[PR] [Unity] [Bugfix] Fix bug in interpolate operator's default mode parameter in PyTorch frontend [tvm]

["Thrsu (via GitHub)" <gi...@apache.org>]

Thrsu opened a new pull request, #15933:
URL: https://github.com/apache/tvm/pull/15933

   This PR fixes a bug in the interpolate operator of the PyTorch frontend in TVM. The bug was caused by incorrectly using the `method` keyword instead of the `mode` keyword when retrieving the default value for the mode parameter. This resulted in incorrect computation of `interpolate` results.
   
   This bug can be reproduced by the below script:
   ```python
   import torch
   from torch import fx
   from torch.nn import Module
   import tvm
   import tvm.testing
   from tvm import relax
   from tvm.relax.frontend.torch import from_fx
   
   input_data = torch.randn([1, 2, 4, 4], dtype=torch.float32)
   class interpolate(Module):
       def forward(self, input):
           return torch.nn.functional.interpolate(input, size=None, scale_factor=2.0, mode='bilinear', align_corners=False,)
   
   model = interpolate().float()
   input_data = [input_data]
   input_names = [f"input{idx}" for idx, _ in enumerate(input_data)]
   input_info = list(zip([list(inp.shape) for inp in input_data], [str(inp.dtype) for inp in input_data]))
   fx_model : torch.fx.GraphModule = fx.symbolic_trace(model)
   with torch.no_grad():
       mod = from_fx(fx_model, input_info)
   
   if torch.cuda.is_available():
       model = model.cuda()
       torch_input = [inp.cuda() for inp in input_data]
   
   with torch.no_grad():
       torch_outputs = model(*[input.clone() for input in input_data])
   torch_outputs = (torch_outputs.cpu().numpy(),)
   
   compiled_input = dict(zip(input_names, [inp.clone().cpu().numpy() for inp in input_data]))
   tvm_input = {}
   for name, inp in compiled_input.items():
       tvm_input[name] = tvm.nd.array(inp)
       target = tvm.target.Target("llvm", host="llvm")
       mod = relax.transform.LegalizeOps()(mod)
       ex = relax.build(mod, target)
       vm = relax.VirtualMachine(ex, tvm.cpu())
   
       tvm_outputs = vm["main"](*[tvm_input[name] for name in input_names])
       tvm_outputs = [tvm_outputs]
   
   for i, torch_output in enumerate(torch_outputs):
       output = tvm_outputs[i].numpy()
       tvm.testing.assert_allclose(torch_output, output, rtol=1e-5, atol=1e-5)
   ```
   
   And here is the traceback information:
   ```
   Traceback (most recent call last):
   ...  
       tvm.testing.assert_allclose(torch_output, output, rtol=1e-5, atol=1e-5)
     File "/workplace/software/tvm/tvm/python/tvm/testing/utils.py", line 120, in assert_allclose
       np.testing.assert_allclose(actual, desired, rtol=rtol, atol=atol, verbose=True)
     File "/workplace/software/miniconda3/envs/tflite/lib/python3.8/site-packages/numpy/testing/_private/utils.py", line 1527, in assert_allclose
       assert_array_compare(compare, actual, desired, err_msg=str(err_msg),
     File "/workplace/software/miniconda3/envs/tflite/lib/python3.8/site-packages/numpy/testing/_private/utils.py", line 844, in assert_array_compare
       raise AssertionError(msg)
   AssertionError: 
   Not equal to tolerance rtol=1e-05, atol=1e-05
   
   Mismatched elements: 120 / 128 (93.8%)
   Max absolute difference: 1.2100129
   Max relative difference: 39.08069
    x: array([[[[-0.407674, -0.087851,  0.551796,  0.303579, -0.832501,
             -0.630166,  0.910585,  1.680961],
            [-0.152558, -0.054653,  0.141159, -0.0371  , -0.589427,...
    y: array([[[[-0.407674, -0.407674,  0.871619,  0.871619, -1.400542,
             -1.400542,  1.680961,  1.680961],
            [-0.407674, -0.407674,  0.871619,  0.871619, -1.400542,...
   ```


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Re: [PR] [Unity] [Bugfix] Fix bug in interpolate operator's default mode parameter in PyTorch frontend [tvm]

["Hzfengsy (via GitHub)" <gi...@apache.org>]

Hzfengsy merged PR #15933:
URL: https://github.com/apache/tvm/pull/15933


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