Unable to compile for targets other than x86_64

[ghost]

I have tried emitting bundles for targets other than x86_64 to no avail. Even i386 is failing. It has something to do with tensor types. I suppose different targets expect/support different tensor types; however, I made sure that my network is using Float32. So this shouldn’t be a problem for i386. The network looks like this:

graph(%input_data : Float(1, 2)
      %1 : Float(8, 2)
      %2 : Float(8)
      %3 : Float(8, 8)
      %4 : Float(8)
      %5 : Float(8, 8)
      %6 : Float(8)
      %7 : Float(1, 8)
      %8 : Float(1)) {
  %9 : Float(1, 8) = onnx::Gemm[alpha=1, beta=1, transB=1](%input_data, %1, %2), scope: FeedForwardNN/Linear
  %10 : Float(1, 8) = onnx::Relu(%9), scope: FeedForwardNN/ReLU
  %11 : Float(1, 8) = onnx::Gemm[alpha=1, beta=1, transB=1](%10, %3, %4), scope: FeedForwardNN/ReLU
  %12 : Float(1, 8) = onnx::Relu(%11), scope: FeedForwardNN/ReLU
  %13 : Float(1, 8) = onnx::Gemm[alpha=1, beta=1, transB=1](%12, %5, %6), scope: FeedForwardNN/ReLU
  %14 : Float(1, 8) = onnx::Relu(%13), scope: FeedForwardNN/ReLU
  %15 : Float(1, 1) = onnx::Gemm[alpha=1, beta=1, transB=1](%14, %7, %8), scope: FeedForwardNN/ReLU
  %output : Float(1, 1) = onnx::Sigmoid(%15), scope: FeedForwardNN/Sigmoid
  return (%output);
}

The traceback that I get when trying to build is:

(base) wny@wny-Macbook ~/sandbox/nn/xgmr/build/debug/bin $ ./x-model-runner -model=/Users/wny/sandbox/nn/pytorch_tuts/ffnn.onnx -network-name=x -model-input-name=input_data -cpu -emit-bundle=../output -target i386
Assertion failed: ((i >= FTy->getNumParams() || FTy->getParamType(i) == args[i]->getType()) && "Calling a function with a bad signature: argument type mismatch."), function createCall, file ../../lib/LLVMIRCodeGen/LLVMIRGen.cpp, line 558.
0  x-model-runner       0x000000010721e0bc llvm::sys::PrintStackTrace(llvm::raw_ostream&) + 37
1  x-model-runner       0x000000010721e4be SignalHandler(int) + 192
2  libsystem_platform.dylib 0x00007fff713b7b3d _sigtramp + 29
3  libsystem_platform.dylib 0x000000011a0f8938 _sigtramp + 2832469528
4  libsystem_c.dylib        0x00007fff712751c9 abort + 127
5  libsystem_c.dylib        0x00007fff7123d868 basename_r + 0
6  x-model-runner       0x00000001072c7136 glow::LLVMIRGen::createCall(llvm::IRBuilder<llvm::ConstantFolder, llvm::IRBuilderDefaultInserter>&, llvm::Function*, llvm::ArrayRef<llvm::Value*>) + 422
7  x-model-runner       0x00000001072d04e3 glow::LLVMIRGen::generateLLVMIRForInstr(llvm::IRBuilder<llvm::ConstantFolder, llvm::IRBuilderDefaultInserter>&, glow::Instruction const*) + 1603
8  x-model-runner       0x0000000107272111 glow::CPULLVMIRGen::generateLLVMIRForInstr(llvm::IRBuilder<llvm::ConstantFolder, llvm::IRBuilderDefaultInserter>&, glow::Instruction const*) + 2113
9  x-model-runner       0x00000001072c92ed glow::LLVMIRGen::generateLLVMIRForModule(llvm::IRBuilder<llvm::ConstantFolder, llvm::IRBuilderDefaultInserter>&) + 445
10 x-model-runner       0x00000001072718c8 glow::CPULLVMIRGen::generateLLVMIRForModule(llvm::IRBuilder<llvm::ConstantFolder, llvm::IRBuilderDefaultInserter>&) + 40
11 x-model-runner       0x00000001072c448b glow::LLVMIRGen::performCodeGen() + 139
12 x-model-runner       0x000000010727c2c2 glow::BundleSaver::save(llvm::StringRef, llvm::StringRef, llvm::StringRef, llvm::SmallVectorImpl<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> > > const&, llvm::StringRef, llvm::StringRef) 
+ 450
13 x-model-runner       0x00000001072eccd0 glow::LLVMBackend::save(glow::Function*, llvm::StringRef, llvm::StringRef) const + 832
14 x-model-runner       0x00000001071124a4 glow::ExecutionEngine::save(glow::Function*, glow::CompilationOptions const&, llvm::StringRef, llvm::StringRef) + 148
15 x-model-runner       0x0000000106f27e6f glow::Loader::compile(glow::PlaceholderBindings&) + 2415
16 x-model-runner       0x0000000106f4cf85 buildNetwork(glow::Loader&, glow::Type const*) + 117
17 x-model-runner       0x0000000106f4d4a2 main + 242
18 libdyld.dylib            0x00007fff711cced9 start + 1
Abort trap: 6

In case there are any questions about x-model-runner: it is a custom runner that I built based on the image-classifier. The runner itself is working as expected for x86_64 -- it emits bundles that I'm able to link to and run inference.

[ghost]

@opti-mix Right, but that isn't needed anymore given your fix. In other words, we don't need any special modifications anymore. Everything works as expected out of the box.

[opti-mix]

Right, but that isn't needed anymore given your fix. In other words, we don't need any special modifications anymore. Everything works as expected out of the box.

Good to hear. But even if everything works out of the box, I don't think that this whole cross-compilation topic and how you use it is really covered anywhere in the docs. A small example in AOT.md with e.g. a short description of a scenario and a command-line to be used could be really helpful to other users.

[ghost]

@opti-mix Agreed.