forked from mindspore-Ecosystem/mindspore
!9435 [MS][LITE][Develop] GPU Ops for PRRelu Optimization
From: @pengyongrong Reviewed-by: @zhanghaibo5,@ddwsky Signed-off-by:
This commit is contained in:
Gitee
commit
af32a33e60
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@ -1,30 +1,19 @@
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#pragma OPENCL EXTENSION cl_khr_fp16 : enable
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__constant sampler_t smp_zero = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST;
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#define NHWC4 2
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#define NC4HW4 100
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__kernel void PRelu_scalar(__read_only image2d_t input, __write_only image2d_t output, float weight, int4 shape,
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int data_format) {
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int h = get_global_id(0);
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int nh = get_global_id(0);
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int w = get_global_id(1);
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int slice = get_global_id(2);
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int H = shape.y;
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int W = shape.z;
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int SLICES = shape.w;
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if (h >= H || w >= W || slice >= SLICES) {
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int c = get_global_id(2);
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if (nh >= shape.x * shape.y || w >= shape.z || c >= shape.w || shape.y == 0) {
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return;
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}
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int x, y;
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if (data_format == 2) {
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x = w * SLICES + slice;
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y = h;
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} else {
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x = w;
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y = slice * H + h;
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}
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int n = nh / shape.y;
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int h = nh % shape.y;
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int x = w * shape.w + c;
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int y = n * shape.y + h;
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FLT4 out = READ_IMAGE(input, smp_zero, (int2)(x, y));
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if (out.x < 0) {
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out.x *= weight;
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@ -43,25 +32,17 @@ __kernel void PRelu_scalar(__read_only image2d_t input, __write_only image2d_t o
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__kernel void PRelu_vector(__read_only image2d_t input, __write_only image2d_t output, __global FLT4 *weight_vector,
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int4 shape, int data_format) {
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int h = get_global_id(0);
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int nh = get_global_id(0);
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int w = get_global_id(1);
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int slice = get_global_id(2);
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int H = shape.y;
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int W = shape.z;
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int SLICES = shape.w;
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if (h >= H || w >= W || slice >= SLICES) {
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int c = get_global_id(2);
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if (nh >= shape.x * shape.y || w >= shape.z || c >= shape.w || shape.y == 0) {
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return;
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}
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FLT4 weight = weight_vector[slice];
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int x, y;
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if (data_format == 2) {
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x = w * SLICES + slice;
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y = h;
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} else {
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x = w;
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y = slice * H + h;
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}
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int n = nh / shape.y;
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int h = nh % shape.y;
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int x = w * shape.w + c;
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int y = n * shape.y + h;
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FLT4 weight = weight_vector[c];
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FLT4 out = READ_IMAGE(input, smp_zero, (int2)(x, y));
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if (out.x < 0) {
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@ -23,7 +23,7 @@ __kernel void stack_2input_3axis_1inshape(__read_only image2d_t input0, __read_o
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FLT4 result1 = READ_IMAGE(input0, smp_none, (int2)(0, (X)));
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FLT4 result2 = READ_IMAGE(input1, smp_none, (int2)(0, (X)));
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FLT4 result = {result1.x, result2.x, 0, 0};
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WRITE_IMAGE(output, (int2)(coordinate_x_out, (X)), result);
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WRITE_IMAGE(output, (int2)(Y, (X)), result);
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}
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// input -2D -axis = 1
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@ -35,6 +35,10 @@ int BatchNormOpenCLKernel::CheckSpecs() {
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MS_LOG(ERROR) << "in size: " << in_tensors_.size() << ", out size: " << out_tensors_.size();
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return RET_ERROR;
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}
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if (in_tensors_.at(0)->shape().size() == 4) {
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MS_LOG(ERROR) << "The dim of in_tensors->shape must be 4 but your dim is : " << in_tensors_.at(0)->shape().size();
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return RET_ERROR;
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}
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if (in_tensors_.at(0)->shape()[0] > 1) {
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MS_LOG(ERROR) << " Unsupported batch_size >1 ";
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return RET_ERROR;
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@ -48,6 +48,10 @@ int CastOpenCLKernel::CheckSpecs() {
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MS_LOG(ERROR) << "in size: " << in_tensors_.size() << ", out size: " << out_tensors_.size();
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return RET_ERROR;
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}
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if (in_tensors_.at(0)->shape().size() == 4) {
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MS_LOG(ERROR) << "The dim of in_tensors->shape must be 4 but your dim is : " << in_tensors_.at(0)->shape().size();
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return RET_ERROR;
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}
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return RET_OK;
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}
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@ -69,12 +69,19 @@ int ConcatOpenCLKernel::CheckSpecs() {
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return RET_ERROR;
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}
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auto param = reinterpret_cast<ConcatParameter *>(this->op_parameter_);
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auto out_tensors_shape_size = out_tensors_[0]->shape().size();
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MS_LOG(DEBUG) << " concat at axis=: " << param->axis_;
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if (out_tensors_[0]->shape().size() > 4) {
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MS_LOG(ERROR) << " GPU Unsupported shape.size > 4 "
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<< "your shape().size()=: " << out_tensors_[0]->shape().size();
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if (out_tensors_shape_size > 4) {
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MS_LOG(ERROR) << " GPU Unsupported shape.size > 4 ";
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return RET_ERROR;
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}
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for (int i = 0; i < in_tensors_.size(); ++i) {
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auto in_tensors_shape_size = in_tensors_[i]->shape().size();
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if (in_tensors_shape_size > 4) {
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MS_LOG(ERROR) << " GPU Unsupported in_tensor shape.size > 4 ";
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return RET_ERROR;
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}
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}
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axis_ = param->axis_;
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if (axis_ < 0) {
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axis_ += in_tensors_.front()->shape().size();
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@ -83,16 +90,20 @@ int ConcatOpenCLKernel::CheckSpecs() {
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MS_LOG(ERROR) << " only support axis >= 0 and axis <= 3 ";
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return RET_ERROR;
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}
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if (out_tensors_[0]->shape().size() < 4 && op_parameter_->type_ == PrimitiveType_Concat && axis_ != 0) {
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if (out_tensors_[0]->shape().size() == 2) {
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if (out_tensors_shape_size < 4 && Type() == PrimitiveType_Concat && axis_ != 0) {
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if (out_tensors_shape_size == 2) {
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axis_ = axis_ + 2;
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} else if (out_tensors_[0]->shape().size() == 3) {
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} else if (out_tensors_shape_size == 3) {
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axis_ = axis_ + 1;
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} else {
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MS_LOG(ERROR) << " Unsupported axis =: " << axis_ << " shape().size()=: " << out_tensors_[0]->shape().size();
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MS_LOG(ERROR) << " Unsupported axis =: " << axis_ << " shape().size()=: " << out_tensors_shape_size;
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return RET_ERROR;
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}
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}
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if (in_tensors_.size() < 2 || in_tensors_.size() > 6) {
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MS_LOG(ERROR) << "unsupported input size :" << in_tensors_.size();
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return RET_ERROR;
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}
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return RET_OK;
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}
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@ -161,12 +172,7 @@ int ConcatOpenCLKernel::Prepare() {
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if (axis_ == 3 && !Align_) {
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kernel_name += "Input" + std::to_string(in_tensors_.size()) + "UnAlign";
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} else {
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if (2 <= in_tensors_.size() && in_tensors_.size() <= 6) {
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kernel_name += std::to_string(in_tensors_.size()) + "inputaxis" + std::to_string(axis_);
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} else {
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MS_LOG(ERROR) << " input must be less than 6 and more than 2 ";
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return RET_ERROR;
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}
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}
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kernel_name += "_NHWC4";
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@ -186,7 +192,6 @@ int ConcatOpenCLKernel::Run() {
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if (axis_ == 0) {
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return RunAxis0();
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}
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if (2 <= in_tensors_.size() && in_tensors_.size() <= 6) {
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int arg_cn = 0;
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for (int i = 0; i < in_tensors_.size(); ++i) {
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ocl_runtime_->SetKernelArg(kernel_, arg_cn++, in_tensors_[i]->data_c());
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@ -196,10 +201,6 @@ int ConcatOpenCLKernel::Run() {
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} else {
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ocl_runtime_->SetKernelArg(kernel_, arg_cn++, out_tensors_[0]->data_c());
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}
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} else {
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MS_LOG(ERROR) << "unsupported input size :" << in_tensors_.size();
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return RET_ERROR;
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}
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ocl_runtime_->RunKernel(kernel_, global_range_, local_range_, nullptr, &event_);
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return RET_OK;
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}
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@ -34,16 +34,22 @@ namespace mindspore::kernel {
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int PowerOpenCLKernel::CheckSpecs() {
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auto param = reinterpret_cast<PowerParameter *>(this->op_parameter_);
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broadcast_ = param->broadcast_;
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if (!(broadcast_ && in_tensors_.size() == 1)) {
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if ((in_tensors_.size() != 1 && in_tensors_.size() != 2) || out_tensors_.size() != 1) {
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MS_LOG(ERROR) << "in size: " << in_tensors_.size() << "out size: " << out_tensors_.size();
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return RET_ERROR;
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}
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if (in_tensors_.size() == 1 && !broadcast_) {
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MS_LOG(ERROR) << "broadcast is supported when in_tensors_.size() == 1 ";
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return RET_ERROR;
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}
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if (in_tensors_.size() == 2 && in_tensors_.at(0)->shape().size() != in_tensors_.at(1)->shape().size()) {
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MS_LOG(ERROR) << "Unsupported input->shape.size " << in_tensors_.at(0)->shape().size()
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<< "!=" << in_tensors_.at(1)->shape().size();
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return RET_ERROR;
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} else if (in_tensors_.size() > 2 || in_tensors_.at(0)->shape().size() > 4) {
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MS_LOG(ERROR) << "Unsupported in_tensors_->shape.size " << in_tensors_.size() << " or "
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<< "in_tensors_[0]->shape().size(): " << in_tensors_.at(0)->shape().size();
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return RET_ERROR;
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}
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if (in_tensors_.at(0)->shape().size() > 4) {
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MS_LOG(ERROR) << "in_tensors_->shape.size must be less than 4";
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return RET_ERROR;
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}
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return RET_OK;
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}
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@ -35,7 +35,8 @@ namespace mindspore::kernel {
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int PReluOpenCLKernel::InitWeights() {
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auto allocator = ocl_runtime_->GetAllocator();
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auto weight_tensor = in_tensors_[1];
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auto weight_tensor = in_tensors_.at(1);
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int C_ = weight_shape_.s[3];
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if (weight_is_scalar) {
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if (weight_tensor->data_type() == kNumberTypeFloat16) {
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weight_scalar_ = static_cast<float>(*reinterpret_cast<float16_t *>(weight_tensor->data_c()));
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@ -74,51 +75,75 @@ int PReluOpenCLKernel::InitWeights() {
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return RET_OK;
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}
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int PReluOpenCLKernel::Init() {
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auto input_tensor = in_tensors_[0];
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auto weight_tensor = in_tensors_[1];
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if (input_tensor->shape().size() != 4) {
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MS_LOG(ERROR) << "PRelu only support dim=4, but your dim=" << input_tensor->shape().size();
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return mindspore::lite::RET_ERROR;
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}
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batch_size_ = input_tensor->Batch();
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C_ = input_tensor->Channel();
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H_ = input_tensor->Height();
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W_ = input_tensor->Width();
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if (batch_size_ != 1) {
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MS_LOG(ERROR) << "Init PRelu kernel failed: Unsupported multi-batch.";
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int PReluOpenCLKernel::CheckSpecs() {
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if (in_tensors_.size() != 2 || out_tensors_.size() != 1) {
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MS_LOG(ERROR) << "PRelu Only supported in_tensors_.size=2 and out_tensors_.size()= 2 but your in_tensors_.size = "
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<< in_tensors_.size() << "out_tensors_.size()=: " << out_tensors_.size();
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return RET_ERROR;
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}
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auto weight_channel = weight_tensor->shape()[0];
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if (weight_channel != 1 && weight_channel != C_) {
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MS_LOG(ERROR)
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<< "PRelu weight channel size must be 1 or must be equal with in_teneors channel size, but your weight size is "
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<< weight_channel << " and your input channel size is " << C_;
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GpuTensorInfo img_info_in_tensors0(in_tensors_[0]);
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GpuTensorInfo img_info_in_tensors1(in_tensors_[1]);
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auto weight_tensor = in_tensors_.at(1);
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auto in_tensor_channel = img_info_in_tensors0.C;
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auto weight_channel = img_info_in_tensors1.C;
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if (weight_channel != 1 && weight_channel != in_tensor_channel) {
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MS_LOG(ERROR) << "PRelu weight must be equal with in_teneors channel size, but your weight size is "
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<< weight_channel << " and your input channel size is " << in_tensor_channel;
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return mindspore::lite::RET_ERROR;
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}
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weight_is_scalar = weight_channel == 1;
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if (weight_tensor->data_type() != kNumberTypeFloat16 && weight_tensor->data_type() != kNumberTypeFloat32) {
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MS_LOG(ERROR) << "PRelu weight must be float32 or float16";
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return RET_ERROR;
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}
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enable_fp16_ = ocl_runtime_->GetFp16Enable();
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return RET_OK;
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}
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void PReluOpenCLKernel::SetConstArgs() {
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int arg_idx = 3;
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out_shape_.s[3] = UP_DIV(out_shape_.s[3], C4NUM);
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ocl_runtime_->SetKernelArg(kernel_, arg_idx++, out_shape_);
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ocl_runtime_->SetKernelArg(kernel_, arg_idx++, 2);
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}
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void PReluOpenCLKernel::SetGlobalLocal() {
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std::vector<size_t> local = {4, 4, 1};
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OH = out_shape_.s[0] * out_shape_.s[1];
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OW = out_shape_.s[2];
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OC = out_shape_.s[3];
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std::vector<size_t> global = {OH, OW, OC};
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AlignGlobalLocal(global, local);
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}
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int PReluOpenCLKernel::Prepare() {
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cl_int4 output_shape = {};
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cl_int4 weight_shape = {};
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for (int i = 0; i < out_tensors_.at(0)->shape().size(); ++i) {
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output_shape.s[i] = out_tensors_.at(0)->shape()[i];
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}
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for (int i = 0; i < in_tensors_.at(1)->shape().size(); ++i) {
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weight_shape.s[i] = in_tensors_.at(1)->shape()[i];
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}
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Broadcast2GpuShape(out_shape_.s, output_shape.s, out_tensors_.at(0)->shape().size(), 1);
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Broadcast2GpuShape(weight_shape_.s, weight_shape.s, in_tensors_.at(1)->shape().size(), 1);
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weight_is_scalar = weight_shape_.s[3] == 1;
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enable_fp16_ = ocl_runtime_->GetFp16Enable();
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std::string source = prelu_source;
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std::string program_name = "PRelu";
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std::string kernel_name = "PRelu_" + std::string(weight_is_scalar ? "scalar" : "vector");
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ocl_runtime_->LoadSource(program_name, source);
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ocl_runtime_->BuildKernel(kernel_, program_name, kernel_name);
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InitWeights();
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MS_LOG(DEBUG) << program_name << " init Done!";
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MS_LOG(DEBUG) << "kernel_name=: " << kernel_name << " init Done!";
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SetConstArgs();
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SetGlobalLocal();
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return mindspore::lite::RET_OK;
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}
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int PReluOpenCLKernel::Run() {
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MS_LOG(DEBUG) << op_parameter_->name_ << " Running!";
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auto CO_SLICES_ = UP_DIV(C_, C4NUM);
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cl_int4 shape = {batch_size_, H_, W_, CO_SLICES_};
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int arg_idx = 0;
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ocl_runtime_->SetKernelArg(kernel_, arg_idx++, in_tensors_[0]->data_c());
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ocl_runtime_->SetKernelArg(kernel_, arg_idx++, out_tensors_[0]->data_c());
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@ -127,13 +152,7 @@ int PReluOpenCLKernel::Run() {
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} else {
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ocl_runtime_->SetKernelArg(kernel_, arg_idx++, weight_vector_, lite::opencl::MemType::BUF);
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}
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ocl_runtime_->SetKernelArg(kernel_, arg_idx++, shape);
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ocl_runtime_->SetKernelArg(kernel_, arg_idx++, 2);
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std::vector<size_t> local = {4, 4, 1};
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std::vector<size_t> global = {static_cast<size_t>(H_), static_cast<size_t>(W_), static_cast<size_t>(CO_SLICES_)};
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AlignGlobalLocal(global, local);
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auto ret = ocl_runtime_->RunKernel(kernel_, global_range_, local_range_);
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auto ret = ocl_runtime_->RunKernel(kernel_, global_range_, local_range_, nullptr, &event_);
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if (ret != mindspore::lite::RET_OK) {
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MS_LOG(ERROR) << "Run kernel " << op_parameter_->name_ << " error.";
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return mindspore::lite::RET_ERROR;
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@ -141,30 +160,6 @@ int PReluOpenCLKernel::Run() {
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return mindspore::lite::RET_OK;
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}
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kernel::LiteKernel *OpenCLPReluKernelCreator(const std::vector<lite::Tensor *> &inputs,
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const std::vector<lite::Tensor *> &outputs, OpParameter *opParameter,
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const lite::InnerContext *ctx, const kernel::KernelKey &desc,
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const lite::PrimitiveC *primitive) {
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if (inputs.empty()) {
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MS_LOG(ERROR) << "Input data size must be greater than 0, but your size is " << inputs.size();
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free(opParameter);
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return nullptr;
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}
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auto *kernel = new (std::nothrow) PReluOpenCLKernel(reinterpret_cast<OpParameter *>(opParameter), inputs, outputs);
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if (kernel == nullptr) {
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MS_LOG(ERROR) << "kernel " << opParameter->name_ << "is nullptr.";
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free(opParameter);
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return nullptr;
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}
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auto ret = kernel->Init();
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if (ret != mindspore::lite::RET_OK) {
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MS_LOG(ERROR) << "Init PRelu kernel failed!";
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delete kernel;
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return nullptr;
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}
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return kernel;
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}
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REG_KERNEL(kGPU, kNumberTypeFloat32, PrimitiveType_PReLU, OpenCLPReluKernelCreator)
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REG_KERNEL(kGPU, kNumberTypeFloat16, PrimitiveType_PReLU, OpenCLPReluKernelCreator)
|
||||
REG_KERNEL(kGPU, kNumberTypeFloat32, PrimitiveType_PReLU, OpenCLKernelCreator<PReluOpenCLKernel>);
|
||||
REG_KERNEL(kGPU, kNumberTypeFloat16, PrimitiveType_PReLU, OpenCLKernelCreator<PReluOpenCLKernel>);
|
||||
} // namespace mindspore::kernel
|
||||
|
|
|
|||
|
|
@ -32,16 +32,20 @@ class PReluOpenCLKernel : public OpenCLKernel {
|
|||
: OpenCLKernel(parameter, inputs, outputs) {}
|
||||
~PReluOpenCLKernel() override = default;
|
||||
|
||||
int Init() override;
|
||||
int Prepare() override;
|
||||
int CheckSpecs() override;
|
||||
void SetConstArgs() override;
|
||||
void SetGlobalLocal() override;
|
||||
int Run() override;
|
||||
int InitWeights() override;
|
||||
|
||||
private:
|
||||
bool enable_fp16_{false};
|
||||
int batch_size_{};
|
||||
int C_{};
|
||||
int H_{};
|
||||
int W_{};
|
||||
uint32_t OH = {1};
|
||||
uint32_t OW = {1};
|
||||
uint32_t OC = {1};
|
||||
cl_int4 weight_shape_{};
|
||||
cl_int4 out_shape_{};
|
||||
void *weight_vector_{nullptr};
|
||||
float weight_scalar_{0.f};
|
||||
bool weight_is_scalar{false};
|
||||
|
|
|
|||
|
|
@ -91,13 +91,13 @@ int SparseToDenseOpenCLKernel::InitWeights() {
|
|||
}
|
||||
|
||||
int SparseToDenseOpenCLKernel::CheckSpecs() {
|
||||
if (in_tensors_[0]->shape().size() > 4 || out_tensors_[0]->shape().size() > 4) {
|
||||
MS_LOG(ERROR) << "Unsupported inputdim: " << in_tensors_[0]->shape().size() << "outdim"
|
||||
<< out_tensors_[0]->shape().size();
|
||||
if (in_tensors_.size() < 3 || out_tensors_.at(0)->shape().size() > 4) {
|
||||
MS_LOG(ERROR) << " only support out_tensors_ dim <= 4 and in_tensors_.size >= 3";
|
||||
return RET_ERROR;
|
||||
}
|
||||
if (out_tensors_[0]->shape().size() > 3 || in_tensors_.size() < 3) {
|
||||
MS_LOG(ERROR) << " only support dim <= 2 and in_tensors_.size >= 3";
|
||||
if (in_tensors_.at(0)->shape().size() > 4 || out_tensors_.at(0)->shape().size() > 4) {
|
||||
MS_LOG(ERROR) << "Unsupported inputdim: " << in_tensors_[0]->shape().size() << "outdim"
|
||||
<< out_tensors_[0]->shape().size();
|
||||
return RET_ERROR;
|
||||
}
|
||||
if (input_dim_ == 2) {
|
||||
|
|
|
|||
|
|
@ -64,7 +64,9 @@ void StackGetWorkGroup(const std::vector<size_t> &global, std::vector<size_t> *l
|
|||
}
|
||||
|
||||
int StackOpenCLKernel::CheckSpecs() {
|
||||
if (in_tensors_[0]->shape().size() > 2 && (axis_ != 0)) {
|
||||
auto param = reinterpret_cast<StackParameter *>(this->op_parameter_);
|
||||
axis_ = param->axis_;
|
||||
if (in_tensors_.size() != 2 && (axis_ != 0)) {
|
||||
MS_LOG(ERROR) << " only support input size = 2 ";
|
||||
return RET_ERROR;
|
||||
}
|
||||
|
|
@ -72,8 +74,6 @@ int StackOpenCLKernel::CheckSpecs() {
|
|||
MS_LOG(ERROR) << " only support dim <= 4 ";
|
||||
return RET_ERROR;
|
||||
}
|
||||
auto param = reinterpret_cast<StackParameter *>(this->op_parameter_);
|
||||
axis_ = param->axis_;
|
||||
axis_ = axis_ < 0 ? axis_ + in_tensors_[0]->shape().size() : axis_;
|
||||
if (axis_ > 3) {
|
||||
MS_LOG(ERROR) << " only support axis <= 3 ";
|
||||
|
|
@ -178,7 +178,7 @@ int StackOpenCLKernel::Run() {
|
|||
}
|
||||
ocl_runtime_->SetKernelArg(kernel_, arg_cn++, out_tensors_[0]->data_c());
|
||||
}
|
||||
ocl_runtime_->RunKernel(kernel_, global_range_, local_range_);
|
||||
ocl_runtime_->RunKernel(kernel_, global_range_, local_range_, nullptr, &event_);
|
||||
return RET_OK;
|
||||
}
|
||||
REG_KERNEL(kGPU, kNumberTypeFloat32, PrimitiveType_Stack, OpenCLKernelCreator<StackOpenCLKernel>);
|
||||
|
|
|
|||
|
|
@ -13,186 +13,56 @@
|
|||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
||||
#include <iostream>
|
||||
#include <memory>
|
||||
|
||||
#include "src/common/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/opencl_subgraph.h"
|
||||
#include "mindspore/lite/src/runtime/kernel/opencl/kernel/prelu.h"
|
||||
#include "ut/src/runtime/kernel/opencl/common.h"
|
||||
#include "mindspore/lite/nnacl/prelu_parameter.h"
|
||||
|
||||
using mindspore::kernel::LiteKernel;
|
||||
using mindspore::kernel::OpenCLSubGraph;
|
||||
using mindspore::kernel::PReluOpenCLKernel;
|
||||
using mindspore::lite::RET_ERROR;
|
||||
using mindspore::lite::RET_OK;
|
||||
|
||||
// PrimitiveType_PReLU: src/ops/populate/p_relu_populate.cc
|
||||
|
||||
namespace mindspore::lite::opencl::test {
|
||||
class TestPReluOpenCL : public CommonTest {};
|
||||
|
||||
void LoadDataPRelu(void *dst, size_t dst_size, const std::string &file_path) {
|
||||
if (file_path.empty()) {
|
||||
memset(dst, 0x00, dst_size);
|
||||
} else {
|
||||
auto src_data = mindspore::lite::ReadFile(file_path.c_str(), &dst_size);
|
||||
memcpy(dst, src_data, dst_size);
|
||||
class TestOpenCL_PRrelu : public CommonTest {};
|
||||
|
||||
namespace {
|
||||
// PrimitiveType_PReLU: src/ops/populate/p_relu_populate.cc
|
||||
OpParameter *CreateParameter() {
|
||||
auto *param = test::CreateParameter<PReluParameter>(schema::PrimitiveType_PReLU);
|
||||
return reinterpret_cast<OpParameter *>(param);
|
||||
}
|
||||
} // namespace
|
||||
|
||||
TEST_F(TestOpenCL_PRrelu, testcase1) {
|
||||
std::vector<int> input_shape1 = {1, 4, 5, 6};
|
||||
std::vector<int> input_shape2 = {1};
|
||||
std::vector<int> output_shape = {1, 4, 5, 6};
|
||||
size_t input1_size, input2_size, output_size;
|
||||
std::string input1Ppath = "./test_data/PRRelufp32_input1.bin";
|
||||
std::string input2Ppath = "./test_data/PRRelufp32_input2.bin";
|
||||
std::string correctOutputPath = "./test_data/PRRelufp32fp32_output.bin";
|
||||
auto input_data1 = reinterpret_cast<float *>(mindspore::lite::ReadFile(input1Ppath.c_str(), &input1_size));
|
||||
auto input_data2 = reinterpret_cast<float *>(mindspore::lite::ReadFile(input2Ppath.c_str(), &input2_size));
|
||||
auto output_data = reinterpret_cast<float *>(mindspore::lite::ReadFile(correctOutputPath.c_str(), &output_size));
|
||||
|
||||
for (auto fp16_enable : {true}) {
|
||||
auto *param = CreateParameter();
|
||||
TestMain({{input_shape1, input_data1, VAR}, {input_shape2, input_data2, CONST_TENSOR}}, {output_shape, output_data},
|
||||
param, fp16_enable, fp16_enable ? 1e-2 : 1e-9);
|
||||
}
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
void CompareOutPRelu(lite::Tensor *output_tensor, const std::string &standard_answer_file) {
|
||||
auto *output_data = reinterpret_cast<T *>(output_tensor->data_c());
|
||||
size_t output_size = output_tensor->Size();
|
||||
auto expect_data = reinterpret_cast<T *>(mindspore::lite::ReadFile(standard_answer_file.c_str(), &output_size));
|
||||
constexpr float atol = 0.0002;
|
||||
for (int i = 0; i < output_tensor->ElementsNum(); ++i) {
|
||||
if (std::fabs(output_data[i] - expect_data[i]) > atol) {
|
||||
printf("error at idx[%d] expect=%.3f output=%.3f\n", i, expect_data[i], output_data[i]);
|
||||
printf("error at idx[%d] expect=%.3f output=%.3f\n", i, expect_data[i], output_data[i]);
|
||||
printf("error at idx[%d] expect=%.3f output=%.3f\n\n\n", i, expect_data[i], output_data[i]);
|
||||
return;
|
||||
}
|
||||
}
|
||||
printf("compare success!\n");
|
||||
printf("compare success!\n");
|
||||
printf("compare success!\n\n\n");
|
||||
}
|
||||
TEST_F(TestOpenCL_PRrelu, testcase2) {
|
||||
std::vector<int> input_shape1 = {1, 4, 5, 6};
|
||||
std::vector<int> input_shape2 = {1, 1, 1, 6};
|
||||
std::vector<int> output_shape = {1, 4, 5, 6};
|
||||
size_t input1_size, input2_size, output_size;
|
||||
std::string input1Ppath = "./test_data/PRRelufp32_input1.bin";
|
||||
std::string input2Ppath = "./test_data/PRRelufp32_input2.bin";
|
||||
std::string correctOutputPath = "./test_data/PRRelufp32fp32_output.bin";
|
||||
auto input_data1 = reinterpret_cast<float *>(mindspore::lite::ReadFile(input1Ppath.c_str(), &input1_size));
|
||||
auto input_data2 = reinterpret_cast<float *>(mindspore::lite::ReadFile(input2Ppath.c_str(), &input2_size));
|
||||
auto output_data = reinterpret_cast<float *>(mindspore::lite::ReadFile(correctOutputPath.c_str(), &output_size));
|
||||
|
||||
template <typename T>
|
||||
void printf_tensor_Prelu(const std::string &log, mindspore::lite::Tensor *in_data, int size) {
|
||||
MS_LOG(INFO) << log;
|
||||
auto input_data = reinterpret_cast<T *>(in_data->data_c());
|
||||
for (int i = 0; i < size; ++i) {
|
||||
printf("%f ", input_data[i]);
|
||||
for (auto fp16_enable : {true}) {
|
||||
auto *param = CreateParameter();
|
||||
TestMain({{input_shape1, input_data1, VAR}, {input_shape2, input_data2, CONST_TENSOR}}, {output_shape, output_data},
|
||||
param, fp16_enable, fp16_enable ? 1e-2 : 1e-9);
|
||||
}
|
||||
printf("\n");
|
||||
MS_LOG(INFO) << "Print tensor done";
|
||||
}
|
||||
|
||||
TEST_F(TestPReluOpenCL, PReluFp32_dim4) {
|
||||
std::string in_file = "/data/local/tmp/in_data.bin";
|
||||
std::string weight_file = "/data/local/tmp/weight_data.bin";
|
||||
std::string standard_answer_file = "/data/local/tmp/caffe_prelu.bin";
|
||||
MS_LOG(INFO) << "-------------------->> Begin test PRelu!";
|
||||
auto ocl_runtime = lite::opencl::OpenCLRuntimeWrapper().GetInstance();
|
||||
ocl_runtime->Init();
|
||||
auto allocator = ocl_runtime->GetAllocator();
|
||||
|
||||
MS_LOG(INFO) << "Init tensors.";
|
||||
std::vector<int> input_shape = {1, 4, 3, 9};
|
||||
auto data_type = kNumberTypeFloat16;
|
||||
ocl_runtime->SetFp16Enable(data_type == kNumberTypeFloat16);
|
||||
schema::Format format = schema::Format_NHWC;
|
||||
auto tensor_type = lite::Tensor::CONST_TENSOR;
|
||||
auto input_tensor = new (std::nothrow) lite::Tensor(data_type, input_shape, format, tensor_type);
|
||||
if (input_tensor == nullptr) {
|
||||
MS_LOG(ERROR) << "new input_tensor error!";
|
||||
return;
|
||||
}
|
||||
auto output_tensor = new (std::nothrow) lite::Tensor(data_type, input_shape, format, tensor_type);
|
||||
if (output_tensor == nullptr) {
|
||||
MS_LOG(ERROR) << "new output_tensor error";
|
||||
delete input_tensor;
|
||||
return;
|
||||
}
|
||||
auto weight_tensor =
|
||||
new (std::nothrow) lite::Tensor(data_type, std::vector<int>{input_shape[3]}, schema::Format_NHWC, tensor_type);
|
||||
if (weight_tensor == nullptr) {
|
||||
MS_LOG(ERROR) << "new weight_tensor error";
|
||||
delete input_tensor;
|
||||
delete output_tensor;
|
||||
return;
|
||||
}
|
||||
std::vector<lite::Tensor *> inputs{input_tensor, weight_tensor};
|
||||
std::vector<lite::Tensor *> outputs{output_tensor};
|
||||
inputs[0]->MallocData(allocator);
|
||||
inputs[1]->MallocData(allocator);
|
||||
|
||||
MS_LOG(INFO) << "initialize input data";
|
||||
LoadDataPRelu(input_tensor->data_c(), input_tensor->Size(), in_file);
|
||||
LoadDataPRelu(weight_tensor->data_c(), weight_tensor->Size(), weight_file);
|
||||
if (ocl_runtime->GetFp16Enable()) {
|
||||
printf_tensor_Prelu<float16_t>("PRELU:FP16--input data", input_tensor, inputs[0]->ElementsNum());
|
||||
printf_tensor_Prelu<float16_t>("PRELU:FP16--weight data", weight_tensor, weight_tensor->ElementsNum());
|
||||
} else {
|
||||
printf_tensor_Prelu<float>("PRELU:FP32--input data", input_tensor, inputs[0]->ElementsNum());
|
||||
printf_tensor_Prelu<float>("PRELU:FP32--weight data", weight_tensor, inputs[1]->ElementsNum());
|
||||
}
|
||||
|
||||
auto param = new (std::nothrow) PReluParameter();
|
||||
if (param == nullptr) {
|
||||
MS_LOG(ERROR) << "new PreluParameter error";
|
||||
delete input_tensor;
|
||||
delete output_tensor;
|
||||
delete weight_tensor;
|
||||
return;
|
||||
}
|
||||
auto prelu_kernel =
|
||||
new (std::nothrow) kernel::PReluOpenCLKernel(reinterpret_cast<OpParameter *>(param), inputs, outputs);
|
||||
if (prelu_kernel == nullptr) {
|
||||
MS_LOG(ERROR) << "new PReluOpenCLKernel error";
|
||||
delete input_tensor;
|
||||
delete output_tensor;
|
||||
delete weight_tensor;
|
||||
delete param;
|
||||
return;
|
||||
}
|
||||
auto ret = prelu_kernel->Init();
|
||||
if (ret != RET_OK) {
|
||||
MS_LOG(ERROR) << "Init prelu kernel error";
|
||||
return;
|
||||
}
|
||||
|
||||
MS_LOG(INFO) << "initialize sub_graph";
|
||||
std::vector<kernel::LiteKernel *> kernels{prelu_kernel};
|
||||
auto *sub_graph = new (std::nothrow) kernel::OpenCLSubGraph({input_tensor}, outputs, kernels, kernels, kernels);
|
||||
if (sub_graph == nullptr) {
|
||||
MS_LOG(ERROR) << "Create kernel sub_graph error";
|
||||
delete input_tensor;
|
||||
delete output_tensor;
|
||||
delete weight_tensor;
|
||||
delete param;
|
||||
delete prelu_kernel;
|
||||
return;
|
||||
}
|
||||
ret = sub_graph->Init();
|
||||
if (ret != RET_OK) {
|
||||
MS_LOG(ERROR) << "Init sub graph error";
|
||||
delete input_tensor;
|
||||
delete output_tensor;
|
||||
delete weight_tensor;
|
||||
delete param;
|
||||
delete sub_graph;
|
||||
return;
|
||||
}
|
||||
|
||||
ret = sub_graph->Run();
|
||||
if (ret != RET_OK) {
|
||||
MS_LOG(ERROR) << "Run sub graph error";
|
||||
delete input_tensor;
|
||||
delete output_tensor;
|
||||
delete weight_tensor;
|
||||
delete param;
|
||||
delete sub_graph;
|
||||
return;
|
||||
}
|
||||
|
||||
if (ocl_runtime->GetFp16Enable()) {
|
||||
printf_tensor_Prelu<float16_t>("PRelu:FP16--output_data", output_tensor, outputs[0]->ElementsNum());
|
||||
CompareOutPRelu<float16_t>(output_tensor, standard_answer_file);
|
||||
} else {
|
||||
printf_tensor_Prelu<float>("PRelu:FP32--output_data", output_tensor, outputs[0]->ElementsNum());
|
||||
CompareOutPRelu<float>(output_tensor, standard_answer_file);
|
||||
}
|
||||
delete input_tensor;
|
||||
delete output_tensor;
|
||||
delete weight_tensor;
|
||||
delete param;
|
||||
delete sub_graph;
|
||||
}
|
||||
} // namespace mindspore::lite::opencl::test
|
||||
|
|
|
|||
Loading…
Reference in New Issue