[AMD][Quantization] Add TritonScaledMMLinearKernel since int8 is broken for AMD (#12282)

Signed-off-by: Randall Smith <Randall.Smith@amd.com>

tests/kernels/test_triton_scaled_mm.py

@@ -39,6 +39,23 @@ def get_8bit_types():
     return types
 
 
+# This test is to check regressions for int8 support on ROCm.
+@pytest.mark.parametrize("model_path", [
+    "neuralmagic/Llama-3.2-1B-quantized.w8a8",
+])
+@pytest.mark.parametrize("max_tokens", [32])
+@pytest.mark.parametrize("num_logprobs", [10])
+@pytest.mark.skipif(not current_platform.is_rocm(),
+                    reason="Should only run on ROCm")
+def test_rocm_compressed_tensors_w8a8(vllm_runner, example_prompts, model_path,
+                                      max_tokens, num_logprobs):
+    dtype = "bfloat16"
+
+    with vllm_runner(model_path, dtype=dtype) as vllm_model:
+        vllm_model.generate_greedy_logprobs(example_prompts, max_tokens,
+                                            num_logprobs)
+
+
 @pytest.mark.parametrize("M", [1, 33, 64, 512])
 @pytest.mark.parametrize("N", [256, 971, 20486])
 @pytest.mark.parametrize("K", [128, 496, 1024])

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

@@ -5,8 +5,8 @@ from vllm.model_executor.layers.quantization.kernels.scaled_mm.cutlass import (
     CutlassScaledMMLinearKernel)
 from vllm.model_executor.layers.quantization.kernels.scaled_mm.ScaledMMLinearKernel import (  # noqa: E501
     ScaledMMLinearKernel, ScaledMMLinearLayerConfig)
-# from vllm.model_executor.layers.quantization.kernels.scaled_mm.triton import (
-#     TritonScaledMMLinear)
+from vllm.model_executor.layers.quantization.kernels.scaled_mm.triton import (
+    TritonScaledMMLinearKernel)
 from vllm.model_executor.layers.quantization.kernels.scaled_mm.xla import (
     XLAScaledMMLinearKernel)
 from vllm.platforms import PlatformEnum, current_platform
@@ -15,9 +15,7 @@ from vllm.platforms import PlatformEnum, current_platform
 _POSSIBLE_KERNELS: Dict[PlatformEnum, List[Type[ScaledMMLinearKernel]]] = {
     PlatformEnum.CPU: [CutlassScaledMMLinearKernel],
     PlatformEnum.CUDA: [CutlassScaledMMLinearKernel],
-    # TODO(rob): Create TritonScaledMMLinear kernel. ROCM will
-    # incorrectly attempt to run AZP models if prompted to.
-    PlatformEnum.ROCM: [CutlassScaledMMLinearKernel],
+    PlatformEnum.ROCM: [TritonScaledMMLinearKernel],
     PlatformEnum.TPU: [XLAScaledMMLinearKernel],
 }
 

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

+from typing import Optional, Tuple
+
+import torch
+
+from vllm.platforms import current_platform
+
+from .cutlass import CutlassScaledMMLinearKernel
+from .ScaledMMLinearKernel import ScaledMMLinearLayerConfig
+
+
+class TritonScaledMMLinearKernel(CutlassScaledMMLinearKernel):
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        return 75
+
+    @classmethod
+    def can_implement(
+            cls, c: ScaledMMLinearLayerConfig) -> Tuple[bool, Optional[str]]:
+        if current_platform.is_cpu():
+            return (
+                False,
+                "TritonScaledMMLinearKernel requires Triton which is not " +
+                "currently supported on CPU.")
+        if not c.input_symmetric:
+            return (False,
+                    "TritonScaledMMLinearKernel only supports symmetric " +
+                    "quantization.")
+        return True, None
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        super().process_weights_after_loading(layer)
+
+    def apply_weights(self,
+                      layer: torch.nn.Module,
+                      x: torch.Tensor,
+                      bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+        return super().apply_weights(layer, x, bias)