I am confused why `relay.floor_mod` allows variable of type `float64` as
parameter. So I wrote the following script:
```
import tvm
from tvm import relay
from tvm.contrib import graph_runtime
import numpy as np
def vmobj_to_list(o, dtype="float64"):
if isinstance(o, tvm.nd.NDArray):
return [o]
elif isinstance(o, tvm.runtime.container.ADT):
result = []
for f in o:
result.extend(vmobj_to_list(f, dtype))
return result
else:
return o
var_0 = relay.var("var_0", dtype = "float64", shape = ())
var_1 = relay.exp(var_0)
const_2 = relay.const([787.644532], dtype="float64")
var_3 = relay.floor_mod(var_1, const_2)
tuple = relay.Tuple([var_3])
F = relay.Function([var_0], tuple)
mod = tvm.IRModule()
mod['main'] = F
mod = relay.transform.InferType()(mod)
graph, lib, params = relay.build(mod, target='llvm')
module = graph_runtime.create(graph, lib, tvm.device('llvm',0))
intrp = relay.build_module.create_executor('graph', mod,
tvm.device('cuda',0),'cuda')
input_0= np.array(415.748715, dtype='float64')
module.set_input('var_0', input_0)
module.set_input(**params)
module.run()
res0_0 = module.get_output(0).asnumpy()
res1 = intrp.evaluate()(input_0)
res1 = vmobj_to_list(res1)
res1_0 = res1[0].asnumpy()
np.testing.assert_allclose(res0_0 ,res1_0, atol=1e-3, rtol=1e-3)
```
A difference between `res0_0` and `res1_0` then was caught by
`assert_allclose`. I think it's due to the fact that `floor_mod` on `float64`
is undefined, which would be interpreted in different ways by LLVM and CUDA. If
it's the case, why TVM still allow `floor_mod` to take `float64` variable as
parameter?
BTW, this is the message thrown by `np.testing.assert_allclose(res0_0 ,res1_0,
atol=1e-3, rtol=1e-3)`:
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