Jesse Gross 0e38297f87 backend: Consistently use int (vs. int64) for tensor shapes ...

Currently there is a mixture of int and int64 used when dealing with
tensor dimensions and shapes, which causes unnecessary conversions -
they all should be the same type.

In general, most interfaces (such as Pytorch) use int64 for
generality but most implementations (such as CUDA) use int32 for
performance. There isn't much benefit to us to being more flexible
than the implementations we are likely to run on.

In addition, as a practical matter, a model with a tensor with a single
dimension larger than 32 bits is unlikely to run on a 32-bit machine.

2025-02-13 17:09:26 -08:00

..

ggml

backend: Consistently use int (vs. int64) for tensor shapes

2025-02-13 17:09:26 -08:00

backend.go

next ollama runner (#7913)

2025-02-13 16:31:21 -08:00