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Fazang

Fazang is a Fortran library for reverse-mode automatic differentiation, inspired by Stan/Math library.

Quick start

Fazang provides user-facing variable var type. It is the type for dependent and independent variables of which derivatives will be calculated.

program var_grad_test
  use fazang ! load Fazang library

  implicit none
  
  real(rk) :: y, fx_d
  type(var) :: f, sigma, mu

  ! data
  y = 1.3d0

  ! independent variables
  mu = var(0.5d0)
  sigma = var(1.2d0)

  ! dependent
  f = var(-0.5d0 * log(2 * pi))
  f = f - log(sigma)
  f = f - 0.5d0 * ((y - mu) / sigma) ** 2.d0;

  ! use grad() to calculate df/d(mu) and df/d(sigma). Each var's
  ! derivative (also called adjoint) can be access through var%adj().

  call f%grad()
  write(*, *) "df/d(mu): ", mu%adj()
  write(*, *) "df/d(sigma): ", sigma%adj()
end program var_grad_test

An alternative to the same problem above is to write a function then use it as a procedure argument to Fazang ‘s gradient funcion.

module func
  use fazang ! load Fazang library
  implicit none

  real(rk), parameter :: y = 1.3d0

contains
  type(var) function f(x)
    type(var), intent(in) :: x(:)
    type(var) :: mu, sigma
    mu = x(1)
    sigma = x(2)
    f = -0.5d0 * log(2 * pi) - log(sigma) - 0.5d0 * ((y - mu) / sigma) ** 2.d0;
  end function f

end module func

program grad_test
  use iso_c_binding
  use fazang
  use func

  implicit none
  
  real(rk) :: fx(3), x(2)
  x = [0.5d0, 1.2d0]

  fx = gradient(f, x)
  write(*, *) "f(x): ", fx(1)
  write(*, *) "df/d(x(1)): ", fx(2)
  write(*, *) "df/d(x(2)): ", fx(3)
end program grad_test

Use Fazang

User guide can be found here.

Build

Fazang uses meson to build.

cd /path/to/fazang
mkdir build
cd build
meson compile

Afterwards one can run the unit tests

meson test

Use the library

Fazang can be accessed by use fazang module.

A variable declared var

type(var) x

can be defined as

x = var()           ! value of x is 0.d0
x = var(1.5d0)      ! value of x is 1.5d0

Fazang overloads instrinc arithmatic unary and binary functions. A list of supported functions can be found in the user guide.

All the downstream variables that depend on a var should also be var

type(var) x, y
x = var(1.d0)
y = sin(x)

The value and the adjoint (derivative) of a var can be accessed using var%val() and var%adj() functions, respectively.

write(*, *) y%val()   ! equals to sin(x%val())
write(*, *) y%adj()   ! equals to 0.d0 before any gradient operations

Fazang’s unary and binary functions are elemental, so they can be extended to arrays.

type(var) a(3), b, c(3), d
a = var([1.d0, 2.d0, 3.d0])
b = var(0.5d0)
c = 2.d0 * a
d = log(b * a * exp(c))

To calculate a dependent variable’s derivatives, call var%grad() function

call d(2)%grad()

and access each upstream variable’s derivative through var%adj() afterwards.

write(*, *) c%adj()    ! should be [0.0, 1.0, 0.0]

Though Fazang uses special storge pattern for array and matrix operations for efficiency purpose, the storage mechanism is transparent to the user.

type(var) :: x(4, 2), y(2, 5), z(4, 5)
real(rk) :: a(4, 2) = reshape([1.d0, 47.d0, 3.d0, 53.d0, 21.d0,&
& 7.d0, 3.d0, 3.d0], [4, 2])
real(rk) :: b(2, 5) = reshape([1.d0, 47.d0, 3.d0, 53.d0, 21.d0,&
& 7.d0, 3.d0, 3.d0, 3.2d0, 8.d0], [2, 5])

x = var(a)
y = var(b)
z = matmul(x, y)
do j = 1, 5
   do i = 1, 4
      call z(i, j)%grad()
      ! ...
      call set_zero_all_adj()  ! reset all adjionts to zero
   end do
end do

ODE sensitivity

Fazang also supports ordinary differential equation sensitivity without explicitly asking for Jacobian. For that the user-defined ODE must include two RHS definitions: one with var parameters, and one real parameters.

module ode_mod
  use fazang
  use, intrinsic :: iso_c_binding
  implicit none

  real(rk), parameter :: omega = 0.5d0
  real(rk), parameter :: d1 = 1.0d0
  real(rk), parameter :: d2 = 1.0d0

contains
  ! right-hand-side for data input
  subroutine eval_rhs(t, y, fy)
    implicit none
    real(c_double), intent(in) :: t, y(:)
    real(c_double), intent(inout) :: fy(size(y))
    fy(1) = y(2)
    fy(2) = sin(omega * d1 * d2 * t)
  end subroutine eval_rhs

  ! right-hand-side for var input with parameters
  ! y, p, and output fy must all be of var type
  subroutine eval_rhs_pvar(t, y, fy, p)
    implicit none
    real(c_double), intent(in) :: t
    type(var), intent(in) :: y(:), p(:)
    type(var), intent(inout) :: fy(size(y))
    fy(1) = y(2)
    fy(2) = sin(p(1) * p(2) * p(3) * t)
  end subroutine eval_rhs_pvar
end module ode_mod

Now we can solve the defined ODE.

program cvodes_demo
  use ode_mod
  use fazang
  implicit none

  type(var) :: yt(2, 3)
  type(cvodes_tol) :: tol
  real(rk), parameter :: ts(3) = [1.2d0, 2.4d0, 4.8d0]
  real(rk), parameter :: y00(2) = [0.2d0, 0.8d0]
  type(var) :: param(3)
  real(rk) :: y0(2), ga(2)
  integer :: i, j

  y0 = y00                      ! init condition
  param = var([omega, d1, d2])  ! parameters
  tol = cvodes_tol(CV_BDF, 1.d-10, 1.d-10, 1000_8)

  yt = cvodes_sol(0.d0, y0, ts, param, eval_rhs,&
       & eval_rhs_pvar, tol)
! ...
end program cvodes_demo

Note that now the call to the solver function cvodes_sol includes argument param as the sensitivity parameters, as well as two RHS functions. After solution the sensitivities are obtained the same way by calling grad and adj functions.

call yt(1, 1) % grad()
write(*, *) "dy_1/ d_omega at time ts(1):", param(1)%adj()

Planned

  • More function and matrices operations
  • DAE solver support
  • Contiguous memory model for large arrays

Name

The library is named after ancient Chinese philosopher Fazang (法藏), who views the cosmos “as an infinite number of interdependent and interpenetrating parts” (一法为因，万法为果；万法为因，一法为果).