jikechao opened a new issue, #14926: URL: https://github.com/apache/tvm/issues/14926
For the layer `InstanceNorm1d` or `InstanceNorm3d`, if attribute `track_running_stats` was set as `True`, TVM will give different inference results with PyTorch. ### Expected behavior For the same input data, TVM and PyTorch give the same inference results. ### Actual behavior  ### Steps to reproduce ``` import torch from tvm import relay import tvm import numpy as np m = torch.nn.InstanceNorm1d(3, track_running_stats=True).eval() input_data = [torch.randn([1, 3, 5], dtype=torch.float32)] torch_outputs = m(*[input_.clone() for input_ in input_data ]) trace = torch.jit.trace(m, input_data) input_shapes = [('input0', torch.Size([1,3,5]))] mod, params = relay.frontend.from_pytorch(trace, input_shapes) with tvm.transform.PassContext(opt_level=3): exe = relay.create_executor('aot', mod=mod, params=params, device=tvm.cpu(0), target='llvm').evaluate() input_tvm = {'input0': np.array(input_data[0], dtype='float32')} tvm_outputs = exe(**input_tvm).asnumpy() np.testing.assert_allclose(torch_outputs, tvm_outputs, rtol=1e-3, atol=1e-3) ``` ### Triage * frontend:torch * needs-triage -- 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]
