[TFLite] Bugfix - ensure pad calcalution to be in int32 #7009
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kevinthesun
reviewed
Dec 1, 2020
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| out = int(math.ceil(float(data) / float(stride))) | ||
| pad = max(0, (out - 1) * stride + kernel - data) | ||
| pad = max(0, (out - 1) * int(stride) + int(kernel) - int(data)) |
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Do we want to somehow test the compilation for skylake/cascadelake target?
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Also, do we know why this fails with int64? AFAIU from your RFC: https://discuss.tvm.apache.org/t/int64-vs-int32-dtype-error/8555 this used to work before.
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I also found the same problem and sent #7030 to fix it. Your modification of |
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@kevinthesun @zhiics @giuseros
TFLite prequantized Resent model was failing to compile. I found that the pad calculation introduce int64 datatype in the shape of max pool2d, that later causes issues with tensorization. Int64 was introduced because of the calculation between python int and np.int32, which results in np.int64. This PR ensures that all the padding calculations happen in python int.
Additionally, I have added a small check to print i64 in AsText. This helped in identifying which op was introducing the int64 datatype.