eml-rewrite 0.5.1

F-family fusion pattern rewriter for symbolic expressions and Python source.

eml-rewrite

Stable beta. Patent pending. Source-available; see LICENSE.

F-family fusion pattern rewriter for symbolic expressions. Detects equivalent rewrites that strictly reduce predicted EML cost (per eml-cost). The library's headline guarantee: every proposed rewrite clears three independent gates (cost, domain, numerical equivalence) before it's surfaced to the caller.

Installation

pip install eml-rewrite

eml-cost is installed as a dependency.

For local development:

git clone https://github.com/almaguer1986/eml-rewrite
cd eml-rewrite
pip install -e ".[dev]"
pytest

Library API

from eml_rewrite import suggest, best, score, Suggestion
import sympy as sp

x = sp.Symbol("x", real=True)

# Find improving rewrites (only those that strictly improve)
sugg = suggest(sp.exp(x) / (1 + sp.exp(x)))
# [Suggestion(pattern_name="sigmoid",
#             rewritten=1/(1 + exp(-x)),
#             score_before=3, score_after=2, reduction=1,
#             domain_required="", domain_verified=True,
#             numerically_verified=True)]

# Pick the lowest-score rewrite, or original if no improvement
best(sp.sinh(x) / sp.cosh(x))
# tanh(x)

best(sp.exp(sp.exp(x)))
# exp(exp(x))   <- original; nothing in library matches

Pattern library (9)

1. exp(x)/(1+exp(x)) -> 1/(1+exp(-x)) — sigmoid canonicalization
2. sinh(x)/cosh(x) -> tanh(x) — F-family fusion
3. (exp(x)+exp(-x))/2 -> cosh(x) — hyperbolic fusion (cosh)
4. (exp(x)-exp(-x))/2 -> sinh(x) — hyperbolic fusion (sinh)
5. sin(x)^2 + cos(x)^2 -> 1 — Pythagorean identity
6. cosh(x)^2 - sinh(x)^2 -> 1 — hyperbolic identity
7. exp(log(x)), log(exp(x)) -> x — inverse pair
8. log(a/b) -> log(a) - log(b) — when score improves
9. log(x^n) -> n*log(x) — when score improves

Real-world examples

Scanning a SciPy-style script

# stats.py
from sympy import Symbol, exp, log
x = Symbol("x", positive=True)
log(x ** 4)                  # gets flagged: log_pow rewrite available
exp(x) / (1 + exp(x))        # gets flagged: sigmoid canonicalization

$ eml-rewrite scan stats.py
stats.py:3  log(x**4)
  -> 4*log(x)  (log_pow, -0 cost units)
stats.py:4  exp(x)/(exp(x) + 1)
  -> 1/(1 + exp(-x))  (sigmoid, -1 cost units)

Found 2 improving rewrite(s) across 1 file(s).

Fixing a PyTorch-flavored model definition

# model.py — math written by hand for clarity, then auto-canonicalized
from sympy import Symbol, exp, sinh, cosh
x = Symbol("x", real=True, positive=True)
sinh(x) / cosh(x)            # → tanh(x)
exp(x) / (1 + exp(x))        # → 1/(1 + exp(-x))

$ eml-rewrite fix model.py
model.py:3  sinh(x)/cosh(x)
  Would rewrite to: tanh(x)
model.py:4  exp(x)/(exp(x) + 1)
  Would rewrite to: 1/(1 + exp(-x))

Would apply 2 rewrite(s) across 1 file(s).
NOTE: v0.1 prints proposed rewrites only.

Surfacing conditional rewrites for editor / notebook UX

$ eml-rewrite analyze "log(x**4)" --include-conditional
Expression:           log(x**4)
  pfaffian_r:           1
  ...

Suggested rewrites (1):
  [log_pow | conditional: x > 0] 4*log(x)  (-0 cost units)

The conditional: annotation appears when the rewrite's domain requirement (here, x > 0) cannot be established by SymPy's assumption system. Without --include-conditional, the rewrite is silently rejected. With it, the requirement is annotated so the caller can prompt or confirm.

Command line

eml-rewrite scan FILE [FILE ...]    # report rewrites without applying
eml-rewrite fix  FILE [FILE ...]    # show rewrites that would be applied
eml-rewrite analyze "exp(sin(x))"   # full Pfaffian profile + suggestions

Example:

$ eml-rewrite analyze "exp(x)/(1 + exp(x))"
Expression:           exp(x)/(exp(x) + 1)
  pfaffian_r:           1
  max_path_r:           1
  eml_depth:            2
  structural_overhead:  2
  corrections:          Corrections(c_osc=0, c_composite=0, delta_fused=0)
  predicted_depth:      3
  is_pfaffian_not_eml:  False

Suggested rewrites (1):
  [sigmoid] 1/(1 + exp(-x))  (-1 cost units)

Non-regression: three layers

Every rewrite returned with only_improvements=True is required to clear three independent gates before being surfaced to the caller.

1. Cost gate

for s in suggest(expr, only_improvements=True):
    assert s.score_after < s.score_before

Strict EML cost reduction (per eml-cost's measure) — the historical headline guarantee. Suggestions whose post-rewrite cost is not strictly lower are filtered out before return.

2. Domain gate

A subset of the rewrites in the library are valid only on a restricted input domain. Examples:

Pattern	Identity	Valid when
log(x^n) -> n*log(x)	real-valued log identity	x > 0
log(a/b) -> log(a) - log(b)	log split	a > 0 and b > 0
exp(log(x)) -> x	inverse pair	x > 0
log(exp(x)) -> x	inverse pair (branch correctness)	x real

For these patterns, the engine probes SymPy's assumption system against the relevant subexpression (expr.is_positive, expr.is_real). A rewrite is accepted only when the assumption required for soundness is established by the assumption system. So:

import sympy as sp
from eml_rewrite import suggest

# Unconstrained symbol — no positivity assumption available.
u = sp.Symbol("u")
suggest(sp.log(u**2))           # []  (log_pow rejected; would silently
                                #      produce a complex value for u<0)

# Same expression with a positive symbol — accepted.
x = sp.Symbol("x", positive=True)
suggest(sp.log(x**2), only_improvements=False)
# [Suggestion(pattern_name='log_pow', rewritten=2*log(x),
#             domain_required='x > 0', domain_verified=True, ...)]

# Conditional mode: surface the rewrite WITH the requirement annotated
# rather than dropping it silently. Useful for editor / notebook UI.
suggest(sp.log(u**2), only_improvements=False, include_conditional=True)
# [Suggestion(pattern_name='log_pow', rewritten=2*log(u),
#             domain_required='u > 0', domain_verified=False, ...)]

3. Numerical-equivalence gate

After the structural and domain gates pass, the engine evaluates both the original and the rewritten expression at sample points drawn from regions consistent with each free symbol's SymPy assumptions (positive, negative, near-zero, moderately large). Evaluation runs at 30 decimal digits via SymPy's evalf, with a fast path that short-circuits when SymPy's simplify proves the identity directly — this avoids false positives on rewrites that improve numerical stability (e.g., cosh(x)^2 - sinh(x)^2 -> 1 at large x, where float-precision evaluation of the original collapses to zero from catastrophic cancellation).

A rewrite that disagrees on any probed point — including a one-sided domain error indicating the rewrite changes the domain of definition — is rejected. Set numerical_verify=False only for benchmarking the structural+domain layers in isolation.

Verifying equivalence directly

from eml_rewrite import verify_equivalence
import sympy as sp

u = sp.Symbol("u")
verify_equivalence(sp.log(u**2), 2 * sp.log(u))
# False  (disagrees on u<0)

verify_equivalence(sp.sin(u)**2 + sp.cos(u)**2, sp.S.One)
# True   (universal identity)

Links

• Project home: monogate.org
• Source: github.com/almaguer1986/eml-rewrite
• Package: pypi.org/project/eml-rewrite
• Companion: eml-cost (required dependency)

License

PROPRIETARY-PRE-RELEASE. See LICENSE.