Merge branch 'main' into main

.github/workflows/cd.yml

@@ -100,7 +100,10 @@ jobs:
           pip install -U pip
           pip install "nbconvert>=7.2.10,<7.3" pytest>=7.2.0 matplotlib>=3.5.1 ipykernel>=6.15.0 nbformat>=5.3.0
           artifact=$(ls | grep lava_dl | grep tar)
-          pip install --no-input $artifact          
+          pip install --no-input $artifact
+          # There is an issue "ImportError: cannot import name 'packaging' from 'pkg_resources'" when 
+          # using setuptools==70.0.0 -- temp fix to downgrade to  setuptools==69.5.1
+          pip install setuptools==69.5.1
           tar -xvf $artifact
           mv ./lava*/tests .
           mv ./lava*/tutorials .
@@ -120,6 +123,9 @@ jobs:
           pip install "nbconvert>=7.2.10,<7.3" pytest>=7.2.0 matplotlib>=3.5.1 ipykernel>=6.15.0 nbformat>=5.3.0
           artifact=$(ls | grep lava_dl | grep whl)
           pip install --no-input $artifact
+          # There is an issue "ImportError: cannot import name 'packaging' from 'pkg_resources'" when 
+          # using setuptools==70.0.0 -- temp fix to downgrade to  setuptools==69.5.1
+          pip install setuptools==69.5.1
           # Change $artifact to tar.gz
           artifact=$(ls | grep lava_dl | grep tar)
           tar -xvf $artifact

.github/workflows/ci.yml

@@ -16,8 +16,12 @@ jobs:
     runs-on: ubuntu-latest
 
     steps:
+    - uses: actions/checkout@v3
+      with:
+        lfs: true
+
     - name: Setup CI
-      uses: lava-nc/ci-setup-composite-action@v1.1
+      uses: lava-nc/ci-setup-composite-action@v1.5.12_py3.10
       with:
         repository: 'Lava-DL'
 
@@ -29,8 +33,12 @@ jobs:
     runs-on: ubuntu-latest
 
     steps:
+    - uses: actions/checkout@v3
+      with:
+        lfs: true
+
     - name: Setup CI
-      uses: lava-nc/ci-setup-composite-action@v1.1
+      uses: lava-nc/ci-setup-composite-action@v1.5.12_py3.10
       with:
         repository: 'Lava-DL'
 
@@ -46,14 +54,22 @@ jobs:
     runs-on: ${{ matrix.operating-system }}
     strategy:
       matrix:
-        operating-system: [ubuntu-latest, windows-latest, macos-latest]
+        operating-system: [ubuntu-latest, windows-latest]
 
     steps:
+    - uses: actions/checkout@v3
+      with:
+        lfs: true
+
     - name: Setup CI
-      uses: lava-nc/ci-setup-composite-action@v1.1
+      uses: lava-nc/ci-setup-composite-action@v1.5.12_py3.10
       with:
         repository: 'Lava-DL'    
 
     - name: Run unit tests
-      run: poetry run pytest
+      run: |
+        # There is an issue "ImportError: cannot import name 'packaging' from 'pkg_resources'" when 
+        # using setuptools==70.0.0 -- temp fix to downgrade to  setuptools==69.5.1
+        poetry run pip install setuptools==69.5.1
+        poetry run pytest
       

.gitignore

@@ -129,3 +129,9 @@ dmypy.json
 
 # Pyre type checker
 .pyre/
+
+#Trained log folders
+#Trained_*
+#Logs_*
+#runs
+

pyproject.toml

@@ -46,7 +46,7 @@ classifiers = [
 "Discussions" = "https://github.com/lava-nc/lava-dl/discussions"
 
 [tool.poetry.dependencies]
-python = ">=3.8, <3.11"
+python = ">=3.10, <3.11"
 
 lava-nc = { git = "https://github.com/lava-nc/lava.git", branch = "main", develop = true }
 

src/lava/lib/dl/netx/blocks/process.py

@@ -172,9 +172,11 @@ def __init__(self, **kwargs: Union[dict, tuple, list, int, bool]) -> None:
             self.synapse = Synapse(
                 weights=weight,
                 delays=delay.astype(int),
+                weight_exp=weight_exponent,
                 max_delay=62,
                 num_weight_bits=num_weight_bits,
                 num_message_bits=self.input_message_bits,
+
             )
 
         if self.shape != self.synapse.a_out.shape:
@@ -197,6 +199,112 @@ def export_hdf5(self, handle: Union[h5py.File, h5py.Group]) -> None:
         raise NotImplementedError
 
 
+class RecurrentDense(AbstractBlock):
+    """RecurrentDense 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.
+    weight_rec : np.ndarray
+        recurrent synaptic weight.
+    delay : np.ndarray
+        synaptic delay.
+    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.
+    sparse_synapse : bool
+        connection is sparse
+    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)
+
+        weight = kwargs.pop('weight')
+        weight_rec = kwargs.pop('weight_rec')
+        delay = kwargs.pop('delay', None)
+        num_weight_bits = kwargs.pop('num_weight_bits', 8)
+        weight_exponent = kwargs.pop('weight_exponent', 0)
+        sparse_synapse = kwargs.pop('sparse_synapse', False)
+
+        if delay is None:
+            if sparse_synapse:
+                Synapse = SparseSynapse
+                weight = csr_matrix(weight)
+            else:
+                Synapse = DenseSynapse
+
+            self.synapse = Synapse(
+                weights=weight,
+                weight_exp=weight_exponent,
+                num_weight_bits=num_weight_bits,
+                num_message_bits=self.input_message_bits,
+                shape=weight.shape
+            )
+            self.synapse_rec = Synapse(
+                weights=weight_rec,
+                weight_exp=weight_exponent,
+                num_weight_bits=num_weight_bits,
+                num_message_bits=self.input_message_bits,
+                shape=weight_rec.shape
+            )
+        else:
+            # TODO test this in greater detail
+            if sparse_synapse:
+                Synapse = DelaySparseSynapse
+                delay[weight == 0] = 0
+                weight = csr_matrix(weight)
+                delay = csr_matrix(delay)
+            else:
+                Synapse = DelayDenseSynapse
+
+            self.synapse = Synapse(
+                weights=weight,
+                delays=delay.astype(int),
+                max_delay=62,
+                num_weight_bits=num_weight_bits,
+                num_message_bits=self.input_message_bits,
+            )
+            self.synapse_rec = Synapse(
+                weights=weight_rec,
+                delays=delay.astype(int),
+                max_delay=62,
+                num_weight_bits=num_weight_bits,
+                num_message_bits=self.input_message_bits,
+            )
+
+        if self.shape != self.synapse.a_out.shape:
+            raise RuntimeError(
+                f'Expected synapse output shape to be {self.shape[-1]}, '
+                f'found {self.synapse.a_out.shape}.'
+            )
+
+        self.neuron = self._neuron(kwargs.pop('bias', None))
+
+        self.inp = InPort(shape=self.synapse.s_in.shape)
+        self.out = OutPort(shape=self.neuron.s_out.shape)
+        self.inp.connect(self.synapse.s_in)
+        self.synapse.a_out.connect(self.neuron.a_in)
+        self.neuron.s_out.connect(self.out)
+
+        self.neuron.s_out.connect(self.synapse_rec.s_in)
+
+        self.synapse_rec.a_out.connect(self.neuron.a_in)
+
+        self._clean()
+
+
 class ComplexDense(AbstractBlock):
     """Dense Complex layer block.
 

src/lava/lib/dl/netx/hdf5.py

@@ -19,7 +19,8 @@
 from lava.lib.dl.slayer.neuron.rf import neuron_params as get_rf_params
 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, ComplexDense
+from lava.lib.dl.netx.blocks.process import Input, Dense, RecurrentDense, \
+    Conv, ComplexDense
 from lava.lib.dl.netx.blocks.models import AbstractPyBlockModel
 
 
@@ -320,7 +321,8 @@ def create_dense(layer_config: h5py.Group,
                      reset_interval: Optional[int] = None,
                      reset_offset: int = 0,
                      spike_exp: int = 6,
-                     sparse_synapse: bool = 0) -> Tuple[Dense, str]:
+                     sparse_synapse: bool = 0,
+                     rec: bool = False) -> Tuple[Dense, str]:
         """Creates dense layer from layer configuration
 
         Parameters
@@ -393,11 +395,24 @@ def create_dense(layer_config: h5py.Group,
 
         else:
             weight = layer_config['weight']
+            if rec:
+                weight_rec = layer_config['weight_rec']
             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
+                if rec:
+                    weight_rec = weight_rec.reshape(shape[0], -1)
+
+            if rec:
+                weight_concat = np.hstack((weight, weight_rec))
+                opt_weight_concat = optimize_weight_bits(weight_concat)
+                weight_concat, num_weight_bits, \
+                    weight_exponent, sign_mode = opt_weight_concat
+                weight = weight_concat[:, :weight.shape[1]]
+                weight_rec = weight_concat[:, weight.shape[1]:]
+            else:
+                opt_weights = optimize_weight_bits(weight)
+                weight, num_weight_bits, \
+                    weight_exponent, sign_mode = opt_weights
 
             # arguments for dense block
             params = {'shape': shape,
@@ -408,6 +423,8 @@ def create_dense(layer_config: h5py.Group,
                       'sign_mode': sign_mode,
                       'input_message_bits': input_message_bits,
                       "sparse_synapse": sparse_synapse}
+            if rec:
+                params['weight_rec'] = weight_rec
 
             if 'delay' in layer_config.keys():
                 delay = layer_config['delay']
@@ -438,7 +455,10 @@ def create_dense(layer_config: h5py.Group,
             if 'bias' in layer_config.keys():
                 params['bias'] = layer_config['bias']
 
-            proc = Dense(**params)
+            if rec:
+                proc = RecurrentDense(**params)
+            else:
+                proc = Dense(**params)
         table_entry = Network._table_str(type_str='Dense', width=1, height=1,
                                          channel=shape[0],
                                          delay='delay' in layer_config.keys())
@@ -619,14 +639,19 @@ def _create(self) -> List[AbstractProcess]:
                 flatten_next = True
                 table = None
 
-            elif layer_type == 'dense':
+            elif layer_type == 'dense' or layer_type == 'dense_rec':
+                if layer_type == 'dense':
+                    rec = False
+                elif layer_type == 'dense_rec':
+                    rec = True
                 layer, table = self.create_dense(
                     layer_config=layer_config[i],
                     input_message_bits=input_message_bits,
                     reset_interval=reset_interval,
                     reset_offset=reset_offset,
                     spike_exp=self.spike_exp,
-                    sparse_synapse=self.sparse_fc_layer)
+                    sparse_synapse=self.sparse_fc_layer,
+                    rec=rec)
                 if i >= self.skip_layers:
                     layers.append(layer)
                     reset_offset += 1

src/lava/lib/dl/netx/utils.py

@@ -47,7 +47,8 @@ def __init__(
             'iDecay', 'refDelay', 'scaleRho', 'tauRho', 'theta', 'vDecay',
             'vThMant', 'wgtExp', 'sinDecay', 'cosDecay'
         ]
-        self.copy_keys = ['weight', 'bias', 'weight/real', 'weight/imag']
+        self.copy_keys = ['weight', 'weight_rec',
+                          'bias', 'weight/real', 'weight/imag']
 
     def keys(self) -> h5py._hl.base.KeysViewHDF5:
         return self.f.keys()