diff --git a/csrc/ops/fused_bucketized_kernel.cu b/csrc/ops/fused_bucketized_kernel.cu
index 99547eb..0a5c7a5 100644
--- a/csrc/ops/fused_bucketized_kernel.cu
+++ b/csrc/ops/fused_bucketized_kernel.cu
@@ -16,6 +16,7 @@ struct BucketizeData {
       : boundaries(boundaries), len(len) {}
 };
 
+#ifndef __HIP_PLATFORM_AMD__
 struct BucketizeFactory {
   __device__ int operator()(const float value, const BucketizeData& data) {
     int bucket = 0;
@@ -35,6 +36,126 @@ struct BucketizeFactory {
     return bucket;
   }
 };
+#else
+#define KBLOCK_SIZE 256
+struct BucketizeFactory {
+  __device__ __forceinline__ int operator()(const float value, 
+                                            const float* boundaries,
+                                            int count) {
+    int c = count;
+    if (c <= 0) return 0;
+    int bucket = 0;
+    // ceil(log2(c)) for positive c
+    int iters = 32 - __builtin_clz(c);
+    #pragma unroll
+    for (int i = 0; i < iters; ++i) {
+      int step = c >> 1;
+      int left = bucket + step;
+      int cond = (value >= boundaries[left]);
+      bucket = cond ? (left + 1) : bucket;
+      c = cond ? (c - step - 1) : step;
+    }
+    return bucket;
+  }
+};
+
+template <typename A, typename B, typename C>
+__global__ __launch_bounds__(KBLOCK_SIZE, 2)
+void fused_bucketize_kernel(const A** a, const B** b, C** c,
+                            int64_t N, int64_t* sizes, int64_t* b_sizes,
+                            BucketizeFactory factory) {
+  int vec_id = blockIdx.y;
+  if (vec_id >= N) return;
+  // use 32-bit indices locally to reduce register pressure
+  int size_local = static_cast<int>(sizes[vec_id]);
+  int b_size_local = static_cast<int>(b_sizes[vec_id]);
+  int threads_num = blockDim.x * gridDim.x;
+  int tid = blockIdx.x * blockDim.x + threadIdx.x;
+
+  extern __shared__ unsigned char shmem[];
+  B* sm_boundaries = reinterpret_cast<B*>(shmem);
+
+  // move data to shared buffer
+  const B* __restrict__ b_vec = b[vec_id];
+  if (threadIdx.x < b_size_local) {
+    sm_boundaries[threadIdx.x] = b_vec[threadIdx.x];
+  }
+  __syncthreads();
+
+  const A* __restrict__ a_vec = a[vec_id];
+  C* __restrict__ c_vec = c[vec_id];
+
+  // Process multiple elements per thread to improve ILP and reduce loop overhead
+  const int UNROLL = 8;
+  for (int index = tid; index < size_local; index += threads_num * UNROLL) {
+    int i0 = index;
+    // software prefetch of a few elements in the unroll group
+    A v0 = (i0 < size_local) ? a_vec[i0] : A(0);
+    A v1 = ((i0 + threads_num) < size_local) ? a_vec[i0 + threads_num] : A(0);
+    A v2 = ((i0 + 2 * threads_num) < size_local) ? a_vec[i0 + 2 * threads_num] : A(0);
+    A v3 = ((i0 + 3 * threads_num) < size_local) ? a_vec[i0 + 3 * threads_num] : A(0);
+    #pragma unroll
+    for (int u = 0; u < UNROLL; ++u) {
+      int ii = i0 + u * threads_num;
+      if (ii < size_local) {
+        A val = (u == 0) ? v0 : (u == 1) ? v1 : (u == 2) ? v2 : (u == 3) ? v3 : a_vec[ii];
+        c_vec[ii] = factory(val, sm_boundaries, b_size_local);
+      }
+    }
+  }
+}
+
+template <typename A, typename B, typename C>
+void fused_bucketize_launcher(const A** a, const B** b, C** c, int64_t* sizes,
+                                 int64_t* b_sizes, int64_t N,
+                                 BucketizeFactory factor,
+                                 bool with_pack,
+                                 hipStream_t stream) {
+  using namespace recis::cuda;
+  int64_t sm_count = get_sm_count();
+  int64_t max_size = 0;
+  for (int64_t i = 0; i < N; ++i) {
+    max_size = std::max(max_size, sizes[i]);
+  }
+  if (max_size == 0) return;
+  
+ // copy sizes to device
+  int64_t* d_sizes = cuda_malloc_and_copy<int64_t>(sizes, N, stream);
+  int64_t* d_b_sizes = cuda_malloc_and_copy<int64_t>(b_sizes, N, stream);
+
+  // shared memory size based on maximum boundary size across tensors
+  int64_t max_b_size = 0;
+  for (int64_t i = 0; i < N; ++i) {
+    max_b_size = std::max(max_b_size, b_sizes[i]);
+  }
+  size_t shared_memory_size = static_cast<size_t>(max_b_size + 1) * sizeof(B);
+
+  int maxActiveBlocksPerSM = 0;
+  C10_HIP_CHECK(hipOccupancyMaxActiveBlocksPerMultiprocessor(
+      &maxActiveBlocksPerSM,
+      fused_bucketize_kernel<A, B, C>,
+      KBLOCK_SIZE,
+      shared_memory_size));
+  if (maxActiveBlocksPerSM <= 0) {
+    maxActiveBlocksPerSM = 8; // fallback consistent with original heuristic
+  }
+
+  int64_t block_num = std::min<int64_t>(sm_count * maxActiveBlocksPerSM,
+                                        (max_size + KBLOCK_SIZE - 1) / KBLOCK_SIZE);
+  if (block_num <= 0) block_num = sm_count * maxActiveBlocksPerSM;
+  dim3 grid(block_num, N);
+  dim3 block(KBLOCK_SIZE);
+
+  fused_bucketize_kernel<A, B, C>
+        <<<grid, block, shared_memory_size, stream>>>(a, b, c, N, d_sizes, d_b_sizes,
+                                                    factor);
+  
+  C10_CUDA_KERNEL_LAUNCH_CHECK();
+  C10_CUDA_CHECK(cudaStreamSynchronize(stream));
+  delete_cuda_ptr(d_sizes);
+  delete_cuda_ptr(d_b_sizes);
+}
+#endif
 
 void fused_bucketized_cuda(std::vector<torch::Tensor>& inputs,
                            std::vector<torch::Tensor>& outputs,
@@ -45,6 +166,29 @@ void fused_bucketized_cuda(std::vector<torch::Tensor>& inputs,
   std::vector<int64_t> sizes(N);
   CudaVecParam<float*> inputs_ptrs(N, stream);
   CudaVecParam<int64_t*> outputs_ptrs(N, stream);
+  
+#ifdef __HIP_PLATFORM_AMD__
+  CudaVecParam<float*> boundaries_ptrs(N, stream);
+  std::vector<int64_t> b_sizes(N);
+  for (int64_t i = 0; i < N; ++i) {
+    sizes[i] = inputs[i].numel();
+    inputs_ptrs[i] = inputs[i].data_ptr<float>();
+    outputs_ptrs[i] = outputs[i].data_ptr<int64_t>();
+    boundaries_ptrs[i] = boundaries[i].data_ptr<float>();
+    b_sizes[i] = static_cast<int>(boundaries[i].numel());
+  }
+
+  fused_bucketize_launcher<float, float, int64_t>(
+      const_cast<const float**>(inputs_ptrs.data()),
+      const_cast<const float**>(boundaries_ptrs.data()),
+      outputs_ptrs.data(),
+      sizes.data(),
+      b_sizes.data(),
+      N,
+      BucketizeFactory(),
+      false,
+      stream);
+#else
   CudaVecParam<BucketizeData> bucketize_datas(N, stream);
   for (int64_t i = 0; i < N; ++i) {
     sizes[i] = inputs[i].numel();
@@ -57,6 +201,7 @@ void fused_bucketized_cuda(std::vector<torch::Tensor>& inputs,
   fused_element_wise_launcher<float, BucketizeData, int64_t, BucketizeFactory>(
       const_cast<const float**>(inputs_ptrs.data()), bucketize_datas.data(),
       outputs_ptrs.data(), sizes.data(), N, BucketizeFactory(), false, stream);
+#endif
 }
 }  // namespace functional
 }  // namespace recis