Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

let hf trainer handle torch compile #516

Merged
merged 5 commits into from
Sep 13, 2023
Merged

let hf trainer handle torch compile #516

Show file tree
Hide file tree
Changes from 3 commits
Commits
Show all changes
5 commits
Select commit Hold shift + click to select a range
1eafdea
let hf trainer handle torch compile
winglian Aug 31, 2023
72e340a
remove torch compile checks, include option for backend
winglian Aug 31, 2023
f899427
Merge branch 'main' into torch-compile
winglian Aug 31, 2023
a0081c5
suppress torch errors to get further
tmm1 Sep 3, 2023
2362a54
require min torch version of 2.1.0 for torch compile to work
winglian Sep 7, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
4 changes: 4 additions & 0 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -526,6 +526,10 @@ wandb_log_model: # "checkpoint" to log model to wandb Artifacts every `save_step
# where to save the finished model to
output_dir: ./completed-model

# whether to use torch.compile and which backend to use
torch_compile: # bool
torch_compile_backend: # Optional[str]
Copy link
Collaborator

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Is it necessary to open this "backend" config?

Refer to the PyTorch doc for possible values and note that they may change across PyTorch versions.

and reading the source of the torch compile, it seems to only have a condition for inductor.

Copy link
Collaborator

@tmm1 tmm1 Sep 1, 2023 •
edited
Loading

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Not sure; there are other backends but maybe they're not useful?

torch._dynamo.list_backends()

['aot_ts_nvfuser', 'cudagraphs', 'inductor', 'ipex', 'nvprims_nvfuser', 'onnxrt', 'tvm']


# training hyperparameters
gradient_accumulation_steps: 1
micro_batch_size: 2
Expand Down
4 changes: 0 additions & 4 deletions src/axolotl/train.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -80,10 +80,6 @@ def train(

model.config.use_cache = False

if torch.__version__ >= "2" and sys.platform != "win32":
tmm1 marked this conversation as resolved.
Show resolved Hide resolved
LOG.info("Compiling torch model")
model = torch.compile(model)

# go ahead and presave, so we have the adapter config available to inspect
if peft_config:
LOG.info(f"Pre-saving adapter config to {cfg.output_dir}")
Expand Down
7 changes: 7 additions & 0 deletions src/axolotl/utils/trainer.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -579,6 +579,13 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_
if cfg.bench_dataset:
training_arguments_kwargs["bench_dataset"] = cfg.bench_dataset

if cfg.torch_compile:
training_arguments_kwargs["torch_compile"] = cfg.torch_compile
if cfg.torch_compile_backend:
training_arguments_kwargs[
"torch_compile_backend"
] = cfg.torch_compile_backend

# DDP Config
if cfg.ddp_timeout:
training_arguments_kwargs["ddp_timeout"] = cfg.ddp_timeout
Expand Down