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enable group_offload cases and quanto cases on XPU (#11405)

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* enable group_offload cases and quanto cases on XPU

Signed-off-by: YAO Matrix <matrix.yao@intel.com>

* use backend APIs

Signed-off-by: Yao Matrix <matrix.yao@intel.com>

* fix style

Signed-off-by: Yao Matrix <matrix.yao@intel.com>

---------

Signed-off-by: YAO Matrix <matrix.yao@intel.com>
Signed-off-by: Yao Matrix <matrix.yao@intel.com>
This commit is contained in:
Yao Matrix
2025-04-28 09:07:16 +08:00
committed by GitHub
parent aa5f5d41d6
commit 9ce89e2efa
2 changed files with 18 additions and 12 deletions
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4
tests/pipelines/test_pipelines_common.py
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@@ -53,7 +53,7 @@ from diffusers.utils.testing_utils import (
require_accelerator,
require_hf_hub_version_greater,
require_torch,
require_torch_gpu,
require_torch_accelerator,
require_transformers_version_greater,
skip_mps,
torch_device,
@@ -2212,7 +2212,7 @@ class PipelineTesterMixin:
inputs = self.get_dummy_inputs(torch_device)
_ = pipe(**inputs)[0]
@require_torch_gpu
@require_torch_accelerator
def test_group_offloading_inference(self):
if not self.test_group_offloading:
return

26
tests/quantization/quanto/test_quanto.py
View File

@@ -6,10 +6,13 @@ from diffusers import FluxPipeline, FluxTransformer2DModel, QuantoConfig
from diffusers.models.attention_processor import Attention
from diffusers.utils import is_optimum_quanto_available, is_torch_available
from diffusers.utils.testing_utils import (
backend_empty_cache,
backend_reset_peak_memory_stats,
enable_full_determinism,
nightly,
numpy_cosine_similarity_distance,
require_accelerate,
require_big_gpu_with_torch_cuda,
require_big_accelerator,
require_torch_cuda_compatibility,
torch_device,
)
@@ -23,9 +26,11 @@ if is_torch_available():
from ..utils import LoRALayer, get_memory_consumption_stat
enable_full_determinism()
@nightly
@require_big_gpu_with_torch_cuda
@require_big_accelerator
@require_accelerate
class QuantoBaseTesterMixin:
model_id = None
@@ -39,13 +44,13 @@ class QuantoBaseTesterMixin:
_test_torch_compile = False
def setUp(self):
torch.cuda.reset_peak_memory_stats()
torch.cuda.empty_cache()
backend_reset_peak_memory_stats(torch_device)
backend_empty_cache(torch_device)
gc.collect()
def tearDown(self):
torch.cuda.reset_peak_memory_stats()
torch.cuda.empty_cache()
backend_reset_peak_memory_stats(torch_device)
backend_empty_cache(torch_device)
gc.collect()
def get_dummy_init_kwargs(self):
@@ -89,7 +94,7 @@ class QuantoBaseTesterMixin:
self.model_cls._keep_in_fp32_modules = self.keep_in_fp32_module
model = self.model_cls.from_pretrained(**self.get_dummy_model_init_kwargs())
model.to("cuda")
model.to(torch_device)
for name, module in model.named_modules():
if isinstance(module, torch.nn.Linear):
@@ -107,7 +112,7 @@ class QuantoBaseTesterMixin:
init_kwargs.update({"quantization_config": quantization_config})
model = self.model_cls.from_pretrained(**init_kwargs)
model.to("cuda")
model.to(torch_device)
for name, module in model.named_modules():
if name in self.modules_to_not_convert:
@@ -122,7 +127,8 @@ class QuantoBaseTesterMixin:
with self.assertRaises(ValueError):
# Tries with a `device` and `dtype`
model.to(device="cuda:0", dtype=torch.float16)
device_0 = f"{torch_device}:0"
model.to(device=device_0, dtype=torch.float16)
with self.assertRaises(ValueError):
# Tries with a cast
@@ -133,7 +139,7 @@ class QuantoBaseTesterMixin:
model.half()
# This should work
model.to("cuda")
model.to(torch_device)
def test_serialization(self):
model = self.model_cls.from_pretrained(**self.get_dummy_model_init_kwargs())