perf: use common subexpression elimination in AutoSymbolics (#759)

* use cse in AutoSymbolics

closes #758

* Format

---------

Co-authored-by: Guillaume Dalle <22795598+gdalle@users.noreply.github.com>

Author: baggepinnen

DifferentiationInterface/ext/DifferentiationInterfaceSymbolicsExt/onearg.jl

@@ -18,7 +18,9 @@ function DI.prepare_pushforward_nokwarg(
     step_der_var = derivative(f(x_var + t_var * dx_var, context_vars...), t_var)
     pf_var = substitute(step_der_var, Dict(t_var => zero(eltype(x))))
 
-    res = build_function(pf_var, x_var, dx_var, context_vars...; expression=Val(false))
+    res = build_function(
+        pf_var, x_var, dx_var, context_vars...; expression=Val(false), cse=true
+    )
     (pf_exe, pf_exe!) = if res isa Tuple
         res
     elseif res isa RuntimeGeneratedFunction
@@ -102,7 +104,7 @@ function DI.prepare_derivative_nokwarg(
     context_vars = variablize(contexts)
     der_var = derivative(f(x_var, context_vars...), x_var)
 
-    res = build_function(der_var, x_var, context_vars...; expression=Val(false))
+    res = build_function(der_var, x_var, context_vars...; expression=Val(false), cse=true)
     (der_exe, der_exe!) = if res isa Tuple
         res
     elseif res isa RuntimeGeneratedFunction
@@ -177,7 +179,9 @@ function DI.prepare_gradient_nokwarg(
     # Symbolic.gradient only accepts vectors
     grad_var = gradient(f(x_var, context_vars...), vec(x_var))
 
-    res = build_function(grad_var, vec(x_var), context_vars...; expression=Val(false))
+    res = build_function(
+        grad_var, vec(x_var), context_vars...; expression=Val(false), cse=true
+    )
     (grad_exe, grad_exe!) = res
     return SymbolicsOneArgGradientPrep(_sig, grad_exe, grad_exe!)
 end
@@ -254,7 +258,7 @@ function DI.prepare_jacobian_nokwarg(
         jacobian(f(x_var, context_vars...), x_var)
     end
 
-    res = build_function(jac_var, x_var, context_vars...; expression=Val(false))
+    res = build_function(jac_var, x_var, context_vars...; expression=Val(false), cse=true)
     (jac_exe, jac_exe!) = res
     return SymbolicsOneArgJacobianPrep(_sig, jac_exe, jac_exe!)
 end
@@ -333,7 +337,9 @@ function DI.prepare_hessian_nokwarg(
         hessian(f(x_var, context_vars...), vec(x_var))
     end
 
-    res = build_function(hess_var, vec(x_var), context_vars...; expression=Val(false))
+    res = build_function(
+        hess_var, vec(x_var), context_vars...; expression=Val(false), cse=true
+    )
     (hess_exe, hess_exe!) = res
 
     gradient_prep = DI.prepare_gradient_nokwarg(
@@ -420,7 +426,12 @@ function DI.prepare_hvp_nokwarg(
     hvp_vec_var = hess_var * vec(dx_var)
 
     res = build_function(
-        hvp_vec_var, vec(x_var), vec(dx_var), context_vars...; expression=Val(false)
+        hvp_vec_var,
+        vec(x_var),
+        vec(dx_var),
+        context_vars...;
+        expression=Val(false),
+        cse=true,
     )
     (hvp_exe, hvp_exe!) = res
 
@@ -508,7 +519,7 @@ function DI.prepare_second_derivative_nokwarg(
     der_var = derivative(f(x_var, context_vars...), x_var)
     der2_var = derivative(der_var, x_var)
 
-    res = build_function(der2_var, x_var, context_vars...; expression=Val(false))
+    res = build_function(der2_var, x_var, context_vars...; expression=Val(false), cse=true)
     (der2_exe, der2_exe!) = if res isa Tuple
         res
     elseif res isa RuntimeGeneratedFunction

DifferentiationInterface/ext/DifferentiationInterfaceSymbolicsExt/twoarg.jl

@@ -26,7 +26,9 @@ function DI.prepare_pushforward_nokwarg(
     step_der_var = derivative(y_var, t_var)
     pf_var = substitute(step_der_var, Dict(t_var => zero(eltype(x))))
 
-    res = build_function(pf_var, x_var, dx_var, context_vars...; expression=Val(false))
+    res = build_function(
+        pf_var, x_var, dx_var, context_vars...; expression=Val(false), cse=true
+    )
     (pushforward_exe, pushforward_exe!) = res
     return SymbolicsTwoArgPushforwardPrep(_sig, pushforward_exe, pushforward_exe!)
 end
@@ -114,7 +116,7 @@ function DI.prepare_derivative_nokwarg(
     f!(y_var, x_var, context_vars...)
     der_var = derivative(y_var, x_var)
 
-    res = build_function(der_var, x_var, context_vars...; expression=Val(false))
+    res = build_function(der_var, x_var, context_vars...; expression=Val(false), cse=true)
     (der_exe, der_exe!) = res
     return SymbolicsTwoArgDerivativePrep(_sig, der_exe, der_exe!)
 end
@@ -201,7 +203,7 @@ function DI.prepare_jacobian_nokwarg(
         jacobian(y_var, x_var)
     end
 
-    res = build_function(jac_var, x_var, context_vars...; expression=Val(false))
+    res = build_function(jac_var, x_var, context_vars...; expression=Val(false), cse=true)
     (jac_exe, jac_exe!) = res
     return SymbolicsTwoArgJacobianPrep(_sig, jac_exe, jac_exe!)
 end