Inconsistency in merge error when accessing the inner most dim by absolute vs relative indexing

[kevinstephano]

When indexing a 3-D tensor with size [4, 4, 4] with 2 or -1 with merge, I think we should get the same error. Note, the 2nd error also should occur when the dim is specified as value 1 and -2.

With absolute indexing, I get the following error:

import torch
from nvfuser import FusionDefinition

inputs = [
    torch.randn(4, 4, 4, device="cuda"),
]

class InputError(FusionDefinition):
    def definition(self):
        self.t0 = self.from_pytorch(inputs[0])
        self.t1 = self.ops.sum(self.t0, axis=-1)
        self.add_output(self.t1)

    def schedule(self):
        self.sched.merge(fd.t1, 2),

fd = InputError()
_ = fd.execute(inputs)

Error:

RuntimeError: Invalid merge detected, either one or both axes are outside of TensorView's range.

With relative indexing I get:

import torch
from nvfuser import FusionDefinition

inputs = [
   torch.randn(4, 4, 4, device="cuda"),
]

class InputError(FusionDefinition):
   def definition(self):
       self.t0 = self.from_pytorch(inputs[0])
       self.t1 = self.ops.sum(self.t0, axis=-1)
       self.add_output(self.t1)

   def schedule(self):
       self.sched.merge(fd.t1, -1),

fd = InputError()
_ = fd.execute(inputs)

Error:

RuntimeError: Merging IterDomains requires that their iteration types match. Outer: iS3{i0}, Inner: rS5{i2}

[kevinstephano]

Closing, old.