Commit Graph

56 Commits

Author SHA1 Message Date
Dan Zheng
aa66cce808 [AutoDiff upstream] Add differentiation transform.
The differentiation transform does the following:
- Canonicalizes differentiability witnesses by filling in missing derivative
  function entries.
- Canonicalizes `differentiable_function` instructions by filling in missing
  derivative function operands.
- If necessary, performs automatic differentiation: generating derivative
  functions for original functions.
  - When encountering non-differentiability code, produces a diagnostic and
    errors out.

Partially resolves TF-1211: add the main canonicalization loop.

To incrementally stage changes, derivative functions are currently created
with empty bodies that fatal error with a nice message.

Derivative emitters will be upstreamed separately.
2020-04-02 15:43:57 -07:00
Dan Zheng
1308fc69c5 [AutoDiff] Simplify conditions enabling differentiable programming. (#30765)
Previously, two conditions were necessary to enable differentiable programming:
- Using the `-enable-experimental-differentiable-programming` frontend flag.
- Importing the `_Differentiation` module.

Importing the `_Differentiation` module is the true condition because it
contains the required compiler-known `Differentiable` protocol. The frontend
flag is redundant and cumbersome.

Now, the frontend flag is removed.
Importing `_Differentiation` is the only condition.
2020-04-02 03:24:03 -07:00
Marc Rasi
64d0e76dd9 improve differential operator tests 2020-03-30 14:57:59 -07:00
Richard Wei
57d228b39e [AutoDiff upstream] Add differential operators and some utilities.
* Add all [differential operators](https://github.com/apple/swift/blob/master/docs/DifferentiableProgramming.md#list-of-differential-operators).
* Add `withoutDerivative(at:)`, used for efficiently stopping the derivative propagation at a value and causing the derivative at the value to be zero.
* Add utility `differentiableFunction(from:)`, used for creating a `@differentiable` function from an original function and a derivative function.

Mostly work done by @marcrasi and @dan-zheng.
Partially resolves TF-843.

TODO:
* Add `AnyDerivative`.
* Add `Array.differentiableMap(_:)` and `differentiableReduce(_:_:)`.
2020-03-30 14:15:35 -07:00
Dan Zheng
8aac6f9a1a [AutoDiff upstream] Conform floating-point types to Differentiable. (#28718)
Add `Differentiable` conformances for floating-point types to the
`_Differentiation` module. The `TangentVector` associated type for
floating-point types is `Self`.

This design adheres to the differentiable programming manifesto:
docs/DifferentiableProgramming.md.

Partially resolves TF-1052.
2019-12-11 18:43:09 -08:00
Dan Zheng
53e61a9587 [AutoDiff upstream] Add the _Differentiation module. (#27511)
The `_Differentiation` module is the experimental support library for
differentiable programming. It is built when the build-script flag
`--enable-experimental-differentiable-programming` is enabled.

The `Differentiable` protocol generalizes all types that work with
differentiation. It is a core piece of the differentiable programming
project. Other parts depending on the `Differentiable` protocol will
be upstreamed piece by piece.

The `Differentiable` protocol is compiler-known and will be used during
type-checking, SILGen, and the SIL differentiation transform.
2019-11-06 11:31:12 -08:00