Fixes #96064

When deciding whether to fuse nodes, we match indexing like `c0 + 5 * tmp0`, but `tmp0` in the different nodes can refer to totally different values. Even when `tmp0` is the same (like in the added test) inductor still generates wrongly ordered loads and stores (loads come before stores), so better just disable this fusion altogether. We should fix wrong order also:
```
@pointwise(size_hints=[8], filename=__file__, meta={'signature': {0: '*i64', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': 0, 'constants': {}, 'mutated_arg_names': ['out_ptr0'], 'configs': [instance_descriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]})
@triton.jit
def triton_(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 5
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0_load = tl.load(in_ptr0 + (0))
    tmp0 = tl.broadcast_to(tmp0_load, [XBLOCK])
    tmp1 = tl.load(in_ptr1 + (x0), xmask)
    tmp2 = tl.load(out_ptr0 + (x0 + (5*tmp0)), xmask)
    tl.store(out_ptr0 + (x0 + (5*tmp0) + tl.zeros([XBLOCK], tl.int32)), tmp1, xmask)
    tl.store(out_ptr1 + (x0 + tl.zeros([XBLOCK], tl.int32)), tmp2, xmask)
```
Note: we are loading from `out_ptr0` here (that shouldn't happen), we are loading from it before storing to it.
After this PR, the kernel above is split in 2.

Pull Request resolved: pytorch/pytorch#96273
Approved by: https://github.com/jansel

Fixes pytorch#96064

When deciding whether to fuse nodes, we match indexing like `c0 + 5 * tmp0`, but `tmp0` in the different nodes can refer to totally different values. Even when `tmp0` is the same (like in the added test) inductor still generates wrongly ordered loads and stores (loads come before stores), so better just disable this fusion altogether. We should fix wrong order also:
```
@pointwise(size_hints=[8], filename=__file__, meta={'signature': {0: '*i64', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': 0, 'constants': {}, 'mutated_arg_names': ['out_ptr0'], 'configs': [instance_descriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]})
@triton.jit
def triton_(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 5
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0_load = tl.load(in_ptr0 + (0))
    tmp0 = tl.broadcast_to(tmp0_load, [XBLOCK])
    tmp1 = tl.load(in_ptr1 + (x0), xmask)
    tmp2 = tl.load(out_ptr0 + (x0 + (5*tmp0)), xmask)
    tl.store(out_ptr0 + (x0 + (5*tmp0) + tl.zeros([XBLOCK], tl.int32)), tmp1, xmask)
    tl.store(out_ptr1 + (x0 + tl.zeros([XBLOCK], tl.int32)), tmp2, xmask)
```
Note: we are loading from `out_ptr0` here (that shouldn't happen), we are loading from it before storing to it.
After this PR, the kernel above is split in 2.

Pull Request resolved: pytorch#96273
Approved by: https://github.com/jansel