Clean up a few general patterns that are now obviated by canImport
This aligns more generally with the cleanup that the Swift Package
Manager has already done in their automated XCTest-plumbing tool in
apple/swift-package-manager#1826.
`Differentiable` conformance derivation now supports
`Differentiable.zeroTangentVectorInitializer`.
There are two potential cases:
1. Memberwise derivation: done when `TangentVector` can be initialized memberwise.
2. `{ TangentVector.zero }` derivation: done as a fallback.
`zeroTangentVectorInitializer` is a closure that produces a zero tangent vector,
capturing minimal necessary information from `self`.
It is an instance property, unlike the static property `AdditiveArithmetic.zero`,
and should be used by the differentiation transform for correctness.
Remove `Differentiable.zeroTangentVectorInitializer` dummy default implementation.
Update stdlib `Differentiable` conformances and tests.
Clean up DerivedConformanceDifferentiable.cpp cruft.
Resolves TF-1007.
Progress towards TF-1008: differentiation correctness for projection operations.
The derivative wrt `self` should drop the last element from the incoming seed.
Example:
- Incoming seed: [1, 2, 3, 4]
- Derivative wrt `self`: [1, 2, 3]
- Derivative wrt appended element: 4
New files were added in #30875 which did not include os(OpenBSD), so add
this.
add_swift_target_library in AddSwiftStdlib subsequently required
modification. _add_target_variant_link_flags likely needs modification as
well, but this is better suited to a separate PR.
Add `Differentiable.withDerivative(_:)`, a "derivative surgery" API.
`Differentiable.withDerivative(_:)` is an identity function returning `self`.
It takes a closure and applies it to the derivative of the return value, in
contexts where the return value is differentiated with respect to.
JVP functions are forward-mode derivative functions. They take original
arguments and return original results and a differential function. Differential
functions take derivatives wrt arguments and return derivatives wrt results.
`JVPEmitter` is a cloner that emits JVP and differential functions at the same
time. In JVP functions, function applications are replaced with JVP function
applications. In differential functions, function applications are replaced
with differential function applications.
In JVP functions, each basic block takes a differential struct containing callee
differentials. These structs are consumed by differential functions.
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.
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.
* 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(_:_:)`.
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.
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.
