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rewrite mcok tests
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tests/openvino/test_quantization.py

+34-35
Original file line numberDiff line numberDiff line change
@@ -459,57 +459,56 @@ def test_ovmodel_load_with_uncompressed_weights(self, model_cls, model_type):
459459
self.assertEqual(0, num_int8)
460460

461461
def test_ovmodel_load_large_model_with_default_compressed_weights(self):
462-
with unittest.mock.patch("transformers.modeling_utils.ModuleUtilsMixin") as model_mixin_patch:
463-
model_mixin_patch.num_parameters.return_value = 2e9
462+
with unittest.mock.patch("torch.nn.Module.parameters") as model_parameters:
463+
model_parameters.return_value = [torch.ones((2000, 1000, 1000), requires_grad=True)]
464464
with unittest.mock.patch("openvino.runtime.ie_api.Core.read_model") as core_patch:
465465
with unittest.mock.patch("optimum.exporters.openvino.convert._save_model") as save_model_patch:
466466
_ = OVModelForCausalLM.from_pretrained(
467467
MODEL_NAMES["llama"], export=True, compile=False, use_cache=False
468468
)
469-
saving_params = {
470-
"model": unittest.mock.ANY,
471-
"path": unittest.mock.ANY,
472-
"compression_option": "int8",
473-
"compression_ratio": None,
474-
}
475-
save_model_patch.aasert_called_with(saving_params)
469+
save_model_patch.assert_called_with(
470+
unittest.mock.ANY, unittest.mock.ANY, ov_config=OVConfig(quantization_config={"bits": 8})
471+
)
476472

477473
def test_ovmodel_load_large_model_with_uncompressed_weights(self):
478-
with unittest.mock.patch("transformers.modeling_utils.ModuleUtilsMixin") as model_mixin_patch:
479-
model_mixin_patch.num_parameters.return_value = 2e9
474+
with unittest.mock.patch("torch.nn.Module.parameters") as model_parameters:
475+
model_parameters.return_value = [torch.ones((2000, 1000, 1000), requires_grad=True)]
480476
with unittest.mock.patch("openvino.runtime.ie_api.Core.read_model") as core_patch:
481477
with unittest.mock.patch("optimum.exporters.openvino.convert._save_model") as save_model_patch:
482478
_ = OVModelForCausalLM.from_pretrained(
483479
MODEL_NAMES["llama"], export=True, load_in_8bit=False, compile=False, use_cache=False
484480
)
485-
saving_params = {
486-
"model": unittest.mock.ANY,
487-
"path": unittest.mock.ANY,
488-
"compression_option": "fp32",
489-
"compression_ratio": None,
490-
}
491-
save_model_patch.aasert_called_with(saving_params)
481+
save_model_patch.assert_called_with(
482+
unittest.mock.ANY, unittest.mock.ANY, ov_config=OVConfig(dtype="fp32")
483+
)
492484

493485
def test_ovmodel_load_large_model_with_additional_quantization_config(self):
494-
with unittest.mock.patch("transformers.modeling_utils.ModuleUtilsMixin") as model_mixin_patch:
495-
model_mixin_patch.num_parameters.return_value = 2e9
486+
with unittest.mock.patch("torch.nn.Module.parameters") as model_parameters:
487+
model_parameters.return_value = [torch.ones((2000, 1000, 1000), requires_grad=True)]
496488
with unittest.mock.patch("openvino.runtime.ie_api.Core.read_model") as core_patch:
497489
with unittest.mock.patch("optimum.exporters.openvino.convert._save_model") as save_model_patch:
498-
_ = OVModelForCausalLM.from_pretrained(
499-
MODEL_NAMES["llama"],
500-
export=True,
501-
compile=False,
502-
use_cache=False,
503-
quantization_config=OVWeightQuantizationConfig(bits=4, sym=True, group_size=-1, ratio=0.8),
504-
)
505-
# quantization will be performed later, using load_model
506-
saving_params = {
507-
"model": unittest.mock.ANY,
508-
"path": unittest.mock.ANY,
509-
"compression_option": "fp32",
510-
"compression_ratio": None,
511-
}
512-
save_model_patch.aasert_called_with(saving_params)
490+
with unittest.mock.patch("nncf.compress_weights") as compress_weights_patch:
491+
_ = OVModelForCausalLM.from_pretrained(
492+
MODEL_NAMES["llama"],
493+
export=True,
494+
compile=False,
495+
use_cache=False,
496+
quantization_config=OVWeightQuantizationConfig(bits=4, sym=True, group_size=-1, ratio=0.8),
497+
)
498+
# quantization will be performed later, using load_model
499+
save_model_patch.assert_called_with(
500+
unittest.mock.ANY, unittest.mock.ANY, ov_config=OVConfig(dtype="fp32")
501+
)
502+
compression_params = {
503+
"mode": nncf.CompressWeightsMode.INT4_SYM,
504+
"ratio": 0.8,
505+
"group_size": -1,
506+
"all_layers": None,
507+
"sensitivity_metric": None,
508+
"dataset": None,
509+
"ignored_scope": None,
510+
}
511+
compress_weights_patch.assert_called_with(unittest.mock.ANY, **compression_params)
513512

514513

515514
class OVQuantizerQATest(unittest.TestCase):

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