Merge pull request #24552 from fengyuentau:layernorm_backends

dnn: add openvino, opencl and cuda backends for layer normalization layer #24552

Merge after https://github.com/opencv/opencv/pull/24544.

Todo:

- [x] openvino
- [x] opencl
- [x] cuda

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake

modules/dnn/src/cuda/mvn.cu

@@ -68,15 +68,36 @@ namespace raw {
     }
 
     template <class T>
-    __global__ void normalize_mean_variance_channelwise(Span<T> output, View<T> input, View<T> scale, View<T> bias, View<float> means, View<float> stdev, size_type inner_size, size_type C) {
+    __global__ void normalize_mean_variance_channelwise(Span<T> output, View<T> input, View<T> scale, View<T> bias, View<float> means, View<float> inv_stddev, size_type inner_size, size_type C) {
         for (auto idx : grid_stride_range(output.size())) {
             const index_type outer_idx = idx / inner_size;
             const index_type c = outer_idx % C;
-            auto s = static_cast<float>(scale[c]) * stdev[outer_idx];
+            auto s = static_cast<float>(scale[c]) * inv_stddev[outer_idx];
             auto b = static_cast<float>(bias[c]);
             output[idx] = (static_cast<float>(input[idx]) - means[outer_idx]) * s + b;
         }
     }
+
+    template <class T>
+    __global__ void normalize_mean_variance_layernorm(Span<T> output, View<T> input, View<T> scale, View<float> means, View<float> inv_stddev, size_type inner_size) {
+        for (auto idx : grid_stride_range(output.size())) {
+            const index_type outer_idx = idx / inner_size;
+            const index_type inner_idx = idx % inner_size;
+            auto s = static_cast<float>(scale[inner_idx]) * inv_stddev[outer_idx];
+            output[idx] = (static_cast<float>(input[idx]) - means[outer_idx]) * s;
+        }
+    }
+
+    template <class T>
+    __global__ void normalize_mean_variance_layernorm_with_bias(Span<T> output, View<T> input, View<T> scale, View<T> bias, View<float> means, View<float> inv_stddev, size_type inner_size) {
+        for (auto idx : grid_stride_range(output.size())) {
+            const index_type outer_idx = idx / inner_size;
+            const index_type inner_idx = idx % inner_size;
+            auto s = static_cast<float>(scale[inner_idx]) * inv_stddev[outer_idx];
+            auto b = static_cast<float>(bias[inner_idx]);
+            output[idx] = (static_cast<float>(input[idx]) - means[outer_idx]) * s + b;
+        }
+    }
 }
 
 template <class T>
@@ -154,20 +175,54 @@ template void normalize_mean_variance(const Stream&, Span<__half>, View<__half>,
 template void normalize_mean_variance(const Stream&, Span<float>, View<float>, View<float>, View<float>, std::size_t);
 
 template <class T>
-void normalize_mean_variance_channelwise(const Stream& stream, Span<T> output, View<T> input, View<T> scale, View<T> bias, View<float> means, View<float> stdev, std::size_t inner_size, std::size_t C)
+void normalize_mean_variance_channelwise(const Stream& stream, Span<T> output, View<T> input, View<T> scale, View<T> bias, View<float> means, View<float> inv_stddev, std::size_t inner_size, std::size_t C)
 {
     CV_Assert(input.size() == output.size());
     CV_Assert(input.size() / inner_size == means.size());
-    CV_Assert(means.size() == stdev.size());
+    CV_Assert(means.size() == inv_stddev.size());
 
     auto kernel = raw::normalize_mean_variance_channelwise<T>;
     auto policy = make_policy(kernel, output.size(), 0, stream);
-    launch_kernel(kernel, policy, output, input, scale, bias, means, stdev, inner_size, C);
+    launch_kernel(kernel, policy, output, input, scale, bias, means, inv_stddev, inner_size, C);
+}
+
+#if !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530)
+template void normalize_mean_variance_channelwise(const Stream&, Span<__half> /*output*/, View<__half> /*input*/, View<__half> /*scale*/, View<__half> /*bias*/, View<float> /*means*/, View<float> /*inv_stddev*/, std::size_t, std::size_t);
+#endif
+template void normalize_mean_variance_channelwise(const Stream&, Span<float> /*output*/, View<float> /*input*/, View<float> /*scale*/, View<float> /*bias*/, View<float> /*means*/, View<float> /*inv_stddev*/, std::size_t, std::size_t);
+
+template <class T>
+void normalize_mean_variance_layernorm(const Stream& stream, Span<T> output, View<T> input, View<T> scale, View<float> means, View<float> inv_stddev, std::size_t inner_size)
+{
+    CV_Assert(input.size() == output.size());
+    CV_Assert(input.size() / inner_size == means.size());
+    CV_Assert(means.size() == inv_stddev.size());
+
+    auto kernel = raw::normalize_mean_variance_layernorm<T>;
+    auto policy = make_policy(kernel, output.size(), 0, stream);
+    launch_kernel(kernel, policy, output, input, scale, means, inv_stddev, inner_size);
+}
+
+#if !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530)
+template void normalize_mean_variance_layernorm(const Stream&, Span<__half> /*output*/, View<__half> /*input*/, View<__half> /*scale*/, View<float> /*means*/, View<float> /*inv_stddev*/, std::size_t);
+#endif
+template void normalize_mean_variance_layernorm(const Stream&, Span<float> /*output*/, View<float> /*input*/, View<float> /*scale*/, View<float> /*means*/, View<float> /*inv_stddev*/, std::size_t);
+
+template <class T>
+void normalize_mean_variance_layernorm(const Stream& stream, Span<T> output, View<T> input, View<T> scale, View<T> bias, View<float> means, View<float> inv_stddev, std::size_t inner_size)
+{
+    CV_Assert(input.size() == output.size());
+    CV_Assert(input.size() / inner_size == means.size());
+    CV_Assert(means.size() == inv_stddev.size());
+
+    auto kernel = raw::normalize_mean_variance_layernorm_with_bias<T>;
+    auto policy = make_policy(kernel, output.size(), 0, stream);
+    launch_kernel(kernel, policy, output, input, scale, bias, means, inv_stddev, inner_size);
 }
 
 #if !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530)
-template void normalize_mean_variance_channelwise(const Stream&, Span<__half> /*output*/, View<__half> /*input*/, View<__half> /*scale*/, View<__half> /*bias*/, View<float> /*means*/, View<float> /*stdev*/, std::size_t, std::size_t);
+template void normalize_mean_variance_layernorm(const Stream&, Span<__half> /*output*/, View<__half> /*input*/, View<__half> /*scale*/, View<__half> /*bias*/, View<float> /*means*/, View<float> /*inv_stddev*/, std::size_t);
 #endif
-template void normalize_mean_variance_channelwise(const Stream&, Span<float> /*output*/, View<float> /*input*/, View<float> /*scale*/, View<float> /*bias*/, View<float> /*means*/, View<float> /*stdev*/, std::size_t, std::size_t);
+template void normalize_mean_variance_layernorm(const Stream&, Span<float> /*output*/, View<float> /*input*/, View<float> /*scale*/, View<float> /*bias*/, View<float> /*means*/, View<float> /*inv_stddev*/, std::size_t);
 
 }}}} /* namespace cv::dnn::cuda4dnn::kernels */

modules/dnn/src/cuda4dnn/kernels/mvn.hpp

@@ -27,7 +27,13 @@ template <class T>
 void normalize_mean_variance(const csl::Stream& stream, csl::Span<T> output, csl::View<T> input, csl::View<float> means, csl::View<float> scale, std::size_t inner_size);
 
 template <class T>
-void normalize_mean_variance_channelwise(const csl::Stream &stream, csl::Span<T> output, csl::View<T> input, csl::View<T> scale, csl::View<T> bias, csl::View<float> means, csl::View<float> stdev, std::size_t inner_size, std::size_t C);
+void normalize_mean_variance_channelwise(const csl::Stream &stream, csl::Span<T> output, csl::View<T> input, csl::View<T> scale, csl::View<T> bias, csl::View<float> means, csl::View<float> inv_stddev, std::size_t inner_size, std::size_t C);
+
+template <class T>
+void normalize_mean_variance_layernorm(const csl::Stream &stream, csl::Span<T> output, csl::View<T> input, csl::View<T> scale, csl::View<float> means, csl::View<float> inv_stddev, std::size_t inner_size);
+
+template <class T>
+void normalize_mean_variance_layernorm(const csl::Stream &stream, csl::Span<T> output, csl::View<T> input, csl::View<T> scale, csl::View<T> bias, csl::View<float> means, csl::View<float> inv_stddev, std::size_t inner_size);
 
 }}}} /* namespace cv::dnn::cuda4dnn::kernels */
 

modules/dnn/src/cuda4dnn/primitives/layer_norm.hpp

@@ -0,0 +1,93 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#ifndef OPENCV_DNN_SRC_CUDA4DNN_PRIMITIVES_LAYER_NORM_HPP
+#define OPENCV_DNN_SRC_CUDA4DNN_PRIMITIVES_LAYER_NORM_HPP
+
+#include "../../op_cuda.hpp"
+
+#include "../csl/stream.hpp"
+#include "../csl/span.hpp"
+#include "../csl/tensor.hpp"
+#include "../csl/workspace.hpp"
+
+#include "../kernels/fill_copy.hpp"
+#include "../kernels/mvn.hpp"
+
+#include <opencv2/core.hpp>
+
+#include <cstddef>
+#include <vector>
+#include <utility>
+
+namespace cv { namespace dnn { namespace cuda4dnn {
+
+    template <class T>
+    class LayerNormOp final : public CUDABackendNode {
+     public:
+        using wrapper_type = GetCUDABackendWrapperType<T>;
+
+        LayerNormOp(csl::Stream stream_, int normalized_axis, float epsilon_, size_t loops)
+            : stream(std::move(stream_)), epsilon(epsilon_) {
+            CV_CheckGE(normalized_axis, 0, "LayerNorm/CUDA: axis needs to be normalized");
+            axis = static_cast<size_t>(normalized_axis);
+
+            csl::WorkspaceBuilder builder;
+            builder.require<float>(loops);
+            builder.require<float>(loops);
+            scratch_mem_in_bytes = builder.required_workspace_size();
+        }
+
+        void forward(const std::vector<cv::Ptr<BackendWrapper>>& inputs,
+                     const std::vector<cv::Ptr<BackendWrapper>>& outputs,
+                     csl::Workspace& workspace) override {
+            auto input_wrapper = inputs[0].dynamicCast<wrapper_type>();
+            auto scale_wrapper = inputs[1].dynamicCast<wrapper_type>();
+
+            auto input = input_wrapper->getView();
+            auto scale = scale_wrapper->getView();
+
+            auto output_wrapper = outputs[0].dynamicCast<wrapper_type>();
+            auto output = output_wrapper->getSpan();
+
+            auto loops = input.size_range(0, axis);
+            auto norm_size = input.size_range(axis, input.rank());
+            if (norm_size == 1) {
+                kernels::fill<T>(stream, output, 0.f);
+                return;
+            } else {
+                auto ws_allocator = csl::WorkspaceAllocator(workspace);
+
+                auto mean = ws_allocator.get_span<float>(loops);
+                kernels::fill<float>(stream, mean, 0.f);
+
+                auto inv_stddev = ws_allocator.get_span<float>(loops);
+                kernels::fill<float>(stream, inv_stddev, 0.f);
+
+                kernels::reduce_mean_sqr_sum<T>(stream, mean, inv_stddev, input, norm_size);
+                kernels::compute_normalization_scale(stream, inv_stddev, mean, inv_stddev, norm_size, epsilon);
+                if (inputs.size() == 3) {
+                    auto bias_wrapper = inputs[2].dynamicCast<wrapper_type>();
+                    auto bias = bias_wrapper->getView();
+                    kernels::normalize_mean_variance_layernorm<T>(stream, output, input, scale, bias, mean, inv_stddev, norm_size);
+                } else {
+                    kernels::normalize_mean_variance_layernorm<T>(stream, output, input, scale, mean, inv_stddev, norm_size);
+                }
+            }
+        }
+
+        std::size_t get_workspace_memory_in_bytes() const noexcept override { return scratch_mem_in_bytes; }
+
+     private:
+        csl::Stream stream;
+
+        float epsilon;
+        size_t axis;
+
+        std::size_t scratch_mem_in_bytes;
+    };
+
+}}} // cv::dnn::cuda4dnn
+
+#endif // OPENCV_DNN_SRC_CUDA4DNN_PRIMITIVES_LAYER_NORM_HPP

modules/dnn/src/layers/layer_norm.cpp

@@ -9,8 +9,26 @@
 // CANN backend
 #include "../op_cann.hpp"
 
+// OpenVINO backend
+#include "../op_inf_engine.hpp"
+#include "../ie_ngraph.hpp"
+
+// CUDA backend
+#include "../op_cuda.hpp"
+#ifdef HAVE_CUDA
+#include "../cuda4dnn/primitives/layer_norm.hpp"
+using namespace cv::dnn::cuda4dnn;
+#endif
+
+// OpenCL backend
+#ifdef HAVE_OPENCL
+#include "../ocl4dnn/include/math_functions.hpp"
+#include "opencl_kernels_dnn.hpp"
+#endif
+
 namespace cv { namespace dnn {
 
+// https://github.com/onnx/onnx/blob/main/docs/Operators.md#LayerNormalization
 class LayerNormLayerImpl CV_FINAL : public LayerNormLayer
 {
 public:
@@ -25,7 +43,12 @@ public:
 
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
+#ifdef HAVE_INF_ENGINE
+        if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
+            return true;
+#endif
         return backendId == DNN_BACKEND_OPENCV ||
+               backendId == DNN_BACKEND_CUDA   ||
                (backendId == DNN_BACKEND_CANN && axis != -1); // axis=-1 not supported due to 1d mat shape problem
     }
 
@@ -73,6 +96,9 @@ public:
         CV_TRACE_FUNCTION();
         CV_TRACE_ARG_VALUE(name, "name", name.c_str());
 
+        CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget),
+                   forward_ocl(inputs_arr, outputs_arr, internals_arr))
+
         if (inputs_arr.depth() == CV_16S)
         {
             forward_fallback(inputs_arr, outputs_arr, internals_arr);
@@ -95,6 +121,91 @@ public:
         }
     }
 
+#ifdef HAVE_OPENCL
+    bool forward_ocl(InputArrayOfArrays inputs_, OutputArrayOfArrays outputs_, OutputArrayOfArrays internals_) {
+        std::vector<UMat> inputs;
+        std::vector<UMat> outputs;
+
+        inputs_.getUMatVector(inputs);
+        outputs_.getUMatVector(outputs);
+
+        const auto &input = inputs[0], &scale = inputs[1]; // &bias = inputs[2]; // bias is optional
+        auto &output = outputs[0];
+
+        const auto input_shape = shape(input);
+        size_t loops = static_cast<size_t>(total(input_shape, 0, axis)),
+               norm_size = static_cast<size_t>(total(input_shape, axis));
+        float inv_norm_size = 1.f / norm_size;
+
+        const auto &bias = inputs.size() == 3 ? inputs[2] : UMat::zeros(norm_size, 1, CV_32F);
+
+        // no fp16 support
+        if (input.depth() == CV_16S) {
+            return false;
+        }
+
+        String base_opts = format(" -DT=float -DT4=float4 -Dconvert_T=convert_float4");
+
+        // Calculate mean
+        UMat one = UMat::ones(norm_size, 1, CV_32F);
+        UMat mean = UMat(loops, 1, CV_32F);
+        UMat mean_square = UMat(loops, 1, CV_32F);
+        UMat tmp = UMat(loops, norm_size, CV_32F);
+        bool ret = ocl4dnn::ocl4dnnGEMV<float>(ocl4dnn::CblasNoTrans, loops, norm_size, inv_norm_size,
+                                               input, 0, one, 0, 0.f, mean, 0);
+        if (!ret) {
+            return false;
+        }
+        // Calculate mean_square
+        int num_vector = (norm_size % 8 == 0) ? 8 : ((norm_size % 4 == 0) ? 4 : 1);
+        size_t global[] = {loops, static_cast<size_t>(norm_size / num_vector)};
+        String build_opt = format(" -DNUM=%d", num_vector) + base_opts;
+        String mean_square_kernel_name = format("calc_mean%d", num_vector);
+        ocl::Kernel mean_square_kernel(mean_square_kernel_name.c_str(), ocl::dnn::mvn_oclsrc, build_opt + " -DKERNEL_MEAN");
+        if (mean_square_kernel.empty()) {
+            return false;
+        }
+        mean_square_kernel.set(0, ocl::KernelArg::PtrReadOnly(input));
+        mean_square_kernel.set(1, (int)loops);
+        mean_square_kernel.set(2, (int)norm_size);
+        mean_square_kernel.set(3, ocl::KernelArg::PtrReadOnly(mean));
+        mean_square_kernel.set(4, ocl::KernelArg::PtrWriteOnly(tmp));
+        ret = mean_square_kernel.run(2, global, NULL, false);
+        if (!ret) {
+            return false;
+        }
+        ret = ocl4dnn::ocl4dnnGEMV<float>(ocl4dnn::CblasNoTrans, loops, norm_size, inv_norm_size,
+                                          tmp, 0, one, 0, 0.f, mean_square, 0);
+        if (!ret) {
+            return false;
+        }
+        // Calculate instance norm: output = scale * (x - mean) / sqrt(var + eps) + bias
+        String mvn_kernel_name = format("mvn%d", num_vector);
+        build_opt += " -DNORM_VARIANCE -DLAYER_NORM -DKERNEL_MVN";
+        ocl::Kernel mvn_kernel(mvn_kernel_name.c_str(), ocl::dnn::mvn_oclsrc, build_opt);
+        if (mvn_kernel.empty()) {
+            return false;
+        }
+        mvn_kernel.set(0, ocl::KernelArg::PtrReadOnly(input));
+        mvn_kernel.set(1, (int)loops);
+        mvn_kernel.set(2, (int)norm_size);
+        mvn_kernel.set(3, (float)epsilon);
+        mvn_kernel.set(4, ocl::KernelArg::PtrReadOnly(mean));
+        mvn_kernel.set(5, ocl::KernelArg::PtrReadOnly(mean_square));
+        mvn_kernel.set(6, ocl::KernelArg::PtrReadOnly(scale));
+        mvn_kernel.set(7, ocl::KernelArg::PtrReadOnly(bias));
+        mvn_kernel.set(8, (int)1);
+        mvn_kernel.set(9, (float)0.f);
+        mvn_kernel.set(10, ocl::KernelArg::PtrWriteOnly(output));
+        ret = mvn_kernel.run(2, global, NULL, false);
+        if (!ret) {
+            return false;
+        }
+
+        return true;
+    }
+#endif
+
 #ifdef HAVE_CANN
     virtual Ptr<BackendNode> initCann(const std::vector<Ptr<BackendWrapper> > &inputs,
                                       const std::vector<Ptr<BackendWrapper> > &outputs,
@@ -147,6 +258,67 @@ public:
     }
 #endif // HAVE_CANN
 
+#ifdef HAVE_DNN_NGRAPH
+    virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
+                                        const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE {
+        auto ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
+        const auto &input_shape = ieInpNode.get_shape();
+        std::shared_ptr<ngraph::Node> mvn, result;
+
+        // mvn
+#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2021_2)
+        // https://docs.openvino.ai/2021.4/api/ngraph_python_api/_autosummary/ngraph.opset3.mvn.html?highlight=mvn#ngraph.opset3.mvn
+        bool across_channels = false;
+        bool normalize_variance = true;
+        mvn = std::make_shared<ngraph::op::MVN>(ieInpNode, across_channels, normalize_variance, epsilon);
+#else
+        // https://docs.openvino.ai/2023.1/openvino_docs_ops_normalization_MVN_6.html
+        std::vector<int64_t> axes_v(input_shape.size() - axis);
+        std::iota(axes_v.begin(), axes_v.end(), axis);
+        auto axes = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{axes_v.size()}, axes_v.data());
+        bool normalize_variance = true;
+        mvn = std::make_shared<ngraph::op::v6::MVN>(ieInpNode, axes, normalize_variance, epsilon, ngraph::op::MVNEpsMode::INSIDE_SQRT);
+#endif
+
+        // layer norm = scale * mvn + bias
+        auto scale = nodes[1].dynamicCast<InfEngineNgraphNode>()->node;
+        ngraph::Output<ngraph::Node> bias;
+        if (nodes.size() == 3) {
+            bias = nodes[2].dynamicCast<InfEngineNgraphNode>()->node;
+        }
+        if (axis == -1 || axis == input_shape.size() - 1) { // special case for 1D tensor (2D mat)
+            std::vector<int64_t> shared_shape_v(input_shape.size(), 1);
+            shared_shape_v.back() = -1;
+            auto shared_shape = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{shared_shape_v.size()}, shared_shape_v.data());
+            scale  = std::make_shared<ngraph::op::v1::Reshape>(scale, shared_shape, true);
+            if (nodes.size() == 3) {
+                bias  = std::make_shared<ngraph::op::v1::Reshape>(bias, shared_shape, true);
+            }
+        }
+
+        result = std::make_shared<ngraph::op::v1::Multiply>(mvn, scale);
+        if (nodes.size() == 3) {
+            result = std::make_shared<ngraph::op::v1::Add>(result, bias);
+        }
+
+        return Ptr<BackendNode>(new InfEngineNgraphNode(result));
+    }
+#endif // HAVE_DNN_NGRAPH
+
+#ifdef HAVE_CUDA
+    Ptr<BackendNode> initCUDA(void *context_,
+                              const std::vector<Ptr<BackendWrapper>>& inputs,
+                              const std::vector<Ptr<BackendWrapper>>& outputs) override {
+        auto context = reinterpret_cast<csl::CSLContext*>(context_);
+
+        auto input_wrapper = inputs[0].dynamicCast<CUDABackendWrapper>();
+        auto input_shape = input_wrapper->getShape();
+        size_t loops = static_cast<size_t>(total(input_shape, 0, axis));
+
+        return make_cuda_node<cuda4dnn::LayerNormOp>(preferableTarget, std::move(context->stream), axis, epsilon, loops);
+    }
+#endif // HAVE_CUDA
+
 };
 
 Ptr<LayerNormLayer> LayerNormLayer::create(const LayerParams& params)

modules/dnn/src/opencl/mvn.cl

@@ -126,12 +126,18 @@ __kernel void MVN(__global const Dtype* src,
     alpha = 1;
 #endif
 
+#ifdef LAYER_NORM
+    vec_type w = load(bnorm_weight, y), b = load(bnorm_bias, y);
+#else
+
     Dtype w = 1.f, b = 0.f;
 #ifdef FUSE_BATCH_NORM
     w = bnorm_weight[x % channels];
     b = bnorm_bias[x % channels];
 #endif
 
+#endif // LAYER_NORM
+
     vec_type src_vec = load(src, index) - (vec_type)mean_val;
     vec_type dst_vec = src_vec * alpha;
     dst_vec = dst_vec * w + (vec_type)b;

modules/dnn/test/test_onnx_conformance_layer_filter__openvino.inl.hpp

@@ -793,81 +793,43 @@ CASE(test_isinf_positive)
 CASE(test_isnan)
     // no filter
 CASE(test_layer_normalization_2d_axis0)
-#if SKIP_SET_1
-    SKIP_NON_CPU;
-#endif
+    // no filter
 CASE(test_layer_normalization_2d_axis1)
-#if SKIP_SET_1
-    SKIP_NON_CPU;
-#endif
+    // no filter
 CASE(test_layer_normalization_2d_axis_negative_1)
-#if SKIP_SET_1
-    SKIP_NON_CPU;
-#endif
+    // no filter
 CASE(test_layer_normalization_2d_axis_negative_2)
-#if SKIP_SET_1
-    SKIP_NON_CPU;
-#endif
+    // no filter
 CASE(test_layer_normalization_3d_axis0_epsilon)
-#if SKIP_SET_1
-    SKIP_NON_CPU;
-#endif
+    // no filter
 CASE(test_layer_normalization_3d_axis1_epsilon)
-#if SKIP_SET_1
-    SKIP_NON_CPU;
-#endif
+    // no filter
 CASE(test_layer_normalization_3d_axis2_epsilon)
-#if SKIP_SET_1
-    SKIP_NON_CPU;
-#endif
+    // no filter
 CASE(test_layer_normalization_3d_axis_negative_1_epsilon)
-#if SKIP_SET_1
-    SKIP_NON_CPU;
-#endif
+    // no filter
 CASE(test_layer_normalization_3d_axis_negative_2_epsilon)
-#if SKIP_SET_1
-    SKIP_NON_CPU;
-#endif
+    // no filter
 CASE(test_layer_normalization_3d_axis_negative_3_epsilon)
-#if SKIP_SET_1
-    SKIP_NON_CPU;
-#endif
+    // no filter
 CASE(test_layer_normalization_4d_axis0)
-#if SKIP_SET_1
-    SKIP_NON_CPU;
-#endif
+    // no filter
 CASE(test_layer_normalization_4d_axis1)
-#if SKIP_SET_1
-    SKIP_NON_CPU;
-#endif
+    // no filter
 CASE(test_layer_normalization_4d_axis2)
-#if SKIP_SET_1
-    SKIP_NON_CPU;
-#endif
+    // no filter
 CASE(test_layer_normalization_4d_axis3)
-#if SKIP_SET_1
-    SKIP_NON_CPU;
-#endif
+    // no filter
 CASE(test_layer_normalization_4d_axis_negative_1)
-#if SKIP_SET_1
-    SKIP_NON_CPU;
-#endif
+    // no filter
 CASE(test_layer_normalization_4d_axis_negative_2)
-#if SKIP_SET_1
-    SKIP_NON_CPU;
-#endif
+    // no filter
 CASE(test_layer_normalization_4d_axis_negative_3)
-#if SKIP_SET_1
-    SKIP_NON_CPU;
-#endif
+    // no filter
 CASE(test_layer_normalization_4d_axis_negative_4)
-#if SKIP_SET_1
-    SKIP_NON_CPU;
-#endif
+    // no filter
 CASE(test_layer_normalization_default_axis)
-#if SKIP_SET_1
-    SKIP_NON_CPU;
-#endif
+    // no filter
 CASE(test_leakyrelu)
     // no filter
 CASE(test_leakyrelu_default)