[GitHub] [incubator-tvm] nolanliou commented on issue #6268: TVMError: Check failed: it != type_definitions.end(): There is no definition of static_tensor_float32_*

[GitBox <gi...@apache.org>]

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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