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mindspore
提交 b022e3d1 编写于 作者: P Pengyongrong
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add new ops named slice 

add ops supported fp16
上级 b4bc1deb
隐藏空白更改
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Showing with 450 addition and 82 deletion
mindspore/lite/src/runtime/kernel/opencl/cl/batchnorm.cl
浏览文件 @ b022e3d1
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#define INT4 int4
#define INT2 int2
__constant sampler_t smp_none = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_NONE | CLK_FILTER_NEAREST;
......@@ -11,16 +12,16 @@ __kernel void batch_normalization(__read_only image2d_t input, __read_only image
if (X >= input_shape.y || Y >= input_shape.z || Z >= input_shape.w) {
return;
}
FLT4 result = read_imagef(input, smp_none, (int2)((Y)*input_shape.w + Z, (X)));
FLT4 result = READ_IMAGE(input, smp_none, (int2)((Y)*input_shape.w + Z, (X)));
FLT4 result_mean = read_imagef(mean, smp_none, (int2)((Z), (0)));
FLT4 result_var = read_imagef(variance, smp_none, (int2)((Z), (0)));
FLT4 result_scale = read_imagef(scale, smp_none, (int2)((Z), (0)));
FLT4 result_offset = read_imagef(offset, smp_none, (int2)((Z), (0)));
FLT4 result_mean = READ_IMAGE(mean, smp_none, (int2)((Z), (0)));
FLT4 result_var = READ_IMAGE(variance, smp_none, (int2)((Z), (0)));
FLT4 result_scale = READ_IMAGE(scale, smp_none, (int2)((Z), (0)));
FLT4 result_offset = READ_IMAGE(offset, smp_none, (int2)((Z), (0)));
result.x = result_scale.x * ((result.x - result_mean.x) / sqrt(result_var.x + epsilon)) + result_offset.x;
result.y = result_scale.y * ((result.y - result_mean.y) / sqrt(result_var.y + epsilon)) + result_offset.y;
result.z = result_scale.z * ((result.z - result_mean.z) / sqrt(result_var.z + epsilon)) + result_offset.z;
result.w = result_scale.w * ((result.w - result_mean.w) / sqrt(result_var.w + epsilon)) + result_offset.w;
write_imagef(output, (int2)((Y)*input_shape.w + Z, (X)), result);
WRITE_IMAGE(output, (int2)((Y)*input_shape.w + Z, (X)), result);
}
mindspore/lite/src/runtime/kernel/opencl/cl/concat.cl
浏览文件 @ b022e3d1
// #pragma OPENCL EXTENSION cl_khr_fp16 : enable
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
__constant sampler_t smp_none = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_NONE | CLK_FILTER_NEAREST;
__kernel void Concat(__read_only image2d_t input0, __read_only image2d_t input1, __write_only image2d_t output,
......@@ -10,11 +10,11 @@ __kernel void Concat(__read_only image2d_t input0, __read_only image2d_t input1,
return;
}
if (Z < input_channels.x) {
FLT4 result = read_imagef(input0, smp_none, (int2)((Y)*input_channels.x + Z, (X)));
write_imagef(output, (int2)((Y)*output_shape.w + Z, (X)), result);
FLT4 result = READ_IMAGE(input0, smp_none, (int2)((Y)*input_channels.x + Z, (X)));
WRITE_IMAGE(output, (int2)((Y)*output_shape.w + Z, (X)), result);
} else {
FLT4 result = read_imagef(input1, smp_none, (int2)((Y)*input_channels.y + Z - input_channels.x, (X)));
write_imagef(output, (int2)((Y)*output_shape.w + Z, (X)), result);
FLT4 result = READ_IMAGE(input1, smp_none, (int2)((Y)*input_channels.y + Z - input_channels.x, (X)));
WRITE_IMAGE(output, (int2)((Y)*output_shape.w + Z, (X)), result);
}
}
......@@ -27,14 +27,14 @@ __kernel void Concat3input(__read_only image2d_t input0, __read_only image2d_t i
return;
}
if (Z < input_channels.x) {
FLT4 result0 = read_imagef(input0, smp_none, (int2)((Y)*input_channels.x + Z, (X)));
write_imagef(output, (int2)((Y)*output_shape.w + Z, (X)), result0);
FLT4 result0 = READ_IMAGE(input0, smp_none, (int2)((Y)*input_channels.x + Z, (X)));
WRITE_IMAGE(output, (int2)((Y)*output_shape.w + Z, (X)), result0);
} else if (Z < (input_channels.x + input_channels.y)) {
FLT4 result1 = read_imagef(input1, smp_none, (int2)((Y)*input_channels.y + Z - input_channels.x, (X)));
write_imagef(output, (int2)((Y)*output_shape.w + Z, (X)), result1);
FLT4 result1 = READ_IMAGE(input1, smp_none, (int2)((Y)*input_channels.y + Z - input_channels.x, (X)));
WRITE_IMAGE(output, (int2)((Y)*output_shape.w + Z, (X)), result1);
} else {
FLT4 result2 =
read_imagef(input2, smp_none, (int2)((Y)*input_channels.z + Z - input_channels.x - input_channels.y, (X)));
write_imagef(output, (int2)((Y)*output_shape.w + Z, (X)), result2);
READ_IMAGE(input2, smp_none, (int2)((Y)*input_channels.z + Z - input_channels.x - input_channels.y, (X)));
WRITE_IMAGE(output, (int2)((Y)*output_shape.w + Z, (X)), result2);
}
}
mindspore/lite/src/runtime/kernel/opencl/cl/slice.cl
浏览文件 @ b022e3d1
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#define INT2 int2
#define INT4 int4
#define FLT4 float4
__constant sampler_t smp_none = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_NONE | CLK_FILTER_NEAREST;
__kernel void slice(__read_only image2d_t input, __write_only image2d_t output, INT4 input_shape, INT4 out_shape,
INT4 begin, INT2 sharedNoUpdiv) {
......@@ -12,46 +12,43 @@ __kernel void slice(__read_only image2d_t input, __write_only image2d_t output,
FLT4 result;
if (sharedNoUpdiv.x % 4 == 0) {
for (int i = 0; i < out_shape.w; i++) {
result = read_imagef(input, smp_none, (INT2)((Y + begin.z) * input_shape.w + (i + begin.w), (X + begin.y)));
write_imagef(output, (INT2)((Y)*out_shape.w + i, (X)), result);
result = READ_IMAGE(input, smp_none, (INT2)((Y + begin.z) * input_shape.w + (i + begin.w), (X + begin.y)));
WRITE_IMAGE(output, (INT2)((Y)*out_shape.w + i, (X)), result);
}
} else {
int begin_postion = sharedNoUpdiv.y % 4;
FLT4 first = read_imagef(input, smp_none, (INT2)((Y + begin.z) * input_shape.w + begin.w, (X + begin.y)));
FLT4 first = READ_IMAGE(input, smp_none, (INT2)((Y + begin.z) * input_shape.w + begin.w, (X + begin.y)));
if (begin_postion == 1) {
for (int i = 1; i <= out_shape.w; i++) {
FLT4 second =
read_imagef(input, smp_none, (INT2)((Y + begin.z) * input_shape.w + (begin.w + i), (X + begin.y)));
FLT4 second = READ_IMAGE(input, smp_none, (INT2)((Y + begin.z) * input_shape.w + (begin.w + i), (X + begin.y)));
result.x = first.y;
result.y = first.z;
result.z = first.w;
result.w = second.x;
write_imagef(output, (INT2)((Y)*out_shape.w + i - 1, (X)), result);
WRITE_IMAGE(output, (INT2)((Y)*out_shape.w + i - 1, (X)), result);
first.y = second.y;
first.z = second.z;
first.w = second.w;
}
} else if (begin_postion == 2) {
for (int i = 1; i <= out_shape.w; i++) {
FLT4 second =
read_imagef(input, smp_none, (INT2)((Y + begin.z) * input_shape.w + (begin.w + i), (X + begin.y)));
FLT4 second = READ_IMAGE(input, smp_none, (INT2)((Y + begin.z) * input_shape.w + (begin.w + i), (X + begin.y)));
result.x = first.z;
result.y = first.w;
result.z = second.x;
result.w = second.y;
write_imagef(output, (INT2)((Y)*out_shape.w + i - 1, (X)), result);
WRITE_IMAGE(output, (INT2)((Y)*out_shape.w + i - 1, (X)), result);
first.z = second.z;
first.w = second.w;
}
} else {
for (int i = 1; i <= out_shape.w; i++) {
FLT4 second =
read_imagef(input, smp_none, (INT2)((Y + begin.z) * input_shape.w + (begin.w + i), (X + begin.y)));
FLT4 second = READ_IMAGE(input, smp_none, (INT2)((Y + begin.z) * input_shape.w + (begin.w + i), (X + begin.y)));
result.x = first.w;
result.y = second.x;
result.z = second.y;
result.w = second.z;
write_imagef(output, (INT2)((Y)*out_shape.w + i - 1, (X)), result);
WRITE_IMAGE(output, (INT2)((Y)*out_shape.w + i - 1, (X)), result);
first.w = second.w;
}
}
......@@ -64,18 +61,18 @@ __kernel void slice(__read_only image2d_t input, __write_only image2d_t output,
result_fill0.y = 0;
result_fill0.z = 0;
result_fill0.w = 0;
write_imagef(output, (INT2)((Y)*out_shape.w + out_shape.w - 1, (X)), result_fill0);
WRITE_IMAGE(output, (INT2)((Y)*out_shape.w + out_shape.w - 1, (X)), result_fill0);
} else if (size == 2) {
result_fill0.x = result.x;
result_fill0.y = result.y;
result_fill0.z = 0;
result_fill0.w = 0;
write_imagef(output, (INT2)((Y)*out_shape.w + out_shape.w - 1, (X)), result_fill0);
WRITE_IMAGE(output, (INT2)((Y)*out_shape.w + out_shape.w - 1, (X)), result_fill0);
} else if (size == 3) {
result_fill0.x = result.x;
result_fill0.y = result.y;
result_fill0.z = result.z;
result_fill0.w = 0;
write_imagef(output, (INT2)((Y)*out_shape.w + out_shape.w - 1, (X)), result_fill0);
WRITE_IMAGE(output, (INT2)((Y)*out_shape.w + out_shape.w - 1, (X)), result_fill0);
}
}
mindspore/lite/src/runtime/kernel/opencl/kernel/batchnorm.cc
浏览文件 @ b022e3d1
......@@ -38,11 +38,12 @@ int BatchNormOpenCLKernel::GetImageSize(size_t idx, std::vector<size_t> *img_siz
im_dst_y = out_tensors_[0]->Height() * CO4;
im_dst_x = out_tensors_[0]->Width();
}
#ifdef ENABLE_FP16
size_t img_dtype = CL_HALF_FLOAT;
#else
size_t img_dtype = CL_FLOAT;
#endif
auto ocl_runtime = lite::opencl::OpenCLRuntime::GetInstance();
auto enable_fp16_ = ocl_runtime->GetFp16Enable();
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;
......@@ -148,4 +149,5 @@ kernel::LiteKernel *OpenCLBatchnormKernelCreator(const std::vector<lite::tensor:
}
REG_KERNEL(kGPU, kNumberTypeFloat32, PrimitiveType_BatchNorm, OpenCLBatchnormKernelCreator);
REG_KERNEL(kGPU, kNumberTypeFloat16, PrimitiveType_BatchNorm, OpenCLBatchnormKernelCreator);
} // namespace mindspore::kernel
mindspore/lite/src/runtime/kernel/opencl/kernel/concat.cc
浏览文件 @ b022e3d1
......@@ -38,11 +38,12 @@ int ConcatOpenCLKernel::GetImageSize(size_t idx, std::vector<size_t> *img_size)
im_dst_y = out_tensors_[0]->Height() * CO4;
im_dst_x = out_tensors_[0]->Width();
}
#ifdef ENABLE_FP16
size_t img_dtype = CL_HALF_FLOAT;
#else
size_t img_dtype = CL_FLOAT;
#endif
auto ocl_runtime = lite::opencl::OpenCLRuntime::GetInstance();
auto enable_fp16_ = ocl_runtime->GetFp16Enable();
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;
......@@ -225,4 +226,5 @@ kernel::LiteKernel *OpenCLConcatKernelCreator(const std::vector<lite::tensor::Te
}
REG_KERNEL(kGPU, kNumberTypeFloat32, PrimitiveType_Concat, OpenCLConcatKernelCreator);
REG_KERNEL(kGPU, kNumberTypeFloat16, PrimitiveType_Concat, OpenCLConcatKernelCreator);
} // namespace mindspore::kernel
mindspore/lite/src/runtime/kernel/opencl/kernel/matmul.cc
浏览文件 @ b022e3d1
......@@ -175,4 +175,6 @@ kernel::LiteKernel *OpenCLMatMulKernelCreator(const std::vector<lite::tensor::Te
REG_KERNEL(kGPU, kNumberTypeFloat32, PrimitiveType_MatMul, OpenCLMatMulKernelCreator)
REG_KERNEL(kGPU, kNumberTypeFloat32, PrimitiveType_FullConnection, OpenCLMatMulKernelCreator)
REG_KERNEL(kGPU, kNumberTypeFloat16, PrimitiveType_MatMul, OpenCLMatMulKernelCreator)
REG_KERNEL(kGPU, kNumberTypeFloat16, PrimitiveType_FullConnection, OpenCLMatMulKernelCreator)
} // namespace mindspore::kernel
mindspore/lite/src/runtime/kernel/opencl/kernel/slice.cc
浏览文件 @ b022e3d1
......@@ -38,11 +38,12 @@ int SliceOpenCLKernel::GetImageSize(size_t idx, std::vector<size_t> *img_size) {
im_dst_y = out_tensors_[0]->Height() * CO4;
im_dst_x = out_tensors_[0]->Width();
}
#ifdef ENABLE_FP16
size_t img_dtype = CL_HALF_FLOAT;
#else
size_t img_dtype = CL_FLOAT;
#endif
auto ocl_runtime = lite::opencl::OpenCLRuntime::GetInstance();
auto enable_fp16_ = ocl_runtime->GetFp16Enable();
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;
......@@ -143,4 +144,6 @@ kernel::LiteKernel *OpenCLSliceKernelCreator(const std::vector<lite::tensor::Ten
}
REG_KERNEL(kGPU, kNumberTypeFloat32, PrimitiveType_Slice, OpenCLSliceKernelCreator);
REG_KERNEL(kGPU, kNumberTypeFloat16, PrimitiveType_Slice, OpenCLSliceKernelCreator);
} // namespace mindspore::kernel
mindspore/lite/test/ut/src/runtime/kernel/opencl/batchnorm_tests.cc
浏览文件 @ b022e3d1
......@@ -23,9 +23,13 @@
#include "mindspore/lite/src/runtime/kernel/opencl/kernel/batchnorm.h"
namespace mindspore {
class TestBatchnormOpenCL : public mindspore::CommonTest {
class TestBatchnormOpenCLfp32 : public mindspore::CommonTest {
public:
TestBatchnormOpenCL() {}
TestBatchnormOpenCLfp32() {}
};
class TestBatchnormOpenCLfp16 : public mindspore::CommonTest {
public:
TestBatchnormOpenCLfp16() {}
};
template <typename T>
......@@ -35,30 +39,153 @@ void CompareOutputData1(T *output_data, T *correct_data, int size, float err_bou
ASSERT_LE(abs, err_bound);
}
}
TEST_F(TestBatchnormOpenCLfp16, Batchnormfp16input_dim4) {
MS_LOG(INFO) << "begin test";
auto ocl_runtime = lite::opencl::OpenCLRuntime::GetInstance();
ocl_runtime->SetFp16Enable(true);
ocl_runtime->Init();
auto allocator = ocl_runtime->GetAllocator();
MS_LOG(INFO) << "Read tensors from .bin";
std::vector<int> input_shape = {1, 256, 256, 48};
std::vector<int> output_shape = {1, 256, 256, 48};
auto data_type = kNumberTypeFloat32;
auto tensor_type = schema::NodeType_ValueNode;
// get the input from .bin
size_t input_size, output_size;
std::string input_path = "./test_data/batchnorm_in_datafp16.bin";
std::string mean_path = "./test_data/batchnorm_meanfp16.bin";
std::string var_path = "./test_data/batchnorm_varfp16.bin";
std::string offset_path = "./test_data/batchnorm_offsetfp16.bin";
std::string scale_path = "./test_data/batchnorm_scalefp16.bin";
std::string output_path = "./test_data/batchnorm_out_datafp16.bin";
auto input_data = reinterpret_cast<float16_t *>(mindspore::lite::ReadFile(input_path.c_str(), &input_size));
auto correct_data = reinterpret_cast<float16_t *>(mindspore::lite::ReadFile(output_path.c_str(), &output_size));
size_t mean_size, var_size, scale_size, offset_size;
auto mean_data = reinterpret_cast<float16_t *>(mindspore::lite::ReadFile(mean_path.c_str(), &mean_size));
auto var_data = reinterpret_cast<float16_t *>(mindspore::lite::ReadFile(var_path.c_str(), &var_size));
auto scale_data = reinterpret_cast<float16_t *>(mindspore::lite::ReadFile(scale_path.c_str(), &scale_size));
auto offset_data = reinterpret_cast<float16_t *>(mindspore::lite::ReadFile(offset_path.c_str(), &offset_size));
MS_LOG(INFO) << "construct tensors";
lite::tensor::Tensor *tensor_data =
new (std::nothrow) lite::tensor::Tensor(data_type, input_shape, schema::Format_NHWC, tensor_type);
lite::tensor::Tensor *tensor_mean =
new (std::nothrow) lite::tensor::Tensor(data_type, {1, 1, 1, input_shape[3]}, schema::Format_NHWC, tensor_type);
lite::tensor::Tensor *tensor_var =
new (std::nothrow) lite::tensor::Tensor(data_type, {1, 1, 1, input_shape[3]}, schema::Format_NHWC, tensor_type);
lite::tensor::Tensor *tensor_scale =
new (std::nothrow) lite::tensor::Tensor(data_type, {1, 1, 1, input_shape[3]}, schema::Format_NHWC, tensor_type);
lite::tensor::Tensor *tensor_offset =
new (std::nothrow) lite::tensor::Tensor(data_type, {1, 1, 1, input_shape[3]}, schema::Format_NHWC, tensor_type);
if (tensor_data == nullptr || tensor_mean == nullptr || tensor_var == nullptr || tensor_scale == nullptr ||
tensor_offset == nullptr) {
MS_LOG(INFO) << "init tensor failed";
return;
}
auto *output_tensor =
new (std::nothrow) lite::tensor::Tensor(data_type, output_shape, schema::Format_NHWC4, tensor_type);
if (output_tensor == nullptr) {
MS_LOG(INFO) << "init tensor failed";
delete tensor_data;
delete tensor_mean;
delete tensor_var;
delete tensor_scale;
delete tensor_offset;
return;
}
std::vector<lite::tensor::Tensor *> inputs = {tensor_data, tensor_scale, tensor_offset, tensor_mean, tensor_var};
std::vector<lite::tensor::Tensor *> outputs{output_tensor};
MS_LOG(INFO) << "initialize tensors";
auto param = new (std::nothrow) BatchNormParameter();
if (param == nullptr) {
MS_LOG(INFO) << "new BatchNormParameter failed";
for (auto tensor : outputs) {
delete tensor;
}
return;
}
param->epsilon_ = pow(10, -5);
auto *batchnorm_kernel =
new (std::nothrow) kernel::BatchNormOpenCLKernel(reinterpret_cast<OpParameter *>(param), inputs, outputs);
if (batchnorm_kernel == nullptr) {
MS_LOG(INFO) << "new kernel::BatchNorm_kernel failed";
for (auto tensor : outputs) {
delete tensor;
}
delete param;
return;
}
batchnorm_kernel->Init();
TEST_F(TestBatchnormOpenCL, Batchnorminput_dim4) {
// to do allocate memory for inputs and outputs
for (auto &input_tensor : inputs) {
input_tensor->MallocData(allocator);
}
MS_LOG(INFO) << "initialize sub_graph";
std::vector<kernel::LiteKernel *> kernels{batchnorm_kernel};
auto *sub_graph = new (std::nothrow) kernel::SubGraphOpenCLKernel(inputs, outputs, kernels, kernels, kernels);
if (sub_graph == nullptr) {
MS_LOG(INFO) << "new kernel::SubGraphOpenCLKernel failed";
for (auto tensor : outputs) {
delete tensor;
}
delete param;
delete batchnorm_kernel;
return;
}
sub_graph->Init();
MS_LOG(INFO) << "init tensors";
memcpy(inputs[0]->Data(), input_data, input_size);
memcpy(inputs[1]->Data(), scale_data, scale_size);
memcpy(inputs[2]->Data(), offset_data, offset_size);
memcpy(inputs[3]->Data(), mean_data, mean_size);
memcpy(inputs[4]->Data(), var_data, var_size);
std::cout << "==================output data================" << std::endl;
sub_graph->Run();
auto *output_data_gpu = reinterpret_cast<float16_t *>(output_tensor->Data());
CompareOutputData1(output_data_gpu, correct_data, output_tensor->ElementsNum(), 0.0001);
for (auto tensor : inputs) {
delete tensor;
}
for (auto tensor : outputs) {
delete tensor;
}
delete param;
delete batchnorm_kernel;
delete sub_graph;
}
TEST_F(TestBatchnormOpenCLfp32, Batchnormfp32input_dim4) {
MS_LOG(INFO) << "begin test";
auto ocl_runtime = lite::opencl::OpenCLRuntime::GetInstance();
ocl_runtime->Init();
auto allocator = ocl_runtime->GetAllocator();
MS_LOG(INFO) << "Read tensors from .bin";
std::vector<int> input_shape = {1, 256, 256, 16};
std::vector<int> output_shape = {1, 256, 256, 16};
std::vector<int> input_shape = {1, 256, 256, 47};
std::vector<int> output_shape = {1, 256, 256, 47};
auto data_type = kNumberTypeFloat32;
auto tensor_type = schema::NodeType_ValueNode;
// get the input from .bin
size_t input_size, output_size;
std::string input_path = "./test_data/in_data.bin";
std::string mean_path = "./test_data/mean.bin";
std::string var_path = "./test_data/var.bin";
std::string output_path = "./test_data/out_data.bin";
std::string input_path = "./test_data/batchnorm_in_datafp32.bin";
std::string mean_path = "./test_data/batchnorm_meanfp32.bin";
std::string var_path = "./test_data/batchnorm_varfp32.bin";
std::string offset_path = "./test_data/batchnorm_offsetfp32.bin";
std::string scale_path = "./test_data/batchnorm_scalefp32.bin";
std::string output_path = "./test_data/batchnorm_out_datafp32.bin";
auto input_data = reinterpret_cast<float *>(mindspore::lite::ReadFile(input_path.c_str(), &input_size));
auto correct_data = reinterpret_cast<float *>(mindspore::lite::ReadFile(output_path.c_str(), &output_size));
size_t mean_size, var_size;
size_t mean_size, var_size, scale_size, offset_size;
auto mean_data = reinterpret_cast<float *>(mindspore::lite::ReadFile(mean_path.c_str(), &mean_size));
auto var_data = reinterpret_cast<float *>(mindspore::lite::ReadFile(var_path.c_str(), &var_size));
auto scale_data = reinterpret_cast<float *>(mindspore::lite::ReadFile(scale_path.c_str(), &scale_size));
auto offset_data = reinterpret_cast<float *>(mindspore::lite::ReadFile(offset_path.c_str(), &offset_size));
MS_LOG(INFO) << "construct tensors";
lite::tensor::Tensor *tensor_data =
......@@ -131,14 +258,9 @@ TEST_F(TestBatchnormOpenCL, Batchnorminput_dim4) {
}
sub_graph->Init();
MS_LOG(INFO) << "init tensors";
std::cout << "init tensors" << std::endl;
memcpy(inputs[0]->Data(), input_data, input_size);
auto &temp = inputs[1];
auto tensor_temp = reinterpret_cast<float *>(temp->Data());
int UPDIV_tensor_scale = UP_DIV(tensor_scale->ElementsNum(), C4NUM) * 4;
for (int i = 0; i < UPDIV_tensor_scale; ++i) {
tensor_temp[i] = static_cast<float>(1);
}
memcpy(inputs[1]->Data(), scale_data, scale_size);
memcpy(inputs[2]->Data(), offset_data, offset_size);
memcpy(inputs[3]->Data(), mean_data, mean_size);
memcpy(inputs[4]->Data(), var_data, var_size);
std::cout << "==================output data================" << std::endl;
......
mindspore/lite/test/ut/src/runtime/kernel/opencl/concat_tests.cc
浏览文件 @ b022e3d1
......@@ -21,9 +21,10 @@
#include "mindspore/lite/src/runtime/kernel/opencl/subgraph_opencl_kernel.h"
#include "mindspore/lite/src/runtime/kernel/opencl/kernel/concat.h"
void ConcatComputeByCPU_2input_dim4_axis3(const float *input0, const float *input1, float *output,
std::vector<int> input_shape0, std::vector<int> input_shape1,
std::vector<int> output_shape, const int axis) {
template <typename T>
void ConcatComputeByCPU_2input_dim4_axis3(const T *input0, const T *input1, T *output, std::vector<int> input_shape0,
std::vector<int> input_shape1, std::vector<int> output_shape,
const int axis) {
int postion, index0 = 0, index1 = 0;
for (int i = 0; i < output_shape[0]; i++) {
for (int j = 0; j < output_shape[1]; j++) {
......@@ -43,10 +44,10 @@ void ConcatComputeByCPU_2input_dim4_axis3(const float *input0, const float *inpu
}
}
}
void ConcatComputeByCPU_3input_dim4_axis3(float *input0, float *input1, float *input2, float *output,
std::vector<int> input_shape0, std::vector<int> input_shape1,
std::vector<int> input_shape2, std::vector<int> output_shape,
const int axis) {
template <typename T>
void ConcatComputeByCPU_3input_dim4_axis3(T *input0, T *input1, T *input2, T *output, std::vector<int> input_shape0,
std::vector<int> input_shape1, std::vector<int> input_shape2,
std::vector<int> output_shape, const int axis) {
int postion, index0 = 0, index1 = 0, index2 = 0;
for (int i = 0; i < output_shape[0]; i++) {
for (int j = 0; j < output_shape[1]; j++) {
......@@ -82,9 +83,13 @@ void ConcatComputeByCPU_3input_dim4_axis3(float *input0, float *input1, float *i
}
namespace mindspore {
class TestConcatOpenCL : public mindspore::CommonTest {
class TestConcatOpenCLfp32 : public mindspore::CommonTest {
public:
TestConcatOpenCLfp32() {}
};
class TestConcatOpenCLfp16 : public mindspore::CommonTest {
public:
TestConcatOpenCL() {}
TestConcatOpenCLfp16() {}
};
template <typename T>
......@@ -94,18 +99,138 @@ void CompareOutputData1(T *output_data, T *correct_data, int size, float err_bou
ASSERT_LE(abs, err_bound);
}
}
TEST_F(TestConcatOpenCLfp16, ConcatFp16_2input_dim4_axis3) {
MS_LOG(INFO) << "begin test";
auto ocl_runtime = lite::opencl::OpenCLRuntime::GetInstance();
ocl_runtime->SetFp16Enable(true);
ocl_runtime->Init();
auto allocator = ocl_runtime->GetAllocator();
MS_LOG(INFO) << "init tensors";
constexpr int INPUT_NUM = 3;
std::array<std::vector<int>, INPUT_NUM> input_shapes = {
std::vector<int>{1, 16, 256, 80}, std::vector<int>{1, 16, 256, 80}, std::vector<int>{1, 16, 256, 80}};
std::vector<int> output_shape = {1, 16, 256, 240};
auto data_type = kNumberTypeFloat16;
auto tensor_type = schema::NodeType_ValueNode;
std::vector<lite::tensor::Tensor *> inputs;
for (auto &shape : input_shapes) {
auto input_temp = new (std::nothrow) lite::tensor::Tensor(data_type, shape, schema::Format_NHWC4, tensor_type);
inputs.push_back(input_temp);
if (input_temp == nullptr) {
MS_LOG(INFO) << "new input_tensor failed";
return;
}
}
auto *output_tensor =
new (std::nothrow) lite::tensor::Tensor(data_type, output_shape, schema::Format_NHWC4, tensor_type);
if (output_tensor == nullptr) {
MS_LOG(INFO) << "new output_tensor failed";
for (auto tensor : inputs) {
delete tensor;
}
return;
}
std::vector<lite::tensor::Tensor *> outputs{output_tensor};
MS_LOG(INFO) << "input_shapes size=: " << input_shapes.size();
MS_LOG(INFO) << "initialize tensors";
auto param = new (std::nothrow) ConcatParameter();
if (param == nullptr) {
MS_LOG(INFO) << "new ConcatParameter failed";
for (auto tensor : inputs) {
delete tensor;
}
for (auto tensor : outputs) {
delete tensor;
}
return;
}
param->axis_ = 3;
auto *concat_kernel =
new (std::nothrow) kernel::ConcatOpenCLKernel(reinterpret_cast<OpParameter *>(param), inputs, outputs);
if (concat_kernel == nullptr) {
MS_LOG(INFO) << "new kernel::ConcatOpenCLKernel failed";
for (auto tensor : inputs) {
delete tensor;
}
for (auto tensor : outputs) {
delete tensor;
}
delete param;
return;
}
concat_kernel->Init();
// to do allocate memory for inputs and outputs
for (auto &input_tensor : inputs) {
input_tensor->MallocData(allocator);
}
MS_LOG(INFO) << "initialize sub_graph";
std::vector<kernel::LiteKernel *> kernels{concat_kernel};
auto *sub_graph = new (std::nothrow) kernel::SubGraphOpenCLKernel(inputs, outputs, kernels, kernels, kernels);
if (sub_graph == nullptr) {
MS_LOG(INFO) << "new kernel::SubGraphOpenCLKernel failed";
for (auto tensor : inputs) {
delete tensor;
}
for (auto tensor : outputs) {
delete tensor;
}
delete param;
delete concat_kernel;
return;
}
sub_graph->Init();
unsigned int seed = 123;
MS_LOG(INFO) << "initialize input data";
for (auto &input_tensor : inputs) {
auto input_data = reinterpret_cast<float16_t *>(input_tensor->Data());
for (int i = 0; i < input_tensor->ElementsNum(); ++i) {
input_data[i] = static_cast<float16_t>(rand_r(&seed) % 10 + 1);
}
}
// compute the result for CPU
auto *input_data0 = reinterpret_cast<float16_t *>(inputs[0]->Data());
auto *input_data1 = reinterpret_cast<float16_t *>(inputs[1]->Data());
std::vector<float16_t> output_data_cpu(output_shape[0] * output_shape[1] * output_shape[2] * output_shape[3]);
if (inputs.size() == 2) {
ConcatComputeByCPU_2input_dim4_axis3(input_data0, input_data1, output_data_cpu.data(), input_shapes[0],
input_shapes[1], output_shape, param->axis_);
}
if (inputs.size() == 3) {
auto *input_data2 = reinterpret_cast<float16_t *>(inputs[2]->Data());
ConcatComputeByCPU_3input_dim4_axis3(input_data0, input_data1, input_data2, output_data_cpu.data(), input_shapes[0],
input_shapes[1], input_shapes[2], output_shape, param->axis_);
}
std::cout << "==================output data================" << std::endl;
sub_graph->Run();
auto *output_data_gpu = reinterpret_cast<float16_t *>(output_tensor->Data());
CompareOutputData1(output_data_gpu, output_data_cpu.data(), output_tensor->ElementsNum(), 0.00001);
for (auto tensor : inputs) {
delete tensor;
}
for (auto tensor : outputs) {
delete tensor;
}
delete param;
delete concat_kernel;
delete sub_graph;
}
TEST_F(TestConcatOpenCL, ConcatFp32_2input_dim4_axis3) {
TEST_F(TestConcatOpenCLfp32, ConcatFp32_2input_dim4_axis3) {
MS_LOG(INFO) << "begin test";
auto ocl_runtime = lite::opencl::OpenCLRuntime::GetInstance();
ocl_runtime->Init();
auto allocator = ocl_runtime->GetAllocator();
MS_LOG(INFO) << "init tensors";
constexpr int INPUT_NUM = 2;
std::array<std::vector<int>, INPUT_NUM> input_shapes = {std::vector<int>{1, 16, 256, 80},
std::vector<int>{1, 16, 256, 80}};
std::vector<int> output_shape = {1, 16, 256, 160};
constexpr int INPUT_NUM = 3;
std::array<std::vector<int>, INPUT_NUM> input_shapes = {
std::vector<int>{1, 16, 256, 80}, std::vector<int>{1, 16, 256, 80}, std::vector<int>{1, 16, 256, 80}};
std::vector<int> output_shape = {1, 16, 256, 240};
auto data_type = kNumberTypeFloat32;
auto tensor_type = schema::NodeType_ValueNode;
std::vector<lite::tensor::Tensor *> inputs;
......
mindspore/lite/test/ut/src/runtime/kernel/opencl/slice_tests.cc
浏览文件 @ b022e3d1
......@@ -23,9 +23,13 @@
#include "mindspore/lite/src/runtime/kernel/opencl/kernel/slice.h"
namespace mindspore {
class TestSliceOpenCL : public mindspore::CommonTest {
class TestSliceOpenCLfp32 : public mindspore::CommonTest {
public:
TestSliceOpenCL() {}
TestSliceOpenCLfp32() {}
};
class TestSliceOpenCLfp16 : public mindspore::CommonTest {
public:
TestSliceOpenCLfp16() {}
};
template <typename T>
......@@ -36,7 +40,7 @@ void CompareOutputData1(T *output_data, T *correct_data, int size, float err_bou
}
}
TEST_F(TestSliceOpenCL, Sliceinput_dim4) {
TEST_F(TestSliceOpenCLfp32, Slicefp32input_dim4) {
MS_LOG(INFO) << "begin test";
auto ocl_runtime = lite::opencl::OpenCLRuntime::GetInstance();
ocl_runtime->Init();
......@@ -52,8 +56,8 @@ TEST_F(TestSliceOpenCL, Sliceinput_dim4) {
// get the input from .bin
size_t input_size, output_size;
std::string input_path = "./test_data/in_data.bin";
std::string output_path = "./test_data/out_data.bin";
std::string input_path = "./test_data/in_datafp32.bin";
std::string output_path = "./test_data/out_datafp32.bin";
auto input_data = reinterpret_cast<float *>(mindspore::lite::ReadFile(input_path.c_str(), &input_size));
auto correct_data = reinterpret_cast<float *>(mindspore::lite::ReadFile(output_path.c_str(), &output_size));
......@@ -86,7 +90,7 @@ TEST_F(TestSliceOpenCL, Sliceinput_dim4) {
MS_LOG(INFO) << "new SliceParameter failed";
return;
}
for (int i = 0; i < 4; i++) {
for (int i = 0; i < input_shape.size(); i++) {
param->begin_[i] = begin[i];
param->size_[i] = size[i];
}
......@@ -145,4 +149,114 @@ TEST_F(TestSliceOpenCL, Sliceinput_dim4) {
delete slice_kernel;
delete sub_graph;
}
TEST_F(TestSliceOpenCLfp16, Slicefp16input_dim4) {
MS_LOG(INFO) << "begin test";
auto ocl_runtime = lite::opencl::OpenCLRuntime::GetInstance();
ocl_runtime->SetFp16Enable(true);
ocl_runtime->Init();
auto allocator = ocl_runtime->GetAllocator();
MS_LOG(INFO) << "Read tensors from .bin";
std::vector<int> input_shape = {1, 256, 256, 48};
std::vector<int> output_shape = {1, 255, 255, 15};
std::vector<int> begin = {0, 1, 1, 7};
std::vector<int> size = {1, 255, 255, 15};
auto data_type = kNumberTypeFloat16;
auto tensor_type = schema::NodeType_ValueNode;
// get the input from .bin
size_t input_size, output_size;
std::string input_path = "./test_data/in_data.bin";
std::string output_path = "./test_data/out_data.bin";
auto input_data = reinterpret_cast<float16_t *>(mindspore::lite::ReadFile(input_path.c_str(), &input_size));
auto correct_data = reinterpret_cast<float16_t *>(mindspore::lite::ReadFile(output_path.c_str(), &output_size));
MS_LOG(INFO) << "construct tensors";
lite::tensor::Tensor *tensor_data =
new (std::nothrow) lite::tensor::Tensor(data_type, input_shape, schema::Format_NHWC, tensor_type);
if (tensor_data == nullptr) {
MS_LOG(INFO) << "init tensor failed";
return;
}
auto *output_tensor =
new (std::nothrow) lite::tensor::Tensor(data_type, output_shape, schema::Format_NHWC4, tensor_type);
if (output_tensor == nullptr) {
delete tensor_data;
MS_LOG(INFO) << "init tensor failed";
return;
}
std::vector<lite::tensor::Tensor *> inputs = {tensor_data};
std::vector<lite::tensor::Tensor *> outputs = {output_tensor};
MS_LOG(INFO) << "setting SliceParameter";
auto param = new (std::nothrow) SliceParameter();
if (param == nullptr) {
for (auto tensor : inputs) {
delete tensor;
}
for (auto tensor : outputs) {
delete tensor;
}
MS_LOG(INFO) << "new SliceParameter failed";
return;
}
for (int i = 0; i < 4; i++) {
param->begin_[i] = begin[i];
param->size_[i] = size[i];
}
auto *slice_kernel =
new (std::nothrow) kernel::SliceOpenCLKernel(reinterpret_cast<OpParameter *>(param), inputs, outputs);
if (slice_kernel == nullptr) {
for (auto tensor : inputs) {
delete tensor;
}
for (auto tensor : outputs) {
delete tensor;
}
delete param;
MS_LOG(INFO) << "new kernel::slice_kernel failed";
return;
}
slice_kernel->Init();
// to do allocate memory for inputs and outputs
for (auto &input_tensor : inputs) {
input_tensor->MallocData(allocator);
}
MS_LOG(INFO) << "initialize sub_graph";
std::vector<kernel::LiteKernel *> kernels{slice_kernel};
auto *sub_graph = new (std::nothrow) kernel::SubGraphOpenCLKernel(inputs, outputs, kernels, kernels, kernels);
if (sub_graph == nullptr) {
for (auto tensor : inputs) {
delete tensor;
}
for (auto tensor : outputs) {
delete tensor;
}
delete param;
delete slice_kernel;
MS_LOG(INFO) << "new kernel::SubGraphOpenCLKernel failed";
return;
}
sub_graph->Init();
MS_LOG(INFO) << "init tensors";
memcpy(inputs[0]->Data(), input_data, input_size);
std::cout << "==================output data================" << std::endl;
sub_graph->Run();
auto *output_data_gpu = reinterpret_cast<float16_t *>(output_tensor->Data());
CompareOutputData1(output_data_gpu, correct_data, output_tensor->ElementsNum(), 0.0001);
for (auto tensor : inputs) {
delete tensor;
}
for (auto tensor : outputs) {
delete tensor;
}
delete slice_kernel;
delete sub_graph;
}
} // namespace mindspore
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