jikechao opened a new issue, #15284: URL: https://github.com/apache/tvm/issues/15284
### Actual behavior  ### Steps to reproduce ``` import torch from tvm import relay import tvm import numpy as np from torch.nn import Module input_data = torch.randn([1, 2048], dtype=torch.float32) para_1 = torch.randn([1000, 2048], dtype=torch.float32) para_2 = torch.randn([1000], dtype=torch.float32) class linear(Module): def forward(self, *args): return torch.nn.functional.linear(args[0], para_1,para_2,) m = linear().float().eval() torch_outputs = m(input_data) trace = torch.jit.trace(m, input_data) input_shapes = [('input0', torch.Size([1, 2048]))] 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='float32')} tvm_outputs = exe(**input_tvm).asnumpy() np.testing.assert_allclose(torch_outputs, tvm_outputs, rtol=1e-5, atol=1e-5) # the treshold is the same with that in equipped test cases ``` ### Triage * needs-triage * frontend:pytorch -- 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]
