SUMIN/HuggingFace_transformer
1
0
Fork 0
Code Issues Pull Requests Actions 7 Packages Projects Releases Wiki Activity

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

Browse Source 
* 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
Download Patch File Download Diff File Expand all files Collapse all files

23
tests/quantization/torchao_integration/test_torchao.py
View File

@@ -21,10 +21,13 @@ from packaging import version
from transformers import AutoModelForCausalLM, AutoTokenizer, TorchAoConfig
from transformers.testing_utils import (
backend_empty_cache,
get_device_properties,
require_torch_gpu,
require_torch_multi_gpu,
require_torchao,
require_torchao_version_greater_or_equal,
torch_device,
)
from transformers.utils import is_torch_available, is_torchao_available
@@ -131,7 +134,7 @@ class TorchAoTest(unittest.TestCase):
def tearDown(self):
gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)
gc.collect()
def test_int4wo_quant(self):
@@ -260,7 +263,7 @@ class TorchAoTest(unittest.TestCase):
@require_torch_gpu
class TorchAoGPUTest(TorchAoTest):
device = "cuda"
device = torch_device
quant_scheme_kwargs = {"group_size": 32}
def test_int4wo_offload(self):
@@ -397,7 +400,7 @@ class TorchAoSerializationTest(unittest.TestCase):
def tearDown(self):
gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)
gc.collect()
def test_original_model_expected_output(self):
@@ -452,33 +455,33 @@ class TorchAoSerializationW8CPUTest(TorchAoSerializationTest):
@require_torch_gpu
class TorchAoSerializationGPTTest(TorchAoSerializationTest):
quant_scheme, quant_scheme_kwargs = "int4_weight_only", {"group_size": 32}
device = "cuda:0"
device = f"{torch_device}:0"
@require_torch_gpu
class TorchAoSerializationW8A8GPUTest(TorchAoSerializationTest):
quant_scheme, quant_scheme_kwargs = "int8_dynamic_activation_int8_weight", {}
EXPECTED_OUTPUT = "What are we having for dinner?\n\nJessica: (smiling)"
device = "cuda:0"
device = f"{torch_device}:0"
@require_torch_gpu
class TorchAoSerializationW8GPUTest(TorchAoSerializationTest):
quant_scheme, quant_scheme_kwargs = "int8_weight_only", {}
EXPECTED_OUTPUT = "What are we having for dinner?\n\nJessica: (smiling)"
device = "cuda:0"
device = f"{torch_device}:0"
@require_torch_gpu
@require_torchao_version_greater_or_equal("0.10.0")
class TorchAoSerializationFP8GPUTest(TorchAoSerializationTest):
EXPECTED_OUTPUT = "What are we having for dinner?\n\nJessica: (smiling)"
device = "cuda:0"
device = f"{torch_device}:0"
# called only once for all test in this class
@classmethod
def setUpClass(cls):
if not torch.cuda.is_available() or torch.cuda.get_device_capability()[0] < 9:
if not (get_device_properties()[0] == "cuda" and get_device_properties()[1] >= 9):
raise unittest.SkipTest("CUDA compute capability 9.0 or higher required for FP8 tests")
from torchao.quantization import Float8WeightOnlyConfig
@@ -493,12 +496,12 @@ class TorchAoSerializationFP8GPUTest(TorchAoSerializationTest):
@require_torchao_version_greater_or_equal("0.10.0")
class TorchAoSerializationA8W4Test(TorchAoSerializationTest):
EXPECTED_OUTPUT = "What are we having for dinner?\n\nJessica: (smiling)"
device = "cuda:0"
device = f"{torch_device}:0"
# called only once for all test in this class
@classmethod
def setUpClass(cls):
if not torch.cuda.is_available() or torch.cuda.get_device_capability()[0] < 9:
if not (get_device_properties()[0] == "cuda" and get_device_properties()[1] >= 9):
raise unittest.SkipTest("CUDA compute capability 9.0 or higher required for FP8 tests")
from torchao.quantization import Int8DynamicActivationInt4WeightConfig