Merge pull request #17200 from YashasSamaga:cuda4dnn-general-opt1

cuda4dnn: optimizations for swish, mish, sigmoid, region, resize based ops, transpose, identity-conv fusion

* bunch of optimizations

* more accurate implementation for mish

modules/dnn/src/cuda/crop_and_resize.cu

@@ -9,6 +9,7 @@
 #include "types.hpp"
 #include "grid_stride_range.hpp"
 #include "execution.hpp"
+#include "memory.hpp"
 
 #include "../cuda4dnn/csl/stream.hpp"
 #include "../cuda4dnn/csl/tensor.hpp"
@@ -102,10 +103,10 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace kernels {
 
                 #pragma unroll 1 /* disable unrolling */
                 for (int i = 0; i < CHANNELS_PER_ITER; i++) {
-                    auto v_00 = input[in_offset_r0 + in_x0],
-                         v_01 = input[in_offset_r0 + in_x1],
-                         v_10 = input[in_offset_r1 + in_x0],
-                         v_11 = input[in_offset_r1 + in_x1];
+                    auto v_00 = load_ldg(input[in_offset_r0 + in_x0]),
+                         v_01 = load_ldg(input[in_offset_r0 + in_x1]),
+                         v_10 = load_ldg(input[in_offset_r1 + in_x0]),
+                         v_11 = load_ldg(input[in_offset_r1 + in_x1]);
 
                     output[out_idx] =
                         v_00 +

modules/dnn/src/cuda/functors.hpp

@@ -30,8 +30,10 @@ struct tanh_functor {
 template <class T>
 struct swish_functor {
     __device__ T operator()(T value) {
-        using csl::device::sigmoid;
-        return value * sigmoid(value);
+        // f(x) = x * sigmoid(x)
+        using csl::device::fast_divide;
+        using csl::device::fast_exp;
+        return fast_divide(value, static_cast<T>(1) + fast_exp(-value));
     }
 };
 
@@ -44,11 +46,30 @@ struct mish_functor {
     }
 };
 
+template <>
+struct mish_functor<float> {
+    __device__ float operator()(float value) {
+        // f(x) = x * tanh(log1pexp(x));
+        using csl::device::fast_divide;
+        using csl::device::fast_exp;
+
+        auto e = fast_exp(value);
+        if (value <= -18.0f)
+            return value * e;
+
+        auto n = e * e + 2 * e;
+        if (value <= -5.0f)
+            return value * fast_divide(n, n + 2);
+
+        return value - 2 * fast_divide(value, n + 2);
+    }
+};
+
 template <class T>
 struct sigmoid_functor {
     __device__ T operator()(T value) {
-        using csl::device::sigmoid;
-        return sigmoid(value);
+        using csl::device::fast_sigmoid;
+        return fast_sigmoid(value);
     }
 };
 

modules/dnn/src/cuda/math.hpp

@@ -160,6 +160,15 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace csl { namespace de
     template <> inline __device__ __half2 ceil(__half2 value) { return h2ceil(value); }
 #endif
 
+    template <class T> __device__ T fast_divide(T x, T y) { return x / y; }
+    template <> inline __device__ float fast_divide(float x, float y) { return __fdividef(x, y); }
+
+    template <class T> __device__ T fast_exp(T value) { return exp(value); }
+    template <> inline __device__ float fast_exp(float value) { return __expf(value); }
+
+    template <class T> __device__ T fast_sigmoid(T value) { return sigmoid(value); }
+    template <> inline __device__ float fast_sigmoid(float value) { return __fdividef(1, 1 + __expf(-value)); }
+
 }}}}} /* namespace cv::dnn::cuda4dnn::csl::device */
 
 #endif /* OPENCV_DNN_SRC_CUDA_MATH_HPP */

modules/dnn/src/cuda/memory.hpp

@@ -0,0 +1,32 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#ifndef OPENCV_DNN_SRC_CUDA_MEMORY_HPP
+#define OPENCV_DNN_SRC_CUDA_MEMORY_HPP
+
+#include <cuda_runtime.h>
+
+namespace cv { namespace dnn { namespace cuda4dnn { namespace csl { namespace device {
+
+template <class T>
+__device__ T load_ldg(const T& src) {
+#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 350)
+    return __ldg(&src);
+#else
+    return src;
+#endif
+}
+
+template <class T>
+__device__ T load_ldg(const T* src) {
+#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 350)
+    return __ldg(src);
+#else
+    return *src;
+#endif
+}
+
+}}}}} /* namespace cv::dnn::cuda4dnn::csl::device */
+
+#endif /* OPENCV_DNN_SRC_CUDA_MEMORY_HPP */

modules/dnn/src/cuda/permute.cu

@@ -7,7 +7,6 @@
 
 #include "array.hpp"
 #include "types.hpp"
-#include "vector_traits.hpp"
 #include "grid_stride_range.hpp"
 #include "execution.hpp"
 #include "kernel_dispatcher.hpp"
@@ -50,82 +49,60 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace kernels {
             }
         }
 
-        template <class T, int TILE_SIZE, std::size_t N>
+        template <class T, int TILE_SIZE, int ROWS_PER_THREAD>
         __global__ void transpose(Span<T> output, View<T> input, size_type in_width, size_type out_width)
         {
-            using vector_type = get_vector_type_t<T, N>;
-
             __shared__ T tile[TILE_SIZE][TILE_SIZE + 1];
 
-            /* blockDim.y = TILE_SIZE, blockDim.x = TILE_SIZE/N */
-            const index_type in_x = blockIdx.x * TILE_SIZE + threadIdx.x * N;
-            const index_type in_y = blockIdx.y * TILE_SIZE + threadIdx.y;
+            /* blockDim.y = TILE_SIZE / ROWS_PER_THREAD, blockDim.x = TILE_SIZE */
+            const index_type in_x = blockIdx.x * TILE_SIZE + threadIdx.x;
+            const index_type in_y_begin = blockIdx.y * TILE_SIZE + threadIdx.y;
 
             /* Every valid input location has a corresponding output location and vice versa.
              * Hence, if we do not load values into the shared memory for a given location, we
              * also won't read them for storing in the output.
              */
-            if (in_x < in_width && in_y < out_width)
+            for (int j = 0; j < TILE_SIZE; j += TILE_SIZE / ROWS_PER_THREAD)
             {
-                vector_type vec;
-                auto input_vPtr = vector_type::get_pointer(input.data());
-                v_load(vec, input_vPtr[(in_y * in_width + in_x) / N]);
-
-                for (int i = 0; i < vector_type::size(); i++)
-                    tile[threadIdx.y][threadIdx.x * N + i] = vec.data[i];
+                const auto in_y_current = in_y_begin + j;
+                if (in_x < in_width && in_y_current < out_width)
+                    tile[threadIdx.y + j][threadIdx.x] = input[in_y_current * in_width + in_x];
             }
 
             __syncthreads();
 
-            /* Note that `blockDim.x * N` is equal to `blockDim.y`. Since there are an equal
-             * number of them, we can interchange `threadIdx.x` and `threadIdx.y` without changing
-             * result. The advantage of interchanging is that consecutive output indices map to
+            /* We interchange `threadIdx.x` and `threadIdx.y` so that consecutive output indices map to
              * consecutive threads. This would allow writes across threds in a warp to be coalesced.
              */
-            const index_type out_x = blockIdx.y * TILE_SIZE + threadIdx.x * N;
-            const index_type out_y = blockIdx.x * TILE_SIZE + threadIdx.y;
+            const index_type out_x = blockIdx.y * TILE_SIZE + threadIdx.x;
+            const index_type out_y_begin = blockIdx.x * TILE_SIZE + threadIdx.y;
 
-            if (out_x < out_width && out_y < in_width)
+            for (int j = 0; j < TILE_SIZE; j += TILE_SIZE / ROWS_PER_THREAD)
             {
-                vector_type vec;
-                for (int i = 0; i < vector_type::size(); i++)
-                    vec.data[i] = tile[threadIdx.x * N + i][threadIdx.y];
-
-                auto output_vPtr = vector_type::get_pointer(output.data());
-                v_store(output_vPtr[(out_y * out_width + out_x) / N], vec);
+                const auto out_y_current = out_y_begin + j;
+                if (out_x < out_width && out_y_current < in_width)
+                    output[out_y_current * out_width + out_x] = tile[threadIdx.x][threadIdx.y + j];
             }
         }
     }
 
-    template <class T, std::size_t N> static
-    void launch_transpose_kernel(const Stream& stream, Span<T> output, View<T> input, size_type in_width, size_type out_width)
-    {
-        CV_Assert(is_fully_aligned<T>(output, N));
-        CV_Assert(is_fully_aligned<T>(input, N));
-        CV_Assert(in_width % N == 0);
-        CV_Assert(out_width % N == 0);
-
-        constexpr int TILE_SIZE = 32;
-        constexpr int TILE_SIZE_X = TILE_SIZE/N, TILE_SIZE_Y = TILE_SIZE;
-        auto kernel = raw::transpose<T, TILE_SIZE, N>;
-
-        dim3 grid_size((in_width/N + TILE_SIZE_X - 1)/TILE_SIZE_X, (out_width + TILE_SIZE_Y - 1)/TILE_SIZE_Y);
-        dim3 block_size(TILE_SIZE_X, TILE_SIZE_Y);
-        auto policy = execution_policy(grid_size, block_size, stream);
-
-        launch_kernel(kernel, policy, output, input, in_width, out_width);
-    }
-
     template <class T>
     void transpose(const Stream& stream, Span<T> output, View<T> input, std::size_t in_width, std::size_t out_width)
     {
-        if (is_fully_aligned<T>(output, 4) && is_fully_aligned<T>(input, 4) && in_width % 4 == 0 && out_width % 4 == 0) {
-            launch_transpose_kernel<T, 4>(stream, output, input, in_width, out_width);
-        } else if (is_fully_aligned<T>(output, 2) && is_fully_aligned<T>(input, 2) && in_width % 2 == 0 && out_width % 2 == 0) {
-            launch_transpose_kernel<T, 2>(stream, output, input, in_width, out_width);
-        } else {
-            launch_transpose_kernel<T, 1>(stream, output, input, in_width, out_width);
-        }
+        /* Each block processes a TILE_SIZE x TILE_SIZE piece */
+        constexpr int TILE_SIZE = 32;
+
+        /* Each thread processes ROWS_PER_THREAD rows. We do this to decrease the number of threads required
+         * in a block so that the cost of the block-wide synchronization is minimized.
+         */
+        constexpr int ROWS_PER_THREAD = 4;
+
+        dim3 grid_size((in_width + TILE_SIZE - 1) / TILE_SIZE, (out_width + TILE_SIZE - 1) / TILE_SIZE);
+        dim3 block_size(TILE_SIZE, TILE_SIZE / ROWS_PER_THREAD);
+        auto policy = execution_policy(grid_size, block_size, stream);
+
+        auto kernel = raw::transpose<T, TILE_SIZE, ROWS_PER_THREAD>;
+        launch_kernel(kernel, policy, output, input, in_width, out_width);
     }
 
     template void transpose(const Stream&, Span<__half>, View<__half>, std::size_t, std::size_t);

modules/dnn/src/cuda/region.cu

@@ -47,20 +47,20 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace kernels {
                 const auto y = (box_index % batch_inner_size) / row_inner_size;
                 const auto x = (box_index % row_inner_size) / col_inner_size;
 
-                using device::sigmoid;
-                output[box_offset + 0] = (T(x) + sigmoid(input[box_offset + 0])) / T(cols);
-                output[box_offset + 1] = (T(y) + sigmoid(input[box_offset + 1])) / T(rows);
+                using device::fast_sigmoid;
+                output[box_offset + 0] = (T(x) + fast_sigmoid(input[box_offset + 0])) / T(cols);
+                output[box_offset + 1] = (T(y) + fast_sigmoid(input[box_offset + 1])) / T(rows);
 
                 vector2_type bias_xy;
                 v_load(bias_xy, bias_vPtr[box_of_the_cell]);
 
-                using device::exp;
-                output[box_offset + 2] = exp(input[box_offset + 2]) * bias_xy.data[0] / T(width_norm);
-                output[box_offset + 3] = exp(input[box_offset + 3]) * bias_xy.data[1] / T(height_norm);
+                using device::fast_exp;
+                output[box_offset + 2] = fast_exp(input[box_offset + 2]) * bias_xy.data[0] / T(width_norm);
+                output[box_offset + 3] = fast_exp(input[box_offset + 3]) * bias_xy.data[1] / T(height_norm);
 
                 /* squash objectness score into a probability */
-                using device::sigmoid;
-                T objectness_prob = sigmoid(input[box_offset + 4]);
+                using device::fast_sigmoid;
+                T objectness_prob = fast_sigmoid(input[box_offset + 4]);
 
                 /* ignore prediction if the objectness probability is less than the cutoff */
                 if (objectness_prob < object_prob_cutoff)
@@ -91,7 +91,8 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace kernels {
                  * to obtain the actual class probability, we multiply the conditional probability
                  * with the object probability
                  */
-                auto actual_class_prob = objectness_prob * sigmoid(input[idx]);
+                using device::fast_sigmoid;
+                auto actual_class_prob = objectness_prob * fast_sigmoid(input[idx]);
                 if (actual_class_prob <= class_prob_cutoff)
                     actual_class_prob = T(0);
                 output[idx] = actual_class_prob;

modules/dnn/src/cuda/resize.cu

@@ -9,6 +9,7 @@
 #include "types.hpp"
 #include "grid_stride_range.hpp"
 #include "execution.hpp"
+#include "memory.hpp"
 
 #include "../cuda4dnn/csl/stream.hpp"
 #include "../cuda4dnn/csl/tensor.hpp"
@@ -70,7 +71,7 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace kernels {
                 index_type out_idx = c_start * out_image_size + y * out_width + x;
 
                 for (int i = 0; i < CHANNELS_PER_ITER; i++) {
-                    output[out_idx] = input[in_idx];
+                    output[out_idx] = load_ldg(input[in_idx]);
 
                     in_idx += in_image_size;
                     out_idx += out_image_size;
@@ -134,10 +135,10 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace kernels {
 
                 #pragma unroll 1 /* disable unrolling to reduce register pressure; not sure how but it works */
                 for (auto c = c_start; c < c_end; c++) {
-                    auto v_00 = input[in_offset_r0 + in_x0],
-                         v_01 = input[in_offset_r0 + in_x1],
-                         v_10 = input[in_offset_r1 + in_x0],
-                         v_11 = input[in_offset_r1 + in_x1];
+                    auto v_00 = load_ldg(input[in_offset_r0 + in_x0]),
+                         v_01 = load_ldg(input[in_offset_r0 + in_x1]),
+                         v_10 = load_ldg(input[in_offset_r1 + in_x0]),
+                         v_11 = load_ldg(input[in_offset_r1 + in_x1]);
 
                     output[out_idx] =
                         v_00 +

modules/dnn/src/cuda/roi_pooling.cu

@@ -10,6 +10,7 @@
 #include "types.hpp"
 #include "grid_stride_range.hpp"
 #include "execution.hpp"
+#include "memory.hpp"
 
 #include "../cuda4dnn/csl/stream.hpp"
 #include "../cuda4dnn/csl/tensor.hpp"
@@ -118,7 +119,7 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace kernels {
                         const auto in_idx = in_offset + iy * in_width;
                         for (auto ix = x_start; ix < x_end; ix++)
                         {
-                            max_val = max(max_val, input[in_idx + ix]);
+                            max_val = max(max_val, load_ldg(input[in_idx + ix]));
                         }
                     }
 

modules/dnn/src/cuda/vector_traits.hpp

@@ -8,6 +8,7 @@
 #include <cuda_runtime.h>
 
 #include "types.hpp"
+#include "memory.hpp"
 
 #include "../cuda4dnn/csl/pointer.hpp"
 
@@ -86,6 +87,16 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace csl { namespace de
         dest.raw = src->raw;
     }
 
+    template <class V>
+    __device__ void v_load_ldg(V& dest, const V& src) {
+        dest.raw = load_ldg(src.raw);
+    }
+
+    template <class V>
+    __device__ void v_load_ldg(V& dest, const V* src) {
+        dest.raw = load_ldg(src->raw);
+    }
+
     template <class V>
     __device__ void v_store(V* dest, const V& src) {
         dest->raw = src.raw;

modules/dnn/src/layers/convolution_layer.cpp

@@ -167,6 +167,10 @@ public:
 
     virtual bool tryFuse(Ptr<Layer>& top) CV_OVERRIDE
     {
+        Ptr<BlankLayer> blank_layer = top.dynamicCast<BlankLayer>();
+        if (blank_layer)
+            return true;
+
         Mat w, b;
         top->getScaleShift(w, b);
         if (!w.empty() || !b.empty())