Type annotations for instruction operands are omitted, e.g.
```
%3 = struct $S(%1, %2)
```
Operand types are redundant anyway and were only used for sanity checking in the SIL parser.
But: operand types _are_ printed if the definition of the operand value was not printed yet.
This happens:
* if the block with the definition appears after the block where the operand's instruction is located
* if a block or instruction is printed in isolation, e.g. in a debugger
The old behavior can be restored with `-Xllvm -sil-print-types`.
This option is added to many existing test files which check for operand types in their check-lines.
Compiler:
- Add `Forward` and `Reverse` to `DifferentiabilityKind`.
- Expand `DifferentiabilityMask` in `ExtInfo` to 3 bits so that it now holds all 4 cases of `DifferentiabilityKind`.
- Parse `@differentiable(reverse)` and `@differentiable(_forward)` declaration attributes and type attributes.
- Emit a warning for `@differentiable` without `reverse`.
- Emit an error for `@differentiable(_forward)`.
- Rename `@differentiable(linear)` to `@differentiable(_linear)`.
- Make `@differentiable(reverse)` type lowering go through today's `@differentiable` code path. We will specialize it to reverse-mode in a follow-up patch.
ABI:
- Add `Forward` and `Reverse` to `FunctionMetadataDifferentiabilityKind`.
- Extend `TargetFunctionTypeFlags` by 1 bit to store the highest bit of differentiability kind (linear). Note that there is a 2-bit gap in `DifferentiabilityMask` which is reserved for `AsyncMask` and `ConcurrentMask`; `AsyncMask` is ABI-stable so we cannot change that.
_Differentiation module:
- Replace all occurrences of `@differentiable` with `@differentiable(reverse)`.
- Delete `_transpose(of:)`.
Resolves rdar://69980056.
`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.
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 test/AutoDiff/lit.local.cfg: run tests only when `differentiable_programming`
is enabled in lit. With this, individual tests no longer need
`REQUIRES: differentiable_programming`.
Move multi-functionality SIL tests from test/AutoDiff/SIL/Serialization to
test/AutoDiff/SIL.
Garden test filenames.
Add `differentiable_function` and `differentiable_function_extract`
instructions.
`differentiable_function` creates a `@differentiable` function-typed
value from an original function operand and derivative function operands
(optional).
`differentiable_function_extract` extracts either the original or
derivative function value from a `@differentiable` function.
The differentiation transform canonicalizes `differentiable_function`
instructions, filling in derivative function operands if missing.
Resolves TF-1139 and TF-1140.
This is necessary because the `Differentiable` protocol exists in stdlib core
on `tensorflow` branch but in the `_Differentiation` module on `master` branch.
The robust solution is to add auto-import `_Differentiation` logic to `tensorflow`.
The `differentiability_witness_function` instruction looks up a
differentiability witness function (JVP, VJP, or transpose) for a referenced
function via SIL differentiability witnesses.
Add round-trip parsing/serialization and IRGen tests.
Notes:
- Differentiability witnesses for linear functions require more support.
`differentiability_witness_function [transpose]` instructions do not yet
have IRGen.
- Nothing currently generates `differentiability_witness_function` instructions.
The differentiation transform does, but it hasn't been upstreamed yet.
Resolves TF-1141.
There is currently a difference between the tensorflow branch and the
master branch. On tensorflow, the differentiability support is merged
into the standard library. This changes the decoration of the witness.
Loosen the test to accept either.
We should change the tensorflow branch to generate the
`_Differentiation` module with the support and then auto-import the
module in the longer term. This can be gated by the
`-enable-experimental-autodifferentiation` flag to the driver to gain
the same behaviour on both the branches.
SIL differentiability witnesses are a new top-level SIL construct mapping
an "original" SIL function and derivative configuration to derivative SIL
functions.
This patch adds `SILDifferentiabilityWitness` IRGen.
`SILDifferentiabilityWitness` has a fixed `{ i8*, i8* }` layout:
JVP and VJP derivative function pointers.
Resolves TF-1146.
SIL differentiability witnesses are a new top-level SIL construct mapping
an "original" SIL function and derivative configuration to derivative SIL
functions.
This patch adds `SILDifferentiabilityWitness` serialization/deserialization.
Resolves TF-1136.
The `@noDerivative` attribute marks the non-differentiability parameters of a
`@differentiable` function type. All parameters except those marked with
`@noDerivative` are differentiability parameters.
For example, `@differentiable (Float, @noDerivative Float) -> Float` is only
differentiable with respect to its first parameter.
The `@noDerivative` attribute is represented as a
`SILParameterDifferentiability` bit on `SILParameterInfo`.
Add round-trip serialization tests.
Resolves TF-872.
