Improve topk performance. (#21087)

* Improve topk performance.

give 200000 data to compute topk,
before opt: cost 1s
after opt: cost 0.0028s.

* Refine return value.
* Add cuda util funtions.
* Fix ComputeBlockSize bug & refine comments.
Signed-off-by: Nzhaoyuchen <zhaoyuchen01@baidu.com>

paddle/fluid/operators/top_k_op.cc

@@ -42,6 +42,11 @@ class TopkOp : public framework::OperatorWithKernel {
 
     framework::DDim dims = input_dims;
     dims[dims.size() - 1] = k;
+    // If has K as tensor, set k=-1 as not know real size at this time.
+    if (ctx->HasInput("K")) {
+      dims[dims.size() - 1] = -1;
+    }
+
     ctx->SetOutputDim("Out", dims);
     ctx->SetOutputDim("Indices", dims);
     ctx->ShareLoD("X", "Out");

paddle/fluid/operators/top_k_op.cu

@@ -12,11 +12,20 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License. */
 
+#include "cub/cub.cuh"
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/operators/top_k_op.h"
 #include "paddle/fluid/platform/cuda_device_function.h"
 #include "paddle/fluid/platform/float16.h"
 
+// set cub base traits in order to handle float16
+namespace cub {
+template <>
+struct NumericTraits<paddle::platform::float16>
+    : BaseTraits<FLOATING_POINT, true, false, uint16_t,
+                 paddle::platform::float16> {};
+}  // namespace cub
+
 namespace paddle {
 namespace operators {
 
@@ -303,6 +312,157 @@ inline static int GetDesiredBlockDim(int dim) {
   }
 }
 
+// Iter for move to next row
+struct SegmentOffsetIter {
+  EIGEN_DEVICE_FUNC
+  explicit SegmentOffsetIter(int num_cols) : num_cols_(num_cols) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int operator()(int idx) const {
+    return idx * num_cols_;
+  }
+
+  int num_cols_;
+};
+
+// Iter using into a column
+struct ColumnIndexIter {
+  explicit ColumnIndexIter(int num_cols) : num_cols_(num_cols) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int operator()(
+      const Eigen::array<int, 1>& ix) const {
+    return ix[0] % num_cols_;
+  }
+
+  int num_cols_;
+};
+
+__global__ void InitIndex(int64_t* indices, int num_rows, int num_cols) {
+  int col_id = threadIdx.x;
+  int row_id = blockIdx.x;
+
+  for (int j = row_id; j < num_rows; j += gridDim.x) {
+    for (int i = col_id; i < num_cols; i += blockDim.x) {
+      indices[j * num_cols + i] = i;
+    }
+  }
+}
+
+template <typename T>
+bool SortTopk(const platform::CUDADeviceContext& ctx,
+              const framework::Tensor* input_tensor, const size_t num_cols,
+              const size_t num_rows, size_t k, framework::Tensor* out_tensor,
+              framework::Tensor* indices_tensor) {
+  auto cu_stream = ctx.stream();
+
+  Tensor input_indices;
+  const std::vector<int64_t> dims = {static_cast<int64_t>(num_rows),
+                                     static_cast<int64_t>(num_cols)};
+  auto dim = framework::make_ddim(dims);
+  input_indices.Resize(dim);
+  // input_indices.Resize(num_rows*num_cols);
+  input_indices.mutable_data<int64_t>(ctx.GetPlace());
+  size_t temp_storage_bytes = -1;
+
+  auto ComputeBlockSize = [](int col) {
+    if (col > 512)
+      return 1024;
+    else if (col > 256 && col <= 512)
+      return 512;
+    else if (col > 128 && col <= 256)
+      return 256;
+    else if (col > 64 && col <= 128)
+      return 128;
+    else
+      return 64;
+  };
+
+  int block_size = ComputeBlockSize(num_cols);
+
+  int maxGridDimX = ctx.GetCUDAMaxGridDimSize().x;
+  // actually, int num_rows < max_grid_size
+  int grid_size = num_rows < maxGridDimX ? num_rows : maxGridDimX;
+  // Init a index array
+  InitIndex<<<grid_size, block_size, 0, cu_stream>>>(
+      input_indices.data<int64_t>(), num_rows, num_cols);
+
+  // create iter for counting input
+  cub::CountingInputIterator<int> counting_iter(0);
+  // segment_offset is used for move to next row
+  cub::TransformInputIterator<int, SegmentOffsetIter,
+                              cub::CountingInputIterator<int>>
+      segment_offsets_t(counting_iter, SegmentOffsetIter(num_cols));
+
+  T* sorted_values_ptr;
+  int64_t* sorted_indices_ptr;
+
+  Tensor temp_values;
+  Tensor temp_indices;
+
+  const T* input = input_tensor->data<T>();
+  T* values = out_tensor->data<T>();
+  int64_t* indices = indices_tensor->mutable_data<int64_t>(ctx.GetPlace());
+
+  if (k == num_cols) {
+    // Doing a full sort.
+    sorted_values_ptr = values;
+    sorted_indices_ptr = indices;
+  } else {
+    temp_values.Resize(dim);
+    temp_indices.Resize(dim);
+    sorted_values_ptr = temp_values.mutable_data<T>(ctx.GetPlace());
+    sorted_indices_ptr = temp_indices.mutable_data<int64_t>(ctx.GetPlace());
+  }
+
+  // Get temp storage buffer size, maybe can allocate a fixed buffer to save
+  // time.
+  auto err = cub::DeviceSegmentedRadixSort::SortPairsDescending(
+      nullptr, temp_storage_bytes, input, sorted_values_ptr,
+      input_indices.data<int64_t>(), sorted_indices_ptr, num_cols * num_rows,
+      num_rows, segment_offsets_t, segment_offsets_t + 1, 0, sizeof(T) * 8,
+      cu_stream);
+  if (err != cudaSuccess) {
+    LOG(ERROR)
+        << "TopKOP failed as could not launch "
+           "cub::DeviceSegmentedRadixSort::SortPairsDescending to calculate "
+           "temp_storage_bytes, status: "
+        << cudaGetErrorString(err);
+    return false;
+  }
+  Tensor temp_storage;
+  temp_storage.mutable_data<uint8_t>(ctx.GetPlace(), temp_storage_bytes);
+
+  err = cub::DeviceSegmentedRadixSort::SortPairsDescending(
+      temp_storage.data<uint8_t>(), temp_storage_bytes, input,
+      sorted_values_ptr, input_indices.data<int64_t>(), sorted_indices_ptr,
+      num_cols * num_rows, num_rows, segment_offsets_t, segment_offsets_t + 1,
+      0, sizeof(T) * 8, cu_stream);
+  if (err != cudaSuccess) {
+    LOG(ERROR)
+        << "TopKOP failed as could not launch "
+           "cub::DeviceSegmentedRadixSort::SortPairsDescending to sort input, "
+           "temp_storage_bytes: "
+        << temp_storage_bytes << ", status: " << cudaGetErrorString(err);
+    return false;
+  }
+  auto& dev = *ctx.eigen_device();
+  if (k < num_cols) {
+    // copy sliced data to output.
+    const Eigen::DSizes<Eigen::DenseIndex, 2> slice_indices{0, 0};
+    const Eigen::DSizes<Eigen::DenseIndex, 2> slice_sizes{num_rows, k};
+    auto e_indices = EigenMatrix<int64_t>::From(*indices_tensor, dim);
+    auto e_tmp_indices = EigenMatrix<int64_t>::From(temp_indices);
+
+    std::vector<int> odims = {static_cast<int>(num_rows), static_cast<int>(k)};
+    auto dim = framework::make_ddim(odims);
+    auto e_values = EigenMatrix<T>::From(*out_tensor, dim);
+    auto e_tmp_values = EigenMatrix<T>::From(temp_values);
+
+    e_indices.device(dev) = e_tmp_indices.slice(slice_indices, slice_sizes);
+    e_values.device(dev) = e_tmp_values.slice(slice_indices, slice_sizes);
+  }
+  return true;
+}
+
 #define FIXED_BLOCK_DIM_BASE(dim, ...) \
   case (dim): {                        \
     constexpr auto kBlockDim = (dim);  \
@@ -340,13 +500,24 @@ class TopkOpCUDAKernel : public framework::OpKernel<T> {
     const T* input_data = input->data<T>();
     T* output_data = output->mutable_data<T>(ctx.GetPlace());
     // FIXME(typhoonzero): data is always converted to type T?
-    int64_t* indices_data = indices->mutable_data<int64_t>(ctx.GetPlace());
 
     framework::DDim inputdims = input->dims();
     const size_t input_height = framework::product(
         framework::slice_ddim(inputdims, 0, inputdims.size() - 1));
     const size_t input_width = inputdims[inputdims.size() - 1];
+    const auto& dev_ctx = ctx.cuda_device_context();
 
+    if ((input_width <= 1024 || k >= 128 || k == input_width)) {
+      if (SortTopk<T>(dev_ctx, input, input_width, input_height, k, output,
+                      indices)) {
+        // Successed, return.
+        return;
+      } else {
+        LOG(INFO) << "TopKOP: Some errors happened when use cub sorting, use "
+                     "default topk kernel.";
+      }
+    }
+    int64_t* indices_data = indices->mutable_data<int64_t>(ctx.GetPlace());
     if (k > input_width) k = input_width;
 
     // NOTE: pass lds and dim same to input width.
@@ -354,7 +525,6 @@ class TopkOpCUDAKernel : public framework::OpKernel<T> {
     // TODO(typhoonzero): refine this kernel.
     const int kMaxHeight = 2048;
     int gridx = input_height < kMaxHeight ? input_height : kMaxHeight;
-    auto& dev_ctx = ctx.cuda_device_context();
     switch (GetDesiredBlockDim(input_width)) {
       FIXED_BLOCK_DIM(
           KeMatrixTopK<T, 5,

paddle/fluid/platform/device_context.cc

@@ -216,6 +216,7 @@ CUDADeviceContext::CUDADeviceContext(CUDAPlace place) : place_(place) {
   compute_capability_ = GetCUDAComputeCapability(place_.device);
   multi_process_ = GetCUDAMultiProcessors(place_.device);
   max_threads_per_mp_ = GetCUDAMaxThreadsPerMultiProcessor(place_.device);
+  max_grid_dim_size_ = GetGpuMaxGridDimSize(place_.device);
   PADDLE_ENFORCE_CUDA_SUCCESS(cudaStreamCreate(&stream_));
   eigen_stream_.reset(new EigenCudaStreamDevice());
   eigen_stream_->Reinitialize(&stream_, place);
@@ -342,6 +343,10 @@ bool CUDADeviceContext::tensor_core_available() const {
   return cublas_tensor_core_handle_ != nullptr;
 }
 
+dim3 CUDADeviceContext::GetCUDAMaxGridDimSize() const {
+  return max_grid_dim_size_;
+}
+
 cudnnHandle_t CUDADeviceContext::cudnn_handle() const { return cudnn_handle_; }
 
 CudnnWorkspaceHandle CUDADeviceContext::cudnn_workspace_handle() const {

paddle/fluid/platform/device_context.h

@@ -96,6 +96,9 @@ class CUDADeviceContext : public DeviceContext {
   /*! \brief  Return the max physical thread count in the device context */
   int GetMaxPhysicalThreadCount() const;
 
+  /*! \brief  Return the max grid dim size in the device context */
+  dim3 GetCUDAMaxGridDimSize() const;
+
   /*! \brief  Return eigen device in the device context. */
   Eigen::GpuDevice* eigen_device() const;
 
@@ -184,6 +187,7 @@ class CUDADeviceContext : public DeviceContext {
   int driver_version_;
   int multi_process_;
   int max_threads_per_mp_;
+  dim3 max_grid_dim_size_;
 
   // StreamCallbackManager is thread-safe
   std::unique_ptr<StreamCallbackManager> callback_manager_;

paddle/fluid/platform/gpu_info.cc

@@ -99,6 +99,33 @@ int GetCUDAComputeCapability(int id) {
   return major * 10 + minor;
 }
 
+dim3 GetGpuMaxGridDimSize(int id) {
+  PADDLE_ENFORCE_LT(id, GetCUDADeviceCount(), "id must less than GPU count");
+  dim3 ret;
+  int size;
+  auto error_code_x = cudaDeviceGetAttribute(&size, cudaDevAttrMaxGridDimX, id);
+  PADDLE_ENFORCE_EQ(error_code_x, 0,
+                    "cudaDevAttrMaxGridDimX failed in "
+                    "paddle::platform::GpuMaxGridDimSize, error code : %d, %s",
+                    error_code_x, CudaErrorWebsite());
+  ret.x = size;
+
+  auto error_code_y = cudaDeviceGetAttribute(&size, cudaDevAttrMaxGridDimY, id);
+  PADDLE_ENFORCE_EQ(error_code_y, 0,
+                    "cudaDevAttrMaxGridDimY failed in "
+                    "paddle::platform::GpuMaxGridDimSize, error code : %d, %s",
+                    error_code_y, CudaErrorWebsite());
+  ret.y = size;
+
+  auto error_code_z = cudaDeviceGetAttribute(&size, cudaDevAttrMaxGridDimZ, id);
+  PADDLE_ENFORCE_EQ(error_code_z, 0,
+                    "cudaDevAttrMaxGridDimZ failed in "
+                    "paddle::platform::GpuMaxGridDimSize, error code : %d, %s",
+                    error_code_z, CudaErrorWebsite());
+  ret.z = size;
+  return ret;
+}
+
 int GetCUDARuntimeVersion(int id) {
   PADDLE_ENFORCE_LT(id, GetCUDADeviceCount(), "id must less than GPU count");
   int runtime_version = 0;

paddle/fluid/platform/gpu_info.h

@@ -48,6 +48,9 @@ int GetCUDAMaxThreadsPerMultiProcessor(int i);
 //! Get the current GPU device id in system.
 int GetCurrentDeviceId();
 
+//! Get the maximum GridDim size for GPU buddy allocator.
+dim3 GetGpuMaxGridDimSize(int);
+
 //! Get a list of device ids from environment variable or use all.
 std::vector<int> GetSelectedDevices();