[Kernel][Quantization] Integrate block-quantized CUTLASS kernels for DeepSeekV3 (#12587)

Integrates the block-quantized kernels introduced in
https://github.com/vllm-project/vllm/pull/11868 for use in linear
layers.

Signed-off-by: Tyler Michael Smith <tyler@neuralmagic.com>

vllm/model_executor/layers/quantization/fp8.py

@@ -21,7 +21,8 @@ from vllm.model_executor.layers.quantization.utils.quant_utils import (
     is_layer_skipped)
 from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
     all_close_1d, apply_fp8_linear, convert_to_channelwise,
-    cutlass_fp8_supported, normalize_e4m3fn_to_e4m3fnuz, per_tensor_dequantize,
+    cutlass_block_fp8_supported, cutlass_fp8_supported,
+    normalize_e4m3fn_to_e4m3fnuz, per_tensor_dequantize,
     requantize_with_max_scale)
 from vllm.model_executor.parameter import (BlockQuantScaleParameter,
                                            ModelWeightParameter,
@@ -133,6 +134,7 @@ class Fp8LinearMethod(LinearMethodBase):
     def __init__(self, quant_config: Fp8Config):
         self.quant_config = quant_config
         self.cutlass_fp8_supported = cutlass_fp8_supported()
+        self.cutlass_block_fp8_supported = cutlass_block_fp8_supported()
 
         # For GPUs that lack FP8 hardware support, we can leverage the Marlin
         # kernel for fast weight-only FP8 quantization
@@ -359,6 +361,7 @@ class Fp8LinearMethod(LinearMethodBase):
                 weight_scale=layer.weight_scale_inv,
                 input_scale=layer.input_scale,
                 bias=bias,
+                cutlass_block_fp8_supported=self.cutlass_block_fp8_supported,
             )
 
         return apply_fp8_linear(