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let hf trainer handle torch compile #516

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merged 5 commits into from
Sep 13, 2023
Merged

let hf trainer handle torch compile #516

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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
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4 changes: 4 additions & 0 deletions README.md
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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]
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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.

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@tmm1 tmm1 Sep 1, 2023 •
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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
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4 changes: 0 additions & 4 deletions src/axolotl/train.py
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Expand Up @@ -80,10 +80,6 @@ def train(

model.config.use_cache = False

if torch.__version__ >= "2" and sys.platform != "win32":
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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}")
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16 changes: 16 additions & 0 deletions src/axolotl/utils/trainer.py
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Expand Up @@ -11,6 +11,7 @@
from typing import Optional, Union

import numpy as np
import torch
import torch.cuda
import transformers
from datasets import Dataset, set_caching_enabled
Expand Down Expand Up @@ -579,6 +580,21 @@ 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:
if torch.__version__ < "2.1.0": # pylint: disable=protected-access
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@tmm1 lmk if you think this is good enough for now

LOG.warning("torch>=2.1.0 required for torch_compile to work properly")
else:
import torch._dynamo # pylint: disable=redefined-outer-name

torch._dynamo.config.suppress_errors = ( # pylint: disable=protected-access
True
)
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
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