This PR adds a new option to the function `simpHaveTelescope` in which
the `have` telescope is simplified in two passes:
* In the first pass, only the values and the body are simplified.
* In the second pass, unused declarations are eliminated.
This new mode eliminates **superlinear** behavior in the benchmark
`simp_3.lean`. Note that the kernel type checker still **exhibits**
quadratic behavior in this example, because it **does not have support**
for expanding a `have`/`let` telescope in a single step.
This PR reorganizes the monad hierarchy for symbolic computation in
Lean.
## Motivation
We want a clean layering where:
1. A foundational monad (`SymM`) provides maximally shared terms and
structural/syntactic `isDefEq`
2. `GrindM` builds on this foundation, adding E-graphs, congruence
closure, and decision procedures
3. Symbolic execution / VCGen uses `GrindM` directly without introducing
a third monad
## Changes
The core symbolic computation layer still lives in `Lean.Meta.Sym`. This
monad (`SymM`) provides:
- Maximally shared terms with pointer-based equality
- Structural/syntactic `isDefEq` and matching (no reduction, predictable
cost)
- Monotonic local contexts (no `revert` or `clear`), enabling O(1)
metavariable validation
- Efficient `intro`, `apply`, and `simp` implementations
The name "Sym" reflects that this is infrastructure for symbolic
computation: symbolic simulation, verification condition generation, and
decision procedures.
### Updated hierarchy
```
Lean.Meta.Sym -- SymM: shared terms, syntactic isDefEq, intro, apply, simp
Lean.Meta.Grind -- GrindM: E-graphs, congruence closure (extends SymM)
```
Symbolic execution is a usage pattern of `GrindM` operating on
`Grind.Goal`, not a separate monad. This keeps the API surface minimal:
users learn two monads, and VCGen is "how you use `GrindM`" (for users
that want to use `grind`) rather than a third abstraction to understand.
This PR adds a `done` flag to the result returned by `Simproc`s in
`Sym.simp`.
The `done` flag controls whether simplification should continue after
the result:
- `done = false` (default): Continue with subsequent simplification
steps
- `done = true`: Stop processing, return this result as final
## Use cases for `done = true`
### In `pre` simprocs
Skip simplification of certain subterms entirely:
```
def skipLambdas : Simproc := fun e =>
if e.isLambda then return .rfl (done := true)
else return .rfl
```
### In `post` simprocs
Perform single-pass normalization without recursive simplification:
```
def singlePassNormalize : Simproc := fun e =>
if let some (e', h) ← tryNormalize e then
return .step e' h (done := true)
else return .rfl
```
With `done = true`, the result `e'` won't be recursively simplified.
This PR adds support for simplifying lambda expressions in `Sym.simp`.
It is much more efficient than standard simp for very large lambda
expressions with many binders. The key idea is to generate a custom
function extensionality theorem for the type of the lambda being
simplified.
This technique is compatible with the standard `simp` tactic, and will
be ported in a separate PR.
<img width="581" height="455" alt="image"
src="https://github.com/user-attachments/assets/5911dc6c-03f0-48ed-843b-b8cb4f67ee61"
/>
### `lambda` benchmark summary
| Lambda size | MetaM (ms) | SymM (ms) | Speedup |
|-------------|------------|-----------|---------|
| 50 | 22.7 | 0.74 | ~31× |
| 100 | 120.5 | 1.75 | ~69× |
| 150 | 359.6 | 2.90 | ~124× |
| 200 | 809.5 | 4.51 | ~180× |
This PR ensures that `Sym.simp` checks thresholds for maximum recursion
depth and maximum number of steps. It also invokes `checkSystem`.
Additionally, this PR simplifies the main loop. Assigned metavariables
and `zetaDelta` reduction are now handled by installing `pre`/`post`
methods.
This PR adds `getMatch` and `getMatchWithExtra` for retrieving patterns
from
discrimination trees in the symbolic simulation framework.
The PR also adds uses `DiscrTree` to implement indexing in `Sym.simp`.
