Summary
The codebase has 5+ independent random tensor generation implementations that should be consolidated onto modelkit.core.model_input_generator.
Duplication Inventory
| Location |
Status |
core/model_input_generator.py |
CANONICAL - keep |
onnx/io.py:InputTensorSpec.to_tensor() |
Keep (different scope) |
commands/perf.py |
FIXED - now uses model_input_generator |
data/random_dataset.py |
LEGACY - superseded by datasets/random_dataset.py |
tests/optim/conftest.py:generate_random_inputs() |
Should migrate |
tests/optim/pipes/test_pipe_fusion_direct.py |
Should migrate |
Remaining Work
- Remove legacy
data/random_dataset.py
- Migrate test input helpers to use
generate_dummy_inputs_from_specs()
- Integrate with
winml.io.outputs for richer specs from model I/O config
Summary
The codebase has 5+ independent random tensor generation implementations that should be consolidated onto
modelkit.core.model_input_generator.Duplication Inventory
core/model_input_generator.pyonnx/io.py:InputTensorSpec.to_tensor()commands/perf.pydata/random_dataset.pytests/optim/conftest.py:generate_random_inputs()tests/optim/pipes/test_pipe_fusion_direct.pyRemaining Work
data/random_dataset.pygenerate_dummy_inputs_from_specs()winml.io.outputsfor richer specs from model I/O config