ROCm · alextmagro · May 14, 2026 · May 18, 2026 · May 19, 2026 · May 21, 2026
@@ -86,3 +86,4 @@ add_te_benchmark(bench_quantize_mxfp8_fused cast/bench_quantize_mxfp8_fused.cpp)
 add_te_benchmark(bench_dequantize_mxfp8 cast/bench_dequantize_mxfp8.cpp)
 add_te_benchmark(bench_gated_mxfp8 cast/bench_gated_mxfp8.cpp)
 add_te_benchmark(bench_casttranspose cast/bench_casttranspose.cpp)
+add_te_benchmark(bench_multi_cast_transpose cast/bench_multi_cast_transpose.cpp)
@@ -0,0 +1,242 @@
+/*************************************************************************
+ * Copyright (c) 2026, Advanced Micro Devices, Inc. All rights reserved.
+ *
+ * License for AMD contributions = MIT. See LICENSE for more information
+ ************************************************************************/
+
+#include <benchmark/benchmark.h>
+#include <hip/hip_runtime.h>
+#include <hip/hip_bfloat16.h>
+#include "amd_detail/hip_float8.h"
+
+#include "benchmark_utils.h"
+
+#include "transformer_engine/transpose_hip.h"
+#include "transformer_engine/transformer_engine_hip.h"
+
+#include <algorithm>
+#include <cmath>
+#include <numeric>
+#include <random>
+#include <string>
+#include <vector>
+
+using namespace te_bench;
+using namespace transformer_engine;
+using fp8_e4m3 = test::fp8e4m3;
+
+// MoE shapes from Qwen3-235B and DeepSeek-V3
+// Args: {total_tokens, cols, num_experts, top_k, routing_mode}
+#define MOE_BALANCED                     \
+  ->Args({4096,  4096, 128, 8, 0})       \
+  ->Args({8192,  4096, 128, 8, 0})       \
+  ->Args({16384, 4096, 128, 8, 0})       \
+  ->Args({4096,  1536, 128, 8, 0})       \
+  ->Args({8192,  1536, 128, 8, 0})       \
+  ->Args({16384, 1536, 128, 8, 0})       \
+  ->Args({4096,  3072, 128, 8, 0})       \
+  ->Args({8192,  3072, 128, 8, 0})       \
+  ->Args({16384, 3072, 128, 8, 0})       \
+  ->Args({4096,  7168, 256, 8, 0})       \
+  ->Args({8192,  7168, 256, 8, 0})       \
+  ->Args({16384, 7168, 256, 8, 0})       \
+  ->Args({4096,  2048, 256, 8, 0})       \
+  ->Args({8192,  2048, 256, 8, 0})       \
+  ->Args({16384, 2048, 256, 8, 0})       \
+  ->Args({4096,  4096, 256, 8, 0})       \
+  ->Args({8192,  4096, 256, 8, 0})       \
+  ->Args({16384, 4096, 256, 8, 0})
+
+#define MOE_SKEWED                       \
+  ->Args({4096,  4096, 128, 8, 1})       \
+  ->Args({8192,  4096, 128, 8, 1})       \
+  ->Args({16384, 4096, 128, 8, 1})       \
+  ->Args({4096,  1536, 128, 8, 1})       \
+  ->Args({8192,  1536, 128, 8, 1})       \
+  ->Args({16384, 1536, 128, 8, 1})       \
+  ->Args({4096,  3072, 128, 8, 1})       \
+  ->Args({8192,  3072, 128, 8, 1})       \
+  ->Args({16384, 3072, 128, 8, 1})       \
+  ->Args({4096,  7168, 256, 8, 1})       \
+  ->Args({8192,  7168, 256, 8, 1})       \
+  ->Args({16384, 7168, 256, 8, 1})       \
+  ->Args({4096,  2048, 256, 8, 1})       \
+  ->Args({8192,  2048, 256, 8, 1})       \
+  ->Args({16384, 2048, 256, 8, 1})       \
+  ->Args({4096,  4096, 256, 8, 1})       \
+  ->Args({8192,  4096, 256, 8, 1})       \
+  ->Args({16384, 4096, 256, 8, 1})
+
+namespace {
+
+static const uint64_t kRunSeed = std::random_device{}();
+static constexpr size_t kPadMultiple = 16;
+
+static uint64_t derive_seed(size_t a, size_t b, size_t c, size_t d, size_t e) {
+  uint64_t h = kRunSeed;
+  h ^= a; h *= 1099511628211ULL;
+  h ^= b; h *= 1099511628211ULL;
+  h ^= c; h *= 1099511628211ULL;
+  h ^= d; h *= 1099511628211ULL;
+  h ^= e; h *= 1099511628211ULL;
+  return h;
+}
+
+static std::vector<size_t> simulate_topk_balanced(
+    size_t total_tokens, size_t num_experts, size_t top_k, uint64_t seed)
+{
+  std::vector<size_t> counts(num_experts, 0);
+  std::mt19937_64 gen(seed);
+
+  std::vector<size_t> experts(num_experts);
+  std::iota(experts.begin(), experts.end(), 0);
+
+  for (size_t t = 0; t < total_tokens; t++) {
+    for (size_t k = 0; k < top_k; k++) {
+      std::uniform_int_distribution<size_t> dist(k, num_experts - 1);
+      std::swap(experts[k], experts[dist(gen)]);
+      counts[experts[k]]++;
+    }
+  }
+  return counts;
+}
+
+static std::vector<size_t> simulate_topk_skewed(
+    size_t total_tokens, size_t num_experts, size_t top_k, uint64_t seed)
+{
+  std::vector<size_t> counts(num_experts, 0);
+  std::mt19937_64 gen(seed);
+
+  std::vector<double> weights(num_experts);
+  for (size_t i = 0; i < num_experts; i++)
+    weights[i] = 1.0 / std::pow(static_cast<double>(i + 1), 0.7);
+
+  std::shuffle(weights.begin(), weights.end(), gen);
+  std::discrete_distribution<size_t> wdist(weights.begin(), weights.end());
+
+  std::vector<bool> used(num_experts, false);
+  std::vector<size_t> used_list;
+  used_list.reserve(top_k);
+
+  for (size_t t = 0; t < total_tokens; t++) {
+    used_list.clear();
+    for (size_t k = 0; k < top_k; k++) {
+      size_t e;
+      do { e = wdist(gen); } while (used[e]);
+      used[e] = true;
+      used_list.push_back(e);
+      counts[e]++;
+    }
+    for (size_t e : used_list) used[e] = false;
+  }
+  return counts;
+}
+
+template <typename IType>
+static void BM_MultiCastTranspose(benchmark::State &state) {
+  const size_t total_tokens = state.range(0);
+  const size_t cols         = state.range(1);
+  const size_t num_experts  = state.range(2);
+  const size_t top_k        = state.range(3);
+  const size_t routing_mode = state.range(4);
+
+  uint64_t seed = derive_seed(total_tokens, cols, num_experts, top_k, routing_mode);
+
+  auto counts = (routing_mode == 0)
+      ? simulate_topk_balanced(total_tokens, num_experts, top_k, seed)
+      : simulate_topk_skewed(total_tokens, num_experts, top_k, seed);
+
+  size_t min_tok = *std::min_element(counts.begin(), counts.end());
+  size_t max_tok = *std::max_element(counts.begin(), counts.end());
+  size_t sum_tok = std::accumulate(counts.begin(), counts.end(), size_t(0));
+
+  DType itype = std::is_same_v<IType, float>       ? DType::kFloat32 :
+                std::is_same_v<IType, hip_bfloat16> ? DType::kBFloat16 :
+                                                       DType::kFloat16;
+
+  std::string pfx = "mct_" + std::to_string(total_tokens) + "_"
+                   + std::to_string(cols) + "_" + std::to_string(num_experts)
+                   + "_" + std::to_string(routing_mode);
+
+  std::vector<NVTETensor> nvte_in(num_experts), nvte_out(num_experts);
+
+  for (size_t e = 0; e < num_experts; e++) {
+    size_t rows = ((std::max(counts[e], size_t(1)) + kPadMultiple - 1)
+                   / kPadMultiple) * kPadMultiple;
+    std::string in_name  = pfx + "_in_"  + std::to_string(e);
+    std::string out_name = pfx + "_out_" + std::to_string(e);
+
+    auto &input = TensorCache::get_or_create(
+        in_name, {rows, cols}, itype,
+        true, false, NVTE_DELAYED_TENSOR_SCALING, true);
+
+    auto &output = TensorCache::get_or_create(
+        out_name, {rows, cols}, DType::kFloat8E4M3,
+        true, true, NVTE_DELAYED_TENSOR_SCALING, false);
+
+    output.set_scale(1.0f);
+
+    nvte_in[e]  = input.data();
+    nvte_out[e] = output.data();
+  }
+
+  hipStream_t stream;
+  HIP_CHECK(hipStreamCreate(&stream));
+
+  hipEvent_t start, stop;
+  HIP_CHECK(hipEventCreate(&start));
+  HIP_CHECK(hipEventCreate(&stop));
+
+  nvte_multi_cast_transpose(num_experts, nvte_in.data(), nvte_out.data(), stream);
+  HIP_CHECK(hipStreamSynchronize(stream));
+  warmup_gpu();
+
+  for (auto _ : state) {
+    HIP_CHECK(hipEventRecord(start, stream));
+    nvte_multi_cast_transpose(num_experts, nvte_in.data(), nvte_out.data(), stream);
+    HIP_CHECK(hipEventRecord(stop, stream));
+    HIP_CHECK(hipEventSynchronize(stop));
+
+    float ms = 0;
+    HIP_CHECK(hipEventElapsedTime(&ms, start, stop));
+    state.SetIterationTime(ms / 1000.0);
+  }
+
+  HIP_CHECK(hipEventDestroy(start));
+  HIP_CHECK(hipEventDestroy(stop));
+
+  size_t total_bytes = 0;
+  for (size_t e = 0; e < num_experts; e++) {
+    size_t rows = ((std::max(counts[e], size_t(1)) + kPadMultiple - 1)
+                   / kPadMultiple) * kPadMultiple;
+    total_bytes += rows * cols * sizeof(IType);
+    total_bytes += rows * cols * sizeof(fp8_e4m3) * 2;
+  }
+  set_bytes_processed(state, total_bytes);
+
+  state.counters["experts"]  = num_experts;
+  state.counters["cols"]     = cols;
+  state.counters["avg_tok"]  = static_cast<double>(sum_tok) / num_experts;
+  state.counters["min_tok"]  = min_tok;
+  state.counters["max_tok"]  = max_tok;
+
+  HIP_CHECK(hipStreamDestroy(stream));
+}
+
+}  // namespace
+
+#define REGISTER_MCT(ITYPE, INAME)                                            \
+  BENCHMARK_TEMPLATE(BM_MultiCastTranspose, ITYPE)                            \
+    ->Name("BM_MultiCastTranspose/" INAME "_E4M3/moe")                        \
+    MOE_BALANCED                                                              \
+    ->Unit(benchmark::kMicrosecond)                                           \
+    ->UseManualTime();                                                        \
+  BENCHMARK_TEMPLATE(BM_MultiCastTranspose, ITYPE)                            \
+    ->Name("BM_MultiCastTranspose/" INAME "_E4M3/moe_skewed")                \
+    MOE_SKEWED                                                                \
+    ->Unit(benchmark::kMicrosecond)                                           \
+    ->UseManualTime();
+
+REGISTER_MCT(hip_bfloat16, "BF16")
+
+BENCHMARK_MAIN();
@@ -27,6 +27,7 @@ main() {
         "bench_dequantize_mxfp8"
         "bench_gated_mxfp8"
         "bench_casttranspose"
+        "bench_multi_cast_transpose"
     )
 
     FAILED_BENCHMARKS=()

@@ -16,6 +16,10 @@
 
 namespace transformer_engine {
 
+#ifdef __HIP_PLATFORM_AMD__
+#include "rocm_multi_cast_transpose.cuh"
+#endif  // #ifdef __HIP_PLATFORM_AMD__
+
 namespace {
 
 // Parameters to tune
@@ -235,6 +239,42 @@ void multi_cast_transpose(const std::vector<Tensor*> input_list, std::vector<Ten
                input.data.shape);
   }
 
+#ifdef __HIP_PLATFORM_AMD__
+  {
+    const size_t n = input_list.size();
+    TRANSFORMER_ENGINE_TYPE_SWITCH_INPUT(
+        itype, InputType,
+        TRANSFORMER_ENGINE_TYPE_SWITCH_OUTPUT(
+            otype, OutputType,
+
+            std::vector<const InputType *> in_ptrs(n);
+            std::vector<OutputType *> out_c_ptrs(n);
+            std::vector<OutputType *> out_t_ptrs(n);
+            std::vector<const float *> scale_ptrs(n);
+            std::vector<float *> amax_ptrs(n);
+            std::vector<float *> sinv_ptrs(n);
+            std::vector<size_t> rows(n);
+            std::vector<size_t> cols(n);
+
+            for (size_t i = 0; i < n; i++) {
+              in_ptrs[i]    = reinterpret_cast<const InputType *>(input_list[i]->data.dptr);
+              out_c_ptrs[i] = reinterpret_cast<OutputType *>(output_list[i]->data.dptr);
+              out_t_ptrs[i] = reinterpret_cast<OutputType *>(output_list[i]->columnwise_data.dptr);
+              scale_ptrs[i] = reinterpret_cast<const float *>(output_list[i]->scale.dptr);
+              amax_ptrs[i]  = reinterpret_cast<float *>(output_list[i]->amax.dptr);
+              sinv_ptrs[i]  = reinterpret_cast<float *>(output_list[i]->scale_inv.dptr);
+              rows[i]       = input_list[i]->data.shape[0];
+              cols[i]       = input_list[i]->data.shape[1];
+            }
+
+            rocm_multi_cast_transpose_dispatch<InputType, OutputType>(n, in_ptrs.data(), out_c_ptrs.data(),
+                      out_t_ptrs.data(), scale_ptrs.data(), amax_ptrs.data(), sinv_ptrs.data(), rows.data(),
+                      cols.data(), stream);
+        ); // NOLINT(*)
+    );     // NOLINT(*)
+    NVTE_CHECK_CUDA(cudaGetLastError());
+  }
+#else
   // Input matrices are divided into tiles
   // Note: Each tile is a warp_size x warp_size grid of nvec_out x nvec_in subtiles
   const int tile_dim_m = THREADS_PER_WARP * desired_store_size * 8 / typeToNumBits(otype);
@@ -328,6 +368,7 @@ void multi_cast_transpose(const std::vector<Tensor*> input_list, std::vector<Ten
     );                                                                              // NOLINT(*)
     NVTE_CHECK_CUDA(cudaGetLastError());
   }
+#endif  // #ifdef __HIP_PLATFORM_AMD__
 }
 
 }  // namespace transformer_engine