feat: add reduce to Ops.jl (#840)

* adding reduce to Ops.jl

* Update src/Ops.jl

* change Ops.reduce test case to reflect stablehlo semantics

* Run through formatter

* add docstring as comments suggest

* Update src/Ops.jl

* Update src/Ops.jl

src/Ops.jl

@@ -2313,4 +2313,101 @@ Produces a [`Reactant.MLIR.Dialects.sdy.sharding_constraint`](@ref) operation wi
     end
 end
 
+"""
+    reduce(
+        x::TracedRArray{T},
+        init_values::TracedRNumber{T},
+        dimensions::Vector{Int},
+        fn::Function,
+        location=mlir_stacktrace("rand", @__FILE__, @__LINE__),
+    )
+
+Applies a reduction function `fn` along the specified `dimensions` of input `x`, starting from `init_values`.
+
+# Arguments
+
+- `x`: The input array.
+- `init_values`: The initial value.
+- `dimensions`: The dimensions to reduce along.
+- `fn`: A binary operator.
+
+!!! warning
+    This reduction operation follows StableHLO semantics. The key difference between this operation and Julia's built-in `reduce` is explained below:
+
+    - The function `fn` and the initial value `init_values` must form a **monoid**, meaning:
+      - `fn` must be an **associative** binary operation.
+      - `init_values` must be the **identity element** associated with `fn`.
+    - This constraint ensures consistent results across all implementations.
+
+    If `init_values` is not the identity element of `fn`, the results may vary between CPU and GPU executions. For example:
+
+    ```julia
+    A = [1 3; 2 4;;; 5 7; 6 8;;; 9 11; 10 12]
+    init_values = 2
+    dimensions = [1, 3]
+    ```
+
+    - **CPU version & Julia's `reduce`**:
+      - Reduce along dimension 1 → `[(15) (21); (18) (24)]`
+      - Reduce along dimension 3 → `[(33 + 2)  (45 + 2)]` → `[35 47]`
+    
+    - **GPU version**:
+      - Reduce along dimension 1 → `[(15 + 2) (21 + 2); (18 + 2) (24 + 2)]`
+      - Reduce along dimension 3 → `[37 49]`
+"""
+@noinline function reduce(
+    x::TracedRArray{T},
+    init_values::TracedRNumber{T},
+    dimensions::Vector{Int},
+    fn::Function,
+    location=mlir_stacktrace("reduce", @__FILE__, @__LINE__),
+) where {T}
+    reduced_shape = Tuple(deleteat!(collect(size(x)), dimensions))
+
+    result_type = mlir_type(TracedRArray{T,length(reduced_shape)}, reduced_shape)
+
+    sample_inputs = [
+        Reactant.TracedUtils.promote_to(TracedRNumber{T}, 0),
+        Reactant.TracedUtils.promote_to(TracedRNumber{T}, 0)
+    ]
+
+    func =
+        Reactant.TracedUtils.make_mlir_fn(
+            fn,
+            (sample_inputs),
+            (),
+            "reduce_fn",
+            false;
+            args_in_result=:none,
+            return_dialect=:stablehlo,
+        ).f
+    @assert MLIR.IR.nregions(func) == 1
+    fn_name = String(
+        MLIR.IR.attr(func, String(MLIR.API.mlirSymbolTableGetSymbolAttributeName()))
+    )
+    ftype_attr = MLIR.IR.attr(func, "function_type")
+    ftype = MLIR.IR.Type(ftype_attr)
+    @assert MLIR.IR.result(ftype) == MLIR.IR.TensorType((), MLIR.IR.Type(T)) error (
+        "$fn return type is not tensor<i1>"
+    )
+    fn = MLIR.IR.Region()
+    MLIR.API.mlirRegionTakeBody(fn, MLIR.IR.region(func, 1))
+    MLIR.IR.rmfromparent!(func)
+
+    dimensions = MLIR.IR.Attribute(dimensions .- 1)
+
+    res = MLIR.IR.result(
+        stablehlo.reduce(
+            [x.mlir_data],
+            [init_values.mlir_data];
+            result_0=[result_type],
+            dimensions=dimensions,
+            body=fn,
+            location=location,
+        ),
+    )
+
+    return TracedRArray{T,length(reduced_shape)}((), res, reduced_shape)
+end
+
 end # module Ops

test/ops.jl

@@ -1071,3 +1071,33 @@ end
 
     @test Reactant.@jit(f_multiple_hlo_calls(x_reactant, y_reactant))[1] ≈ (x .+ y) .* y
 end
+
+@testset "reduce" begin
+    # stablehlo reduce collapse the dimension so that (1,3) beomces (3, )
+    # while Julia reduce retains (1, 3). The test will fail despite elements being equal
+    function squeeze_dims(r)
+        return dropdims(r; dims=tuple(findall(size(r) .== 1)...))
+    end
+
+    # Floating point operation is not associative
+    A = rand(Int64, 3, 4, 5)
+    A_ra = Reactant.to_rarray(A)
+    init = 1
+    init_ra = @jit Reactant.Ops.constant(init)
+    dims = [2]
+    r_hlo = @jit Reactant.Ops.reduce(A_ra, init_ra, dims, *)
+    r = reduce(*, A; dims=dims, init=init)
+    @test r_hlo ≈ squeeze_dims(r)
+
+    dims = [1, 3]
+    init = 0
+    init_ra = @jit Reactant.Ops.constant(init)
+    r_hlo = @jit Reactant.Ops.reduce(A_ra, init_ra, dims, +)
+    r = reduce(+, A; dims=dims, init=init)
+    @test r_hlo ≈ squeeze_dims(r)
+
+    dims = [1, 2, 3]
+    r_hlo = @jit Reactant.Ops.reduce(A_ra, init_ra, dims, +)
+    r = reduce(+, A; dims=dims, init=init)
+    @test r_hlo ≈ squeeze_dims(r)
+end