diff --git a/Project.toml b/Project.toml
index 881541e4d..6764a580c 100644
--- a/Project.toml
+++ b/Project.toml
@@ -46,6 +46,7 @@ SymPy = "24249f21-da20-56a4-8eb1-6a02cf4ae2e6"
 [extensions]
 SymbolicsGroebnerExt = "Groebner"
 SymbolicsPreallocationToolsExt = ["ForwardDiff", "PreallocationTools"]
+SymbolicsForwardDiffExt = "ForwardDiff"
 SymbolicsSymPyExt = "SymPy"
 
 [compat]
diff --git a/ext/SymbolicsForwardDiffExt.jl b/ext/SymbolicsForwardDiffExt.jl
new file mode 100644
index 000000000..480fc83f5
--- /dev/null
+++ b/ext/SymbolicsForwardDiffExt.jl
@@ -0,0 +1,256 @@
+module SymbolicsForwardDiffExt
+
+using ForwardDiff
+using ForwardDiff.NaNMath
+using ForwardDiff.DiffRules
+using ForwardDiff: value, Dual, partials
+using Symbolics
+
+# The method generation in this file have been adapted from
+# https://github.com/JuliaDiff/ForwardDiff.jl/blob/v0.10.36/src/dual.jl
+
+const AMBIGUOUS_TYPES = (Num,)
+
+####################################
+# N-ary Operation Definition Tools #
+####################################
+
+macro define_binary_dual_op(f, xy_body, x_body, y_body, Ts)
+    FD = ForwardDiff
+    defs = quote end
+    for R in Ts
+        expr = quote
+            @inline $(f)(x::$FD.Dual{Tx}, y::$R) where {Tx} = $x_body
+            @inline $(f)(x::$R, y::$FD.Dual{Ty}) where {Ty} = $y_body
+        end
+        append!(defs.args, expr.args)
+    end
+    return esc(defs)
+end
+
+macro define_ternary_dual_op(f, xyz_body, xy_body, xz_body, yz_body, x_body, y_body, z_body, Ts)
+    FD = ForwardDiff
+    defs = quote end
+    for R in Ts
+        expr = quote
+            @inline $(f)(x::$FD.Dual{Txy}, y::$FD.Dual{Txy}, z::$R) where {Txy} = $xy_body
+            @inline $(f)(x::$FD.Dual{Tx}, y::$FD.Dual{Ty}, z::$R)  where {Tx, Ty} = Ty ≺ Tx ? $x_body : $y_body
+            @inline $(f)(x::$FD.Dual{Txz}, y::$R, z::$FD.Dual{Txz}) where {Txz} = $xz_body
+            @inline $(f)(x::$FD.Dual{Tx}, y::$R, z::$FD.Dual{Tz}) where {Tx,Tz} = Tz ≺ Tx ? $x_body : $z_body
+            @inline $(f)(x::$R, y::$FD.Dual{Tyz}, z::$FD.Dual{Tyz}) where {Tyz} = $yz_body
+            @inline $(f)(x::$R, y::$FD.Dual{Ty}, z::$FD.Dual{Tz}) where {Ty,Tz} = Tz ≺ Ty ? $y_body : $z_body
+        end
+        append!(defs.args, expr.args)
+        for Q in Ts
+            Q === R && continue
+            expr = quote
+                @inline $(f)(x::$FD.Dual{Tx}, y::$R, z::$Q) where {Tx} = $x_body
+                @inline $(f)(x::$R, y::$FD.Dual{Ty}, z::$Q) where {Ty} = $y_body
+                @inline $(f)(x::$R, y::$Q, z::$FD.Dual{Tz}) where {Tz} = $z_body
+            end
+            append!(defs.args, expr.args)
+        end
+        expr = quote
+            @inline $(f)(x::$FD.Dual{Tx}, y::$R, z::$R) where {Tx} = $x_body
+            @inline $(f)(x::$R, y::$FD.Dual{Ty}, z::$R) where {Ty} = $y_body
+            @inline $(f)(x::$R, y::$R, z::$FD.Dual{Tz}) where {Tz} = $z_body
+        end
+        append!(defs.args, expr.args)
+    end
+    return esc(defs)
+end
+
+function binary_dual_definition(M, f, Ts)
+    FD = ForwardDiff
+    dvx, dvy = DiffRules.diffrule(M, f, :vx, :vy)
+    Mf = M == :Base ? f : :($M.$f)
+    xy_work = FD.qualified_cse!(quote
+        val = $Mf(vx, vy)
+        dvx = $dvx
+        dvy = $dvy
+    end)
+    dvx, _ = DiffRules.diffrule(M, f, :vx, :y)
+    x_work = FD.qualified_cse!(quote
+        val = $Mf(vx, y)
+        dvx = $dvx
+    end)
+    _, dvy = DiffRules.diffrule(M, f, :x, :vy)
+    y_work = FD.qualified_cse!(quote
+        val = $Mf(x, vy)
+        dvy = $dvy
+    end)
+    expr = quote
+        @define_binary_dual_op(
+            $M.$f,
+            begin
+                vx, vy = $FD.value(x), $FD.value(y)
+                $xy_work
+                return $FD.dual_definition_retval(Val{Txy}(), val, dvx, $FD.partials(x), dvy, $FD.partials(y))
+            end,
+            begin
+                vx = $FD.value(x)
+                $x_work
+                return $FD.dual_definition_retval(Val{Tx}(), val, dvx, $FD.partials(x))
+            end,
+            begin
+                vy = $FD.value(y)
+                $y_work
+                return $FD.dual_definition_retval(Val{Ty}(), val, dvy, $FD.partials(y))
+            end,
+            $Ts
+        )
+    end
+    return expr
+end
+
+###################################
+# General Mathematical Operations #
+###################################
+
+for (M, f, arity) in DiffRules.diffrules(filter_modules = nothing)
+    if (M, f) in ((:Base, :^), (:NaNMath, :pow), (:Base, :/), (:Base, :+), (:Base, :-), (:Base, :sin), (:Base, :cos))
+        continue  # Skip methods which we define elsewhere.
+    elseif !(isdefined(@__MODULE__, M) && isdefined(getfield(@__MODULE__, M), f))
+        continue  # Skip rules for methods not defined in the current scope
+    end
+    if arity == 1
+        # no-op
+    elseif arity == 2
+        eval(binary_dual_definition(M, f, AMBIGUOUS_TYPES))
+    else
+        # no-op
+    end
+end
+
+#################
+# Special Cases #
+#################
+
+# +/- #
+#-----#
+
+@eval begin
+    @define_binary_dual_op(
+        Base.:+,
+        begin
+            vx, vy = value(x), value(y)
+            Dual{Txy}(vx + vy, partials(x) + partials(y))
+        end,
+        Dual{Tx}(value(x) + y, partials(x)),
+        Dual{Ty}(x + value(y), partials(y)),
+        $AMBIGUOUS_TYPES
+    )
+end
+
+@eval begin
+    @define_binary_dual_op(
+        Base.:-,
+        begin
+            vx, vy = value(x), value(y)
+            Dual{Txy}(vx - vy, partials(x) - partials(y))
+        end,
+        Dual{Tx}(value(x) - y, partials(x)),
+        Dual{Ty}(x - value(y), -partials(y)),
+        $AMBIGUOUS_TYPES
+    )
+end
+
+# / #
+#---#
+
+# We can't use the normal diffrule autogeneration for this because (x/y) === (x * (1/y))
+# doesn't generally hold true for floating point; see issue #264
+@eval begin
+    @define_binary_dual_op(
+        Base.:/,
+        begin
+            vx, vy = value(x), value(y)
+            Dual{Txy}(vx / vy, _div_partials(partials(x), partials(y), vx, vy))
+        end,
+        Dual{Tx}(value(x) / y, partials(x) / y),
+        begin
+            v = value(y)
+            divv = x / v
+            Dual{Ty}(divv, -(divv / v) * partials(y))
+        end,
+        $AMBIGUOUS_TYPES
+    )
+end
+
+# exponentiation #
+#----------------#
+
+for f in (:(Base.:^), :(NaNMath.pow))
+    @eval begin
+        @define_binary_dual_op(
+            $f,
+            begin
+                vx, vy = value(x), value(y)
+                expv = ($f)(vx, vy)
+                powval = vy * ($f)(vx, vy - 1)
+                if isconstant(y)
+                    logval = one(expv)
+                elseif iszero(vx) && vy > 0
+                    logval = zero(vx)
+                else
+                    logval = expv * log(vx)
+                end
+                new_partials = _mul_partials(partials(x), partials(y), powval, logval)
+                return Dual{Txy}(expv, new_partials)
+            end,
+            begin
+                v = value(x)
+                expv = ($f)(v, y)
+                if y == zero(y) || iszero(partials(x))
+                    new_partials = zero(partials(x))
+                else
+                    new_partials = partials(x) * y * ($f)(v, y - 1)
+                end
+                return Dual{Tx}(expv, new_partials)
+            end,
+            begin
+                v = value(y)
+                expv = ($f)(x, v)
+                deriv = (iszero(x) && v > 0) ? zero(expv) : expv*log(x)
+                return Dual{Ty}(expv, deriv * partials(y))
+            end,
+            $AMBIGUOUS_TYPES
+        )
+    end
+end
+
+# hypot #
+#-------#
+
+@eval begin
+    @define_ternary_dual_op(
+        Base.hypot,
+        calc_hypot(x, y, z, Txyz),
+        calc_hypot(x, y, z, Txy),
+        calc_hypot(x, y, z, Txz),
+        calc_hypot(x, y, z, Tyz),
+        calc_hypot(x, y, z, Tx),
+        calc_hypot(x, y, z, Ty),
+        calc_hypot(x, y, z, Tz),
+        $AMBIGUOUS_TYPES
+    )
+end
+
+# muladd #
+#--------#
+
+@eval begin
+    @define_ternary_dual_op(
+        Base.muladd,
+        calc_muladd_xyz(x, y, z),                         # xyz_body
+        calc_muladd_xy(x, y, z),                          # xy_body
+        calc_muladd_xz(x, y, z),                          # xz_body
+        Base.muladd(y, x, z),                             # yz_body
+        Dual{Tx}(muladd(value(x), y, z), partials(x) * y), # x_body
+        Base.muladd(y, x, z),                             # y_body
+        Dual{Tz}(muladd(x, y, value(z)), partials(z)),     # z_body
+        $AMBIGUOUS_TYPES
+    )
+end
+
+end
diff --git a/test/forwarddiff_symbolic_dual_ops.jl b/test/forwarddiff_symbolic_dual_ops.jl
new file mode 100644
index 000000000..1b89c79e6
--- /dev/null
+++ b/test/forwarddiff_symbolic_dual_ops.jl
@@ -0,0 +1,109 @@
+using ForwardDiff
+using Symbolics
+using Symbolics.SymbolicUtils
+using Symbolics.SymbolicUtils.SpecialFunctions
+using Symbolics.NaNMath
+using Test
+
+SF = SymbolicUtils.SpecialFunctions
+
+@variables x
+
+# Test functions from Symbolics #
+#-------------------------------#
+
+for f ∈ SymbolicUtils.basic_monadic
+    fun = eval(:(ξ ->($f)(ξ)))
+
+    fd = ForwardDiff.derivative(fun, x)
+    sym = Symbolics.Differential(x)(fun(x)) |> expand_derivatives
+
+    @test isequal(fd, sym)
+end
+
+for f ∈ SymbolicUtils.monadic
+    # The polygamma and trigamma functions seem to be missing rules in ForwardDiff.
+    # The abs rule uses conditionals and cannot be used with Symbolics.Num.
+    # acsc, asech, NanMath.log2 and NaNMath.log10 are tested separately
+    if f ∈ (abs, SF.polygamma, SF.trigamma, acsc, acsch, asech, NaNMath.log2, NaNMath.log10)
+        continue
+    end
+
+    fun = eval(:(ξ ->($f)(ξ)))
+
+    fd = ForwardDiff.derivative(fun, x)
+    sym = Symbolics.Differential(x)(fun(x)) |> expand_derivatives
+
+    @test isequal(fd, sym)
+end
+
+# These are evaluated numerically. For some reason isequal evaluates to false for the symbolic expressions.
+for f ∈ (acsc, asech, NaNMath.log2, NaNMath.log10)
+    fun = eval(:(ξ ->($f)(ξ)))
+
+    fd = ForwardDiff.derivative(fun, 1.0)
+    sym = Symbolics.Differential(x)(fun(x)) |> expand_derivatives
+
+    @test fd ≈ substitute(sym, Dict(x => 1.0))
+end
+
+for f ∈ SymbolicUtils.basic_diadic
+    if f ∈ (//,)
+        continue
+    end
+
+    fun = eval(:(ξ ->($f)(ξ, 2.0)))
+
+    fd = ForwardDiff.derivative(fun, x)
+    sym = Symbolics.Differential(x)(fun(x)) |> expand_derivatives
+
+    @test isequal(fd, sym)
+end
+
+for f ∈ SymbolicUtils.diadic
+    if f ∈ (max, min, NaNMath.atanh, mod, rem, copysign, besselj, bessely, besseli, besselk)
+        continue
+    end
+
+    fun = eval(:(ξ ->($f)(ξ, 2.0)))
+
+    fd = ForwardDiff.derivative(fun, x)
+    sym = Symbolics.Differential(x)(fun(x)) |> expand_derivatives
+
+    @test isequal(fd, sym)
+end
+
+for f ∈ (NaNMath.atanh,)
+    fun = eval(:(ξ ->($f)(ξ)))
+
+    fd = ForwardDiff.derivative(fun, x)
+    sym = Symbolics.Differential(x)(fun(x)) |> expand_derivatives
+
+    @test isequal(fd, sym)
+end
+
+for f ∈ (besselj, bessely, besseli, besselk)
+    fun = eval(:(ξ ->($f)(ξ, 2)))
+
+    fd = ForwardDiff.derivative(fun, x)
+    sym = Symbolics.Differential(x)(fun(x)) |> expand_derivatives
+
+    @test isequal(fd, sym)
+end
+
+# Additionally test these definitions from ForwardDiff #
+#------------------------------------------------------#
+
+# https://github.com/JuliaDiff/ForwardDiff.jl/blob/d3002093beb88ff0b98ed178377961dfd55c1247/src/dual.jl#L599
+# and
+# https://github.com/JuliaDiff/ForwardDiff.jl/blob/d3002093beb88ff0b98ed178377961dfd55c1247/src/dual.jl#L683
+for f ∈ (hypot, muladd)
+    fun = eval(:(ξ ->($f)(ξ, 2.0, 3.0)))
+
+    fd = ForwardDiff.derivative(fun, 5.0)
+    sym = Symbolics.Differential(x)(fun(x)) |> expand_derivatives
+
+    @test fd ≈ substitute(sym, Dict(x => 5.0))
+end
+
+# fma is not defined for Symbolics.Num
diff --git a/test/runtests.jl b/test/runtests.jl
index d8e6e544c..7d42fb555 100644
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -33,6 +33,7 @@ if GROUP == "All" || GROUP == "Core"
     @safetestset "Linear Solver Test" begin include("linear_solver.jl") end
     @safetestset "Algebraic Solver Test" begin include("solver.jl") end
     @safetestset "Overloading Test" begin include("overloads.jl") end
+    @safetestset "ForwardDiff Extension Test" begin include("forwarddiff_symbolic_dual_ops.jl") end
     @safetestset "Nested ForwardDiff Sparsity Test" begin include("nested_forwarddiff_sparsity.jl") end
     @safetestset "Build Function Test" begin include("build_function.jl") end
     @safetestset "Build Function Array Test" begin include("build_function_arrayofarray.jl") end