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Use explicit tiny-Qwen2_5_VL model_id parameter in CI tests #4325

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Oct 29, 2025
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Merged

Use explicit tiny-Qwen2_5_VL model_id parameter in CI tests #4325

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bfa80f6
Use explicit tiny-Qwen2_5_VL model_id param in CI tests
albertvillanova Oct 23, 2025
035b847
Merge branch 'main' into refactor-ci-explicit-model-id-tiny-qwen2.5-vl
qgallouedec Oct 28, 2025
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52 changes: 40 additions & 12 deletions tests/test_grpo_trainer.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1305,8 +1305,14 @@ def reward_func(completions, **kwargs):
new_param = trainer.model.get_parameter(n)
assert not torch.equal(param, new_param), f"Parameter {n} has not changed."

@pytest.mark.parametrize(
"model_id",
[
"trl-internal-testing/tiny-Qwen2_5_VLForConditionalGeneration",
],
)
@require_vision
def test_training_vlm_beta_non_zero(self):
def test_training_vlm_beta_non_zero(self, model_id):
dataset = load_dataset("trl-internal-testing/zen-image", "conversational_prompt_only", split="train")

def reward_func(completions, **kwargs):
Expand All @@ -1323,7 +1329,7 @@ def reward_func(completions, **kwargs):
report_to="none",
)
trainer = GRPOTrainer(
model="trl-internal-testing/tiny-Qwen2_5_VLForConditionalGeneration",
model=model_id,
reward_funcs=reward_func,
args=training_args,
train_dataset=dataset,
Expand All @@ -1345,12 +1351,16 @@ def reward_func(completions, **kwargs):
new_param = trainer.model.get_parameter(n)
assert not torch.equal(param, new_param), f"Parameter {n} has not changed."

@pytest.mark.parametrize(
"model_id",
[
"trl-internal-testing/tiny-Qwen2_5_VLForConditionalGeneration",
],
)
@require_vision
@require_peft
def test_training_vlm_peft(self):
model = AutoModelForImageTextToText.from_pretrained(
"trl-internal-testing/tiny-Qwen2_5_VLForConditionalGeneration"
)
def test_training_vlm_peft(self, model_id):
model = AutoModelForImageTextToText.from_pretrained(model_id)
base_param_names = [f"base_model.model.{n}" for n, _ in model.named_parameters()]
dataset = load_dataset("trl-internal-testing/zen-image", "conversational_prompt_only", split="train")

Expand Down Expand Up @@ -1388,8 +1398,14 @@ def reward_func(completions, **kwargs):
elif "base_layer" not in n: # We expect the peft params to be different (except for the base layer)
assert not torch.allclose(param, new_param), f"Parameter {n} has not changed."

@pytest.mark.parametrize(
"model_id",
[
"trl-internal-testing/tiny-Qwen2_5_VLForConditionalGeneration",
],
)
@require_vision
def test_training_vlm_and_importance_sampling(self):
def test_training_vlm_and_importance_sampling(self, model_id):
dataset = load_dataset("trl-internal-testing/zen-image", "conversational_prompt_only", split="train")

def reward_func(completions, **kwargs):
Expand All @@ -1406,7 +1422,7 @@ def reward_func(completions, **kwargs):
report_to="none",
)
trainer = GRPOTrainer(
model="trl-internal-testing/tiny-Qwen2_5_VLForConditionalGeneration",
model=model_id,
reward_funcs=reward_func,
args=training_args,
train_dataset=dataset,
Expand All @@ -1428,9 +1444,15 @@ def reward_func(completions, **kwargs):
new_param = trainer.model.get_parameter(n)
assert not torch.equal(param, new_param), f"Parameter {n} has not changed."

@pytest.mark.parametrize(
"model_id",
[
"trl-internal-testing/tiny-Qwen2_5_VLForConditionalGeneration",
],
)
@require_vision
@require_liger_kernel
def test_training_vlm_and_liger(self):
def test_training_vlm_and_liger(self, model_id):
dataset = load_dataset("trl-internal-testing/zen-image", "conversational_prompt_only", split="train")

def reward_func(completions, **kwargs):
Expand All @@ -1448,7 +1470,7 @@ def reward_func(completions, **kwargs):
report_to="none",
)
trainer = GRPOTrainer(
model="trl-internal-testing/tiny-Qwen2_5_VLForConditionalGeneration",
model=model_id,
reward_funcs=reward_func,
args=training_args,
train_dataset=dataset,
Expand Down Expand Up @@ -1515,8 +1537,14 @@ def reward_func(completions, **kwargs):
new_param = trainer.model.get_parameter(n)
assert not torch.equal(param, new_param), f"Parameter {n} has not changed."

@pytest.mark.parametrize(
"model_id",
[
"trl-internal-testing/tiny-Qwen2_5_VLForConditionalGeneration",
],
)
@require_vision
def test_training_vlm_multi_image(self):
def test_training_vlm_multi_image(self, model_id):
dataset = load_dataset("trl-internal-testing/zen-multi-image", "conversational_prompt_only", split="train")

def reward_func(completions, **kwargs):
Expand All @@ -1533,7 +1561,7 @@ def reward_func(completions, **kwargs):
report_to="none",
)
trainer = GRPOTrainer(
model="trl-internal-testing/tiny-Qwen2_5_VLForConditionalGeneration",
model=model_id,
reward_funcs=reward_func,
args=training_args,
train_dataset=dataset,
Expand Down
32 changes: 24 additions & 8 deletions tests/test_rloo_trainer.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1129,8 +1129,14 @@ def reward_func(completions, **kwargs):
new_param = trainer.model.get_parameter(n)
assert not torch.equal(param, new_param), f"Parameter {n} has not changed."

@pytest.mark.parametrize(
"model_id",
[
"trl-internal-testing/tiny-Qwen2_5_VLForConditionalGeneration",
],
)
@require_vision
def test_training_vlm_beta_non_zero(self):
def test_training_vlm_beta_non_zero(self, model_id):
dataset = load_dataset("trl-internal-testing/zen-image", "conversational_prompt_only", split="train")

def reward_func(completions, **kwargs):
Expand All @@ -1147,7 +1153,7 @@ def reward_func(completions, **kwargs):
report_to="none",
)
trainer = RLOOTrainer(
model="trl-internal-testing/tiny-Qwen2_5_VLForConditionalGeneration",
model=model_id,
reward_funcs=reward_func,
args=training_args,
train_dataset=dataset,
Expand All @@ -1169,12 +1175,16 @@ def reward_func(completions, **kwargs):
new_param = trainer.model.get_parameter(n)
assert not torch.equal(param, new_param), f"Parameter {n} has not changed."

@pytest.mark.parametrize(
"model_id",
[
"trl-internal-testing/tiny-Qwen2_5_VLForConditionalGeneration",
],
)
@require_vision
@require_peft
def test_training_vlm_peft(self):
model = AutoModelForImageTextToText.from_pretrained(
"trl-internal-testing/tiny-Qwen2_5_VLForConditionalGeneration"
)
def test_training_vlm_peft(self, model_id):
model = AutoModelForImageTextToText.from_pretrained(model_id)
base_param_names = [f"base_model.model.{n}" for n, _ in model.named_parameters()]
dataset = load_dataset("trl-internal-testing/zen-image", "conversational_prompt_only", split="train")

Expand Down Expand Up @@ -1257,8 +1267,14 @@ def reward_func(completions, **kwargs):
new_param = trainer.model.get_parameter(n)
assert not torch.equal(param, new_param), f"Parameter {n} has not changed."

@pytest.mark.parametrize(
"model_id",
[
"trl-internal-testing/tiny-Qwen2_5_VLForConditionalGeneration",
],
)
@require_vision
def test_training_vlm_multi_image(self):
def test_training_vlm_multi_image(self, model_id):
dataset = load_dataset("trl-internal-testing/zen-multi-image", "conversational_prompt_only", split="train")

def reward_func(completions, **kwargs):
Expand All @@ -1275,7 +1291,7 @@ def reward_func(completions, **kwargs):
report_to="none",
)
trainer = RLOOTrainer(
model="trl-internal-testing/tiny-Qwen2_5_VLForConditionalGeneration",
model=model_id,
reward_funcs=reward_func,
args=training_args,
train_dataset=dataset,
Expand Down
30 changes: 24 additions & 6 deletions tests/test_sft_trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1381,13 +1381,19 @@ def test_train_vlm(self, model_id):
continue
assert not torch.allclose(param, new_param, rtol=1e-12, atol=1e-12), f"Param {n} is not updated"

@pytest.mark.parametrize(
"model_id",
[
"trl-internal-testing/tiny-Qwen2_5_VLForConditionalGeneration",
],
)
@pytest.mark.xfail(
parse_version(transformers.__version__) < parse_version("4.57.0"),
reason="Mixing text-only and image+text examples is only supported in transformers >= 4.57.0",
strict=False,
)
@require_vision
def test_train_vlm_multi_image(self):
def test_train_vlm_multi_image(self, model_id):
# Get the dataset
dataset = load_dataset(
"trl-internal-testing/zen-multi-image", "conversational_prompt_completion", split="train"
Expand All @@ -1400,7 +1406,7 @@ def test_train_vlm_multi_image(self):
report_to="none",
)
trainer = SFTTrainer(
model="trl-internal-testing/tiny-Qwen2_5_VLForConditionalGeneration",
model=model_id,
args=training_args,
train_dataset=dataset,
)
Expand All @@ -1419,8 +1425,14 @@ def test_train_vlm_multi_image(self):
new_param = trainer.model.get_parameter(n)
assert not torch.allclose(param, new_param, rtol=1e-12, atol=1e-12), f"Param {n} is not updated"

@pytest.mark.parametrize(
"model_id",
[
"trl-internal-testing/tiny-Qwen2_5_VLForConditionalGeneration",
],
)
@require_vision
def test_train_vlm_prompt_completion(self):
def test_train_vlm_prompt_completion(self, model_id):
# Get the dataset
dataset = load_dataset("trl-internal-testing/zen-image", "conversational_prompt_completion", split="train")

Expand All @@ -1431,7 +1443,7 @@ def test_train_vlm_prompt_completion(self):
report_to="none",
)
trainer = SFTTrainer(
model="trl-internal-testing/tiny-Qwen2_5_VLForConditionalGeneration",
model=model_id,
args=training_args,
train_dataset=dataset,
)
Expand Down Expand Up @@ -1519,15 +1531,21 @@ def test_train_vlm_gemma_3n(self):
continue
assert not torch.allclose(param, new_param, rtol=1e-12, atol=1e-12), f"Param {n} is not updated"

@pytest.mark.parametrize(
"model_id",
[
"trl-internal-testing/tiny-Qwen2_5_VLForConditionalGeneration",
],
)
@require_vision
def test_train_vlm_text_only_data(self):
def test_train_vlm_text_only_data(self, model_id):
# Get the dataset
dataset = load_dataset("trl-internal-testing/zen", "conversational_language_modeling", split="train")

# Initialize the trainer
training_args = SFTConfig(output_dir=self.tmp_dir, report_to="none")
trainer = SFTTrainer(
model="trl-internal-testing/tiny-Qwen2_5_VLForConditionalGeneration",
model=model_id,
args=training_args,
train_dataset=dataset,
)
Expand Down
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