coffezhou opened a new issue, #18594: URL: https://github.com/apache/tvm/issues/18594
### Expected behavior The AveragePool operator in TVM should produce right shape. ### Actual behavior For the following model, <img width="810" height="580" alt="Image" src="https://github.com/user-attachments/assets/8d018e15-e353-4e46-82ad-3a4a55e9e4fc"; /> when ceil_mode = 1, the shapes of maxpool_output and avgpool_output are as follows: ``` maxpool_output (1, 3, 17, 17) avgpool_output (1, 3, 9, 9) maxpool_output (1, 3, 17, 17) avgpool_output (1, 3, 10, 10) ``` For the documents of [AveragePool](https://onnx.ai/onnx/operators/onnx__AveragePool.html), if ceil_mode = 1, the shape can be calculated by the following formula: ``` output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i] + 1) ``` that is, ceil((17+1-1*(2-1)-1)/2 + 1) = 9, which indicates that TVM produces wrong shape of average_output. I also try to set ceil_mode = 0, the results are as follows: ``` maxpool_output (1, 3, 17, 17) avgpool_output (1, 3, 9, 9) maxpool_output (1, 3, 17, 17) avgpool_output (1, 3, 9, 9) ``` In this case, the results are right. ### Environment OS: Ubuntu 20.04 TVM: 0.23.dev0 (f4e28d315) onnxruntime: 1.23.2 ### Steps to reproduce This bug can be reproduced by the following code with the model in the attachment. ```python from typing import Dict, List, Literal, Optional import sys import os import numpy as np import onnx import onnxruntime from onnx import ModelProto, TensorProto, helper import tvm import tvm.testing from tvm import relax from tvm.relax.frontend.onnx import from_onnx import argparse import pickle def test( model: ModelProto, inputs: Optional[Dict[str, np.ndarray]] = None, ir_version: int = 8, opset: int = 14, ) -> None: # Configure model format. if ir_version is not None: model.ir_version = ir_version if opset is not None: model.opset_import[0].version = opset with open("inputs.pkl", 'rb') as fp: inputs = pickle.load(fp) # Run the model through onnx to get the expected result. try: ort_session = onnxruntime.InferenceSession( model.SerializeToString(), providers=["CPUExecutionProvider"] ) ort_output = ort_session.run([], inputs) except Exception as e: print(e) print("This model cannot be executed by onnxruntime!") sys.exit(1) print('maxpool_output', ort_output[0].shape) print('avgpool_output', ort_output[1].shape) tvm_model = from_onnx(model, opset=opset, keep_params_in_input=True) tvm_model = relax.transform.DecomposeOpsForInference()(tvm_model) tvm_model = relax.transform.LegalizeOps()(tvm_model) tvm_model, params = relax.frontend.detach_params(tvm_model) with tvm.transform.PassContext(opt_level=3): ex = tvm.compile(tvm_model, target="llvm") vm = relax.VirtualMachine(ex, tvm.cpu()) input_list = [ inputs[key.name_hint] for key in tvm_model["main"].params if key.name_hint in inputs ] if params: input_list += params["main"] # Run model and check outputs. vm.set_input("main", *input_list) vm.invoke_stateful("main") tvm_output = vm.get_outputs("main") print('maxpool_output', tvm_output[0].shape) print('avgpool_output', tvm_output[1].shape) if __name__ == "__main__": onnx_model = onnx.load("22.onnx") test(onnx_model) ``` <!-- Failed to upload "testcase.zip" --> In the attachment, 11.onnx is the model with ceil_mode=1, 22.onnx is ceil_mode=0. ### Triage Please refer to the list of label tags [here](https://github.com/apache/tvm/wiki/Issue-Triage-Labels) to find the relevant tags and add them below in a bullet format (example below). * 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]
