Method exec_internal

dnn/src/rocm/convolution/forward/matmul.cpp:37–66

template <typename T>
void ConvolutionForwardImpl::AlgoMatmul::exec_internal(const ExecArgs& args) {
    auto&& fm = args.filter_meta;
    size_t N = args.src_layout->shape[0], IC = fm.icpg, IH = args.src_layout->shape[2],
           IW = args.src_layout->shape[3], OC = fm.ocpg, OH = args.dst_layout->shape[2],
           OW = args.dst_layout->shape[3], FH = fm.spatial[0], FW = fm.spatial[1],
           PH = fm.padding[0], PW = fm.padding[1], SH = fm.stride[0], SW = fm.stride[1],
           DH = fm.dilation[0], DW = fm.dilation[1];
    auto stream = hip_stream(args.handle);
    auto wbundle = matmul_get_workspace_bundle(args);
    wbundle.set(args.workspace.raw_ptr);
    T* dst_t = static_cast<T*>(wbundle.get(0));
    T* col = static_cast<T*>(wbundle.get(1));
    convolution::im2col<T>(
            args.src_tensor->ptr<T>(), col, N, args.src_layout->stride[0], IC, IH, IW,
            FH, FW, OH, OW, PH, PW, SH, SW, DH, DW, stream);
    TensorLayout Al({OC, IC * FH * FW}, typename DTypeTrait<T>::dtype()),
            Bl({IC * FH * FW, OH * OW * N}, typename DTypeTrait<T>::dtype()),
            Cl({OC, OH * OW * N}, typename DTypeTrait<T>::dtype());
    TensorND A(args.filter_tensor->ptr<T>(), Al), B(col, Bl), C(dst_t, Cl);
    if (fm.should_flip) {
        convolution::flip_filter(args, wbundle.get_workspace(2), A.get_ref_ptr());
    }
    args.handle->matmul_opr()->exec(A, B, C, Workspace());
    TensorLayout C2l({OC * OH * OW, N}, typename DTypeTrait<T>::dtype()), C3l = C2l;
    C3l.stride[0] = 1;
    C3l.stride[1] = args.dst_tensor->layout.stride[0];
    TensorND C2(dst_t, C2l);
    TensorND C3(args.dst_tensor->ptr<T>(), C3l);
    args.handle->relayout_opr()->exec(C2, C3);
}

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