -43,6 +44,7 @@ from test_trainer import TrainerIntegrationCommon, get_regression_trainer # noq
set_seed(42)
MBART_TINY = "sshleifer/tiny-mbart"
T5_SMALL = "t5-small"
def load_json(path):
@@ -61,6 +63,11 @@ def require_deepspeed(test_case):
return test_case
ZERO2 = "zero2"
ZERO3 = "zero3"
stages = [ZERO2, ZERO3]
@require_deepspeed
@require_torch_gpu
class TrainerIntegrationDeepSpeed(TestCasePlus, TrainerIntegrationCommon):
@@ -68,7 +75,19 @@ class TrainerIntegrationDeepSpeed(TestCasePlus, TrainerIntegrationCommon):
This class is for testing directly via get_regression_trainer
It mixes in `TrainerIntegrationCommon` which already has a lot of helper validation methods which we can re-use here.
It mixes in `TrainerIntegrationCommon` which already has a lot of helper validation methods
which we can re-use here.
Important: this class' setup can only work with a single gpu because it runs within the current
pytest worker. For multi-gpu tests use TestDeepSpeedWithLauncher.
Note: if any of the tests of this class get run there will be at least one gpu occupied by them
until this pytest worker exits. This is because the gpu memory allocated by the cuda-kernels
won't be released until this pytest worker exits.
This may appear as some run-away tests if you watch `nvidia-smi` while other tests that fork new
processes are run. So there will be one or two "stale" processes reported in `nvidia-smi`. This
is not a bug.
"""
def setUp(self):
@@ -81,18 +100,28 @@ class TrainerIntegrationDeepSpeed(TestCasePlus, TrainerIntegrationCommon):
self.dist_env_1_gpu = dict(
MASTER_ADDR="localhost", MASTER_PORT="10999", RANK="0", LOCAL_RANK="0", WORLD_SIZE="1"
)
self.ds_config_file = f"{self.test_file_dir_str}/ds_config.json"
with io.open(self.ds_config_file, "r", encoding="utf-8") as f:
self.ds_config_dict = json.load(f)
def test_fake_notebook_no_launcher(self):
# this setup emulates a notebook where a launcher needs to be emulated by hand
with CaptureStd() as cs: # noqa
with mockenv_context(**self.dist_env_1_gpu):
trainer = get_regression_trainer(local_rank=0, deepspeed=self.ds_config_file)
trainer.train()
# fixme:
# assert "DeepSpeed info" in cs.out, "expected DeepSpeed logger output but got none"
self.ds_config_file = {}
self.ds_config_file[ZERO2] = f"{self.test_file_dir_str}/ds_config_zero2.json"
self.ds_config_file[ZERO3] = f"{self.test_file_dir_str}/ds_config_zero3.json"
# use self.get_config_dict(stage) to use these to ensure the original is not modified
self.ds_config_dict = {}
with io.open(self.ds_config_file[ZERO2], "r", encoding="utf-8") as f:
self.ds_config_dict[ZERO2] = json.load(f)
with io.open(self.ds_config_file[ZERO3], "r", encoding="utf-8") as f:
self.ds_config_dict[ZERO3] = json.load(f)
def get_config_dict(self, stage):
""" As the tests modify the dict, always make a copy """
config = deepcopy(self.ds_config_dict[stage])
if stage == ZERO3:
# This setting slows things down, so don't enable it by default unless needed by a test.
# It's in the file as a demo for users since we want everything to work out of the box even if slower.
config["zero_optimization"]["stage3_gather_fp16_weights_on_model_save"] = False
return config
# --- These tests are enough to run on one of zero stages --- #
# Test various combos
# 1. DS scheduler + DS optimizer: this is already tested by most other tests
@@ -103,12 +132,12 @@ class TrainerIntegrationDeepSpeed(TestCasePlus, TrainerIntegrationCommon):
def test_hf_scheduler_hf_optimizer(self):
a = 0
with mockenv_context(**self.dist_env_1_gpu):
ds_config_dict = deepcopy(self.ds_config_dict)
del ds_config_dict["optimizer"] # force default HF Trainer optimizer
del ds_config_dict["scheduler"] # force default HF Trainer scheduler
ds_config_dict["zero_optimization"]["cpu_offload"] = False
ds_config_dict["fp16"]["initial_scale_power"] = 1 # force optimizer on the first step
trainer = get_regression_trainer(a=a, local_rank=0, deepspeed=ds_config_dict)
ds_config_zero2_dict = self.get_config_dict(ZERO2)
del ds_config_zero2_dict["optimizer"] # force default HF Trainer optimizer
del ds_config_zero2_dict["scheduler"] # force default HF Trainer scheduler
ds_config_zero2_dict["zero_optimization"]["cpu_offload"] = False
ds_config_zero2_dict["fp16"]["initial_scale_power"] = 1 # force optimizer on the first step
trainer = get_regression_trainer(a=a, local_rank=0, deepspeed=ds_config_zero2_dict)
trainer.train()
new_a = trainer.model.a.item()
self.assertNotEqual(new_a, a)
@@ -116,11 +145,11 @@ class TrainerIntegrationDeepSpeed(TestCasePlus, TrainerIntegrationCommon):
def test_ds_scheduler_hf_optimizer(self):
a = 0
with mockenv_context(**self.dist_env_1_gpu):
ds_config_dict = deepcopy(self.ds_config_dict)
del ds_config_dict["optimizer"] # force default HF Trainer optimizer
ds_config_dict["zero_optimization"]["cpu_offload"] = False
ds_config_dict["fp16"]["initial_scale_power"] = 1 # force optimizer on the first step
trainer = get_regression_trainer(a=a, local_rank=0, deepspeed=ds_config_dict)
ds_config_zero2_dict = self.get_config_dict(ZERO2)
del ds_config_zero2_dict["optimizer"] # force default HF Trainer optimizer
ds_config_zero2_dict["zero_optimization"]["cpu_offload"] = False
ds_config_zero2_dict["fp16"]["initial_scale_power"] = 1 # force optimizer on the first step
trainer = get_regression_trainer(a=a, local_rank=0, deepspeed=ds_config_zero2_dict)
trainer.train()
new_a = trainer.model.a.item()
self.assertNotEqual(new_a, a)
@@ -128,11 +157,11 @@ class TrainerIntegrationDeepSpeed(TestCasePlus, TrainerIntegrationCommon):
def test_hf_scheduler_ds_optimizer(self):
# this combo is not possible at the moment
with mockenv_context(**self.dist_env_1_gpu):
ds_config_dict = deepcopy(self.ds_config_dict)
del ds_config_dict["scheduler"] # force default HF Trainer scheduler
ds_config_dict["zero_optimization"]["cpu_offload"] = False
ds_config_dict["fp16"]["initial_scale_power"] = 1 # force optimizer on the first step
trainer = get_regression_trainer(local_rank=0, deepspeed=ds_config_dict)
ds_config_zero2_dict = self.get_config_dict(ZERO2)
del ds_config_zero2_dict["scheduler"] # force default HF Trainer scheduler
ds_config_zero2_dict["zero_optimization"]["cpu_offload"] = False
ds_config_zero2_dict["fp16"]["initial_scale_power"] = 1 # force optimizer on the first step
trainer = get_regression_trainer(local_rank=0, deepspeed=ds_config_zero2_dict)
with self.assertRaises(Exception) as context:
trainer.train()
self.assertTrue("HF scheduler + DeepSpeed optimizer combination is not possible" in str(context.exception))
@@ -140,20 +169,38 @@ class TrainerIntegrationDeepSpeed(TestCasePlus, TrainerIntegrationCommon):
def test_hf_optimizer_with_offload(self):
# must not allow non-DS optimizer when using ZERO-offload
with mockenv_context(**self.dist_env_1_gpu):
ds_config_dict = deepcopy(self.ds_config_dict)
del ds_config_dict["optimizer"] # force default HF Trainer optimizer
ds_config_dict["zero_optimization"]["cpu_offload"] = True
ds_config_zero2_dict = self.get_config_dict(ZERO2)
del ds_config_zero2_dict["optimizer"] # force default HF Trainer optimizer
ds_config_zero2_dict["zero_optimization"]["cpu_offload"] = True
# sanity check - should the default config change
assert (
"cpu_offload" in ds_config_dict["zero_optimization"]
and ds_config_dict["zero_optimization"]["cpu_offload"] is True
"cpu_offload" in ds_config_zero2_dict["zero_optimization"]
and ds_config_zero2_dict["zero_optimization"]["cpu_offload"] is True
), "ensure the config is set up correctly"
trainer = get_regression_trainer(local_rank=0, deepspeed=ds_config_dict)
trainer = get_regression_trainer(local_rank=0, deepspeed=ds_config_zero2_dict)
with self.assertRaises(Exception) as context:
trainer.train()
self.assertTrue("ZeRO Offload can only work with DeepSpeed optimizers" in str(context.exception))
def test_early_get_last_lr(self):
# --- These tests need to run on both zero stages --- #
@parameterized.expand(stages)
def test_fake_notebook_no_launcher(self, stage):
# this setup emulates a notebook where a launcher needs to be emulated by hand
# note that unittest resets sys.stdout each test, so `CaptureStd` will work here to capture
# DeepSpeed log if this test happens to run first in this pytest worker. But it will fail if
# it's run not as a first test as `sys.stdout` will no longer be the same. So we either have
# to reset `logger.handlers[0].setStream(sys.stdout)` or directly capture from the logger.
from deepspeed.utils import logger
with CaptureLogger(logger) as cs:
with mockenv_context(**self.dist_env_1_gpu):
trainer = get_regression_trainer(local_rank=0, deepspeed=self.ds_config_file[stage])
trainer.train()
assert "DeepSpeed info" in cs.out, "expected DeepSpeed logger output but got none"
@parameterized.expand(stages)
def test_early_get_last_lr(self, stage):
# with deepspeed's fp16 and dynamic loss scale enabled the optimizer/scheduler steps may
# not run for the first few dozen steps while loss scale is too large, and thus during
# that time `get_last_lr` will fail if called during that warm up stage,
@@ -167,19 +214,24 @@ class TrainerIntegrationDeepSpeed(TestCasePlus, TrainerIntegrationCommon):
b=b,
local_rank=0,
train_len=8,
deepspeed=self.ds_config_file,
deepspeed=self.ds_config_file[stage],
per_device_train_batch_size=8,
logging_steps=1,
)
trainer.train()
no_grad_accum_a = trainer.model.a.item()
post_train_a = trainer.model.a.item()
# XXX: for some reason the following check fails with zero3 - not a broken but a
# different qualitative outcome - need to investigate at some point
if stage == ZERO3:
return
# it's enough that train didn't fail for this test, but we must check that
# optimizer/scheduler didn't run (since if it did this test isn't testing the right thing)
self.assertEqual(no_grad_accum_a, a)
def test_gradient_accumulation(self):
self.assertEqual(post_train_a, a)
@parameterized.expand(stages)
def test_gradient_accumulation(self, stage):
# this test measures that we get identical weights and similar loss with:
# 1. per_device_train_batch_size=8, gradient_accumulation_steps=1
# 2. per_device_train_batch_size=4, gradient_accumulation_steps=2
@@ -201,7 +253,7 @@ class TrainerIntegrationDeepSpeed(TestCasePlus, TrainerIntegrationCommon):
b=b,
local_rank=0,
train_len=train_len,
deepspeed=self.ds_config_file,
deepspeed=self.ds_config_file[stage],
per_device_train_batch_size=8,
gradient_accumulation_steps=1,
)
@@ -218,7 +270,7 @@ class TrainerIntegrationDeepSpeed(TestCasePlus, TrainerIntegrationCommon):
b=b,
local_rank=0,
train_len=train_len,
deepspeed=self.ds_config_file,
deepspeed=self.ds_config_file[stage],
per_device_train_batch_size=4,
gradient_accumulation_steps=2,
)
@@ -235,34 +287,55 @@ class TrainerIntegrationDeepSpeed(TestCasePlus, TrainerIntegrationCommon):
# see the note above how to get identical loss on a small bs
self.assertAlmostEqual(no_grad_accum_loss, yes_grad_accum_loss, places=5)
def check_saved_checkpoints_deepspeed(self, output_dir, freq, total, is_pretrained=True):
def check_saved_checkpoints_deepspeed(self, output_dir, freq, total, stage):
# adapted from TrainerIntegrationCommon.check_saved_checkpoints
file_list = [WEIGHTS_NAME, "training_args.bin", "trainer_state.json", "config.json"]
ds_file_list = ["mp_rank_00_model_states.pt", "zero_pp_rank_0_mp_rank_00optim_states.pt"]
if stage == ZERO2:
ds_file_list = ["mp_rank_00_model_states.pt"]
elif stage == ZERO3:
ds_file_list = ["zero_pp_rank_0_mp_rank_00_model_states.pt"]
else:
raise ValueError(f"unknown stage {stage}")
# XXX: this can be recoded and then removed once we require deepspeed>0.3.13
from packaging import version
import deepspeed
if version.parse(deepspeed.__version__) > version.parse("0.3.13"):
ds_file_list.append("zero_pp_rank_0_mp_rank_00_optim_states.pt")
else:
ds_file_list.append("zero_pp_rank_0_mp_rank_00optim_states.pt")
for step in range(freq, total, freq):
checkpoint = os.path.join(output_dir, f"checkpoint-{step}")
self.assertTrue(os.path.isdir(checkpoint))
self.assertTrue(os.path.isdir(checkpoint), f"[{stage}] {checkpoint} dir is not found")
# common files
for filename in file_list:
self.assertTrue(os.path.isfile(os.path.join(checkpoint, filename)))
path = os.path.join(checkpoint, filename)
self.assertTrue(os.path.isfile(path), f"[{stage}] {path} is not found")
# ds files
ds_path = os.path.join(checkpoint, f"global_step{step}")
for filename in ds_file_list:
# filename = os.path.join(path, filename)
# print(filename)
self.assertTrue(os.path.isfile(os.path.join(ds_path, filename)))
path = os.path.join(ds_path, filename)
self.assertTrue(os.path.isfile(path), f"[{stage}] {path} is not found")
def test_save_checkpoints(self):
@parameterized.expand(stages)
def test_save_checkpoints(self, stage):
# adapted from TrainerIntegrationTest.test_save_checkpoints
output_dir = self.get_auto_remove_tmp_dir()
ds_config_dict = deepcopy(self.ds_config_dict)
ds_config_dict["fp16"]["initial_scale_power"] = 1 # force optimizer on the first step
freq = 5
output_dir = self.get_auto_remove_tmp_dir()
ds_config_dict = self.get_config_dict(stage)
ds_config_dict["fp16"]["initial_scale_power"] = 1 # force optimizer on the first step
if stage == ZERO3:
ds_config_dict["zero_optimization"]["stage3_gather_fp16_weights_on_model_save"] = True
# save checkpoints
with mockenv_context(**self.dist_env_1_gpu):
@@ -274,14 +347,42 @@ class TrainerIntegrationDeepSpeed(TestCasePlus, TrainerIntegrationCommon):
trainer.train()
total = int(self.n_epochs * 64 / self.batch_size)
self.check_saved_checkpoints_deepspeed(output_dir, freq, total)
self.check_saved_checkpoints_deepspeed(output_dir, freq, total, stage)
def test_can_resume_training(self):
@parameterized.expand(stages)
def test_can_resume_training_errors(self, stage):
with mockenv_context(**self.dist_env_1_gpu):
ds_config_dict = self.get_config_dict(stage)
output_dir = self.get_auto_remove_tmp_dir()
trainer = get_regression_trainer(output_dir=output_dir, deepspeed=ds_config_dict)
# 1. fail to find any checkpoint - due a fresh output_dir
with self.assertRaises(Exception) as context:
trainer.train(resume_from_checkpoint=True)
self.assertTrue(
"No valid checkpoint found in output directory" in str(context.exception),
f"got exception: {context.exception}",
)
# 2. fail to find a bogus checkpoint
with self.assertRaises(Exception) as context:
checkpoint = os.path.join(output_dir, "checkpoint-5")
trainer.train(resume_from_checkpoint=f"{checkpoint}-bogus")
self.assertTrue(
"Can't find a valid checkpoint at" in str(context.exception), f"got exception: {context.exception}"
)
@parameterized.expand(stages)
def test_can_resume_training_normal(self, stage):
# adapted from TrainerIntegrationTest.test_can_resume_training
# test normal resume for each stage separately, error-handling is tested in a different test
output_dir = self.get_auto_remove_tmp_dir()
ds_config_dict = deepcopy(self.ds_config_dict)
ds_config_dict = self.get_config_dict(stage)
ds_config_dict["fp16"]["initial_scale_power"] = 1 # force optimizer on the first step
if stage == ZERO3:
ds_config_dict["zero_optimization"]["stage3_gather_fp16_weights_on_model_save"] = True
kwargs = dict(output_dir=output_dir, train_len=128, save_steps=5, learning_rate=0.1, deepspeed=ds_config_dict)
with mockenv_context(**self.dist_env_1_gpu):
@@ -315,70 +416,117 @@ class TrainerIntegrationDeepSpeed(TestCasePlus, TrainerIntegrationCommon):
self.assertEqual(b, b1)
self.check_trainer_state_are_the_same(state, state1)
# Now check failures
# 1. fail to find a bogus checkpoint
trainer = get_regression_trainer(**kwargs)
with self.assertRaises(Exception) as context:
trainer.train(resume_from_checkpoint=f"{checkpoint}-bogus")
self.assertTrue("failed to resume from checkpoint" in str(context.exception))
# 2. fail to find any checkpoint - due a fresh output_dir
output_dir2 = self.get_auto_remove_tmp_dir()
trainer = get_regression_trainer(output_dir=output_dir2, deepspeed=ds_config_dict)
with self.assertRaises(Exception) as context:
trainer.train(resume_from_checkpoint=True)
self.assertTrue("No valid checkpoint found in output directory" in str(context.exception))
@slow
@require_deepspeed
@require_torch_gpu
class TestDeepSpeed(TestCasePlus):
""" This class is for testing via an external script """
class TestDeepSpeedWithLauncher(TestCasePlus):
""" This class is for testing via an external script - can do multiple gpus """
# Tests to devise #
#
# 1. predict_with_generate on multigpu - need to figure out how to give input sequences so that
# the 2 gpus will generate prediction sequences that aren't of the same length - this is because
# we had to code a special feature to sync the gpus when the predicted sequences aren't of the
# same length. In general this will tested as a side-effect through a variety of other tests -
# it'll simply hang trying to synchronize with other gpus if this problem is encountered. So as
# long as we have a few full tests running on zero3 + predict_with_generate this should be
# mostly covered.
#
# but there are 5 variations on beam search in `generate`- with identical code branched with `if
# synced_gpus`
#
# 2. most tests should probably be run on both: zero2 and zero3 configs
#
@require_torch_multi_gpu
def test_basic_distributed(self):
self.run_quick(distributed=True)
@parameterized.expand(stages)
def test_basic_distributed(self, stage):
self.run_and_check(stage=stage, distributed=True)
def test_do_eval_no_train(self):
@parameterized.expand(stages)
def test_do_eval_no_train(self, stage):
# we should not fail if train is skipped
output_dir = self.run_trainer(
self.run_and_check(
stage=stage,
eval_steps=1,
max_len=12,
model_name=MBART_TINY,
num_train_epochs=1,
distributed=False,
extra_args_str="--do_eval",
remove_args_str="--do_train",
do_train=False,
do_eval=True,
)
val_metrics = load_json(os.path.join(output_dir, "eval_results.json"))
assert "eval_bleu" in val_metrics
@parameterized.expand(stages)
def test_resume_train_not_from_ds_checkpoint(self, stage):
# do normal training and then resume not from the deepspeed checkpoint but explicitly from
# the saved model dir
do_train = True
do_eval = False
kwargs = dict(stage=stage, eval_steps=1, distributed=True, do_train=do_train, do_eval=do_eval)
# 1. normal training
output_dir = self.run_and_check(**kwargs)
# 2. now resume explicitly from the saved weights, by passing --model_name_or_path output_dir
# - i.e. the same path the model was saved to in step 1
output_dir = self.run_trainer(**kwargs, model_name=output_dir)
self.do_checks(output_dir, do_train=do_train, do_eval=do_eval)
def do_checks(self, output_dir, do_train=True, do_eval=True):
if do_train:
train_metrics = load_json(os.path.join(output_dir, "train_results.json"))
self.assertIn("train_samples_per_second", train_metrics)
self.assertGreater(train_metrics["train_samples_per_second"], 0.5)
if do_eval:
eval_metrics = load_json(os.path.join(output_dir, "eval_results.json"))
self.assertIn("eval_bleu", eval_metrics)
self.assertGreater(eval_metrics["eval_bleu"], 0)
# XXX: need to do better validation beyond just that the run was successful
def run_quick(self, distributed=True, extra_args_str=None, remove_args_str=None):
def run_and_check(
self,
stage,
eval_steps=10,
distributed=True,
do_train=True,
do_eval=True,
extra_args_str=None,
remove_args_str=None,
):
# we are doing quality testing so using a small real model
output_dir = self.run_trainer(
eval_steps=1,
max_len=12,
model_name=MBART_TINY,
stage=stage,
model_name=T5_SMALL,
eval_steps=eval_steps,
num_train_epochs=1,
do_train=do_train,
do_eval=do_eval,
distributed=distributed,
extra_args_str=extra_args_str,
remove_args_str=remove_args_str,
)
train_metrics = load_json(os.path.join(output_dir, "train_results.json"))
assert "train_runtime" in train_metrics
self.do_checks(output_dir, do_train=do_train, do_eval=do_eval)
return output_dir
def run_trainer(
self,
eval_steps: int,
max_len: str,
stage: str,
model_name: str,
num_train_epochs: int,
eval_steps: int = 10,
num_train_epochs: int = 1,
do_train: bool = False,
do_eval: bool = True,
distributed: bool = True,
extra_args_str: str = None,
remove_args_str: str = None,
):
max_len = 32
data_dir = self.examples_dir / "test_data/wmt_en_ro"
output_dir = self.get_auto_remove_tmp_dir()
args = f"""
@@ -387,41 +535,100 @@ class TestDeepSpeed(TestCasePlus):
--validation_file {data_dir}/val.json
--output_dir {output_dir}
--overwrite_output_dir
--max_train_samples 8
--max_val_samples 8
--max_source_length {max_len}
--max_target_length {max_len}
--val_max_target_length {max_len}
--do_train
--num_train_epochs {str(num_train_epochs)}
--per_device_train_batch_size 4
--learning_rate 3e-3
--warmup_steps 8
--predict_with_generate
--logging_steps 0
--save_steps {str(eval_steps)}
--save_steps 0
--eval_steps {eval_steps}
--group_by_length
--label_smoothing_factor 0.1
--adafactor
--target_lang ro_RO
--source_lang en_XX
--source_lang en
--target_lang ro
""".split()
args.extend(["--source_prefix", '"translate English to Romanian: "'])
actions = 0
if do_train:
actions += 1
args.extend(
f"""
--do_train
--num_train_epochs {str(num_train_epochs)}
--max_train_samples 100
--per_device_train_batch_size 2
--learning_rate 3e-3
""".split()
)
if do_eval:
actions += 1
args.extend(
"""
--do_eval
--max_val_samples 100
--per_device_eval_batch_size 2
""".split()
)
assert actions > 0, "need at least do_train or do_eval for the test to run"
if extra_args_str is not None:
args.extend(extra_args_str.split())
# currently only works for bool args
if remove_args_str is not None:
remove_args = remove_args_str.split()
args = [x for x in args if x not in remove_args]
ds_args = f"--deepspeed {self.test_file_dir_str}/ds_config.json".split()
ds_args = f"--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json".split()
script = [f"{self.examples_dir_str}/seq2seq/run_translation.py"]
num_gpus = get_gpu_count() if distributed else 1
launcher = f"deepspeed --num_gpus {num_gpus}".split()
cmd = launcher + script + args + ds_args
# keep for quick debug
# print(" ".join([f"PYTHONPATH={self.src_dir_str}"] +cmd)); die
# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
execute_subprocess_async(cmd, env=self.get_env())
return output_dir
@parameterized.expand(stages)
def test_clm(self, stage):
# this test exercises model.resize_token_embeddings() which requires param gathering outside
# of forward - it's not used by `run_translation.py`, but it is in `run_clm.py`
data_dir = self.tests_dir / "fixtures"
output_dir = self.get_auto_remove_tmp_dir()
args = f"""
--model_name_or_path sshleifer/tiny-gpt2
--train_file {data_dir}/sample_text.txt
--validation_file {data_dir}/sample_text.txt
--output_dir {output_dir}
--overwrite_output_dir
--do_train
--do_eval
--max_train_samples 10
--max_val_samples 10
--per_device_train_batch_size 5
--per_device_eval_batch_size 5
--num_train_epochs 1
--warmup_steps 8
--block_size 128
""".split()
distributed = True
ds_args = f"--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json".split()
script = [f"{self.examples_dir_str}/language-modeling/run_clm.py"]
num_gpus = get_gpu_count() if distributed else 1
launcher = f"deepspeed --num_gpus {num_gpus}".split()
cmd = launcher + script + args + ds_args
# keep for quick debug
# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
execute_subprocess_async(cmd, env=self.get_env())
return output_dir