The batching pass passes operands to the batched operation as a flat,
one-dimensional vector produced through a `tensor.collapse_shape`
operation collapsing all dimensions of the original tensor of
operands. Similarly, the shape of the result vector of the batched
operation is expanded to the original shape afterwards using a
`tensor.expand_shape` operation.
The pass emits the `tensor.collapse_shape` and `tensor.expand_shape`
operations unconditionally, even for tensors, which already have only
a single dimension. This causes the verifiers of these operations to
fail in some cases, aborting the entire compilation process.
This patch lets the batching pass emit `tensor.collapse_shape` and
`tensor.expand_shape` for batched operands and batched results only if
the rank of the corresponding tensors is greater than one.
This CI "feature" is meant to circumvent the 6 hours hard-limit
for a job in GitHub Action.
The benchmark is done using a matrix which is handled by Slab.
Here's the workflow:
1. ML benchmarks are started in a fire and forget fashion via
start_ml_benchmarks.yml
2. Slab will read ci/slab.toml to get the AWS EC2 configuration
and the matrix parameters
3. Slab will launch at most max_parallel_jobs EC2 instances in
parallel
4. Each job will trigger ml_benchmark_subset.yml which will run
only one of the generated YAML file via make generate-mlbench,
based on the value of the matrix item they were given.
5. As soon as a job is completed, the next one in the matrix
will start promptly.
This is done until all the matrix items are exhausted.
This adds a new end-to-end test `apply_lookup_table_batched`, which
forces batching of Concrete operations when invoking the compiler
engine, indirectly causing the `concrete.bootstrap_lwe` and
`concrete.keyswitch_lwe` operations generated from the
`FHELinalg.apply_lookup_table` operation of the test to be batched
into `concrete.batched_bootstrap_lwe` and
`concrete.batched_keyswitch_lwe` operations. The batched operations
trigger the generation of calls to batching wrapper functions further
down the pipeline, effectively testing the lowering and implementation
of batched operations altogether.
Bench just one compilation option for automatic benchmarks. Only 'loop'
option is tested to take advantage of hardware with a lot of available
CPUs. Running benchmarks with 'default' option is suboptimal for this
kind of hardware since it uses only one CPU.
This also remove time consuming MNIST test, as it should be in ML benchmarks.
Moreover Makefile is fixed to use provided Python executable instead of
relying on system one to generate MLIR Yaml files.
- 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
This moves all tests from
`tests/end_to_end_tests/end_to_end_jit_clear_tensor.cc` to the test
specification in YAML format in
`tests/end_to_end_fixture/end_to_end_clear_tensor.yaml`. Parametric
tests and tests invoking lambdas in loops have been fully unrolled.
This patch adds support for scalar results to the client/server
protocol and tests. In addition to `TensorData`, a new type
`ScalarData` is added. Previous representations of scalar values using
one-dimensional `TensorData` instances have been replaced with proper
instantiations of `ScalarData`.
The generic use of `TensorData` for scalar and tensor values has been
replaced with uses of a new variant `ScalarOrTensorData`, which can
either hold an instance of `TensorData` or `ScalarData`.
This adds a new optional boolean parameter
`use_default_fhe_constraints` to the YAML description of tests that
causes the test backend to use the default FHE constraints if set to
true. This is necessary for clear text tests, since automatic
determination of FHE parameters by the `CompilerEngine` fails for
programs not using encrypted integers and causes the program to abort.
The code in `loadEndToEndDesc` attempts to load the contents of a YAML
file without ensuring that the stream from which the file is read was
set up correctly. As a result, streams in a bad state, e.g., if the
file to be opened does not exist, simply result in an empty string for
the file contents. The test driver cannot distinguish this result from
a valid YAML file that is simply empty.
This patch causes `loadEndToEndDesc` to abort execution whenever a
file input stream could not be set up correctly.
Returning tensors with elements whose width is not equal to 64 results
in garbled data. This commit extends the `TensorData` class used to
represent tensors in JIT compilation with support for signed /
unsigned elements of 8/16/32 and 64 bits, such that all clear text
tensors with up to 64 bits can be represented accurately.
