[Apache TVM Discuss] [Questions] Question: BYOC : replace nn.conv2d() to our nucfpga_conv2d()

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I use the latest version TVM from github.  Now I want I want to customize the 
operator instead of nn.conv2d.   
I have changed these places：

**1.src/relay/backend/contrib/nuc_fpga/codegen.cc**



    GenerateBodyOutput GenerateOpCall(const CallNode* call) {
    const auto* op_node = call->op.as<OpNode>();
    CHECK(op_node) << "Expect OpNode, but got " << call->op->GetTypeKey();
    using ArgFunType = std::function<std::vector<std::string>(const CallNode*)>;
    static const std::map<std::string, std::pair<std::string, ArgFunType>> 
op_map = {
        {"nn.conv2d", {"nuc_fpga_conv2d", Conv2d}},
        {"nn.dense", {"nuc_fpga_dense", Dense}},
    };
    ......
    ......
    TVM_REGISTER_GLOBAL("relay.ext.nuc_fpga").set_body_typed(NucFPGACompiler);

**2.src/runtime/contrib/nuc_fpga/nuc_fpga.cc**
    
    extern "C" void nuc_fpga_conv2d(
        int8_t* feature_map, int8_t* kernel, int* out, 
        int batch, int in_ch, int in_height, int in_width,
        int kernel_number, int group, 
        int pad_height, int pad_width, 
        int kernel_height, int kernel_width,
        int stride_height, int stride_width) {
            printf("Calling From nuc_fpga_conv2d\n");
    }

**3.src/runtime/contrib/nuc_fpga/nuc_fpga.h**

    extern "C" TVM_DLL void nuc_fpga_conv2d(
    int8_t* feature_map, int8_t* kernel, int* out, 
    int batch, int in_ch, int in_height, int in_width,
    int kernel_number, int group, 
    int pad_height, int pad_width, 
    int kernel_height, int kernel_width,
    int stride_height, int stride_width);

**4.python/tvm/relay/op/contrib/nuc_fpga.py**

    _register_external_op_helper("nn.conv2d")


**5.cmake/modules/contrib/NUCFPGA.cmake**

    if(USE_NUCFPGA_CODEGEN STREQUAL "ON")
        file(GLOB NUCFPGA_CODEGEN_SRC src/relay/backend/contrib/nuc_fpga/*.cc)
        list(APPEND COMPILER_SRCS ${NUCFPGA_CODEGEN_SRC})
        file(GLOB NUCFPGA_CONTRIB_SRCS src/runtime/contrib/nuc_fpga/nuc_fpga.cc)
        list(APPEND RUNTIME_SRCS ${NUCFPGA_CONTRIB_SRCS})
        message(STATUS "Build with contrib.nuc_fpga")
    endif()

**6.build/config.cmake**

    set(USE_NUCFPGA_CODEGEN ON)

Then cmake ..   and make j4

When I run the program(Resnet18):

    extern_mod = relay.transform.AnnotateTarget(['nuc_fpga'])(mod)
    extern_mod = relay.transform.MergeCompilerRegions()(extern_mod)
    extern_mod = relay.transform.PartitionGraph()(extern_mod)
    print("extern_mod:", extern_mod)
    target = tvm.target.Target('llvm')
    with tvm.transform.PassContext(opt_level=3):
        grf_mod = relay.build(extern_mod, target=target, params=params)

The graph still is nn.conv2d not the nuc_fpga_conv2d


Part of **extern_mod log** is :

   %0 = **nn.conv2d**(%input0, %conv1.0.weight, strides=[2, 2], padding=[3, 3, 
3, 3], channels=64, 
  kernel_size=[7, 7]) /* ty=Tensor[(1, 64, 112, 112), float32]                  
                       */;

  %1 = nn.bias_add(%0, %conv1.0.bias) /* ty=Tensor[(1, 64, 112, 112), float32] 
*/;

  %2 = nn.relu(%1) /* ty=Tensor[(1, 64, 112, 112), float32] */;

  %3 = nn.max_pool2d(%2, pool_size=[3, 3], strides=[2, 2], padding=[1, 1, 1, 
1]) /* ty=Tensor[(1, 64, 56, 56), float32] */;

  %4 = **nn.conv2**d(%3, %layer1.0.conv1.0.weight, padding=[1, 1, 1, 1], 
channels=64, kernel_size=[3, 3]) /* ty=Tensor[(1, 64, 56, 56), float32] */;

  %5 = nn.bias_add(%4, %layer1.0.conv1.0.bias) /* ty=Tensor[(1, 64, 56, 56), 
float32] */;

  %6 = nn.relu(%5) /* ty=Tensor[(1, 64, 56, 56), float32] */;


**It should be nn.conv2d instead of nuc_fpga_conv2d,but not.**

Did I do something wrong?

Thanks.





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