[ATen][CUDA] Optimize 128 bit vectorization (#152967)

pytorchbot · Aidyn-A · web-flow · commit 24b0c4abfcab · 2025-05-07T11:09:39.000-07:00
[ATen][CUDA] Optimize 128 bit vectorization (#148320) Fixes #147376. As per request: #145746 (review) This PR omits sm80 or older of using vec8 kernels due to long compilation and large binary size. Pull Request resolved: #148320 Approved by: https://github.com/eqy, https://github.com/malfet, https://github.com/atalman (cherry picked from commit 72337bd) Co-authored-by: Aidyn-A <31858918+Aidyn-A@users.noreply.github.com>
diff --git a/aten/src/ATen/native/cuda/CUDALoops.cuh b/aten/src/ATen/native/cuda/CUDALoops.cuh
@@ -133,6 +133,69 @@ constexpr auto calc_io_size(){
 #endif
 }
 
+#ifndef USE_ROCM
+// To save on binary size of libtorch_cuda.so, we split the vectorized_elementwise_kernel
+// into two: one for vec_size=8 and one for vec_size=[2, 4], since vec8 is going to be
+// used on sm_90 and sm_100 exclusively.
+template <int vec_size, typename func_t, typename array_t>
+C10_LAUNCH_BOUNDS_1(num_threads())
+__global__ void vectorized_elementwise_kernel(int N, func_t f, array_t data) {
+  if constexpr (vec_size == 8) {
+#if __CUDA_ARCH__ == 900 || __CUDA_ARCH__ == 1000
+    using traits = function_traits<func_t>;
+    constexpr auto io_size = calc_io_size<func_t>();
+    int remaining = N - io_block_work_size<io_size>() * blockIdx.x;
+
+    if (remaining < io_block_work_size<io_size>()) { // if this block handles the reminder,
+                  // just do a naive unrolled loop
+      auto input_calc = TrivialOffsetCalculator<traits::arity>();
+      auto output_calc = TrivialOffsetCalculator<1>();
+      auto loader = memory::LoadWithoutCast();
+      auto storer = memory::StoreWithoutCast();
+      auto policy = memory::policies::unroll<
+      array_t,
+      decltype(input_calc),
+      decltype(output_calc),
+      memory::LoadWithoutCast,
+      memory::StoreWithoutCast,
+      elems_per_thread<io_size>()>(
+      data, remaining, input_calc, output_calc, loader, storer);
+      elementwise_kernel_helper(f, policy);
+    } else { // if this block has a full `block_work_size` data to handle, use
+        // vectorized memory access
+      elementwise_kernel_helper(
+      f, memory::policies::vectorized<vec_size, array_t, elems_per_thread<io_size>()>(data));
+    }
+#endif // __CUDA_ARCH__ == 900 || __CUDA_ARCH__ == 1000
+  } else {
+    using traits = function_traits<func_t>;
+    constexpr auto io_size = calc_io_size<func_t>();
+    int remaining = N - io_block_work_size<io_size>() * blockIdx.x;
+
+    if (remaining < io_block_work_size<io_size>()) { // if this block handles the reminder,
+                   // just do a naive unrolled loop
+      auto input_calc = TrivialOffsetCalculator<traits::arity>();
+      auto output_calc = TrivialOffsetCalculator<1>();
+      auto loader = memory::LoadWithoutCast();
+      auto storer = memory::StoreWithoutCast();
+      auto policy = memory::policies::unroll<
+      array_t,
+      decltype(input_calc),
+      decltype(output_calc),
+      memory::LoadWithoutCast,
+      memory::StoreWithoutCast,
+      elems_per_thread<io_size>()>(
+      data, remaining, input_calc, output_calc, loader, storer);
+      elementwise_kernel_helper(f, policy);
+    } else { // if this block has a full `block_work_size` data to handle, use
+         // vectorized memory access
+      elementwise_kernel_helper(
+      f, memory::policies::vectorized<vec_size, array_t, elems_per_thread<io_size>()>(data));
+    }
+  }
+}
+
+#else // USE_ROCM
 template <int vec_size, typename func_t, typename array_t>
 C10_LAUNCH_BOUNDS_1(num_threads())
 __global__ void vectorized_elementwise_kernel(int N, func_t f, array_t data) {
@@ -157,15 +220,12 @@ __global__ void vectorized_elementwise_kernel(int N, func_t f, array_t data) {
     elementwise_kernel_helper(f, policy);
   } else { // if this block has a full `block_work_size` data to handle, use
            // vectorized memory access
-#ifdef USE_ROCM
     constexpr auto optimal_vec_size = calc_optimal_vec_size<vec_size, io_size>();
-#else
-    constexpr auto optimal_vec_size = vec_size;
-#endif
     elementwise_kernel_helper(
         f, memory::policies::vectorized<optimal_vec_size, array_t, elems_per_thread<io_size>()>(data));
   }
 }
+#endif // USE_ROCM
 
 template <
     typename func_t,
@@ -212,6 +272,11 @@ static inline void launch_vectorized_kernel(
   // Here we purposely omit vec8 for 1-byte data because of a bug in NVCC
   // that causes some numerical mismatches with uint8 on sm80 and sm90.
   // TODO: Revisit this after CUDA 12.8 update.
+  cudaDeviceProp* p = at::cuda::getDeviceProperties(stream.device().index());
+  const int computeCapability = p->major * 10 + p->minor;
+  if (computeCapability != 90 && computeCapability != 100) {
+    vec_size = std::min<uint16_t>(vec_size, 4);
+  }
   if constexpr (sizeof(cpp_type) < 2) {
     vec_size = std::min<uint16_t>(vec_size, 4);
   }
