In the optimizer, nodes without consumers are identified as outputs.
Since we can now return multiple values, this is inherently buggy,
since a value can then be both returned, and consumed to create another
input.
This commit fixes this by allowing the compiler to tag nodes as being
outputs.
For `ExtractSliceOp` and `InsertSliceOp`, the code performing the
hoisting of indexed operations in the batching pass derives the
indexes of the hoisted operation from the indexes provided by
`ExtractSliceOp::getOffsets()` and
`InsertSliceOp::getOffsets()`. However, these methods only return
dynamic indexes, such that operations with mixed, dynamic and static
offsets are hoisted incorrectly.
This patch replaces the invocations of `ExtractSliceOp::getOffsets()`
and `InsertSliceOp::getOffsets()` with invocations of
`ExtractSliceOp::getMixedOffsets()` and
`InsertSliceOp::getMixedOffsets()`, respectively in order to take into
account both static and dynamic indexes.
The IR generated by the batching pattern introduces intermediate
tensor values omitting the batched dimensions for batched
operands. This happens uncondiationally, leading to the generation of
`tensor.collapse_shape` operations with the same output and inout
shape. However, verification for such operation fails, since the
verifier assumes that the rank of the resulting tensor is reduced at
least by one.
This commit modified the check in `flattenTensor`, such that no
flattening operation is generated if the input and output shapes would
be identical.
Currently, the cleanup pattern matches sequences of `tensor.extract` /
`tensor.extract_slice`, `tensor.insert` / `tensor.insert_slice` and
`scf.yield` operations that are embedded into perfect loop nests whose
IVs are referenced in quasi-affine expressions with constant
steps. The pattern ensures that the extraction and insertion operation
use the same IVs for indexing, but does not check whether they appear
in the same order.
However, the order in which IVs are used for indexing is crucial when
replacing the operations with `tensor.extract_slice` and
`tensor.insert_slice` operations in order to preserve the shape of the
slices and the order of elements.
For example, when the cleanup pattern is applied to the following IR:
```
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%c2 = arith.constant 2 : index
%c3 = arith.constant 3 : index
scf.for %i = %c0 to %c3 step %c1 ... {
scf.for %j = %c0 to %c2 step %c1 ... {
%v = tensor.extract ... [%i, %j]
%t = tensor.insert ... [%j, %i]
scf.yield %t
}
...
}
```
The extracted slice has a shape of 3x2, while the insertion should be
happening with a slice with the shape 2x3.
This commit adds an additional check to the cleanup pattern that
ensures that loop IVs are used for indexing in the same order and
appear the same number of times.
Normalization of indexes in the batching pass currently
unconditionally emits arithmetic operations to shift and scale indexes
regardless of whether these indexes are already normalized. This leads
to unnecessary subtractions with 0 and divisions by 1.
This commit introduces two additional checks to the code normalizing
indexes that prevent arithmetic operations to be emitted for indexes
that do not need to be shifted or scaled.
- added --compress-input compiler option which forces the use of seeded
bootstrap keys and keyswitch keys
- replaced the concrete-cpu FHE implementation with tfhe-rs
Co-authored-by: Nikita Frolov <nf@mkmks.org>
This commit:
+ Adds support for a protocol which enables inter-op between concrete,
tfhe-rs and potentially other contributors to the fhe ecosystem.
+ Gets rid of hand-made serialization in the compiler, and
client/server libs.
+ Refactors client/server libs to allow more pre/post processing of
circuit inputs/outputs.
The protocol is supported by a definition in the shape of a capnp file,
which defines different types of objects among which:
+ ProgramInfo object, which is a precise description of a set of fhe
circuit coming from the same compilation (understand function type
information), and the associated key set.
+ *Key objects, which represent secret/public keys used to
encrypt/execute fhe circuits.
+ Value object, which represent values that can be transferred between
client and server to support calls to fhe circuits.
The hand-rolled serialization that was previously used is completely
dropped in favor of capnp in the whole codebase.
The client/server libs, are refactored to introduce a modular design for
pre-post processing. Reading the ProgramInfo file associated with a
compilation, the client and server libs assemble a pipeline of
transformers (functions) for pre and post processing of values coming in
and out of a circuit. This design properly decouples various aspects of
the processing, and allows these capabilities to be safely extended.
In practice this commit includes the following:
+ Defines the specification in a concreteprotocol package
+ Integrate the compilation of this package as a compiler dependency
via cmake
+ Modify the compiler to use the Encodings objects defined in the
protocol
+ Modify the compiler to emit ProgramInfo files as compilation
artifact, and gets rid of the bloated ClientParameters.
+ Introduces a new Common library containing the functionalities shared
between the compiler and the client/server libs.
+ Introduces a functional pre-post processing pipeline to this common
library
+ Modify the client/server libs to support loading ProgramInfo objects,
and calling circuits using Value messages.
+ Drops support of JIT.
+ Drops support of C-api.
+ Drops support of Rust bindings.
Co-authored-by: Nikita Frolov <nf@mkmks.org>
Instead of having one `getSQManp` implementation per op with a lot of repetition, the noise
calculation is now modular.
- Ops that implements`UnaryEint`/`BinaryInt`/`BinaryEint` interfaces share the operand noise
presence check.
- For many scalar ops no further calculation is needed. If it's not the case, an op can override
`sqMANP`.
- Integer operand types lookups are abstracted into `BinaryInt::operandIntType()`
- Finding largest operand value for a type is abstracted into `BinaryInt::operandMaxConstant`
- Noise calculation for matmul ops is simplified and it's now general enough to work for
`matmul_eint_int`, `matmul_int_eint` and `dot_eint_int` at once.
