Use w8a8 quantized matmul Pallas kernel (#19170)

Signed-off-by: Xiongfei Wei <isaacwxf23@gmail.com>

tests/tpu/test_quantization_accuracy.py

@@ -14,7 +14,7 @@ RTOL = 0.03
 @dataclass
 class GSM8KAccuracyTestConfig:
     model_name: str
-    excepted_value: float
+    expected_value: float
 
     def get_model_args(self) -> str:
         return (f"pretrained={self.model_name},"
@@ -25,13 +25,13 @@ class GSM8KAccuracyTestConfig:
 ACCURACY_CONFIGS = [
     GSM8KAccuracyTestConfig(
         model_name="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
-        excepted_value=0.76),  # no bias
+        expected_value=0.76),  # no bias
     # NOTE(rob): We cannot re-initialize vLLM in the same process for TPU,
     # so only one of these tests can run in a single call to pytest. As
     # a follow up, move this into the LM-EVAL section of the CI.
     # GSM8KAccuracyTestConfig(
     #     model_name="neuralmagic/Qwen2-7B-Instruct-quantized.w8a8",
-    #     excepted_value=0.66),  # bias in QKV layers
+    #     expected_value=0.66),  # bias in QKV layers
 ]
 
 
@@ -45,7 +45,7 @@ def test_gsm8k_correctness(config: GSM8KAccuracyTestConfig):
         batch_size="auto",
     )
 
-    EXPECTED_VALUE = config.excepted_value
+    EXPECTED_VALUE = config.expected_value
     measured_value = results["results"][TASK][FILTER]
     assert (measured_value - RTOL < EXPECTED_VALUE
             and measured_value + RTOL > EXPECTED_VALUE