Support Windows x86 builds

[eerhardt]

System information

• OS version/distro: Windows
• .NET Version (eg., dotnet --info): All - desktop, .net core

Issue

• What did you do? Try to use ML.NET in an x86 process
• What happened? It doesn't work because CpuMathNative can't be loaded into an x86 process
• What did you expect? I expected it to work in a Windows x86 process.

Notes

There are cases where developers are forced to use x86 processes. For example, if their hosting environment only supports x86 processes. Or if they are using other native libraries that are only available for x86, and they don't want to (or can't) spin up multiple processes (x64 for ML.NET and x86 for other native libraries).

[Ivanidzo4ka]

Is this good place for discussion?
From C# point of view, we currently have SseUtils, AvxUtils classes which we call directly. We need to change our code to use something like CpuUtils which will perform platform validation and instruction availability and fallback on proper implementation.

From native point of view, is it possible to have x64 and x86 code in one library or we need to compile two version?

Or shall we get rid of native code and switch to hardware Intrinsics in .core 2.1?

[eerhardt]

From native point of view, is it possible to have x64 and x86 code in one library or we need to compile two version?

We compile 2 versions - one for x64 and one for x86. These two versions get put into different folders in our NuGet package runtimes\win-{arch}\CpuMathNative.dll.

Then depending how the architecture used by the application, they get the correct assembly for the specified architecture. This is exactly how we support macOS and Linux assemblies as well - each native asset is in a folder in the NuGet package, and it gets picked out correctly.

In the case of a "portable" application (one that can run anywhere on any processor), all the assets across all the architectures are published into a runtimes folder. The user can then move that same app between multiple OS/archs and the app still runs because all the assets are there.

Or shall we get rid of native code and switch to hardware Intrinsics in .core 2.1?

Hardware intrinsics only work in .NET Core, so we would no longer be a netstandard library.
Note: we also have the FastTree native code in C++.

[fiigii]

Or shall we get rid of native code and switch to hardware Intrinsics in .core 2.1?

Hardware intrinsic in .NET Core 2.1 supports 64-bit and 32-bit both, so switching to use HW intrinsic will solve the problem for .NET Core build at least. We can keep the native code for other platform builds (e.g., .NET framework, Mono, etc.).

[rowanmiller]

Adding a +1 to x86 support. I hit this issue when building out a demo, as I was working in a WPF app that used some UI controls that required x86. There aren't x64 alternatives for the controls, so moving wasn't an option.

[dan-drews]

Adding another +1 to this. I looked to add this to an older application that is set to be x86. Will look to change that to be 64-bit (I'm assuming without issue) in the near future in order to use this.

I could spin up a new web service to handle the ML predictions, but that'll take a while to get the traction. Thankfully, our application is not complicated, so converting to x64 should go off without a hitch, but I can only assume that others will expect this support.

[Anipik]

Another +1 for x86 . I was trying to use ML.net in azure functions. But Azure Functions only support x86 processes. And for training as well predicting the value we require CpuMathNative.
The error occurs while calculating L2Norm

Microsoft.ML.Runtime.Data.LpNormNormalizerTransform.GetGetterCore.AnonymousMethod__5(ref Microsoft.ML.Runtime.Data.VBuffer<float> dst) Line 434
 	Microsoft.ML.Data.dll!Microsoft.ML.Runtime.Data.ConcatTransform.MakeGetter.AnonymousMethod__0(ref Microsoft.ML.Runtime.Data.VBuffer<float> dst) Line 835
 	Microsoft.ML.Data.dll!Microsoft.ML.Runtime.Data.ConcatTransform.MakeGetter.AnonymousMethod__0(ref Microsoft.ML.Runtime.Data.VBuffer<float> dst) Line 835
 	Microsoft.ML.Data.dll!Microsoft.ML.Runtime.Data.SchemaBindablePredictorWrapperBase.GetValueGetter.AnonymousMethod__0(ref Microsoft.ML.Runtime.Data.VBuffer<float> dst) Line 157
 	Microsoft.ML.Data.dll!Microsoft.ML.Runtime.Data.PredictedLabelScorerBase.EnsureCachedPosition<Microsoft.ML.Runtime.Data.VBuffer<float>>(ref long cachedPosition, ref Microsoft.ML.Runtime.Data.VBuffer<float> score, Microsoft.ML.Runtime.Data.IRow boundRow, Microsoft.ML.Runtime.Data.ValueGetter<Microsoft.ML.Runtime.Data.VBuffer<float>> scoreGetter) Line 443	C#
 	Microsoft.ML.Data.dll!Microsoft.ML.Runtime.Data.MultiClassClassifierScorer.GetPredictedLabelGetter.AnonymousMethod__0(ref uint dst) Line 564
 	Microsoft.ML.Data.dll!Microsoft.ML.Runtime.Data.KeyToValueTransform.KeyToValueMap<uint, Microsoft.ML.Runtime.Data.DvText>.GetMappingGetter.AnonymousMethod__0(ref Microsoft.ML.Runtime.Data.DvText dst) Line 309
 	Microsoft.ML.Data.dll!Microsoft.ML.Runtime.Data.DataViewUtils.Splitter.InPipe.Impl<Microsoft.ML.Runtime.Data.DvText>.Fill() Line 732
 	Microsoft.ML.Data.dll!Microsoft.ML.Runtime.Data.DataViewUtils.Splitter.Consolidator.ConsolidateCore.AnonymousMethod__2() Line 418
 	System.Threading.Thread.dll!System.Threading.Thread.ThreadMain_ThreadStart() Line 93
 	System.Private.CoreLib.dll!System.Threading.ThreadHelper.ThreadStart_Context(object state) Line 62
 	System.Private.CoreLib.dll!System.Threading.ExecutionContext.RunInternal(System.Threading.ExecutionContext executionContext, System.Threading.ContextCallback callback, object state) Line 167
 	System.Private.CoreLib.dll!System.Threading.ThreadHelper.ThreadStart() Line 91	

[klausmh]

Is there any work or decision on this? I am looking to integrate into a scenario where we need both x86 and x64.

[danmoseley]

@tannergooding is going to have a quick look to see whether it's trivial.

[tannergooding]

Looks like most of the infrastructure for building 32-bit native binaries already exists (just need to ensure /p:TargetArchitecture=x86, I tested by setting an environment variable).

The mlnetmkldeps package needs to be updated to also include x86 libraries and the build system likely needs some easy mechanism to pass through x86 as the target architecture.

[tannergooding]

With #860, build succeeds for everything except the copying the native Tensorflow dependency.

Tensorflow is 64-bit only, with no planned support for 32-bit (from what I can tell). Building from source (on Windows) also requires building several other build dependencies from source to produce a 32-bit build.

Ignoring Tensorflow, the following test assemblies fully pass:

• Microsoft.ML.CodeAnalyzer.Tests (13 Passed, 0 Failed, 0 Skipped)
• Microsoft.ML.CpuMath.UnitTests.netstandard (72 Passed, 0 Failed, 0 Skipped)
• Microsoft.ML.StaticPipelineTesting (10 Passed, 0 Failed, 0 Skipped)
• Microsoft.ML.Sweeper.Tests (21 Passed, 0 Failed, 0 Skipped)

The following had failures:

• Microsoft.ML.Core.Tests (90 Passed, 4 Failed, 1 Skipped)
  • Failures: Microsoft.ML.Core.Tests.txt
    • 2: Unable to find lib_lightgbm
    • 2: Unable to find tensorflow
• Microsoft.ML.Predictor.Tests (23 Passed, 26 Failed, 56 Skipped)
  • Failures: Microsoft.ML.Predictor.Tests.txt
    • 25: Output and baseline mismatch
    • 1: Unabled to find lib_lightgbm
• Microsoft.ML.TestFramework (13 Passed, 1 Failed, 57 Skipped)
  • Failures: Microsoft.ML.TestFramework.txt
    • 1: Output and baseline mismatch
• Microsoft.ML.Tests (101 Passed, 10 Failed, 5 Skipped)
  • Failures: Microsoft.ML.Tests.txt
    • 2: Unable to find lib_lightgbm
    • 6: Unable to find tensorflow
    • 1: Output and baseline mismatch
    • 1: Expected vs Actual different
• Microsoft.ML.FSharp.Tests (0 Passed, 3 Failed, 0 Skipped)
  • Failures: Microsoft.ML.FSharp.Tests.txt
    • Attempt to load program with incorrect format

[eerhardt]

Unabled to find lib_lightgbm

LightGBM is a separate project (https://github.com/Microsoft/LightGBM/) and I don't believe they have support for x86 currently. @guolinke - do you have plans for making Windows x86 binaries available?

@tannergooding - if not, we will need to treat LightGBM like we do for TensorFlow.

Microsoft.ML.FSharp.Tests - Attempt to load program with incorrect format

@dsyme - any thoughts here why the F# tests wouldn't support x86?

[tannergooding]

For F#, based on the failure, they are likely compiled for 64-bit only. Will confirm after lunch.

[tannergooding]

Yep. F# is explicitly targeting x64: https://github.com/dotnet/machinelearning/blob/master/test/Microsoft.ML.FSharp.Tests/Microsoft.ML.FSharp.Tests.fsproj#L11

Changing it to AnyCPU works and all 3 tests pass.