Mohamad11Dab opened a new issue, #17987: URL: https://github.com/apache/tvm/issues/17987
Thanks for participating in the TVM community! We use https://discuss.tvm.ai for any general usage questions and discussions. The issue tracker is used for actionable items such as feature proposals discussion, roadmaps, and bug tracking. You are always welcomed to post on the forum first :smile_cat: Issues that are inactive for a period of time may get closed. We adopt this policy so that we won't lose track of actionable issues that may fall at the bottom of the pile. Feel free to reopen a new one if you feel there is an additional problem that needs attention when an old one gets closed. ### Expected behavior Consistent Result between TVM and ONNXRuntime ### Actual behavior `––––– MISMATCH DETECTED ––––– Not equal to tolerance rtol=0.01, atol=0.001 Mismatched elements: 65536 / 65536 (100%) Max absolute difference: 0.00585903 Max relative difference: 0.8311606 x: array([[[[0.00119, 0.00119, 0.00119, ..., 0.00119, 0.00119, 0.00119], [0.00119, 0.00119, 0.00119, ..., 0.00119, 0.00119, 0.00119], [0.00119, 0.00119, 0.00119, ..., 0.00119, 0.00119, 0.00119],... y: array([[[[0.007049, 0.007049, 0.007049, ..., 0.007049, 0.007049, 0.007049], [0.007049, 0.007049, 0.007049, ..., 0.007049, 0.007049,...` ### Environment TVM: 0.17.0 ONNXRuntime: 1.16.3 ### Steps to reproduce ```python import torch import torch.nn as nn import onnx import onnxruntime as ort import numpy as np import tempfile import torch.nn.functional as F import tvm from tvm import relay from tvm.contrib import graph_executor from numpy.testing import assert_allclose class SimpleBugModel(nn.Module): def __init__(self): super().__init__() self.input_conv = nn.Conv2d(in_channels=3, out_channels=16, kernel_size=1) self.block5 = nn.InstanceNorm2d(num_features=16) def forward(self, x): x = self.input_conv(x) x=F.softplus(x) x = torch.tanh(x) x = torch.ceil(x) x = F.gelu(x) x = F.interpolate(x, scale_factor=2.0, mode='nearest') x = self.block5(x) return x def main(): model = SimpleBugModel() model.eval() dummy = torch.randn(1, 3, 32, 32, dtype=torch.float32) with tempfile.NamedTemporaryFile(suffix=".onnx", delete=False) as tmp: onnx_path = tmp.name torch.onnx.export( model, dummy, onnx_path, opset_version=19, input_names=["input"], output_names=["output"], ) ## run ONNX-Runtime ort_sess = ort.InferenceSession(onnx_path, providers=["CPUExecutionProvider"]) ort_out = ort_sess.run(None, {"input": dummy.numpy()})[0] ### compile & run TVM onnx_model = onnx.load(onnx_path) shape_dict = {"input": dummy.numpy().shape} mod, params = relay.frontend.from_onnx(onnx_model, shape_dict, freeze_params=True) with tvm.transform.PassContext(opt_level=4): lib = relay.build(mod, target="llvm", params=params) m = graph_executor.GraphModule(lib["default"](tvm.cpu())) m.set_input("input", tvm.nd.array(dummy.numpy())) m.run() tvm_out = m.get_output(0) tvm_out = tvm_out.numpy() try: assert_allclose(ort_out, tvm_out, rtol=1e-2, atol=1e-3, equal_nan=True) except AssertionError as e: print("––––– MISMATCH DETECTED –––––") print(e) # just the assertion message except Exception as e: print("––––– UNEXPECTED ERROR DURING COMPARISON –––––") print(f"{type(e).__name__}: {e}") if __name__ == "__main__": main() ``` -- 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]
