DNN: make MatMul support 3D or 4D with broadcast

[WanliZhong]

Merge with extra: opencv/opencv_extra#1018

This PR follows the #22775

The main purpose of this PR is making MatMul support the broadcast that the second input has less dimention than the first one. And let the operation support SIMD and multi-thread. Beacuse it doesn't support 1D Mat, only support MatMul like

2x3x4 mul 4x5 -> 2x3x5
2x3x4x5 mul 3x5x6 -> 2x3x4x6

• 2 const inputs: create a virtual layer for the first input
• 1 const input with CPU (whether or not using broadcast): use the SIMD and multi-thread flow which for InnerProduct
• 1 const input with CUDA: broadcast inputs will fallback to CPU. Inputs with the same shape will use the cuda.

Pull Request Readiness Checklist

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

• I agree to contribute to the project under Apache 2 License.
• 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
• The PR is proposed to the proper branch
• There is a reference to the original bug report and related work
• There is accuracy test, performance test and test data in opencv_extra repository, if applicable
  Patch to opencv_extra has the same branch name.
• The feature is well documented and sample code can be built with the project CMake

[WanliZhong]

Because the upstream has supported tranA and tranB, I will fix the confilcts.

[asmorkalov]

@zihaomu @rogday Your turn.

[zihaomu]

Thanks for your contribution. LGTM! 👍

[alalek]

Merge branch '4.x' into improve_matmul
fix conflicts

Use git rebase instead of merge commits to have clear changes. GitHub has issues with handling PRs which includes merge commits.
PR should have 1 commit. This is stated in contribution guidelines.

[WanliZhong]

I'm sorry I forgot to squash it. I will squash it later.

[rogday]

👍

[alalek]

There is failed OpenCL FP16 test:

[ RUN      ] Test_ONNX_layers.MatMul_init/1, where GetParam() = OCV/OCL_FP16
[ INFO:0@189.433] global onnx_importer.cpp:822 populateNet DNN/ONNX: loading ONNX v8 model produced by 'matmul_2d_init'. Number of nodes = 1, initializers = 1, inputs = 2, outputs = 1
[ INFO:0@189.433] global onnx_importer.cpp:724 parseOperatorSet DNN/ONNX: ONNX opset version = 17
[ INFO:0@189.433] global onnx_importer.cpp:991 handleNode DNN/ONNX: processing node with 2 inputs and 1 outputs: [MatMul]:(onnx_node_output_0!output) from domain='ai.onnx'
[ INFO:0@189.434] global onnx_importer.cpp:822 populateNet DNN/ONNX: loading ONNX v8 model produced by 'matmul_3d_init'. Number of nodes = 1, initializers = 1, inputs = 2, outputs = 1
[ INFO:0@189.434] global onnx_importer.cpp:724 parseOperatorSet DNN/ONNX: ONNX opset version = 17
[ INFO:0@189.434] global onnx_importer.cpp:991 handleNode DNN/ONNX: processing node with 2 inputs and 1 outputs: [MatMul]:(onnx_node_output_0!output) from domain='ai.onnx'
[ INFO:0@189.434] global onnx_importer.cpp:822 populateNet DNN/ONNX: loading ONNX v8 model produced by 'matmul_4d_init'. Number of nodes = 1, initializers = 1, inputs = 2, outputs = 1
[ INFO:0@189.434] global onnx_importer.cpp:724 parseOperatorSet DNN/ONNX: ONNX opset version = 17
[ INFO:0@189.434] global onnx_importer.cpp:991 handleNode DNN/ONNX: processing node with 2 inputs and 1 outputs: [MatMul]:(onnx_node_output_0!output) from domain='ai.onnx'
[ INFO:0@189.434] global onnx_importer.cpp:822 populateNet DNN/ONNX: loading ONNX v8 model produced by 'matmul_init_2'. Number of nodes = 2, initializers = 2, inputs = 3, outputs = 1
[ INFO:0@189.434] global onnx_importer.cpp:724 parseOperatorSet DNN/ONNX: ONNX opset version = 17
[ INFO:0@189.434] global onnx_importer.cpp:991 handleNode DNN/ONNX: processing node with 2 inputs and 1 outputs: [MatMul]:(onnx_node_output_0!outputY) from domain='ai.onnx'
[ INFO:0@189.434] global onnx_importer.cpp:991 handleNode DNN/ONNX: processing node with 2 inputs and 1 outputs: [Add]:(onnx_node_output_0!output) from domain='ai.onnx'
[ INFO:0@189.435] global onnx_importer.cpp:822 populateNet DNN/ONNX: loading ONNX v8 model produced by 'matmul_init_bcast'. Number of nodes = 1, initializers = 1, inputs = 2, outputs = 1
[ INFO:0@189.435] global onnx_importer.cpp:724 parseOperatorSet DNN/ONNX: ONNX opset version = 17
[ INFO:0@189.435] global onnx_importer.cpp:991 handleNode DNN/ONNX: processing node with 2 inputs and 1 outputs: [MatMul]:(onnx_node_output_0!output) from domain='ai.onnx'
/build/precommit_opencl_linux/4.x/opencv/modules/dnn/test/test_common.impl.hpp:74: Failure
Expected: (normL1) <= (l1), actual: 1.22411 vs 0.004
  |ref| = 6.9979562759399414
/build/precommit_opencl_linux/4.x/opencv/modules/dnn/test/test_common.impl.hpp:77: Failure
Expected: (normInf) <= (lInf), actual: 6.99796 vs 0.02
  |ref| = 6.9979562759399414
[ INFO:0@189.435] global ts.cpp:850 testTearDown Memory_usage (OpenCL): 3960 (base=0  current=0)
[  FAILED  ] Test_ONNX_layers.MatMul_init/1, where GetParam() = OCV/OCL_FP16 (2 ms)