[BugFix][TVMScript] Fix invalid f-string format spec causing TypeError on Python 3.14 (#19362)
## Problem
On Python 3.14, any use of TVMScript raises a `TypeError` before the
module body is even parsed:
```
TypeError: unsupported format string passed to type.__format__
```
The traceback points to
`python/tvm/script/parser/core/diagnostics.py:120`:
```python
raise TypeError(f"Source for {obj:!r} not found")
```
## Root Cause
`{obj:!r}` is an invalid f-string expression. The `:` introduces a
`format_spec`, so `!r` is passed to `type.__format__` as a format string
— which it does not support.
The intended syntax for a `repr()` conversion is `{obj!r}` (no colon).
Python 3.14 re-implemented f-string parsing under [PEP
701](https://peps.python.org/pep-0701/) and now strictly validates
format specs, surfacing this latent bug. Python 3.10–3.13 silently
passed the invalid spec to `__format__` and happened not to raise in
most code paths, so the bug went unnoticed.
## Fix
```diff
- raise TypeError(f"Source for {obj:!r} not found")
+ raise TypeError(f"Source for {obj!r} not found")
```
One character change. Valid across all Python versions >= 3.6.
## Testing
Verified on Python 3.14.2 (darwin/arm64):
- TVMScript `ir_module` + `prim_func` parses and compiles correctly
after the fix
- Full TVMScript test suite: **628 passed, 1 xfailed** (the 1 failure in
`test_tvmscript_roundtrip.py::test_roundtrip[relax_symbolic_size_var]`
is pre-existing and unrelated to this change)Documentation | Contributors | Community | Release Notes
Apache TVM is an open machine learning compilation framework, following the following principles:
TVM is licensed under the Apache-2.0 license.
Check out the TVM Documentation site for installation instructions, tutorials, examples, and more. The Getting Started with TVM tutorial is a great place to start.
TVM adopts the Apache committer model. We aim to create an open-source project maintained and owned by the community. Check out the Contributor Guide.
TVM started as a research project for deep learning compilation. The first version of the project benefited a lot from the following projects:
Since then, the project has gone through several rounds of redesigns. The current design is also drastically different from the initial design, following the development trend of the ML compiler community.
The most recent version focuses on a cross-level design with TensorIR as the tensor-level representation and Relax as the graph-level representation and Python-first transformations. The project's current design goal is to make the ML compiler accessible by enabling most transformations to be customizable in Python and bringing a cross-level representation that can jointly optimize computational graphs, tensor programs, and libraries. The project is also a foundation infra for building Python-first vertical compilers for domains, such as LLMs.