[Bug][Frontend][ONNX] Tile operator incorrectly requires 'repeats' to be a constant tensor

[huenwei-arch]

Expected behavior

According to the ONNX Tile specification (Opset 13+), the repeats input is a 1D tensor of integers. The specification allows repeats to be a Graph Input (a dynamic tensor provided at runtime). TVM should be able to handle this by mapping it to a dynamic Relax operator that accepts a tensor for the repeats argument.

Actual behavior

The TVM ONNX frontend incorrectly mandates that repeats must be a compile-time constant (Initializer).

• Static Mode: When repeats is an Initializer, the conversion succeeds, and the output matches ONNX Runtime.
• Dynamic Mode: When repeats is a Graph Input, the conversion fails with an explicit error: Dynamic reps for Tile are supported yet.. Additionally, the BlockBuilder is destroyed with remaining blocks due to the unhandled exception during conversion.

Reproduction Log:

>>> Testing STATIC repeats mode...
  [ORT] Model is valid. Output shape: (4, 6)
  [TVM] Conversion Successful!
  [Result] Success: Shapes match (4, 6)

>>> Testing DYNAMIC repeats mode...
  [ORT] Model is valid. Output shape: (4, 6)
  [TVM] Conversion Failed!
  Error Message: Dynamic reps for Tile are supported yet. src/relax/ir/block_builder.cc:65: Warning: BlockBuilder destroyed with remaining blocks!

Environment

OS:
Linux-5.15.0-139-generic-x86_64-with-glibc2.31

TVM Version:
0.24.dev0

ONNX Version:
1.20.1

ONNX Runtime Version:
1.24.1

NumPy Version:
2.4.2

Steps to reproduce

import onnx
import numpy as np
import onnxruntime as ort
from onnx import helper, TensorProto
import tvm
from tvm import relax
from tvm.relax.frontend.onnx import from_onnx

def create_tile_model(is_dynamic):
    input_shape = [2, 3]
    repeats_val = [2, 2]
    
    x_info = helper.make_tensor_value_info('X', TensorProto.FLOAT, input_shape)
    inputs = [x_info]
    initializers = []
    
    if is_dynamic:
        r_info = helper.make_tensor_value_info('R', TensorProto.INT64, [2])
        inputs.append(r_info)
        tile_inputs = ['X', 'R']
        model_name = 'DynamicTileModel'
    else:
        r_init = helper.make_tensor('R_const', TensorProto.INT64, [2], repeats_val)
        initializers.append(r_init)
        tile_inputs = ['X', 'R_const']
        model_name = 'StaticTileModel'

    node = helper.make_node('Tile', inputs=tile_inputs, outputs=['Y'])
    graph = helper.make_graph(
        [node], model_name, inputs,
        [helper.make_tensor_value_info('Y', TensorProto.FLOAT, [None, None])],
        initializer=initializers
    )
    return helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])

def test_tvm_tile(is_dynamic):
    mode = "DYNAMIC" if is_dynamic else "STATIC"
    print(f"\n>>> Testing {mode} repeats mode...")
    
    model = create_tile_model(is_dynamic)
    x_np = np.ones([2, 3]).astype(np.float32)
    r_np = np.array([2, 2], dtype=np.int64)

    # 1. Verify with ONNX Runtime
    try:
        sess = ort.InferenceSession(model.SerializeToString())
        feeds = {'X': x_np}
        if is_dynamic: feeds['R'] = r_np
        ort_res = sess.run(None, feeds)[0]
        print(f"  [ORT] Model is valid. Output shape: {ort_res.shape}")
    except Exception as e:
        print(f"  [ORT] Model error: {e}")
        return

    # 2. Verify with TVM
    try:
        tvm_mod = from_onnx(model)
        print(f"  [TVM] Conversion Successful!")
        
        target = tvm.target.Target("llvm")
        exe = relax.build(tvm_mod, target)
        vm = relax.VirtualMachine(exe, tvm.cpu())
        
        # Using positional arguments for VM call
        args = [tvm.runtime.tensor(x_np)]
        if is_dynamic: args.append(tvm.runtime.tensor(r_np))
        
        tvm_res = vm["main"](*args).numpy()
        np.testing.assert_allclose(ort_res, tvm_res)
        print(f"  [Result] Success: Shapes match {tvm_res.shape}")

    except Exception as e:
        print(f"  [TVM] Conversion Failed!")
        error_msg = str(e).splitlines()[-1]
        print(f"  Error Message: {error_msg}")

if __name__ == "__main__":
    test_tvm_tile(is_dynamic=False)
    test_tvm_tile(is_dynamic=True)

Triage

• relax:frontend:onnx
• needs-triage

cc @KJlaccHoeUM9l