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enable awq tests #1195

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Mar 19, 2025
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6 changes: 6 additions & 0 deletions .github/workflows/test_ipex.yml
Original file line number Diff line number Diff line change
Expand Up @@ -44,6 +44,12 @@ jobs:
cd bitsandbytes
pip install .

- name: Install autoawq
run: |
git clone https://github.com/casper-hansen/AutoAWQ.git
cd AutoAWQ
pip install .

- name: Assert versions
run: |
python -c "import torch; print(torch.__version__); assert torch.__version__.startswith('${{ matrix.torch-version }}'.replace('.*', ''))"
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39 changes: 39 additions & 0 deletions tests/ipex/test_modeling.py
Original file line number Diff line number Diff line change
Expand Up @@ -483,6 +483,45 @@ def test_bnb(self):
self.assertTrue(torch.allclose(outputs.logits, loaded_model_outputs.logits, atol=1e-7))
self.assertTrue(torch.allclose(outputs.logits, init_model_outputs.logits, atol=1e-7))

@unittest.skipIf(not is_auto_awq_available(), reason="Test requires autoawq")
def test_awq(self):
model_id = "PrunaAI/JackFram-llama-68m-AWQ-4bit-smashed"
set_seed(SEED)
dtype = torch.float16 if IS_XPU_AVAILABLE else torch.float32
# Test model forward do not need cache.
ipex_model = IPEXModelForCausalLM.from_pretrained(model_id, torch_dtype=dtype, device_map=DEVICE)
self.assertIsInstance(ipex_model.config, PretrainedConfig)
tokenizer = AutoTokenizer.from_pretrained(model_id)
tokens = tokenizer(
"This is a sample",
return_tensors="pt",
return_token_type_ids=False,
).to(DEVICE)
inputs = ipex_model.prepare_inputs_for_generation(**tokens)
outputs = ipex_model(**inputs)

self.assertIsInstance(outputs.logits, torch.Tensor)

transformers_model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=dtype, device_map=DEVICE)
with torch.no_grad():
transformers_outputs = transformers_model(**tokens)

# Test re-load model
with tempfile.TemporaryDirectory() as tmpdirname:
ipex_model.save_pretrained(tmpdirname)
loaded_model = self.IPEX_MODEL_CLASS.from_pretrained(tmpdirname, torch_dtype=dtype, device_map=DEVICE)
loaded_model_outputs = loaded_model(**inputs)

# Test init method
init_model = self.IPEX_MODEL_CLASS(transformers_model)
init_model_outputs = init_model(**inputs)

# Compare tensor outputs
self.assertTrue(torch.allclose(outputs.logits, transformers_outputs.logits, atol=5e-2))
# To avoid float pointing error
self.assertTrue(torch.allclose(outputs.logits, loaded_model_outputs.logits, atol=1e-7))
self.assertTrue(torch.allclose(outputs.logits, init_model_outputs.logits, atol=1e-7))


class IPEXModelForAudioClassificationTest(unittest.TestCase):
IPEX_MODEL_CLASS = IPEXModelForAudioClassification
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