diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 71ff7c6ad..497a33c34 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -21,7 +21,7 @@ jobs: lfs: true - name: setup CI - uses: lava-nc/ci-setup-composite-action@v1.2 + uses: lava-nc/ci-setup-composite-action@v1.5.10_py3.10 with: repository: 'Lava' @@ -39,7 +39,7 @@ jobs: lfs: true - name: setup CI - uses: lava-nc/ci-setup-composite-action@v1.2 + uses: lava-nc/ci-setup-composite-action@v1.5.10_py3.10 with: repository: 'Lava' @@ -55,14 +55,14 @@ jobs: runs-on: ${{ matrix.operating-system }} strategy: matrix: - operating-system: [ubuntu-latest, windows-latest, macos-13] + operating-system: [ubuntu-latest, macos-13] steps: - uses: actions/checkout@v3 with: lfs: true - name: setup CI - uses: lava-nc/ci-setup-composite-action@v1.2 + uses: lava-nc/ci-setup-composite-action@v1.5.10_py3.10 with: repository: 'Lava' diff --git a/RELEASE.md b/RELEASE.md deleted file mode 100644 index 1852313be..000000000 --- a/RELEASE.md +++ /dev/null @@ -1,82 +0,0 @@ -_Lava_ v0.4.0 brings initial support to compile and run models on Loihi 2 via Intel’s cloud hosted Loihi systems through participation in the Intel Neuromorphic Research Community (INRC). In addition, new tutorials and documentation explain how to build Lava Processes written in Python or C for CPU and Loihi backends (C and Loihi tutorials available via the INRC). - -While this release offers few high-level application examples, Lava v0.4.0 provides major enhancements to the overall Lava architecture. It forms the basis for the open-source community to enable the full Loihi feature set, such as on-chip learning, convolutional connectivity, or accelerated spike IO. The Lava Compiler and Runtime architecture has also been generalized allowing extension to other backends or neuromorphic processors. - -## New Features and Improvements -Features marked with * are available as part of the Loihi 2 extension. -- *Extended Process library including new ProcessModels and additional improvements: - - LIF, Sigma-Delta, and Dense Processes execute on Loihi NeuroCores. - - Prototype Convolutional Process added. - - Sending and receiving spikes to NeuroCores via embedded processes that can be programmed in C with examples included. - - All Lava Processes now list all constructor arguments explicitly with type annotations. -- *Added high-level API to develop custom ProcessModels that use Loihi 2 features: - - Loihi NeuroCores can be programmed in Python by allocating neural network resources like Axons, Synapses or Neurons. In particular, Loihi 2 NeuroCore Neurons can be configured by writing highly flexible assembly programs. - - Loihi embedded processors can be programmed in C. But unlike the prior NxSDK, no knowledge of low-level registers details is required anymore. Instead, the C API mirrors the high-level Python API to interact with other processes via channels. -- Compiler and Runtime support for Loihi 2: - - General redesign of Compiler and Runtime architecture to support compilation of Processes that execute across a heterogenous backend of different compute resources. CPU and Loihi are supported via separate sub compilers. - - *The Loihi NeuroCore sub compiler automatically distributes neural network resources across multiple cores. - - *The Runtime supports direct channel-based communication between Processes running on Loihi NeuroCores, embedded CPUs or host CPUs written in Python or C. Of all combinations, only Python<->C and C<->NeuroCore are currently supported. - - *Added support to access Process Variables on Loihi NeuroCores at runtime via Var.set and Var.get(). -- New tutorials and improved class and method docstrings explain how new Lava features can be used such as *NeuroCore and *embedded processor programming. -- An extended suite of unit tests and new *integration tests validate the correctness of the Lava framework. - - -## Bug Fixes and Other Changes - -- Support for virtual ports on multiple incoming connections (Python Processes only) (Issue [#223](https://github.com/lava-nc/lava/issues/223), PR [#224](https://github.com/lava-nc/lava/pull/224)) -- Added conda install instructions (PR [#225](https://github.com/lava-nc/lava/pull/225)) -- Var.set/get() works when RunContinuous RunMode is used (Issue [#255](https://github.com/lava-nc/lava/issues/255), PR [#256](https://github.com/lava-nc/lava/pull/256)) -- Successful execution of tutorials now covered by unit tests (Issue [#243](https://github.com/lava-nc/lava/issues/243), PR [#244](https://github.com/lava-nc/lava/pull/244)) -- Fixed PYTHONPATH in tutorial_01 (Issue [#45](https://github.com/lava-nc/lava/issues/45), PR [#239](https://github.com/lava-nc/lava/pull/239)) -- Fixed output of tutorial_07 (Issue [#249](https://github.com/lava-nc/lava/issues/249), PR [#253](https://github.com/lava-nc/lava/pull/253)) - -## Breaking Changes - -- Process constructors for standard library processes now require explicit keyword/value pairs and do not accept arbitrary input arguments via **kwargs anymore. This might break some workloads. -- use_graded_spike kwarg has been changed to num_message_bits for all the built-in processes. -- shape kwarg has been removed from Dense process. It is automatically inferred from the weight parameter’s shape. -- Conv Process has additional arguments weight_exp and num_weight_bits that are relevant for fixed-point implementations. -- The sign_mode argument in the Dense Process is now an enum rather than an integer. -- New parameters u and v in the LIF Process enable setting initial values for current and voltage. -- The bias parameter in the LIF Process has been renamed to bias_mant. - - -## Known Issues - -- Lava does currently not support on-chip learning, Loihi 1 and a variety of connectivity compression features such as convolutional encoding. -- All Processes in a network must currently be connected via channels. Running unconnected Processes using NcProcessModels in parallel currently gives incorrect results. -- Only one instance of a Process targeting an embedded processor (using CProcessModel) can currently be created. Creating multiple instances in a network, results in an error. As a workaround, the behavior of multiple Processes can be fused into a single CProcessModel. -- Direct channel connections between Processes using a PyProcessModel and NcProcessModel are not supported. -- In the scenario that InputAxons are duplicated across multiple cores and users expect to inject spikes based on the declared port size, then the current implementation leads to buffer overflows and memory corruption. -- Channel communication between PyProcessModels is slow. -- The Lava Compiler is still inefficient and in need of improvement to performance and memory utilization. -- Virtual ports are only supported between Processes using PyProcModels, but not between Processes when CProcModels or NcProcModels are involved. In addition, VirtualPorts do not support concatenation yet. -- Joining and forking of virtual ports is not supported. -- The Monitor Process does currently only support probing of a single Var per Process implemented via a PyProcessModel. The Monitor Process does currently not support probing of Vars mapped to NeuroCores. -- Despite new docstrings, type annotations, and parameter descriptions to most of the public user-facing API, some parts of the code still have limited documentation and are missing type annotations. - - -## What's Changed -* Virtual ports on multiple incoming connections by @mathisrichter in https://github.com/lava-nc/lava/pull/224 -* Add conda install to README by @Tobias-Fischer in https://github.com/lava-nc/lava/pull/225 -* PYTHONPATH fix in tutorial by @jlubo in https://github.com/lava-nc/lava/pull/239 -* Fix tutorial04_execution.ipynb by @mgkwill in https://github.com/lava-nc/lava/pull/241 -* Tutorial tests by @mgkwill in https://github.com/lava-nc/lava/pull/244 -* Update README.md remove vlab instructions by @mgkwill in https://github.com/lava-nc/lava/pull/248 -* Tutorial bug fix by @PhilippPlank in https://github.com/lava-nc/lava/pull/253 -* Fix get set var by @PhilippPlank in https://github.com/lava-nc/lava/pull/256 -* Update runtime_service.py by @PhilippPlank in https://github.com/lava-nc/lava/pull/258 -* Release/v0.4.0 by @mgkwill in https://github.com/lava-nc/lava/pull/265 - -## Thanks to our Contributors - -- Intel Corporation: All contributing members of the Intel Neuromorphic Computing Lab - -### Open-source community: -- [Tobias-Fischer](https://github.com/Tobias-Fischer), Tobias Fischer -- [jlubo](https://github.com/jlubo), Jannik Luboeinski - -## New Contributors -* @jlubo made their first contribution in https://github.com/lava-nc/lava/pull/239 - -**Full Changelog**: https://github.com/lava-nc/lava/compare/v0.3.0...v0.4.0 diff --git a/poetry.lock b/poetry.lock index fccb79cfd..da3da826a 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,14 +1,14 @@ -# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. 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[[package]] @@ -4190,13 +4129,13 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "virtualenv" -version = "20.25.1" +version = "20.26.3" description = "Virtual Python Environment builder" optional = false python-versions = ">=3.7" files = [ - {file = "virtualenv-20.25.1-py3-none-any.whl", hash = "sha256:961c026ac520bac5f69acb8ea063e8a4f071bcc9457b9c1f28f6b085c511583a"}, - {file = "virtualenv-20.25.1.tar.gz", hash = "sha256:e08e13ecdca7a0bd53798f356d5831434afa5b07b93f0abdf0797b7a06ffe197"}, + {file = "virtualenv-20.26.3-py3-none-any.whl", hash = "sha256:8cc4a31139e796e9a7de2cd5cf2489de1217193116a8fd42328f1bd65f434589"}, + {file = "virtualenv-20.26.3.tar.gz", hash = "sha256:4c43a2a236279d9ea36a0d76f98d84bd6ca94ac4e0f4a3b9d46d05e10fea542a"}, ] [package.dependencies] @@ -4205,7 +4144,7 @@ filelock = ">=3.12.2,<4" platformdirs = ">=3.9.1,<5" [package.extras] -docs = ["furo (>=2023.7.26)", "proselint (>=0.13)", "sphinx (>=7.1.2)", "sphinx-argparse (>=0.4)", "sphinxcontrib-towncrier 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"sha256:2884ed22e7d8961de1c9a05142eb69a247f120291bc0206a00a7642f09b5b715"}, + {file = "zipp-3.19.2-py3-none-any.whl", hash = "sha256:f091755f667055f2d02b32c53771a7a6c8b47e1fdbc4b72a8b9072b3eef8015c"}, + {file = "zipp-3.19.2.tar.gz", hash = "sha256:bf1dcf6450f873a13e952a29504887c89e6de7506209e5b1bcc3460135d4de19"}, ] [package.extras] -docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] -testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"] +doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] +test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"] [metadata] lock-version = "2.0" -python-versions = ">=3.9, <3.11" -content-hash = "5f586164dce49b4cfd4de650399a7879ce1e166a361e273d9699abb2535d6c11" +python-versions = ">=3.10, <3.11" +content-hash = "699b81595c61a64c61d4b845d250e948d8230a063d676e81570b3556a47a3f44" diff --git a/pyproject.toml b/pyproject.toml index 13ca68f12..6393c7dba 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -10,7 +10,7 @@ packages = [ {include = "tests"} ] include = ["tutorials"] -version = "0.9.0.dev0" +version = "0.11.0.dev0" readme = "README.md" description = "A Software Framework for Neuromorphic Computing" homepage = "https://lava-nc.org/" @@ -46,13 +46,13 @@ classifiers = [ "Discussions" = "https://github.com/lava-nc/lava/discussions" [tool.poetry.dependencies] -python = ">=3.9, <3.11" +python = ">=3.10, <3.11" numpy = "^1.24.4" scipy = "^1.10.1" networkx = "<=2.8.7" asteval = "^0.9.31" -scikit-learn = "^1.3.1" +scikit-learn = "^1.5.0" notebook = "^7.0.6" bokeh = "^3.3.4" diff --git a/src/lava/magma/compiler/channel_map.py b/src/lava/magma/compiler/channel_map.py index 2eadd52cc..5c963792c 100644 --- a/src/lava/magma/compiler/channel_map.py +++ b/src/lava/magma/compiler/channel_map.py @@ -11,6 +11,7 @@ from lava.magma.compiler.utils import PortInitializer from lava.magma.core.process.ports.ports import AbstractPort from lava.magma.core.process.ports.ports import AbstractSrcPort, AbstractDstPort +from lava.magma.core.process.process import AbstractProcess @dataclass(eq=True, frozen=True) @@ -27,6 +28,10 @@ class Payload: dst_port_initializer: PortInitializer = None +def lmt_init_id(): + return -1 + + class ChannelMap(dict): """The ChannelMap is used by the SubCompilers during compilation to communicate how they are planning to partition Processes onto their @@ -35,7 +40,7 @@ class ChannelMap(dict): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self._initializers_lookup = dict() - self._lmt_allocation_dict: ty.Dict[int, int] = defaultdict(lambda: -1) + self._lmt_allocation_dict: ty.Dict[int, int] = defaultdict(lmt_init_id) def __setitem__( self, key: PortPair, value: Payload, dict_setitem=dict.__setitem__ @@ -91,7 +96,8 @@ def from_proc_groups(self, return channel_map @classmethod - def _get_port_pairs_from_proc_groups(self, proc_groups: ty.List[ProcGroup]): + def _get_port_pairs_from_proc_groups(cls, + proc_groups: ty.List[ProcGroup]): """Loop over processes connectivity and get all connected port pairs.""" processes = list(itertools.chain.from_iterable(proc_groups)) port_pairs = [] @@ -102,7 +108,7 @@ def _get_port_pairs_from_proc_groups(self, proc_groups: ty.List[ProcGroup]): for src_port in src_ports: dst_ports = src_port.get_dst_ports() for dst_port in dst_ports: - if self._is_leaf_process_port(dst_port, processes): + if cls._is_leaf_process_port(dst_port, processes): port_pairs.append(PortPair(src=src_port, dst=dst_port)) return port_pairs @@ -111,9 +117,9 @@ def _is_leaf_process_port(dst_port, processes): dst_process = dst_port.process return True if dst_process in processes else False - def set_port_initializer( - self, port: AbstractPort, port_initializer: PortInitializer - ): + def set_port_initializer(self, + port: AbstractPort, + port_initializer: PortInitializer): if port in self._initializers_lookup.keys(): raise AssertionError( "An initializer for this port has already " "been assigned." @@ -125,3 +131,77 @@ def get_port_initializer(self, port): def has_port_initializer(self, port) -> bool: return port in self._initializers_lookup + + def write_to_cache(self, + cache_object: ty.Dict[ty.Any, ty.Any], + proc_to_procname_map: ty.Dict[AbstractProcess, str]): + cache_object["lmt_allocation"] = self._lmt_allocation_dict + + initializers_serializable: ty.List[ty.Tuple[str, str, + PortInitializer]] = [] + port: AbstractPort + pi: PortInitializer + for port, pi in self._initializers_lookup.items(): + procname = proc_to_procname_map[port.process] + if procname.startswith("Process_"): + msg = f"Unable to Cache. " \ + f"Please give unique names to every process. " \ + f"Violation Name: {procname=}" + raise Exception(msg) + + initializers_serializable.append((procname, port.name, pi)) + cache_object["initializers"] = initializers_serializable + + cm_serializable: ty.List[ty.Tuple[ty.Tuple[str, str], + ty.Tuple[str, str], + Payload]] = [] + port_pair: PortPair + payload: Payload + for port_pair, payload in self.items(): + src_port: AbstractPort = ty.cast(AbstractPort, port_pair.src) + dst_port: AbstractPort = ty.cast(AbstractPort, port_pair.dst) + src_proc_name: str = proc_to_procname_map[src_port.process] + src_port_info = (src_proc_name, src_port.name) + dst_proc_name: str = proc_to_procname_map[dst_port.process] + dst_port_info = (dst_proc_name, dst_port.name) + if src_proc_name.startswith("Process_") or \ + dst_proc_name.startswith("Process_"): + msg = f"Unable to Cache. " \ + f"Please give unique names to every process. " \ + f"Violation Name: {src_proc_name=} {dst_proc_name=}" + raise Exception(msg) + + cm_serializable.append((src_port_info, dst_port_info, payload)) + cache_object["channelmap_dict"] = cm_serializable + + def read_from_cache(self, + cache_object: ty.Dict[ty.Any, ty.Any], + procname_to_proc_map: ty.Dict[str, AbstractProcess]): + self._lmt_allocation_dict = cache_object["lmt_allocation"] + initializers_serializable = cache_object["initializers"] + cm_serializable = cache_object["channelmap_dict"] + + for procname, port_name, pi in initializers_serializable: + process: AbstractProcess = procname_to_proc_map[procname] + port: AbstractPort = getattr(process, port_name) + self._initializers_lookup[port] = pi + + src_port_info: ty.Tuple[str, str] + dst_port_info: ty.Tuple[str, str] + payload: Payload + for src_port_info, dst_port_info, payload in cm_serializable: + src_port_process: AbstractProcess = procname_to_proc_map[ + src_port_info[0]] + src: AbstractPort = getattr(src_port_process, + src_port_info[1]) + dst_port_process: AbstractProcess = procname_to_proc_map[ + dst_port_info[0]] + dst: AbstractPort = getattr(dst_port_process, + dst_port_info[1]) + for port_pair, pld in self.items(): + s, d = port_pair.src, port_pair.dst + if s.name == src.name and d.name == dst.name and \ + s.process.name == src_port_process.name and \ + d.process.name == dst_port_process.name: + pld.src_port_initializer = payload.src_port_initializer + pld.dst_port_initializer = payload.dst_port_initializer diff --git a/src/lava/magma/compiler/compiler.py b/src/lava/magma/compiler/compiler.py index 1daddb2ad..e447b488c 100644 --- a/src/lava/magma/compiler/compiler.py +++ b/src/lava/magma/compiler/compiler.py @@ -4,6 +4,8 @@ import itertools import logging +import os +import pickle # noqa: S403 # nosec import typing as ty from collections import OrderedDict, defaultdict @@ -219,6 +221,35 @@ def _compile_proc_groups( The global dict-like ChannelMap given as input but with values updated according to partitioning done by subcompilers. """ + procname_to_proc_map: ty.Dict[str, AbstractProcess] = {} + proc_to_procname_map: ty.Dict[AbstractProcess, str] = {} + for proc_group in proc_groups: + for p in proc_group: + procname_to_proc_map[p.name] = p + proc_to_procname_map[p] = p.name + + if self._compile_config.get("cache", False): + cache_dir = self._compile_config["cache_dir"] + if os.path.exists(os.path.join(cache_dir, "cache")): + with open(os.path.join(cache_dir, "cache"), "rb") \ + as cache_file: + cache_object = pickle.load(cache_file) # noqa: S301 # nosec + + proc_builders_values = cache_object["procname_to_proc_builder"] + proc_builders = {} + for proc_name, pb in proc_builders_values.items(): + proc = procname_to_proc_map[proc_name] + proc_builders[proc] = pb + pb.proc_params = proc.proc_params + + channel_map.read_from_cache(cache_object, procname_to_proc_map) + print(f"\nBuilders and Channel Map loaded from " + f"Cache {cache_dir}\n") + return proc_builders, channel_map + + # Get manual partitioning, if available + partitioning = self._compile_config.get("partitioning", None) + # Create the global ChannelMap that is passed between # SubCompilers to communicate about Channels between Processes. @@ -238,7 +269,8 @@ def _compile_proc_groups( subcompilers.append(pg_subcompilers) # Compile this ProcGroup. - self._compile_proc_group(pg_subcompilers, channel_map) + self._compile_proc_group(pg_subcompilers, channel_map, + partitioning) # Flatten the list of all SubCompilers. subcompilers = list(itertools.chain.from_iterable(subcompilers)) @@ -248,6 +280,28 @@ def _compile_proc_groups( subcompilers, channel_map ) + if self._compile_config.get("cache", False): + cache_dir = self._compile_config["cache_dir"] + os.makedirs(cache_dir, exist_ok=True) + cache_object = {} + # Validate All Processes are Named + procname_to_proc_builder = {} + for p, pb in proc_builders.items(): + if p.name in procname_to_proc_builder or \ + "Process_" in p.name: + msg = f"Unable to Cache. " \ + f"Please give unique names to every process. " \ + f"Violation Name: {p.name=}" + raise Exception(msg) + procname_to_proc_builder[p.name] = pb + pb.proc_params = None + cache_object["procname_to_proc_builder"] = procname_to_proc_builder + channel_map.write_to_cache(cache_object, proc_to_procname_map) + with open(os.path.join(cache_dir, "cache"), "wb") as cache_file: + pickle.dump(cache_object, cache_file) + for p, pb in proc_builders.items(): + pb.proc_params = p.proc_params + print(f"\nBuilders and Channel Map stored to Cache {cache_dir}\n") return proc_builders, channel_map @staticmethod @@ -353,7 +407,8 @@ def _create_subcompilers( @staticmethod def _compile_proc_group( - subcompilers: ty.List[AbstractSubCompiler], channel_map: ChannelMap + subcompilers: ty.List[AbstractSubCompiler], channel_map: ChannelMap, + partitioning: ty.Dict[str, ty.Dict] ) -> None: """For a given list of SubCompilers that have been initialized with the Processes of a single ProcGroup, iterate through the compilation @@ -369,6 +424,8 @@ def _compile_proc_group( channel_map : ChannelMap The global ChannelMap that contains information about Channels between Processes. + partitioning: ty.Dict + Optional manual mapping dictionary used by ncproc compiler. """ channel_map_prev = None @@ -381,7 +438,7 @@ def _compile_proc_group( for subcompiler in subcompilers: # Compile the Processes registered with each SubCompiler and # update the ChannelMap. - channel_map = subcompiler.compile(channel_map) + channel_map = subcompiler.compile(channel_map, partitioning) @staticmethod def _extract_proc_builders( @@ -713,8 +770,8 @@ def _create_runtime_service_builder( model_ids: ty.List[int] = [p.id for p in sync_domain.processes] rs_kwargs = { - "c_builders" : list(c_builders.values()), - "nc_builders" : list(nc_builders.values()) + "c_builders": list(c_builders.values()), + "nc_builders": list(nc_builders.values()) } if isinstance(run_cfg, AbstractLoihiHWRunCfg): rs_kwargs["callback_fxs"] = run_cfg.callback_fxs diff --git a/src/lava/magma/compiler/mapper.py b/src/lava/magma/compiler/mapper.py index e05939f89..b3ddb170f 100644 --- a/src/lava/magma/compiler/mapper.py +++ b/src/lava/magma/compiler/mapper.py @@ -162,6 +162,10 @@ def map_cores(self, executable: Executable, address.update(chips) break if len(address) > 1 and hasattr(var_model, "address"): + print('=' * 50) + print('Note to JOYESH from the future:') + print('Add logic to make multichip conv input work for YOLO.') + print('=' * 50) raise ValueError("Lava Compiler doesn't support port" "splitting currently. MultiChip " "Not Supported ") diff --git a/src/lava/magma/compiler/subcompilers/py/pyproc_compiler.py b/src/lava/magma/compiler/subcompilers/py/pyproc_compiler.py index d916a1e52..c5948a399 100644 --- a/src/lava/magma/compiler/subcompilers/py/pyproc_compiler.py +++ b/src/lava/magma/compiler/subcompilers/py/pyproc_compiler.py @@ -30,6 +30,7 @@ ImplicitVarPort, VarPort, ) +from lava.magma.core.process.ports.connection_config import ConnectionConfig from lava.magma.core.process.process import AbstractProcess from lava.magma.compiler.subcompilers.constants import SPIKE_BLOCK_CORE @@ -88,7 +89,8 @@ def __init__( super().__init__(proc_group, compile_config) self._spike_io_counter_offset: Offset = Offset() - def compile(self, channel_map: ChannelMap) -> ChannelMap: + def compile(self, channel_map: ChannelMap, + partitioning: ty.Dict = None) -> ChannelMap: return self._update_channel_map(channel_map) def __del__(self): @@ -189,7 +191,11 @@ def _create_inport_initializers( pi.embedded_counters = \ np.arange(counter_start_idx, counter_start_idx + num_counters, dtype=np.int32) - pi.connection_config = list(port.connection_configs.values())[0] + if port.connection_configs.values(): + conn_config = list(port.connection_configs.values())[0] + else: + conn_config = ConnectionConfig() + pi.connection_config = conn_config port_initializers.append(pi) self._tmp_channel_map.set_port_initializer(port, pi) else: @@ -209,7 +215,7 @@ def _create_outport_initializers( self, process: AbstractProcess ) -> ty.List[PortInitializer]: port_initializers = [] - for port in list(process.out_ports): + for k, port in enumerate(list(process.out_ports)): pi = PortInitializer( port.name, port.shape, @@ -218,6 +224,11 @@ def _create_outport_initializers( self._compile_config["pypy_channel_size"], port.get_incoming_transform_funcs(), ) + if port.connection_configs.values(): + conn_config = list(port.connection_configs.values())[k] + else: + conn_config = ConnectionConfig() + pi.connection_config = conn_config port_initializers.append(pi) self._tmp_channel_map.set_port_initializer(port, pi) return port_initializers diff --git a/src/lava/magma/compiler/var_model.py b/src/lava/magma/compiler/var_model.py index e33f3fbd9..94097e71e 100644 --- a/src/lava/magma/compiler/var_model.py +++ b/src/lava/magma/compiler/var_model.py @@ -269,6 +269,14 @@ class NcSpikeIOVarModel(NcVarModel): interface: SpikeIOInterface = SpikeIOInterface.ETHERNET spike_io_port: SpikeIOPort = SpikeIOPort.ETHERNET spike_io_mode: SpikeIOMode = SpikeIOMode.TIME_COMPARE + ethernet_chip_id: ty.Optional[ty.Tuple[int, int, int]] = None + ethernet_chip_idx: ty.Optional[int] = None decode_config: ty.Optional[DecodeConfig] = None time_compare: ty.Optional[TimeCompare] = None spike_encoder: ty.Optional[SpikeEncoder] = None + + +@dataclass +class NcConvSpikeInVarModel(NcSpikeIOVarModel): + # Tuple will be in the order of [atom_paylod, atom_axon, addr_idx] + region_map: ty.List[ty.List[ty.Tuple[int, int, int]]] = None diff --git a/src/lava/magma/core/model/py/model.py b/src/lava/magma/core/model/py/model.py index 40235d4e1..ec6c252bc 100644 --- a/src/lava/magma/core/model/py/model.py +++ b/src/lava/magma/core/model/py/model.py @@ -759,6 +759,10 @@ def run_async(self) -> None: if py_loihi_model.post_guard(self): py_loihi_model.run_post_mgmt(self) self.time_step += 1 + # self.advance_to_time_step(self.time_step) + for port in self.py_ports: + if isinstance(port, PyOutPort): + port.advance_to_time_step(self.time_step) py_async_model = type( name, diff --git a/src/lava/magma/core/process/ports/connection_config.py b/src/lava/magma/core/process/ports/connection_config.py index 86b0a6d3d..2b5aca897 100644 --- a/src/lava/magma/core/process/ports/connection_config.py +++ b/src/lava/magma/core/process/ports/connection_config.py @@ -14,6 +14,7 @@ # expressly stated in the License. from dataclasses import dataclass from enum import IntEnum, Enum +import typing as ty class SpikeIOInterface(IntEnum): @@ -54,3 +55,6 @@ class ConnectionConfig: spike_io_mode: SpikeIOMode = SpikeIOMode.TIME_COMPARE num_time_buckets: int = 1 << 16 ethernet_mac_address: str = "0x90e2ba01214c" + loihi_mac_address: str = "0x0015edbeefed" + ethernet_chip_id: ty.Optional[ty.Tuple[int, int, int]] = None + ethernet_chip_idx: ty.Optional[int] = None diff --git a/src/lava/proc/clp/id_broadcast/models.py b/src/lava/proc/clp/id_broadcast/models.py new file mode 100644 index 000000000..96b7f283b --- /dev/null +++ b/src/lava/proc/clp/id_broadcast/models.py @@ -0,0 +1,32 @@ +# Copyright (C) 2022 Intel Corporation +# SPDX-License-Identifier: BSD-3-Clause +# See: https://spdx.org/licenses/ + +import numpy as np +from lava.magma.core.decorator import implements, requires +from lava.magma.core.model.py.model import PyLoihiProcessModel +from lava.magma.core.model.py.ports import PyInPort, PyOutPort +from lava.magma.core.model.py.type import LavaPyType +from lava.magma.core.resources import CPU +from lava.magma.core.sync.protocols.loihi_protocol import LoihiProtocol + +from lava.proc.clp.id_broadcast.process import IdBroadcast + + +@implements(proc=IdBroadcast, protocol=LoihiProtocol) +@requires(CPU) +class IdBroadcastModel(PyLoihiProcessModel): + """CPU model for the IdBroadcast process. + + The process sends out a graded spike with payload equal to a_in. + """ + a_in: PyInPort = LavaPyType(PyInPort.VEC_DENSE, int) + s_out: PyOutPort = LavaPyType(PyOutPort.VEC_DENSE, int) + val: np.ndarray = LavaPyType(np.ndarray, np.int32) + + def run_spk(self): + """Execute spiking phase, send value of a_in.""" + + a_in_data = self.a_in.recv() + self.val = a_in_data + self.s_out.send(a_in_data) diff --git a/src/lava/proc/clp/id_broadcast/process.py b/src/lava/proc/clp/id_broadcast/process.py new file mode 100644 index 000000000..6e4e27c85 --- /dev/null +++ b/src/lava/proc/clp/id_broadcast/process.py @@ -0,0 +1,34 @@ +# Copyright (C) 2022 Intel Corporation +# SPDX-License-Identifier: BSD-3-Clause +# See: https://spdx.org/licenses/ + +import typing as ty + +from lava.magma.core.process.ports.ports import InPort, OutPort +from lava.magma.core.process.variable import Var +from lava.magma.core.process.process import AbstractProcess, LogConfig + + +class IdBroadcast(AbstractProcess): + """Process that sends out a graded spike with payload equal to a_in. + + Parameters + ---------- + shape : tuple(int) + Shape of the population of process units. + name : str + Name of the Process. Default is 'Process_ID', where ID is an + integer value that is determined automatically. + log_config : LogConfig + Configuration options for logging. + """ + + def __init__(self, *, + shape: ty.Tuple[int, ...] = (1,), + name: ty.Optional[str] = None, + log_config: ty.Optional[LogConfig] = None) -> None: + super().__init__(shape=shape, name=name, log_config=log_config) + self.a_in = InPort(shape=shape) + self.s_out = OutPort(shape=shape) + + self.val = Var(shape=shape, init=0) diff --git a/src/lava/proc/graded/models.py b/src/lava/proc/graded/models.py index 969caa1d4..870b0b301 100644 --- a/src/lava/proc/graded/models.py +++ b/src/lava/proc/graded/models.py @@ -11,7 +11,8 @@ from lava.magma.core.decorator import implements, requires, tag from lava.magma.core.model.py.model import PyLoihiProcessModel -from lava.proc.graded.process import GradedVec, NormVecDelay, InvSqrt +from lava.proc.graded.process import (GradedVec, GradedReluVec, + NormVecDelay, InvSqrt) class AbstractGradedVecModel(PyLoihiProcessModel): @@ -51,6 +52,43 @@ class PyGradedVecModelFixed(AbstractGradedVecModel): exp: np.ndarray = LavaPyType(np.ndarray, np.int32, precision=24) +class AbstractGradedReluVecModel(PyLoihiProcessModel): + """Implementation of GradedReluVec""" + + a_in = None + s_out = None + v = None + vth = None + exp = None + + def run_spk(self) -> None: + """The run function that performs the actual computation during + execution orchestrated by a PyLoihiProcessModel using the + LoihiProtocol. + """ + a_in_data = self.a_in.recv() + self.v += a_in_data + + is_spike = self.v > self.vth + sp_out = self.v * is_spike + + self.v[:] = 0 + + self.s_out.send(sp_out) + + +@implements(proc=GradedReluVec, protocol=LoihiProtocol) +@requires(CPU) +@tag('fixed_pt') +class PyGradedReluVecModelFixed(AbstractGradedReluVecModel): + """Fixed point implementation of GradedVec""" + a_in = LavaPyType(PyInPort.VEC_DENSE, np.int32, precision=24) + s_out = LavaPyType(PyOutPort.VEC_DENSE, np.int32, precision=24) + vth: np.ndarray = LavaPyType(np.ndarray, np.int32, precision=24) + v: np.ndarray = LavaPyType(np.ndarray, np.int32, precision=24) + exp: np.ndarray = LavaPyType(np.ndarray, np.int32, precision=24) + + @implements(proc=NormVecDelay, protocol=LoihiProtocol) @requires(CPU) @tag('fixed_pt') diff --git a/src/lava/proc/graded/process.py b/src/lava/proc/graded/process.py index ed7c41b3f..25c881b50 100644 --- a/src/lava/proc/graded/process.py +++ b/src/lava/proc/graded/process.py @@ -34,6 +34,45 @@ class GradedVec(AbstractProcess): Graded spike vector layer. Transmits accumulated input as graded spike with no dynamics. + v[t] = a_in + s_out = v[t] * (|v[t]| > vth) + + Parameters + ---------- + shape: tuple(int) + number and topology of neurons + vth: int + threshold for spiking + exp: int + fixed point base + """ + + def __init__( + self, + shape: ty.Tuple[int, ...], + vth: ty.Optional[int] = 1, + exp: ty.Optional[int] = 0) -> None: + + super().__init__(shape=shape) + + self.a_in = InPort(shape=shape) + self.s_out = OutPort(shape=shape) + + self.v = Var(shape=shape, init=0) + self.vth = Var(shape=(1,), init=vth) + self.exp = Var(shape=(1,), init=exp) + + @property + def shape(self) -> ty.Tuple[int, ...]: + """Return shape of the Process.""" + return self.proc_params['shape'] + + +class GradedReluVec(AbstractProcess): + """GradedReluVec + Graded spike vector layer. Transmits accumulated input as + graded spike with no dynamics. + v[t] = a_in s_out = v[t] * (v[t] > vth) diff --git a/src/lava/proc/io/extractor.py b/src/lava/proc/io/extractor.py index 7316ac17f..48ea87de6 100644 --- a/src/lava/proc/io/extractor.py +++ b/src/lava/proc/io/extractor.py @@ -10,7 +10,9 @@ from lava.magma.core.resources import CPU from lava.magma.core.decorator import implements, requires from lava.magma.core.model.py.model import PyLoihiProcessModel +from lava.magma.core.model.py.model import PyAsyncProcessModel from lava.magma.core.sync.protocols.loihi_protocol import LoihiProtocol +from lava.magma.core.sync.protocols.async_protocol import AsyncProtocol from lava.magma.core.model.py.type import LavaPyType from lava.magma.core.model.py.ports import PyInPort, PyOutPort, PyRefPort from lava.magma.compiler.channels.pypychannel import PyPyChannel @@ -124,6 +126,27 @@ def run_spk(self) -> None: self.in_port.recv()) +@implements(proc=Extractor, protocol=AsyncProtocol) +@requires(CPU) +class PyLoihiExtractorModelAsync(PyAsyncProcessModel): + in_port: PyInPort = LavaPyType(PyInPort.VEC_DENSE, float) + out_port: PyOutPort = LavaPyType(PyOutPort.VEC_DENSE, float) + + def __init__(self, proc_params: dict) -> None: + super().__init__(proc_params=proc_params) + + channel_config = self.proc_params["channel_config"] + + self._send = channel_config.get_send_full_function() + self.time_step = 1 + + def run_async(self) -> None: + while self.time_step != self.num_steps + 1: + self._send(self.out_port.csp_ports[-1], + self.in_port.recv()) + self.time_step += 1 + + class VarWire(AbstractProcess): """VarWire allows non-Lava code, such as a third-party Python library to tap data from a Lava Process Variable (Var) while the Lava Runtime is diff --git a/src/lava/proc/io/injector.py b/src/lava/proc/io/injector.py index 78d200fac..65ad0c8d0 100644 --- a/src/lava/proc/io/injector.py +++ b/src/lava/proc/io/injector.py @@ -10,7 +10,9 @@ from lava.magma.core.resources import CPU from lava.magma.core.decorator import implements, requires from lava.magma.core.model.py.model import PyLoihiProcessModel +from lava.magma.core.model.py.model import PyAsyncProcessModel from lava.magma.core.sync.protocols.loihi_protocol import LoihiProtocol +from lava.magma.core.sync.protocols.async_protocol import AsyncProtocol from lava.magma.core.model.py.type import LavaPyType from lava.magma.core.model.py.ports import PyInPort, PyOutPort from lava.proc.io import utils @@ -44,6 +46,7 @@ class Injector(AbstractProcess): buffer is full and how the dst_port behaves when the buffer is empty and not empty. """ + def __init__(self, shape: ty.Tuple[int, ...], buffer_size: ty.Optional[int] = 50, @@ -122,3 +125,47 @@ def run_spk(self) -> None: elements_in_buffer) self.out_port.send(data) + + def __del__(self) -> None: + self._p_to_pm_dst_port.join() + + +@implements(proc=Injector, protocol=AsyncProtocol) +@requires(CPU) +class PyLoihiInjectorModelAsync(PyAsyncProcessModel): + """PyAsyncProcessModel for the Injector Process.""" + in_port: PyInPort = LavaPyType(PyInPort.VEC_DENSE, float) + out_port: PyOutPort = LavaPyType(PyOutPort.VEC_DENSE, float) + + def __init__(self, proc_params: dict) -> None: + super().__init__(proc_params=proc_params) + + shape = self.proc_params["shape"] + channel_config = self.proc_params["channel_config"] + self._zeros = np.zeros(shape) + + self._receive_when_empty = channel_config.get_receive_empty_function() + self._receive_when_not_empty = \ + channel_config.get_receive_not_empty_function() + self.time_step = 1 + + def run_async(self) -> None: + while self.time_step != self.num_steps + 1: + self._zeros.fill(0) + elements_in_buffer = self.in_port.csp_ports[-1]._queue.qsize() + + if elements_in_buffer == 0: + data = self._receive_when_empty( + self.in_port, + self._zeros) + else: + data = self._receive_when_not_empty( + self.in_port, + self._zeros, + elements_in_buffer) + + self.out_port.send(data) + self.time_step += 1 + + def __del__(self) -> None: + self._p_to_pm_dst_port.join() diff --git a/src/lava/proc/s4d/models.py b/src/lava/proc/s4d/models.py index c409ba3af..520f445f1 100644 --- a/src/lava/proc/s4d/models.py +++ b/src/lava/proc/s4d/models.py @@ -7,12 +7,77 @@ from lava.proc.sdn.models import AbstractSigmaDeltaModel from lava.magma.core.decorator import implements, requires, tag from lava.magma.core.sync.protocols.loihi_protocol import LoihiProtocol -from lava.proc.s4d.process import SigmaS4dDelta, SigmaS4dDeltaLayer +from lava.proc.s4d.process import SigmaS4dDelta, SigmaS4dDeltaLayer, S4d from lava.magma.core.resources import CPU from lava.magma.core.model.py.ports import PyInPort, PyOutPort from lava.magma.core.model.py.type import LavaPyType from lava.magma.core.model.sub.model import AbstractSubProcessModel from lava.proc.sparse.process import Sparse +from lava.magma.core.model.py.model import PyLoihiProcessModel + + +@implements(proc=S4d, protocol=LoihiProtocol) +@requires(CPU) +@tag('floating_pt') +class S4dModel(PyLoihiProcessModel): + a_in = LavaPyType(PyInPort.VEC_DENSE, float) + s_out = LavaPyType(PyOutPort.VEC_DENSE, float) + s4_exp: np.ndarray = LavaPyType(np.ndarray, np.int32, precision=3) + inp_exp: np.ndarray = LavaPyType(np.ndarray, np.int32, precision=3) + + # S4 variables + s4_state: np.ndarray = LavaPyType(np.ndarray, complex) + a: np.ndarray = LavaPyType(np.ndarray, complex) + b: np.ndarray = LavaPyType(np.ndarray, complex) + c: np.ndarray = LavaPyType(np.ndarray, complex) + + def __init__(self, proc_params: Dict[str, Any]) -> None: + """ + Neuron model that implements S4D + (as described by Gu et al., 2022) dynamics. + + Relevant parameters in proc_params + -------------------------- + a: np.ndarray + Diagonal elements of the state matrix of the discretized S4D model. + b: np.ndarray + Diagonal elements of the input matrix of the discretized S4D model. + c: np.ndarray + Diagonal elements of the output matrix of the discretized S4D model. + s4_state: np.ndarray + State vector of the S4D discretized model. + """ + super().__init__(proc_params) + self.a = self.proc_params['a'] + self.b = self.proc_params['b'] + self.c = self.proc_params['c'] + self.s4_state = self.proc_params['s4_state'] + + def run_spk(self) -> None: + """Performs S4D dynamics. + + This function simulates the behavior of a linear time-invariant system + with diagonalized state-space representation. + (For reference see Gu et al., 2022) + + The state-space equations are given by: + s4_state_{k+1} = A * s4_state_k + B * input_k + act_k = C * s4_state_k + + where: + - s4_state_k is the state vector at time step k, + - input_k is the input vector at time step k, + - act_k is the output vector at time step k, + - A is the diagonal state matrix, + - B is the diagonal input matrix, + - C is the diagonal output matrix. + + The function computes the next output step of the + system for the given input signal. + """ + inp = self.a_in.recv() + self.s4_state = (self.s4_state * self.a + inp * self.b) + self.s_out.send(np.real(self.c * self.s4_state * 2)) class AbstractSigmaS4dDeltaModel(AbstractSigmaDeltaModel): diff --git a/src/lava/proc/s4d/process.py b/src/lava/proc/s4d/process.py index 218e7292c..a99d3e431 100644 --- a/src/lava/proc/s4d/process.py +++ b/src/lava/proc/s4d/process.py @@ -10,6 +10,83 @@ from lava.proc.sdn.process import ActivationMode, SigmaDelta +class S4d(AbstractProcess): + def __init__( + self, + shape: ty.Tuple[int, ...], + a: float, + b: float, + c: float, + s4_state: ty.Optional[int] = 0, + s4_exp: ty.Optional[int] = 0, + inp_exp: ty.Optional[int] = 0) -> None: + """ + Neuron process that implements S4D (described by + Gu et al., 2022) dynamics. + + This process simulates the behavior of a linear time-invariant system + with diagonal state-space representation. + The state-space equations are given by: + s4_state_{k+1} = A * s4_state_k + B * inp_k + act_k = C * s4_state_k + + where: + - s4_state_k is the state vector at time step k, + - inp_k is the input vector at time step k, + - act_k is the output vector at time step k, + - A is the diagonal state matrix, + - B is the diagonal input matrix, + - C is the diagonal output matrix. + + Parameters + ---------- + shape: Tuple + Shape of the sigma process. + vth: int or float + Threshold of the delta encoder. + a: np.ndarray + Diagonal elements of the state matrix of the S4D model. + b: np.ndarray + Diagonal elements of the input matrix of the S4D model. + c: np.ndarray + Diagonal elements of the output matrix of the S4D model. + s4_state: int or float + Initial state of the S4D model. + s4_exp: int + Scaling exponent with base 2 for the S4 state variables. + Note: This should only be used for nc models. + Default is 0. + inp_exp: int + Bit precision of the input signal. + Note: This should only be used for nc models. + Default is 0. + """ + + super().__init__(shape=shape, + a=a, + b=b, + c=c, + s4_state=s4_state, + s4_exp=s4_exp, + inp_exp=inp_exp) + # Ports + self.a_in = InPort(shape=shape) + self.s_out = OutPort(shape=shape) + + # Variables for S4 + self.a = Var(shape=shape, init=a) + self.b = Var(shape=shape, init=b) + self.c = Var(shape=shape, init=c) + self.s4_state = Var(shape=shape, init=s4_state) + self.s4_exp = Var(shape=(1,), init=s4_exp) + self.inp_exp = Var(shape=(1,), init=inp_exp) + + @property + def shape(self) -> ty.Tuple[int, ...]: + """Return shape of the Process.""" + return self.proc_params['shape'] + + class SigmaS4dDelta(SigmaDelta, AbstractProcess): def __init__( self, diff --git a/tests/lava/magma/compiler/test_compiler.py b/tests/lava/magma/compiler/test_compiler.py index 1046253c3..876def248 100644 --- a/tests/lava/magma/compiler/test_compiler.py +++ b/tests/lava/magma/compiler/test_compiler.py @@ -170,6 +170,10 @@ def create_mock_proc_groups() -> ty.List[ProcGroup]: py_pg2_p1 = Mock(spec_set=AbstractProcess()) py_pg2_p2 = Mock(spec_set=AbstractProcess()) + py_pg1_p1.configure_mock(name='py_pg1_p1') + py_pg2_p1.configure_mock(name='py_pg2_p1') + py_pg2_p2.configure_mock(name='py_pg2_p2') + proc_list = [py_pg1_p1, py_pg2_p1, py_pg2_p2] proc_model_types = [ @@ -213,7 +217,8 @@ def create_patches( and the compile() method returns the given ChannelMap unchanged. .""" - def compile_return(channel_map: ChannelMap) -> ChannelMap: + def compile_return(channel_map: ChannelMap, + partitioning=None) -> ChannelMap: return channel_map py_patch = patch( @@ -387,13 +392,13 @@ def test_compile_proc_group_single_loop(self) -> None: subcompilers = [py_proc_compiler] # Call the method to be tested. - self.compiler._compile_proc_group(subcompilers, channel_map) + self.compiler._compile_proc_group(subcompilers, channel_map, None) # Check that it called compile() on every SubCompiler instance # exactly once. After that, the while loop should exit because the # ChannelMap instance has not changed. for sc in subcompilers: - sc.compile.assert_called_once_with({}) + sc.compile.assert_called_once_with({}, None) def test_compile_proc_group_multiple_loops(self) -> None: """Test whether the correct methods are called on all objects when @@ -420,13 +425,15 @@ def test_compile_proc_group_multiple_loops(self) -> None: subcompilers = [py_proc_compiler] # Call the method to be tested. - self.compiler._compile_proc_group(subcompilers, channel_map) + self.compiler._compile_proc_group(subcompilers, channel_map, + None) # Check that it called compile() on every SubCompiler instance # exactly once. After that, the while loop should exit because the # ChannelMap instance has not changed. for sc in subcompilers: - sc.compile.assert_called_with({**channel_map1, **channel_map2}) + sc.compile.assert_called_with({**channel_map1, **channel_map2}, + None) self.assertEqual(sc.compile.call_count, 3) def test_extract_proc_builders(self) -> None: diff --git a/tests/lava/proc/graded/test_graded.py b/tests/lava/proc/graded/test_graded.py index b2acbe882..3c65b6087 100644 --- a/tests/lava/proc/graded/test_graded.py +++ b/tests/lava/proc/graded/test_graded.py @@ -6,7 +6,8 @@ import numpy as np from scipy.sparse import csr_matrix -from lava.proc.graded.process import GradedVec, NormVecDelay, InvSqrt +from lava.proc.graded.process import (GradedVec, GradedReluVec, + NormVecDelay, InvSqrt) from lava.proc.graded.models import inv_sqrt from lava.proc.dense.process import Dense from lava.proc.sparse.process import Sparse @@ -59,7 +60,7 @@ def test_gradedvec_dot_dense(self): self.assertTrue(np.all(out_data[:, (3, 7)] == expected_out[:, (2, 6)])) def test_gradedvec_dot_sparse(self): - """Tests that GradedVec and Dense computes dot product.""" + """Tests that GradedVec and Sparse computes dot product""" num_steps = 10 v_thresh = 1 @@ -99,6 +100,91 @@ def test_gradedvec_dot_sparse(self): self.assertTrue(np.all(out_data[:, (3, 7)] == expected_out[:, (2, 6)])) +class TestGradedReluVecProc(unittest.TestCase): + """Tests for GradedReluVec""" + + def test_gradedreluvec_dot_dense(self): + """Tests that GradedReluVec and Dense computes dot product""" + num_steps = 10 + v_thresh = 1 + + weights1 = np.zeros((10, 1)) + weights1[:, 0] = (np.arange(10) - 5) * 0.2 + + inp_data = np.zeros((weights1.shape[1], num_steps)) + inp_data[:, 2] = 1000 + inp_data[:, 6] = 20000 + + weight_exp = 7 + weights1 *= 2**weight_exp + weights1 = weights1.astype('int') + + dense1 = Dense(weights=weights1, num_message_bits=24, + weight_exp=-weight_exp) + vec1 = GradedReluVec(shape=(weights1.shape[0],), + vth=v_thresh) + + generator = io.source.RingBuffer(data=inp_data) + logger = io.sink.RingBuffer(shape=(weights1.shape[0],), + buffer=num_steps) + + generator.s_out.connect(dense1.s_in) + dense1.a_out.connect(vec1.a_in) + vec1.s_out.connect(logger.a_in) + + vec1.run(condition=RunSteps(num_steps=num_steps), + run_cfg=Loihi2SimCfg(select_tag='fixed_pt')) + out_data = logger.data.get().astype('int') + vec1.stop() + + ww = np.floor(weights1 / 2) * 2 + expected_out = np.floor((ww @ inp_data) / 2**weight_exp) + expected_out *= expected_out > v_thresh + + self.assertTrue(np.all(out_data[:, (3, 7)] == expected_out[:, (2, 6)])) + + def test_gradedreluvec_dot_sparse(self): + """Tests that GradedReluVec and Sparse computes dot product""" + num_steps = 10 + v_thresh = 1 + + weights1 = np.zeros((10, 1)) + weights1[:, 0] = (np.arange(10) - 5) * 0.2 + + inp_data = np.zeros((weights1.shape[1], num_steps)) + inp_data[:, 2] = 1000 + inp_data[:, 6] = 20000 + + weight_exp = 7 + weights1 *= 2**weight_exp + weights1 = weights1.astype('int') + + sparse1 = Sparse(weights=csr_matrix(weights1), + num_message_bits=24, + weight_exp=-weight_exp) + vec1 = GradedReluVec(shape=(weights1.shape[0],), + vth=v_thresh) + + generator = io.source.RingBuffer(data=inp_data) + logger = io.sink.RingBuffer(shape=(weights1.shape[0],), + buffer=num_steps) + + generator.s_out.connect(sparse1.s_in) + sparse1.a_out.connect(vec1.a_in) + vec1.s_out.connect(logger.a_in) + + vec1.run(condition=RunSteps(num_steps=num_steps), + run_cfg=Loihi2SimCfg(select_tag='fixed_pt')) + out_data = logger.data.get().astype('int') + vec1.stop() + + ww = np.floor(weights1 / 2) * 2 + expected_out = np.floor((ww @ inp_data) / 2**weight_exp) + expected_out *= expected_out > v_thresh + + self.assertTrue(np.all(out_data[:, (3, 7)] == expected_out[:, (2, 6)])) + + class TestInvSqrtProc(unittest.TestCase): """Tests for inverse square process.""" diff --git a/tests/lava/proc/s4d/dA_complex.npy b/tests/lava/proc/s4d/dA_complex.npy new file mode 100644 index 000000000..1d61ccc37 --- /dev/null +++ b/tests/lava/proc/s4d/dA_complex.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:342546700242502415e6c81e2b620c915250f56447b7d46eb56feec9e0bb1629 +size 80128 diff --git a/tests/lava/proc/s4d/dB_complex.npy b/tests/lava/proc/s4d/dB_complex.npy new file mode 100644 index 000000000..e5c858679 --- /dev/null +++ b/tests/lava/proc/s4d/dB_complex.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1195dc519e74ffe535918b6112a080e7b0458d61189c9a8e51cf3ea30edd8ec9 +size 80128 diff --git a/tests/lava/proc/s4d/dC_complex.npy b/tests/lava/proc/s4d/dC_complex.npy new file mode 100644 index 000000000..185cb40fa --- /dev/null +++ b/tests/lava/proc/s4d/dC_complex.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7db2dbb793555e0836e557db355d2f6861b32a92b431443bf4c102e4626bf473 +size 80128 diff --git a/tests/lava/proc/s4d/test_models.py b/tests/lava/proc/s4d/test_models.py index c08e19cf8..64fe33da8 100644 --- a/tests/lava/proc/s4d/test_models.py +++ b/tests/lava/proc/s4d/test_models.py @@ -2,18 +2,104 @@ # SPDX-License-Identifier: BSD-3-Clause # See: https://spdx.org/licenses/ + import unittest import numpy as np from typing import Tuple import lava.proc.io as io from lava.magma.core.run_conditions import RunSteps from lava.proc.sdn.process import ActivationMode, SigmaDelta -from lava.proc.s4d.process import SigmaS4dDelta, SigmaS4dDeltaLayer +from lava.proc.s4d.process import S4d, SigmaS4dDelta, SigmaS4dDeltaLayer from lava.proc.sparse.process import Sparse from lava.magma.core.run_configs import Loihi2SimCfg from tests.lava.proc.s4d.utils import get_coefficients, run_original_model +class TestS4DModel(unittest.TestCase): + """Tests for S4d neuron""" + def run_in_sim( + self, + inp: np.ndarray, + a: np.ndarray, + b: np.ndarray, + c: np.ndarray, + num_steps: int, + model_dim: int, + d_states: int, + ) -> Tuple[np.ndarray]: + + # Get S4D matrices + a = a[:model_dim * d_states] + b = b[:model_dim * d_states] + c = c[:model_dim * d_states] + + # Setup network: input -> expansion -> S4D neuron -> output + kron_matrix = np.kron(np.eye(model_dim), np.ones((d_states, ))) + spiker = io.source.RingBuffer(data=inp) + sparse_1 = Sparse(weights=kron_matrix.T, num_message_bits=24) + neuron = S4d(shape=((model_dim * d_states,)), + a=a, + b=b, + c=c) + + receiver = io.sink.RingBuffer(buffer=num_steps, + shape=(model_dim * d_states,)) + spiker.s_out.connect(sparse_1.s_in) + sparse_1.a_out.connect(neuron.a_in) + neuron.s_out.connect(receiver.a_in) + + run_cfg = Loihi2SimCfg(select_tag="floating_pt") + neuron.run( + condition=RunSteps(num_steps=num_steps), run_cfg=run_cfg) + received_data_sim = receiver.data.get() + neuron.stop() + + return received_data_sim + + def compare_s4d_model_to_original_equations(self, + model_dim: int = 10, + d_states: int = 5, + n_steps: int = 5, + inp_exp: int = 5, + is_real: bool = False) -> None: + + """Asserts that the floating point lava simulation for S4d outputs + exactly the same values as the original equations.""" + a, b, c = get_coefficients(is_real=is_real) + np.random.seed(0) + inp = (np.random.random((model_dim, n_steps)) * 2**inp_exp).astype(int) + out_lava = self.run_in_sim(inp=inp, + num_steps=n_steps, + a=a, + b=b, + c=c, + model_dim=model_dim, + d_states=d_states) + out_original_equations = run_original_model(inp=inp, + num_steps=n_steps, + model_dim=model_dim, + d_states=d_states, + a=a, + b=b, + c=c, + perform_reduction=False) + + np.testing.assert_array_equal(out_original_equations[:, :-1], + out_lava[:, 1:]) + + def test_s4d_real_model_single_hidden_state(self) -> None: + self.compare_s4d_model_to_original_equations(is_real=True, d_states=1) + + def test_s4d_real_model_multiple_hidden_state(self) -> None: + self.compare_s4d_model_to_original_equations(is_real=True, d_states=5) + + def test_s4d_complex_model_single_hidden_state(self) -> None: + self.compare_s4d_model_to_original_equations(is_real=False, d_states=1) + + def test_s4d_complex_model_multiple_hidden_state(self) -> None: + self.compare_s4d_model_to_original_equations(is_real=False, d_states=5) + + class TestSigmaS4DDeltaModels(unittest.TestCase): """Tests for SigmaS4Delta neuron""" def run_in_lava( diff --git a/tests/lava/proc/s4d/test_process.py b/tests/lava/proc/s4d/test_process.py index 5f6cbcc9c..cd6b5f2d3 100644 --- a/tests/lava/proc/s4d/test_process.py +++ b/tests/lava/proc/s4d/test_process.py @@ -4,7 +4,34 @@ import unittest import numpy as np -from lava.proc.s4d.process import SigmaS4dDelta, SigmaS4dDeltaLayer +from lava.proc.s4d.process import SigmaS4dDelta, SigmaS4dDeltaLayer, S4d + + +class TestS4dProcess(unittest.TestCase): + """Tests for S4d Class""" + + def test_init(self) -> None: + """Tests instantiation of S4d""" + shape = 10 + s4_exp = 12 + inp_exp = 8 + a = np.ones(shape) * 0.5 + b = np.ones(shape) * 0.8 + c = np.ones(shape) * 0.9 + s4d = S4d(shape=(shape,), + s4_exp=s4_exp, + inp_exp=inp_exp, + a=a, + b=b, + c=c) + + self.assertEqual(s4d.shape, (shape,)) + self.assertEqual(s4d.s4_exp.init, s4_exp) + self.assertEqual(s4d.inp_exp.init, inp_exp) + np.testing.assert_array_equal(s4d.a.init, a) + np.testing.assert_array_equal(s4d.b.init, b) + np.testing.assert_array_equal(s4d.c.init, c) + self.assertEqual(s4d.s4_state.init, 0) class TestSigmaS4dDeltaProcess(unittest.TestCase): @@ -37,7 +64,7 @@ def test_init(self) -> None: self.assertEqual(sigma_s4_delta.state_exp.init, state_exp) self.assertEqual(sigma_s4_delta.s4_state.init, 0) - # default sigmadelta params - inherited from SigmaDelta class + # default sigma-delta params - inherited from SigmaDelta class self.assertEqual(sigma_s4_delta.cum_error.init, False) self.assertEqual(sigma_s4_delta.spike_exp.init, 0) self.assertEqual(sigma_s4_delta.bias.init, 0) diff --git a/tests/lava/proc/s4d/utils.py b/tests/lava/proc/s4d/utils.py index 3afd48b2e..a323da338 100644 --- a/tests/lava/proc/s4d/utils.py +++ b/tests/lava/proc/s4d/utils.py @@ -4,16 +4,23 @@ import os import numpy as np -from typing import Tuple +from typing import List, Tuple -def get_coefficients() -> [np.ndarray, np.ndarray, np.ndarray]: +def get_coefficients( + is_real: bool = True) -> [np.ndarray, np.ndarray, np.ndarray]: curr_dir = os.path.dirname(os.path.realpath(__file__)) - # Initialize A, B and C with values trained on efficientnet features. - s4d_A = np.load(curr_dir + "/s4d_A.dat.npy").flatten() - s4d_B = np.load(curr_dir + "/s4d_B.dat.npy").flatten() - s4d_C = np.load(curr_dir + "/s4d_C.dat.npy").flatten().flatten() + # Initialize A, B and C with values + if is_real: + s4d_A = np.load(curr_dir + "/s4d_A.dat.npy").flatten() + s4d_B = np.load(curr_dir + "/s4d_B.dat.npy").flatten() + s4d_C = np.load(curr_dir + "/s4d_C.dat.npy").flatten().flatten() + else: + s4d_A = np.load(curr_dir + "/dA_complex.npy").flatten() + s4d_B = np.load(curr_dir + "/dB_complex.npy").flatten() + s4d_C = np.load(curr_dir + "/dC_complex.npy").flatten().flatten() + return s4d_A, s4d_B, s4d_C @@ -24,9 +31,10 @@ def run_original_model( d_states: int, a: np.ndarray, b: np.ndarray, - c: np.ndarray) -> Tuple[np.ndarray]: + c: np.ndarray, + perform_reduction: bool = True) -> Tuple[np.ndarray]: """ - Run original S4d model. + Run original S4d model in full precision. This function simulates the behavior of a linear time-invariant system with diagonalized state-space representation. (S4D) @@ -50,7 +58,7 @@ def run_original_model( Parameters ---------- - input: np.ndarray + inp: np.ndarray Input signal to the model. num_steps: int Number of time steps to simulate the model. @@ -79,9 +87,10 @@ def run_original_model( out = np.zeros((model_dim * d_states, num_steps)) s4_state = np.zeros((model_dim * d_states,)).flatten() - for idx, inp in enumerate(expanded_inp.T): - s4_state = np.multiply(s4_state, a) + np.multiply(inp, b) - out[:, idx] = np.multiply(c, s4_state) * 2 + for idx, data_in in enumerate(expanded_inp.T): + s4_state = s4_state * a + data_in * b + out[:, idx] = np.real(c * s4_state * 2) - out = np.matmul(expansion_weights, out) + if perform_reduction: + out = np.matmul(expansion_weights, out) return out