Use w8a8 quantized matmul Pallas kernel

requirements/tpu.txt

@@ -18,9 +18,9 @@ setuptools==78.1.0
 --find-links https://storage.googleapis.com/libtpu-releases/index.html
 --find-links https://storage.googleapis.com/jax-releases/jax_nightly_releases.html
 --find-links https://storage.googleapis.com/jax-releases/jaxlib_nightly_releases.html
-torch==2.9.0.dev20250703
-torchvision==0.24.0.dev20250703
-torch_xla[tpu, pallas] @ https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.8.0.dev20250703-cp39-cp39-linux_x86_64.whl ; python_version == "3.9"
-torch_xla[tpu, pallas] @ https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.8.0.dev20250703-cp310-cp310-linux_x86_64.whl ; python_version == "3.10"
-torch_xla[tpu, pallas] @ https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.8.0.dev20250703-cp311-cp311-linux_x86_64.whl ; python_version == "3.11"
+torch==2.9.0.dev20250711
+torchvision==0.24.0.dev20250711
+torch_xla[tpu, pallas] @ https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.9.0.dev20250711-cp39-cp39-linux_x86_64.whl ; python_version == "3.9"
+torch_xla[tpu, pallas] @ https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.9.0.dev20250711-cp310-cp310-linux_x86_64.whl ; python_version == "3.10"
+torch_xla[tpu, pallas] @ https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.9.0.dev20250711-cp311-cp311-linux_x86_64.whl ; python_version == "3.11"
 

tests/tpu/test_quantization_accuracy.py

@@ -14,7 +14,7 @@
 @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 @@ def get_model_args(self) -> str:
 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

tests/v1/tpu/test_basic.py

@@ -145,3 +145,35 @@ def test_gemma3_27b_with_text_input_and_tp(
         for output, answer in zip(vllm_outputs, answers):
             generated_text = output[1]
             assert answer in generated_text
+
+
+@pytest.mark.skipif(not current_platform.is_tpu(),
+                    reason="This is a basic test for TPU only")
+def test_w8a8_quantization(
+    vllm_runner: type[VllmRunner],
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    model = "neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a8"
+    max_tokens = 5
+    tensor_parallel_size = 1
+    max_num_seqs = 4
+
+    prompt = "The next numbers of the sequence " + ", ".join(
+        str(i) for i in range(1024)) + " are:"
+    example_prompts = [prompt]
+
+    with monkeypatch.context() as m:
+        m.setenv("VLLM_USE_V1", "1")
+
+        with vllm_runner(
+                model,
+                max_num_batched_tokens=64,
+                max_model_len=4096,
+                gpu_memory_utilization=0.7,
+                max_num_seqs=max_num_seqs,
+                tensor_parallel_size=tensor_parallel_size) as vllm_model:
+            vllm_outputs = vllm_model.generate_greedy(example_prompts,
+                                                      max_tokens)
+        output = vllm_outputs[0][1]
+
+        assert "1024" in output or "0, 1" in output

vllm/model_executor/layers/quantization/kernels/scaled_mm/xla.py

@@ -90,16 +90,15 @@ def apply_weights(self,
                       bias: Optional[torch.Tensor] = None) -> torch.Tensor:
         w_q, w_s, _, _, _ = self._get_weight_params(layer)
 
-        import torch_xla.experimental.xla_quantized_matmul  # noqa: F401
-        out = torch.ops.xla.quantized_matmul(x,
-                                             w_q,
-                                             w_s,
-                                             zero_point=None,
-                                             block_size=-1,
-                                             int4_weight=False,
-                                             quantize_activation=True)
-        # `quantized_matmul` output is fp32, cast it down to bf16 for perf
-        out = out.to(x.dtype)
+        # Required to register custom ops.
+        import torch_xla.experimental.custom_kernel  # noqa: F401
+        out = torch.ops.xla.quantized_matmul_int8(
+            x,
+            w_q,
+            w_s,
+            quantize_activation=True,
+        )
+
         # Explicitly capture control flow to make dynamo happy.
         # https://pytorch.org/docs/main/generated/exportdb/index.html#cond-branch-class-method # noqa: E501
         return cond(bias is None, self.no_add_bias, self.add_bias, [out, bias])