@huningxin i think it would still be [0, N-1] range? The dimensions are of type uint32_t so it could exceed int32_t range, but I don't think in reality we have tensors that big?

this also exposes another issue - both CoreML and tflite backends actually use int32_t for tensor dimensions so if we have tensor that needs to use that extra bit from uint32_t they won't work on these backends. Should we actually consider use int32_t for dimensions?

@philloooo

i think it would still be [0, N-1] range? The dimensions are of type uint32_t so it could exceed int32_t range,

Yes, then would it be [0, min(N-1, MAX_INT)] range? And should we add a validation step to ensure the size of the dimension being reduced can be indexed by that range?

but I don't think in reality we have tensors that big?

I haven't seen any tensor of a model has such big dimension.

this also exposes another issue - both CoreML and tflite backends actually use int32_t for tensor dimensions so if we have tensor that needs to use that extra bit from uint32_t they won't work on these backends

Yes, AFAIK, the current Chromium TFLite prototype reports error when the dimension size is too large for int32: ToSignedDimensions().

Should we actually consider use int32_t for dimensions?

FYI, #279 replaces the int32_t dimension with uint32_t because there are no use cases for negative values.

We may want to reconsider that given it is not widely supported by currently targeting backends, CoreML / TFLite.

I haven't seen any tensor of a model has such big dimension.

While we closed issue #456, this table is still relevant: #456 (comment) (and the max for Core ML is 5D). The dimensions will always be non-negative and within MAX_INT

While we closed issue #456, this table is still relevant: #456 (comment) (and the max for Core ML is 5D). The dimensions will always be non-negative and within MAX_INT

Apologies, that table is referring to max rank while we're talking about the max dimension here

Yes, then would it be [0, min(N-1, MAX_INT)] range? And should we add a validation step to ensure the size of the dimension being reduced can be indexed by that range?

+1, though we should consider [0, MAX_INT] (or even something less than MAX_INT). In that case, we could make WebNN's argmin/argmax return just int32 or uint32 and backends which natively return other types can losslessly cast the result to int32 (which might be able to be optimized away in many cases)

@huningxin I've created a separate issue #734 for int32 vs uint32.
For this issue for now, I've added a validation step you suggested, which we can remove later when #734 is resolved. Given the validation step, we can keep the return value to be still range of [0, N-1]