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Right after MacOS upgrade to Sonoma 14.1.2, the MPS implementation runs with log on tabnet_pretrain() and tabnet_fit() with the following log
loc("outputTensor"("(mpsFileLoc): /AppleInternal/Library/BuildRoots/495c257e-668e-11ee-93ce-926038f30c31/Library/Caches/com.apple.xbs/Sources/MetalPerformanceShadersGraph/mpsgraph/MetalPerformanceShadersGraph/Core/Files/MPSGraphUtilities.mm":745:0)): error: 'anec.gain_offset_control' op result #0 must be 4D/5D memref of 16-bit float or 8-bit signed integer or 8-bit unsigned integer values, but got 'memref<1x256x1x73xi1>'
It seems it can be treated as a warning (end-user perspective)
Please report any occurrence where the training fails.
ReprEx
data("ames", package="modeldata")
ids<- sample(nrow(ames), 256)
small_ames<-ames[ids,]
x<-ames[ids,-which(names(ames) =="Sale_Price")]
y<-ames[ids,]$Sale_Price# ames common models
library(tabnet)
ames_pretrain<- tabnet_pretrain(x, y, epoch=2, checkpoint_epochs=1,
verbose=FALSE)
#> loc("outputTensor"("(mpsFileLoc): /AppleInternal/Library/BuildRoots/495c257e-668e-11ee-93ce-926038f30c31/Library/Caches/com.apple.xbs/Sources/Metal#> PerformanceShadersGraph/mpsgraph/MetalPerformanceShadersGraph/Core/Files/MPSGraphUtilities.mm":745:0)): error: 'anec.gain_offset_control' op result #0#> must be 4D/5D memref of 16-bit float or 8-bit signed integer or 8-bit unsigned integer values, but got 'memref<1x256x1x73xi1>'
cregouby
changed the title
MPS implementation in MacOS Sonoma 14.1.2 raises errors on tensor anec.gain_offset_control shape
MPS implementation in MacOS Sonoma 14.1.2 raises warning errors on tensor anec.gain_offset_control shape
Jan 12, 2024
cregouby
changed the title
MPS implementation in MacOS Sonoma 14.1.2 raises warning errors on tensor anec.gain_offset_control shape
MPS implementation in MacOS Sonoma 14.1.2+ raises warning errors on tensor anec.gain_offset_control shape
Jan 12, 2024
Current situation
Right after MacOS upgrade to Sonoma 14.1.2, the MPS implementation runs with log on
tabnet_pretrain()
andtabnet_fit()
with the following logIt seems it can be treated as a warning (end-user perspective)
Please report any occurrence where the training fails.
ReprEx
Created on 2024-01-05 with reprex v2.0.2
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