mvn layer ocl update

it fuse ocl kernels to reduce kernel enqueue

Signed-off-by: Li Peng <peng.li@intel.com>

diff --git a/modules/dnn/src/layers/mvn_layer.cpp b/modules/dnn/src/layers/mvn_layer.cpp
index a74bc0e..c911b74 100644 (file)
--- a/modules/dnn/src/layers/mvn_layer.cpp
+++ b/modules/dnn/src/layers/mvn_layer.cpp
@@ -93,6 +93,67 @@ public:
     }
 
 #ifdef HAVE_OPENCL
+    bool fast_forward_ocl(std::vector<UMat> &inputs, std::vector<UMat> &outputs)
+    {
+        if( fuse_batch_norm && scale.empty())
+        {
+            bnorm->getScaleShift(scale, shift);
+            bnorm_weight = scale.getUMat(ACCESS_READ);
+            bnorm_bias = shift.getUMat(ACCESS_READ);
+        }
+
+        int splitDim = (acrossChannels) ? 1 : 2;
+        for (size_t inpIdx = 0; inpIdx < inputs.size(); inpIdx++)
+        {
+            UMat &inpMat = inputs[inpIdx];
+            UMat &outMat = outputs[inpIdx];
+            int newRows = total(shape(inpMat), 0, splitDim);
+
+            MatShape s = shape(newRows, inpMat.total() / newRows);
+            UMat oneMat = UMat::ones(s[1], 1, CV_32F);
+            UMat meanMat = UMat(s[0], 1, CV_32F);
+            UMat tmpMat  = UMat(s[0], s[1], CV_32F);
+            float alpha = 1.0f / s[1];
+
+            String buildopt = "-DNUM=4";
+            ocl::Kernel k("mean_fuse4", ocl::dnn::mvn_oclsrc, buildopt);
+            size_t localsize[] = { 128 };
+            size_t globalsize[] = { (size_t)s[0] / 4 * localsize[0] };
+
+            int argId = 0;
+            k.set(argId++, ocl::KernelArg::PtrReadOnly(inpMat));
+            k.set(argId++, (int)s[1]);
+            k.set(argId++, alpha);
+            k.set(argId++, ocl::KernelArg::PtrWriteOnly(meanMat));
+            k.set(argId++, ocl::KernelArg::PtrWriteOnly(tmpMat));
+            k.set(argId++, NULL, localsize[0] * sizeof(cl_float4));
+            bool ret = k.run(1, globalsize, localsize, false);
+            if (!ret)
+                return false;
+
+            buildopt += format(" %s %s", (fuse_batch_norm) ? "-DFUSE_BATCH_NORM" : "",
+                               (fuse_relu) ? "-DFUSE_RELU" : "");
+
+            ocl::Kernel k1("mvn_fuse4", ocl::dnn::mvn_oclsrc, buildopt);
+            argId = 0;
+            k1.set(argId++, ocl::KernelArg::PtrReadOnly(tmpMat));
+            k1.set(argId++, ocl::KernelArg::PtrReadOnly(inpMat));
+            k1.set(argId++, ocl::KernelArg::PtrReadOnly(meanMat));
+            k1.set(argId++, (int)s[1]);
+            k1.set(argId++, (float)alpha);
+            k1.set(argId++, (float)eps);
+            k1.set(argId++, (float)relu_slope);
+            k1.set(argId++, ocl::KernelArg::PtrReadOnly(bnorm_weight));
+            k1.set(argId++, ocl::KernelArg::PtrReadOnly(bnorm_bias));
+            k1.set(argId++, ocl::KernelArg::PtrWriteOnly(outMat));
+            k1.set(argId++, NULL, localsize[0] * sizeof(cl_float4));
+            ret = k1.run(1, globalsize, localsize, false);
+            if (!ret)
+                return false;
+        }
+        return true;
+    }
+
     bool forward_ocl(InputArrayOfArrays inputs_, OutputArrayOfArrays outputs_, OutputArrayOfArrays internals_)
     {
         std::vector<UMat> inputs;
@@ -101,6 +162,15 @@ public:
         inputs_.getUMatVector(inputs);
         outputs_.getUMatVector(outputs);
 
+        int splitDim = (acrossChannels) ? 1 : 2;
+        int row_size = total(shape(inputs[0]), 0, splitDim);
+        int plane_size = total(shape(inputs[0]), splitDim);
+        if (normVariance && (row_size % 4 == 0) && (plane_size % 4 == 0))
+        {
+            bool ret = fast_forward_ocl(inputs, outputs);
+            return ret;
+        }
+
         if( fuse_batch_norm && scale.empty())
         {
             bnorm->getScaleShift(scale, shift);
@@ -112,11 +182,7 @@ public:
         {
             UMat &inpMat = inputs[inpIdx];
             UMat &outMat = outputs[inpIdx];
-
-            int splitDim = (acrossChannels) ? 1 : 2;
-            int i, newRows = 1;
-            for( i = 0; i < splitDim; i++ )
-                newRows *= inpMat.size[i];
+            int newRows = total(shape(inpMat), 0, splitDim);
 
             MatShape s = shape(newRows, inpMat.total() / newRows);
             UMat oneMat = UMat::ones(s[1], 1, CV_32F);

diff --git a/modules/dnn/src/opencl/mvn.cl b/modules/dnn/src/opencl/mvn.cl
index cc059ee..9f8ab57 100644 (file)
--- a/modules/dnn/src/opencl/mvn.cl
+++ b/modules/dnn/src/opencl/mvn.cl
@@ -50,18 +50,24 @@
     #define vec_type Dtype8
     #define CALC_MEAN calc_mean8
     #define MVN mvn8
+    #define MEAN_FUSE mean_fuse8
+    #define MVN_FUSE mvn_fuse8
 #elif NUM == 4
     #define load(src, index) vload4(0, src + index)
     #define store(vec, dst, index) vstore4(vec, 0, dst + index)
     #define vec_type Dtype4
     #define CALC_MEAN calc_mean4
     #define MVN mvn4
+    #define MEAN_FUSE mean_fuse4
+    #define MVN_FUSE mvn_fuse4
 #elif NUM == 1
     #define load(src, index) src[index]
     #define store(vec, dst, index) dst[index] = vec
     #define vec_type Dtype
     #define CALC_MEAN calc_mean1
     #define MVN mvn1
+    #define MEAN_FUSE mean_fuse1
+    #define MVN_FUSE mvn_fuse1
 #endif
 
 __kernel void CALC_MEAN(__global const Dtype* src,
@@ -128,3 +134,177 @@ __kernel void MVN(__global const Dtype* src,
 
     store(dst_vec, dst, index);
 }
+
+__kernel void MEAN_FUSE(__global const Dtype * A,
+                        unsigned int A_col_size,
+                        float alpha,
+                        __global Dtype4 * result,
+                        __global Dtype * B,
+                        __local Dtype4 * work)
+{
+    unsigned int row_gid = get_group_id(0);
+    unsigned int lid = get_local_id(0);
+    const __global Dtype *src0_read = A + row_gid * 4 * A_col_size;
+    __global Dtype *dst0_read = B + row_gid * 4 * A_col_size;
+    Dtype4 dot0, dot1, dot2, dot3;
+    dot0 = dot1 = dot2 = dot3 = (Dtype4)(0.f);
+
+    unsigned int i = lid;
+    const Dtype4 b0 = (Dtype4)1.f;
+    while( i < A_col_size / 4)
+    {
+        const Dtype4 a0 = vload4(i, src0_read);
+        const Dtype4 a1 = vload4(i, src0_read + A_col_size);
+        const Dtype4 a2 = vload4(i, src0_read + 2 * A_col_size);
+        const Dtype4 a3 = vload4(i, src0_read + 3 * A_col_size);
+
+        dot0 += a0;
+        dot1 += a1;
+        dot2 += a2;
+        dot3 += a3;
+
+        i += get_local_size(0);
+    }
+
+    work[lid].s0 = dot(dot0, b0);
+    work[lid].s1 = dot(dot1, b0);
+    work[lid].s2 = dot(dot2, b0);
+    work[lid].s3 = dot(dot3, b0);
+
+    for(unsigned int stride=get_local_size(0)/2 ; stride>0 ; stride>>=1)
+    {
+        barrier(CLK_LOCAL_MEM_FENCE);
+        if(lid < stride)
+            work[lid] += work[lid+stride];
+    }
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    if(lid == 0)
+    {
+        result[row_gid] = alpha * work[0];
+    }
+
+    Dtype4 sum = work[0] * alpha;
+    i = lid;
+    while( i < A_col_size / 4)
+    {
+        const Dtype4 a0 = vload4(i, src0_read);
+        const Dtype4 a1 = vload4(i, src0_read + A_col_size);
+        const Dtype4 a2 = vload4(i, src0_read + 2 * A_col_size);
+        const Dtype4 a3 = vload4(i, src0_read + 3 * A_col_size);
+
+        dot0 = native_powr(a0 - (Dtype4)sum.x, 2);
+        dot1 = native_powr(a1 - (Dtype4)sum.y, 2);
+        dot2 = native_powr(a2 - (Dtype4)sum.z, 2);
+        dot3 = native_powr(a3 - (Dtype4)sum.w, 2);
+
+        vstore4(dot0, i, dst0_read);
+        vstore4(dot1, i, dst0_read + A_col_size);
+        vstore4(dot2, i, dst0_read + 2 * A_col_size);
+        vstore4(dot3, i, dst0_read + 3 * A_col_size);
+
+        i += get_local_size(0);
+    }
+}
+
+__kernel void MVN_FUSE(__global const Dtype * tmp,
+                       __global const Dtype * A,
+                       __global const Dtype4 * mean,
+                       unsigned int A_col_size,
+                       const float alpha_val,
+                       const float eps,
+                       const float relu_slope,
+                       __global const Dtype4 * bnorm_weight,
+                       __global const Dtype4 * bnorm_bias,
+                       __global Dtype * B,
+                       __local Dtype4 * work)
+{
+    unsigned int row_gid = get_group_id(0);
+    unsigned int lid = get_local_id(0);
+    const __global Dtype *src0_read = tmp + row_gid * 4 * A_col_size;
+    const __global Dtype *src1_read = A + row_gid * 4 * A_col_size;
+    __global Dtype *dst0_read = B + row_gid * 4 * A_col_size;
+    Dtype4 dot0, dot1, dot2, dot3;
+    dot0 = dot1 = dot2 = dot3 = (Dtype4)(0.f);
+
+    unsigned int i = lid;
+    const Dtype4 b0 = (Dtype4)1.f;
+    while( i < A_col_size / 4)
+    {
+        const Dtype4 a0 = vload4(i, src0_read);
+        const Dtype4 a1 = vload4(i, src0_read + A_col_size);
+        const Dtype4 a2 = vload4(i, src0_read + 2 * A_col_size);
+        const Dtype4 a3 = vload4(i, src0_read + 3 * A_col_size);
+
+        dot0 += a0;
+        dot1 += a1;
+        dot2 += a2;
+        dot3 += a3;
+
+        i += get_local_size(0);
+    }
+
+    work[lid].s0 = dot(dot0, b0);
+    work[lid].s1 = dot(dot1, b0);
+    work[lid].s2 = dot(dot2, b0);
+    work[lid].s3 = dot(dot3, b0);
+
+    for(unsigned int stride=get_local_size(0)/2 ; stride>0 ; stride>>=1)
+    {
+        barrier(CLK_LOCAL_MEM_FENCE);
+        if(lid < stride)
+            work[lid] += work[lid+stride];
+    }
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    Dtype4 mean_val = mean[row_gid];
+    Dtype4 dev_val = sqrt(work[0] * alpha_val) + (Dtype4)eps;
+    Dtype4 alpha = (Dtype4)1.f / dev_val;
+
+    Dtype4 w = (Dtype4)1.f;
+    Dtype4 b = (Dtype4)0.f;
+#ifdef FUSE_BATCH_NORM
+    w = bnorm_weight[row_gid];
+    b = bnorm_bias[row_gid];
+#endif
+
+    i = lid;
+    while( i < A_col_size / 4)
+    {
+        const Dtype4 a0 = vload4(i, src1_read);
+        const Dtype4 a1 = vload4(i, src1_read + A_col_size);
+        const Dtype4 a2 = vload4(i, src1_read + 2 * A_col_size);
+        const Dtype4 a3 = vload4(i, src1_read + 3 * A_col_size);
+
+        dot0 = (a0 - (Dtype4)mean_val.x) * alpha.x;
+        dot1 = (a1 - (Dtype4)mean_val.y) * alpha.y;
+        dot2 = (a2 - (Dtype4)mean_val.z) * alpha.z;
+        dot3 = (a3 - (Dtype4)mean_val.w) * alpha.w;
+
+        dot0 = dot0 * w.x + (Dtype4)b.x;
+        dot1 = dot1 * w.y + (Dtype4)b.y;
+        dot2 = dot2 * w.z + (Dtype4)b.z;
+        dot3 = dot3 * w.w + (Dtype4)b.w;
+
+#ifdef FUSE_RELU
+        Dtype4 new0 = dot0 * relu_slope;
+        dot0 = select(new0, dot0, dot0 > (Dtype4)0.f);
+
+        Dtype4 new1 = dot1 * relu_slope;
+        dot1 = select(new1, dot1, dot1 > (Dtype4)0.f);
+
+        Dtype4 new2 = dot2 * relu_slope;
+        dot2 = select(new2, dot2, dot2 > (Dtype4)0.f);
+
+        Dtype4 new3 = dot3 * relu_slope;
+        dot3 = select(new3, dot3, dot3 > (Dtype4)0.f);
+#endif
+
+        vstore4(dot0, i, dst0_read);
+        vstore4(dot1, i, dst0_read + A_col_size);
+        vstore4(dot2, i, dst0_read + 2 * A_col_size);
+        vstore4(dot3, i, dst0_read + 3 * A_col_size);
+
+        i += get_local_size(0);
+    }
+}