[Perf] Use NVIDIA hardware-accelerated instruction for float to fp8_e4m3 quantization (vllm-project#24757)

Signed-off-by: elvischenv <[email protected]>
Signed-off-by: bruceszchen <[email protected]>

Author: elvischenv

csrc/quantization/fp8/common.cuh

@@ -5,7 +5,9 @@
 
 #include <cmath>
 
-#ifdef USE_ROCM
+#ifndef USE_ROCM
+  #include "nvidia/quant_utils.cuh"
+#else
   #include "amd/quant_utils.cuh"
 #endif
 
@@ -48,7 +50,9 @@ __device__ __forceinline__ fp8_type scaled_fp8_conversion(float const val,
   float r =
       fmaxf(-quant_type_max_v<fp8_type>, fminf(x, quant_type_max_v<fp8_type>));
 #ifndef USE_ROCM
-  return static_cast<fp8_type>(r);
+  // Use hardware cvt instruction for fp8 on nvidia
+  // Currently only support fp8_type = c10::Float8_e4m3fn
+  return fp8::vec_conversion<fp8_type, float>(r);
 #else
   // Use hardware cvt instruction for fp8 on rocm
   return fp8::cvt_c10<fp8_type>(r);

csrc/quantization/fp8/nvidia/quant_utils.cuh

@@ -12,13 +12,26 @@ namespace vllm {
 namespace fp8 {
   #ifdef ENABLE_FP8
 
-    #if 0  // Disable the following code to reduce the binary size.
 template <typename Tout, typename Tin>
-__inline__ __device__ Tout
-vec_conversion(const Tin &x, const __nv_fp8_interpretation_t fp8_type) {
+__inline__ __device__ Tout vec_conversion(
+    const Tin& x, const __nv_fp8_interpretation_t fp8_type = __NV_E4M3) {
   return x;
 }
 
+// float -> c10::Float8_e4m3fn
+template <>
+__inline__ __device__ c10::Float8_e4m3fn
+vec_conversion<c10::Float8_e4m3fn, float>(
+    const float& a, const __nv_fp8_interpretation_t fp8_type) {
+    #if defined(__CUDA_ARCH__) && __CUDA_ARCH__ < 800
+  return static_cast<c10::Float8_e4m3fn>(a);
+    #else
+  return c10::Float8_e4m3fn(__nv_cvt_float_to_fp8(a, __NV_SATFINITE, fp8_type),
+                            c10::Float8_e4m3fn::from_bits());
+    #endif
+}
+
+    #if 0  // Disable the following code to reduce the binary size.
 // fp8 -> half
 template <>
 __inline__ __device__ uint16_t vec_conversion<uint16_t, uint8_t>(