Optimize tbe_input_combine_with_length_cuda on AMD

Summary:
X-link: facebookresearch/FBGEMM#1496

DPER frontend benchmark show tbe_input_combine_with_length_cuda as one of the top contributors to local net latency on CMF fully remote model. Especially on AMD, where latency for this kernel is ~2x of NVIDIA (albeit AMD executes with more kernels in parallel). 

VEC_WIDTH=32 Increase items processed on AMD per thread, improving memory access patterns and taking advantage of AMD GPU larger memory bandwidth.

Reviewed By: q10

Differential Revision: D75886673

Author: JChunX

fbgemm_gpu/src/input_combine_ops/input_combine.cu

@@ -24,7 +24,11 @@ DEVICE_INLINE void vec_copy_with_implicit_type_cast(
     const uint64_t src_bound) {
   // TODO: Use vector load/store if address aligns with the vector type
   const src_t* const src = reinterpret_cast<src_t*>(src_addr);
+#ifdef __HIP_PLATFORM_AMD__
+#pragma unroll 4
+#else
 #pragma unroll
+#endif
   for (uint64_t i = 0; i < VEC_WIDTH && src_offset + i < src_bound; i++) {
     dst[dst_offset + i] = src[src_offset + i];
   }
@@ -130,9 +134,12 @@ std::tuple<Tensor, Tensor, Tensor> tbe_input_combine_with_length_cuda(
           .dtype(at::kFloat)
           .device(at::kCUDA, at::cuda::current_device()));
 
-  // Each thread loads 4 elements (rule of thumb; should work well with 32-bit
-  // inputs)
-  constexpr uint32_t VEC_WIDTH = 4;
+// Each thread loads VEC_WIDTH elements (tuned for specific hardware)
+#ifdef __HIP_PLATFORM_AMD__
+  constexpr uint32_t VEC_WIDTH = 32;
+#else
+  constexpr uint32_t VEC_WIDTH = 8;
+#endif
   constexpr uint32_t NUM_WARPS_PER_BLOCK = kMaxThreads / kWarpSize;
   const auto num_warps_per_list =
       div_round_up(max_list_size, kWarpSize * VEC_WIDTH);