nolanliou commented on issue #6268:
URL: https://github.com/apache/incubator-tvm/issues/6268#issuecomment-685459847
Sample code to reproduce the problem of inputs' type not matching.
```
import numpy as np
import torch
import tvm
from tvm import relay
import tvm.contrib.graph_runtime as runtime
def fn(x):
y = (x - 1).long()
y = y.to(dtype=torch.float32)
y = y[:, 0]
return y
jit = torch.jit.trace(fn, (torch.ones(4, 5, dtype=torch.int64),))
torch.jit.save(jit, "test.jit")
jit = torch.jit.load("test.jit", map_location=torch.device('cpu'))
jit.eval()
inputs = torch.randint(low=0, high=10, size=(4, 5), dtype=torch.int64)
print(jit(inputs))
shape_list = [('input', [4, 5])]
mod, params = relay.frontend.pytorch.from_pytorch(jit,
shape_list,
default_dtype="float32")
with tvm.transform.PassContext(opt_level=3):
json, lib, params = relay.build(mod, target="llvm", params=params)
ctx = tvm.cpu(0)
inputs = tvm.nd.array(inputs.cpu().numpy().astype(np.int64), ctx)
runtime = tvm.contrib.graph_runtime.create(json, lib, ctx)
runtime.set_input(**params)
runtime.set_input('input', inputs)
runtime.run()
tvm_result = runtime.get_output(0).asnumpy()
print(tvm_result)
```
There is no type of information in the JIT file, seems TVM cannot support
the situation (load from JIT file) yet.
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