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mvn layer ocl update

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it fuse ocl kernels to reduce kernel enqueue

Signed-off-by: Li Peng <peng.li@intel.com>
This commit is contained in:
Li Peng 2018-01-31 19:28:58 +08:00
parent 83b16ab7b7
commit 6aec71d7ee
2 changed files with 251 additions and 5 deletions
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76
modules/dnn/src/layers/mvn_layer.cpp
View File

@ -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);

180
modules/dnn/src/opencl/mvn.cl
View File

@ -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);
}
}