From 6fa054b8f5c9476dcdeacd851c092e494b294b2c Mon Sep 17 00:00:00 2001 From: Jurado Date: Mon, 26 Sep 2022 15:27:29 -0400 Subject: [PATCH 1/7] Added support for regular rf dense layers on netx. Implementation not fully tested --- src/lava/lib/dl/netx/blocks/models.py | 8 ++- src/lava/lib/dl/netx/blocks/process.py | 70 +++++++++++++++++- src/lava/lib/dl/netx/hdf5.py | 88 ++++++++++++++++++++++- src/lava/lib/dl/netx/utils.py | 10 ++- src/lava/lib/dl/slayer/block/base.py | 15 ++-- src/lava/lib/dl/slayer/synapse/complex.py | 4 +- 6 files changed, 178 insertions(+), 17 deletions(-) diff --git a/src/lava/lib/dl/netx/blocks/models.py b/src/lava/lib/dl/netx/blocks/models.py index 2b0f4d2b..81798b0d 100644 --- a/src/lava/lib/dl/netx/blocks/models.py +++ b/src/lava/lib/dl/netx/blocks/models.py @@ -11,7 +11,7 @@ from lava.magma.core.sync.protocols.loihi_protocol import LoihiProtocol from lava.magma.core.resources import CPU -from lava.lib.dl.netx.blocks.process import Input, Dense, Conv +from lava.lib.dl.netx.blocks.process import Input, Dense, Conv, DenseComplex @requires(CPU) @@ -51,6 +51,12 @@ def __init__(self, proc: AbstractProcess) -> None: class PyDenseModel(AbstractPyBlockModel): def __init__(self, proc: AbstractProcess) -> None: super().__init__(proc) + +@implements(proc=DenseComplex, protocol=LoihiProtocol) +class PyDenseComplexModel(AbstractPyBlockModel): + def __init__(self, proc: AbstractProcess) -> None: + super().__init__(proc) + @implements(proc=Conv, protocol=LoihiProtocol) diff --git a/src/lava/lib/dl/netx/blocks/process.py b/src/lava/lib/dl/netx/blocks/process.py index cf3651ac..f1cd345c 100644 --- a/src/lava/lib/dl/netx/blocks/process.py +++ b/src/lava/lib/dl/netx/blocks/process.py @@ -105,10 +105,8 @@ def __init__(self, **kwargs: Union[dict, tuple, list, int, bool]) -> None: def export_hdf5(self, handle: Union[h5py.File, h5py.Group]) -> None: raise NotImplementedError - class Dense(AbstractBlock): """Dense layer block. - Parameters ---------- shape : tuple or list @@ -164,6 +162,74 @@ def export_hdf5(self, handle: Union[h5py.File, h5py.Group]) -> None: raise NotImplementedError +class DenseComplex(AbstractBlock): + """Dense layer block. + + Parameters + ---------- + shape : tuple or list + shape of the layer block in (x, y, z)/WHC format. + neuron_params : dict, optional + dictionary of neuron parameters. Defaults to None. + weight : np.ndarray + synaptic weight. + bias : np.ndarray or None + bias of neuron. None means no bias. Defaults to None. + has_graded_input : dict + flag for graded spikes at input. Defaults to False. + num_weight_bits : int + number of weight bits. Defaults to 8. + weight_exponent : int + weight exponent value. Defaults to 0. + input_message_bits : int, optional + number of message bits in input spike. Defaults to 0 meaning unary + spike. + """ + + def __init__(self, **kwargs: Union[dict, tuple, list, int, bool]) -> None: + super().__init__(**kwargs) + + num_weight_bits_real = kwargs.pop('num_weight_bits_real', 8) + num_weight_bits_imag = kwargs.pop('num_weight_bits_imag', 8) + + weight_exponent_real = kwargs.pop('weight_exponent_real', 0) + weight_exponent_imag = kwargs.pop('weight_exponent_imag', 0) + weight_real = kwargs.pop('weight_real') + weight_imag = kwargs.pop('weight_imag') + + self.neuron = self._neuron(kwargs.pop('bias', None)) + + self.real_synapse = DenseSynapse( + weights=weight_real, + weight_exp=weight_exponent_real, + num_weight_bits=num_weight_bits_real, + num_message_bits=self.input_message_bits, + ) + self.imag_synapse = DenseSynapse( + weights=weight_imag, + weight_exp=weight_exponent_imag, + num_weight_bits=num_weight_bits_imag, + num_message_bits=self.input_message_bits, + ) + + if self.shape != self.real_synapse.a_out.shape or self.shape != self.imag_synapse.a_out.shape: + raise RuntimeError( + f'Expected synapse output shape to be {self.shape[-1]}, ' + f'found {self.synapse.a_out.shape}.' + ) + + self.inp = InPort(shape=self.real_synapse.s_in.shape) + self.out = OutPort(shape=self.neuron.s_out.shape) + self.inp.connect(self.real_synapse.s_in) + self.inp.connect(self.imag_synapse.s_in) + self.real_synapse.a_out.connect(self.neuron.a_real_in) + self.imag_synapse.a_out.connect(self.neuron.a_imag_in) + self.neuron.s_out.connect(self.out) + self._clean() + + def export_hdf5(self, handle: Union[h5py.File, h5py.Group]) -> None: + raise NotImplementedError + class Conv(AbstractBlock): """Conv layer block. diff --git a/src/lava/lib/dl/netx/hdf5.py b/src/lava/lib/dl/netx/hdf5.py index c782e927..90498c10 100644 --- a/src/lava/lib/dl/netx/hdf5.py +++ b/src/lava/lib/dl/netx/hdf5.py @@ -6,6 +6,7 @@ from typing import List, Optional, Tuple, Union from lava.magma.core.decorator import implements from lava.magma.core.sync.protocols.loihi_protocol import LoihiProtocol +from lava.proc.rf.process import RF import numpy as np import h5py @@ -15,7 +16,7 @@ from lava.proc.sdn.process import Sigma, Delta, SigmaDelta from lava.lib.dl.netx.utils import NetDict from lava.lib.dl.netx.utils import optimize_weight_bits -from lava.lib.dl.netx.blocks.process import Input, Dense, Conv +from lava.lib.dl.netx.blocks.process import Input, Dense, Conv, DenseComplex from lava.lib.dl.netx.blocks.models import AbstractPyBlockModel @@ -135,6 +136,17 @@ def get_neuron_params(neuron_config: h5py.Group, 'state_exp': 6, 'num_message_bits': num_message_bits} return neuron_params + elif neuron_type in ['RF_PHASE']: + if num_message_bits is None: + num_message_bits = 0 # default value + neuron_process = RF + neuron_params = { + 'neuron_proc': neuron_process, + 'vth': neuron_config['vThMant'], + 'sin_decay': neuron_config['sinDecay'] - 1, + 'cos_decay': neuron_config['cosDecay'] - 1, + } + return neuron_params @staticmethod def _table_str(type_str: str = '', @@ -221,6 +233,7 @@ def create_input(layer_config: h5py.Group) -> Tuple[Input, str]: return Input(**params), table_entry + @staticmethod def create_dense(layer_config: h5py.Group, input_message_bits: int = 0) -> Tuple[Dense, str]: @@ -269,6 +282,62 @@ def create_dense(layer_config: h5py.Group, return Dense(**params), table_entry + @staticmethod + def create_complex_dense(layer_config: h5py.Group, + input_message_bits: int = 0) -> Tuple[Dense, str]: + """Creates dense layer from layer configuration + + Parameters + ---------- + layer_config : h5py.Group + hdf5 handle to layer description. + input_message_bits : int, optional + number of message bits in input spike. Defaults to 0 meaning unary + spike. + + Returns + ------- + AbstractProcess + dense block process. + str + table entry string for process. + """ + shape = (np.prod(layer_config['shape']),) + neuron_params = Network.get_neuron_params(layer_config['neuron']) + weight_real = layer_config['weight_real'] + weight_imag = layer_config['weight_imag'] + if weight_real.ndim == 1: + weight_real = weight_real.reshape(shape[0], -1) + weight_imag = weight_imag.reshape(shape[0], -1) + + opt_weights_real = optimize_weight_bits(weight_real) + opt_weights_imag = optimize_weight_bits(weight_imag) + weight_real, num_weight_bits_real, weight_exponent_real, sign_mode_real = opt_weights_real + weight_imag, num_weight_bits_imag, weight_exponent_imag, sign_mode_imag = opt_weights_imag + + # arguments for dense block + params = {'shape': shape, + 'neuron_params': neuron_params, + 'weight_real': weight_real, + 'weight_imag': weight_imag, + 'num_weight_bits_real': num_weight_bits_real, + 'num_weight_bits_imag': num_weight_bits_imag, + 'weight_exponent_real': weight_exponent_real, + 'weight_exponent_imag': weight_exponent_imag, + 'sign_mode_real': sign_mode_real, + 'sign_mode_imag': sign_mode_imag, + 'input_message_bits': input_message_bits} + + # optional arguments + if 'bias' in layer_config.keys(): + params['bias'] = layer_config['bias'] + + table_entry = Network._table_str(type_str='Dense', width=1, height=1, + channel=shape[0], + delay='delay' in layer_config.keys()) + + return DenseComplex(**params), table_entry + @staticmethod def create_conv(layer_config: h5py.Group, input_shape: Tuple[int, int, int], @@ -369,7 +438,6 @@ def _create(self) -> List[AbstractProcess]: for i in range(num_layers): layer_type = layer_config[i]['type'] - if layer_type == 'input': layer, table = self.create_input(layer_config[i]) layers.append(layer) @@ -423,7 +491,21 @@ def _create(self) -> List[AbstractProcess]: else: if len(layers) > 1: layers[-2].out.connect(layers[-1].inp) - + elif layer_type == "dense_comp": + layer, table = self.create_complex_dense( + layer_config=layer_config[i], + input_message_bits=input_message_bits + ) + layers.append(layer) + input_message_bits = layer.output_message_bits + if flatten_next: + layers[-2].out.transpose([2, 1, 0]).flatten().connect( + layers[-1].inp + ) + flatten_next = False + else: + if len(layers) > 1: + layers[-2].out.connect(layers[-1].inp) elif layer_type == 'average': raise NotImplementedError(f'{layer_type} is not implemented.') diff --git a/src/lava/lib/dl/netx/utils.py b/src/lava/lib/dl/netx/utils.py index 667640a9..59bf94cc 100644 --- a/src/lava/lib/dl/netx/utils.py +++ b/src/lava/lib/dl/netx/utils.py @@ -7,6 +7,7 @@ import h5py import numpy as np from enum import IntEnum, unique +import torch @unique @@ -44,9 +45,9 @@ def __init__( self.array_keys = [ 'shape', 'stride', 'padding', 'dilation', 'groups', 'delay', 'iDecay', 'refDelay', 'scaleRho', 'tauRho', 'theta', 'vDecay', - 'vThMant', 'wgtExp', + 'vThMant', 'wgtExp', 'sinDecay', 'cosDecay' ] - self.copy_keys = ['weight', 'bias'] + self.copy_keys = ['weight', 'bias', 'weight_real', 'weight_imag'] def keys(self) -> h5py._hl.base.KeysViewHDF5: return self.f.keys() @@ -62,7 +63,10 @@ def __getitem__(self, key: str) -> h5py.Dataset: else: value = value[()] return value.decode('ascii') - elif key in self.copy_keys: + elif key in self.copy_keys: + # if len(self.f[key]) == 2:# this is needed to extract the complex val weights + # vals = np.stack([i[()].astype(int).copy() for i in self.f[key].values()]) + # return vals return self.f[key][()].astype(int).copy() elif key in self.array_keys: return self.f[key][()] diff --git a/src/lava/lib/dl/slayer/block/base.py b/src/lava/lib/dl/slayer/block/base.py index 3a68d57a..555b763e 100644 --- a/src/lava/lib/dl/slayer/block/base.py +++ b/src/lava/lib/dl/slayer/block/base.py @@ -546,10 +546,6 @@ def weight(s): def delay(d): return torch.floor(d.delay).flatten().cpu().data.numpy() - # dense descriptors - handle.create_dataset( - 'type', (1, ), 'S10', ['dense'.encode('ascii', 'ignore')] - ) handle.create_dataset('shape', data=np.array(self.neuron.shape)) handle.create_dataset('inFeatures', data=self.synapse.in_channels) handle.create_dataset('outFeatures', data=self.synapse.out_channels) @@ -558,16 +554,21 @@ def delay(d): self.synapse.disable_weight_norm() if hasattr(self.synapse, 'imag'): # complex synapse handle.create_dataset( - 'weight/real', + 'weight_real', data=weight(self.synapse.real) ) handle.create_dataset( - 'weight/imag', + 'weight_imag', data=weight(self.synapse.imag) ) + type_str = "dense_comp" else: handle.create_dataset('weight', data=weight(self.synapse)) - + type_str = "dense" + + handle.create_dataset( + 'type', (1, ), 'S10', [type_str.encode('ascii', 'ignore')] + ) # bias has_norm = False if hasattr(self.neuron, 'norm'): diff --git a/src/lava/lib/dl/slayer/synapse/complex.py b/src/lava/lib/dl/slayer/synapse/complex.py index a3ee4bf5..47348bfc 100644 --- a/src/lava/lib/dl/slayer/synapse/complex.py +++ b/src/lava/lib/dl/slayer/synapse/complex.py @@ -105,7 +105,9 @@ def __init__( weight_scale, weight_norm, pre_hook_fx ) - + self.in_channels = self.real.in_channels + self.out_channels = self.real.out_channels + self.weight_norm_enabled = self.real.weight_norm_enabled class Conv(ComplexLayer): """Convolution complex-synapse layer. From 275b4fc9bc8514b5472bc4eb4e86a0f2ca518237 Mon Sep 17 00:00:00 2001 From: Jurado Date: Tue, 27 Sep 2022 15:02:58 -0400 Subject: [PATCH 2/7] Refactored code based on bamsumit suggestion. Added netx support for rf_iz.Dense --- src/lava/lib/dl/netx/blocks/models.py | 6 +- src/lava/lib/dl/netx/blocks/process.py | 2 +- src/lava/lib/dl/netx/hdf5.py | 140 ++++++++++++------------- src/lava/lib/dl/netx/utils.py | 5 +- src/lava/lib/dl/slayer/block/base.py | 12 ++- 5 files changed, 77 insertions(+), 88 deletions(-) diff --git a/src/lava/lib/dl/netx/blocks/models.py b/src/lava/lib/dl/netx/blocks/models.py index 81798b0d..541c1c8b 100644 --- a/src/lava/lib/dl/netx/blocks/models.py +++ b/src/lava/lib/dl/netx/blocks/models.py @@ -11,7 +11,7 @@ from lava.magma.core.sync.protocols.loihi_protocol import LoihiProtocol from lava.magma.core.resources import CPU -from lava.lib.dl.netx.blocks.process import Input, Dense, Conv, DenseComplex +from lava.lib.dl.netx.blocks.process import Input, Dense, Conv, ComplexDense @requires(CPU) @@ -52,8 +52,8 @@ class PyDenseModel(AbstractPyBlockModel): def __init__(self, proc: AbstractProcess) -> None: super().__init__(proc) -@implements(proc=DenseComplex, protocol=LoihiProtocol) -class PyDenseComplexModel(AbstractPyBlockModel): +@implements(proc=ComplexDense, protocol=LoihiProtocol) +class PyComplexDenseModel(AbstractPyBlockModel): def __init__(self, proc: AbstractProcess) -> None: super().__init__(proc) diff --git a/src/lava/lib/dl/netx/blocks/process.py b/src/lava/lib/dl/netx/blocks/process.py index f1cd345c..f582f840 100644 --- a/src/lava/lib/dl/netx/blocks/process.py +++ b/src/lava/lib/dl/netx/blocks/process.py @@ -162,7 +162,7 @@ def export_hdf5(self, handle: Union[h5py.File, h5py.Group]) -> None: raise NotImplementedError -class DenseComplex(AbstractBlock): +class ComplexDense(AbstractBlock): """Dense layer block. Parameters diff --git a/src/lava/lib/dl/netx/hdf5.py b/src/lava/lib/dl/netx/hdf5.py index 90498c10..c0f1547f 100644 --- a/src/lava/lib/dl/netx/hdf5.py +++ b/src/lava/lib/dl/netx/hdf5.py @@ -7,6 +7,7 @@ from lava.magma.core.decorator import implements from lava.magma.core.sync.protocols.loihi_protocol import LoihiProtocol from lava.proc.rf.process import RF +from lava.proc.rf_iz.process import RF_IZ import numpy as np import h5py @@ -16,7 +17,7 @@ from lava.proc.sdn.process import Sigma, Delta, SigmaDelta from lava.lib.dl.netx.utils import NetDict from lava.lib.dl.netx.utils import optimize_weight_bits -from lava.lib.dl.netx.blocks.process import Input, Dense, Conv, DenseComplex +from lava.lib.dl.netx.blocks.process import Input, Dense, Conv, ComplexDense from lava.lib.dl.netx.blocks.models import AbstractPyBlockModel @@ -147,6 +148,17 @@ def get_neuron_params(neuron_config: h5py.Group, 'cos_decay': neuron_config['cosDecay'] - 1, } return neuron_params + elif neuron_type in ['RF_IZHIKEVICH']: + if num_message_bits is None: + num_message_bits = 0 # default value + neuron_process = RF_IZ + neuron_params = { + 'neuron_proc': neuron_process, + 'vth': neuron_config['vThMant'], + 'sin_decay': neuron_config['sinDecay'] - 1, + 'cos_decay': neuron_config['cosDecay'] - 1, + } + return neuron_params @staticmethod def _table_str(type_str: str = '', @@ -256,87 +268,65 @@ def create_dense(layer_config: h5py.Group, """ shape = (np.prod(layer_config['shape']),) neuron_params = Network.get_neuron_params(layer_config['neuron']) - weight = layer_config['weight'] - if weight.ndim == 1: - weight = weight.reshape(shape[0], -1) - - opt_weights = optimize_weight_bits(weight) - weight, num_weight_bits, weight_exponent, sign_mode = opt_weights - - # arguments for dense block - params = {'shape': shape, - 'neuron_params': neuron_params, - 'weight': weight, - 'num_weight_bits': num_weight_bits, - 'weight_exponent': weight_exponent, - 'sign_mode': sign_mode, - 'input_message_bits': input_message_bits} - - # optional arguments - if 'bias' in layer_config.keys(): - params['bias'] = layer_config['bias'] - - table_entry = Network._table_str(type_str='Dense', width=1, height=1, - channel=shape[0], - delay='delay' in layer_config.keys()) - - return Dense(**params), table_entry + if layer_config["complex_synapse"]: + weight_real = layer_config['weight_real'] + weight_imag = layer_config['weight_imag'] + if weight_real.ndim == 1: + weight_real = weight_real.reshape(shape[0], -1) + weight_imag = weight_imag.reshape(shape[0], -1) + + opt_weights_real = optimize_weight_bits(weight_real) + opt_weights_imag = optimize_weight_bits(weight_imag) + weight_real, num_weight_bits_real, weight_exponent_real, sign_mode_real = opt_weights_real + weight_imag, num_weight_bits_imag, weight_exponent_imag, sign_mode_imag = opt_weights_imag + + # arguments for dense block + params = {'shape': shape, + 'neuron_params': neuron_params, + 'weight_real': weight_real, + 'weight_imag': weight_imag, + 'num_weight_bits_real': num_weight_bits_real, + 'num_weight_bits_imag': num_weight_bits_imag, + 'weight_exponent_real': weight_exponent_real, + 'weight_exponent_imag': weight_exponent_imag, + 'sign_mode_real': sign_mode_real, + 'sign_mode_imag': sign_mode_imag, + 'input_message_bits': input_message_bits} + + # optional arguments + if 'bias' in layer_config.keys(): + params['bias'] = layer_config['bias'] + + proc = ComplexDense(**params) + + else: + weight = layer_config['weight'] + if weight.ndim == 1: + weight = weight.reshape(shape[0], -1) - @staticmethod - def create_complex_dense(layer_config: h5py.Group, - input_message_bits: int = 0) -> Tuple[Dense, str]: - """Creates dense layer from layer configuration + opt_weights = optimize_weight_bits(weight) + weight, num_weight_bits, weight_exponent, sign_mode = opt_weights - Parameters - ---------- - layer_config : h5py.Group - hdf5 handle to layer description. - input_message_bits : int, optional - number of message bits in input spike. Defaults to 0 meaning unary - spike. + # arguments for dense block + params = {'shape': shape, + 'neuron_params': neuron_params, + 'weight': weight, + 'num_weight_bits': num_weight_bits, + 'weight_exponent': weight_exponent, + 'sign_mode': sign_mode, + 'input_message_bits': input_message_bits} - Returns - ------- - AbstractProcess - dense block process. - str - table entry string for process. - """ - shape = (np.prod(layer_config['shape']),) - neuron_params = Network.get_neuron_params(layer_config['neuron']) - weight_real = layer_config['weight_real'] - weight_imag = layer_config['weight_imag'] - if weight_real.ndim == 1: - weight_real = weight_real.reshape(shape[0], -1) - weight_imag = weight_imag.reshape(shape[0], -1) - - opt_weights_real = optimize_weight_bits(weight_real) - opt_weights_imag = optimize_weight_bits(weight_imag) - weight_real, num_weight_bits_real, weight_exponent_real, sign_mode_real = opt_weights_real - weight_imag, num_weight_bits_imag, weight_exponent_imag, sign_mode_imag = opt_weights_imag - - # arguments for dense block - params = {'shape': shape, - 'neuron_params': neuron_params, - 'weight_real': weight_real, - 'weight_imag': weight_imag, - 'num_weight_bits_real': num_weight_bits_real, - 'num_weight_bits_imag': num_weight_bits_imag, - 'weight_exponent_real': weight_exponent_real, - 'weight_exponent_imag': weight_exponent_imag, - 'sign_mode_real': sign_mode_real, - 'sign_mode_imag': sign_mode_imag, - 'input_message_bits': input_message_bits} + # optional arguments + if 'bias' in layer_config.keys(): + params['bias'] = layer_config['bias'] - # optional arguments - if 'bias' in layer_config.keys(): - params['bias'] = layer_config['bias'] + proc = Dense(**params) table_entry = Network._table_str(type_str='Dense', width=1, height=1, - channel=shape[0], - delay='delay' in layer_config.keys()) + channel=shape[0], + delay='delay' in layer_config.keys()) + return proc, table_entry - return DenseComplex(**params), table_entry @staticmethod def create_conv(layer_config: h5py.Group, diff --git a/src/lava/lib/dl/netx/utils.py b/src/lava/lib/dl/netx/utils.py index 59bf94cc..d44af870 100644 --- a/src/lava/lib/dl/netx/utils.py +++ b/src/lava/lib/dl/netx/utils.py @@ -45,7 +45,7 @@ def __init__( self.array_keys = [ 'shape', 'stride', 'padding', 'dilation', 'groups', 'delay', 'iDecay', 'refDelay', 'scaleRho', 'tauRho', 'theta', 'vDecay', - 'vThMant', 'wgtExp', 'sinDecay', 'cosDecay' + 'vThMant', 'wgtExp', 'sinDecay', 'cosDecay', "complex_synapse" ] self.copy_keys = ['weight', 'bias', 'weight_real', 'weight_imag'] @@ -64,9 +64,6 @@ def __getitem__(self, key: str) -> h5py.Dataset: value = value[()] return value.decode('ascii') elif key in self.copy_keys: - # if len(self.f[key]) == 2:# this is needed to extract the complex val weights - # vals = np.stack([i[()].astype(int).copy() for i in self.f[key].values()]) - # return vals return self.f[key][()].astype(int).copy() elif key in self.array_keys: return self.f[key][()] diff --git a/src/lava/lib/dl/slayer/block/base.py b/src/lava/lib/dl/slayer/block/base.py index 555b763e..92514bad 100644 --- a/src/lava/lib/dl/slayer/block/base.py +++ b/src/lava/lib/dl/slayer/block/base.py @@ -546,13 +546,19 @@ def weight(s): def delay(d): return torch.floor(d.delay).flatten().cpu().data.numpy() + handle.create_dataset( + 'type', (1, ), 'S10', ['dense'.encode('ascii', 'ignore')] + ) + handle.create_dataset('shape', data=np.array(self.neuron.shape)) handle.create_dataset('inFeatures', data=self.synapse.in_channels) handle.create_dataset('outFeatures', data=self.synapse.out_channels) if self.synapse.weight_norm_enabled: self.synapse.disable_weight_norm() + if hasattr(self.synapse, 'imag'): # complex synapse + handle.create_dataset("complex_synapse", data=np.array(True)) # TODO handle.create_dataset( 'weight_real', data=weight(self.synapse.real) @@ -561,14 +567,10 @@ def delay(d): 'weight_imag', data=weight(self.synapse.imag) ) - type_str = "dense_comp" else: + handle.create_dataset("complex_synapse", data=np.array(False)) handle.create_dataset('weight', data=weight(self.synapse)) - type_str = "dense" - handle.create_dataset( - 'type', (1, ), 'S10', [type_str.encode('ascii', 'ignore')] - ) # bias has_norm = False if hasattr(self.neuron, 'norm'): From 1fd58dd1127b410c4a46debc0e97b5ba90fe0b6e Mon Sep 17 00:00:00 2001 From: Jurado Date: Wed, 9 Nov 2022 15:29:08 -0500 Subject: [PATCH 3/7] Added unit tests for rf netx blocks. Conv not included --- src/lava/lib/dl/netx/blocks/models.py | 8 +- src/lava/lib/dl/netx/blocks/process.py | 32 +++++++- src/lava/lib/dl/netx/hdf5.py | 30 +++---- src/lava/lib/dl/netx/utils.py | 2 +- src/lava/lib/dl/slayer/block/base.py | 4 +- src/lava/lib/dl/slayer/neuron/rf.py | 4 +- src/lava/lib/dl/slayer/neuron/rf_iz.py | 4 +- tests/lava/lib/dl/netx/test_blocks.py | 105 ++++++++++++++++++++++++- 8 files changed, 159 insertions(+), 30 deletions(-) diff --git a/src/lava/lib/dl/netx/blocks/models.py b/src/lava/lib/dl/netx/blocks/models.py index 541c1c8b..a6708cce 100644 --- a/src/lava/lib/dl/netx/blocks/models.py +++ b/src/lava/lib/dl/netx/blocks/models.py @@ -11,7 +11,7 @@ from lava.magma.core.sync.protocols.loihi_protocol import LoihiProtocol from lava.magma.core.resources import CPU -from lava.lib.dl.netx.blocks.process import Input, Dense, Conv, ComplexDense +from lava.lib.dl.netx.blocks.process import Input, ComplexInput, Dense, Conv, ComplexDense @requires(CPU) @@ -46,6 +46,10 @@ class PyInputModel(AbstractPyBlockModel): def __init__(self, proc: AbstractProcess) -> None: super().__init__(proc) +@implements(proc=ComplexInput, protocol=LoihiProtocol) +class PyComplexInputModel(AbstractPyBlockModel): + def __init__(self, proc: AbstractProcess) -> None: + super().__init__(proc) @implements(proc=Dense, protocol=LoihiProtocol) class PyDenseModel(AbstractPyBlockModel): @@ -57,8 +61,6 @@ class PyComplexDenseModel(AbstractPyBlockModel): def __init__(self, proc: AbstractProcess) -> None: super().__init__(proc) - - @implements(proc=Conv, protocol=LoihiProtocol) class PyConvModel(AbstractPyBlockModel): def __init__(self, proc: AbstractProcess) -> None: diff --git a/src/lava/lib/dl/netx/blocks/process.py b/src/lava/lib/dl/netx/blocks/process.py index 4e42177b..14277dc8 100644 --- a/src/lava/lib/dl/netx/blocks/process.py +++ b/src/lava/lib/dl/netx/blocks/process.py @@ -175,8 +175,6 @@ class ComplexDense(AbstractBlock): synaptic real weight. weight_imag : np.ndarray synaptic imag weight. - bias : np.ndarray or None - bias of neuron. None means no bias. Defaults to None. has_graded_input : dict flag for graded spikes at input. Defaults to False. num_weight_bits_real : int @@ -203,8 +201,7 @@ def __init__(self, **kwargs: Union[dict, tuple, list, int, bool]) -> None: weight_real = kwargs.pop('weight_real') weight_imag = kwargs.pop('weight_imag') - self.neuron = self._neuron(kwargs.pop('bias', None)) - + self.neuron = self._neuron(None) self.real_synapse = DenseSynapse( weights=weight_real, weight_exp=weight_exponent_real, @@ -236,6 +233,33 @@ def __init__(self, **kwargs: Union[dict, tuple, list, int, bool]) -> None: def export_hdf5(self, handle: Union[h5py.File, h5py.Group]) -> None: raise NotImplementedError +class ComplexInput(AbstractBlock): + """Input layer block. + + Parameters + ---------- + shape : tuple or list + shape of the layer block in (x, y, z)/WHC format. + neuron_params : dict, optional + dictionary of neuron parameters. Defaults to None. + """ + + def __init__(self, **kwargs: Union[dict, tuple, list, int, bool]) -> None: + super().__init__(**kwargs) + self.neuron = self._neuron(None) + + self.inp = InPort(shape=self.neuron.a_real_in.shape) + self.inp.connect(self.neuron.a_real_in) + self.inp.connect(self.neuron.a_imag_in) + self.out = OutPort(shape=self.neuron.s_out.shape) + self.neuron.s_out.connect(self.out) + + self._clean() + + def export_hdf5(self, handle: Union[h5py.File, h5py.Group]) -> None: + raise NotImplementedError + + class Conv(AbstractBlock): """Conv layer block. diff --git a/src/lava/lib/dl/netx/hdf5.py b/src/lava/lib/dl/netx/hdf5.py index 9ca09566..9e83b77d 100644 --- a/src/lava/lib/dl/netx/hdf5.py +++ b/src/lava/lib/dl/netx/hdf5.py @@ -316,8 +316,8 @@ def create_dense(layer_config: h5py.Group, reset_interval=reset_interval, reset_offset=reset_offset) if layer_config["complex_synapse"]: - weight_real = layer_config['weight_real'] - weight_imag = layer_config['weight_imag'] + weight_real = layer_config['weight/real'] + weight_imag = layer_config['weight/imag'] if weight_real.ndim == 1: weight_real = weight_real.reshape(shape[0], -1) weight_imag = weight_imag.reshape(shape[0], -1) @@ -329,16 +329,16 @@ def create_dense(layer_config: h5py.Group, # arguments for dense block params = {'shape': shape, - 'neuron_params': neuron_params, - 'weight_real': weight_real, - 'weight_imag': weight_imag, - 'num_weight_bits_real': num_weight_bits_real, - 'num_weight_bits_imag': num_weight_bits_imag, - 'weight_exponent_real': weight_exponent_real, - 'weight_exponent_imag': weight_exponent_imag, - 'sign_mode_real': sign_mode_real, - 'sign_mode_imag': sign_mode_imag, - 'input_message_bits': input_message_bits} + 'neuron_params': neuron_params, + 'weight_real': weight_real, + 'weight_imag': weight_imag, + 'num_weight_bits_real': num_weight_bits_real, + 'num_weight_bits_imag': num_weight_bits_imag, + 'weight_exponent_real': weight_exponent_real, + 'weight_exponent_imag': weight_exponent_imag, + 'sign_mode_real': sign_mode_real, + 'sign_mode_imag': sign_mode_imag, + 'input_message_bits': input_message_bits} proc = ComplexDense(**params) @@ -361,9 +361,9 @@ def create_dense(layer_config: h5py.Group, proc = Dense(**params) - # optional arguments - if 'bias' in layer_config.keys(): - params['bias'] = layer_config['bias'] + # optional arguments + if 'bias' in layer_config.keys(): + params['bias'] = layer_config['bias'] table_entry = Network._table_str(type_str='Dense', width=1, height=1, channel=shape[0], diff --git a/src/lava/lib/dl/netx/utils.py b/src/lava/lib/dl/netx/utils.py index 5e2129b3..edc2b8a1 100644 --- a/src/lava/lib/dl/netx/utils.py +++ b/src/lava/lib/dl/netx/utils.py @@ -48,7 +48,7 @@ def __init__( 'iDecay', 'refDelay', 'scaleRho', 'tauRho', 'theta', 'vDecay', 'vThMant', 'wgtExp', 'sinDecay', 'cosDecay', "complex_synapse" ] - self.copy_keys = ['weight', 'bias', 'weight_real', 'weight_imag'] + self.copy_keys = ['weight', 'bias', 'weight/real', 'weight/imag'] def keys(self) -> h5py._hl.base.KeysViewHDF5: return self.f.keys() diff --git a/src/lava/lib/dl/slayer/block/base.py b/src/lava/lib/dl/slayer/block/base.py index 94d2bce0..9de09526 100644 --- a/src/lava/lib/dl/slayer/block/base.py +++ b/src/lava/lib/dl/slayer/block/base.py @@ -560,11 +560,11 @@ def delay(d): if hasattr(self.synapse, 'imag'): # complex synapse handle.create_dataset("complex_synapse", data=np.array(True)) # TODO handle.create_dataset( - 'weight_real', + 'weight/real', data=weight(self.synapse.real) ) handle.create_dataset( - 'weight_imag', + 'weight/imag', data=weight(self.synapse.imag) ) else: diff --git a/src/lava/lib/dl/slayer/neuron/rf.py b/src/lava/lib/dl/slayer/neuron/rf.py index 260527b7..3817992f 100644 --- a/src/lava/lib/dl/slayer/neuron/rf.py +++ b/src/lava/lib/dl/slayer/neuron/rf.py @@ -39,8 +39,8 @@ def neuron_params(device_params, scale=1 << 6, p_scale=1 << 12): dictionary of neuron parameters that can be used to initialize neuron class. """ - sin_decay = device_params['sinDecay'] / p_scale, - cos_decay = device_params['cosDecay'] / p_scale, + sin_decay = device_params['sinDecay'] / p_scale + cos_decay = device_params['cosDecay'] / p_scale decay = 1 - np.sqrt(sin_decay ** 2 + cos_decay ** 2) frequency = np.arctan2(sin_decay, cos_decay) / 2 / np.pi return { diff --git a/src/lava/lib/dl/slayer/neuron/rf_iz.py b/src/lava/lib/dl/slayer/neuron/rf_iz.py index 6740a2f3..893758c7 100644 --- a/src/lava/lib/dl/slayer/neuron/rf_iz.py +++ b/src/lava/lib/dl/slayer/neuron/rf_iz.py @@ -41,8 +41,8 @@ def neuron_params(device_params, scale=1 << 6, p_scale=1 << 12): dictionary of neuron parameters that can be used to initialize neuron class. """ - sin_decay = device_params['sinDecay'] / p_scale, - cos_decay = device_params['cosDecay'] / p_scale, + sin_decay = device_params['sinDecay'] / p_scale + cos_decay = device_params['cosDecay'] / p_scale decay = 1 - np.sqrt(sin_decay ** 2 + cos_decay ** 2) frequency = np.arctan2(sin_decay, cos_decay) / 2 / np.pi return { diff --git a/tests/lava/lib/dl/netx/test_blocks.py b/tests/lava/lib/dl/netx/test_blocks.py index 2664b7a6..673f4802 100644 --- a/tests/lava/lib/dl/netx/test_blocks.py +++ b/tests/lava/lib/dl/netx/test_blocks.py @@ -16,10 +16,12 @@ from lava.proc.io.source import RingBuffer as SendProcess from lava.proc.io.sink import RingBuffer as ReceiveProcess from lava.proc.lif.process import LIF +from lava.proc.rf.process import RF +from lava.proc.rf_iz.process import RF_IZ from lava.proc.sdn.process import Sigma, Delta, SigmaDelta from lava.proc.conv import utils -from lava.lib.dl.netx.blocks.process import Dense, Conv, Input +from lava.lib.dl.netx.blocks.process import Dense, Conv, Input, ComplexDense, ComplexInput verbose = True if (('-v' in sys.argv) or ('--verbose' in sys.argv)) else False @@ -223,7 +225,108 @@ def test_dense(self) -> None: f'Error was {s_error}.' ) +class TestRFBlocks(unittest.TestCase): + def test_input(self) -> None: + """Tests input rf block driven by known input.""" + num_steps = 2000 + rf_params = {'vth': 1.1, + 'period': 7, + 'state_exp': 6, + 'decay_bits': 12, + 'alpha': .05} + + input_blk = ComplexInput( + shape=(200,), + neuron_params={'neuron_proc': RF_IZ, **rf_params}, + ) + source = SendProcess(data=np.load(root + '/gts/complex_dense/in.npy')) + source.s_out.connect(input_blk.inp) + sink = ReceiveProcess(shape=input_blk.out.shape, buffer=num_steps) + input_blk.out.connect(sink.a_in) + + run_condition = RunSteps(num_steps=num_steps) + run_config = TestRunConfig(select_tag='fixed_pt') + input_blk.run(condition=run_condition, run_cfg=run_config) + output = sink.data.get() + input_blk.stop() + + gt = np.load(root + '/gts/complex_dense/current.npy') + + error = np.abs(output - gt).sum() + if verbose: + print('Input spike error:', error) + if HAVE_DISPLAY: + plt.figure() + out_ae = np.argwhere(output.reshape((-1, num_steps)) > 0) + gt_ae = np.argwhere(gt.reshape((-1, num_steps)) > 0) + plt.plot(gt_ae[:, 1], + gt_ae[:, 0], + '.', markersize=15, label='Ground Truth') + plt.plot(out_ae[:, 1], out_ae[:, 0], '.', label='Input Block') + plt.xlabel('Time') + plt.ylabel('Neuron ID') + plt.legend() + plt.show() + + self.assertTrue( + error == 0, + f'Output spike and ground truth do not match for Input block. ' + f'Found {output[output != gt] = } and {gt[output != gt] = }. ' + f'Error was {error}.' + ) + + def test_dense(self) -> None: + """Tests RF dense block driven by known input.""" + num_steps = 2000 + rf_params = {'vth': 25, + 'period': 11, + 'state_exp': 6, + 'decay_bits': 12, + 'alpha': .05} + + dense_blk = ComplexDense( + shape=(256,), + neuron_params={'neuron_proc': RF, **rf_params}, + weight_real = np.load(root + '/gts/complex_dense/weight_r.npy'), + weight_imag = np.load(root + '/gts/complex_dense/weight_img.npy'), + ) + + source = SendProcess(data=np.load(root + '/gts/complex_dense/in.npy')) + sink = ReceiveProcess(shape=dense_blk.out.shape, buffer=num_steps) + source.s_out.connect(dense_blk.inp) + dense_blk.out.connect(sink.a_in) + + run_condition = RunSteps(num_steps=num_steps) + run_config = TestRunConfig(select_tag='fixed_pt') + dense_blk.run(condition=run_condition, run_cfg=run_config) + s = sink.data.get() + dense_blk.stop() + + s_gt = np.load(root + '/gts/complex_dense/out.npy') + s_error = np.abs(s - s_gt).sum() + + if verbose: + print('Dense spike error:', s_error) + if HAVE_DISPLAY: + plt.figure() + out_ae = np.argwhere(s.reshape((-1, num_steps)) > 0) + gt_ae = np.argwhere(s_gt.reshape((-1, num_steps)) > 0) + plt.plot(gt_ae[:, 1], + gt_ae[:, 0], + '.', markersize=15, label='Ground Truth') + plt.plot(out_ae[:, 1], out_ae[:, 0], '.', label='Input Block') + plt.xlabel('Time') + plt.ylabel('Neuron ID') + plt.legend() + plt.show() + + self.assertTrue( + s_error == 0, + f'Output spike and ground truth do not match for Dense block. ' + f'Found {s[s != s_gt] = } and {s_gt[s != s_gt] = }. ' + f'Error was {s_error}.' + ) class TestSDNBlocks(unittest.TestCase): def test_input(self) -> None: """Tests SDN input block driven by known input.""" From 3081cd9821d88df54c22a7ab428a29191371559d Mon Sep 17 00:00:00 2001 From: Jurado Date: Wed, 9 Nov 2022 15:42:42 -0500 Subject: [PATCH 4/7] Added complex_dense folder for unit tests --- .../lib/dl/netx/gts/complex_dense/current.npy | Bin 0 -> 3200128 bytes .../lava/lib/dl/netx/gts/complex_dense/in.npy | Bin 0 -> 3200128 bytes .../lib/dl/netx/gts/complex_dense/out.npy | Bin 0 -> 4096128 bytes .../dl/netx/gts/complex_dense/weight_img.npy | Bin 0 -> 409728 bytes .../dl/netx/gts/complex_dense/weight_r.npy | Bin 0 -> 409728 bytes 5 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 tests/lava/lib/dl/netx/gts/complex_dense/current.npy create mode 100644 tests/lava/lib/dl/netx/gts/complex_dense/in.npy create mode 100644 tests/lava/lib/dl/netx/gts/complex_dense/out.npy create mode 100644 tests/lava/lib/dl/netx/gts/complex_dense/weight_img.npy create mode 100644 tests/lava/lib/dl/netx/gts/complex_dense/weight_r.npy diff --git 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z-0$}}tW0-0zpzMgx1RzF?#uxFkA`T1bx%**xfd(&etI_zWq2){k#{L;^F ze&W@q&KG=e-Lrk@+xMndq=#NQ@Wb~$@r@4erJPQFnOA?}GhOCHe%M3bmATir z$Z7vO4s^rEPha~OJUn9-65afPg9m=&W{$u1F;DNg3c>y2wXZoi`uXo%PNzOP)mtCl z=v3Ed?^5^u?=aHW_;3=Pn}gT)l0LfZE#E! z-RCR)=Iucr{q`Dp=rMn6ymaD~)91axH!^>&BA_3>e){3S74@~g;CZi}`#dqx*OdOmeNz|)UU zKGNSlCwD*aM;?0k^UNHGN%5NJ`;k8S=&%=jI^pnbKM&oY$3Fa!o8RAq*0l=B-n%~c zVPEHT?&tQ_C%5N5r_Vlg%k3kokJ(>+zMxs;h#Cu0KyfgE7MSUav zcl!9!x>AROUow0vlo26>Ziw?b3Ws9t}gDl za_9K@e@5rIO1M6}bkpTNMDyO0BRC?z=^f$YD?ahBx%BgU*CD5qKX8uBt3UYH_&jvk zU$h_n@c7q#!7H@ypMTo(%(;HP)BRpG>HWfshfi`oz!{l0=ic}U-`?)2SigTBXFoXj z_4FMHbSUgPuV=MO*O!Lg_FGraWKxAAiB-iz(~^AmoD=g0ax8Uz@^Jx j=J1EMzyHFX{MQGUe(y7UeCoU2E55 Date: Wed, 9 Nov 2022 16:09:31 -0500 Subject: [PATCH 5/7] Small fix to create dense --- src/lava/lib/dl/netx/hdf5.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/src/lava/lib/dl/netx/hdf5.py b/src/lava/lib/dl/netx/hdf5.py index 9e83b77d..5ccda92d 100644 --- a/src/lava/lib/dl/netx/hdf5.py +++ b/src/lava/lib/dl/netx/hdf5.py @@ -315,7 +315,8 @@ def create_dense(layer_config: h5py.Group, neuron_params = Network.get_neuron_params(layer_config['neuron'], reset_interval=reset_interval, reset_offset=reset_offset) - if layer_config["complex_synapse"]: + # check to see for nested weights + if isinstance(layer_config["weight"], NetDict): weight_real = layer_config['weight/real'] weight_imag = layer_config['weight/imag'] if weight_real.ndim == 1: @@ -343,6 +344,7 @@ def create_dense(layer_config: h5py.Group, proc = ComplexDense(**params) else: + print("WENT INTO REGULAR STATEMENT") weight = layer_config['weight'] if weight.ndim == 1: weight = weight.reshape(shape[0], -1) @@ -359,12 +361,11 @@ def create_dense(layer_config: h5py.Group, 'sign_mode': sign_mode, 'input_message_bits': input_message_bits} - proc = Dense(**params) - # optional arguments if 'bias' in layer_config.keys(): params['bias'] = layer_config['bias'] + proc = Dense(**params) table_entry = Network._table_str(type_str='Dense', width=1, height=1, channel=shape[0], delay='delay' in layer_config.keys()) From 079dcf03c42ac4c8567af98529a346586748ae1b Mon Sep 17 00:00:00 2001 From: Michael Jurado Date: Thu, 10 Nov 2022 10:22:10 -0500 Subject: [PATCH 6/7] Fixed linting problems --- src/lava/lib/dl/netx/blocks/models.py | 11 +++++--- src/lava/lib/dl/netx/blocks/process.py | 7 +++-- src/lava/lib/dl/netx/hdf5.py | 33 +++++++++++------------ src/lava/lib/dl/netx/utils.py | 2 +- src/lava/lib/dl/slayer/block/base.py | 4 +-- src/lava/lib/dl/slayer/synapse/complex.py | 2 ++ tests/lava/lib/dl/netx/test_blocks.py | 10 ++++--- 7 files changed, 41 insertions(+), 28 deletions(-) diff --git a/src/lava/lib/dl/netx/blocks/models.py b/src/lava/lib/dl/netx/blocks/models.py index a6708cce..8f4787d6 100644 --- a/src/lava/lib/dl/netx/blocks/models.py +++ b/src/lava/lib/dl/netx/blocks/models.py @@ -11,7 +11,8 @@ from lava.magma.core.sync.protocols.loihi_protocol import LoihiProtocol from lava.magma.core.resources import CPU -from lava.lib.dl.netx.blocks.process import Input, ComplexInput, Dense, Conv, ComplexDense +from lava.lib.dl.netx.blocks.process import Input, ComplexInput, Dense, Conv,\ + ComplexDense @requires(CPU) @@ -46,21 +47,25 @@ class PyInputModel(AbstractPyBlockModel): def __init__(self, proc: AbstractProcess) -> None: super().__init__(proc) + @implements(proc=ComplexInput, protocol=LoihiProtocol) class PyComplexInputModel(AbstractPyBlockModel): def __init__(self, proc: AbstractProcess) -> None: super().__init__(proc) + @implements(proc=Dense, protocol=LoihiProtocol) class PyDenseModel(AbstractPyBlockModel): def __init__(self, proc: AbstractProcess) -> None: super().__init__(proc) - + + @implements(proc=ComplexDense, protocol=LoihiProtocol) class PyComplexDenseModel(AbstractPyBlockModel): def __init__(self, proc: AbstractProcess) -> None: super().__init__(proc) - + + @implements(proc=Conv, protocol=LoihiProtocol) class PyConvModel(AbstractPyBlockModel): def __init__(self, proc: AbstractProcess) -> None: diff --git a/src/lava/lib/dl/netx/blocks/process.py b/src/lava/lib/dl/netx/blocks/process.py index 14277dc8..f18cd223 100644 --- a/src/lava/lib/dl/netx/blocks/process.py +++ b/src/lava/lib/dl/netx/blocks/process.py @@ -105,6 +105,7 @@ def __init__(self, **kwargs: Union[dict, tuple, list, int, bool]) -> None: def export_hdf5(self, handle: Union[h5py.File, h5py.Group]) -> None: raise NotImplementedError + class Dense(AbstractBlock): """Dense layer block. Parameters @@ -163,7 +164,7 @@ def export_hdf5(self, handle: Union[h5py.File, h5py.Group]) -> None: class ComplexDense(AbstractBlock): - """Dense layer block. + """Dense Complex layer block. Parameters ---------- @@ -215,7 +216,7 @@ def __init__(self, **kwargs: Union[dict, tuple, list, int, bool]) -> None: num_message_bits=self.input_message_bits, ) - if self.shape != self.real_synapse.a_out.shape or self.shape != self.imag_synapse.a_out.shape: + if self.shape != self.real_synapse.a_out.shape: raise RuntimeError( f'Expected synapse output shape to be {self.shape[-1]}, ' f'found {self.synapse.a_out.shape}.' @@ -228,11 +229,13 @@ def __init__(self, **kwargs: Union[dict, tuple, list, int, bool]) -> None: self.real_synapse.a_out.connect(self.neuron.a_real_in) self.imag_synapse.a_out.connect(self.neuron.a_imag_in) self.neuron.s_out.connect(self.out) + self._clean() def export_hdf5(self, handle: Union[h5py.File, h5py.Group]) -> None: raise NotImplementedError + class ComplexInput(AbstractBlock): """Input layer block. diff --git a/src/lava/lib/dl/netx/hdf5.py b/src/lava/lib/dl/netx/hdf5.py index 5ccda92d..dec08379 100644 --- a/src/lava/lib/dl/netx/hdf5.py +++ b/src/lava/lib/dl/netx/hdf5.py @@ -182,7 +182,7 @@ def get_neuron_params(neuron_config: h5py.Group, 'neuron_proc': neuron_process, 'vth': neuron_config['vThMant'], 'period': neuron_params['period'], - 'alpha': neuron_params['decay'], + 'alpha': neuron_params['decay'], 'state_exp': 6, 'decay_bits': 12 } @@ -282,7 +282,6 @@ def create_input(layer_config: h5py.Group, return Input(**params), table_entry - @staticmethod def create_dense(layer_config: h5py.Group, input_message_bits: int = 0, @@ -316,7 +315,7 @@ def create_dense(layer_config: h5py.Group, reset_interval=reset_interval, reset_offset=reset_offset) # check to see for nested weights - if isinstance(layer_config["weight"], NetDict): + if isinstance(layer_config["weight"], NetDict): weight_real = layer_config['weight/real'] weight_imag = layer_config['weight/imag'] if weight_real.ndim == 1: @@ -325,10 +324,12 @@ def create_dense(layer_config: h5py.Group, opt_weights_real = optimize_weight_bits(weight_real) opt_weights_imag = optimize_weight_bits(weight_imag) - weight_real, num_weight_bits_real, weight_exponent_real, sign_mode_real = opt_weights_real - weight_imag, num_weight_bits_imag, weight_exponent_imag, sign_mode_imag = opt_weights_imag + weight_real, num_weight_bits_real, weight_exponent_real,\ + sign_mode_real = opt_weights_real + weight_imag, num_weight_bits_imag, weight_exponent_imag,\ + sign_mode_imag = opt_weights_imag - # arguments for dense block + # arguments for complex dense block params = {'shape': shape, 'neuron_params': neuron_params, 'weight_real': weight_real, @@ -342,9 +343,8 @@ def create_dense(layer_config: h5py.Group, 'input_message_bits': input_message_bits} proc = ComplexDense(**params) - + else: - print("WENT INTO REGULAR STATEMENT") weight = layer_config['weight'] if weight.ndim == 1: weight = weight.reshape(shape[0], -1) @@ -354,12 +354,12 @@ def create_dense(layer_config: h5py.Group, # arguments for dense block params = {'shape': shape, - 'neuron_params': neuron_params, - 'weight': weight, - 'num_weight_bits': num_weight_bits, - 'weight_exponent': weight_exponent, - 'sign_mode': sign_mode, - 'input_message_bits': input_message_bits} + 'neuron_params': neuron_params, + 'weight': weight, + 'num_weight_bits': num_weight_bits, + 'weight_exponent': weight_exponent, + 'sign_mode': sign_mode, + 'input_message_bits': input_message_bits} # optional arguments if 'bias' in layer_config.keys(): @@ -367,11 +367,10 @@ def create_dense(layer_config: h5py.Group, proc = Dense(**params) table_entry = Network._table_str(type_str='Dense', width=1, height=1, - channel=shape[0], - delay='delay' in layer_config.keys()) + channel=shape[0], + delay='delay' in layer_config.keys()) return proc, table_entry - @staticmethod def create_conv(layer_config: h5py.Group, input_shape: Tuple[int, int, int], diff --git a/src/lava/lib/dl/netx/utils.py b/src/lava/lib/dl/netx/utils.py index edc2b8a1..bdb2d0f5 100644 --- a/src/lava/lib/dl/netx/utils.py +++ b/src/lava/lib/dl/netx/utils.py @@ -64,7 +64,7 @@ def __getitem__(self, key: str) -> h5py.Dataset: else: value = value[()] return value.decode('ascii') - elif key in self.copy_keys: + elif key in self.copy_keys: return self.f[key][()].astype(int).copy() elif key in self.array_keys: return self.f[key][()] diff --git a/src/lava/lib/dl/slayer/block/base.py b/src/lava/lib/dl/slayer/block/base.py index 9de09526..e505adc4 100644 --- a/src/lava/lib/dl/slayer/block/base.py +++ b/src/lava/lib/dl/slayer/block/base.py @@ -558,7 +558,7 @@ def delay(d): self.synapse.disable_weight_norm() if hasattr(self.synapse, 'imag'): # complex synapse - handle.create_dataset("complex_synapse", data=np.array(True)) # TODO + handle.create_dataset("complex_synapse", data=np.array(True)) handle.create_dataset( 'weight/real', data=weight(self.synapse.real) @@ -570,7 +570,7 @@ def delay(d): else: handle.create_dataset("complex_synapse", data=np.array(False)) handle.create_dataset('weight', data=weight(self.synapse)) - + # bias has_norm = False if hasattr(self.neuron, 'norm'): diff --git a/src/lava/lib/dl/slayer/synapse/complex.py b/src/lava/lib/dl/slayer/synapse/complex.py index 47348bfc..302e5da1 100644 --- a/src/lava/lib/dl/slayer/synapse/complex.py +++ b/src/lava/lib/dl/slayer/synapse/complex.py @@ -108,6 +108,8 @@ def __init__( self.in_channels = self.real.in_channels self.out_channels = self.real.out_channels self.weight_norm_enabled = self.real.weight_norm_enabled + + class Conv(ComplexLayer): """Convolution complex-synapse layer. diff --git a/tests/lava/lib/dl/netx/test_blocks.py b/tests/lava/lib/dl/netx/test_blocks.py index 673f4802..fd968545 100644 --- a/tests/lava/lib/dl/netx/test_blocks.py +++ b/tests/lava/lib/dl/netx/test_blocks.py @@ -21,7 +21,8 @@ from lava.proc.sdn.process import Sigma, Delta, SigmaDelta from lava.proc.conv import utils -from lava.lib.dl.netx.blocks.process import Dense, Conv, Input, ComplexDense, ComplexInput +from lava.lib.dl.netx.blocks.process import Dense, Conv, Input, ComplexDense,\ + ComplexInput verbose = True if (('-v' in sys.argv) or ('--verbose' in sys.argv)) else False @@ -225,6 +226,7 @@ def test_dense(self) -> None: f'Error was {s_error}.' ) + class TestRFBlocks(unittest.TestCase): def test_input(self) -> None: @@ -288,8 +290,8 @@ def test_dense(self) -> None: dense_blk = ComplexDense( shape=(256,), neuron_params={'neuron_proc': RF, **rf_params}, - weight_real = np.load(root + '/gts/complex_dense/weight_r.npy'), - weight_imag = np.load(root + '/gts/complex_dense/weight_img.npy'), + weight_real=np.load(root + '/gts/complex_dense/weight_r.npy'), + weight_imag=np.load(root + '/gts/complex_dense/weight_img.npy'), ) source = SendProcess(data=np.load(root + '/gts/complex_dense/in.npy')) @@ -327,6 +329,8 @@ def test_dense(self) -> None: f'Found {s[s != s_gt] = } and {s_gt[s != s_gt] = }. ' f'Error was {s_error}.' ) + + class TestSDNBlocks(unittest.TestCase): def test_input(self) -> None: """Tests SDN input block driven by known input.""" From 988573a4d3c1a2f3378342210fd3475ea4707f40 Mon Sep 17 00:00:00 2001 From: PhilippPlank <32519998+PhilippPlank@users.noreply.github.com> Date: Thu, 17 Nov 2022 15:31:43 +0100 Subject: [PATCH 7/7] Update poetry.lock Use latest lava-nc for CI tests, which includes the rf processes. --- poetry.lock | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/poetry.lock b/poetry.lock index 532eb143..0cbbddbb 100644 --- a/poetry.lock +++ b/poetry.lock @@ -816,7 +816,7 @@ scipy = "^1.8.0" type = "git" url = "https://github.com/lava-nc/lava.git" reference = "main" -resolved_reference = "d321a8b01b7cac017c52f196329c4e486558b8b7" +resolved_reference = "5f7faa7ea19667bc3e0991413e4cd7edbd794220" [[package]] name = "linecache2"