Before submitting an issue, please make sure it hasn't been already addressed by searching through the existing and past issues.

Describe the bug

Found that after upgrading to 0.40 tag a model conversion workflow which previously worked began failing. This is due to the additional input validation checks added to reference runner:

.

When model input is something like ['batch', 1, 1, 1], input_shape in ReferenceRunner becomes [0, 1, 1, 1]. Only dim_value field is being checked, which does not exist for a dynamic input (instead it is dim_param field), this results in 0 value for all dynamic inputs. This is later checked exactly against the incoming calibration data and results in input validation failing.

A side note, args for calibration_data in convert_to_mixed_precision are just "str" | None.

ReferenceRunner does support passing an OrderedDict

It is nicer as a user to just pass through a Dict in-memory rather than creating a file just to pass the path along, updating the calibration_data args to match what ReferenceRunner can use would be nice.

Steps/Code to reproduce bug

import numpy as np
import onnx
from modelopt.onnx.autocast.convert import convert_to_mixed_precision

input = onnx.helper.make_tensor_value_info(
"input", onnx.TensorProto.FLOAT, ["batch", 1]
)
output = onnx.helper.make_tensor_value_info(
"output", onnx.TensorProto.FLOAT, ["batch", 1]
)

node = onnx.helper.make_node(
"Relu",
["input"],
["output"],
)
graph = onnx.helper.make_graph(
nodes=[node],
name="foo",
inputs=[input],
outputs=[output],
initializer=[],
)
model = onnx.helper.make_model(
graph,
)
onnx.save(model, "foo.onnx")
input_arr = np.ones((1, 1),dtype=np.float32)
np.savez("calibration_data.npz", input=input_arr)
convert_to_mixed_precision("foo.onnx", calibration_data="calibration_data.npz")
`

Expected behavior

Expect the above reproducer to pass, currently get ValueError on the dimension check
ValueError: Input shape from 'input' does not match provided input shape: [0, 1] vs [1, 1]. Please make sure that your calibration data matches the ONNX input shapes

Who can help?

• ?

System information

• Container used (if applicable): n/a
• OS (e.g., Ubuntu 22.04, CentOS 7, Windows 10): SUSE Linux Enterprise Server 15 SP6
• CPU architecture (x86_64, aarch64): x86_64
• GPU name (e.g. H100, A100, L40S): NVIDIA RTX A4000
• GPU memory size: 15.0 GB
• Number of GPUs: 1
• Library versions (if applicable):
  • Python: 3.12.11
  • ModelOpt version or commit hash: 0.40.0
  • CUDA: 13.0
  • PyTorch: n/a
  • Transformers: n/a
  • TensorRT-LLM: ?
  • ONNXRuntime: 1.23.2
  • TensorRT: 10.13