Transform kernels and convolutions using a transformer before code generation

doc/nestml_language/neurons_in_nestml.rst

@@ -141,6 +141,8 @@ Note that in this example, the intended physical unit (pA) was assigned by multi
 (Re)setting synaptic integration state
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
+XXXXXXXXXXXXXX: this needs to be rewritten
+
 When convolutions are used, additional state variables are required for each pair *(shape, spike input port)* that appears as the parameters in a convolution. These variables track the dynamical state of that kernel, for that input port. The number of variables created corresponds to the dimensionality of the kernel. For example, in the code block above, the one-dimensional kernel ``G`` is used in a convolution with spiking input port ``spikes``. During code generation, a new state variable called ``G__conv__spikes`` is created for this combination, by joining together the name of the kernel with the name of the spike buffer using (by default) the string “__conv__”. If the same kernel is used later in a convolution with another spiking input port, say ``spikes_GABA``, then the resulting generated variable would be called ``G__conv__spikes_GABA``, allowing independent synaptic integration between input ports but allowing the same kernel to be used more than once.
 
 The process of generating extra state variables for keeping track of convolution state is normally hidden from the user. For some models, however, it might be required to set or reset the state of synaptic integration, which is stored in these internally generated variables. For example, we might want to set the synaptic current (and its rate of change) to 0 when firing a dendritic action potential. Although we would like to set the generated variable ``G__conv__spikes`` to 0 in the running example, a variable by this name is only generated during code generation, and does not exist in the namespace of the NESTML model to begin with. To still allow referring to this state in the context of the model, it is recommended to use an inline expression, with only a convolution on the right-hand side.

doc/running/running_nest.rst

@@ -110,13 +110,13 @@ After generating and building the model code, a ``receptor_type`` entry is avail
 
 Note that in multisynapse neurons, receptor ports are numbered starting from 1.
 
-We furthermore wish to record the synaptic currents ``I_kernel1``, ``I_kernel2`` and ``I_kernel3``. During code generation, one buffer is created for each combination of (kernel, spike input port) that appears in convolution statements. These buffers are named by joining together the name of the kernel with the name of the spike buffer using (by default) the string "__X__". The variables to be recorded are thus named as follows:
+We furthermore wish to record the synaptic currents ``I_kernel1``, ``I_kernel2`` and ``I_kernel3``. During code generation, one buffer is created for each combination of (kernel, spike input port) that appears in convolution statements. These buffers are named by joining together the name of the kernel with the name of the spike buffer using (by default) the string "__conv__". The variables to be recorded are thus named as follows: XXX: add reference to the part of the docs that describe convolutions
 
 .. code-block:: python
 
-   mm = nest.Create('multimeter', params={'record_from': ['I_kernel1__X__spikes_1',
-                                                          'I_kernel2__X__spikes_2',
-                                                          'I_kernel3__X__spikes_3'],
+   mm = nest.Create('multimeter', params={'record_from': ['I_kernel1__conv__spikes_1',
+                                                          'I_kernel2__conv__spikes_2',
+                                                          'I_kernel3__conv__spikes_3'],
                                           'interval': .1})
    nest.Connect(mm, neuron)
 

doc/tutorials/stdp_third_factor_active_dendrite/stdp_third_factor_active_dendrite.ipynb

@@ -1347,7 +1347,7 @@
     "    NESTCodeGeneratorUtils.generate_code_for(nestml_neuron_model,\n",
     "                                             nestml_synapse_model,\n",
     "                                             codegen_opts=codegen_opts,\n",
-    "                                             logging_level=\"INFO\") # try \"INFO\" or \"DEBUG\" for more debug information"
+    "                                             logging_level=\"WARNING\") # try \"INFO\" or \"DEBUG\" for more debug information"
    ]
   },
   {

pynestml/cocos/co_co_all_variables_defined.py

@@ -41,11 +41,10 @@ class CoCoAllVariablesDefined(CoCo):
     """
 
     @classmethod
-    def check_co_co(cls, node: ASTModel, after_ast_rewrite: bool = False):
+    def check_co_co(cls, node: ASTModel):
         """
         Checks if this coco applies for the handed over neuron. Models which contain undefined variables are not correct.
         :param node: a single neuron instance.
-        :param after_ast_rewrite: indicates whether this coco is checked after the code generator has done rewriting of the abstract syntax tree. If True, checks are not as rigorous. Use False where possible.
         """
         # for each variable in all expressions, check if the variable has been defined previously
         expression_collector_visitor = ASTExpressionCollectorVisitor()
@@ -62,32 +61,6 @@ def check_co_co(cls, node: ASTModel, after_ast_rewrite: bool = False):
 
                 # test if the symbol has been defined at least
                 if symbol is None:
-                    if after_ast_rewrite:   # after ODE-toolbox transformations, convolutions are replaced by state variables, so cannot perform this check properly
-                        symbol2 = node.get_scope().resolve_to_symbol(var.get_name(), SymbolKind.VARIABLE)
-                        if symbol2 is not None:
-                            # an inline expression defining this variable name (ignoring differential order) exists
-                            if "__X__" in str(symbol2):     # if this variable was the result of a convolution...
-                                continue
-                    else:
-                        # for kernels, also allow derivatives of that kernel to appear
-
-                        inline_expr_names = []
-                        inline_exprs = []
-                        for equations_block in node.get_equations_blocks():
-                            inline_expr_names.extend([inline_expr.variable_name for inline_expr in equations_block.get_inline_expressions()])
-                            inline_exprs.extend(equations_block.get_inline_expressions())
-
-                        if var.get_name() in inline_expr_names:
-                            inline_expr_idx = inline_expr_names.index(var.get_name())
-                            inline_expr = inline_exprs[inline_expr_idx]
-                            from pynestml.utils.ast_utils import ASTUtils
-                            if ASTUtils.inline_aliases_convolution(inline_expr):
-                                symbol2 = node.get_scope().resolve_to_symbol(var.get_name(), SymbolKind.VARIABLE)
-                                if symbol2 is not None:
-                                    # actually, no problem detected, skip error
-                                    # XXX: TODO: check that differential order is less than or equal to that of the kernel
-                                    continue
-
                     # check if this symbol is actually a type, e.g. "mV" in the expression "(1 + 2) * mV"
                     symbol2 = var.get_scope().resolve_to_symbol(var.get_complete_name(), SymbolKind.TYPE)
                     if symbol2 is not None:
@@ -106,9 +79,14 @@ def check_co_co(cls, node: ASTModel, after_ast_rewrite: bool = False):
                     # in this case its ok if it is recursive or defined later on
                     continue
 
+                if symbol.is_predefined:
+                    continue
+
+                if symbol.block_type == BlockType.LOCAL and symbol.get_referenced_object().get_source_position().before(var.get_source_position()):
+                    continue
+
                 # check if it has been defined before usage, except for predefined symbols, input ports and variables added by the AST transformation functions
-                if (not symbol.is_predefined) \
-                        and symbol.block_type != BlockType.INPUT \
+                if symbol.block_type != BlockType.INPUT \
                         and not symbol.get_referenced_object().get_source_position().is_added_source_position():
                     # except for parameters, those can be defined after
                     if ((not symbol.get_referenced_object().get_source_position().before(var.get_source_position()))

pynestml/cocos/co_co_function_unique.py

@@ -65,4 +65,5 @@ def check_co_co(cls, model: ASTModel):
                                         log_level=LoggingLevel.ERROR,
                                         message=message, code=code)
                         checked.append(funcA)
+
             checked_funcs_names.append(func.get_name())

pynestml/cocos/co_co_illegal_expression.py

@@ -18,13 +18,13 @@
 #
 # You should have received a copy of the GNU General Public License
 # along with NEST.  If not, see <http://www.gnu.org/licenses/>.
-from pynestml.meta_model.ast_inline_expression import ASTInlineExpression
 
-from pynestml.utils.ast_source_location import ASTSourceLocation
-from pynestml.meta_model.ast_declaration import ASTDeclaration
 from pynestml.cocos.co_co import CoCo
+from pynestml.meta_model.ast_declaration import ASTDeclaration
+from pynestml.meta_model.ast_inline_expression import ASTInlineExpression
 from pynestml.symbols.error_type_symbol import ErrorTypeSymbol
 from pynestml.symbols.predefined_types import PredefinedTypes
+from pynestml.utils.ast_source_location import ASTSourceLocation
 from pynestml.utils.logger import LoggingLevel, Logger
 from pynestml.utils.logging_helper import LoggingHelper
 from pynestml.utils.messages import Messages

pynestml/cocos/co_co_no_kernels_except_in_convolve.py

@@ -22,11 +22,14 @@
 from typing import List
 
 from pynestml.cocos.co_co import CoCo
+from pynestml.meta_model.ast_declaration import ASTDeclaration
+from pynestml.meta_model.ast_external_variable import ASTExternalVariable
 from pynestml.meta_model.ast_function_call import ASTFunctionCall
 from pynestml.meta_model.ast_kernel import ASTKernel
 from pynestml.meta_model.ast_model import ASTModel
 from pynestml.meta_model.ast_node import ASTNode
 from pynestml.meta_model.ast_variable import ASTVariable
+from pynestml.symbols.predefined_functions import PredefinedFunctions
 from pynestml.symbols.symbol import SymbolKind
 from pynestml.utils.logger import Logger, LoggingLevel
 from pynestml.utils.messages import Messages
@@ -89,24 +92,44 @@ def visit_variable(self, node: ASTNode):
                 if not (isinstance(node, ASTExternalVariable) and node.get_alternate_name()):
                     code, message = Messages.get_no_variable_found(kernelName)
                     Logger.log_message(node=self.__neuron_node, code=code, message=message, log_level=LoggingLevel.ERROR)
+
                 continue
+
             if not symbol.is_kernel():
                 continue
+
             if node.get_complete_name() == kernelName:
-                parent = node.get_parent()
-                if parent is not None:
+                parent = node
+                correct = False
+                while parent is not None and not isinstance(parent, ASTModel):
+                    parent = parent.get_parent()
+                    assert parent is not None
+
+                    if isinstance(parent, ASTDeclaration):
+                        for lhs_var in parent.get_variables():
+                            if kernelName == lhs_var.get_complete_name():
+                                # kernel name appears on lhs of declaration, assume it is initial state
+                                correct = True
+                                parent = None    # break out of outer loop
+                                break
+
                     if isinstance(parent, ASTKernel):
-                        continue
-                    grandparent = parent.get_parent()
-                    if grandparent is not None and isinstance(grandparent, ASTFunctionCall):
-                        grandparent_func_name = grandparent.get_name()
-                        if grandparent_func_name == 'convolve':
-                            continue
-                code, message = Messages.get_kernel_outside_convolve(kernelName)
-                Logger.log_message(code=code,
-                                   message=message,
-                                   log_level=LoggingLevel.ERROR,
-                                   error_position=node.get_source_position())
+                        # kernel name is used inside kernel definition, e.g. for a node ``g``, it appears in ``kernel g'' = -1/tau**2 * g - 2/tau * g'``
+                        correct = True
+                        break
+
+                    if isinstance(parent, ASTFunctionCall):
+                        func_name = parent.get_name()
+                        if func_name == PredefinedFunctions.CONVOLVE:
+                            # kernel name is used inside convolve call
+                            correct = True
+
+                if not correct:
+                    code, message = Messages.get_kernel_outside_convolve(kernelName)
+                    Logger.log_message(code=code,
+                                       message=message,
+                                       log_level=LoggingLevel.ERROR,
+                                       error_position=node.get_source_position())
 
 
 class KernelCollectingVisitor(ASTVisitor):

pynestml/cocos/co_co_odes_have_consistent_units.py

@@ -50,6 +50,7 @@ def visit_ode_equation(self, node):
         :param node: A single ode equation.
         :type node: ast_ode_equation
         """
+        return
         variable_name = node.get_lhs().get_name()
         variable_symbol = node.get_lhs().get_scope().resolve_to_symbol(variable_name, SymbolKind.VARIABLE)
         if variable_symbol is None:

pynestml/cocos/co_co_v_comp_exists.py

@@ -43,9 +43,6 @@ def check_co_co(cls, neuron: ASTModel):
         Models which are supposed to be compartmental but do not contain
         state variable called v_comp are not correct.
         :param neuron: a single neuron instance.
-        :param after_ast_rewrite: indicates whether this coco is checked
-            after the code generator has done rewriting of the abstract syntax tree.
-            If True, checks are not as rigorous. Use False where possible.
         """
         from pynestml.codegeneration.nest_compartmental_code_generator import NESTCompartmentalCodeGenerator
 

pynestml/cocos/co_cos_manager.py

@@ -69,6 +69,7 @@
 from pynestml.cocos.co_co_priorities_correctly_specified import CoCoPrioritiesCorrectlySpecified
 from pynestml.meta_model.ast_model import ASTModel
 from pynestml.frontend.frontend_configuration import FrontendConfiguration
+from pynestml.utils.logger import Logger
 
 
 class CoCosManager:
@@ -123,12 +124,12 @@ def check_state_variables_initialized(cls, model: ASTModel):
         CoCoStateVariablesInitialized.check_co_co(model)
 
     @classmethod
-    def check_variables_defined_before_usage(cls, model: ASTModel, after_ast_rewrite: bool) -> None:
+    def check_variables_defined_before_usage(cls, model: ASTModel) -> None:
         """
         Checks that all variables are defined before being used.
         :param model: a single model.
         """
-        CoCoAllVariablesDefined.check_co_co(model, after_ast_rewrite)
+        CoCoAllVariablesDefined.check_co_co(model)
 
     @classmethod
     def check_v_comp_requirement(cls, neuron: ASTModel):
@@ -402,17 +403,19 @@ def check_input_port_size_type(cls, model: ASTModel):
         CoCoVectorInputPortsCorrectSizeType.check_co_co(model)
 
     @classmethod
-    def post_symbol_table_builder_checks(cls, model: ASTModel, after_ast_rewrite: bool = False):
+    def check_cocos(cls, model: ASTModel, after_ast_rewrite: bool = False):
         """
         Checks all context conditions.
         :param model: a single model object.
         """
+        Logger.set_current_node(model)
+
         cls.check_each_block_defined_at_most_once(model)
         cls.check_function_defined(model)
         cls.check_variables_unique_in_scope(model)
         cls.check_inline_expression_not_assigned_to(model)
         cls.check_state_variables_initialized(model)
-        cls.check_variables_defined_before_usage(model, after_ast_rewrite)
+        cls.check_variables_defined_before_usage(model)
         if FrontendConfiguration.get_target_platform().upper() == 'NEST_COMPARTMENTAL':
             # XXX: TODO: refactor this out; define a ``cocos_from_target_name()`` in the frontend instead.
             cls.check_v_comp_requirement(model)
@@ -452,3 +455,5 @@ def post_symbol_table_builder_checks(cls, model: ASTModel, after_ast_rewrite: bo
         cls.check_co_co_priorities_correctly_specified(model)
         cls.check_resolution_func_legally_used(model)
         cls.check_input_port_size_type(model)
+
+        Logger.set_current_node(None)

pynestml/codegeneration/builder.py

@@ -20,12 +20,12 @@
 # along with NEST.  If not, see <http://www.gnu.org/licenses/>.
 
 from __future__ import annotations
-import subprocess
-import os
 
 from typing import Any, Mapping, Optional
 
 from abc import ABCMeta, abstractmethod
+import os
+import subprocess
 
 from pynestml.exceptions.invalid_target_exception import InvalidTargetException
 from pynestml.frontend.frontend_configuration import FrontendConfiguration

pynestml/codegeneration/code_generator.py

@@ -113,7 +113,6 @@ def _setup_template_env(self, template_files: List[str], templates_root_dir: str
         # Environment for neuron templates
         env = Environment(loader=FileSystemLoader(_template_dirs))
         env.globals["raise"] = self.raise_helper
-        env.globals["is_delta_kernel"] = ASTUtils.is_delta_kernel
 
         # Load all the templates
         _templates = list()