jikechao opened a new issue, #14998:
URL: https://github.com/apache/tvm/issues/14998

   For the op `torch.nn.functional.avg_pool1d()`, if the attribute 
`ceil_mode=True`, the PyTorch and TVM give different output results and the 
output_shape is different. 
   
   For [pytorch official documentation], we can know that: 
   
   **"ceil_mode – when True, will use ceil instead of floor to compute the 
output shape. Default: False"**.
   
   To figure out this exception. I further check the source code in Lines 66-78 
in the file: [src/relay/op/nn/pooling.cc 
](https://github.com/apache/tvm/blob/main/src/relay/op/nn/pooling.cc)
   
   
![image](https://github.com/apache/tvm/assets/29506758/c73938f1-56ad-43db-be77-7722675c5aab)
   
   It seems that the source code takes into consideration about handling the 
`ceil_mode=True`
   
   
   
   ### Expected behavior
   PyTorch and TVM have the same inference results
   
   ### Actual behavior
   
![image](https://github.com/apache/tvm/assets/29506758/d69a3d74-e4c8-41e7-a4e9-f8383c540b01)
   
   
   
   ### Environment
   TVM: 0.13.dev0
   PyTorch: 1.13.1+cu117
   
   
   ### Steps to reproduce
   ```
   import torch
   from tvm import relay
   import tvm
   import numpy as np
   from torch.nn import Module
   
   para_0 = torch.randn([1, 6, 4], dtype=torch.float64)
   class avg_pool1d(Module):
           def forward(self, *args):
               return torch.nn.functional.avg_pool1d(args[0], kernel_size=[1], 
stride=2, ceil_mode=True)
   m = avg_pool1d().float().eval()
   
   
   input_data = para_0
   
   torch_outputs = m(input_data)
   
   trace = torch.jit.trace(m, input_data)
   input_shapes = [('input0', torch.Size([1, 6, 4]))]
   
   mod, params = relay.frontend.from_pytorch(trace, input_shapes)
   #print(mod)
   with tvm.transform.PassContext(opt_level=3):
       exe = relay.create_executor('graph', mod=mod, params=params, 
device=tvm.device('llvm', 0), target='llvm').evaluate()
   
   input_tvm = {'input0': np.array(input_data, dtype='float64')}
   tvm_outputs = exe(**input_tvm).asnumpy()
   
   np.testing.assert_allclose(torch_outputs, tvm_outputs, rtol=1e-3, atol=1e-3)
   ```
   
   ### Triage
   
   * frontend:pytorch
   
   
   


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