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Fix torch_dtype handling in {DPO,SFT}Trainer when provided via CLI #1807

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Merged
kashif merged 5 commits into from
Jul 5, 2024
Merged

Fix torch_dtype handling in {DPO,SFT}Trainer when provided via CLI #1807

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9cf00a0
Fix `torch_dtype` handling through CLI
alvarobartt Jul 5, 2024
513036f
Add `torch_dtype` tests in `test_{dpo,sft}_trainer.py`
alvarobartt Jul 5, 2024
25a1bf7
Forward contribution credits
yash-srivastava19 Jul 5, 2024
6622c06
Merge branch 'main' into torch-dtype-cli-fix
alvarobartt Jul 5, 2024
28424ae
Run `make precommit`
alvarobartt Jul 5, 2024
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64 changes: 64 additions & 0 deletions tests/test_dpo_trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,9 +11,11 @@
# 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 tempfile
import unittest

import pytest
import torch
from datasets import Dataset, features
from parameterized import parameterized
Expand Down Expand Up @@ -840,6 +842,68 @@ def test_dpo_lora_force_use_ref(self):
# train the model
trainer.train()

def test_dpo_trainer_torch_dtype(self):
# See https://github.com/huggingface/trl/issues/1751
dummy_dataset = self._init_dummy_dataset()
with tempfile.TemporaryDirectory() as tmp_dir:
dpo_config = DPOConfig(
output_dir=tmp_dir,
per_device_train_batch_size=2,
max_steps=1,
model_init_kwargs={"torch_dtype": "float16"},
ref_model_init_kwargs={"torch_dtype": "float16"},
)

trainer = DPOTrainer(
model=self.model_id,
ref_model=self.model_id,
tokenizer=self.tokenizer,
args=dpo_config,
train_dataset=dummy_dataset,
)
assert trainer.model.config.torch_dtype == torch.float16
assert trainer.ref_model.config.torch_dtype == torch.float16

# Now test when `torch_dtype` is provided but is wrong to either the model or the ref_model
with tempfile.TemporaryDirectory() as tmp_dir:
dpo_config = DPOConfig(
output_dir=tmp_dir,
per_device_train_batch_size=2,
max_steps=1,
model_init_kwargs={"torch_dtype": -1},
)

with pytest.raises(
ValueError,
match="Invalid `torch_dtype` passed to the DPOConfig. Expected a string with either `torch.dtype` or 'auto', but got -1.",
):
_ = DPOTrainer(
model=self.model_id,
tokenizer=self.tokenizer,
args=dpo_config,
train_dataset=dummy_dataset,
)

with tempfile.TemporaryDirectory() as tmp_dir:
dpo_config = DPOConfig(
output_dir=tmp_dir,
per_device_train_batch_size=2,
max_steps=1,
ref_model_init_kwargs={"torch_dtype": -1},
)

with pytest.raises(
ValueError,
match="Invalid `torch_dtype` passed to the DPOConfig. Expected a string with either `torch.dtype` or 'auto', but got -1.",
):
_ = DPOTrainer(
model=self.model_id,
ref_model=self.model_id,
tokenizer=self.tokenizer,
args=dpo_config,
train_dataset=dummy_dataset,
)

def test_dpo_loss_alpha_div_f(self):
model_id = "trl-internal-testing/tiny-random-LlamaForCausalLM"
tokenizer = AutoTokenizer.from_pretrained(model_id)
Expand Down
42 changes: 42 additions & 0 deletions tests/test_sft_trainer.py
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Expand Up @@ -1273,3 +1273,45 @@ def __call__(self, examples):
assert trainer.state.log_history[0]["eval_loss"] is not None

assert "model.safetensors" in os.listdir(tmp_dir + "/checkpoint-2")

def test_sft_trainer_torch_dtype(self):
# See https://github.com/huggingface/trl/issues/1751
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = SFTConfig(
output_dir=tmp_dir,
eval_strategy="steps",
max_steps=4,
eval_steps=2,
save_steps=2,
per_device_train_batch_size=2,
model_init_kwargs={"torch_dtype": torch.float16},
)
trainer = SFTTrainer(
model=self.model_id,
args=training_args,
train_dataset=self.train_dataset,
eval_dataset=self.eval_dataset,
)
assert trainer.model.config.torch_dtype == torch.float16

# Now test when `torch_dtype` is provided but is wrong
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = SFTConfig(
output_dir=tmp_dir,
eval_strategy="steps",
max_steps=4,
eval_steps=2,
save_steps=2,
per_device_train_batch_size=2,
model_init_kwargs={"torch_dtype": -1},
)
with pytest.raises(
ValueError,
match="Invalid `torch_dtype` passed to the SFTConfig. Expected a string with either `torch.dtype` or 'auto', but got -1.",
):
_ = SFTTrainer(
model=self.model_id,
args=training_args,
train_dataset=self.train_dataset,
eval_dataset=self.eval_dataset,
)
35 changes: 25 additions & 10 deletions trl/trainer/dpo_trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -181,11 +181,19 @@ def __init__(
)
else:
model_init_kwargs = args.model_init_kwargs
model_init_kwargs["torch_dtype"] = (
model_init_kwargs["torch_dtype"]
if model_init_kwargs["torch_dtype"] in ["auto", None]
else getattr(torch, model_init_kwargs["torch_dtype"])
)

torch_dtype = model_init_kwargs["torch_dtype"]
if torch_dtype is not None:
# Convert to `torch.dtype` if an str is passed
if isinstance(torch_dtype, str) and torch_dtype != "auto":
torch_dtype = getattr(torch, torch_dtype)

if torch_dtype != "auto" and not isinstance(torch_dtype, torch.dtype):
raise ValueError(
f"Invalid `torch_dtype` passed to the DPOConfig. Expected a string with either `torch.dtype` or 'auto', but got {torch_dtype}."
)

model_init_kwargs["torch_dtype"] = torch_dtype

if ref_model_init_kwargs is not None:
warnings.warn(
Expand All @@ -201,11 +209,18 @@ def __init__(
)
else:
ref_model_init_kwargs = args.ref_model_init_kwargs
ref_model_init_kwargs["torch_dtype"] = (
ref_model_init_kwargs["torch_dtype"]
if ref_model_init_kwargs["torch_dtype"] in ["auto", None]
else getattr(torch, ref_model_init_kwargs["torch_dtype"])
)
torch_dtype = ref_model_init_kwargs["torch_dtype"]
if torch_dtype is not None:
# Convert to `torch.dtype` if an str is passed
if isinstance(torch_dtype, str) and torch_dtype != "auto":
torch_dtype = getattr(torch, torch_dtype)

if torch_dtype != "auto" and not isinstance(torch_dtype, torch.dtype):
raise ValueError(
f"Invalid `torch_dtype` passed to the DPOConfig. Expected a string with either `torch.dtype` or 'auto', but got {torch_dtype}."
)

ref_model_init_kwargs["torch_dtype"] = torch_dtype

if isinstance(model, str):
warnings.warn(
Expand Down
18 changes: 13 additions & 5 deletions trl/trainer/sft_trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -162,11 +162,19 @@ def __init__(
raise ValueError("You passed model_init_kwargs to the SFTConfig, but your model is already instantiated.")
else:
model_init_kwargs = args.model_init_kwargs
model_init_kwargs["torch_dtype"] = (
model_init_kwargs["torch_dtype"]
if model_init_kwargs["torch_dtype"] in ["auto", None]
else getattr(torch, model_init_kwargs["torch_dtype"])
)

torch_dtype = model_init_kwargs["torch_dtype"]
if torch_dtype is not None:
# Convert to `torch.dtype` if an str is passed
if isinstance(torch_dtype, str) and torch_dtype != "auto":
torch_dtype = getattr(torch, torch_dtype)

if torch_dtype != "auto" and not isinstance(torch_dtype, torch.dtype):
raise ValueError(
f"Invalid `torch_dtype` passed to the SFTConfig. Expected a string with either `torch.dtype` or 'auto', but got {torch_dtype}."
)

model_init_kwargs["torch_dtype"] = torch_dtype

if infinite is not None:
warnings.warn(
Expand Down