Importing third-party packages before TensorFlow causes a runtime error

[zachgrayio]

Continuing our discussion from the group here.

Full background - I've just copied my comment directly from the group:

I've had some success in using third-party SPM packages by creating a dynamic library and linking to it when launching the REPL, however, it seems like the import order of TensorFlow vs other packages is important; importing the 3rd-party lib first causes a C++ runtime error in TensorFlow.

Here's some snippets:

Package.swift

import PackageDescription

let package = Package(
    name: "TFExample",
    products: [
        .library(
            name: "TFExample",
            type: .dynamic,    // allow use of this package and it's deps from the REPL
            targets: ["TFExample"]
        )
    ],
    dependencies: [
        .package(url: "https://github.com/ReactiveX/RxSwift.git", "4.0.0" ..< "5.0.0")
    ],
    targets: [
        .target(
            name: "TFExample",
            dependencies: ["RxSwift"]),
        .testTarget(
            name: "TFExampleTests",
            dependencies: ["TFExample"]),
    ]
)

... then we just fetch dependencies and build with vanilla commands, then invoke the REPL:

Invocation

swift -I/usr/lib/swift/clang/include -I/usr/src/TFExample/.build/debug -L/usr/src/TFExample/.build/debug -lTFExample

At this point, I'm able to import RxSwift and TensorFlow in the REPL without errors in any order; however, when I actually interact with the packages, the incorrect import order does result in a runtime error:

Scenario 1 (OK)

  1> import TensorFlow
  2> import RxSwift
  3>  _ = Observable.from([1,2]).subscribe(onNext: { print($0) })
1
2
  4> var x = Tensor([[1, 2], [3, 4]])
2018-04-27 17:13:12.514107: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
x: TensorFlow.Tensor<Double> = [[1.0, 2.0], [3.0, 4.0]]

Scenario 2 (runtime error)

  1> import RxSwift
  2> import TensorFlow
  3> _ = Observable.from([1,2]).subscribe(onNext: { print($0) })
1
2
  4> var x = Tensor([[1, 2], [3, 4]])
x: TensorFlow.Tensor<Double> =terminate called after throwing an instance of 'std::logic_error'
  what():  basic_string::_M_construct null not valid

The full process is outlined here if more detail is necessary: https://github.com/zachgrayio/swift-tensorflow/blob/example/package/README.md#run-with-dependencies-advanced

[dan-zheng]

Thanks for providing so much detail!
I'm looking into this now.

[dan-zheng]

I was able to replicate the issue:

$ docker run --rm --privileged --cap-add sys_ptrace -it -v ${PWD}:/usr/src zachgray/swift-tensorflow:4.2 swift -I/usr/lib/swift/clang/include -I/usr/src/TFExample/.build/debug -L/usr/src/TFExample/.build/debug -lTFExample
Welcome to Swift version 4.2-dev (LLVM 04bdb56f3d, Clang b44dbbdf44). Type :help for assistance.
  1> import TensorFlow
  2> import RxSwift
  3> Tensor(1)
error: Couldn't lookup symbols:
  protocol witness table for Swift.Double : TensorFlow.AccelerableByTensorFlow in TensorFlow
  _swift_tfc_StartTensorComputation
  _swift_tfc_FinishTensorComputation
  direct field offset for TensorFlow.TensorHandle.cTensorHandle : Swift.OpaquePointer
  type metadata accessor for TensorFlow.TensorHandle

  3> var x = Tensor([[1, 2], [3, 4]])
x: TensorFlow.Tensor<Double> =terminate called after throwing an instance of 'std::logic_error'
  what():  basic_string::_M_construct null not valid

The solution is to add an extra -lswiftTensorFlow flag:

$ docker run --rm --privileged --cap-add sys_ptrace -it -v ${PWD}:/usr/src zachgray/swift-tensorflow:4.2 swift -I/usr/lib/swift/clang/include -I/usr/src/TFExample/.build/debug -L/usr/src/TFExample/.build/debug -lTFExample -lswiftTensorFlow
Welcome to Swift version 4.2-dev (LLVM 04bdb56f3d, Clang b44dbbdf44). Type :help for assistance.
  1> import RxSwift
  2> import TensorFlow
  3> _ = Observable.from([1,2]).subscribe(onNext: { print($0) })
1
2
  4> var x = Tensor([[1, 2], [3, 4]])
2018-04-27 23:07:35.467557: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 AVX512F FMA
x: TensorFlow.Tensor<Double> = [[1.0, 2.0], [3.0, 4.0]]

I tested the Swift interpreter by putting the code into test.swift, then running:
docker run --rm --privileged --cap-add sys_ptrace -it -v ${PWD}:/usr/src zachgray/swift-tensorflow:4.2 swift -I/usr/lib/swift/clang/include -I/usr/src/TFExample/.build/debug -L/usr/src/TFExample/.build/debug -lTFExample -O /usr/src/test.swift

This worked without specifying -lswiftTensorFlow, suggesting the problem is probably REPL-specific and involves linker flags.

On Linux, the Swift shared runtime library path is found at <path_to_toolchain>/usr/lib/swift/linux. It contains shared libraries like libswiftCore.so, libswiftTensorFlow.so, libswiftPython.so, etc.

In lib/Driver/Toolchains.cpp (used by the interpreter/compiler), toolchains::GenericUnix::constructInvocation automatically adds flags that add the Swift shared runtime library path and link libswiftCore.so. Ostensibly, there's other logic for handling other libraries in the same path (like libswiftPython.so) but I couldn't find it.

The REPL uses entirely separate logic for linking libraries (somewhere in google/swift-lldb). I'll do some digging and try to fix this.

This linking is probably related to #5.

[zachgrayio]

@dan-zheng - nice work man. This is exactly what I was missing. See the following:

docker run --rm --privileged --cap-add sys_ptrace -itv ${PWD}:/usr/src \
    zachgray/swift-tensorflow:4.2 \
    swift \
    -I/usr/lib/swift/clang/include \
    -I/usr/src/TFExample/.build/debug \
    -L/usr/src/TFExample/.build/debug \
    -lswiftPython \
    -lswiftTensorFlow \
    -lTFExample

Welcome to Swift version 4.2-dev (LLVM 04bdb56f3d, Clang b44dbbdf44). Type :help for assistance.
  1> import RxSwift
  2> import Python
  3> import TensorFlow
  4> var x = Tensor([[1, 2], [3, 4]])
2018-04-28 00:11:10.828554: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
x: TensorFlow.Tensor<Double> = [[1.0, 2.0], [3.0, 4.0]]
  5> _ = Observable.from([1,2]).subscribe(onNext: { print($0) })
1
2
  6> var x: PyValue = [1, "hello", 3.14]
x: Python.PyValue = [1, 'hello', 3.14]
  7> :exit

** edited formatting

[dan-zheng]

I'm working on a simple fix now.
Regarding import order: I didn't notice errors when importing Python before TensorFlow so that's the order I'll use.

[dan-zheng]

I believe this is fixed in 1969380.
You can try the pre-built packages from 05-10 to verify.

[pschuh]

This should be able to be solved by -module-link-name . That avoids this hack into the compiler.