[CUDA] Fix custom kernel cache collision for same name, different source

[HaoXuAI]

Fixes #4275.

get_jit_module keys the module cache on the kernel name alone (jit_module.cpp:471-485), so a second custom kernel sharing a name silently runs the first one's compiled code — no error, wrong results.

This is the CUDA counterpart of #3832, fixed for Metal in #3833. It is worse here: on Metal the stale-source invalidation worked across eval boundaries and only failed within a single batch, whereas get_jit_module has no invalidation at all, so the collision persists for the lifetime of the process.

The fix hashes the source into the module name, the same way #3833 did. The kernel name passed to get_kernel() is unchanged, so lookup inside the module still works.

It also covers a second path with the same root cause: read_cached_ptx() runs unconditionally (jit_module.cpp:407) even when use_disk_cache is false, so a stale on-disk PTX under the same name could be picked up too.

Testing

test_cuda_kernel_same_name_different_source mirrors the Metal regression test from #3833. Verified on an L40S (sm_89) with CUDA 12.6 — two kernels sharing an entry name but differing in body, with a separate mx.eval between them:

	before	after
first source (x * 2)	[0, 2, 4] correct	correct
second source (x + 100)	[0, 2, 4] — kernel A's code	[100, 101, 102] correct

The rest of test_fast.py is unaffected: 24 passed / 4 skipped on CUDA, 27 passed / 1 skipped on Metal.

I ran into this through mx.fast.precompiled_cuda_kernel with Triton output, where it is easy to hit by accident since Triton names kernels after the Python function.