Int64 support for UpsampleNearest3d

[kdrozd-dev]

Fixes: #2510.

Align with changes from: pytorch/pytorch#144865. Improve error messages to resemble the cuda ones and change the relevant int32 variables to int64.

[pbielak]

Looks good, but similar to my changes with the MaxPool3D kernel (see PR: #3558 and #3632 ), we should check the impact of the int64_t "hardcoding". Maybe a dispatch over int32/int64 would give better perf results.

[kdrozd-dev]

Looks good, but similar to my changes with the MaxPool3D kernel (see PR: #3558 and #3632 ), we should check the impact of the int64_t "hardcoding". Maybe a dispatch over int32/int64 would give better perf results.

Seems like a good idea will switch to dispatch approach after a quick benchmark

[kdrozd-dev]

3 runs with 20 execs per test case each

Case	Shape	dtype	Output numel	Speedup (med)	Speedup (avg)	Stable
small-f32	(2, 64, 16, 16, 16)	float32	4M	0.869x	0.899x	no
small-bf16	(2, 64, 16, 16, 16)	bfloat16	4M	1.044x	1.062x	no
small-f16	(2, 64, 16, 16, 16)	float16	4M	1.482x	1.287x	no
med-f32	(4, 128, 32, 32, 32)	float32	134M	1.034x	0.869x	no
med-bf16	(4, 128, 32, 32, 32)	bfloat16	134M	0.986x	1.089x	no
med-f16	(4, 128, 32, 32, 32)	float16	134M	0.925x	0.848x	no
large-f32	(1, 32, 64, 128, 128)	float32	268M	1.053x	1.053x	YES
large-bf16	(1, 32, 64, 128, 128)	bfloat16	268M	1.058x	1.058x	YES
xl-bf16	(1, 64, 64, 128, 256)	bfloat16	1.07B	1.054x	1.054x	YES
xl-f32	(1, 64, 64, 128, 256)	float32	1.07B	1.055x	1.055x	YES
med-exact-f32	(4, 128, 32, 32, 32)	float32	134M	0.847x	0.845x	no
med-scale3-f32	(2, 64, 16, 16, 16)	float32	14M	1.302x	1.063x	no

The benchmark shows that for stable test cases dispatch based approach is faster by around 5%. For small kernels results were mostly noise and varied greatly.