Add support for Apples' Metal Performance Shaders (MPS) in pytorch #2037

demq · 2022-09-27T10:31:43Z

Description

Pytorch 1.12 supports Apple’s Metal Performance Shaders (MPS) for accelerated training/inference. A simple test on HuggingFace models shows 2x-3x improvements on the inference latency over the CPU cores on an Apple M1Pro with 8 CPU and 14 GPU cores:

--------------------------------------------------------------------------------
Model:              roberta-hf
Device:             cpu()
Samples:            34
Model Input Length: 384
Metrics:
	Total: 140.206 ± 5.880      msec
	Inference: 138.794 ± 5.713      msec
	Preprocess: 0.441 ± 0.553      msec
	Postprocess: 0.824 ± 0.567      msec
--------------------------------------------------------------------------------
Model:              roberta-hf
Device:             mps(-1)
Samples:            34
Model Input Length: 384
Metrics:
	Total: 65.500 ± 9.166      msec
	Inference: 41.235 ± 6.198      msec
	Preprocess: 1.059 ± 0.338      msec
	Postprocess: 23.206 ± 6.794      msec

--------------------------------------------------------------------------------

This improvement has been requested by me in the DJL help slack channel, as well as more recently in #2018 .

Brief description of what this PR is about

Adds a new "MPS" device type and maps to the corresponding pytorch device type.

Some caveats:

The "MPS" mode only works if torch::jit::load(path, device, map); is called with device= torch::nullopt as in torch the model gets deserialized in 'legacy' mode, which only support CPU and GPU devices. The model get desiralized on the CPU and then can be converted to MPS. For this, in traced torch model's directory in the file "serving.properties" the option.mapLocation=false should be set, which triggers this implementation already present in DJL native module.
Alternatively, the the following check can be added the pytorch-native module:

djl/engines/pytorch/pytorch-native/src/main/native/ai_djl_pytorch_jni_PyTorchLibrary_inference.cc

Lines 49 to 52 in f097502

    
           if (jmap_location) { 
        
             module = torch::jit::load(path, device, map); 
        
             module.eval(); 
        
           } else {

if (jmap_location && (device.is_cpu() || device.is_cuda())) {

An env variable s of hould be setPYTORCH_ENABLE_MPS_FALLBACK=1 as a work around a pytorch implementation deficiency of 'aten::index.Tensor' for MPS, see General MPS op coverage tracking issue pytorch/pytorch#77764

…gine.

frankfliu · 2022-09-27T18:45:22Z

api/src/main/java/ai/djl/Device.java

@@ -36,6 +36,7 @@ public final class Device {

    private static final Device CPU = new Device(Type.CPU, -1);
    private static final Device GPU = Device.of(Type.GPU, 0);
+    private static final Device MPS = Device.of(Type.MPS, -1);


MPS is PyTorch specific, for the time being, it's better keep it PyTorch only. We can add to Device class when it become standard.

I am not too sure how to avoid this part "cleanly" without having to rewrite a bunch of code in pytorche's JniUtils.java, as most of the functions there use a Device object to infer the PtDeviceType:

djl/engines/pytorch/pytorch-engine/src/main/java/ai/djl/pytorch/jni/JniUtils.java

Lines 191 to 202 in 8fce4eb

public static PtNDArray createZerosNdArray(

PtNDManager manager, Shape shape, DataType dType, Device device, SparseFormat fmt) {

int layoutVal = layoutMapper(fmt, device);

return new PtNDArray(

manager,

PyTorchLibrary.LIB.torchZeros(

shape.getShape(),

dType.ordinal(),

layoutVal,

new int[] {PtDeviceType.toDeviceType(device), device.getDeviceId()},

false));

}

One way is to change the PtDeviceType:: toDeviceType(Device device) to somehow return the code for MPS device on osx-aarm64 systems, but I don't know how to do that:

djl/engines/pytorch/pytorch-engine/src/main/java/ai/djl/pytorch/engine/PtDeviceType.java

Lines 28 to 38 in 8fce4eb

public static int toDeviceType(Device device) {

String deviceType = device.getDeviceType();

if (Device.Type.CPU.equals(deviceType)) {

return 0;

} else if (Device.Type.GPU.equals(deviceType)) {

return 1;

} else {

throw new IllegalArgumentException("Unsupported device: " + device.toString());

}

}

frankfliu · 2022-09-27T18:46:20Z

api/src/main/java/ai/djl/Device.java

    /** Contains device type string constants. */
    public interface Type {
        String CPU = "cpu";
        String GPU = "gpu";
+        String MPS = "mps";


We don't need make changes in Device class. Let's keep it private in PyTorch for now

I agree that this might create some false expectations form users that Apple MPS is supported for all the engines, but the current solution means that apple silicone users don't need to rewrite any of their existing code. Perhaps adding a documentation that MPS will only work for pytorch?

The users can simply specify "mps" as their device type in their model Criteria when running on Mac M1/M2 and get the acceleration working. Otherwise the developers would need to add pytorch and OSX-specific code to support "mps", making it less likely to be used. The device type, on the other hand, is usually just a system setting / cli argument to ensure let's say the GPU acceleration can be switched on/off.

codecov-commenter · 2022-09-28T20:54:27Z

Codecov Report

Base: 72.08% // Head: 69.53% // Decreases project coverage by -2.55% ⚠️

Coverage data is based on head (b9df963) compared to base (bb5073f).
Patch coverage: 68.32% of modified lines in pull request are covered.

Additional details and impacted files

@@             Coverage Diff              @@
##             master    #2037      +/-   ##
============================================
- Coverage     72.08%   69.53%   -2.56%     
- Complexity     5126     5953     +827     
============================================
  Files           473      597     +124     
  Lines         21970    26498    +4528     
  Branches       2351     2880     +529     
============================================
+ Hits          15838    18426    +2588     
- Misses         4925     6687    +1762     
- Partials       1207     1385     +178

Impacted Files	Coverage Δ
api/src/main/java/ai/djl/modality/cv/Image.java	`69.23% <ø> (-4.11%)`	⬇️
...rc/main/java/ai/djl/modality/cv/MultiBoxPrior.java	`76.00% <ø> (ø)`
...rc/main/java/ai/djl/modality/cv/output/Joints.java	`71.42% <ø> (ø)`
.../main/java/ai/djl/modality/cv/output/Landmark.java	`100.00% <ø> (ø)`
...main/java/ai/djl/modality/cv/output/Rectangle.java	`72.41% <0.00%> (ø)`
...i/djl/modality/cv/translator/BigGANTranslator.java	`21.42% <0.00%> (-5.24%)`	⬇️
...odality/cv/translator/BigGANTranslatorFactory.java	`33.33% <0.00%> (+8.33%)`	⬆️
.../cv/translator/InstanceSegmentationTranslator.java	`0.00% <0.00%> (-86.59%)`	⬇️
...nslator/InstanceSegmentationTranslatorFactory.java	`7.14% <0.00%> (-11.04%)`	⬇️
.../cv/translator/SemanticSegmentationTranslator.java	`0.00% <0.00%> (ø)`
... and 512 more

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Add support for Apples' Metal Performance Shaders (MPS) in pytorch en…

f223987

…gine.

demq requested review from zachgk and frankfliu as code owners September 27, 2022 10:31

Add support for Apples' Metal Performance Shaders (MPS) in pytorch en…

e7dd131

…gine.

frankfliu reviewed Sep 27, 2022

View reviewed changes

Add Unit test

b9df963

frankfliu approved these changes Sep 28, 2022

View reviewed changes

frankfliu merged commit 1d0bc77 into deepjavalibrary:master Sep 28, 2022

Ubehebe mentioned this pull request Apr 5, 2023

Segfault from Dataset.prepare() on MPS device #2504

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Add support for Apples' Metal Performance Shaders (MPS) in pytorch #2037

Add support for Apples' Metal Performance Shaders (MPS) in pytorch #2037

demq commented Sep 27, 2022 •

edited

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frankfliu Sep 27, 2022

demq Sep 28, 2022 •

edited

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frankfliu Sep 27, 2022

demq Sep 28, 2022 •

edited

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codecov-commenter commented Sep 28, 2022

	if (jmap_location) {
	module = torch::jit::load(path, device, map);
	module.eval();
	} else {

	public static PtNDArray createZerosNdArray(
	PtNDManager manager, Shape shape, DataType dType, Device device, SparseFormat fmt) {
	int layoutVal = layoutMapper(fmt, device);
	return new PtNDArray(
	manager,
	PyTorchLibrary.LIB.torchZeros(
	shape.getShape(),
	dType.ordinal(),
	layoutVal,
	new int[] {PtDeviceType.toDeviceType(device), device.getDeviceId()},
	false));
	}

	public static int toDeviceType(Device device) {
	String deviceType = device.getDeviceType();

	if (Device.Type.CPU.equals(deviceType)) {
	return 0;
	} else if (Device.Type.GPU.equals(deviceType)) {
	return 1;
	} else {
	throw new IllegalArgumentException("Unsupported device: " + device.toString());
	}
	}

Add support for Apples' Metal Performance Shaders (MPS) in pytorch #2037

Add support for Apples' Metal Performance Shaders (MPS) in pytorch #2037

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