Add documentation on Moose - introduction and protocol implementation guide

documentation/DevelopmentGuide.md

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+# Moose
+Moose is a framework for secure multi-party computation, which offers an easy to use numpy-like Python API that allows users to build machine learning models that perform inference on encrypted data.
+
+
+### How to implement new protocols in Moose
+Example: How to implement an operation edsl.ones(), which creates a vector of ones with a given lenght
+
+Summary
+- pymoose/pymoose/edsl/base.py
+
+    Add class OnesExperssion
+
+    Add def ones(), which returns OnesExpression(placement, inputs, vtype)
+
+- pymoose/pymoose/edsl/tracer.py
+
+    Add def visit_OnesExpression to class AstTracer, which returns a logical computation (by mapping expressions to operations that are added to computational graph
+
+- pymoose/pymoose/computation/utils.py
+
+    Add ops.OnesOperation to SUPPORTED_TYPES
+
+    This script takes a computational graph and serializes it using msgpack to output a binary that can be passed into Rust
+
+- pymoose/pymoose/computation/operations.py
+
+    Add class OnesOperation
+
+- pymoose/src/bindings.py
+
+    Nothing to add
+
+    These are the bindings between Python and Rust
+
+- pymoose/src/computation.rs
+
+    Add OnesOperation(PyOnesOperation) to enum PyOperation
+
+    Add struct PyOnesOperation(name, inputs, placement_name, signature)
+
+    Add OnesOperation to TryFrom
+
+- moose/src/computation.rs
+
+    Add Ones to operators![]
+
+    Add pub struct OnesOp
+
+- moose/src/compilation/well-formed.rs
+
+    Add Ones(op) => DispatchKernel
+
+- moose/src/execution/asynchronous.rs, symbolic.rs, synchronous.rs
+
+    Add Ones(op) => DispatchKernel
+
+- moose/src/floatingpoint/ops.rs
+
+    Add impl OnesOp
+
+- moose/src/host/ops.rs (if the protocol is for host placements)
+
+    Add impl OnesOp

documentation/Introduction.md

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+# Moose
+Moose is a framework for secure multi-party computation, which offers an easy to use numpy-like Python API that allows users to build machine learning models that perform inference on encrypted data.
+
+
+### Threat model at Cape: honest, but curious
+It is permissible that if parties collude with each other, they can use result values to reconstruct inputs
+This threat model is less strict than active security, which takes into account that perhaps not every party follows given protocols
+
+### Moose has 3 (main) components: 
+- EDSL (embedded domain specific language) - PyMoose
+- Elk compiler
+- Moose runtime
+
+### Running onnx with PyMoose
+Given an onnx file as input, a Moose computation is produced as follows:
+- Convert onnx to PyMoose predictor
+- PyMoose predictor builds an edsl computation using its `predictor_factory` method
+- edsl computation is traced by ASTTracer into a python Computation object
+- Python Computation serialized to msgpack; msgpack deserialized into Moose’s Computation struct
+- Dump Moose Computation to textual format; output the result
+
+### Interface between PyMoose and Moose
+- pymoose/pymoose/computation is the interface between PyMoose and Moose
+- pymoose/pymoose/computation/utils.py specifies how to serialize PyMoose computations (Python) to msgpack
+- moose/src/computations.rs deserializes msgpack to Moose computations, matching Python operators, values, and types with Rust analogues
+A computation is represented as a graph, where nodes = operators and edges = Values. Each edge has a Rust type and a Moose type  
+
+### Levels of computations/Lowering
+- Lowering is implemented via multiple dispatch of “kernel” functions
+- Logical level (Python edsl) = top level
+- Lower from top level through intermediate representations (IR) levels to reach the bottom level (like traversing a tree in depth first search manner)
+- The final level is the runtime kernel, which is a (usually pure) Rust function/closure that can be executed at runtime
+- Each “level” is represented by a particular kernel; kernels are loosely organized into “dialects” (similar to Tensorflow MLIR)
+
+    A level can, for example, relate to placement.
+    Example: The logical level is higher than the “replicated” level, which defines replicated protocol implementations
+    Example: The replicated level is higher than the “host” level, because replicated protocols consist of collections of host operations
+
+    A level can also relate to different abstractions of values/types
+    Example: Floatingpoint is an abstraction over Host/Mirrored implementations of floatingpoint ops.
+    Example: HostFixedTensor is higher than HostRingTensor, because it consists of a HostRingTensor with additional metadata (integral/fractional precision)
+
+### Placements
+- Host = data is on just 1 machine
+- Mirrored = data is on several machines (the machines have the same data and perform the same operations to represent public data)
+- Replicated = data is secret shared across several machines
+- Additive = alternative secret sharing scheme, currently only used in subprotocols for replicated placement
+
+### Truncation
+Numbers are represented using fixed point representation: 128 bits for integer and fractional part.
+Results of operations on fixed point numbers must be truncated in some cases  (e.g.: multiplication) to ensure that the result has the same number integer and fractional bits as the inputs. I.e.: fixed point representation must match for inputs and outputs. 
+
+### Sessions
+There are 3 types of Sessions based on execution patterns:
+- Asynchronous (“prod”) 
+    Performs lowering until a runtime kernel is reached, then schedule a Tokio task
+- Synchronous (“dev”)
+- Symbolic (“graph analysis and optimization”) 
+    One can leverage symbolic execution to lower a computation without actually executing it (i.e.: run the computation with symbolic values instead of concrete values for its args). Depth-first lowering stops when a runtime kernel is reached; return computation graph after full graph has been lowered.
+
+### SymbolicStrategy
+More flexible API for symbolic execution. Allows the user to drop out of the usual lowering process used by the Sync/Async Session structs, e.g. to enforce graph-wide properties or make edits to the graph before continuing lowering
+API is currently incomplete/undefined, but will be designed to be convenient to write compile-time optimizations in the future
+
+### Runtime session attributes
+Role = host placement
+Identity = the host assigned to the role (e.g.: a string (Alice), IP address with a port)