[Refactor][Kernel] Add global helper to deduplicate vectorized memory ops

csrc/cuda_vec_utils.cuh

@@ -0,0 +1,334 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+#pragma once
+
+#include <c10/util/BFloat16.h>
+#include <c10/util/Half.h>
+#include <cassert>
+
+#ifdef USE_ROCM
+  #include <hip/hip_runtime.h>
+#else
+  #include <cuda_bf16.h>
+  #include <cuda_fp16.h>
+  #include <cuda_runtime.h>
+#endif
+
+// Device-side: SM100+ architecture with CUDA 12.9+ toolkit, which
+// together enable 256-bit (v8.u32) PTX load/store instructions.
+// Use for PTX instruction selection with architecture fallback paths.
+#if !defined(USE_ROCM) && defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 1000 && \
+    defined(CUDA_VERSION) && CUDA_VERSION >= 12090
+  #define VLLM_256B_PTX_ENABLED 1
+#else
+  #define VLLM_256B_PTX_ENABLED 0
+#endif
+
+namespace vllm {
+
+// ============================================================
+// Types and traits
+// ============================================================
+
+// 256-bit (32-byte) aligned vector type: 8 x uint32_t
+struct alignas(32) u32x8_t {
+  uint32_t d[8];
+};
+
+// VecTraits — select between 128-bit (int4) and 256-bit
+// (u32x8_t) vector types at compile time.
+template <bool support_256>
+struct VecTraits;
+
+template <>
+struct VecTraits<true> {
+  static constexpr int ARCH_MAX_VEC_SIZE = 32;
+  using vec_t = u32x8_t;
+};
+
+template <>
+struct VecTraits<false> {
+  static constexpr int ARCH_MAX_VEC_SIZE = 16;
+  using vec_t = int4;
+};
+
+// PackedTypeConverter — map between CUDA scalar and packed types
+//   half  <-> half2,  __nv_bfloat16 <-> __nv_bfloat162, etc.
+template <typename T>
+struct PackedTypeConverter {
+  static_assert(sizeof(T) == 0,
+                "PackedTypeConverter is not specialized for this type.");
+};
+
+template <>
+struct PackedTypeConverter<half2> {
+  using Type = half;
+};
+
+template <>
+struct PackedTypeConverter<half> {
+  using Type = half2;
+};
+
+template <>
+struct PackedTypeConverter<__nv_bfloat162> {
+  using Type = __nv_bfloat16;
+};
+
+template <>
+struct PackedTypeConverter<__nv_bfloat16> {
+  using Type = __nv_bfloat162;
+};
+
+template <>
+struct PackedTypeConverter<float> {
+  using Type = float2;
+};
+
+template <>
+struct PackedTypeConverter<float2> {
+  using Type = float;
+};
+
+template <>
+struct PackedTypeConverter<c10::Half> {
+  using Type = half2;
+};
+
+template <>
+struct PackedTypeConverter<c10::BFloat16> {
+  using Type = __nv_bfloat162;
+};
+
+// CUDATypeConverter — map PyTorch scalar types to CUDA scalar
+//   c10::Half -> half,  c10::BFloat16 -> __nv_bfloat16
+template <typename T>
+struct CUDATypeConverter {
+  using Type = T;
+};
+
+template <>
+struct CUDATypeConverter<c10::Half> {
+  using Type = half;
+};
+
+template <>
+struct CUDATypeConverter<c10::BFloat16> {
+  using Type = __nv_bfloat16;
+};
+
+// PackedVec — typed vector container for packed element access.
+//   Derives alignment and element count from VecTraits.
+//   Type is the CUDA scalar type (e.g. half, __nv_bfloat16).
+template <class Type, bool use_256b>
+struct alignas(VecTraits<use_256b>::ARCH_MAX_VEC_SIZE) PackedVec {
+  static constexpr int NUM_ELTS =
+      VecTraits<use_256b>::ARCH_MAX_VEC_SIZE /
+      sizeof(typename PackedTypeConverter<Type>::Type);
+  typename PackedTypeConverter<Type>::Type elts[NUM_ELTS];
+};
+
+// ============================================================
+// Load / store primitives
+// ============================================================
+
+// 256-bit load / store — SM100+ only (PTX v8 instructions).
+__device__ __forceinline__ void ld256(u32x8_t& val, const u32x8_t* ptr) {
+#if VLLM_256B_PTX_ENABLED
+  asm volatile("ld.global.nc.v8.u32 {%0,%1,%2,%3,%4,%5,%6,%7}, [%8];\n"
+               : "=r"(val.d[0]), "=r"(val.d[1]), "=r"(val.d[2]), "=r"(val.d[3]),
+                 "=r"(val.d[4]), "=r"(val.d[5]), "=r"(val.d[6]), "=r"(val.d[7])
+               : "l"(ptr));
+#else
+  assert(false && "ld256 requires SM100+ with CUDA 12.9+");
+#endif
+}
+
+__device__ __forceinline__ void st256(u32x8_t& val, u32x8_t* ptr) {
+#if VLLM_256B_PTX_ENABLED
+  asm volatile("st.global.v8.u32 [%0], {%1,%2,%3,%4,%5,%6,%7,%8};\n"
+               :
+               : "l"(ptr), "r"(val.d[0]), "r"(val.d[1]), "r"(val.d[2]),
+                 "r"(val.d[3]), "r"(val.d[4]), "r"(val.d[5]), "r"(val.d[6]),
+                 "r"(val.d[7])
+               : "memory");
+#else
+  assert(false && "st256 requires SM100+ with CUDA 12.9+");
+#endif
+}
+
+// Generic ld256 / st256 for any 32-byte aligned type (e.g. PackedVec).
+// Non-template overloads above are preferred for u32x8_t.
+template <typename T>
+__device__ __forceinline__ void ld256(T& val, const T* ptr) {
+  static_assert(sizeof(T) == 32, "ld256 requires a 32-byte type");
+  ld256(reinterpret_cast<u32x8_t&>(val), reinterpret_cast<const u32x8_t*>(ptr));
+}
+
+template <typename T>
+__device__ __forceinline__ void st256(T& val, T* ptr) {
+  static_assert(sizeof(T) == 32, "st256 requires a 32-byte type");
+  st256(reinterpret_cast<u32x8_t&>(val), reinterpret_cast<u32x8_t*>(ptr));
+}
+
+// 128-bit load / store via __ldg (read-only cache hint).
+template <typename T>
+__device__ __forceinline__ void ld128(T& val, const T* ptr) {
+  static_assert(sizeof(T) == 16, "ld128 requires a 16-byte type");
+  *reinterpret_cast<int4*>(&val) = __ldg(reinterpret_cast<const int4*>(ptr));
+}
+
+template <typename T>
+__device__ __forceinline__ void st128(T& val, T* ptr) {
+  static_assert(sizeof(T) == 16, "st128 requires a 16-byte type");
+  *reinterpret_cast<int4*>(ptr) = *reinterpret_cast<int4*>(&val);
+}
+
+// 256-bit cache-streaming (.cs) load / store  — SM100+ only.
+__forceinline__ __device__ u32x8_t ld256_cs(const u32x8_t* addr) {
+#if VLLM_256B_PTX_ENABLED
+  u32x8_t val;
+  asm volatile("ld.global.cs.v8.u32 {%0,%1,%2,%3,%4,%5,%6,%7}, [%8];"
+               : "=r"(val.d[0]), "=r"(val.d[1]), "=r"(val.d[2]), "=r"(val.d[3]),
+                 "=r"(val.d[4]), "=r"(val.d[5]), "=r"(val.d[6]), "=r"(val.d[7])
+               : "l"(addr));
+  return val;
+#else
+  assert(false && "ld256_cs requires SM100+ with CUDA 12.9+");
+  return {};
+#endif
+}
+
+__forceinline__ __device__ void st256_cs(u32x8_t* addr, u32x8_t val) {
+#if VLLM_256B_PTX_ENABLED
+  asm volatile(
+      "st.global.cs.v8.u32 [%0], {%1,%2,%3,%4,%5,%6,%7,%8};" ::"l"(addr),
+      "r"(val.d[0]), "r"(val.d[1]), "r"(val.d[2]), "r"(val.d[3]), "r"(val.d[4]),
+      "r"(val.d[5]), "r"(val.d[6]), "r"(val.d[7]));
+#else
+  assert(false && "st256_cs requires SM100+ with CUDA 12.9+");
+#endif
+}
+
+// 32-bit cache-streaming (.cs) load / store  — SM100+ only.
+__forceinline__ __device__ int ld32_cs(const int* addr) {
+#if VLLM_256B_PTX_ENABLED
+  int val;
+  asm volatile("ld.global.cs.b32 %0, [%1];" : "=r"(val) : "l"(addr));
+  return val;
+#else
+  assert(false && "ld32_cs requires SM100+ with CUDA 12.9+");
+  return 0;
+#endif
+}
+
+__forceinline__ __device__ void st32_cs(int* addr, int val) {
+#if VLLM_256B_PTX_ENABLED
+  asm volatile("st.global.cs.b32 [%0], %1;" ::"l"(addr), "r"(val));
+#else
+  assert(false && "st32_cs requires SM100+ with CUDA 12.9+");
+#endif
+}
+
+// Predicated 256-bit / 128-bit cache-global (.cg) loads.
+// Returns zero if pred is false.  SM100+ only.
+__device__ __forceinline__ void ld256_cg_or_zero(u32x8_t& val, const void* ptr,
+                                                 bool pred) {
+#if VLLM_256B_PTX_ENABLED
+  asm volatile(
+      "{\n"
+      "  .reg .pred pr;\n"
+      "  setp.ne.u32 pr, %8, 0;\n"
+      "  mov.u32 %0, 0;\n"
+      "  mov.u32 %1, 0;\n"
+      "  mov.u32 %2, 0;\n"
+      "  mov.u32 %3, 0;\n"
+      "  mov.u32 %4, 0;\n"
+      "  mov.u32 %5, 0;\n"
+      "  mov.u32 %6, 0;\n"
+      "  mov.u32 %7, 0;\n"
+      "  @pr ld.global.cg.v8.u32 {%0,%1,%2,%3,%4,%5,%6,%7}, [%9];\n"
+      "}\n"
+      : "=r"(val.d[0]), "=r"(val.d[1]), "=r"(val.d[2]), "=r"(val.d[3]),
+        "=r"(val.d[4]), "=r"(val.d[5]), "=r"(val.d[6]), "=r"(val.d[7])
+      : "r"((int)pred), "l"(ptr));
+#else
+  assert(false && "ld256_cg_or_zero requires SM100+ with CUDA 12.9+");
+#endif
+}
+
+__device__ __forceinline__ void ld128_cg_or_zero(uint4& val, const void* ptr,
+                                                 bool pred) {
+#if VLLM_256B_PTX_ENABLED
+  uint32_t r0, r1, r2, r3;
+
+  asm volatile(
+      "{\n"
+      "  .reg .pred pr;\n"
+      "  setp.ne.u32 pr, %4, 0;\n"
+      "  mov.u32 %0, 0;\n"
+      "  mov.u32 %1, 0;\n"
+      "  mov.u32 %2, 0;\n"
+      "  mov.u32 %3, 0;\n"
+      "  @pr ld.global.cg.v4.u32 {%0,%1,%2,%3}, [%5];\n"
+      "}\n"
+      : "=r"(r0), "=r"(r1), "=r"(r2), "=r"(r3)
+      : "r"((int)pred), "l"(ptr));
+
+  val = uint4{r0, r1, r2, r3};
+#else
+  assert(false && "ld128_cg_or_zero requires SM100+ with CUDA 12.9+");
+#endif
+}
+
+// ============================================================
+// Alignment helpers
+// ============================================================
+
+__host__ __device__ __forceinline__ bool is_16byte_aligned(const void* ptr) {
+  return (reinterpret_cast<uintptr_t>(ptr) & 15) == 0;
+}
+
+__host__ __device__ __forceinline__ bool is_32byte_aligned(const void* ptr) {
+  return (reinterpret_cast<uintptr_t>(ptr) & 31) == 0;
+}
+
+// ============================================================
+// Packed type conversion and arithmetic
+// ============================================================
+
+template <typename packed_t>
+__device__ __forceinline__ float2 cast_to_float2(const packed_t& val) {
+  if constexpr (std::is_same_v<packed_t, __nv_bfloat162>) {
+    return __bfloat1622float2(val);
+  } else if constexpr (std::is_same_v<packed_t, __half2>) {
+    return __half22float2(val);
+  } else if constexpr (std::is_same_v<packed_t, float2>) {
+    return float2(val);
+  }
+}
+
+template <typename packed_t>
+__device__ __forceinline__ packed_t cast_to_packed(const float2& val) {
+  if constexpr (std::is_same_v<packed_t, __nv_bfloat162>) {
+    return __float22bfloat162_rn(val);
+  } else if constexpr (std::is_same_v<packed_t, __half2>) {
+    return __float22half2_rn(val);
+  } else if constexpr (std::is_same_v<packed_t, float2>) {
+    return float2(val);
+  }
+}
+
+template <typename packed_t>
+__device__ __forceinline__ packed_t packed_mul(const packed_t& x,
+                                               const packed_t& y) {
+  if constexpr (std::is_same_v<packed_t, __nv_bfloat162> ||
+                std::is_same_v<packed_t, __half2>) {
+    return __hmul2(x, y);
+  } else if constexpr (std::is_same_v<packed_t, float2>) {
+    return make_float2(x.x * y.x, x.y * y.y);
+  }
+}
+
+}  // namespace vllm