this is a first commit to support operations on U64 by decomposing them
into smaller chunks (32 chunks of 2 bits). This commit introduce the
lowering pass that will be later populated to support other operations.
Use `OpConversionPattern` instead of `OpRewritePattern` for operation
conversion during dialect conversion. This makes explicit and in-place
type conversions unnecessary, since `OpConversionPattern` already
properly converts operand types and provides them to the rewrite rule
through an operation adaptor.
The main contributions of this commit are the two class templates
`TypeConvertingReinstantiationPattern` and
`GenericOneToOneOpConversionPattern`.
The former allows for the definition of a simple replacement rule that
re-instantiates an operation after the types of its operands have been
converted. This is especially useful for type-polymorphic operations
during dialect conversion.
The latter allows for the definition of patterns, where one operation
needs to be replaced with a different operation after conversion of
its operands.
The default implementations for the class templates provide
conversions rules for operations that have a generic builder method
that takes the desired return type(s), the operands and (optionally) a
set of attributes. How attributes are discarded during a conversion
(either by omitting the builder argument or by passing an empty set of
attributes) can be defined through specialization of
`ReinstantiationAttributeDismissalStrategy`.
Custom replacement rules that deviate from the scheme above should be
implemented by specializing
`TypeConvertingReinstantiationPattern::matchAndRewrite()` and
`GenericOneToOneOpConversionPattern::matchAndRewrite()`.
This adds a new pass `ExtractSDGOps`, which scans a function for
operations that implement `SDFGConvertibleOpInterface`, replaces them
with SDFG processes and constructs an SDFG graph around the processes.
Initialization and teardown of the SDFG graph are embedded into the
function and take place at the beginning of the function and before
the function's terminator, respectively.
The pass can be invoked using concretecompiler by specifying the new
compilation option `--emit-sdfg-ops` or programmatically on a
`CompilerEngine` using the new compilation option `extractSDFGOps`.
This adds a new operation interface `SDFGConvertibleOpInterface` that
allows an operation to specify how it is converted to an SDFG
process. The interface consists of a single method `convert` that
receives as the arguments the DFG created using `SDFG.init`, a set of
SDFG input streams corresponding to the operands and a set of output
streams for results. The order of the input and output streams
corresponds to the order of the operands and output values,
respectively.
This adds a new dialect called "SDFG" for data flow graphs. An SDFG
data flow graph is composed of a set of processes, connected through
data streams. Special streams allow for data to be injected into and
to be retrieved from the data flow graph.
The dialect is intended to be lowered to API calls that allow for
offloading of the graph on hardware accelerators.
- unify CPU and GPU bootstrapping operations
- remove operations to build GLWE from table: this is now done in
wrapper functions
- remove GPU memory management operations: done in wrappers now, but we
will have to think about how to deal with it later in MLIR
The bufferization of the BConcrete dialect emits calls to Concrete
wrapper functions and casts all memrefs to ranked memrefs with dynamic
strides and an implicit identity layout map. The implicit identity map
does not allow for casts of memrefs with non-zero offsets, e.g.,
resulting from folding of memrefs related to intermediate results
passed as operands to the operation implemented by a wrapper.
Casting to memrefs symbolic offsets in the layout map (e.g.,
`[d0, d1, ...](s0, s1, ...) -> (d0 + s0, d1 + s1, ...)`) allows
for more flexibility, in particular this adds support for memrefs
with non-zero, constant offsets returned by operations generating
intermediate results.
For now what it works are only levelled ops with user parameters. (take a look to the tests)
Done:
- Add parameters to the fhe parameters to support CRT-based large integers
- Add command line options and tests options to allows the user to give those new parameters
- Update the dialects and pipeline to handle new fhe parameters for CRT-based large integers
- Update the client parameters and the client library to handle the CRT-based large integers
Todo:
- Plug the optimizer to compute the CRT-based large interger parameters
- Plug the pbs for the CRT-based large integer
