The code for Tier 2 is now only compiled when configured
with `--enable-experimental-jit[=yes|interpreter]`.
We drop support for `PYTHON_UOPS` and -`Xuops`,
but you can disable the interpreter or JIT
at runtime by setting `PYTHON_JIT=0`.
You can also build it without enabling it by default
using `--enable-experimental-jit=yes-off`;
enable with `PYTHON_JIT=1`.
On Windows, the `build.bat` script supports
`--experimental-jit`, `--experimental-jit-off`,
`--experimental-interpreter`.
In the C code, `_Py_JIT` is defined as before
when the JIT is enabled; the new variable
`_Py_TIER2` is defined when the JIT *or* the
interpreter is enabled. It is actually a bitmask:
1: JIT; 2: default-off; 4: interpreter.
Introduce a unified 16-bit backoff counter type (``_Py_BackoffCounter``),
shared between the Tier 1 adaptive specializer and the Tier 2 optimizer. The
API used for adaptive specialization counters is changed but the behavior is
(supposed to be) identical.
The behavior of the Tier 2 counters is changed:
- There are no longer dynamic thresholds (we never varied these).
- All counters now use the same exponential backoff.
- The counter for ``JUMP_BACKWARD`` starts counting down from 16.
- The ``temperature`` in side exits starts counting down from 64.
Changes to the function version cache:
- In addition to the function object, also store the code object,
and allow the latter to be retrieved even if the function has been evicted.
- Stop assigning new function versions after a critical attribute (e.g. `__code__`)
has been modified; the version is permanently reset to zero in this case.
- Changes to `__annotations__` are no longer considered critical. (This fixes gh-109998.)
Changes to the Tier 2 optimization machinery:
- If we cannot map a function version to a function, but it is still mapped to a code object,
we continue projecting the trace.
The operand of the `_PUSH_FRAME` and `_POP_FRAME` opcodes can be either NULL,
a function object, or a code object with the lowest bit set.
This allows us to trace through code that calls an ephemeral function,
i.e., a function that may not be alive when we are constructing the executor,
e.g. a generator expression or certain nested functions.
We will lose globals removal inside such functions,
but we can still do other peephole operations
(and even possibly [call inlining](https://github.com/python/cpython/pull/116290),
if we decide to do it), which only need the code object.
As before, if we cannot retrieve the code object from the cache, we stop projecting.
There are now at least two bytecodes that may attempt to optimize,
JUMP_BACK, and more recently, COLD_EXIT.
Only the JUMP_BACK was counting the attempt in the stats.
This moves that counter to uop_optimize itself so it should
always happen no matter where it is called from.
This undoes the *temporary* default disabling of the T2 optimizer pass in gh-115860.
- Add a new test that reproduces Brandt's example from gh-115859; it indeed crashes before gh-116028 with PYTHONUOPSOPTIMIZE=1
- Re-enable the optimizer pass in T2, stop checking PYTHONUOPSOPTIMIZE
- Rename the env var to disable T2 entirely to PYTHON_UOPS_OPTIMIZE (must be explicitly set to 0 to disable)
- Fix skipIf conditions on tests in test_opt.py accordingly
- Export sym_is_bottom() (for debugging)
- Fix various things in the `_BINARY_OP_` specializations in the abstract interpreter:
- DECREF(temp)
- out-of-space check after sym_new_const()
- add sym_matches_type() checks, so even if we somehow reach a binary op with symbolic constants of the wrong type on the stack we won't trigger the type assert
The theory is that even if we saw a jump go in the same direction the
last 16 times we got there, we shouldn't be overly confident that it's
still going to go the same way in the future. This PR makes it so that
in the extreme cases, the confidence is multiplied by 0.9 instead of
remaining unchanged. For unpredictable jumps, there is no difference
(still 0.5). For somewhat predictable jumps, we interpolate.
* Rename `_testinternalcapi.get_{uop,counter}_optimizer` to `new_*_optimizer`
* Use `_PyUOpName()` instead of` _PyOpcode_uop_name[]`
* Add `target` to executor iterator items -- `list(ex)` now returns `(opcode, oparg, target, operand)` quadruples
* Add executor methods `get_opcode()` and `get_oparg()` to get `vmdata.opcode`, `vmdata.oparg`
* Define a helper for printing uops, and unify various places where they are printed
* Add a hack to summarize_stats.py to fix legacy uop names (e.g. `POP_TOP` -> `_POP_TOP`)
* Define helpers in `test_opt.py` for accessing the set or list of opnames of an executor
Add an option (--enable-experimental-jit for configure-based builds
or --experimental-jit for PCbuild-based ones) to build an
*experimental* just-in-time compiler, based on copy-and-patch (https://fredrikbk.com/publications/copy-and-patch.pdf).
See Tools/jit/README.md for more information on how to install the required build-time tooling.
Fix a few places where the lltrace debug output printed ``(null)`` instead of an opcode name, because it was calling ``_PyUOpName()`` on a Tier-1 opcode.
This uses the new mechanism whereby certain uops
are replaced by others during translation,
using the `_PyUop_Replacements` table.
We further special-case the `_FOR_ITER_TIER_TWO` uop
to update the deoptimization target to point
just past the corresponding `END_FOR` opcode.
Two tiny code cleanups are also part of this PR.
- Double max trace size to 256
- Add a dependency on executor_cases.c.h for ceval.o
- Mark `_SPECIALIZE_UNPACK_SEQUENCE` as `TIER_ONE_ONLY`
- Add debug output back showing the optimized trace
- Bunch of cleanups to Tools/cases_generator/
* Replace jumps with deopts in tier 2
* Fewer special cases of uop names
* Add target field to uop IR
* Remove more redundant SET_IP and _CHECK_VALIDITY micro-ops
* Extend whitelist of non-escaping API functions.
