Differentiable programming proves to be useful in a wide variety of areas such as scientific computing and artificial intelligence. For instance, a controller optimization system equipped with differentiable simulators converges one to four orders of magnitude faster than those using model-free reinforcement learning algorithms.12
Imagine that you have the following kernel:
x = ti.field(float, ())y = ti.field(float, ()) @ti.kerneldef compute_y(): y[None] = ti.sin(x[None])
Now if you want to get the derivative of
y with respect to
dy/dx, it's straightforward to write out the gradient kernel manually:
x = ti.field(dtype=ti.f32, shape=())y = ti.field(dtype=ti.f32, shape=())dy_dx = ti.field(dtype=ti.f32, shape=()) @ti.kerneldef compute_dy_dx(): dy_dx[None] = ti.cos(x[None])
However, as you make a change to
compute_y, you will have to rework the gradient formula
by hand and update
compute_dy_dx accordingly. Apparently, when
the kernel becomes larger and gets frequently updated, this manual workflow is
really error-prone and hard to maintain.
If you run into this situation, Taichi's handy automatic differentiation (autodiff)
system comes to the rescue! Taichi supports gradient evaluation through
ti.Tape() or the more flexible
Let's still take the
compute_y kernel above for an explanation.
ti.Tape() is the easiest way to obtain a kernel that computes
needs_grad=Trueoption when declaring fields involved in the derivative chain.
- Use context manager
with ti.Tape(y):to capture the kernel invocations which you want to automatically differentiate.
dy/dxvalue at current
xis available at
Here's the full code snippet elaborating the steps above:
x = ti.field(dtype=ti.f32, shape=(), needs_grad=True)y = ti.field(dtype=ti.f32, shape=(), needs_grad=True) @ti.kerneldef compute_y(): y[None] = ti.sin(x[None]) with ti.Tape(y): compute_y() print('dy/dx =', x.grad[None], ' at x =', x[None])
A common problem in physical simulation is that it's usually easy to compute
energy but hard to compute force on every particle,
e.g Bond bending (and torsion) in molecular dynamics
and FEM with hyperelastic energy functions.
Recall that we can differentiate
(negative) potential energy to get forces:
F_i = -dU / dx_i. So once you have coded
a kernel that computes the potential energy, you may use Taichi's autodiff
system to obtain the derivatives and then
F_i on each particle.
Take examples/simulation/ad_gravity.py as an example:
import taichi as titi.init() N = 8dt = 1e-5 x = ti.Vector.field(2, dtype=ti.f32, shape=N, needs_grad=True) # particle positionsv = ti.Vector.field(2, dtype=ti.f32, shape=N) # particle velocitiesU = ti.field(dtype=ti.f32, shape=(), needs_grad=True) # potential energy @ti.kerneldef compute_U(): for i, j in ti.ndrange(N, N): r = x[i] - x[j] # r.norm(1e-3) is equivalent to ti.sqrt(r.norm()**2 + 1e-3) # This is to prevent 1/0 error which can cause wrong derivative U[None] += -1 / r.norm(1e-3) # U += -1 / |r| @ti.kerneldef advance(): for i in x: v[i] += dt * -x.grad[i] # dv/dt = -dU/dx for i in x: x[i] += dt * v[i] # dx/dt = v def substep(): with ti.Tape(loss=U): # Kernel invocations in this scope will later contribute to partial derivatives of # U with respect to input variables such as x. compute_U( ) # The tape will automatically compute dU/dx and save the results in x.grad advance() @ti.kerneldef init(): for i in x: x[i] = [ti.random(), ti.random()] init()gui = ti.GUI('Autodiff gravity')while gui.running: for i in range(50): substep() gui.circles(x.to_numpy(), radius=3) gui.show()
ti.Tape(U) must be a 0D field.
To use autodiff with multiple output variables, please see the
kernel.grad() usage below.
ti.Tape(U) will automatically set
U[None] to 0 on
As mentioned above,
ti.Tape() can only track a 0D field as the output variable.
If there're multiple output variables that you want to back-propagate
gradients to inputs,
kernel.grad() should be used instead of
import taichi as titi.init() N = 16 x = ti.field(dtype=ti.f32, shape=N, needs_grad=True)loss = ti.field(dtype=ti.f32, shape=(), needs_grad=True)loss2 = ti.field(dtype=ti.f32, shape=(), needs_grad=True) @ti.kerneldef func(): for i in x: loss[None] += x[i] ** 2 loss2[None] += x[i] for i in range(N): x[i] = iloss.grad[None] = 1loss2.grad[None] = 1 func()func.grad()for i in range(N): assert x.grad[i] == i * 2 + 1
It might be tedious to write out
need_grad=True for every input in a complicated use case.
Alternatively, Taichi provides an API
ti.root.lazy_grad() that automatically places the
gradient fields following the layout of their primal fields.
kernel.grad(), it's recommended to always run forward kernel before backward, e.g.
kernel(); kernel.grad(). If global fields used in the derivative calculation get mutated in the forward run, skipping
kernel() breaks global data access rule #1 below and might produce incorrect gradients.
Unlike tools such as TensorFlow where immutable output buffers are generated, the imperative programming paradigm adopted in Taichi allows programmers to freely modify global fields.
To make automatic differentiation well-defined under this setting, the following rules are enforced when writing differentiable programs in Taichi:
Currently Taichi's autodiff implementation doesn't save intermediate results of global fields which might be used in the backward pass. Therefore mutation is forbidden once you've read from a global field.
Global Data Access Rule #1
Once you read an element in a field, the element cannot be mutated anymore.
import taichi as titi.init() N = 16 x = ti.field(dtype=ti.f32, shape=N, needs_grad=True)loss = ti.field(dtype=ti.f32, shape=(), needs_grad=True)b = ti.field(dtype=ti.f32, shape=(), needs_grad=True) @ti.kerneldef func_broke_rule_1(): # BAD: broke global data access rule #1, reading global field and before mutation is done. loss[None] = x * b[None] b[None] += 100 @ti.kerneldef func_equivalent(): loss[None] = x * 10 for i in range(N): x[i] = ib[None] = 10loss.grad[None] = 1 with ti.Tape(loss): func_broke_rule_1()# Call func_equivalent to see the correct result# with ti.Tape(loss): # func_equivalent() assert x.grad == 10.0
Global Data Access Rule #2
If a global field element is written more than once, then starting from the second write, the write must come in the form of an atomic add ("accumulation", using
ti.atomic_add or simply
+= violates rule #1 above since it reads the old value before computing the sum, it is the only special case of "read before mutation" that Taichi allows in the autodiff system.
import taichi as titi.init() N = 16 x = ti.field(dtype=ti.f32, shape=N, needs_grad=True)loss = ti.field(dtype=ti.f32, shape=(), needs_grad=True) @ti.kerneldef func_break_rule_2(): loss[None] += x ** 2 # Bad: broke global data access rule #2, it's not an atomic_add. loss[None] *= x @ti.kerneldef func_equivalent(): loss[None] = (2 + x ** 2) * x for i in range(N): x[i] = iloss.grad[None] = 1loss[None] = 2 func_break_rule_2()func_break_rule_2.grad()# Call func_equivalent to see the correct result# func_equivalent()# func_equivalent.grad()assert x.grad == 4.0assert x.grad == 3.0
Kernel Simplicity Rule
Kernel body must consist of multiple simply nested for-loops. For example, each for-loop can either contain exactly one (nested) for-loop (and no other statements), or a group of statements without loops.
@ti.kerneldef differentiable_task1(): # Good: simple for loop for i in x: x[i] = y[i] @ti.kerneldef differentiable_task2(): # Good: one nested for loop for i in range(10): for j in range(20): for k in range(300): ... do whatever you want, as long as there are no loops @ti.kerneldef differentiable_task3(): # Bad: the outer for loop contains two for loops. for i in range(10): for j in range(20): ... for j in range(20): ... @ti.kerneldef differentiable_task4(): # Bad: mixed usage of for-loop and a statement without looping. Please split them into two kernels. loss[None] += x for i in range(10): ...
Taichi programs that violate this rule will result in an error.
Violation of rules above might result in incorrect gradient result without a proper error. We're actively working on improving the error reporting mechanism for it. Please feel free to open a github issue if you see any silent wrong results.
static for-loops (e.g.
for i in ti.static(range(4))) will get
unrolled by the Python frontend preprocessor and therefore does not
count as a level of loop.
For instance, we can rewrite
differentiable_task3 listed above using
@ti.kerneldef differentiable_task3(): # Good: ti.static unrolls the inner loops so that it now only has one simple for loop. for i in range(10): for j in ti.static(range(20)): ... for j in ti.static(range(20)): ...
Sometimes user may want to override the gradients provided by the Taichi autodiff system. For example, when differentiating a 3D singular value decomposition (SVD) used in an iterative
solver, it is preferred to use a manually engineered SVD derivative subroutine for better numerical stability.
Taichi provides two decorators
ti.ad.grad_for to overwrite the default
automatic differentiation behavior.
Here's a simple example to use customized gradient function in autodiff:
import taichi as titi.init() x = ti.field(ti.f32)total = ti.field(ti.f32)n = 128ti.root.dense(ti.i, n).place(x)ti.root.place(total)ti.root.lazy_grad() @ti.kerneldef func(mul: ti.f32): for i in range(n): ti.atomic_add(total[None], x[i] * mul) @ti.ad.grad_replaceddef forward(mul): func(mul) func(mul) @ti.ad.grad_for(forward)def backward(mul): func.grad(mul) with ti.Tape(loss=total): forward(4) assert x.grad == 4
Customized gradient function works with both
kernel.grad(). More examples can be found at
Another use case of customized gradient function is checkpointing. We can use recomputation to save memory space through
a user-defined gradient function.
demonstrates that by defining a customized gradient function that recomputes the grid states during backward,
we can reuse the grid states and allocate only one copy compared to
O(n) copies in a native implementation
without customized gradient function.
The DiffTaichi repo contains 10 differentiable physical simulators built with Taichi differentiable programming. A few examples with neural network controllers optimized using differentiable simulators and brute-force gradient descent: