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add judgement for CL

This commit is contained in:
pengyongrong 2020-08-03 10:55:53 +08:00
parent 6a1e6b01f7
commit d831f8df5e
9 changed files with 194 additions and 319 deletions
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4
build.sh
View File

@ -510,12 +510,8 @@ gene_ocl_program() {
build_opencl() {
cd ${BASEPATH}
if [[ ! -d "third_party/OpenCL-Headers" ]]; then
git submodule update --init third_party/OpenCL-Headers
fi
if [[ ! -d "third_party/OpenCL-CLHPP" ]]; then
git submodule update --init third_party/OpenCL-CLHPP
fi
if [[ "${OPENCL_OFFLINE_COMPILE}" == "on" ]]; then
gene_ocl_program
else

42
mindspore/lite/src/runtime/kernel/opencl/cl/fp16/depthwise_conv2d.cl
View File

@ -13,19 +13,9 @@
__constant sampler_t smp_edge = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP_TO_EDGE | CLK_FILTER_NEAREST;
__constant sampler_t smp_none = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_NONE | CLK_FILTER_NEAREST;
__constant sampler_t smp_zero = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST;
__kernel void DepthwiseConv2d_NC4HW4(
__global FLT4* src_data,
__global FLT4* filters,
__global FLT4* biases,
float relu_clip1,
__global FLT4* dst_data,
int2 kernel_size,
int2 stride,
int2 padding,
int2 dilation,
int4 src_size,
int4 dst_size
) {
__kernel void DepthwiseConv2d_NC4HW4(__global FLT4 *src_data, __global FLT4 *filters, __global FLT4 *biases,
float relu_clip1, __global FLT4 *dst_data, int2 kernel_size, int2 stride,
int2 padding, int2 dilation, int4 src_size, int4 dst_size) {
int X = get_global_id(0);
int Y = get_global_id(1);
int Z = get_global_id(2);
@ -42,31 +32,21 @@ __global FLT4* dst_data,
bool outside_x = x_c < 0 || x_c >= src_size.x;
if (!outside_x && !outside_y) {
FLT4 f = filters[fx_c];
FLT4 src_final =src_data[(((Z) * src_size.y + (y_c)) * src_size.x + (x_c))];
FLT4 src_final = src_data[(((Z)*src_size.y + (y_c)) * src_size.x + (x_c))];
r += TO_ACCUM_TYPE(src_final * f);
};
}
fx_c++;
}
}
FLT4 bias_val = biases[Z];
FLT4 res0 = TO_FLT4(r) + bias_val;
res0 = clamp(res0, (FLT)(0.0f), (FLT)(relu_clip1));
dst_data[(((Z) * dst_size.y + (Y)) * dst_size.x + (X))] = res0;
dst_data[(((Z)*dst_size.y + (Y)) * dst_size.x + (X))] = res0;
}
__kernel void DepthwiseConv2d_NHWC4(
__global FLT4* src_data,
__global FLT4* filters,
__global FLT4* biases,
float relu_clip1,
__global FLT4* dst_data,
int2 kernel_size,
int2 stride,
int2 padding,
int2 dilation,
int4 src_size,
int4 dst_size
) {
__kernel void DepthwiseConv2d_NHWC4(__global FLT4 *src_data, __global FLT4 *filters, __global FLT4 *biases,
float relu_clip1, __global FLT4 *dst_data, int2 kernel_size, int2 stride,
int2 padding, int2 dilation, int4 src_size, int4 dst_size) {
int X = get_global_id(0);
int Y = get_global_id(1);
int Z = get_global_id(2);
@ -83,9 +63,9 @@ __global FLT4* dst_data,
bool outside_x = x_c < 0 || x_c >= src_size.x;
if (!outside_x && !outside_y) {
FLT4 f = filters[fx_c];
FLT4 src_final =src_data[((y_c * src_size.x + x_c) * src_size.z + Z)];
FLT4 src_final = src_data[((y_c * src_size.x + x_c) * src_size.z + Z)];
r += TO_ACCUM_TYPE(src_final * f);
};
}
fx_c++;
}
}

5
mindspore/lite/src/runtime/kernel/opencl/cl/fp16/matmul.cl
View File

@ -1,9 +1,8 @@
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#define FLT4 half4
#define FLT16 half16
__kernel void MatMul(__global FLT4 *x, __global FLT16 *weight,
__global FLT4 *buffer, __global FLT4 *bias, int2 offset_ci,
int2 offset_co, int has_bias) {
__kernel void MatMul(__global FLT4 *x, __global FLT16 *weight, __global FLT4 *buffer, __global FLT4 *bias,
int2 offset_ci, int2 offset_co, int has_bias) {
int2 gid = (int2)(get_global_id(0), get_global_id(1));
int2 lid = (int2)(get_local_id(0), get_local_id(1));
FLT4 s = (FLT4)(0.0f);

1
mindspore/lite/src/runtime/kernel/opencl/cl/fp32/avg_pool2d.cl
View File

@ -31,7 +31,6 @@ __kernel void AvgPooling2d(__global float4 *input, __global float4 *output, cons
__constant sampler_t smp_zero = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST;
__kernel void AvgPooling2dImage2d(__read_only image2d_t input, __write_only image2d_t output, const int4 input_shape,
const int4 output_shape, const int2 stride, const int2 kernel_size,
const int2 padding) {

105
mindspore/lite/src/runtime/kernel/opencl/cl/fp32/convolution.cl
View File

@ -1,18 +1,14 @@
#define CI_TILE 4
#define CO_TILE 4
#define UP_DIV(x, y) (((x) + (y) - (1)) / (y))
//#define __global
//#pragma OPENCL EXTENSION cl_arm_printf : enable
__kernel void convolution_NHWC_OHWI(__global float *input,
__global float *weight,
__global float *bias,
// #define __global
// #pragma OPENCL EXTENSION cl_arm_printf : enable
__kernel void convolution_NHWC_OHWI(__global float *input, __global float *weight, __global float *bias,
__global float *output,
const int4 input_shape, // NHWC
const int4 output_shape, // NHWC
const int4 kernel_stride, // kernelHW_strideHW
const int4 pad) // top bottom left right
{
const int4 pad) {
int ow = get_global_id(0);
int oh = get_global_id(1);
int co_slice = get_global_id(2);
@ -25,23 +21,17 @@ __kernel void convolution_NHWC_OHWI(__global float *input,
int CI_SLICES = UP_DIV(CI, CI_TILE);
int CO_SLICES = UP_DIV(CO, CO_TILE);
if (oh >= OH || ow >= OW || co_slice >= CO_SLICES)
return;
if (oh >= OH || ow >= OW || co_slice >= CO_SLICES) return;
float4 acc = (float4)(0.0f, 0.0f, 0.0f, 0.0f);
for (int kh = 0; kh < KH; ++kh)
{
for (int kh = 0; kh < KH; ++kh) {
int ih = kh + oh * strideH - padTop;
for (int kw = 0; kw < KW; ++kw)
{
for (int kw = 0; kw < KW; ++kw) {
int iw = kw + ow * strideW - padLeft;
for (int ci_slice = 0; ci_slice < CI_SLICES; ++ci_slice)
{
for (int ci_inner = 0; ci_inner < CI_TILE; ++ci_inner)
{
for (int ci_slice = 0; ci_slice < CI_SLICES; ++ci_slice) {
for (int ci_inner = 0; ci_inner < CI_TILE; ++ci_inner) {
int ci = ci_slice * CI_TILE + ci_inner;
if (ci >= CI)
break;
if (ci >= CI) break;
int input_idx = ih * IW * CI + iw * CI + ci;
float value = 0;
@ -51,10 +41,7 @@ __kernel void convolution_NHWC_OHWI(__global float *input,
value = input[input_idx];
int CO_OFFSET = KH * KW * CI;
int weight_idx = (co_slice * CO_TILE) * CO_OFFSET +
kh * KW * CI +
kw * CI +
ci;
int weight_idx = (co_slice * CO_TILE) * CO_OFFSET + kh * KW * CI + kw * CI + ci;
acc.x += weight[weight_idx + 0 * CO_OFFSET] * value;
acc.y += weight[weight_idx + 1 * CO_OFFSET] * value;
acc.z += weight[weight_idx + 2 * CO_OFFSET] * value;
@ -64,44 +51,32 @@ __kernel void convolution_NHWC_OHWI(__global float *input,
}
}
int output_idx = oh * OW * CO + ow * CO + (co_slice * CO_TILE);
if (co_slice < CO_SLICES - 1 || CO % CO_TILE == 0)
{
if (co_slice < CO_SLICES - 1 || CO % CO_TILE == 0) {
output[output_idx + 0] = acc.x + bias[co_slice * CO_TILE + 0];
output[output_idx + 1] = acc.y + bias[co_slice * CO_TILE + 1];
output[output_idx + 2] = acc.z + bias[co_slice * CO_TILE + 2];
output[output_idx + 3] = acc.w + bias[co_slice * CO_TILE + 3];
}
else if (CO % CO_TILE == 1)
{
} else if (CO % CO_TILE == 1) {
output[output_idx + 0] = acc.x + bias[co_slice * CO_TILE + 0];
}
else if (CO % CO_TILE == 2)
{
} else if (CO % CO_TILE == 2) {
output[output_idx + 0] = acc.x + bias[co_slice * CO_TILE + 0];
output[output_idx + 1] = acc.y + bias[co_slice * CO_TILE + 1];
}
else if (CO % CO_TILE == 3)
{
} else if (CO % CO_TILE == 3) {
output[output_idx + 0] = acc.x + bias[co_slice * CO_TILE + 0];
output[output_idx + 1] = acc.y + bias[co_slice * CO_TILE + 1];
output[output_idx + 2] = acc.z + bias[co_slice * CO_TILE + 2];
}
}
//#pragma OPENCL EXTENSION cl_khr_fp16 : enable
//#define FLT4 half4
// #pragma OPENCL EXTENSION cl_khr_fp16 : enable
// #define FLT4 half4
#define FLT4 float4
__kernel void convolution_NHWC4_OHWIIO_float8(__global FLT4 *input,
__global FLT4 *weight,
__global FLT4 *bias,
__kernel void convolution_NHWC4_OHWIIO_float8(__global FLT4 *input, __global FLT4 *weight, __global FLT4 *bias,
__global FLT4 *output,
const int4 input_shape, // NHWC
const int4 output_shape, // NHWC
const int4 kernel_stride, // kernelHW_strideHW
const int4 pad) // top bottom left right
{
const int4 pad) {
int oh = get_global_id(0); // [0, OH)
int ow = get_global_id(1); // [0, OW)
int co_slice = get_global_id(2); // [0, UP_DIV(CO, CO_TILE) )
@ -114,22 +89,16 @@ __kernel void convolution_NHWC4_OHWIIO_float8(__global FLT4 *input,
int strideH = kernel_stride.z, strideW = kernel_stride.w;
int padTop = pad.x, padLeft = pad.z;
if (oh >= OH || ow >= OW || 2 * co_slice >= CO_SLICES)
return;
if (2 * co_slice + 1 >= CO_SLICES)
{
if (oh >= OH || ow >= OW || 2 * co_slice >= CO_SLICES) return;
if (2 * co_slice + 1 >= CO_SLICES) {
FLT4 out0_c4 = (FLT4)(0.0f, 0.0f, 0.0f, 0.0f);
__global FLT4 *w0_ic1_oc4 = weight + (2 * co_slice + 0) * KH * KW * CI_SLICES * CI_TILE;
for (int kh = 0; kh < KH; ++kh)
{
for (int kh = 0; kh < KH; ++kh) {
int ih = kh + oh * strideH - padTop;
for (int kw = 0; kw < KW; ++kw)
{
for (int kw = 0; kw < KW; ++kw) {
int iw = kw + ow * strideW - padLeft;
if (ih >= 0 && ih < IH && iw >= 0 && iw < IW)
{
for (int ci_slice = 0; ci_slice < CI_SLICES; ci_slice++)
{
if (ih >= 0 && ih < IH && iw >= 0 && iw < IW) {
for (int ci_slice = 0; ci_slice < CI_SLICES; ci_slice++) {
FLT4 in_c4 = input[ih * IW * CI_SLICES + iw * CI_SLICES + ci_slice];
out0_c4 += w0_ic1_oc4[0] * in_c4.x;
out0_c4 += w0_ic1_oc4[1] * in_c4.y;
@ -137,32 +106,24 @@ __kernel void convolution_NHWC4_OHWIIO_float8(__global FLT4 *input,
out0_c4 += w0_ic1_oc4[3] * in_c4.w;
w0_ic1_oc4 += 4;
}
}
else
{
} else {
w0_ic1_oc4 += 4 * CI_SLICES;
}
}
}
output[oh * OW * CO_SLICES + ow * CO_SLICES + 2 * co_slice + 0] = out0_c4 + bias[2 * co_slice + 0];
}
else
{
} else {
FLT4 out0_c4 = (FLT4)(0.0f, 0.0f, 0.0f, 0.0f);
FLT4 out1_c4 = (FLT4)(0.0f, 0.0f, 0.0f, 0.0f);
__global FLT4 *w0_ic1_oc4 = weight + (2 * co_slice + 0) * KH * KW * CI_SLICES * CI_TILE;
__global FLT4 *w1_ic1_oc4 = weight + (2 * co_slice + 1) * KH * KW * CI_SLICES * CI_TILE;
for (int kh = 0; kh < KH; ++kh)
{
for (int kh = 0; kh < KH; ++kh) {
int ih = kh + oh * strideH - padTop;
for (int kw = 0; kw < KW; ++kw)
{
for (int kw = 0; kw < KW; ++kw) {
int iw = kw + ow * strideW - padLeft;
if (ih >= 0 && ih < IH && iw >= 0 && iw < IW)
{
if (ih >= 0 && ih < IH && iw >= 0 && iw < IW) {
int idx = ih * IW * CI_SLICES + iw * CI_SLICES;
for (int ci_slice = 0; ci_slice < CI_SLICES; ci_slice++)
{
for (int ci_slice = 0; ci_slice < CI_SLICES; ci_slice++) {
FLT4 in_c4 = input[idx + ci_slice];
out0_c4 += w0_ic1_oc4[0] * in_c4.x;
@ -177,9 +138,7 @@ __kernel void convolution_NHWC4_OHWIIO_float8(__global FLT4 *input,
out1_c4 += w1_ic1_oc4[3] * in_c4.w;
w1_ic1_oc4 += 4;
}
}
else
{
} else {
w0_ic1_oc4 += 4 * CI_SLICES;
w1_ic1_oc4 += 4 * CI_SLICES;
}

128
mindspore/lite/src/runtime/kernel/opencl/cl/fp32/depthwise_conv2d.cl
View File

@ -8,18 +8,9 @@
#define TO_FLT4 convert_float4
#endif
__constant sampler_t sampler_zero = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST;
__kernel void DepthwiseConv2d_IMG_NC4HW4(
__read_only image2d_t src_data,
__global FLT4* filter,
__global FLT4* bias,
float relu_clip1,
__write_only image2d_t dst_data,
int2 kernel_size,
int2 stride,
int2 padding,
int2 dilation,
int4 src_size,
int4 dst_size) {
__kernel void DepthwiseConv2d_IMG_NC4HW4(__read_only image2d_t src_data, __global FLT4 *filter, __global FLT4 *bias,
float relu_clip1, __write_only image2d_t dst_data, int2 kernel_size,
int2 stride, int2 padding, int2 dilation, int4 src_size, int4 dst_size) {
int X = get_global_id(0);
int Y = get_global_id(1);
int Z = get_global_id(2);
@ -36,32 +27,23 @@ __write_only image2d_t dst_data,
bool outside_x = x_c < 0 || x_c >= src_size.x;
if (!outside_x && !outside_y) {
FLT4 f = filter[fx_c];
//FLT4 src_final =src_data[(((Z) * src_size.y + (y_c)) * src_size.x + (x_c))];
FLT4 src_final =read_imagef(src_data, sampler_zero, (int2)(x_c, (Z * src_size.y + y_c)));
// FLT4 src_final =src_data[(((Z) * src_size.y + (y_c)) * src_size.x + (x_c))];
FLT4 src_final = read_imagef(src_data, sampler_zero, (int2)(x_c, (Z * src_size.y + y_c)));
r += TO_FLT4(src_final * f);
};
}
fx_c++;
}
}
FLT4 bias_val = bias[Z];
FLT4 res0 = TO_FLT4(r) + bias_val;
res0 = clamp(res0, (FLT)(0.0f), (FLT)(relu_clip1));
//dst_data[(((Z) * dst_size.y + (Y)) * dst_size.x + (X))] = res0;
// dst_data[(((Z) * dst_size.y + (Y)) * dst_size.x + (X))] = res0;
write_imagef(dst_data, (int2)(X, (Z * dst_size.y + Y)), res0);
}
__kernel void DepthwiseConv2d_IMG_NHWC4(
__read_only image2d_t src_data,
__global FLT4* filter,
__global FLT4* bias,
float relu_clip1,
__write_only image2d_t dst_data,
int2 kernel_size,
int2 stride,
int2 padding,
int2 dilation,
int4 src_size,
int4 dst_size) {
__kernel void DepthwiseConv2d_IMG_NHWC4(__read_only image2d_t src_data, __global FLT4 *filter, __global FLT4 *bias,
float relu_clip1, __write_only image2d_t dst_data, int2 kernel_size,
int2 stride, int2 padding, int2 dilation, int4 src_size, int4 dst_size) {
int X = get_global_id(0);
int Y = get_global_id(1);
int Z = get_global_id(2);
@ -78,32 +60,23 @@ __write_only image2d_t dst_data,
bool outside_x = x_c < 0 || x_c >= src_size.x;
if (!outside_x && !outside_y) {
FLT4 f = filter[fx_c];
//FLT4 src_final =src_data[((y_c * src_size.x + x_c) * src_size.z + Z)];
FLT4 src_final =read_imagef(src_data, sampler_zero, (int2)(Z+x_c*src_size.z, y_c));
// FLT4 src_final =src_data[((y_c * src_size.x + x_c) * src_size.z + Z)];
FLT4 src_final = read_imagef(src_data, sampler_zero, (int2)(Z + x_c * src_size.z, y_c));
r += TO_FLT4(src_final * f);
};
}
fx_c++;
}
}
FLT4 bias_val = bias[Z];
FLT4 res0 = TO_FLT4(r) + bias_val;
res0 = clamp(res0, (FLT)(0.0f), (FLT)(relu_clip1));
//dst_data[((Y * dst_size.x + X) * dst_size.z + Z)] = res0;
write_imagef(dst_data, (int2)(X*dst_size.z+Z, Y), res0);
// dst_data[((Y * dst_size.x + X) * dst_size.z + Z)] = res0;
write_imagef(dst_data, (int2)(X * dst_size.z + Z, Y), res0);
}
__kernel void DepthwiseConv2d_IMG_NHWC4_1x1(
__read_only image2d_t src_data,
__global FLT4* filter,
__global FLT4* bias,
float relu_clip1,
__write_only image2d_t dst_data,
int2 kernel_size,
int2 stride,
int2 padding,
int2 dilation,
int4 src_size,
int4 dst_size) {
__kernel void DepthwiseConv2d_IMG_NHWC4_1x1(__read_only image2d_t src_data, __global FLT4 *filter, __global FLT4 *bias,
float relu_clip1, __write_only image2d_t dst_data, int2 kernel_size,
int2 stride, int2 padding, int2 dilation, int4 src_size, int4 dst_size) {
int X = get_global_id(0);
int Y = get_global_id(1);
int Z = get_global_id(2);
@ -120,30 +93,21 @@ __write_only image2d_t dst_data,
bool outside_x = x_c < 0 || x_c >= src_size.x;
if (!outside_x && !outside_y) {
FLT4 f = filter[fx_c];
//FLT4 src_final =src_data[((y_c * src_size.x + x_c) * src_size.z + Z)];
// FLT4 src_final =src_data[((y_c * src_size.x + x_c) * src_size.z + Z)];
FLT4 src_final = read_imagef(src_data, sampler_zero, (int2)(Z, (y_c * src_size.x + x_c) * src_size.z));
r += TO_FLT4(src_final * f);
};
}
}
}
FLT4 bias_val = bias[Z];
FLT4 res0 = TO_FLT4(r) + bias_val;
res0 = clamp(res0, (FLT)(0.0f), (FLT)(relu_clip1));
//dst_data[((Y * dst_size.x + X) * dst_size.z + Z)] = res0;
// dst_data[((Y * dst_size.x + X) * dst_size.z + Z)] = res0;
write_imagef(dst_data, (int2)(Z, (Y * dst_size.x + X) * dst_size.z), res0);
}
__kernel void DepthwiseConv2d_BUF_NC4HW4(
__global FLT4* src_data,
__global FLT4* filter,
__global FLT4* bias,
float relu_clip1,
__global FLT4* dst_data,
int2 kernel_size,
int2 stride,
int2 padding,
int2 dilation,
int4 src_size,
int4 dst_size) {
__kernel void DepthwiseConv2d_BUF_NC4HW4(__global FLT4 *src_data, __global FLT4 *filter, __global FLT4 *bias,
float relu_clip1, __global FLT4 *dst_data, int2 kernel_size, int2 stride,
int2 padding, int2 dilation, int4 src_size, int4 dst_size) {
int X = get_global_id(0);
int Y = get_global_id(1);
int Z = get_global_id(2);
@ -160,30 +124,21 @@ __global FLT4* dst_data,
bool outside_x = x_c < 0 || x_c >= src_size.x;
if (!outside_x && !outside_y) {
FLT4 f = filter[fx_c];
FLT4 src_final =src_data[(((Z) * src_size.y + (y_c)) * src_size.x + (x_c))];
FLT4 src_final = src_data[(((Z)*src_size.y + (y_c)) * src_size.x + (x_c))];
r += TO_FLT4(src_final * f);
};
}
fx_c++;
}
}
FLT4 bias_val = bias[Z];
FLT4 res0 = TO_FLT4(r) + bias_val;
res0 = clamp(res0, (FLT)(0.0f), (FLT)(relu_clip1));
dst_data[(((Z) * dst_size.y + (Y)) * dst_size.x + (X))] = res0;
dst_data[(((Z)*dst_size.y + (Y)) * dst_size.x + (X))] = res0;
}
__kernel void DepthwiseConv2d_BUF_NHWC4(
__global FLT4* src_data,
__global FLT4* filter,
__global FLT4* bias,
float relu_clip1,
__global FLT4* dst_data,
int2 kernel_size,
int2 stride,
int2 padding,
int2 dilation,
int4 src_size,
int4 dst_size) {
__kernel void DepthwiseConv2d_BUF_NHWC4(__global FLT4 *src_data, __global FLT4 *filter, __global FLT4 *bias,
float relu_clip1, __global FLT4 *dst_data, int2 kernel_size, int2 stride,
int2 padding, int2 dilation, int4 src_size, int4 dst_size) {
int X = get_global_id(0);
int Y = get_global_id(1);
int Z = get_global_id(2);
@ -200,9 +155,9 @@ __global FLT4* dst_data,
bool outside_x = x_c < 0 || x_c >= src_size.x;
if (!outside_x && !outside_y) {
FLT4 f = filter[fx_c];
FLT4 src_final =src_data[((y_c * src_size.x + x_c) * src_size.z + Z)];
FLT4 src_final = src_data[((y_c * src_size.x + x_c) * src_size.z + Z)];
r += TO_FLT4(src_final * f);
};
}
fx_c++;
}
}
@ -212,18 +167,9 @@ __global FLT4* dst_data,
dst_data[((Y * dst_size.x + X) * dst_size.z + Z)] = res0;
}
__kernel void DepthwiseConv2d_BUF_NHWC4_1x1(
__global FLT4* src_data,
__global FLT4* filter,
__global FLT4* bias,
float relu_clip1,
__global FLT4* dst_data,
int2 kernel_size,
int2 stride,
int2 padding,
int2 dilation,
int4 src_size,
int4 dst_size) {
__kernel void DepthwiseConv2d_BUF_NHWC4_1x1(__global FLT4 *src_data, __global FLT4 *filter, __global FLT4 *bias,
float relu_clip1, __global FLT4 *dst_data, int2 kernel_size, int2 stride,
int2 padding, int2 dilation, int4 src_size, int4 dst_size) {
int X = get_global_id(0);
int Y = get_global_id(1);
int Z = get_global_id(2);
@ -240,9 +186,9 @@ __global FLT4* dst_data,
bool outside_x = x_c < 0 || x_c >= src_size.x;
if (!outside_x && !outside_y) {
FLT4 f = filter[fx_c];
FLT4 src_final =src_data[((y_c * src_size.x + x_c) * src_size.z + Z)];
FLT4 src_final = src_data[((y_c * src_size.x + x_c) * src_size.z + Z)];
r += TO_FLT4(src_final * f);
};
}
}
}
FLT4 bias_val = bias[Z];

5
mindspore/lite/src/runtime/kernel/opencl/cl/fp32/matmul.cl
View File

@ -1,8 +1,7 @@
#define FLT4 float4
#define FLT16 float16
__kernel void MatMul(__global FLT4 *x, __global FLT16 *weight,
__global FLT4 *buffer, __global FLT4 *bias, int2 offset_ci,
int2 offset_co, int has_bias) {
__kernel void MatMul(__global FLT4 *x, __global FLT16 *weight, __global FLT4 *buffer, __global FLT4 *bias,
int2 offset_ci, int2 offset_co, int has_bias) {
int2 gid = (int2)(get_global_id(0), get_global_id(1));
int2 lid = (int2)(get_local_id(0), get_local_id(1));
FLT4 s = (FLT4)(0.0f);

7
mindspore/lite/src/runtime/kernel/opencl/cl/fp32/softmax.cl
View File

@ -1,14 +1,11 @@
#define SLICES 4
int DivideRoundUp(int n, int div)
{
int DivideRoundUp(int n, int div) {
int q = n / div;
return n % div == 0 ? q : q + 1;
}
__kernel void SoftMax(__global float4 *input,
__global float4 *output,
const int4 input_shape) {
__kernel void SoftMax(__global float4 *input, __global float4 *output, const int4 input_shape) {
int X = get_global_id(0); // width
int Y = get_global_id(1); // height
int H = input_shape.y;