This changes the semantics of `HLFHE.dot_eint_int` from memref-based
reference semantics to tensor-based value semantics. The former:
"HLFHE.dot_eint_int"(%arg0, %arg1, %arg2) :
(memref<Nx!HLFHE.eint<0>>, memref<Nxi32>, memref<!HLFHE.eint<0>>) -> ()
becomes:
"HLFHE.dot_eint_int"(%arg0, %arg1) :
(tensor<Nx!HLFHE.eint<0>>, tensor<Nxi32>) -> !HLFHE.eint<0>
As a side effect, data-flow analyses become much easier. With the
previous memref type of the plaintext argument it is difficult to
check whether the plaintext values are statically defined constants or
originate from a memory region changed at execution time (e.g., for
analyses evaluating the impact on noise). Changing the plaintext type
from `memref` to `vector` makes such analyses significantly easier.
-Encoding Cleartext to Plaintext Op
-Encoding Int to Plaintext Op
-Cleartext/Plaintext should add a bits field as `p`
-Op to create a constant Cleartext
-IntToCleartextOp: kind of casting an int to be later encoded, I'm not
sure if there is a better mechanism for this (e.g. auto casting in Ops),
but we currently need a way to encode int into plaintext, and we need to
go through cleartexts
also fixes an issue regarding populateWithGenerated, which can be
duplicated across different pattern files. So I redefined a different
function that is more unique to the pass that should be ran, and hide
the populateWithGenerated from the global namespace
* feat(compiler): low level fhe dialect
* feat(compiler): using generated printer/parser in LowLFHE
* feat(compiler): new types and ops for LowLFHE
* tests(compiler): LowLFHE types and ops
* feat(compiler): fill ops
* cleanup
* summary + description
* tests(compiler): use new CLI args
* formatting
- feat(compiler): python bindings
- build: update docker image for python bindings
- pin pybind11 to 2.6.2, 2.7 is not having correct include_dirs set (still
a question why?)
- using generated parser/printer
This adds a new command line option
`--convert-hlfhe-tensor-ops-to-linalg` that invokes a conversion pass
replacing any HLFHE tensor operation with an appropriate instance of
`linalg.generic`.
