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!5614 [MS][LITE][GPU]fix transpose softmax reshape bug 

Merge pull request !5614 from chenzupeng/master-lite
tags/v1.0.0
mindspore-ci-bot Gitee 5 years ago
parent
c2fddb56c8 bde334411c
commit
8f106d685a
13 changed files with 320 additions and 232 deletions
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  1. +6
    -3
      mindspore/lite/src/runtime/kernel/opencl/cl/reshape.cl
  2. +125
    -15
      mindspore/lite/src/runtime/kernel/opencl/cl/softmax.cl
  3. +0
    -104
      mindspore/lite/src/runtime/kernel/opencl/cl/softmax1x1.cl
  4. +7
    -5
      mindspore/lite/src/runtime/kernel/opencl/cl/transpose.cl
  5. +2
    -1
      mindspore/lite/src/runtime/kernel/opencl/kernel/pooling2d.cc
  6. +24
    -8
      mindspore/lite/src/runtime/kernel/opencl/kernel/reshape.cc
  7. +42
    -23
      mindspore/lite/src/runtime/kernel/opencl/kernel/softmax.cc
  8. +1
    -0
      mindspore/lite/src/runtime/kernel/opencl/kernel/softmax.h
  9. +2
    -4
      mindspore/lite/src/runtime/kernel/opencl/kernel/to_format.cc
  10. +7
    -5
      mindspore/lite/src/runtime/kernel/opencl/kernel/transpose.cc
  11. +3
    -3
      mindspore/lite/test/ut/src/runtime/kernel/opencl/reshape_tests.cc
  12. +99
    -59
      mindspore/lite/test/ut/src/runtime/kernel/opencl/softmax_tests.cc
  13. +2
    -2
      mindspore/lite/test/ut/src/runtime/kernel/opencl/transpose_tests.cc

+ 6
- 3
mindspore/lite/src/runtime/kernel/opencl/cl/reshape.cl View File

@@ -1,11 +1,14 @@
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
__constant sampler_t smp_zero = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST;
__kernel void reshape(__read_only image2d_t src_data, __write_only image2d_t dst_data, int4 size) {
__kernel void reshape(__read_only image2d_t src_data, __write_only image2d_t dst_data, int4 size, int4 size_out) {
int X = get_global_id(0);
int Y = get_global_id(1);
int Z = get_global_id(2);
if (X >= size.x || Y >= size.y || Z >= size.z) {
if (X >= size_out.x || Y >= size_out.y || Z >= size_out.z) {
return;
}
WRITE_IMAGE(dst_data, (int2)(Y * size.z + Z, X), READ_IMAGE(src_data, smp_zero, (int2)(Y * size.z + Z, X)));
int out_index = X * size_out.y + Y;
int ih = out_index / size.y;
int iw = out_index % size.y;
WRITE_IMAGE(dst_data, (int2)(Y * size.z + Z, X), READ_IMAGE(src_data, smp_zero, (int2)(iw * size.z + Z, ih)));
}

+ 125
- 15
mindspore/lite/src/runtime/kernel/opencl/cl/softmax.cl View File

@@ -1,16 +1,21 @@
__kernel void SoftMax_BUF(__global float4 *input, __global float4 *output, const int4 input_shape) {
int X = get_global_id(0);
int Y = get_global_id(1);
#ifdef cl_khr_fp16
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#endif
__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 SoftMax_BUF(__read_only image2d_t input, __global FLT4 *output, const int4 input_shape) {
int X = get_global_id(0); // H
int Y = get_global_id(1); // W
int H = input_shape.x;
int W = input_shape.y;
int C = input_shape.z;
int S = input_shape.w;

if (X >= W || Y >= H) return;
if (X >= H || Y >= W) return;

float sum = 0.0f;
FLT sum = 0.0f;
for (int d = 0; d < S; ++d) {
float4 t = input[(Y * W + X * H) * C + d];
FLT4 t = READ_IMAGE(input, smp_zero, (int2)(Y * S + d, X));
sum += exp(t.x);
if (d * 4 + 1 < C) sum += exp(t.y);
if (d * 4 + 2 < C) sum += exp(t.z);
@@ -18,15 +23,17 @@ __kernel void SoftMax_BUF(__global float4 *input, __global float4 *output, const
}

for (int d = 0; d < S; ++d) {
float4 t = input[(Y * W + X * H) * C + d];
FLT4 t = READ_IMAGE(input, smp_zero, (int2)(Y * S + d, X));
t = exp(t) / sum;
float4 result = convert_float4(t);
output[(Y * W + X * H) * C + d] = result;
__global FLT *output_flt = (__global FLT *)output;
output_flt += (X * W + Y) * C + 4 * d;
output_flt[0] = t.x;
if (d * 4 + 1 < C) output_flt[1] += t.y;
if (d * 4 + 2 < C) output_flt[2] += t.z;
if (d * 4 + 3 < C) output_flt[3] += t.w;
}
}

__constant sampler_t smp_none = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_NONE | CLK_FILTER_NEAREST;

__kernel void SoftMax_IMG(__read_only image2d_t input, __write_only image2d_t output, const int4 input_shape) {
int X = get_global_id(0);
int Y = get_global_id(1);
@@ -34,7 +41,7 @@ __kernel void SoftMax_IMG(__read_only image2d_t input, __write_only image2d_t ou

float sum = 0.0f;
for (int d = 0; d < input_shape.w; ++d) {
float4 t = read_imagef(input, smp_none, (int2)(Y * input_shape.w + d, X));
FLT4 t = READ_IMAGE(input, smp_none, (int2)(Y * input_shape.w + d, X));
sum += exp(t.x);
if (d * 4 + 1 < input_shape.z) sum += exp(t.y);
if (d * 4 + 2 < input_shape.z) sum += exp(t.z);
@@ -42,9 +49,112 @@ __kernel void SoftMax_IMG(__read_only image2d_t input, __write_only image2d_t ou
}

for (int d = 0; d < input_shape.w; ++d) {
float4 t = read_imagef(input, smp_none, (int2)(Y * input_shape.w + d, X));
FLT4 t = READ_IMAGE(input, smp_none, (int2)(Y * input_shape.w + d, X));
t = exp(t) / sum;
float4 result = convert_float4(t);
write_imagef(output, (int2)(Y * input_shape.w + d, X), result);
FLT4 result = TO_FLT4(t);
WRITE_IMAGE(output, (int2)(Y * input_shape.w + d, X), result);
}
}

__kernel void SoftMax1x1_IMG(__read_only image2d_t input, __write_only image2d_t output, const FLT4 mask,
const int slices, const int slices_x32) {
int tid = get_local_id(0);
int slices_count = 0;
int offset = 0;
FLT sum = 0.0f;
do {
int z = offset + tid;
if (z < slices) {
FLT4 mask_temp = z == slices - 1 ? mask : (FLT4)(1.0f);
FLT4 src = READ_IMAGE(input, smp_none, (int2)(0, 0));
sum += dot(mask_temp, exp(src));
offset += 32;
}
slices_count++;
} while (slices_count < slices_x32);

__local FLT4 tmp[8];
__local FLT *tmpx1 = (__local FLT *)tmp;
tmpx1[tid] = sum;
barrier(CLK_LOCAL_MEM_FENCE);
if (tid == 0) {
sum = dot((FLT4)(1.0f), tmp[0]);
sum += dot((FLT4)(1.0f), tmp[1]);
sum += dot((FLT4)(1.0f), tmp[2]);
sum += dot((FLT4)(1.0f), tmp[3]);
sum += dot((FLT4)(1.0f), tmp[4]);
sum += dot((FLT4)(1.0f), tmp[5]);
sum += dot((FLT4)(1.0f), tmp[6]);
sum += dot((FLT4)(1.0f), tmp[7]);
tmpx1[0] = 1.0f / sum;
}
barrier(CLK_LOCAL_MEM_FENCE);
sum = tmpx1[0];

offset = 0;
slices_count = 0;
do {
int z = offset + tid;
if (z < slices) {
FLT4 res = TO_FLT4(exp(READ_IMAGE(input, smp_none, (int2)(0, 0))) * sum);
WRITE_IMAGE(output, (int2)(0, 0), res);
offset += 32;
}
slices_count++;
} while (slices_count < slices_x32);
}

__kernel void SoftMax1x1_BUF(__read_only image2d_t input, __global FLT4 *output, const float4 mask, const int slices,
const int slices_x32) {
int tid = get_local_id(0);
FLT sum = 0.0f;
for (size_t i = tid; i < slices - 1; i += 32) {
FLT4 src = READ_IMAGE(input, smp_zero, (int2)(i, 0));
sum += dot((FLT4)(1.0f), exp(src));
}
if ((slices - 1) % 32 == tid) {
FLT4 src = READ_IMAGE(input, smp_zero, (int2)(slices - 1, 0));

sum += dot(TO_FLT4(mask), exp(src));
}

__local FLT4 tmp[8];
__local FLT *tmpx1 = (__local FLT *)tmp;
tmpx1[tid] = sum;
barrier(CLK_LOCAL_MEM_FENCE);
if (tid == 0) {
sum = dot((FLT4)(1.0f), tmp[0]);
sum += dot((FLT4)(1.0f), tmp[1]);
sum += dot((FLT4)(1.0f), tmp[2]);
sum += dot((FLT4)(1.0f), tmp[3]);
sum += dot((FLT4)(1.0f), tmp[4]);
sum += dot((FLT4)(1.0f), tmp[5]);
sum += dot((FLT4)(1.0f), tmp[6]);
sum += dot((FLT4)(1.0f), tmp[7]);
tmpx1[0] = 1.0f / sum;
}
barrier(CLK_LOCAL_MEM_FENCE);
sum = tmpx1[0];
for (size_t i = tid; i < slices - 1; i += 32) {
FLT4 result = READ_IMAGE(input, smp_zero, (int2)(i, 0));
result = exp(result) * sum;
output[i] = result;
}
if ((slices - 1) % 32 == tid) {
FLT4 result = READ_IMAGE(input, smp_zero, (int2)(slices - 1, 0));
result = exp(result) * sum;
__global FLT4 *remain_ptr4 = output;
remain_ptr4 += slices - 1;
__global FLT *remain_ptr = (__global FLT *)remain_ptr4;
remain_ptr[0] = result.x;
if (mask.y > 0.f) {
remain_ptr[1] = result.y;
}
if (mask.z > 0.f) {
remain_ptr[2] = result.z;
}
if (mask.w > 0.f) {
remain_ptr[3] = result.w;
}
}
}

+ 0
- 104
mindspore/lite/src/runtime/kernel/opencl/cl/softmax1x1.cl View File

@@ -1,104 +0,0 @@
__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;
// what is mask and args.slices_x32
__kernel void SoftMax1x1_IMG(__read_only image2d_t input, __write_only image2d_t output, const float4 mask,
const int slices, const int slices_x32) {
int tid = get_local_id(0);
int slices_count = 0;
int offset = 0;
float sum = 0.0f;
do {
int z = offset + tid;
if (z < slices) {
float4 mask_temp = z == slices - 1 ? mask : (float4)(1.0f);
float4 src = read_imagef(input, smp_none, (int2)(0, 0));
sum += dot(mask_temp, exp(src));
offset += 32;
}
slices_count++;
} while (slices_count < slices_x32);

__local float4 tmp[8];
__local float *tmpx1 = (__local float *)tmp;
tmpx1[tid] = sum;
barrier(CLK_LOCAL_MEM_FENCE);
if (tid == 0) {
sum = dot((float4)(1.0f), tmp[0]);
sum += dot((float4)(1.0f), tmp[1]);
sum += dot((float4)(1.0f), tmp[2]);
sum += dot((float4)(1.0f), tmp[3]);
sum += dot((float4)(1.0f), tmp[4]);
sum += dot((float4)(1.0f), tmp[5]);
sum += dot((float4)(1.0f), tmp[6]);
sum += dot((float4)(1.0f), tmp[7]);
tmpx1[0] = 1.0f / sum;
}
barrier(CLK_LOCAL_MEM_FENCE);
sum = tmpx1[0];

offset = 0;
slices_count = 0;
do {
int z = offset + tid;
if (z < slices) {
float4 res = convert_float4(exp(read_imagef(input, smp_none, (int2)(0, 0))) * sum);
write_imagef(output, (int2)(0, 0), res);
offset += 32;
}
slices_count++;
} while (slices_count < slices_x32);
}

__kernel void SoftMax1x1_BUF(__read_only image2d_t input, __global float4 *output, const float4 mask, const int slices,
const int slices_x32) {
int tid = get_local_id(0);
float sum = 0.0f;
for (size_t i = tid; i < slices - 1; i += 32) {
float4 src = read_imagef(input, smp_zero, (int2)(i, 0));
sum += dot((float4)(1.0f), exp(src));
}
if ((slices - 1) % 32 == tid) {
float4 src = read_imagef(input, smp_zero, (int2)(slices - 1, 0));
sum += dot(mask, exp(src));
}

__local float4 tmp[8];
__local float *tmpx1 = (__local float *)tmp;
tmpx1[tid] = sum;
barrier(CLK_LOCAL_MEM_FENCE);
if (tid == 0) {
sum = dot((float4)(1.0f), tmp[0]);
sum += dot((float4)(1.0f), tmp[1]);
sum += dot((float4)(1.0f), tmp[2]);
sum += dot((float4)(1.0f), tmp[3]);
sum += dot((float4)(1.0f), tmp[4]);
sum += dot((float4)(1.0f), tmp[5]);
sum += dot((float4)(1.0f), tmp[6]);
sum += dot((float4)(1.0f), tmp[7]);
tmpx1[0] = 1.0f / sum;
}
barrier(CLK_LOCAL_MEM_FENCE);
sum = tmpx1[0];
for (size_t i = tid; i < slices - 1; i += 32) {
float4 result = read_imagef(input, smp_zero, (int2)(i, 0));
result = exp(result) * sum;
output[i] = result;
}
if ((slices - 1) % 32 == tid) {
float4 result = read_imagef(input, smp_zero, (int2)(slices - 1, 0));
result = exp(result) * sum;
__global float4 *remain_ptr4 = output;
remain_ptr4 += slices - 1;
__global float *remain_ptr = (__global float *)remain_ptr4;
remain_ptr[0] = result.x;
if (mask.y > 0.f) {
remain_ptr[1] = result.y;
}
if (mask.z > 0.f) {
remain_ptr[2] = result.z;
}
if (mask.w > 0.f) {
remain_ptr[3] = result.w;
}
}
}

+ 7
- 5
mindspore/lite/src/runtime/kernel/opencl/cl/transpose.cl View File

@@ -1,4 +1,6 @@
#ifdef cl_khr_fp16
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#endif
__constant sampler_t smp_zero = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST;
__kernel void transpose_IMG(__read_only image2d_t src_data, __write_only image2d_t dst_data, int2 HW, int2 C) {
int X = get_global_id(0);
@@ -41,7 +43,7 @@ __kernel void transpose_IMG(__read_only image2d_t src_data, __write_only image2d
WRITE_IMAGE(dst_data, (int2)(X, 4 * Y + 3), result[3]);
}

__kernel void transpose_BUF(__read_only image2d_t src_data, global FLT4 *dst_data, int2 HW, int2 C) {
__kernel void transpose_BUF(__read_only image2d_t src_data, global FLT4 *dst_data, int2 HW, int2 C, int W) {
int X = get_global_id(0);
int Y = get_global_id(1);
if (X >= HW.y || Y >= C.y) {
@@ -52,10 +54,10 @@ __kernel void transpose_BUF(__read_only image2d_t src_data, global FLT4 *dst_dat
result[1] = (FLT4)(0.0f);
result[2] = (FLT4)(0.0f);
result[3] = (FLT4)(0.0f);
FLT4 x0 = READ_IMAGE(src_data, smp_zero, (int2)(Y, 4 * X));
FLT4 x1 = READ_IMAGE(src_data, smp_zero, (int2)(Y, 4 * X + 1));
FLT4 x2 = READ_IMAGE(src_data, smp_zero, (int2)(Y, 4 * X + 2));
FLT4 x3 = READ_IMAGE(src_data, smp_zero, (int2)(Y, 4 * X + 3));
FLT4 x0 = READ_IMAGE(src_data, smp_zero, (int2)((4 * X) % W * C.y + Y, (4 * X) / W));
FLT4 x1 = READ_IMAGE(src_data, smp_zero, (int2)((4 * X + 1) % W * C.y + Y, (4 * X + 1) / W));
FLT4 x2 = READ_IMAGE(src_data, smp_zero, (int2)((4 * X + 2) % W * C.y + Y, (4 * X + 2) / W));
FLT4 x3 = READ_IMAGE(src_data, smp_zero, (int2)((4 * X + 3) % W * C.y + Y, (4 * X + 3) / W));
result[0].x = x0.x;
result[0].y = x1.x;
result[0].z = x2.x;


+ 2
- 1
mindspore/lite/src/runtime/kernel/opencl/kernel/pooling2d.cc View File

@@ -65,7 +65,8 @@ int PoolingOpenCLKernel::Init() {
kernel_ = ocl_runtime->GetKernelFromBinary(kernel_name);
#else
if (out_mem_type_ == OpenCLMemType::BUF) {
kernel_name += "_BUF";
MS_LOG(ERROR) << "buffer output not support yet.";
return RET_ERROR;
} else {
kernel_name += "_IMG";
}


+ 24
- 8
mindspore/lite/src/runtime/kernel/opencl/kernel/reshape.cc View File

@@ -68,10 +68,16 @@ int ReshapeOpenCLKernel::ReSize() { return RET_OK; }

int ReshapeOpenCLKernel::GetImageSize(size_t idx, std::vector<size_t> *img_size) {
size_t im_dst_x, im_dst_y;
std::vector<int> shapex = in_tensors_[0]->shape();
int h = shapex[1];
int w = shapex[2];
int c = shapex[3];
std::vector<int> shapex = out_tensors_[0]->shape();
int h, w, c;
if (shapex.size() == 2) {
h = w = 1;
c = shapex[1];
} else {
h = shapex[1];
w = shapex[2];
c = shapex[3];
}
im_dst_x = w * UP_DIV(c, C4NUM);
im_dst_y = h;
size_t img_dtype = CL_FLOAT;
@@ -91,13 +97,23 @@ int ReshapeOpenCLKernel::Run() {
int w = shapex[2];
int c = shapex[3];
int c4 = UP_DIV(c, C4NUM);
int oh, ow;
if (out_tensors_[0]->shape().size() == 2) {
oh = ow = 1;
} else {
oh = out_tensors_[0]->shape()[1];
ow = out_tensors_[0]->shape()[2];
}
auto ocl_runtime = lite::opencl::OpenCLRuntime::GetInstance();
std::vector<size_t> local = {};
std::vector<size_t> global = {(size_t)h, (size_t)w, (size_t)c4};
std::vector<size_t> global = {(size_t)oh, (size_t)ow, (size_t)c4};
cl_int4 size = {h, w, c4, 1};
ocl_runtime->SetKernelArg(kernel_, 0, in_tensors_[0]->Data());
ocl_runtime->SetKernelArg(kernel_, 1, out_tensors_[0]->Data());
ocl_runtime->SetKernelArg(kernel_, 2, size);
cl_int4 size_out = {oh, ow, c4, 1};
int arg_idx = 0;
ocl_runtime->SetKernelArg(kernel_, arg_idx++, in_tensors_[0]->Data());
ocl_runtime->SetKernelArg(kernel_, arg_idx++, out_tensors_[0]->Data());
ocl_runtime->SetKernelArg(kernel_, arg_idx++, size);
ocl_runtime->SetKernelArg(kernel_, arg_idx++, size_out);
ocl_runtime->RunKernel(kernel_, global, local, nullptr);
return RET_OK;
}


+ 42
- 23
mindspore/lite/src/runtime/kernel/opencl/kernel/softmax.cc View File

@@ -23,7 +23,6 @@
#include "src/runtime/kernel/opencl/utils.h"
#ifndef PROGRAM_WITH_IL
#include "src/runtime/kernel/opencl/cl/softmax.cl.inc"
#include "src/runtime/kernel/opencl/cl/softmax1x1.cl.inc"
#endif

using mindspore::kernel::KERNEL_ARCH::kGPU;
@@ -42,8 +41,8 @@ std::vector<float> SoftmaxOpenCLKernel::GetMaskForLastChannel(int channels) {
}

int SoftmaxOpenCLKernel::InitGlobalSize() {
const size_t global_x = out_tensors_[0]->Height();
const size_t global_y = out_tensors_[0]->Width();
const size_t global_x = out_tensors_[0]->shape()[1];
const size_t global_y = out_tensors_[0]->shape()[2];
const size_t global_z = 1;
global_size_ = {global_x, global_y, global_z};
return lite::RET_OK;
@@ -74,11 +73,10 @@ int SoftmaxOpenCLKernel::GetImageSize(size_t idx, std::vector<size_t> *img_size)
im_dst_x = out_tensors_[0]->Width() * CO4;
im_dst_y = out_tensors_[0]->Height();
}
#ifdef ENABLE_FP16
size_t img_dtype = CL_HALF_FLOAT;
#else
size_t img_dtype = CL_FLOAT;
#endif
if (enable_fp16_) {
img_dtype = CL_HALF_FLOAT;
}
img_size->clear();
std::vector<size_t> vec{im_dst_x, im_dst_y, img_dtype};
*img_size = vec;
@@ -90,27 +88,28 @@ int SoftmaxOpenCLKernel::Init() {
std::string program_name = "SoftMax";
std::string source = softmax_source;
runtime_ = lite::opencl::OpenCLRuntime::GetInstance();
enable_fp16_ = runtime_->GetFp16Enable();
// framework not set this param yet! just use default.
if (parameter_->axis_ == -1) {
parameter_->axis_ = 1;
}
if (in_tensors_[0]->shape().size() == 4 && parameter_->axis_ == 3) {
if (in_tensors_[0]->shape().size() == 4) {
// support 4d tensor
onexone_flag_ = false;
} else if (in_tensors_[0]->shape().size() == 2 && parameter_->axis_ == 1) {
} else if (in_tensors_[0]->shape().size() == 2) {
// support 2d tensor
kernel_name += "1x1";
program_name += "1x1";
source = softmax1x1_source;
onexone_flag_ = true;
} else {
MS_LOG(EXCEPTION) << "Init `Softmax` kernel failed: Unsupported axis: " << parameter_->axis_;
MS_LOG(ERROR) << "Init `Softmax` kernel failed: Unsupported shape size: " << in_tensors_[0]->shape().size();
return RET_ERROR;
}
#ifdef PROGRAM_WITH_IL
kernel_ = ocl_runtime->GetKernelFromBinary(kernel_name);
#else
if (!is_image_out_) {
out_mem_type_ = OpenCLMemType::BUF;
} else {
MS_LOG(ERROR) << "image2d output not support yet.";
return RET_ERROR;
}
if (out_mem_type_ == OpenCLMemType::BUF) {
kernel_name += "_BUF";
@@ -124,12 +123,23 @@ int SoftmaxOpenCLKernel::Init() {
runtime_->BuildKernel(kernel_, program_name, kernel_name, build_options);
#endif
in_ori_format_ = in_tensors_[0]->GetFormat();
in_tensors_[0]->SetFormat(schema::Format_NHWC4);
out_ori_format_ = out_tensors_[0]->GetFormat();
out_tensors_[0]->SetFormat(schema::Format_NHWC4);
if (!is_image_out_) {
out_ori_format_ = schema::Format_NC;
out_tensors_[0]->SetFormat(schema::Format_NC);
if (in_tensors_[0]->shape().size() == 2) {
in_tensors_[0]->SetFormat(schema::Format_NC4);
} else {
in_tensors_[0]->SetFormat(schema::Format_NHWC4);
}

if (is_image_out_) {
if (out_tensors_[0]->shape().size() == 2) {
out_ori_format_ = schema::Format_NC;
out_tensors_[0]->SetFormat(schema::Format_NC4);
} else {
out_ori_format_ = schema::Format_NHWC;
out_tensors_[0]->SetFormat(schema::Format_NHWC4);
}
} else {
out_tensors_[0]->SetFormat(out_ori_format_);
}
MS_LOG(DEBUG) << kernel_name << " Init Done!";
return lite::RET_OK;
@@ -147,17 +157,25 @@ int SoftmaxOpenCLKernel::Run() {
cl_float4 mask = {mask_[0], mask_[1], mask_[2], mask_[3]};

runtime_->SetKernelArg(kernel_, arg_idx++, in_tensors_[0]->Data());
runtime_->SetKernelArg(kernel_, arg_idx++, out_tensors_[0]->Data());
if (is_image_out_) {
runtime_->SetKernelArg(kernel_, arg_idx++, out_tensors_[0]->Data());
} else {
runtime_->SetKernelArg(kernel_, arg_idx++, out_tensors_[0]->Data(), lite::opencl::MemType::BUF);
}
runtime_->SetKernelArg(kernel_, arg_idx++, mask);
runtime_->SetKernelArg(kernel_, arg_idx++, slices);
runtime_->SetKernelArg(kernel_, arg_idx, slices_x32);
SetWorkGroupSize1x1();
} else {
int slices = UP_DIV(out_tensors_[0]->Channel(), C4NUM);
cl_int4 input_shape = {in_tensors_[0]->Height(), in_tensors_[0]->Width(), in_tensors_[0]->Channel(), slices};
int slices = UP_DIV(out_tensors_[0]->shape()[3], C4NUM);
cl_int4 input_shape = {in_tensors_[0]->shape()[1], in_tensors_[0]->shape()[2], in_tensors_[0]->shape()[3], slices};

runtime_->SetKernelArg(kernel_, arg_idx++, in_tensors_[0]->Data());
runtime_->SetKernelArg(kernel_, arg_idx++, out_tensors_[0]->Data());
if (is_image_out_) {
runtime_->SetKernelArg(kernel_, arg_idx++, out_tensors_[0]->Data());
} else {
runtime_->SetKernelArg(kernel_, arg_idx++, out_tensors_[0]->Data(), lite::opencl::MemType::BUF);
}
runtime_->SetKernelArg(kernel_, arg_idx, input_shape);
SetWorkGroupSize();
}
@@ -193,4 +211,5 @@ kernel::LiteKernel *OpenCLSoftMaxKernelCreator(const std::vector<lite::tensor::T
}

REG_KERNEL(kGPU, kNumberTypeFloat32, PrimitiveType_SoftMax, OpenCLSoftMaxKernelCreator)
REG_KERNEL(kGPU, kNumberTypeFloat16, PrimitiveType_SoftMax, OpenCLSoftMaxKernelCreator)
} // namespace mindspore::kernel

+ 1
- 0
mindspore/lite/src/runtime/kernel/opencl/kernel/softmax.h View File

@@ -52,6 +52,7 @@ class SoftmaxOpenCLKernel : public OpenCLKernel {
std::vector<size_t> local_size_;
std::vector<size_t> global_size_;
bool is_image_out_{false};
bool enable_fp16_{false};
};

} // namespace mindspore::kernel


+ 2
- 4
mindspore/lite/src/runtime/kernel/opencl/kernel/to_format.cc View File

@@ -119,11 +119,9 @@ int ToFormatOpenCLKernel::GetImageSize(size_t idx, std::vector<size_t> *img_size
im_dst_x = w * UP_DIV(c, C4NUM);
im_dst_y = h;
} else if (out_tensors_[0]->GetFormat() == schema::Format_NC4) {
const int h = 1;
const int w = 1;
int c = shapex[1];
im_dst_x = w * UP_DIV(c, C4NUM);
im_dst_y = h;
im_dst_x = UP_DIV(c, C4NUM);
im_dst_y = 1;
} else {
MS_LOG(ERROR) << "Unsupported format. " << out_tensors_[0]->GetFormat();
return RET_ERROR;


+ 7
- 5
mindspore/lite/src/runtime/kernel/opencl/kernel/transpose.cc View File

@@ -69,7 +69,7 @@ int TransposeOpenCLKernel::ReSize() { return RET_OK; }

int TransposeOpenCLKernel::GetImageSize(size_t idx, std::vector<size_t> *img_size) {
size_t im_dst_x, im_dst_y;
im_dst_x = UP_DIV(out_tensors_[0]->Height() * out_tensors_[0]->Width(), C4NUM);
im_dst_x = out_tensors_[0]->Height() * UP_DIV(out_tensors_[0]->Width(), C4NUM);
im_dst_y = out_tensors_[0]->Channel();
size_t img_dtype = CL_FLOAT;
if (enable_fp16_) {
@@ -96,10 +96,12 @@ int TransposeOpenCLKernel::Run() {

cl_int2 HW = {h * w, hw4};
cl_int2 C = {c, c4};
ocl_runtime->SetKernelArg(kernel_, 0, in_tensors_[0]->Data());
ocl_runtime->SetKernelArg(kernel_, 1, out_tensors_[0]->Data());
ocl_runtime->SetKernelArg(kernel_, 2, HW);
ocl_runtime->SetKernelArg(kernel_, 3, C);
int arg_idx = 0;
ocl_runtime->SetKernelArg(kernel_, arg_idx++, in_tensors_[0]->Data());
ocl_runtime->SetKernelArg(kernel_, arg_idx++, out_tensors_[0]->Data());
ocl_runtime->SetKernelArg(kernel_, arg_idx++, HW);
ocl_runtime->SetKernelArg(kernel_, arg_idx++, C);
ocl_runtime->SetKernelArg(kernel_, arg_idx++, w);
ocl_runtime->RunKernel(kernel_, global, local, nullptr);
return RET_OK;
}


+ 3
- 3
mindspore/lite/test/ut/src/runtime/kernel/opencl/reshape_tests.cc View File

@@ -86,14 +86,14 @@ void RunTestCaseReshape(const std::vector<int> &shape, void *input_data, void *o
inputs[0]->SetData(nullptr);
outputs[0]->SetData(nullptr);

MS_LOG(INFO) << "Test ReshapeFp32 passed";
MS_LOG(INFO) << "Test Reshape passed";
lite::opencl::OpenCLRuntime::DeleteInstance();
}

TEST_F(TestReshapeOpenCL, ReshapeFp32) {
int c = 7;
std::vector<int> shape = {c};
std::vector<float> input_data = {0.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f};
std::vector<float> input_data = {0.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f};
std::vector<float> output_data = {0.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f};

RunTestCaseReshape(shape, input_data.data(), output_data.data(), false);
@@ -102,7 +102,7 @@ TEST_F(TestReshapeOpenCL, ReshapeFp32) {
TEST_F(TestReshapeOpenCL, ReshapeFp16) {
int c = 7;
std::vector<int> shape = {c};
std::vector<float16_t> input_data = {0.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f};
std::vector<float16_t> input_data = {0.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f};
std::vector<float16_t> output_data = {0.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f};

RunTestCaseReshape(shape, input_data.data(), output_data.data(), true);


+ 99
- 59
mindspore/lite/test/ut/src/runtime/kernel/opencl/softmax_tests.cc View File

@@ -17,94 +17,134 @@
#include <memory>
#include "mindspore/core/utils/log_adapter.h"
#include "common/common_test.h"
#include "mindspore/lite/src/common/file_utils.h"
#include "mindspore/lite/src/runtime/opencl/opencl_runtime.h"
#include "mindspore/lite/src/runtime/kernel/opencl/subgraph_opencl_kernel.h"
#include "mindspore/lite/src/runtime/kernel/opencl/kernel/softmax.h"
#include "mindspore/lite/test/ut/src/runtime/kernel/opencl/utils_tests.h"

namespace mindspore {
class TestSoftmaxOpenCL : public mindspore::CommonTest {
public:
TestSoftmaxOpenCL() {}
};

class TestSoftmaxOpenCL : public mindspore::CommonTest {};

void RunTestCase(std::vector<int> input_shape, std::vector<int> output_shape, std::string input_file,
std::string expect_file, SoftmaxParameter *param, schema::Format format) {
void RunTestCaseSoftmax(const std::vector<int> &shape, void *input_data, void *output_data, bool enable_fp16) {
auto ocl_runtime = lite::opencl::OpenCLRuntime::GetInstance();
ocl_runtime->Init();
size_t dtype_size = sizeof(float);
if (enable_fp16) {
ocl_runtime->SetFp16Enable(true);
dtype_size = sizeof(float16_t);
}
auto allocator = ocl_runtime->GetAllocator();

// define tensor
MS_LOG(INFO) << "defineTensor";
auto data_type = kNumberTypeFloat32;
auto tensorType = schema::NodeType_ValueNode;
auto input_tensor = new (std::nothrow) lite::tensor::Tensor(data_type, input_shape, format, tensorType);
auto output_tensor = new (std::nothrow) lite::tensor::Tensor(data_type, output_shape, format, tensorType);
if (input_tensor == nullptr) {
MS_LOG(ERROR) << "input tensor null";
int n, h, w, c;
bool is_2d = false;
if (shape.size() == 2) {
is_2d = true;
h = w = 1;
n = shape[0];
c = shape[1];
} else {
n = shape[0];
h = shape[1];
w = shape[2];
c = shape[3];
}
std::vector<int> input_shape = {n, h, w, c};
if (is_2d) {
input_shape = {n, c};
}
auto input_format = is_2d ? schema::Format_NC : schema::Format_NHWC;
auto input_dtype = enable_fp16 ? kNumberTypeFloat16 : kNumberTypeFloat32;
auto tensor_x_ptr = std::make_unique<lite::tensor::Tensor>(TypeId(input_dtype), input_shape, input_format);
auto tensor_x = tensor_x_ptr.get();
if (tensor_x == nullptr) {
MS_LOG(ERROR) << "tensor_x create error.";
return;
}
if (output_tensor == nullptr) {
MS_LOG(ERROR) << "output tensor null";
auto tensor_out_ptr = std::make_unique<lite::tensor::Tensor>(TypeId(input_dtype), input_shape, input_format);
auto tensor_out = tensor_out_ptr.get();
if (tensor_out == nullptr) {
MS_LOG(ERROR) << "tensor_out create error.";
return;
}
std::vector<lite::tensor::Tensor *> inputs{input_tensor};
std::vector<lite::tensor::Tensor *> outputs{output_tensor};

// run
MS_LOG(INFO) << "NewOpenCLKernel";
auto *kernel = new kernel::SoftmaxOpenCLKernel(reinterpret_cast<OpParameter *>(param), inputs, outputs);
if (kernel == nullptr) {
MS_LOG(ERROR) << "kernel null";
std::vector<lite::tensor::Tensor *> inputs{tensor_x};
std::vector<lite::tensor::Tensor *> outputs{tensor_out};
auto arith_kernel_ptr = std::make_unique<kernel::SoftmaxOpenCLKernel>(nullptr, inputs, outputs);
auto arith_kernel = arith_kernel_ptr.get();
if (arith_kernel == nullptr) {
MS_LOG(ERROR) << "arith_kernel create error.";
return;
}
MS_LOG(INFO) << "KernelInit";
kernel->Init();
arith_kernel->Init();

std::vector<kernel::LiteKernel *> kernels{kernel};
inputs[0]->MallocData(allocator);
auto *pGraph = new (std::nothrow) kernel::SubGraphOpenCLKernel(inputs, outputs, kernels, kernels, kernels);

std::vector<kernel::LiteKernel *> kernels{arith_kernel};
auto pGraph_ptr = std::make_unique<kernel::SubGraphOpenCLKernel>(inputs, outputs, kernels, kernels, kernels);
auto pGraph = pGraph_ptr.get();
if (pGraph == nullptr) {
MS_LOG(ERROR) << "pGraph null";
MS_LOG(ERROR) << "pGraph create error.";
return;
}
MS_LOG(INFO) << "pGraphinit";
pGraph->Init();

// load data
MS_LOG(INFO) << "load data1";
LoadTestData(input_tensor->Data(), input_tensor->Size(), input_file);
auto *input_data = reinterpret_cast<float *>(input_tensor->Data());
printf("\ninput[0:10]:");
for (int i = 0; i < 10; i++) {
printf("[%d]:%.3f ", i, input_data[i]);
}
printf("\n\n");

MS_LOG(INFO) << "Run";
memcpy(inputs[0]->Data(), input_data, inputs[0]->ElementsNum() * dtype_size);
pGraph->Run();

MS_LOG(INFO) << "compare result";
CompareOutput(output_tensor, expect_file, static_cast<float>(1e-5));
for (auto tensor : inputs) {
delete tensor;
if (enable_fp16) {
CompareOutput(outputs[0]->Data(), output_data, outputs[0]->ElementsNum(), static_cast<float16_t>(1e-3), 2e-2);
} else {
CompareOutput(outputs[0]->Data(), output_data, outputs[0]->ElementsNum(), static_cast<float>(1e-5));
}
for (auto tensor : outputs) {
delete tensor;
}
delete kernel;
delete pGraph;
inputs[0]->SetData(nullptr);
outputs[0]->SetData(nullptr);

MS_LOG(INFO) << "Test Softmax passed";
lite::opencl::OpenCLRuntime::DeleteInstance();
}

TEST_F(TestSoftmaxOpenCL, Softmax_1) {
std::vector<int> input_shape = {1, 2, 2, 8};
std::vector<int> output_shape = {1, 2, 2, 8};
std::string input_file = "softmax_in.bin";
std::string expect_file = "softmax_out.bin";
auto param = new (std::nothrow) SoftmaxParameter;
param->axis_ = 3;
schema::Format format = schema::Format_NHWC4;
TEST_F(TestSoftmaxOpenCL, Softmax2DFp32) {
int n = 1;
int c = 10;
std::vector<int> shape = {n, c};
std::vector<float> input_data = {1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f};
std::vector<float> output_data = {0.1f, 0.1f, 0.1f, 0.1f, 0.1f, 0.1f, 0.1f, 0.1f, 0.1f, 0.1f};

RunTestCaseSoftmax(shape, input_data.data(), output_data.data(), false);
}

TEST_F(TestSoftmaxOpenCL, Softmax2DFp16) {
int n = 1;
int c = 10;
std::vector<int> shape = {n, c};
std::vector<float16_t> input_data = {1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f};
std::vector<float16_t> output_data = {0.1f, 0.1f, 0.1f, 0.1f, 0.1f, 0.1f, 0.1f, 0.1f, 0.1f, 0.1f};

RunTestCase(input_shape, output_shape, input_file, expect_file, param, format);
RunTestCaseSoftmax(shape, input_data.data(), output_data.data(), true);
}

TEST_F(TestSoftmaxOpenCL, Softmax4DFp32) {
int n = 1;
int h = 2;
int w = 1;
int c = 5;
std::vector<int> shape = {n, h, w, c};
std::vector<float> input_data = {1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f};
std::vector<float> output_data = {0.2f, 0.2f, 0.2f, 0.2f, 0.2f, 0.2f, 0.2f, 0.2f, 0.2f, 0.2f};

RunTestCaseSoftmax(shape, input_data.data(), output_data.data(), false);
}

TEST_F(TestSoftmaxOpenCL, Softmax4DFp16) {
int n = 1;
int h = 2;
int w = 1;
int c = 5;
std::vector<int> shape = {n, h, w, c};
std::vector<float16_t> input_data = {1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f};
std::vector<float16_t> output_data = {0.2f, 0.2f, 0.2f, 0.2f, 0.2f, 0.2f, 0.2f, 0.2f, 0.2f, 0.2f};

RunTestCaseSoftmax(shape, input_data.data(), output_data.data(), true);
}
} // namespace mindspore

+ 2
- 2
mindspore/lite/test/ut/src/runtime/kernel/opencl/transpose_tests.cc View File

@@ -117,8 +117,8 @@ TEST_F(TestTransposeOpenCL, TransposeFp32) {
}

TEST_F(TestTransposeOpenCL, TransposeFp16) {
int h = 4;
int w = 1;
int h = 2;
int w = 2;
int c = 3;
std::vector<int> shape = {h, w, c};
std::vector<float16_t> input_data = {0.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f, 11.0f};


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