fix rocm test

Author: jindavid

test/test_rocm.jl

@@ -5,38 +5,38 @@ function run_test(N, n, T, solver_type)
 
     # Set up tensors based on solver type
     if solver_type == :batched
-        MT = ROCArray{T}
+        M = ROCArray
 
         # Initialize the solver
-        data = initialize_batched(N, n, T)
+        data = initialize_batched(N, n, T, M)
 
         # Create test data
-        A_tensor = MT(zeros(n, n, N))
-        B_tensor = MT(zeros(n, n, N-1))
+        A_tensor = M(zeros(n, n, N))
+        B_tensor = M(zeros(n, n, N-1))
 
         # Generate positive definite A matrices and random B matrices
         AMDGPU.@allowscalar for i in 1:N
             temp = randn(T, n, n)
-            A_tensor[:, :, i] .= MT(temp * temp' + n * I)
+            A_tensor[:, :, i] .= M(temp * temp' + n * I)
         end
 
         AMDGPU.@allowscalar for i in 1:N-1
             temp = randn(T, n, n)
-            B_tensor[:, :, i] .= MT(temp)
+            B_tensor[:, :, i] .= M(temp)
         end
 
         # Copy data to solver
         copyto!(data.A_tensor, A_tensor)
         copyto!(data.B_tensor, B_tensor)
 
         # Create solution vector x and right-hand side d
-        x_list = Vector{MT}(undef, N)
+        x_list = Vector{M}(undef, N)
         for i in 1:N
-            x_list[i] = MT(rand(T, n, 1))
+            x_list[i] = M(rand(T, n, 1))
         end
 
         # Compute right-hand side d = Ax
-        d_tensor = MT(zeros(T, n, 1, N))
+        d_tensor = M(zeros(T, n, 1, N))
         d_tensor[:, :, 1] = A_tensor[:, :, 1] * x_list[1] + B_tensor[:, :, 1] * x_list[2]
         @views for i = 2:N-1
             d_tensor[:, :, i] = B_tensor[:, :, i-1]' * x_list[i-1] + A_tensor[:, :, i] * x_list[i] + B_tensor[:, :, i] * x_list[i+1]
@@ -47,38 +47,38 @@ function run_test(N, n, T, solver_type)
         copyto!(data.d_tensor, d_tensor)
 
     elseif solver_type == :sequential
-        MT = ROCArray{T}
+        M = ROCArray
 
         # Initialize the solver
-        data = initialize_seq(N, n, T)
+        data = initialize_seq(N, n, T, M)
 
         # Create test data
-        A_tensor = MT(zeros(n, n, N))
-        B_tensor = MT(zeros(n, n, N-1))
+        A_tensor = M(zeros(n, n, N))
+        B_tensor = M(zeros(n, n, N-1))
 
         # Generate positive definite A matrices and random B matrices
         AMDGPU.@allowscalar for i in 1:N
             temp = randn(T, n, n)
-            A_tensor[:, :, i] .= MT(temp * temp' + n * I)
+            A_tensor[:, :, i] .= M(temp * temp' + n * I)
         end
 
         AMDGPU.@allowscalar for i in 1:N-1
             temp = randn(T, n, n)
-            B_tensor[:, :, i] .= MT(temp)
+            B_tensor[:, :, i] .= M(temp)
         end
 
         # Copy data to solver
         copyto!(data.A_tensor, A_tensor)
         copyto!(data.B_tensor, B_tensor)
 
         # Create solution vector x and right-hand side d
-        x_list = Vector{MT}(undef, N)
+        x_list = Vector{M}(undef, N)
         for i in 1:N
-            x_list[i] = MT(rand(T, n, 1))
+            x_list[i] = M(rand(T, n, 1))
         end
 
         # Compute right-hand side d = Ax
-        d_tensor = MT(zeros(T, n, 1, N))
+        d_tensor = M(zeros(T, n, 1, N))
         d_tensor[:, :, 1] = A_tensor[:, :, 1] * x_list[1] + B_tensor[:, :, 1] * x_list[2]
         @views for i = 2:N-1
             d_tensor[:, :, i] = B_tensor[:, :, i-1]' * x_list[i-1] + A_tensor[:, :, i] * x_list[i] + B_tensor[:, :, i] * x_list[i+1]