Re: [I] [Bug] [Relay] Wrong inference reusult about Pytorch model with dropout layer [tvm]

["hxzd5568 (via GitHub)" <gi...@apache.org>]

hxzd5568 commented on issue #15005:
URL: https://github.com/apache/tvm/issues/15005#issuecomment-1988540791

   The issue may be caused by PyTorch. The TVM is right. The dropout in the evalution mode should be set to be disabled. (see the design aim at https://pytorch.org/docs/stable/_modules/torch/nn/modules/dropout.html#Dropout2d)
   
   Dropout is designed to be only applied during training, so the output and the input should be the same. 
   And from the experiment, TVM models' output and input are the same.
   
   
   ```
   import torch
   from tvm import relay
   import tvm
   import numpy as np
   from torch.nn import Module
   
   input_data = torch.randn([5], dtype=torch.float64)
   class alpha_dropout(Module):
           def forward(self, *args):
               return torch.nn.functional.alpha_dropout(args[0], 0.2,training=True)
   m = alpha_dropout().float().eval()
   
   # TVM is right, but pytorch is wrong.
   
   torch_outputs = m(input_data)
   trace = torch.jit.trace(m, input_data)
   input_shapes = [('input0', torch.Size([5]))]
   
   mod, params = relay.frontend.from_pytorch(trace, input_shapes)
   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()
   try:
       np.testing.assert_allclose(input_data, tvm_outputs, rtol=1e-3, atol=1e-3)
       print('tvm result and the truth are the same')
   except:
       print('tvm & truth are diff')
        
   
   try:
       np.testing.assert_allclose(torch_outputs, tvm_outputs, rtol=1e-3, atol=1e-3)
       print('tvm result and the torch result are the same')
   except:
       print('tvm & torch are diff')
   
   
   
   
   # =================== for functional.dropout =============
   
   class dropout1(Module):
           def forward(self, *args):
               return torch.nn.functional.dropout(args[0], 0.2)
   m = dropout1().float().eval()
   
   torch_outputs = m(input_data)
   trace = torch.jit.trace(m, input_data)
   input_shapes = [('input0', torch.Size([5]))]
   
   mod, params = relay.frontend.from_pytorch(trace, input_shapes)
   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()
   try:
       np.testing.assert_allclose(input_data, tvm_outputs, rtol=1e-3, atol=1e-3)
       print('tvm result and the truth are the same')
   except:
       print('tvm & truth are diff')
        
   
   try:
       np.testing.assert_allclose(torch_outputs, tvm_outputs, rtol=1e-3, atol=1e-3)
       print('tvm result and the torch result are the same')
   except:
       print('tvm & torch are diff')
   
   
   # =================== disabling the 'training' option can solve the issue =============
       
   input_data = torch.randn([5], dtype=torch.float64)
   class alpha_dropout(Module):
           def forward(self, *args):
               return torch.nn.functional.alpha_dropout(args[0], 0.2,training=False)
   
   m = alpha_dropout().float().eval()
   torch_outputs = m(input_data)
   trace = torch.jit.trace(m, input_data)
   input_shapes = [('input0', torch.Size([5]))]
   
   mod, params = relay.frontend.from_pytorch(trace, input_shapes)
   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()
   try:
       np.testing.assert_allclose(input_data, tvm_outputs, rtol=1e-3, atol=1e-3)
       print('tvm result and the truth are the same')
   except:
       print('tvm & truth are diff')
        
   
   try:
       np.testing.assert_allclose(torch_outputs, tvm_outputs, rtol=1e-3, atol=1e-3)
       print('tvm result and the torch result are the same')
   except:
       print('tvm & torch are diff')
   
   ```


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