Merge pull request #405 from SciML/enzyme_batch

Fix enzyme batch mode

ext/LinearSolveEnzymeExt.jl

@@ -58,14 +58,14 @@ function EnzymeCore.EnzymeRules.reverse(config, func::Const{typeof(LinearSolve.i
             d_b .= 0
         end
     else
-        for i in 1:EnzymeRules.width(config)
-            if d_A !== prob_d_A[i]
-                prob_d_A[i] .+= d_A[i]
-                d_A[i] .= 0
+        for (_prob_d_A,_d_A,_prob_d_b, _d_b) in zip(prob_d_A, d_A, prob_d_b, d_b)
+            if _d_A !== _prob_d_A
+                _prob_d_A .+= _d_A
+                _d_A .= 0
             end
-            if d_b !== prob_d_b[i]
-                prob_d_b[i] .+= d_b[i]
-                d_b[i] .= 0
+            if _d_b !== _prob_d_b
+                _prob_d_b .+= _d_b
+                _d_b .= 0
             end
         end
     end

test/enzyme.jl

@@ -33,15 +33,25 @@ b1 = rand(n);
 db1 = zeros(n);
 db12 = zeros(n);
 
-#=
-# Batch test fails
-# Captured in MWE: https://github.com/EnzymeAD/Enzyme.jl/issues/1075
+# Batch test
+n = 4
+A = rand(n, n);
+dA = zeros(n, n);
+dA2 = zeros(n, n);
+b1 = rand(n);
+db1 = zeros(n);
+db12 = zeros(n);
 
-function fbatch(y, A, b1; alg = LUFactorization())
+function f(A, b1; alg = LUFactorization())
     prob = LinearProblem(A, b1)
-
     sol1 = solve(prob, alg)
+    s1 = sol1.u
+    norm(s1)
+end
 
+function fbatch(y, A, b1; alg = LUFactorization())
+    prob = LinearProblem(A, b1)
+    sol1 = solve(prob, alg)
     s1 = sol1.u
     y[1] = norm(s1)
     nothing
@@ -50,16 +60,28 @@ end
 y = [0.0]
 dy1 = [1.0]
 dy2 = [1.0]
+Enzyme.autodiff(Reverse, fbatch, Duplicated(y, dy1), Duplicated(copy(A), dA), Duplicated(copy(b1), db1))
+
+@test y[1] ≈ f(copy(A),b1)
+dA_2 = ForwardDiff.gradient(x->f(x,eltype(x).(b1)), copy(A))
+db1_2 = ForwardDiff.gradient(x->f(eltype(x).(A),x), copy(b1))
+
+@test dA ≈ dA_2
+@test db1 ≈ db1_2
+
+y .= 0
+dy1 .= 1
+dy2 .= 1
+dA .= 0
+dA2 .= 0
+db1 .= 0
+db12 .= 0
 Enzyme.autodiff(Reverse, fbatch, BatchDuplicated(y, (dy1, dy2)), BatchDuplicated(copy(A), (dA, dA2)), BatchDuplicated(copy(b1), (db1, db12)))
 
-dA2 = ForwardDiff.gradient(x->f(x,eltype(x).(b1)), copy(A))
-db12 = ForwardDiff.gradient(x->f(eltype(x).(A),x), copy(b1))
-
-@test_broken dA ≈ dA_2
-@test_broken dA2 ≈ dA_2
-@test_broken db1 ≈ db1_2
-@test_broken db12 ≈ db1_2
-=#
+@test dA ≈ dA_2
+@test db1 ≈ db1_2
+@test dA2 ≈ dA_2
+@test db12 ≈ db1_2
 
 function f(A, b1, b2; alg = LUFactorization())
     prob = LinearProblem(A, b1)