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switch to device agnostic device calling for test cases (#38247)

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* use device agnostic APIs in test cases

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

* fix style

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

* add one more

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

* xpu now supports integer device id, aligning to CUDA behaviors

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

* update to use device_properties

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

* fix style

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

* update comment

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

* fix comments

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

* fix style

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

---------

Signed-off-by: Matrix Yao <matrix.yao@intel.com>
Signed-off-by: YAO Matrix <matrix.yao@intel.com>
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Yao Matrix
2025-05-26 16:18:53 +08:00
committed by GitHub
parent cba279f46c
commit a5a0c7b888
39 changed files with 259 additions and 389 deletions
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18
tests/quantization/bnb/test_mixed_int8.py
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@@ -31,6 +31,8 @@ from transformers import (
from transformers.models.opt.modeling_opt import OPTAttention
from transformers.testing_utils import (
apply_skip_if_not_implemented,
backend_empty_cache,
backend_torch_accelerator_module,
is_accelerate_available,
is_bitsandbytes_available,
is_torch_available,
@@ -137,7 +139,7 @@ class MixedInt8Test(BaseMixedInt8Test):
del self.model_8bit
gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)
def test_get_keys_to_not_convert(self):
r"""
@@ -484,7 +486,7 @@ class MixedInt8T5Test(unittest.TestCase):
avoid unexpected behaviors. Please see: https://discuss.pytorch.org/t/how-can-we-release-gpu-memory-cache/14530/27
"""
gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)
def test_inference_without_keep_in_fp32(self):
r"""
@@ -599,7 +601,7 @@ class MixedInt8ModelClassesTest(BaseMixedInt8Test):
del self.seq_to_seq_model
gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)
def test_correct_head_class(self):
r"""
@@ -631,7 +633,7 @@ class MixedInt8TestPipeline(BaseMixedInt8Test):
del self.pipe
gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)
def test_pipeline(self):
r"""
@@ -872,10 +874,10 @@ class MixedInt8TestTraining(BaseMixedInt8Test):
model = AutoModelForCausalLM.from_pretrained(self.model_name, load_in_8bit=True)
model.train()
if torch.cuda.is_available():
self.assertEqual(set(model.hf_device_map.values()), {torch.cuda.current_device()})
elif torch.xpu.is_available():
self.assertEqual(set(model.hf_device_map.values()), {f"xpu:{torch.xpu.current_device()}"})
if torch_device in ["cuda", "xpu"]:
self.assertEqual(
set(model.hf_device_map.values()), {backend_torch_accelerator_module(torch_device).current_device()}
)
else:
self.assertTrue(all(param.device.type == "cpu" for param in model.parameters()))