testiong low precision ipex model

Author: jiqing-feng

tests/ipex/test_modeling.py

@@ -144,6 +144,26 @@ def test_pipeline(self, model_arch):
         _ = pipe(text)
         self.assertEqual(pipe.device, model.device)
 
+    @parameterized.expand(SUPPORTED_ARCHITECTURES)
+    def test_low_precision(self, model_arch):
+        model_id = MODEL_NAMES[model_arch]
+        ipex_model = self.IPEX_MODEL_CLASS.from_pretrained(model_id, export=True, torch_dtype=torch.bfloat16)
+        self.assertEqual(ipex_model._dtype, torch.bfloat16)
+        transformers_model = self.IPEX_MODEL_CLASS.auto_model_class.from_pretrained(
+            model_id, torch_dtype=torch.bfloat16
+        )
+        tokenizer = AutoTokenizer.from_pretrained(model_id)
+        inputs = "This is a sample input"
+        tokens = tokenizer(inputs, return_tensors="pt")
+        with torch.no_grad():
+            transformers_outputs = transformers_model(**tokens)
+        outputs = ipex_model(**tokens)
+        # Compare tensor outputs
+        for output_name in {"logits", "last_hidden_state"}:
+            if output_name in transformers_outputs:
+                self.assertEqual(outputs[output_name].dtype, torch.bfloat16)
+                self.assertTrue(torch.allclose(outputs[output_name], transformers_outputs[output_name], atol=1e-1))
+
 
 class IPEXModelForSequenceClassificationTest(IPEXModelTest):
     IPEX_MODEL_CLASS = IPEXModelForTokenClassification