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Revert xfail for MoE LoRA tests now that peft#3602 fixed the autocast dtype bug #6919

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Merged
albertvillanova merged 1 commit into from
Aug 25, 2026
Merged

Revert xfail for MoE LoRA tests now that peft#3602 fixed the autocast dtype bug #6919

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16 changes: 0 additions & 16 deletions tests/test_dpo_trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -733,14 +733,6 @@ def test_train_dense_with_peft_config_lora(self):
assert not torch.equal(param, new_param), f"Parameter {n} has not changed."

@require_peft
@pytest.mark.xfail(
is_peft_available() and Version(peft.__version__).is_devrelease,
reason=(
"peft's LoRA parametrization for MoE expert parameters returns bf16 under autocast, which "
"`register_parametrization` rejects (see #6914)."
),
strict=True,
)
def test_train_moe_with_peft_config(self):
model_id = "trl-internal-testing/tiny-GptOssForCausalLM"
model = AutoModelForCausalLM.from_pretrained(model_id, dtype="float32")
Expand Down Expand Up @@ -809,14 +801,6 @@ def test_train_peft_model(self):
assert not torch.equal(param, new_param), f"Parameter {n} has not changed."

@require_peft
@pytest.mark.xfail(
is_peft_available() and Version(peft.__version__).is_devrelease,
reason=(
"peft's LoRA parametrization for MoE expert parameters returns bf16 under autocast, which "
"`register_parametrization` rejects (see #6914)."
),
strict=True,
)
def test_train_moe_peft_model(self):
# Regression test for https://github.com/huggingface/trl/issues/5222. Before PEFT 0.20.0, only one adapter per
# model was supported when the LoRA config uses `target_parameters` (see peft#3340, fixed in peft#3350), so no
Expand Down
8 changes: 0 additions & 8 deletions tests/test_grpo_trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -930,14 +930,6 @@ def test_train_peft_model(self):
assert not torch.equal(param, new_param), f"Parameter {n} has not changed."

@require_peft
@pytest.mark.xfail(
is_peft_available() and Version(peft.__version__).is_devrelease,
reason=(
"peft's LoRA parametrization for MoE expert parameters returns bf16 under autocast, which "
"`register_parametrization` rejects (see #6914)."
),
strict=True,
)
def test_train_moe_peft_model(self):
# Regression test for https://github.com/huggingface/trl/issues/5222. Before PEFT 0.20.0, only one adapter per
# model was supported when the LoRA config uses `target_parameters` (see peft#3340, fixed in peft#3350), so no
Expand Down
8 changes: 0 additions & 8 deletions tests/test_kto_trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -813,14 +813,6 @@ def test_train_peft_model(self):
assert not torch.equal(param, new_param), f"Parameter {n} has not changed."

@require_peft
@pytest.mark.xfail(
is_peft_available() and Version(peft.__version__).is_devrelease,
reason=(
"peft's LoRA parametrization for MoE expert parameters returns bf16 under autocast, which "
"`register_parametrization` rejects (see #6914)."
),
strict=True,
)
def test_train_moe_peft_model(self):
# Regression test for https://github.com/huggingface/trl/issues/5222. Before PEFT 0.20.0, only one adapter per
# model was supported when the LoRA config uses `target_parameters` (see peft#3340, fixed in peft#3350), so no
Expand Down
10 changes: 0 additions & 10 deletions tests/test_reward_trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,7 +18,6 @@
import pytest
import torch
from datasets import DatasetDict, IterableDatasetDict, load_dataset
from packaging.version import Version
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from transformers.utils import is_peft_available

Expand All @@ -29,7 +28,6 @@


if is_peft_available():
import peft
from peft import LoraConfig, get_peft_model


Expand Down Expand Up @@ -344,14 +342,6 @@ def test_train_dense_with_peft_config(self):
assert not torch.equal(param, new_param), f"Parameter {n} has not changed."

@require_peft
@pytest.mark.xfail(
is_peft_available() and Version(peft.__version__).is_devrelease,
reason=(
"peft's LoRA parametrization for MoE expert parameters returns bf16 under autocast, which "
"`register_parametrization` rejects (see #6914)."
),
strict=True,
)
def test_train_moe_with_peft_config(self):
model_id = "trl-internal-testing/tiny-Qwen3MoeForCausalLM"
model = AutoModelForSequenceClassification.from_pretrained(model_id, dtype="float32")
Expand Down
8 changes: 0 additions & 8 deletions tests/test_rloo_trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -559,14 +559,6 @@ def test_train_peft_model(self):
assert not torch.equal(param, new_param), f"Parameter {n} has not changed."

@require_peft
@pytest.mark.xfail(
is_peft_available() and Version(peft.__version__).is_devrelease,
reason=(
"peft's LoRA parametrization for MoE expert parameters returns bf16 under autocast, which "
"`register_parametrization` rejects (see #6914)."
),
strict=True,
)
def test_train_moe_peft_model(self):
# Regression test for https://github.com/huggingface/trl/issues/5222. Before PEFT 0.20.0, only one adapter per
# model was supported when the LoRA config uses `target_parameters` (see peft#3340, fixed in peft#3350), so no
Expand Down
8 changes: 0 additions & 8 deletions tests/test_sft_trainer.py
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Expand Up @@ -753,14 +753,6 @@ def test_train_with_peft_config_prompt_tuning(self, peft_type):
assert not torch.equal(param, new_param), f"Parameter {n} has not changed."

@require_peft
@pytest.mark.xfail(
is_peft_available() and Version(peft.__version__).is_devrelease,
reason=(
"peft's LoRA parametrization for MoE expert parameters returns bf16 under autocast, which "
"`register_parametrization` rejects (see #6914)."
),
strict=True,
)
def test_train_moe_with_peft_config(self):
model_id = "trl-internal-testing/tiny-GptOssForCausalLM"
model = AutoModelForCausalLM.from_pretrained(model_id, dtype="float32")
Expand Down
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