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[tests] properly skip tests instead of return (#11771)

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model test updates
This commit is contained in:
Sayak Paul
2025-06-23 11:59:59 +05:30
committed by GitHub
parent f20b83a04f
commit fbddf02807
1 changed files with 31 additions and 26 deletions
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57
tests/models/test_modeling_common.py
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@@ -30,6 +30,7 @@ from collections import defaultdict
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
import pytest
import requests_mock
import safetensors.torch
import torch
@@ -938,8 +939,9 @@ class ModelTesterMixin:
@require_torch_accelerator_with_training
def test_enable_disable_gradient_checkpointing(self):
# Skip test if model does not support gradient checkpointing
if not self.model_class._supports_gradient_checkpointing:
return # Skip test if model does not support gradient checkpointing
pytest.skip("Gradient checkpointing is not supported.")
init_dict, _ = self.prepare_init_args_and_inputs_for_common()
@@ -957,8 +959,9 @@ class ModelTesterMixin:
@require_torch_accelerator_with_training
def test_effective_gradient_checkpointing(self, loss_tolerance=1e-5, param_grad_tol=5e-5, skip: set[str] = {}):
# Skip test if model does not support gradient checkpointing
if not self.model_class._supports_gradient_checkpointing:
return # Skip test if model does not support gradient checkpointing
pytest.skip("Gradient checkpointing is not supported.")
# enable deterministic behavior for gradient checkpointing
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
@@ -1015,8 +1018,9 @@ class ModelTesterMixin:
def test_gradient_checkpointing_is_applied(
self, expected_set=None, attention_head_dim=None, num_attention_heads=None, block_out_channels=None
):
# Skip test if model does not support gradient checkpointing
if not self.model_class._supports_gradient_checkpointing:
return # Skip test if model does not support gradient checkpointing
pytest.skip("Gradient checkpointing is not supported.")
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
@@ -1073,7 +1077,7 @@ class ModelTesterMixin:
model = self.model_class(**init_dict).to(torch_device)
if not issubclass(model.__class__, PeftAdapterMixin):
return
pytest.skip(f"PEFT is not supported for this model ({model.__class__.__name__}).")
torch.manual_seed(0)
output_no_lora = model(**inputs_dict, return_dict=False)[0]
@@ -1128,7 +1132,7 @@ class ModelTesterMixin:
model = self.model_class(**init_dict).to(torch_device)
if not issubclass(model.__class__, PeftAdapterMixin):
return
pytest.skip(f"PEFT is not supported for this model ({model.__class__.__name__}).")
denoiser_lora_config = LoraConfig(
r=4,
@@ -1159,7 +1163,7 @@ class ModelTesterMixin:
model = self.model_class(**init_dict).to(torch_device)
if not issubclass(model.__class__, PeftAdapterMixin):
return
pytest.skip(f"PEFT is not supported for this model ({model.__class__.__name__}).")
denoiser_lora_config = LoraConfig(
r=rank,
@@ -1196,7 +1200,7 @@ class ModelTesterMixin:
model = self.model_class(**init_dict).to(torch_device)
if not issubclass(model.__class__, PeftAdapterMixin):
return
pytest.skip(f"PEFT is not supported for this model ({model.__class__.__name__}).")
denoiser_lora_config = LoraConfig(
r=4,
@@ -1233,10 +1237,10 @@ class ModelTesterMixin:
@require_torch_accelerator
def test_cpu_offload(self):
if self.model_class._no_split_modules is None:
pytest.skip("Test not supported for this model as `_no_split_modules` is not set.")
config, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**config).eval()
if model._no_split_modules is None:
return
model = model.to(torch_device)
@@ -1263,10 +1267,10 @@ class ModelTesterMixin:
@require_torch_accelerator
def test_disk_offload_without_safetensors(self):
if self.model_class._no_split_modules is None:
pytest.skip("Test not supported for this model as `_no_split_modules` is not set.")
config, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**config).eval()
if model._no_split_modules is None:
return
model = model.to(torch_device)
@@ -1296,10 +1300,10 @@ class ModelTesterMixin:
@require_torch_accelerator
def test_disk_offload_with_safetensors(self):
if self.model_class._no_split_modules is None:
pytest.skip("Test not supported for this model as `_no_split_modules` is not set.")
config, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**config).eval()
if model._no_split_modules is None:
return
model = model.to(torch_device)
@@ -1324,10 +1328,10 @@ class ModelTesterMixin:
@require_torch_multi_accelerator
def test_model_parallelism(self):
if self.model_class._no_split_modules is None:
pytest.skip("Test not supported for this model as `_no_split_modules` is not set.")
config, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**config).eval()
if model._no_split_modules is None:
return
model = model.to(torch_device)
@@ -1426,10 +1430,10 @@ class ModelTesterMixin:
@require_torch_accelerator
def test_sharded_checkpoints_device_map(self):
if self.model_class._no_split_modules is None:
pytest.skip("Test not supported for this model as `_no_split_modules` is not set.")
config, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**config).eval()
if model._no_split_modules is None:
return
model = model.to(torch_device)
torch.manual_seed(0)
@@ -1497,7 +1501,7 @@ class ModelTesterMixin:
def test_layerwise_casting_training(self):
def test_fn(storage_dtype, compute_dtype):
if torch.device(torch_device).type == "cpu" and compute_dtype == torch.bfloat16:
return
pytest.skip("Skipping test because CPU doesn't go well with bfloat16.")
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
@@ -1617,6 +1621,9 @@ class ModelTesterMixin:
@parameterized.expand([False, True])
@require_torch_accelerator
def test_group_offloading(self, record_stream):
if not self.model_class._supports_group_offloading:
pytest.skip("Model does not support group offloading.")
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
torch.manual_seed(0)
@@ -1633,8 +1640,6 @@ class ModelTesterMixin:
return model(**inputs_dict)[0]
model = self.model_class(**init_dict)
if not getattr(model, "_supports_group_offloading", True):
return
model.to(torch_device)
output_without_group_offloading = run_forward(model)
@@ -1670,13 +1675,13 @@ class ModelTesterMixin:
@require_torch_accelerator
@torch.no_grad()
def test_group_offloading_with_layerwise_casting(self, record_stream, offload_type):
if not self.model_class._supports_group_offloading:
pytest.skip("Model does not support group offloading.")
torch.manual_seed(0)
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
if not getattr(model, "_supports_group_offloading", True):
return
model.to(torch_device)
model.eval()
_ = model(**inputs_dict)[0]
@@ -1698,13 +1703,13 @@ class ModelTesterMixin:
@require_torch_accelerator
@torch.no_grad()
def test_group_offloading_with_disk(self, record_stream, offload_type):
if not self.model_class._supports_group_offloading:
pytest.skip("Model does not support group offloading.")
torch.manual_seed(0)
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
if not getattr(model, "_supports_group_offloading", True):
return
torch.manual_seed(0)
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)