dutZ1855 opened a new issue, #18592: URL: https://github.com/apache/tvm/issues/18592
from this model <img width="218" height="281" alt="Image" src="https://github.com/user-attachments/assets/6428b18f-551e-44b8-8a59-492141feb000"; /> ### Expected behavior ONNX `Min` supports NumPy-style broadcasting across inputs. For this repro: - `relu_1` has shape `(30,)` - `atan` has shape `(1,)` - `Min(relu_1, atan)` should broadcast and produce shape `(30,)` [ONNX Broadcasting](https://github.com/onnx/onnx/blob/main/docs/Broadcasting.md ) ### Actual behavior ONNX Runtime runs the model successfully and produces a valid output. ``` [ort] OK - out shape= (30,) dtype= float32 min/max= (1.4074832, 1.4074832) ``` TVM Relax ONNX frontend fails during import (`from_onnx`) with: `Concat expects the input tensors to have the same shape on every dimension except the one indicated by the input axis. However, the input contains tensors whose shapes on dimension 1 is T.int64(30) and T.int64(1)` ### Environment Operating System：Ubuntu 22.04.4 LTS TVM version：0.23.0dev pytorch version:2.9.1 ort version:1.23.2 onnx version: 1.20.0 python:3.11.14 ### Steps to reproduce This bug can be reproduced by the following code with the model in the attachment. As shown in the code, the model can be executed by onnxruntime. However, TVM crashes when importing the model. ``` from __future__ import annotations import argparse import pickle import sys from pathlib import Path import numpy as np import onnx def _load_oracle_inputs(path: Path) -> dict[str, np.ndarray]: obj = pickle.loads(path.read_bytes()) inp = obj.get("input") if not isinstance(inp, dict): raise ValueError(f"oracle.pkl missing 'input' dict: keys={list(obj.keys())}") return {k: np.array(v) for k, v in inp.items()} def main() -> int: ap = argparse.ArgumentParser() ap.add_argument("--model", type=Path, default=Path("model.onnx")) ap.add_argument("--oracle", type=Path, default=Path("oracle.pkl")) args = ap.parse_args() model_path = args.model.resolve() oracle_path = args.oracle.resolve() onnx_model = onnx.load(model_path.as_posix()) inputs = _load_oracle_inputs(oracle_path) print("[model]", model_path) print("[oracle]", oracle_path) print("[inputs]", {k: v.shape for k, v in inputs.items()}) # ORT: expected OK try: import onnxruntime as ort # type: ignore ort_session = ort.InferenceSession(onnx_model.SerializeToString(), providers=["CPUExecutionProvider"]) ort_out = ort_session.run(None, inputs) out_names = [o.name for o in ort_session.get_outputs()] print("[ort] OK") for name, val in zip(out_names, ort_out): arr = np.array(val) print(" -", name, "shape=", arr.shape, "dtype=", arr.dtype, "min/max=", (arr.min(), arr.max())) except Exception as e: print("[ort] FAILED:", type(e).__name__, e) return 1 # TVM: expected FAIL during from_onnx try: import tvm # type: ignore from tvm import relax # type: ignore from tvm.relax.frontend.onnx import from_onnx # type: ignore shape_dict = {k: v.shape for k, v in inputs.items()} print("[tvm] shape_dict:", shape_dict) tvm_mod = from_onnx(onnx_model, shape_dict=shape_dict) if isinstance(tvm_mod, (list, tuple)): tvm_mod = tvm_mod[0] # If it unexpectedly imports, try build+run. tgt = tvm.target.Target("llvm") pipeline = relax.pipeline.get_default_pipeline(tgt) with tvm.transform.PassContext(opt_level=3): ex = relax.build(tvm_mod, target=tgt, relax_pipeline=pipeline) vm = relax.VirtualMachine(ex, tvm.cpu()) vm.set_input("main", **inputs) vm.invoke_stateful("main") out = vm.get_outputs("main") print("[tvm] UNEXPECTED: succeeded. output type:", type(out)) except Exception as e: print("[tvm] EXPECTED FAIL:", type(e).__name__, str(e).splitlines()[0]) return 0 return 0 if __name__ == "__main__": raise SystemExit(main()) ``` [model.zip](https://github.com/user-attachments/files/24255783/model.zip) ### Triage * 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] --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
