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') ``` -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
