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Unverified Commit d49e2dd5 authored by Will Berman's avatar Will Berman Committed by GitHub
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manual check for checkpoints_total_limit instead of using accelerate (#3681) 

* manual check for checkpoints_total_limit instead of using accelerate

* remove controlnet_conditioning_embedding_out_channels
parent 7bfd2375
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Showing with 1007 additions and 95 deletions
examples/controlnet/train_controlnet.py
View file @ d49e2dd5
......@@ -18,6 +18,7 @@ import logging
import math
import os
import random
import shutil
from pathlib import Path
import accelerate
......@@ -307,11 +308,7 @@ def parse_args(input_args=None):
"--checkpoints_total_limit",
type=int,
default=None,
help=(
"Max number of checkpoints to store. Passed as `total_limit` to the `Accelerator` `ProjectConfiguration`."
" See Accelerator::save_state https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.save_state"
" for more details"
),
help=("Max number of checkpoints to store."),
)
parser.add_argument(
"--resume_from_checkpoint",
......@@ -716,9 +713,7 @@ def collate_fn(examples):
def main(args):
logging_dir = Path(args.output_dir, args.logging_dir)
accelerator_project_config = ProjectConfiguration(
total_limit=args.checkpoints_total_limit, project_dir=args.output_dir, logging_dir=logging_dir
)
accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
......@@ -1060,6 +1055,26 @@ def main(args):
if accelerator.is_main_process:
if global_step % args.checkpointing_steps == 0:
# _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
if args.checkpoints_total_limit is not None:
checkpoints = os.listdir(args.output_dir)
checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))
# before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
if len(checkpoints) >= args.checkpoints_total_limit:
num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
removing_checkpoints = checkpoints[0:num_to_remove]
logger.info(
f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
)
logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")
for removing_checkpoint in removing_checkpoints:
removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)
shutil.rmtree(removing_checkpoint)
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
accelerator.save_state(save_path)
logger.info(f"Saved state to {save_path}")
......
examples/custom_diffusion/train_custom_diffusion.py
View file @ d49e2dd5
......@@ -21,6 +21,7 @@ import logging
import math
import os
import random
import shutil
import warnings
from pathlib import Path
......@@ -446,11 +447,7 @@ def parse_args(input_args=None):
"--checkpoints_total_limit",
type=int,
default=None,
help=(
"Max number of checkpoints to store. Passed as `total_limit` to the `Accelerator` `ProjectConfiguration`."
" See Accelerator::save_state https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.save_state"
" for more docs"
),
help=("Max number of checkpoints to store."),
)
parser.add_argument(
"--resume_from_checkpoint",
......@@ -637,9 +634,7 @@ def parse_args(input_args=None):
def main(args):
logging_dir = Path(args.output_dir, args.logging_dir)
accelerator_project_config = ProjectConfiguration(
total_limit=args.checkpoints_total_limit, project_dir=args.output_dir, logging_dir=logging_dir
)
accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
......@@ -1171,6 +1166,26 @@ def main(args):
if global_step % args.checkpointing_steps == 0:
if accelerator.is_main_process:
# _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
if args.checkpoints_total_limit is not None:
checkpoints = os.listdir(args.output_dir)
checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))
# before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
if len(checkpoints) >= args.checkpoints_total_limit:
num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
removing_checkpoints = checkpoints[0:num_to_remove]
logger.info(
f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
)
logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")
for removing_checkpoint in removing_checkpoints:
removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)
shutil.rmtree(removing_checkpoint)
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
accelerator.save_state(save_path)
logger.info(f"Saved state to {save_path}")
......
examples/dreambooth/train_dreambooth.py
View file @ d49e2dd5
......@@ -20,6 +20,7 @@ import itertools
import logging
import math
import os
import shutil
import warnings
from pathlib import Path
......@@ -771,9 +772,7 @@ def encode_prompt(text_encoder, input_ids, attention_mask, text_encoder_use_atte
def main(args):
logging_dir = Path(args.output_dir, args.logging_dir)
accelerator_project_config = ProjectConfiguration(
total_limit=args.checkpoints_total_limit, project_dir=args.output_dir, logging_dir=logging_dir
)
accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
......@@ -1270,12 +1269,33 @@ def main(args):
global_step += 1
if accelerator.is_main_process:
images = []
if global_step % args.checkpointing_steps == 0:
# _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
if args.checkpoints_total_limit is not None:
checkpoints = os.listdir(args.output_dir)
checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))
# before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
if len(checkpoints) >= args.checkpoints_total_limit:
num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
removing_checkpoints = checkpoints[0:num_to_remove]
logger.info(
f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
)
logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")
for removing_checkpoint in removing_checkpoints:
removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)
shutil.rmtree(removing_checkpoint)
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
accelerator.save_state(save_path)
logger.info(f"Saved state to {save_path}")
images = []
if args.validation_prompt is not None and global_step % args.validation_steps == 0:
images = log_validation(
text_encoder,
......
examples/dreambooth/train_dreambooth_lora.py
View file @ d49e2dd5
......@@ -20,6 +20,7 @@ import itertools
import logging
import math
import os
import shutil
import warnings
from pathlib import Path
......@@ -276,11 +277,7 @@ def parse_args(input_args=None):
"--checkpoints_total_limit",
type=int,
default=None,
help=(
"Max number of checkpoints to store. Passed as `total_limit` to the `Accelerator` `ProjectConfiguration`."
" See Accelerator::save_state https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.save_state"
" for more docs"
),
help=("Max number of checkpoints to store."),
)
parser.add_argument(
"--resume_from_checkpoint",
......@@ -653,9 +650,7 @@ def encode_prompt(text_encoder, input_ids, attention_mask, text_encoder_use_atte
def main(args):
logging_dir = Path(args.output_dir, args.logging_dir)
accelerator_project_config = ProjectConfiguration(
total_limit=args.checkpoints_total_limit, project_dir=args.output_dir, logging_dir=logging_dir
)
accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
......@@ -1221,6 +1216,26 @@ def main(args):
if accelerator.is_main_process:
if global_step % args.checkpointing_steps == 0:
# _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
if args.checkpoints_total_limit is not None:
checkpoints = os.listdir(args.output_dir)
checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))
# before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
if len(checkpoints) >= args.checkpoints_total_limit:
num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
removing_checkpoints = checkpoints[0:num_to_remove]
logger.info(
f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
)
logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")
for removing_checkpoint in removing_checkpoints:
removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)
shutil.rmtree(removing_checkpoint)
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
accelerator.save_state(save_path)
logger.info(f"Saved state to {save_path}")
......
examples/instruct_pix2pix/train_instruct_pix2pix.py
View file @ d49e2dd5
......@@ -20,6 +20,7 @@ import argparse
import logging
import math
import os
import shutil
from pathlib import Path
import accelerate
......@@ -327,11 +328,7 @@ def parse_args():
"--checkpoints_total_limit",
type=int,
default=None,
help=(
"Max number of checkpoints to store. Passed as `total_limit` to the `Accelerator` `ProjectConfiguration`."
" See Accelerator::save_state https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.save_state"
" for more docs"
),
help=("Max number of checkpoints to store."),
)
parser.add_argument(
"--resume_from_checkpoint",
......@@ -387,9 +384,7 @@ def main():
),
)
logging_dir = os.path.join(args.output_dir, args.logging_dir)
accelerator_project_config = ProjectConfiguration(
total_limit=args.checkpoints_total_limit, project_dir=args.output_dir, logging_dir=logging_dir
)
accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
mixed_precision=args.mixed_precision,
......@@ -867,6 +862,26 @@ def main():
if global_step % args.checkpointing_steps == 0:
if accelerator.is_main_process:
# _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
if args.checkpoints_total_limit is not None:
checkpoints = os.listdir(args.output_dir)
checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))
# before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
if len(checkpoints) >= args.checkpoints_total_limit:
num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
removing_checkpoints = checkpoints[0:num_to_remove]
logger.info(
f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
)
logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")
for removing_checkpoint in removing_checkpoints:
removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)
shutil.rmtree(removing_checkpoint)
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
accelerator.save_state(save_path)
logger.info(f"Saved state to {save_path}")
......
examples/test_examples.py
View file @ d49e2dd5
......@@ -435,8 +435,10 @@ class ExamplesTestsAccelerate(unittest.TestCase):
pipe(prompt, num_inference_steps=2)
# check checkpoint directories exist
self.assertTrue(os.path.isdir(os.path.join(tmpdir, "checkpoint-2")))
self.assertTrue(os.path.isdir(os.path.join(tmpdir, "checkpoint-4")))
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
{"checkpoint-2", "checkpoint-4"},
)
# check can run an intermediate checkpoint
unet = UNet2DConditionModel.from_pretrained(tmpdir, subfolder="checkpoint-2/unet")
......@@ -474,12 +476,15 @@ class ExamplesTestsAccelerate(unittest.TestCase):
pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)
pipe(prompt, num_inference_steps=2)
# check old checkpoints do not exist
self.assertFalse(os.path.isdir(os.path.join(tmpdir, "checkpoint-2")))
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
{
# no checkpoint-2 -> check old checkpoints do not exist
# check new checkpoints exist
self.assertTrue(os.path.isdir(os.path.join(tmpdir, "checkpoint-4")))
self.assertTrue(os.path.isdir(os.path.join(tmpdir, "checkpoint-6")))
"checkpoint-4",
"checkpoint-6",
},
)
def test_text_to_image_checkpointing_use_ema(self):
pretrained_model_name_or_path = "hf-internal-testing/tiny-stable-diffusion-pipe"
......@@ -516,8 +521,10 @@ class ExamplesTestsAccelerate(unittest.TestCase):
pipe(prompt, num_inference_steps=2)
# check checkpoint directories exist
self.assertTrue(os.path.isdir(os.path.join(tmpdir, "checkpoint-2")))
self.assertTrue(os.path.isdir(os.path.join(tmpdir, "checkpoint-4")))
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
{"checkpoint-2", "checkpoint-4"},
)
# check can run an intermediate checkpoint
unet = UNet2DConditionModel.from_pretrained(tmpdir, subfolder="checkpoint-2/unet")
......@@ -556,9 +563,773 @@ class ExamplesTestsAccelerate(unittest.TestCase):
pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)
pipe(prompt, num_inference_steps=2)
# check old checkpoints do not exist
self.assertFalse(os.path.isdir(os.path.join(tmpdir, "checkpoint-2")))
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
{
# no checkpoint-2 -> check old checkpoints do not exist
# check new checkpoints exist
self.assertTrue(os.path.isdir(os.path.join(tmpdir, "checkpoint-4")))
self.assertTrue(os.path.isdir(os.path.join(tmpdir, "checkpoint-6")))
"checkpoint-4",
"checkpoint-6",
},
)
def test_text_to_image_checkpointing_checkpoints_total_limit(self):
pretrained_model_name_or_path = "hf-internal-testing/tiny-stable-diffusion-pipe"
prompt = "a prompt"
with tempfile.TemporaryDirectory() as tmpdir:
# Run training script with checkpointing
# max_train_steps == 7, checkpointing_steps == 2, checkpoints_total_limit == 2
# Should create checkpoints at steps 2, 4, 6
# with checkpoint at step 2 deleted
initial_run_args = f"""
examples/text_to_image/train_text_to_image.py
--pretrained_model_name_or_path {pretrained_model_name_or_path}
--dataset_name hf-internal-testing/dummy_image_text_data
--resolution 64
--center_crop
--random_flip
--train_batch_size 1
--gradient_accumulation_steps 1
--max_train_steps 7
--learning_rate 5.0e-04
--scale_lr
--lr_scheduler constant
--lr_warmup_steps 0
--output_dir {tmpdir}
--checkpointing_steps=2
--checkpoints_total_limit=2
--seed=0
""".split()
run_command(self._launch_args + initial_run_args)
pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)
pipe(prompt, num_inference_steps=2)
# check checkpoint directories exist
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
# checkpoint-2 should have been deleted
{"checkpoint-4", "checkpoint-6"},
)
def test_text_to_image_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):
pretrained_model_name_or_path = "hf-internal-testing/tiny-stable-diffusion-pipe"
prompt = "a prompt"
with tempfile.TemporaryDirectory() as tmpdir:
# Run training script with checkpointing
# max_train_steps == 9, checkpointing_steps == 2
# Should create checkpoints at steps 2, 4, 6, 8
initial_run_args = f"""
examples/text_to_image/train_text_to_image.py
--pretrained_model_name_or_path {pretrained_model_name_or_path}
--dataset_name hf-internal-testing/dummy_image_text_data
--resolution 64
--center_crop
--random_flip
--train_batch_size 1
--gradient_accumulation_steps 1
--max_train_steps 9
--learning_rate 5.0e-04
--scale_lr
--lr_scheduler constant
--lr_warmup_steps 0
--output_dir {tmpdir}
--checkpointing_steps=2
--seed=0
""".split()
run_command(self._launch_args + initial_run_args)
pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)
pipe(prompt, num_inference_steps=2)
# check checkpoint directories exist
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
{"checkpoint-2", "checkpoint-4", "checkpoint-6", "checkpoint-8"},
)
# resume and we should try to checkpoint at 10, where we'll have to remove
# checkpoint-2 and checkpoint-4 instead of just a single previous checkpoint
resume_run_args = f"""
examples/text_to_image/train_text_to_image.py
--pretrained_model_name_or_path {pretrained_model_name_or_path}
--dataset_name hf-internal-testing/dummy_image_text_data
--resolution 64
--center_crop
--random_flip
--train_batch_size 1
--gradient_accumulation_steps 1
--max_train_steps 11
--learning_rate 5.0e-04
--scale_lr
--lr_scheduler constant
--lr_warmup_steps 0
--output_dir {tmpdir}
--checkpointing_steps=2
--resume_from_checkpoint=checkpoint-8
--checkpoints_total_limit=3
--seed=0
""".split()
run_command(self._launch_args + resume_run_args)
pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)
pipe(prompt, num_inference_steps=2)
# check checkpoint directories exist
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
{"checkpoint-6", "checkpoint-8", "checkpoint-10"},
)
def test_text_to_image_lora_checkpointing_checkpoints_total_limit(self):
pretrained_model_name_or_path = "hf-internal-testing/tiny-stable-diffusion-pipe"
prompt = "a prompt"
with tempfile.TemporaryDirectory() as tmpdir:
# Run training script with checkpointing
# max_train_steps == 7, checkpointing_steps == 2, checkpoints_total_limit == 2
# Should create checkpoints at steps 2, 4, 6
# with checkpoint at step 2 deleted
initial_run_args = f"""
examples/text_to_image/train_text_to_image_lora.py
--pretrained_model_name_or_path {pretrained_model_name_or_path}
--dataset_name hf-internal-testing/dummy_image_text_data
--resolution 64
--center_crop
--random_flip
--train_batch_size 1
--gradient_accumulation_steps 1
--max_train_steps 7
--learning_rate 5.0e-04
--scale_lr
--lr_scheduler constant
--lr_warmup_steps 0
--output_dir {tmpdir}
--checkpointing_steps=2
--checkpoints_total_limit=2
--seed=0
--num_validation_images=0
""".split()
run_command(self._launch_args + initial_run_args)
pipe = DiffusionPipeline.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-pipe", safety_checker=None
)
pipe.load_lora_weights(tmpdir)
pipe(prompt, num_inference_steps=2)
# check checkpoint directories exist
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
# checkpoint-2 should have been deleted
{"checkpoint-4", "checkpoint-6"},
)
def test_text_to_image_lora_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):
pretrained_model_name_or_path = "hf-internal-testing/tiny-stable-diffusion-pipe"
prompt = "a prompt"
with tempfile.TemporaryDirectory() as tmpdir:
# Run training script with checkpointing
# max_train_steps == 9, checkpointing_steps == 2
# Should create checkpoints at steps 2, 4, 6, 8
initial_run_args = f"""
examples/text_to_image/train_text_to_image_lora.py
--pretrained_model_name_or_path {pretrained_model_name_or_path}
--dataset_name hf-internal-testing/dummy_image_text_data
--resolution 64
--center_crop
--random_flip
--train_batch_size 1
--gradient_accumulation_steps 1
--max_train_steps 9
--learning_rate 5.0e-04
--scale_lr
--lr_scheduler constant
--lr_warmup_steps 0
--output_dir {tmpdir}
--checkpointing_steps=2
--seed=0
--num_validation_images=0
""".split()
run_command(self._launch_args + initial_run_args)
pipe = DiffusionPipeline.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-pipe", safety_checker=None
)
pipe.load_lora_weights(tmpdir)
pipe(prompt, num_inference_steps=2)
# check checkpoint directories exist
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
{"checkpoint-2", "checkpoint-4", "checkpoint-6", "checkpoint-8"},
)
# resume and we should try to checkpoint at 10, where we'll have to remove
# checkpoint-2 and checkpoint-4 instead of just a single previous checkpoint
resume_run_args = f"""
examples/text_to_image/train_text_to_image_lora.py
--pretrained_model_name_or_path {pretrained_model_name_or_path}
--dataset_name hf-internal-testing/dummy_image_text_data
--resolution 64
--center_crop
--random_flip
--train_batch_size 1
--gradient_accumulation_steps 1
--max_train_steps 11
--learning_rate 5.0e-04
--scale_lr
--lr_scheduler constant
--lr_warmup_steps 0
--output_dir {tmpdir}
--checkpointing_steps=2
--resume_from_checkpoint=checkpoint-8
--checkpoints_total_limit=3
--seed=0
--num_validation_images=0
""".split()
run_command(self._launch_args + resume_run_args)
pipe = DiffusionPipeline.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-pipe", safety_checker=None
)
pipe.load_lora_weights(tmpdir)
pipe(prompt, num_inference_steps=2)
# check checkpoint directories exist
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
{"checkpoint-6", "checkpoint-8", "checkpoint-10"},
)
def test_unconditional_checkpointing_checkpoints_total_limit(self):
with tempfile.TemporaryDirectory() as tmpdir:
initial_run_args = f"""
examples/unconditional_image_generation/train_unconditional.py
--dataset_name hf-internal-testing/dummy_image_class_data
--model_config_name_or_path diffusers/ddpm_dummy
--resolution 64
--output_dir {tmpdir}
--train_batch_size 1
--num_epochs 1
--gradient_accumulation_steps 1
--ddpm_num_inference_steps 2
--learning_rate 1e-3
--lr_warmup_steps 5
--checkpointing_steps=2
--checkpoints_total_limit=2
""".split()
run_command(self._launch_args + initial_run_args)
# check checkpoint directories exist
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
# checkpoint-2 should have been deleted
{"checkpoint-4", "checkpoint-6"},
)
def test_unconditional_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):
with tempfile.TemporaryDirectory() as tmpdir:
initial_run_args = f"""
examples/unconditional_image_generation/train_unconditional.py
--dataset_name hf-internal-testing/dummy_image_class_data
--model_config_name_or_path diffusers/ddpm_dummy
--resolution 64
--output_dir {tmpdir}
--train_batch_size 1
--num_epochs 1
--gradient_accumulation_steps 1
--ddpm_num_inference_steps 2
--learning_rate 1e-3
--lr_warmup_steps 5
--checkpointing_steps=1
""".split()
run_command(self._launch_args + initial_run_args)
# check checkpoint directories exist
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
{"checkpoint-1", "checkpoint-2", "checkpoint-3", "checkpoint-4", "checkpoint-5", "checkpoint-6"},
)
resume_run_args = f"""
examples/unconditional_image_generation/train_unconditional.py
--dataset_name hf-internal-testing/dummy_image_class_data
--model_config_name_or_path diffusers/ddpm_dummy
--resolution 64
--output_dir {tmpdir}
--train_batch_size 1
--num_epochs 2
--gradient_accumulation_steps 1
--ddpm_num_inference_steps 2
--learning_rate 1e-3
--lr_warmup_steps 5
--resume_from_checkpoint=checkpoint-6
--checkpointing_steps=2
--checkpoints_total_limit=3
""".split()
run_command(self._launch_args + resume_run_args)
# check checkpoint directories exist
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
{"checkpoint-8", "checkpoint-10", "checkpoint-12"},
)
def test_textual_inversion_checkpointing(self):
with tempfile.TemporaryDirectory() as tmpdir:
test_args = f"""
examples/textual_inversion/textual_inversion.py
--pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-pipe
--train_data_dir docs/source/en/imgs
--learnable_property object
--placeholder_token <cat-toy>
--initializer_token a
--validation_prompt <cat-toy>
--validation_steps 1
--save_steps 1
--num_vectors 2
--resolution 64
--train_batch_size 1
--gradient_accumulation_steps 1
--max_train_steps 3
--learning_rate 5.0e-04
--scale_lr
--lr_scheduler constant
--lr_warmup_steps 0
--output_dir {tmpdir}
--checkpointing_steps=1
--checkpoints_total_limit=2
""".split()
run_command(self._launch_args + test_args)
# check checkpoint directories exist
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
{"checkpoint-2", "checkpoint-3"},
)
def test_textual_inversion_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):
with tempfile.TemporaryDirectory() as tmpdir:
test_args = f"""
examples/textual_inversion/textual_inversion.py
--pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-pipe
--train_data_dir docs/source/en/imgs
--learnable_property object
--placeholder_token <cat-toy>
--initializer_token a
--validation_prompt <cat-toy>
--validation_steps 1
--save_steps 1
--num_vectors 2
--resolution 64
--train_batch_size 1
--gradient_accumulation_steps 1
--max_train_steps 3
--learning_rate 5.0e-04
--scale_lr
--lr_scheduler constant
--lr_warmup_steps 0
--output_dir {tmpdir}
--checkpointing_steps=1
""".split()
run_command(self._launch_args + test_args)
# check checkpoint directories exist
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
{"checkpoint-1", "checkpoint-2", "checkpoint-3"},
)
resume_run_args = f"""
examples/textual_inversion/textual_inversion.py
--pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-pipe
--train_data_dir docs/source/en/imgs
--learnable_property object
--placeholder_token <cat-toy>
--initializer_token a
--validation_prompt <cat-toy>
--validation_steps 1
--save_steps 1
--num_vectors 2
--resolution 64
--train_batch_size 1
--gradient_accumulation_steps 1
--max_train_steps 4
--learning_rate 5.0e-04
--scale_lr
--lr_scheduler constant
--lr_warmup_steps 0
--output_dir {tmpdir}
--checkpointing_steps=1
--resume_from_checkpoint=checkpoint-3
--checkpoints_total_limit=2
""".split()
run_command(self._launch_args + resume_run_args)
# check checkpoint directories exist
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
{"checkpoint-3", "checkpoint-4"},
)
def test_instruct_pix2pix_checkpointing_checkpoints_total_limit(self):
with tempfile.TemporaryDirectory() as tmpdir:
test_args = f"""
examples/instruct_pix2pix/train_instruct_pix2pix.py
--pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
--dataset_name=hf-internal-testing/instructpix2pix-10-samples
--resolution=64
--random_flip
--train_batch_size=1
--max_train_steps=7
--checkpointing_steps=2
--checkpoints_total_limit=2
--output_dir {tmpdir}
--seed=0
""".split()
run_command(self._launch_args + test_args)
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
{"checkpoint-4", "checkpoint-6"},
)
def test_instruct_pix2pix_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):
with tempfile.TemporaryDirectory() as tmpdir:
test_args = f"""
examples/instruct_pix2pix/train_instruct_pix2pix.py
--pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
--dataset_name=hf-internal-testing/instructpix2pix-10-samples
--resolution=64
--random_flip
--train_batch_size=1
--max_train_steps=9
--checkpointing_steps=2
--output_dir {tmpdir}
--seed=0
""".split()
run_command(self._launch_args + test_args)
# check checkpoint directories exist
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
{"checkpoint-2", "checkpoint-4", "checkpoint-6", "checkpoint-8"},
)
resume_run_args = f"""
examples/instruct_pix2pix/train_instruct_pix2pix.py
--pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
--dataset_name=hf-internal-testing/instructpix2pix-10-samples
--resolution=64
--random_flip
--train_batch_size=1
--max_train_steps=11
--checkpointing_steps=2
--output_dir {tmpdir}
--seed=0
--resume_from_checkpoint=checkpoint-8
--checkpoints_total_limit=3
""".split()
run_command(self._launch_args + resume_run_args)
# check checkpoint directories exist
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
{"checkpoint-6", "checkpoint-8", "checkpoint-10"},
)
def test_dreambooth_checkpointing_checkpoints_total_limit(self):
with tempfile.TemporaryDirectory() as tmpdir:
test_args = f"""
examples/dreambooth/train_dreambooth.py
--pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
--instance_data_dir=docs/source/en/imgs
--output_dir={tmpdir}
--instance_prompt=prompt
--resolution=64
--train_batch_size=1
--gradient_accumulation_steps=1
--max_train_steps=6
--checkpoints_total_limit=2
--checkpointing_steps=2
""".split()
run_command(self._launch_args + test_args)
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
{"checkpoint-4", "checkpoint-6"},
)
def test_dreambooth_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):
with tempfile.TemporaryDirectory() as tmpdir:
test_args = f"""
examples/dreambooth/train_dreambooth.py
--pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
--instance_data_dir=docs/source/en/imgs
--output_dir={tmpdir}
--instance_prompt=prompt
--resolution=64
--train_batch_size=1
--gradient_accumulation_steps=1
--max_train_steps=9
--checkpointing_steps=2
""".split()
run_command(self._launch_args + test_args)
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
{"checkpoint-2", "checkpoint-4", "checkpoint-6", "checkpoint-8"},
)
resume_run_args = f"""
examples/dreambooth/train_dreambooth.py
--pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
--instance_data_dir=docs/source/en/imgs
--output_dir={tmpdir}
--instance_prompt=prompt
--resolution=64
--train_batch_size=1
--gradient_accumulation_steps=1
--max_train_steps=11
--checkpointing_steps=2
--resume_from_checkpoint=checkpoint-8
--checkpoints_total_limit=3
""".split()
run_command(self._launch_args + resume_run_args)
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
{"checkpoint-6", "checkpoint-8", "checkpoint-10"},
)
def test_dreambooth_lora_checkpointing_checkpoints_total_limit(self):
with tempfile.TemporaryDirectory() as tmpdir:
test_args = f"""
examples/dreambooth/train_dreambooth_lora.py
--pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
--instance_data_dir=docs/source/en/imgs
--output_dir={tmpdir}
--instance_prompt=prompt
--resolution=64
--train_batch_size=1
--gradient_accumulation_steps=1
--max_train_steps=6
--checkpoints_total_limit=2
--checkpointing_steps=2
""".split()
run_command(self._launch_args + test_args)
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
{"checkpoint-4", "checkpoint-6"},
)
def test_dreambooth_lora_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):
with tempfile.TemporaryDirectory() as tmpdir:
test_args = f"""
examples/dreambooth/train_dreambooth_lora.py
--pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
--instance_data_dir=docs/source/en/imgs
--output_dir={tmpdir}
--instance_prompt=prompt
--resolution=64
--train_batch_size=1
--gradient_accumulation_steps=1
--max_train_steps=9
--checkpointing_steps=2
""".split()
run_command(self._launch_args + test_args)
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
{"checkpoint-2", "checkpoint-4", "checkpoint-6", "checkpoint-8"},
)
resume_run_args = f"""
examples/dreambooth/train_dreambooth_lora.py
--pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
--instance_data_dir=docs/source/en/imgs
--output_dir={tmpdir}
--instance_prompt=prompt
--resolution=64
--train_batch_size=1
--gradient_accumulation_steps=1
--max_train_steps=11
--checkpointing_steps=2
--resume_from_checkpoint=checkpoint-8
--checkpoints_total_limit=3
""".split()
run_command(self._launch_args + resume_run_args)
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
{"checkpoint-6", "checkpoint-8", "checkpoint-10"},
)
def test_controlnet_checkpointing_checkpoints_total_limit(self):
with tempfile.TemporaryDirectory() as tmpdir:
test_args = f"""
examples/controlnet/train_controlnet.py
--pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
--dataset_name=hf-internal-testing/fill10
--output_dir={tmpdir}
--resolution=64
--train_batch_size=1
--gradient_accumulation_steps=1
--max_train_steps=6
--checkpoints_total_limit=2
--checkpointing_steps=2
--controlnet_model_name_or_path=hf-internal-testing/tiny-controlnet
""".split()
run_command(self._launch_args + test_args)
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
{"checkpoint-4", "checkpoint-6"},
)
def test_controlnet_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):
with tempfile.TemporaryDirectory() as tmpdir:
test_args = f"""
examples/controlnet/train_controlnet.py
--pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
--dataset_name=hf-internal-testing/fill10
--output_dir={tmpdir}
--resolution=64
--train_batch_size=1
--gradient_accumulation_steps=1
--controlnet_model_name_or_path=hf-internal-testing/tiny-controlnet
--max_train_steps=9
--checkpointing_steps=2
""".split()
run_command(self._launch_args + test_args)
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
{"checkpoint-2", "checkpoint-4", "checkpoint-6", "checkpoint-8"},
)
resume_run_args = f"""
examples/controlnet/train_controlnet.py
--pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
--dataset_name=hf-internal-testing/fill10
--output_dir={tmpdir}
--resolution=64
--train_batch_size=1
--gradient_accumulation_steps=1
--controlnet_model_name_or_path=hf-internal-testing/tiny-controlnet
--max_train_steps=11
--checkpointing_steps=2
--resume_from_checkpoint=checkpoint-8
--checkpoints_total_limit=3
""".split()
run_command(self._launch_args + resume_run_args)
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
{"checkpoint-8", "checkpoint-10", "checkpoint-12"},
)
def test_custom_diffusion_checkpointing_checkpoints_total_limit(self):
with tempfile.TemporaryDirectory() as tmpdir:
test_args = f"""
examples/custom_diffusion/train_custom_diffusion.py
--pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
--instance_data_dir=docs/source/en/imgs
--output_dir={tmpdir}
--instance_prompt=<new1>
--resolution=64
--train_batch_size=1
--modifier_token=<new1>
--dataloader_num_workers=0
--max_train_steps=6
--checkpoints_total_limit=2
--checkpointing_steps=2
""".split()
run_command(self._launch_args + test_args)
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
{"checkpoint-4", "checkpoint-6"},
)
def test_custom_diffusion_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):
with tempfile.TemporaryDirectory() as tmpdir:
test_args = f"""
examples/custom_diffusion/train_custom_diffusion.py
--pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
--instance_data_dir=docs/source/en/imgs
--output_dir={tmpdir}
--instance_prompt=<new1>
--resolution=64
--train_batch_size=1
--modifier_token=<new1>
--dataloader_num_workers=0
--max_train_steps=9
--checkpointing_steps=2
""".split()
run_command(self._launch_args + test_args)
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
{"checkpoint-2", "checkpoint-4", "checkpoint-6", "checkpoint-8"},
)
resume_run_args = f"""
examples/custom_diffusion/train_custom_diffusion.py
--pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
--instance_data_dir=docs/source/en/imgs
--output_dir={tmpdir}
--instance_prompt=<new1>
--resolution=64
--train_batch_size=1
--modifier_token=<new1>
--dataloader_num_workers=0
--max_train_steps=11
--checkpointing_steps=2
--resume_from_checkpoint=checkpoint-8
--checkpoints_total_limit=3
""".split()
run_command(self._launch_args + resume_run_args)
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
{"checkpoint-6", "checkpoint-8", "checkpoint-10"},
)
examples/text_to_image/train_text_to_image.py
View file @ d49e2dd5
......@@ -18,6 +18,7 @@ import logging
import math
import os
import random
import shutil
from pathlib import Path
import accelerate
......@@ -362,11 +363,7 @@ def parse_args():
"--checkpoints_total_limit",
type=int,
default=None,
help=(
"Max number of checkpoints to store. Passed as `total_limit` to the `Accelerator` `ProjectConfiguration`."
" See Accelerator::save_state https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.save_state"
" for more docs"
),
help=("Max number of checkpoints to store."),
)
parser.add_argument(
"--resume_from_checkpoint",
......@@ -427,9 +424,7 @@ def main():
)
logging_dir = os.path.join(args.output_dir, args.logging_dir)
accelerator_project_config = ProjectConfiguration(
total_limit=args.checkpoints_total_limit, project_dir=args.output_dir, logging_dir=logging_dir
)
accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
......@@ -909,6 +904,26 @@ def main():
if global_step % args.checkpointing_steps == 0:
if accelerator.is_main_process:
# _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
if args.checkpoints_total_limit is not None:
checkpoints = os.listdir(args.output_dir)
checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))
# before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
if len(checkpoints) >= args.checkpoints_total_limit:
num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
removing_checkpoints = checkpoints[0:num_to_remove]
logger.info(
f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
)
logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")
for removing_checkpoint in removing_checkpoints:
removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)
shutil.rmtree(removing_checkpoint)
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
accelerator.save_state(save_path)
logger.info(f"Saved state to {save_path}")
......
examples/text_to_image/train_text_to_image_lora.py
View file @ d49e2dd5
......@@ -19,6 +19,7 @@ import logging
import math
import os
import random
import shutil
from pathlib import Path
import datasets
......@@ -327,11 +328,7 @@ def parse_args():
"--checkpoints_total_limit",
type=int,
default=None,
help=(
"Max number of checkpoints to store. Passed as `total_limit` to the `Accelerator` `ProjectConfiguration`."
" See Accelerator::save_state https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.save_state"
" for more docs"
),
help=("Max number of checkpoints to store."),
)
parser.add_argument(
"--resume_from_checkpoint",
......@@ -368,9 +365,7 @@ def main():
args = parse_args()
logging_dir = Path(args.output_dir, args.logging_dir)
accelerator_project_config = ProjectConfiguration(
total_limit=args.checkpoints_total_limit, project_dir=args.output_dir, logging_dir=logging_dir
)
accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
......@@ -809,6 +804,26 @@ def main():
if global_step % args.checkpointing_steps == 0:
if accelerator.is_main_process:
# _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
if args.checkpoints_total_limit is not None:
checkpoints = os.listdir(args.output_dir)
checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))
# before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
if len(checkpoints) >= args.checkpoints_total_limit:
num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
removing_checkpoints = checkpoints[0:num_to_remove]
logger.info(
f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
)
logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")
for removing_checkpoint in removing_checkpoints:
removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)
shutil.rmtree(removing_checkpoint)
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
accelerator.save_state(save_path)
logger.info(f"Saved state to {save_path}")
......@@ -903,6 +918,7 @@ def main():
if accelerator.is_main_process:
for tracker in accelerator.trackers:
if len(images) != 0:
if tracker.name == "tensorboard":
np_images = np.stack([np.asarray(img) for img in images])
tracker.writer.add_images("test", np_images, epoch, dataformats="NHWC")
......
examples/textual_inversion/textual_inversion.py
View file @ d49e2dd5
......@@ -18,6 +18,7 @@ import logging
import math
import os
import random
import shutil
import warnings
from pathlib import Path
......@@ -394,11 +395,7 @@ def parse_args():
"--checkpoints_total_limit",
type=int,
default=None,
help=(
"Max number of checkpoints to store. Passed as `total_limit` to the `Accelerator` `ProjectConfiguration`."
" See Accelerator::save_state https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.save_state"
" for more docs"
),
help=("Max number of checkpoints to store."),
)
parser.add_argument(
"--resume_from_checkpoint",
......@@ -566,9 +563,7 @@ class TextualInversionDataset(Dataset):
def main():
args = parse_args()
logging_dir = os.path.join(args.output_dir, args.logging_dir)
accelerator_project_config = ProjectConfiguration(
total_limit=args.checkpoints_total_limit, project_dir=args.output_dir, logging_dir=logging_dir
)
accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
mixed_precision=args.mixed_precision,
......@@ -887,6 +882,26 @@ def main():
if accelerator.is_main_process:
if global_step % args.checkpointing_steps == 0:
# _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
if args.checkpoints_total_limit is not None:
checkpoints = os.listdir(args.output_dir)
checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))
# before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
if len(checkpoints) >= args.checkpoints_total_limit:
num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
removing_checkpoints = checkpoints[0:num_to_remove]
logger.info(
f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
)
logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")
for removing_checkpoint in removing_checkpoints:
removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)
shutil.rmtree(removing_checkpoint)
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
accelerator.save_state(save_path)
logger.info(f"Saved state to {save_path}")
......
examples/unconditional_image_generation/train_unconditional.py
View file @ d49e2dd5
......@@ -3,6 +3,7 @@ import inspect
import logging
import math
import os
import shutil
from pathlib import Path
from typing import Optional
......@@ -245,11 +246,7 @@ def parse_args():
"--checkpoints_total_limit",
type=int,
default=None,
help=(
"Max number of checkpoints to store. Passed as `total_limit` to the `Accelerator` `ProjectConfiguration`."
" See Accelerator::save_state https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.save_state"
" for more docs"
),
help=("Max number of checkpoints to store."),
)
parser.add_argument(
"--resume_from_checkpoint",
......@@ -287,9 +284,7 @@ def get_full_repo_name(model_id: str, organization: Optional[str] = None, token:
def main(args):
logging_dir = os.path.join(args.output_dir, args.logging_dir)
accelerator_project_config = ProjectConfiguration(
total_limit=args.checkpoints_total_limit, project_dir=args.output_dir, logging_dir=logging_dir
)
accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
......@@ -607,6 +602,26 @@ def main(args):
global_step += 1
if global_step % args.checkpointing_steps == 0:
# _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
if args.checkpoints_total_limit is not None:
checkpoints = os.listdir(args.output_dir)
checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))
# before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
if len(checkpoints) >= args.checkpoints_total_limit:
num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
removing_checkpoints = checkpoints[0:num_to_remove]
logger.info(
f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
)
logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")
for removing_checkpoint in removing_checkpoints:
removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)
shutil.rmtree(removing_checkpoint)
if accelerator.is_main_process:
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
accelerator.save_state(save_path)
......
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