[GitHub] [tvm] AndrewZhaoLuo opened a new issue #8058: Type checking sometimes allows negative dimensions in shape

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

AndrewZhaoLuo opened a new issue #8058:
URL: https://github.com/apache/tvm/issues/8058


   The main example I have is here where avg pool expects an output with negative dimensions in the shape:
   
   ```
   from tvm.relay.testing import densenet
   
   mod, mod_params = densenet.get_workload(classes=5, batch_size=1, image_shape=(1, 224, 224))
   mod_params["data"] = np.random.uniform(-10, 10, (1, 1, 224, 224)).astype("float32")
   
   infer_type_pass = InferType()
   out = infer_type_pass(mod)
   body_typed = out["main"].body
   
   print(body_typed)
   ```
   
   Which produces output including:
   
   `%487 = nn.avg_pool2d(%486, pool_size=[7, 7], padding=[0, 0, 0, 0]) /* ty=Tensor[(1, 1, -5, -5), float32] */;`
   
   *What is expected*:
   
   The type checker will yell at you if it detects nonsensical shapes.
   
   *What can be done*:
   
   This can be done by having the shape relation function for each op check for negative shapes. Alternatively we can determine that negative shapes are inherently nonsensical for every operation and put the check more in the infer type pass.


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