`DifferentiableFunctionInst` now stores result indices.
`SILAutoDiffIndices` now stores result indices instead of a source index.
`@differentiable` SIL function types may now have multiple differentiability
result indices and `@noDerivative` resutls.
`@differentiable` AST function types do not have `@noDerivative` results (yet),
so this functionality is not exposed to users.
Resolves TF-689 and TF-1256.
Infrastructural support for TF-983: supporting differentiation of `apply`
instructions with multiple active semantic results.
Support differentiation of `is` and `as?` operators.
These operators lower to branching cast SIL instructions, requiring control
flow differentiation support.
Resolves SR-12898.
Ensure that semantic member accesors have empty linear map structs.
Semantic member accessor VJPs do not accumulate any callee pullbacks.
Resolves SR-12800: SIL verification error.
Fix ownership errors in semantic member accessor pullbacks:
- Getter pullbacks: emit copy value operation before storing value.
- Setter pullbacks: track loaded value as a temporary, to be destroyed.
Add tests covering trivial, non-trivial, and address-only properties.
Resolves SR-12778 and SR-12779.
Add special-case VJP generation support for "semantic member accessors".
This is necessary to avoid activity analysis related diagnostics and simplifies
generated code.
Fix "wrapped property mutability" check in `Differentiable` derived conformnances.
This resolves SR-12642.
Add e2e test using real world property wrappers (`@Lazy` and `@Clamping`).
Support differentiation of property wrapper wrapped value getters and setters.
Create new pullback generation code path for "semantic member accessors".
"Semantic member accessors" are attached to member properties that have a
corresponding tangent stored property in the parent `TangentVector` type.
These accessors have special-case pullback generation based on their semantic
behavior. Currently, only getters and setters are supported.
This special-case pullback generation is currently used for stored property
accessors and property wrapper wrapped value accessors. In the future, it can
also be used to support `@differentiable(useInTangentVector)` computed
properties: SR-12636.
User-defined accesors cannot use this code path because they may use custom
logic that does not semantically perform a member access.
Resolves SR-12639.
For accessors: make `AbstractFunctionDecl::getDerivativeFunctionConfigurations`
resolve configurations from parent storage declaration `@differentiable`
attributes.
Fixes "no `@differentiable` attribute" non-differentiability error for accessors
whose parent storage declaration `@differentiable` attributes have not been
type-checked (e.g. because the storage declarations are in another file).
Add protocol requirement and class member storage declaration tests.
Resolves TF-1234.
In `AbstractFunctionDecl::getDerivativeFunctionConfigurations`, type-check
`@differentiable` attributes. This is important to populate derivative
configurations for original functions in other files.
Resolves TF-1271.
Exposes TF-1272: fix derivative configurations for cross-file `@derivative`
attributes. This is a more difficult issue.
Add DifferentiabilityWitnessDevirtualizer: an optimization pass that
devirtualizes `differentiability_witness_function` instructions into
`function_ref` instructions.
Co-authored-by: Dan Zheng <danielzheng@google.com>
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.
Canonicalizes `differentiable_function` instructions by filling in missing
derivative function operands.
Derivative function emission rules, based on the original function value:
- `function_ref`: look up differentiability witness with the exact or a minimal
superset derivative configuration. Emit a `differentiability_witness_function`
for the derivative function.
- `witness_method`: emit a `witness_method` with the minimal superset derivative
configuration for the derivative function.
- `class_method`: emit a `class_method` with the minimal superset derivative
configuration for the derivative function.
If an *actual* emitted derivative function has a superset derivative
configuration versus the *desired* derivative configuration, create a "subset
parameters thunk" to thunk the actual derivative to the desired type.
For `differentiable_function` instructions formed from curry thunk applications:
clone the curry thunk (with type `(Self) -> (T, ...) -> U`) and create a new
version with type `(Self) -> @differentiable (T, ...) -> U`.
Progress towards TF-1211.
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.
